World Development Indicators 2011

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WORLD DEVELOPMENT INDICATORS

Low income Afghanistan Bangladesh Benin Burkina Faso Burundi Cambodia Central African Republic Chad Comoros Congo, Dem. Rep. Eritrea Ethiopia Gambia, The Ghana Guinea Guinea-Bissau Haiti Kenya Korea, Dem. Rep. Kyrgyz Republic Lao PDR Liberia Madagascar Malawi Mali Mauritania Mozambique Myanmar Nepal Niger Rwanda Sierra Leone Solomon Islands Somalia Tajikistan Tanzania Togo Uganda Zambia Zimbabwe Lower middle income Angola Armenia Belize Bhutan Bolivia Cameroon Cape Verde China Congo, Rep. Côte d'Ivoire Djibouti Ecuador Egypt, Arab Rep. El Salvador Georgia Guatemala Guyana

Honduras India Indonesia Iraq Jordan Kiribati Kosovo Lesotho Maldives Marshall Islands Micronesia, Fed. Sts. Moldova Mongolia Morocco Nicaragua Nigeria Pakistan Papua New Guinea Paraguay Philippines Samoa São Tomé and Principe Senegal Sri Lanka Sudan Swaziland Syrian Arab Republic Thailand Timor-Leste Tonga Tunisia Turkmenistan Tuvalu Ukraine Uzbekistan Vanuatu Vietnam West Bank and Gaza Yemen, Rep. Upper middle income Albania Algeria American Samoa Antigua and Barbuda Argentina Azerbaijan Belarus Bosnia and Herzegovina Botswana Brazil Bulgaria Chile Colombia Costa Rica Cuba Dominica Dominican Republic Fiji Gabon

Grenada Iran, Islamic Rep. Jamaica Kazakhstan Lebanon Libya Lithuania Macedonia, FYR Malaysia Mauritius Mayotte Mexico Montenegro Namibia Palau Panama Peru Romania Russian Federation Serbia Seychelles South Africa St. Kitts and Nevis St. Lucia St. Vincent and the Grenadines Suriname Turkey Uruguay Venezuela, RB High income Andorra Aruba Australia Austria Bahamas, The Bahrain Barbados Belgium Bermuda Brunei Darussalam Canada Cayman Islands Channel Islands Croatia Cyprus Czech Republic Denmark Equatorial Guinea Estonia Faeroe Islands Finland France French Polynesia Germany Gibraltar Greece Greenland Guam

Hong Kong SAR, China Hungary Iceland Ireland Isle of Man Israel Italy Japan Korea, Rep. Kuwait Latvia Liechtenstein Luxembourg Macao SAR, China Malta Monaco Netherlands Netherlands Antilles New Caledonia New Zealand Northern Mariana Islands Norway Oman Poland Portugal Puerto Rico Qatar San Marino Saudi Arabia Singapore Slovak Republic Slovenia Spain Sweden Switzerland Trinidad and Tobago Turks and Caicos Islands United Arab Emirates United Kingdom United States Virgin Islands (U.S.)

INCOME MAP

The world by income

Designed and edited by Communications Development Incorporated, Washington, D.C., with Peter Grundy Art & Design, London

2011

WORLD DEVELOPMENT INDICATORS

Copyright 2011 by the International Bank for Reconstruction and Development/THE WORLD BANK 1818 H Street NW, Washington, D.C. 20433 USA All rights reserved Manufactured in the United States of America First printing April 2011 This volume is a product of the staff of the Development Data Group of the World Bank’s Development Economics Vice Presidency, and the judgments herein do not necessarily reflect the views of the World Bank’s Board of Executive Directors or the countries they represent. The World Bank does not guarantee the accuracy of the data included in this publication and accepts no responsibility whatsoever for any consequence of their use. The boundaries, colors, denominations, and other information shown on any map in this volume do not imply on the part of the World Bank any judgment on the legal status of any territory or the endorsement or acceptance of such boundaries. This publication uses the Robinson projection for maps, which represents both area and shape reasonably well for most of the earth’s surface. Nevertheless, some distortions of area, shape, distance, and direction remain. The material in this publication is copyrighted. Requests for permission to reproduce portions of it should be sent to the Office of the Publisher at the address in the copyright notice above. The World Bank encourages dissemination of its work and will normally give permission promptly and, when reproduction is for noncommercial purposes, without asking a fee. Permission to photocopy portions for classroom use is granted through the Copyright Center, Inc., Suite 910, 222 Rosewood Drive, Danvers, MA 01923 USA. Photo credits: Front cover, Curt Carnemark/World Bank; page xxiv, Curt Carnemark/World Bank; page 30, Trevor Samson/World Bank; page 122, Curt Carnemark/World Bank; page 188, Curt Carnemark/World Bank; page 262, Ray Witlin/World Bank; page 318, Curt Carnemark/World Bank. If you have questions or comments about this product, please contact: Development Data Group The World Bank 1818 H Street NW, Room MC2-812, Washington, D.C. 20433 USA Hotline: 800 590 1906 or 202 473 7824; fax 202 522 1498 Email: [email protected] Web site: www.worldbank.org or data.worldbank.org ISBN 978-0-8213-8709-2 ECO-AUDIT Environmental Benefits Statement The World Bank is committed to preserving endangered forests and natural resources. The Office of the Publisher has chosen to print World Development Indicators 2011 on recycled paper with 50 percent post-consumer fiber in accordance with the recommended standards for paper usage set by the Green Press Initiative, a nonprofit program supporting publishers in using fiber that is not sourced from endangered forests. For more information, visit www. greenpressinitiative.org. Saved: 91 trees 29 million Btu of total energy 8,609 pounds of net greenhouse gases 41,465 gallons of waste water 2,518 pounds of solid waste

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WORLD DEVELOPMENT INDICATORS

PREFACE World Development Indicators 2011, the 15th edition in its current format, aims to provide relevant, high-quality, internationally comparable statistics about development and the quality of people’s lives around the globe. This latest printed volume is one of a group of products; others include an online dataset, accessible at http://data.worldbank. org; the popular Little Data Book series; and DataFinder, a data query and charting application for mobile devices. Fifteen years ago, World Development Indicators was overhauled and redesigned, organizing the data to present an integrated view of development, with the goal of putting these data in the hands of policymakers, development specialists, students, and the public in a way that makes the data easy to use. Although there have been small changes, the format has stood the test of time, and this edition employs the same sections as the first one: world view, people, environment, economy, states and markets, and global links. Technical innovation and the rise of connected computing devices have gradually changed the way users obtain and consume the data in the World Development Indicators database. Last year saw a more abrupt change: the decision in April 2010 to make the dataset freely available resulted in a large, immediate increase in the use of the on-line resources. Perhaps more important has been the shift in how the data are used. Software developers are now free to use the data in applications they develop—and they are doing just that. We applaud and encourage all efforts to use the World Bank’s databases in creative ways to solve the world’s most pressing development challenges. This edition of World Development Indicators focuses on the impact of the decision to make data freely available under an open license and with better online tools. To help those who wish to use and reuse the data in these new ways, the section introductions discuss key issues in measuring the economic and social phenomena described in the tables and charts and introduce new sources of data. World Development Indicators is possible only through the excellent collaboration of many partners who provide the data that form part of this collection, and we thank them all: the United Nations family, the International Monetary Fund, the World Trade Organization, the Organisation for Economic Co-operation and Development, the statistical offices of more than 200 economies, and countless others who make this unique product possible. As always, we welcome your ideas for making the data in World Development Indicators useful and relevant for improving the lives of people around the world.

Shaida Badiee Director Development Economics Data Group

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ACKNOWLEDGMENTS This book was prepared by a team led by Soong Sup Lee under the management of Neil Fantom and comprising Awatif Abuzeid, Mehdi Akhlaghi, Azita Amjadi, Uranbileg Batjargal, Maja Bresslauer, David Cieslikowski, Mahyar EshraghTabary, Shota Hatakeyama, Masako Hiraga, Bala Bhaskar Naidu Kalimili, Buyant Khaltarkhuu, Elysee Kiti, Alison Kwong, Ibrahim Levent, Johan Mistiaen, Sulekha Patel, William Prince, Premi Rathan Raj, Evis Rucaj, Eric Swanson, Jomo Tariku, and Estela Zamora, working closely with other teams in the Development Economics Vice Presidency’s Development Data Group. World Development Indicators electronic products were prepared by a team led by Reza Farivari, consisting of Ramvel Chandrasekaran, Ying Chi, Jean-Pierre Djomalieu, Ramgopal Erabelly, Shelley Fu, Gytis Kanchas, Ugendran Makhachkala, Vilas Mandlekar, Nacer Megherbi, Parastoo Oloumi, Malarvizhi Veerappan, and Vera Wen. The work was carried out under the direction of Shaida Badiee. Valuable advice was provided by Shahrokh Fardoust. The choice of indicators and text content was shaped through close consultation with and substantial contributions from staff in the World Bank’s four thematic networks—Sustainable Development, Human Development, Poverty Reduction and Economic Management, and Financial and Private Sector Development—and staff of the International Finance Corporation and the Multilateral Investment Guarantee Agency. Most important, the team received substantial help, guidance, and data from external partners. For individual acknowledgments of contributions to the book’s content, please see Credits. For a listing of our key partners, see Partners. Communications Development Incorporated (CDI) provided editorial services, led by Meta de Coquereaumont, Bruce Ross-Larson, and Christopher Trott. Jomo Tariku designed the cover, Deborah Arroyo and Elaine Wilson typeset the book, and Katrina Van Duyn provided proofreading. Azita Amjadi and Alison Kwong oversaw the production process. Staff from External Affairs Office of the Publisher oversaw printing and dissemination of the book.

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TABLE OF CONTENTS FRONT

Preface Acknowledgments Partners Users guide

1. WORLD VIEW Introduction

1.1 1.2 1.3 1.4 1.5 1.6 1a 1b 1c 1d 1e 1f 1g 1h 1i 1j 1k 1l 1.2a 1.3a 1.4a

Tables Size of the economy Millennium Development Goals: eradicating poverty and saving lives Millennium Development Goals: protecting our common environment Millennium Development Goals: overcoming obstacles Women in development Key indicators for other economies Text figures, tables, and boxes Use of World Bank data has risen with the launch of the Open Data Initiative Terms of use for World Bank data Access to information at the World Bank Progress toward eradicating poverty Progress toward universal primary education completion Progress toward gender parity Progress toward reducing child mortality Progress toward improving maternal health HIV incidence is remaining stable or decreasing in many developing countries, but many lack data Progress on access to an improved water source Progress on access to improved sanitation Official development assistance provided by Development Assistance Committee members Location of indicators for Millennium Development Goals 1–4 Location of indicators for Millennium Development Goals 5–7 Location of indicators for Millennium Development Goal 8

v vii xii xxii

1 10 14 18 22 24 28

1 2 3 4 4 4 5 5 5 6 6 7 17 21 23

Introduction

2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11 2.12 2.13 2.14 2.15 2.16 2.17 2.18 2.19 2.20 2.21 2.22 2a 2b 2c 2d 2e 2f 2g 2h 2i 2.6a 2.8a 2.8b 2.8c 2.13a 2.17a

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2. PEOPLE Tables Population dynamics Labor force structure Employment by economic activity Decent work and productive employment Unemployment Children at work Poverty rates at national poverty lines Poverty rates at international poverty lines Distribution of income or consumption Assessing vulnerability and security Education inputs Participation in education Education efficiency Education completion and outcomes Education gaps by income and gender Health systems Health information Disease prevention coverage and quality Reproductive health Nutrition Health risk factors and future challenges Mortality Text figures, tables, and boxes Maternal mortality ratios have declined in all developing country regions since 1990 Maternal mortality ratios have declined fastest among low- and lower middle-income countries but remain high The births of many children in Asia and Africa go unregistered In Nigeria, children’s births are more likely to be unregistered in rural areas . . . . . . in poor households . . . . . . and where the mother has a lower education level Most people live in countries with low-quality cause of death statistics More countries used surveys for mortality statistics, but civil registration did not expand Estimates of infant mortality in the Philippines differ by source The largest sector for child labor remains agriculture, and the majority of children work as unpaid family members While the number of people living on less than $1.25 a day has fallen, the number living on $1.25–$2.00 a day has increased Poverty rates have begun to fall Regional poverty estimates There are more overage children among the poor in primary school in Zambia South Asia has the highest number of unregistered births

31 36 40 44 48 52 56 60 63 68 72 76 80 84 88 92 94 98 102 106 110 114 118

31 31 32 33 33 33 34 34 35 59 65 65 66 87 101

3. ENVIRONMENT 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 3.11 3.12 3.13 3.14 3.15 3.16 3a 3b 3.1a 3.2a 3.2b 3.3a 3.3b

Introduction

123

Tables Rural population and land use Agricultural inputs Agricultural output and productivity Deforestation and biodiversity Freshwater Water pollution Energy production and use Energy dependency and efficiency and carbon dioxide emissions Trends in greenhouse gas emissions Sources of electricity Urbanization Urban housing conditions Traffic and congestion Air pollution Government commitment Contribution of natural resources to gross domestic product

126 130 134 138 142 146 150 154 158 162 166 170 174 178 180 184

Text figures, tables, and boxes The 10 countries with the highest natural resource rents are primarily oil and gas producers Countries with negative adjusted net savings are depleting natural capital without replacing it and are becoming poorer What is rural? Urban? Nearly 40 percent of land globally is devoted to agriculture Rainfed agriculture plays a significant role in Sub-Saharan agriculture where about 95 percent of cropland depends on precipitation, 2008 The food production index has increased steadily since early 1960, and the index for low-income economies has been higher than the world average since early 2000 Cereal yield in Sub-Saharan Africa increased between 1990 and 2009 but still is the lowest among the regions

3.4a 3.5a 3.5b 3.6a 3.7a 3.7b 3.8a 3.9a 3.9b 3.10a 3.10b 3.11a

124 124 129 133

3.11b 3.12a 3.13a

133

3.13b 3.16a

137

3.16b

At least 33 percent of assessed species are estimated to be threatened 141 Agriculture is still the largest user of water, accounting for some 70 percent of global withdrawals . . . 145 . . . and approaching 90 percent in some developing regions 145 Emissions of organic water pollutants vary among countries from 1990 to 2007 149 A person in a high-income economy uses more than 14 times as much energy on average as a person in a low-income economy in 2008 153 Fossil fuels are still the primary global energy source in 2008 153 High-income economies depend on imported energy 157 The six largest contributors to methane emissions account for about 50 percent of emissions 161 The five largest contributors to nitrous oxide emissions account for about 50 percent of emissions 161 More than 50 percent of electricity in Latin America is produced by hydropower 165 Lower middle-income countries produce the majority of their power from coal 165 Urban population is increasing in developing economies, especially in low and lower middle-income economies 169 Latin America and Caribbean has the greatest share of urban population, even greater than the high-income economies in 2009 169 Selected housing indicators for smaller economies 173 Biogasoline consumption as a share of total consumption is highest in Brazil . . . 177 . . . but the United States consumes the most biogasoline 177 Oil dominates the contribution of natural resources in the Middle East and North Africa 187 Upper middle-income countries have the highest contribution of natural resources to GDP 187

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TABLE OF CONTENTS 4. ECONOMY

4.a 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.10 4.11 4.12 4.13 4.14 4.15 4.16 4.17 4a 4b 4c 4d 4e 4f 4g 4.3a 4.4a 4.5a 4.6a 4.7a 4.9a 4.10a 4.12a 4.13a 4.14a 4.17a

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Introduction

189

Tables Recent economic performance Growth of output Structure of output Structure of manufacturing Structure of merchandise exports Structure of merchandise imports Structure of service exports Structure of service imports Structure of demand Growth of consumption and investment Toward a broader measure of national income Toward a broader measure of saving Central government finances Central government expenses Central government revenues Monetary indicators Exchange rates and prices Balance of payments current account

192 194 198 202 206 210 214 218 222 226 230 234 238 242 246 250 254 258

Text figures, tables, and boxes Differences in GDP growth among developing country regions Developing countries are contributing more to global growth Economies—both developing and high income—rebounded in 2010 Revisions to GDP decline over time, and GDP data become more stable on average Ghana’s revised GDP was 60 percent higher in the new base year, 2006 Revised data for Ghana show a larger share of services in GDP Commission on the Measurement of Economic and Social Progress Manufacturing continues to show strong growth in East Asia and Pacific through 2009 Developing economies’ share of world merchandise exports continues to expand Top 10 developing economy exporters of merchandise goods in 2009 Top 10 developing economy exporters of commercial services in 2009 The mix of commercial service imports by developing economies is changing GDP per capita is still lagging in some regions GDP and adjusted net national income in Sub-Saharan Africa, 2000–09 Twenty selected economies had a central government debt to GDP ratio of 65 percent or higher Interest payments are a large part of government expenses for some developing economies Rich economies rely more on direct taxes Top 15 economies with the largest reserves in 2009

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189 189 190 190 190 190 191 205 209 213 217 221 229 233 241 245 249 261

5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5.11 5.12 5.13 5a 5b 5c 5d

5. STATES AND MARKETS Introduction

263

Tables Private sector in the economy Business environment: Enterprise Surveys Business environment: Doing Business indicators Stock markets Financial access, stability, and efficiency Tax policies Military expenditures and arms transfers Fragile situations Public policies and institutions Transport services Power and communications The information age Science and technology

266 270 274 278 282 286 290 294 298 302 306 310 314

Text figures, tables, and boxes The average business in Latin America and the Caribbean spends about 400 hours a year in preparing, filing, and paying business taxes, 2009 Firms in East Asia and the Pacific have the lowest business tax rate, 2010 Two approaches to collecting business environment data: Doing Business and Enterprise Surveys People living in developing countries of East Asia and Pacific have more commercial bank accounts than those in other developing country regions, 2009

264 264 265

265

6. GLOBAL LINKS Introduction

6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 6.10 6.11 6.12 6.13 6.14 6.15 6.16 6.17 6.18 6.19

Tables Integration with the global economy Growth of merchandise trade Direction and growth of merchandise trade High-income economy trade with low- and middle-income economies Direction of trade of developing economies Primary commodity prices Regional trade blocs Tariff barriers Trade facilitation External debt Ratios for external debt Global private financial flows Net official financial flows Financial flows from Development Assistance Committee members Allocation of bilateral aid from Development Assistance Committee members Aid dependency Distribution of net aid by Development Assistance Committee members Movement of people across borders Travel and tourism

319 324 328 332

6a 6b 6c 6d 6e

335 338 341 344 348 352 356 360 364 368

6f

372

6.5a

374 376

6.6a 6.7a 6.11a

380 384 388

6g 6.3a 6.4a

6.16a 6.17a

Text figures, tables, and boxes Source of data for bilateral trade flows Trade in professional services faces the highest barriers Discrepancies persist in measures of FDI net flows Source of data on FDI At least 30 percent of remittance inflows go unrecorded by the sending economies Migrants originating from low- and middle-income economies and residing in high-income economies rose fivefold over 1960–2000 The ratio of central government debt to GDP has increased for most economies, 2007–10 More than half of the world’s merchandise trade takes place between high-income economies. But low- and middle-income economies’ participation in the global trade has increased in the past 15 years Low-income economies have a small market share in the global market of various commodities Developing economies are trading more with other developing economies Primary commodity prices soared again in 2010 Global Preferential Trade Agreements Database Ratio of debt services to exports for middle-income economies have sharply increased in 2009 as export revenues declined Official development assistance from non-DAC donors, 2005–09 Beyond the DAC: The role of other providers of development assistance

320 320 321 322 323

323 323

334 337 340 343 347 363 379 383

BACK Primary data documentation Statistical methods Credits Bibliography Index of indicators

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393 404 406 408 418

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PARTNERS Defining, gathering, and disseminating international statistics is a collective effort of many people and organizations. The indicators presented in World Development Indicators are the fruit of decades of work at many levels, from the field workers who administer censuses and household surveys to the committees and working parties of the national and international statistical agencies that develop the nomenclature, classifications, and standards fundamental to an international statistical system. Nongovernmental organizations and the private sector have also made important contributions, both in gathering primary data and in organizing and publishing their results. And academic researchers have played a crucial role in developing statistical methods and carrying on a continuing dialogue about the quality and interpretation of statistical indicators. All these contributors have a strong belief that available, accurate data will improve the quality of public and private decisionmaking. The organizations listed here have made World Development Indicators possible by sharing their data and their expertise with us. More important, their collaboration contributes to the World Bank’s efforts, and to those of many others, to improve the quality of life of the world’s people. We acknowledge our debt and gratitude to all who have helped to build a base of comprehensive, quantitative information about the world and its people. For easy reference, Web addresses are included for each listed organization. The addresses shown were active on March 1, 2011. Information about the World Bank is also provided.

International and government agencies Carbon Dioxide Information Analysis Center The Carbon Dioxide Information Analysis Center (CDIAC) is the primary global climate change data and information analysis center of the U.S. Department of Energy. The CDIAC’s scope includes anything that would potentially be of value to those concerned with the greenhouse effect and global climate change, including concentrations of carbon dioxide and other radiatively active gases in the atmosphere, the role of the terrestrial biosphere and the oceans in the biogeochemical cycles of greenhouse gases, emissions of carbon dioxide to the atmosphere, long-term climate trends, the effects of elevated carbon dioxide on vegetation, and the vulnerability of coastal areas to rising sea levels. For more information, see http://cdiac.esd.ornl.gov/.

Deutsche Gesellschaft für Internationale Zusammenarbeit The Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH is a German government-owned corporation for international cooperation with worldwide operations. GIZ’s aim is to positively shape political, economic, ecological, and social development in partner countries, thereby improving people’s living conditions and prospects. For more information, see www.giz.de/.

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Food and Agriculture Organization The Food and Agriculture Organization, a specialized agency of the United Nations, was founded in October 1945 with a mandate to raise nutrition levels and living standards, to increase agricultural productivity, and to better the condition of rural populations. The organization provides direct development assistance; collects, analyzes, and disseminates information; offers policy and planning advice to governments; and serves as an international forum for debate on food and agricultural issues. For more information, see www.fao.org/.

Internal Displacement Monitoring Centre The Internal Displacement Monitoring Centre was established in 1998 by the Norwegian Refugee Council and is the leading international body monitoring conflict-induced internal displacement worldwide. The center contributes to improving national and international capacities to protect and assist the millions of people around the globe who have been displaced within their own country as a result of conflicts or human rights violations. For more information, see www.internal-displacement.org/.

International Civil Aviation Organization The International Civil Aviation Organization (ICAO), a specialized agency of the United Nations, is responsible for establishing international standards and recommended practices and procedures for the technical, economic, and legal aspects of international civil aviation operations. ICAO’s strategic objectives include enhancing global aviation safety and security and the efficiency of aviation operations, minimizing the adverse effect of global civil aviation on the environment, maintaining the continuity of aviation operations, and strengthening laws governing international civil aviation. For more information, see www.icao.int/.

International Energy Agency The International Energy Agency (IEA) was founded in 1973/74 with a mandate to facilitate cooperation among the IEA member countries to increase energy efficiency, promoting use of clean energy and technology, and diversify their energy sources while protecting the environment. IEA publishes annual and quarterly statistical publications covering both OECD and non-OECD countries’ statistics on oil, gas, coal, electricity and renewable sources of energy, energy supply and consumption, and energy prices and taxes. IEA also contributes in analysis of all aspects of sustainable development globally and provides policy recommendations. For more information, see www.iea.org/.

International Labour Organization The International Labour Organization (ILO), a specialized agency of the United Nations, seeks the promotion of social justice and internationally recognized human and labor rights. ILO helps advance the creation of decent jobs and the kinds of economic and working conditions that give working people and business people

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PARTNERS a stake in lasting peace, prosperity, and progress. As part of its mandate, the ILO maintains an extensive statistical publication program. For more information, see www.ilo.org/.

International Monetary Fund The International Monetary Fund (IMF) is an international organization of 187 member countries established to promote international monetary cooperation, a stable system of exchange rates, and the balanced expansion of international trade and to foster economic growth and high levels of employment. The IMF reviews national, regional, and global economic and financial developments; provides policy advice to member countries; and serves as a forum where they can discuss the national, regional, and global consequences of their policies. The IMF also makes financing temporarily available to member countries to help them address balance of payments problems. Among the IMF’s core missions are the collection and dissemination of high-quality macroeconomic and financial statistics as an essential prerequisite for formulating appropriate policies. The IMF provides technical assistance and training to member countries in areas of its core expertise, including the development of economic and financial data in accordance with international standards. For more information, see www.imf.org/.

International Telecommunication Union The International Telecommunication Union (ITU) is the leading UN agency for information and communication technologies. ITU’s mission is to enable the growth and sustained development of telecommunications and information networks and to facilitate universal access so that people everywhere can participate in, and benefit from, the emerging information society and global economy. A key priority lies in bridging the so-called Digital Divide by building information and communication infrastructure, promoting adequate capacity building, and developing confidence in the use of cyberspace through enhanced online security. ITU also concentrates on strengthening emergency communications for disaster prevention and mitigation. For more information, see www.itu.int/.

National Science Foundation The National Science Foundation (NSF) is an independent U.S. government agency whose mission is to promote the progress of science; to advance the national health, prosperity, and welfare; and to secure the national defense. NSF’s goals—discovery, learning, research infrastructure, and stewardship—provide an integrated strategy to advance the frontiers of knowledge, cultivate a world-class, broadly inclusive science and engineering workforce, expand the scientific literacy of all citizens, build the nation’s research capability through investments in advanced instrumentation and facilities, and support excellence in science and engineering research and education through a capable and responsive organization. For more information, see www.nsf.gov/.

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Organisation for Economic Co-operation and Development The Organisation for Economic Co-operation and Development (OECD) includes 34 member countries sharing a commitment to democratic government and the market economy to support sustainable economic growth, boost employment, raise living standards, maintain financial stability, assist other countries’ economic development, and contribute to growth in world trade. With active relationships with some 100 other countries, it has a global reach. It is best known for its publications and statistics, which cover economic and social issues from macroeconomics to trade, education, development, and science and innovation. The Development Assistance Committee (DAC, www.oecd.org/dac/) is one of the principal bodies through which the OECD deals with issues related to cooperation with developing countries. The DAC is a key forum of major bilateral donors, who work together to increase the effectiveness of their common efforts to support sustainable development. The DAC concentrates on two key areas: the contribution of international development to the capacity of developing countries to participate in the global economy and the capacity of people to overcome poverty and participate fully in their societies. For more information, see www.oecd.org/.

Stockholm International Peace Research Institute The Stockholm International Peace Research Institute (SIPRI) conducts research on questions of conflict and cooperation of importance for international peace and security, with the aim of contributing to an understanding of the conditions for peaceful solutions to international conflicts and for a stable peace. SIPRI’s main publication, SIPRI Yearbook, is an authoritive and independent source on armaments and arms control and other conflict and security issues. For more information, see www.sipri.org/.

Understanding Children’s Work As part of broader efforts to develop effective and long-term solutions to child labor, the International Labour Organization, the United Nations Children’s Fund (UNICEF), and the World Bank initiated the joint interagency research program “Understanding Children’s Work and Its Impact” in December 2000. The Understanding Children’s Work (UCW) project was located at UNICEF’s Innocenti Research Centre in Florence, Italy, until June 2004, when it moved to the Centre for International Studies on Economic Growth in Rome. The UCW project addresses the crucial need for more and better data on child labor. UCW’s online database contains data by country on child labor and the status of children. For more information, see www.ucw-project.org/.

United Nations The United Nations currently has 192 member states. The purposes of the United Nations, as set forth in its charter, are to maintain international peace and security; to develop friendly relations among nations; to cooperate in solving international economic, social, cultural, and humanitarian problems and in promoting respect for human rights and fundamental freedoms; and to be a center for harmonizing the actions of nations in attaining these ends. For more information, see www.un.org/.

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PARTNERS United Nations Centre for Human Settlements, Global Urban Observatory The Urban Indicators Programme of the United Nations Human Settlements Programme was established to address the urgent global need to improve the urban knowledge base by helping countries and cities design, collect, and apply policy-oriented indicators related to development at the city level. With the Urban Indicators and Best Practices programs, the Global Urban Observatory is establishing a worldwide information, assessment, and capacity-building network to help governments, local authorities, the private sector, and nongovernmental and other civil society organizations. For more information, see www.unhabitat.org/.

United Nations Children’s Fund The United Nations Children’s Fund (UNICEF) works with other UN bodies and with governments and nongovernmental organizations to improve children’s lives in more than 190 countries through various programs in education and health. UNICEF focuses primarily on five areas: child survival and development, basic education and gender equality (including girls’ education), child protection, HIV/AIDS, and policy advocacy and partnerships. For more information, see www.unicef.org/.

United Nations Conference on Trade and Development The United Nations Conference on Trade and Development (UNCTAD) is the principal organ of the United Nations General Assembly in the field of trade and development. Its mandate is to accelerate economic growth and development, particularly in developing countries. UNCTAD discharges its mandate through policy analysis; intergovernmental deliberations, consensus building, and negotiation; monitoring, implementation, and follow-up; and technical cooperation. For more information, see www.unctad.org/.

United Nations Department of Peacekeeping Operations The United Nations Department of Peacekeeping Operations contributes to the most important function of the United Nations—maintaining international peace and security. The department helps countries torn by conflict to create the conditions for lasting peace. The first peacekeeping mission was established in 1948 and has evolved to meet the demands of different conflicts and a changing political landscape. Today’s peacekeepers undertake a wide variety of complex tasks, from helping build sustainable institutions of governance, to monitoring human rights, to assisting in security sector reform, to disarmaming, demobilizing, and reintegrating former combatants. For more information, see www.un.org/en/peacekeeping/.

United Nations Educational, Scientific, and Cultural Organization, Institute for Statistics The United Nations Educational, Scientific, and Cultural Organization (UNESCO) is a specialized agency of the United Nations that promotes international cooperation among member states and associate members in education, science, culture, and communications. The UNESCO Institute for Statistics is the organization’s

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statistical branch, established in July 1999 to meet the growing needs of UNESCO member states and the international community for a wider range of policy-relevant, timely, and reliable statistics on these topics. For more information, see www.uis.unesco.org/.

United Nations Environment Programme The mandate of the United Nations Environment Programme is to provide leadership and encourage partnership in caring for the environment by inspiring, informing, and enabling nations and people to improve their quality of life without compromising that of future generations. For more information, see www.unep.org/.

United Nations Industrial Development Organization The United Nations Industrial Development Organization was established to act as the central coordinating body for industrial activities and to promote industrial development and cooperation at the global, regional, national, and sectoral levels. Its mandate is to help develop scientific and technological plans and programs for industrialization in the public, cooperative, and private sectors. For more information, see www.unido.org/.

United Nations Office on Drugs and Crime The United Nations Office on Drugs and Crime was established in 1977 and is a global leader in the fight against illicit drugs and international crime. The office assists member states in their struggle against illicit drugs, crime, and terrorism by helping build capacity, conducting research and analytical work, and assisting in the ratification and implementation of relevant international treaties and domestic legislation related to drugs, crime, and terrorism. For more information, see www.unodc.org/.

The UN Refugee Agency The UN Refugee Agency (UNHCR) is mandated to lead and coordinate international action to protect refugees and resolve refugee problems worldwide. Its primary purpose is to safeguard the rights and well-being of refugees. UNHCR also collects and disseminates statistics on refugees. For more information, see www.unhcr.org/.

Upsalla Conflict Data Program The Upsalla Conflict Data Program has collected information on armed violence since 1946 and is one of the most accurate and well used data sources on global armed conflicts. Its definition of armed conflict is becoming a standard in how conflicts are systematically defined and studied. In addition to data collection on armed violence, its researchers conduct theoretically and empirically based analyses of the causes, escalation, spread, prevention, and resolution of armed conflict. For more information, see www.pcr.uu.se/research/UCDP/.

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PARTNERS World Bank The World Bank is a vital source of financial and technical assistance for developing countries. The World Bank is made up of two unique development institutions owned by 187 member countries—the International Bank for Reconstruction and Development (IBRD) and the International Development Association (IDA). These institutions play different but collaborative roles to advance the vision of an inclusive and sustainable globalization. The IBRD focuses on middle-income and creditworthy poor countries, while IDA focuses on the poorest countries. Together they provide low-interest loans, interest-free credits, and grants to developing countries for a wide array of purposes, including investments in education, health, public administration, infrastructure, financial and private sector development, agriculture, and environmental and natural resource management. The World Bank’s work focuses on achieving the Millennium Development Goals by working with partners to alleviate poverty. For more information, see http://data.worldbank.org/.

World Health Organization The objective of the World Health Organization (WHO), a specialized agency of the United Nations, is the attainment by all people of the highest possible level of health. It is responsible for providing leadership on global health matters, shaping the health research agenda, setting norms and standards, articulating evidence-based policy options, providing technical support to countries, and monitoring and assessing health trends. For more information, see www.who.int/.

World Intellectual Property Organization The World Intellectual Property Organization (WIPO) is a specialized agency of the United Nations dedicated to developing a balanced and accessible international intellectual property (IP) system, which rewards creativity, stimulates innovation, and contributes to economic development while safeguarding the public interest. WIPO carries out a wide variety of tasks related to the protection of IP rights. These include developing international IP laws and standards, delivering global IP protection services, encouraging the use of IP for economic development, promoting better understanding of IP, and providing a forum for debate. For more information, see www.wipo.int/.

World Tourism Organization The World Tourism Organization is an intergovernmental body entrusted by the United Nations with promoting and developing tourism. It serves as a global forum for tourism policy issues and a source of tourism know-how. For more information, see www.unwto.org/.

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2011 World Development Indicators

World Trade Organization The World Trade Organization (WTO) is the only international organization dealing with the global rules of trade between nations. Its main function is to ensure that trade flows as smoothly, predictably, and freely as possible. It does this by administering trade agreements, acting as a forum for trade negotiations, settling trade disputes, reviewing national trade policies, assisting developing countries in trade policy issues—through technical assistance and training programs—and cooperating with other international organizations. At the heart of the system—known as the multilateral trading system—are the WTO’s agreements, negotiated and signed by a large majority of the world’s trading nations and ratified by their parliaments. For more information, see www.wto.org/.

Private and nongovernmental organizations Containerisation International Containerisation International Yearbook is one of the most authoritative reference books on the container industry. The information can be accessed on the Containerisation International Web site, which also provides a comprehensive online daily business news and information service for the container industry. For more information, see www.ci-online.co.uk/.

DHL DHL provides shipping and customized transportation solutions for customers in more than 220 countries and territories. It offers expertise in express, air, and ocean freight; overland transport; contract logistics solutions; and international mail services. For more information, see www.dhl.com/.

International Institute for Strategic Studies The International Institute for Strategic Studies (IISS) provides information and analysis on strategic trends and facilitates contacts between government leaders, business people, and analysts that could lead to better public policy in international security and international relations. The IISS is a primary source of accurate, objective information on international strategic issues. For more information, see www.iiss.org/.

International Road Federation The International Road Federation (IRF) is a nongovernmental, not-for-profit organization whose mission is to encourage and promote development and maintenance of better, safer, and more sustainable roads and road networks. Working together with its members and associates, the IRF promotes social and economic benefits that flow from well planned and environmentally sound road transport networks. It helps put in place technological solutions and management practices that provide maximum economic and social returns from national road investments. The IRF works in all aspects of road policy and development worldwide with governments and financial institutions, members, and the community of road professionals. For more information, see www.irfnet.org/.

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PARTNERS Netcraft Netcraft provides Internet security services such as antifraud and antiphishing services, application testing, code reviews, and automated penetration testing. Netcraft also provides research data and analysis on many aspects of the Internet and is a respected authority on the market share of web servers, operating systems, hosting providers, Internet service providers, encrypted transactions, electronic commerce, scripting languages, and content technologies on the Internet. For more information, see http://news.netcraft.com/.

PricewaterhouseCoopers PricewaterhouseCoopers provides industry-focused services in the fields of assurance, tax, human resources, transactions, performance improvement, and crisis management services to help address client and stakeholder issues. For more information, see www.pwc.com/.

Standard & Poor’s Standard & Poor’s is the world’s foremost provider of independent credit ratings, indexes, risk evaluation, investment research, and data. S&P’s Global Stock Markets Factbook draws on data from S&P’s Emerging Markets Database (EMDB) and other sources covering data on more than 100 markets with comprehensive market profiles for 82 countries. Drawing a sample of stocks in each EMDB market, Standard & Poor’s calculates indexes to serve as benchmarks that are consistent across national boundaries. For more information, see www.standardandpoors.com/.

World Conservation Monitoring Centre The World Conservation Monitoring Centre provides information on the conservation and sustainable use of the world’s living resources and helps others to develop information systems of their own. It works in close collaboration with a wide range of people and organizations to increase access to the information needed for wise management of the world’s living resources. For more information, see www.unep-wcmc.org/.

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2011 World Development Indicators

World Economic Forum The World Economic Forum (WEF) is an independent international organization committed to improving the state of the world by engaging leaders in partnerships to shape global, regional, and industry agendas. Economic research at the WEF—led by the Global Competitiveness Programme—focuses on identifying the impediments to growth so that strategies to achieve sustainable economic progress, reduce poverty, and increase prosperity can be developed. The WEF’s competitiveness reports range from global coverage, such as Global Competitiveness Report, to regional and topical coverage, such as Africa Competitiveness Report, The Lisbon Review, and Global Information Technology Report. For more information, see www.weforum.org/.

World Resources Institute The World Resources Institute is an independent center for policy research and technical assistance on global environmental and development issues. The institute provides—and helps other institutions provide— objective information and practical proposals for policy and institutional change that will foster environmentally sound, socially equitable development. The institute’s current areas of work include trade, forests, energy, economics, technology, biodiversity, human health, climate change, sustainable agriculture, resource and environmental information, and national strategies for environmental and resource management. For more information, see www.wri.org/.

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USERS GUIDE Tables

gap-filled estimates for missing data and by an s, for

complex technical and conceptual problems that can-

The tables are numbered by section and display the

simple totals, where they do not), median values (m),

not be resolved unequivocally. Data coverage may

identifying icon of the section. Countries and econo-

weighted averages (w), or simple averages (u). Gap

not be complete because of special circumstances

mies are listed alphabetically (except for Hong Kong

filling of amounts not allocated to countries may result

affecting the collection and reporting of data, such

SAR, China, which appears after China). Data are

in discrepancies between subgroup aggregates and

as problems stemming from conflicts.

shown for 155 economies with populations of more

overall totals. For further discussion of aggregation

than 1 million, as well as for Taiwan, China, in selected

methods, see Statistical methods.

tables. Table 1.6 presents selected indicators for 58

For these reasons, although data are drawn from sources thought to be the most authoritative, they should be construed only as indicating trends and

other economies—small economies with populations

Aggregate measures for regions

characterizing major differences among economies

between 30,000 and 1 million and smaller econo-

The aggregate measures for regions include only

rather than as offering precise quantitative mea-

mies if they are members of the International Bank

low- and middle-income economies including econo-

sures of those differences. Discrepancies in data

for Reconstruction and Development (IBRD) or, as it

mies with populations of less than 1 million listed

presented in different editions of World Development

is commonly known, the World Bank. Data for these

in table 1.6.

Indicators reflect updates by countries as well as

economies are included on the World Development

The country composition of regions is based on the

revisions to historical series and changes in meth-

Indicators CD-ROM and the World Bank’s Open Data

World Bank’s analytical regions and may differ from

odology. Thus readers are advised not to compare

website at data.worldbank.org/.

common geographic usage. For regional classifica-

data series between editions of World Development

The term country, used interchangeably with

tions, see the map on the inside back cover and the

Indicators or between different World Bank publica-

economy, does not imply political independence, but

list on the back cover flap. For further discussion of

tions. Consistent time-series data for 1960–2009

refers to any territory for which authorities report

aggregation methods, see Statistical methods.

are available on the World Development Indicators

separate social or economic statistics. When avail-

CD-ROM and at data.worldbank.org/.

able, aggregate measures for income and regional

Statistics

groups appear at the end of each table.

Except where otherwise noted, growth rates are

Data are shown for economies as they were con-

in real terms. (See Statistical methods for information

Indicators are shown for the most recent year or

stituted in 2009, and historical data are revised to

on the methods used to calculate growth rates.) Data

period for which data are available and, in most tables,

reflect current political arrangements. Exceptions are

for some economic indicators for some economies

for an earlier year or period (usually 1990 or 1995 in

noted throughout the tables.

are presented in fiscal years rather than calendar

this edition). Time-series data for all 213 economies

Additional information about the data is provided

years; see Primary data documentation. All dollar fig-

are available on the World Development Indicators CD-

in Primary data documentation. That section sum-

ures are current U.S. dollars unless otherwise stated.

ROM and at data.worldbank.org/.

marizes national and international efforts to improve

The methods used for converting national currencies

Known deviations from standard definitions or

basic data collection and gives country-level informa-

are described in Statistical methods.

breaks in comparability over time or across countries

tion on primary sources, census years, fiscal years,

are either footnoted in the tables or noted in About

statistical methods and concepts used, and other

Country notes

the data. When available data are deemed to be

background information. Statistical methods provides

•

too weak to provide reliable measures of levels and

technical information on some of the general calcula-

trends or do not adequately adhere to international

tions and formulas used throughout the book.

include data for Hong Kong SAR, China; Macao SAR, China; or Taiwan, China. •

standards, the data are not shown.

Unless otherwise noted, data for China do not

Data for Indonesia include Timor-Leste through 1999 unless otherwise noted.

Data consistency, reliability, and comparability •

Montenegro declared independence from Serbia

Aggregate measures for income groups

Considerable effort has been made to standardize

The aggregate measures for income groups include

the data, but full comparability cannot be assured,

and Montenegro on June 3, 2006. Where avail-

213 economies (the economies listed in the main

and care must be taken in interpreting the indicators.

able, data for each country are shown separately.

tables plus those in table 1.6) whenever data are

Many factors affect data availability, comparability,

However, for the Serbia listing, some indicators

available. To maintain consistency in the aggregate

and reliability: statistical systems in many develop-

continue to include data for Montenegro through

measures over time and between tables, missing

ing economies are still weak; statistical methods,

2005; these data are footnoted in the tables.

data are imputed where possible. The aggregates

coverage, practices, and definitions differ widely; and

Moreover, data from 1999 onward for Serbia for

are totals (designated by a t if the aggregates include

cross-country and intertemporal comparisons involve

most indicators exclude data for Kosovo, 1999

xxii

2011 World Development Indicators

being the year when Kosovo became a territory

more. The 17 participating member countries of the

under international administration pursuant to

Euro area are presented as a subgroup under high-

UN Security Council Resolution 1244 (1999); any

income economies. Estonia joined the Euro area on

exceptions are noted. Kosovo became a World

January 1, 2011.

Bank member on June 29, 2009; available data are •

shown separately for Kosovo in the main tables.

Symbols

Netherlands Antilles ceased to exist on October

..

10, 2010. Curaçao and St. Maarten became

means that data are not available or that aggregates

countries within the Kingdom of the Netherlands.

cannot be calculated because of missing data in the

Bonaire, St. Eustatius, and Saba became special

years shown.

municipalities of the Netherlands. 0 or 0.0

Classification of economies

means zero or small enough that the number would

For operational and analytical purposes the World

round to zero at the displayed number of decimal

Bank’s main criterion for classifying economies is

places.

gross national income (GNI) per capita (calculated by the World Bank Atlas method). Every economy

/

is classified as low income, middle income (subdi-

in dates, as in 2003/04, means that the period of

vided into lower middle and upper middle), or high

time, usually 12 months, straddles two calendar

income. For income classifications see the map on

years and refers to a crop year, a survey year, or a

the inside front cover and the list on the front cover

fiscal year.

flap. Low- and middle-income economies are sometimes referred to as developing economies. The term

$

is used for convenience; it is not intended to imply

means current U.S. dollars unless otherwise noted.

that all economies in the group are experiencing similar development or that other economies have

>

reached a preferred or final stage of development.

means more than.

Note that classification by income does not necessarily reflect development status. Because GNI per

<

capita changes over time, the country composition

means less than.

of income groups may change from one edition of World Development Indicators to the next. Once the

Data presentation conventions

classification is fixed for an edition, based on GNI

•

A blank means not applicable or, for an aggregate, not analytically meaningful.

per capita in the most recent year for which data are available (2009 in this edition), all historical data

•

A billion is 1,000 million.

presented are based on the same country grouping.

•

A trillion is 1,000 billion.

Low-income economies are those with a GNI per

•

Figures in italics refer to years or periods other

capita of $995 or less in 2009. Middle-income econ-

than those specified or to growth rates calculated

omies are those with a GNI per capita of more than $995 but less than $12,196. Lower middle-income and upper middle-income economies are separated

for less than the full period specified. •

Data for years that are more than three years from the range shown are footnoted.

at a GNI per capita of $3,945. High-income economies are those with a GNI per capita of $12,196 or

The cutoff date for data is February 1, 2011.

2011 World Development Indicators

xxiii

WORLD VIEW

Introduction

“Our aim is for open data, open knowledge, and open solutions.” —Robert Zoellick, Georgetown University, September 2010

W

orld Development Indicators provides a comprehensive selection of national and international data that focus attention on critical development issues, facilitate research, encourage debate and analysis of policy options, and monitor progress toward development goals. Organized around six themes—world view, people, environment, economy, states and markets, and global links—the book contains more than 800 indicators for 155 economies with a population of 1 million people or more, together with relevant aggregates. The online database includes more than 1,100 indicators for 213 economies, with many time series extending back to 1960. In 2010, to improve the impact of the indicators and to provide a platform for others to use the data to solve pressing development challenges, the World Development Indicators database and many other public databases maintained by the World Bank were made available as open data: free of charge, in accessible nonproprietary formats on the World Wide Web. This year, the first part of the introduction to the World View section provides an overview of the initiative, the impact of moving to an open data platform, a brief survey of the global open data movement, and an examination of its relevance to development. The second part reviews progress toward the Millennium Development Goals—whose target date of 2015 is now just four years away.

The World Bank Open Data Initiative The Open Data Initiative is a new strategy for reaching data users and a major change in the Bank’s business model for data, which had previously been a subscription-based model for licensing data access and use, using a network of university libraries, development agencies, and private firms, and free access provided through the World Bank’s Public Information Centers and depository libraries. At the time of the open data announcement there were around 140,000 regular users of the subscription database annually—a substantial number for a highly specialized data product. But providing free and easier access to the databases has had an immediate and lasting impact on data use. Since April 2010 the new data website—http://data.worldbank.

org—has recorded well over 20 million page views. And at the time of printing this edition of World Development Indicators, it provides data to more than 100,000 unique visitors each week, three times as many as before (figure 1a). Making the World Development Indicators and other databases free was only the first step in creating an open data environment. Open data should mean that users can access and search public datasets at no cost, combine data from different sources, add data and select data records to include or exclude in derived works, change the format or structure of the data, and give away or sell any products they create. For the World Bank, this required designing new user interfaces and developing new search tools to more easily find and report the data. It also required a new license defining the terms of

1

Use of World Bank data has risen with the launch of the Open Data Initiative

1a

Weekly unique visitors to http://data.worldbank.org (thousands) 125 April 2010 Launch of the Open Data Initiative

100 75

Recess period for US and European academic teaching institutions

50 25 0 January 2010

April 2010

July 2010

October 2010

January 2011

Source: World Bank staff calculations from Omniture data.

2011 World Development Indicators

1

1b

Terms of use for World Bank data

Why do open data need to be licensed? Because a license conveys certain rights to the licensee—in this case, the data user—while protecting the interests of the licensor. If there is no explicit license attached to a dataset, users may be uncertain of their rights. Can they republish these data? Can they include them in a new dataset along with data from other sources? Can they give them away or resell them? Intellectual property laws differ by country. In an international environment where data are published on the World Wide Web, it may not be clear what law applies. Lacking a license, a cautious data user would assume that he or she should seek permission of the dataset owner or publisher, creating a real or imagined impediment to using the data. A license can help encourage data use by making clear exactly what is permitted, true even for free data. Use of data in the World Bank’s Data Catalog is governed by the Terms of Use of Datasets posted at http://data.worldbank.org. The terms follow the general model of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0) and the Open Data Commons Attribution License (www.opendatacommons.org/licenses/by/1.0). These licenses require users to acknowledge the original source when they publish or reuse the data, particularly important for World Development Indicators, where many datasets are obtained from sources such as specialized UN and international agencies. The terms of use impose some further limitations, still within the spirit of an open data license: users may not claim endorsement by the World Bank or use its name or logos without permission. Acknowledging data sources is good practice, regardless of the terms of a license. Identifying sources makes it possible for others to locate the same or similar data. And credit to data producers or publisher recognizes their effort and encourages them to continue. The World Bank’s Terms of Use for Datasets provide a suggested form of attribution: The World Bank: Dataset name: Data source. The information for completing this form of attribution is available in the metadata supplied with data downloaded from http://data.worldbank.org.

use for data (box 1b). And it required new thinking to promote the use and reuse of data. To reach out to new audiences and communities of data users, the World Bank organized a global “Apps for Development” competition—one of the first of its kind—inviting developers to create new applications for desktop computers or mobile devices using World Bank datasets, including World Development Indicators data. Open data and open government Advocates of greater transparency in public agencies—the open government movement— have been among the most vocal proponents of open data. Likewise, those seeking databases to build new applications have supported freedom of information laws and unrestricted access to data created by public agencies. Opening public databases empowers people because data are essential for monitoring the performance of governments and the impact of public policies on citizens. For advocates of open data, governments are vast repositories of statistical and nonstatistical information with unrealized potential for creative applications. The political, philosophical, and economic impulses for open data and open government are often linked. One advocate of open data writes, “The term ‘Open Data’ refers to the philosophical and methodological approach to the democratization of data

2

2011 World Development Indicators

enabling citizens to access and create value through the reuse of public sector information” (Rahemtulla 2011). The Sunlight Foundation, a U.S.-based civil society organization, describes its goals as “improving access to government information by making it available online, indeed redefining ‘public’ information as meaning ‘online,’ and . . . creating new tools and websites to enable individuals and communities to better access that information and put it to use. . . . We want to catalyze greater government transparency by engaging individual citizens and communities— technologists, policy wonks, open government advocates, and ordinary citizens —demanding policies that will enable all of us to hold government accountable” (http://sunlightfoundation. com/about/). Digital information and communication technologies permitting dissemination of large amounts of data at little or no cost have strengthened the argument for providing free access to public sector information. Pollock (2010) estimates the direct benefit to the U.K. public of providing free access to public sector information that was previously sold to be £1.6–£6 billion, 4–15 times the forgone sales revenues of £400 million. Additional indirect benefits come from new products and services using open datasets or complementary products and services and from reducing the transaction costs to data users and reusers. Open data and open government initiatives have progressed farther in rich countries than in developing ones. This may reflect a lack of political will or popular demand, but it often reflects a lack of technical capacity and resources to make data available in accessible formats. A study commissioned by the Transparency and Accountability Initiative (Hogge 2010) identified three drivers behind the success of the U.K. and U.S. data.gov initiatives: • Civil society, particularly a small and motivated group of “civic hackers” responsible for developing grassroots political engagement websites. • An engaged and well resourced “middle layer” of skilled government bureaucrats. • A top-level mandate, motivated by an outside force (in the United Kingdom) or a refreshed political administration hungry for change (in the United States). Statistical offices exemplify the “middle layer” of a government bureaucracy, uniquely

WORLD VIEW

skilled in collecting and organizing large datasets. But even they may lack the motivation or resources to make their products freely available to the public unless they enjoy full support from the top. In developing countries aid donors can act as fourth driver by providing technical assistance and funding for open data projects and by modeling transparency in their own practices. The International Aid Transparency Initiative— the World Bank is a founding member—aims to create a global repository of information on aid flows, starting from the commitment of funding from donors and continuing through its disbursement to recipient countries, the allocation of aid money in national budgets, the procurement of goods and services, and the measurement of results. To fulfill the initiative’s goal of providing a complete accounting of aid to the citizens of donor and developing countries will require cooperation among donors and recipients. Terminology and coding systems must be standardized and agreements reached on everything from the timing of reports to the mechanisms for posting and accessing the datasets. In many cases donor governments and international agencies will have to change their rules on access to information to provide full transparency to their aid programs (box 1c). For more information on the initiative, see www. aidtransparency.net. Mapping for results—making data not just accessible but useful The new Access to Information Policy and the Open Data Initiative provide much greater access to the World Bank Group’s knowledge resources than before. But accessible information is not the same as usable information. Project documents contain a wealth of data about planned activities—for instance, on their location. But it may be difficult for many interested parties, such as project beneficiaries, citizen groups, and civil society organizations, to extract and visualize relevant data from long texts or tables. To help solve this problem, the World Bank, on a pilot basis, has started to provide geolocation codes along with data and information about the projects that it supports. The objective is to improve aid effectiveness through enhanced transparency and accountability of project activities. Location information makes

1c

Access to information at the World Bank

Opening the World Bank’s databases is part of a broader effort to introduce greater transparency in the World Bank’s operations, and a new policy on information disclosure went into effect on July 1, 2010. Besides formalizing the Open Data Initiative, the Access to Information Policy (www.worldbank.org/wbaccess) establishes the principle that the World Bank will disclose any information in its possession that is not on a specific list of exceptions. In the past, only documents selected for disclosure were available to the public. The new policy reverses the process and presumes that most information is disclosable. Exceptions include personal information and staff records, internal deliberations and administrative matters, and information received in confidence from clients and third parties. Some documents with restricted access are subject to a declassification schedule, ensuring that they will become available to the public in due course. A process for requesting documents has also been established that allows users to search for documents by country and topic in seven languages.

the data become “local” and much more accessible and relevant to project stakeholders. The data are open and available directly to software developers though an application programming interface and through an interactive web-based application called Mapping for Results (http:// maps.worldbank.org). In keeping with the philosophy of the Open Data Initiative, the Mapping for Results application uses the dataset of geo-located project activities and combines the data with subnational human and social development indicators, such as child mortality rates, poverty incidence, malnutrition, and population measures. But even more value may lie in what other researchers and software developers might do with the data, combining them with their own data or with data from other sources, performing their own analysis, or providing applications that help citizens and beneficiaries connect directly with the project during implementation, through feedback or other mechanisms.

Countdown to the Millennium Development Goals in 2015 There are four years to the target date for the Millennium Development Goals (MDGs). The MDGs have focused the world’s attention on the living conditions of billions of people who live in poor and developing countries and on the need to improve the quality, frequency, and timeliness of the statistics used to track their progress. Progress toward the MDGs has been marked by slow changes in outcome indicators and by improvements in data availability. World Development Indicators has monitored global and regional trends in poverty reduction, education, health, and the environment since 1997. After the UN Millennium Summit in 2000, World Development Indicators began closely tracking the progress of countries 2011 World Development Indicators

3

Progress toward eradicating poverty Share of countries making progress toward reducing extreme poverty by half (percent) 100

1d Reached target Off track Seriously off track

On track Insufficient data

50

0

50

100 2004 140 countries

2011 144 countries

Source: World Bank staff estimates.

Progress toward universal primary education completion Share of countries making progress toward full completion of primary education (percent)

1e

Reached target Off track Seriously off track

On track Insufficient data

100

50

0

50

100

2004 140 countries

2011 144 countries

Source: World Bank staff estimates.

Progress toward gender parity Share of countries making progress toward gender parity in primary and secondary education (percent) 100

1f Reached On track target Off track Seriously off track Insufficient data

50

0

50

100 2004 140 countries Source: World Bank staff estimates.

4

2011 World Development Indicators

2011 144 countries

against the targets selected for the MDGs. The MDGs highlight important outcomes, but the focus on this limited set of indicators should not obscure the fact that development is a complex process whose course is determined in part by geographic location, historical circumstances, institutional capacity, and uncontrollable events such as weather and natural disasters. Success or failure, while not arbitrary or entirely accidental, still has a large component of chance. This review employs the same assessment method that World Development Indicators has used since 2004 to track progress of countries toward the time-bound and quantified targets of the MDGs. Countries are “on track” if their past progress equals or exceeds the rate of change necessary to reach an MDG target. A few countries have already reached their targets. They are counted as having achieved the goal, although some may slip back. Countries making less than necessary progress are “off track,” or “seriously off track” if their past rate progress would not allow them to reach the target even in another 25 years. The remaining countries do not have sufficient data to evaluate their progress—in some cases because there are no data for the benchmark period of 1990–99 and in others because more recent data are missing. But the situation is improving: starting from the earliest World Development Indicators progress assessments in 2004 (based on data for 1990–2002), the number of countries with insufficient data has fallen, enhancing our picture of progress toward the MDGs. For more information on the work of the World Bank and its partners to achieve the MDGs, see www.worldbank.org/mdgs, which includes a link to the World Bank’s MDG eAtlas. Goal 1. Eradicate extreme poverty and hunger The number of people living on less than $1.25 a day fell from 1.8 billion in 1990 to 1.4 billion in 2005. New global and regional estimates, to become available later in 2011, are likely to show a continuation of past trends, although the financial crisis of 2008 and the recent surge in food prices will have slowed progress in some countries. Because household income and expenditure surveys are expensive and time consuming, they are not conducted frequently and there are often difficulties in making reliable comparisons over time or across countries.

WORLD VIEW

For 140 developing countries, figure 1d compares the progress assessments in 2005 and in 2011, based on available data. Forty-three countries are on track or have reached the target of cutting the extreme poverty rate in half, twice as many as in 2005. They include China, Brazil, and the Russian Federation. India, with more than 400 million people living in poverty lags behind, but with faster economic growth may well reach the 2015 target. Goal 2. Achieve universal primary education The goal of providing universal primary education has proved surprisingly hard to achieve. Completion rates measure the proportion of children enrolled in the final year of primary education after adjusting for repetition. In 2011, 49 countries had achieved or were on track to achieve 100 percent primary completion rates, only three more than in 2004, and the number of countries seriously off track has increased, especially in Sub-Saharan Africa (figure 1e). There are more and better data, but the goal remains elusive. Goal 3. Promote gender equality Gender equality and empowering women foster progress toward all the Millennium Development Goals. Equality of educational opportunities, measured by the ratio of girls’ to boys’ enrollments in primary and secondary education, is a starting point. Since the 2004 assessment, the number of countries on track to reach the target has increased steadily, driven by rising enrollments of girls, and the number of countries without sufficient data to measure progress has dropped (figure 1f). Goal 4. Reduce child mortality Of 144 countries with data in February 2011, 11 had achieved a two-thirds reduction in their under-five child mortality rate, and another 25 were on track to do so (figure 1g). This is remarkable progress since 2004, but more than 100 countries remain off track, and only a few of them are likely to reach the MDG target by 2015. Measuring child mortality is the product of a successful collaboration of international statisticians. By bringing together the most reliable data from multiple sources and applying appropriate estimation methods, consistent time series comparable across countries are available for monitoring this important indicator. More information about data sources

Progress toward reducing child mortality

1g Reached On track target Off track Seriously off track Insufficient data

Share of countries making progress toward reducing under-five child mortality by two-thirds (percent) 100

50

0

50

100 2004 140 countries

2011 144 countries

Source: World Bank staff estimates.

Progress toward improving maternal health

1h Reached On track target Off track Seriously off track Insufficient data

Share of countries making progress toward providing skilled attendants at births (percent) 100

50

0

50

100 2004 140 countries

2011 144 countries

Source: World Bank staff estimates.

HIV incidence is remaining stable or decreasing in many developing countries, but many lack data

1i

Change in HIV incidence rate, 2001–09 (number of developing countries) 100

75

50

25

0

Incidence increased by more than 25%

Stable

Incidence decreased by more than 25%

No data

Source: Joint United Nations Programme on HIV/AIDS.

2011 World Development Indicators

5

Progress on access to an improved water source Share of countries reducing proportion of population without access to an improved water source by half (percent)

1j Reached On track target Off track Seriously off track Insufficient data

100

50

0

50

100 2004 140 countries

2011 144 countries

Source: World Bank staff estimates.

Progress on access to improved sanitation Share of countries making progress toward improved sanitation (percent)

1k Reached target Off track Seriously off track

On track Insufficient data

100

50

0

50

100

2004 140 countries

2011 144 countries

Source: World Bank staff estimates.

and estimation methods is available at www. childmortality.org. Goal 5. Improve maternal health Reliable measurements of maternal mortality are difficult to obtain. Many national estimates are not comparable over time or across countries because of differences in methods and estimation techniques. Consistently modeled estimates that became available only recently show that 30 countries are on track to achieve a three-quarter reduction in their maternal mortality ratio and that 94 are off track or seriously off track. Figure 1h compares the availability of skilled birth attendants, a critical factor for reducing maternal and infant deaths, using data from the 2004 and 2011 World Development

6

2011 World Development Indicators

Indicators. While the number of countries seriously off track has increased, the number without adequate data has decreased, and the number providing skilled attendants at birth has risen 35 percent. Goal 6. Combat HIV/AIDS, malaria, and other diseases When the MDGs were formulated, the HIV/AIDS epidemic was spreading rapidly, engulfing many poor countries in Southern Africa. Data on the extent of the epidemic were derived from sentinel sites and limited reporting through health systems. The goal refers to halting and reversing the spread of HIV/AIDS. Under the circumstances it was impossible to set time-bound quantified targets. Now the statistical record is beginning to improve. UNAIDS, in its 2010 Report on the Global AIDS Epidemic, estimates that the annual number of new HIV infections has fallen 21 percent since its peak in 1997 (figure 1i). But reliable estimates of incidence are available for only 60 developing countries and do not include Brazil, China, and the Russian Federation. Goal 7. Ensure environmental sustainability Reversing environmental losses and ensuring a sustainable flow of services from the Earth’s resources have many dimensions: preserving forests, protecting plant and animal species, reducing carbon emissions, and limiting and adapting to the effects of climate change. Improving the built environment is also important. The MDGs set targets for reducing the proportion of people without access to safe water and sanitation by half. The ability to measure progress toward both targets has improved signifi cantly since 2004, and almost half the developing countries with sufficient data are on track to meet the water target (figure 1j). Progress in providing access to sanitation has been slower: almost half the countries are seriously off track (figure 1k). Goal 8. Develop a global partnership for development Partnership between high-income and developing economies, fundamental to achieving the MDGs, rests on four pillars: reducing external debt of developing countries, increasing their access to markets in OECD countries, realizing the benefits of new technologies and essential drugs, and providing financing for development programs in the poorest countries. Following

WORLD VIEW

the adoption of the MDGs, the International Conference on Financing for Development in 2002 urged developed countries “to make concrete efforts toward the target of 0.7 percent of gross national income [GNI] as official development assistance to developing countries.” Since then many countries have increased their official development assistance, but few have reached the target of 0.7 percent (figure 1l). In 2009, five countries provided more than 0.7 percent of their GNI as aid, but their share of total aid was only 15 percent. The largest share of total aid was provided by 10 donors that gave 0.3–0.7 percent of their GNI. The largest single donor, the United States, provided 0.21 percent of its GNI as official development assistance.

Ofﬁcial development assistance provided by Development Assistance Committee members Official development assistance provided, by share of GNI (2009 $ billions)

1l

0.7% GNI or more 0.3% to <0.7% GNI 0.2% to <0.3% GNI <0.2% GNI

150

100

50

0 2000

2009

Source: World Bank staff estimates.

2011 World Development Indicators

7

Millennium Development Goals Goals and targets from the Millennium Declaration

Indicators for monitoring progress

Goal 1 Eradicate extreme poverty and hunger Target 1.A Halve, between 1990 and 2015, the proportion of people whose income is less than $1 a day

1.1

Target 1.B Achieve full and productive employment and decent work for all, including women and young people

1.2 1.3 1.4 1.5 1.6 1.7

Target 1.C Halve, between 1990 and 2015, the proportion of people who suffer from hunger

1.8 1.9

Goal 2 Achieve universal primary education Target 2.A Ensure that by 2015 children everywhere, boys and girls alike, will be able to complete a full course of primary schooling

2.1 2.2 2.3

Goal 3 Promote gender equality and empower women Target 3.A Eliminate gender disparity in primary and secondary education, preferably by 2005, and in all levels of education no later than 2015

3.1 3.2 3.3

Goal 4 Reduce child mortality Target 4.A Reduce by two-thirds, between 1990 and 2015, the under-five mortality rate

Goal 5 Improve maternal health Target 5.A Reduce by three-quarters, between 1990 and 2015, the maternal mortality ratio Target 5.B Achieve by 2015 universal access to reproductive health

Target 6.B Achieve by 2010 universal access to treatment for HIV/AIDS for all those who need it Target 6.C Have halted by 2015 and begun to reverse the incidence of malaria and other major diseases

Net enrollment ratio in primary education Proportion of pupils starting grade 1 who reach last grade of primary education Literacy rate of 15- to 24-year-olds, women and men Ratios of girls to boys in primary, secondary, and tertiary education Share of women in wage employment in the nonagricultural sector Proportion of seats held by women in national parliament

4.1 4.2 4.3

Under-five mortality rate Infant mortality rate Proportion of one-year-old children immunized against measles

5.1 5.2 5.3 5.4 5.5

Maternal mortality ratio Proportion of births attended by skilled health personnel Contraceptive prevalence rate Adolescent birth rate Antenatal care coverage (at least one visit and at least four visits) Unmet need for family planning

5.6 Goal 6 Combat HIV/AIDS, malaria, and other diseases Target 6.A Have halted by 2015 and begun to reverse the spread of HIV/AIDS

Proportion of population below $1 purchasing power parity (PPP) a day1 Poverty gap ratio [incidence × depth of poverty] Share of poorest quintile in national consumption Growth rate of GDP per person employed Employment to population ratio Proportion of employed people living below $1 (PPP) a day Proportion of own-account and contributing family workers in total employment Prevalence of underweight children under five years of age Proportion of population below minimum level of dietary energy consumption

HIV prevalence among population ages 15–24 years Condom use at last high-risk sex Proportion of population ages 15–24 years with comprehensive, correct knowledge of HIV/AIDS 6.4 Ratio of school attendance of orphans to school attendance of nonorphans ages 10–14 years 6.5 Proportion of population with advanced HIV infection with access to antiretroviral drugs 6.6 Incidence and death rates associated with malaria 6.7 Proportion of children under age five sleeping under insecticide-treated bednets 6.8 Proportion of children under age five with fever who are treated with appropriate antimalarial drugs 6.9 Incidence, prevalence, and death rates associated with tuberculosis 6.10 Proportion of tuberculosis cases detected and cured under directly observed treatment short course

6.1 6.2 6.3

The Millennium Development Goals and targets come from the Millennium Declaration, signed by 189 countries, including 147 heads of state and government, in September 2000 (www. un.org/millennium/declaration/ares552e.htm) as updated by the 60th UN General Assembly in September 2005. The revised Millennium Development Goal (MDG) monitoring framework shown here, including new targets and indicators, was presented to the 62nd General Assembly, with new numbering as recommended by the Inter-agency and Expert Group on MDG Indicators at its 12th meeting on 14 November 2007. The goals and targets are interrelated and should be seen as a whole. They represent a partnership between the developed countries and the developing countries “to create an environment—at the national and global levels alike—which is conducive to development and the elimination of poverty.” All indicators should be disaggregated by sex and urban-rural location as far as possible.

8

2011 World Development Indicators

Goals and targets from the Millennium Declaration

Indicators for monitoring progress

Goal 7 Ensure environmental sustainability Target 7.A Integrate the principles of sustainable development into country policies and programs and reverse the loss of environmental resources

7.1 7.2

Target 7.B Reduce biodiversity loss, achieving, by 2010, a significant reduction in the rate of loss

Target 7.C Halve by 2015 the proportion of people without sustainable access to safe drinking water and basic sanitation Target 7.D Achieve by 2020 a significant improvement in the lives of at least 100 million slum dwellers Goal 8 Develop a global partnership for development Target 8.A Develop further an open, rule-based, predictable, nondiscriminatory trading and financial system

Proportion of land area covered by forest Carbon dioxide emissions, total, per capita and per $1 GDP (PPP) 7.3 Consumption of ozone-depleting substances 7.4 Proportion of fish stocks within safe biological limits 7.5 Proportion of total water resources used 7.6 Proportion of terrestrial and marine areas protected 7.7 Proportion of species threatened with extinction 7.8 Proportion of population using an improved drinking water source 7.9 Proportion of population using an improved sanitation facility 7.10 Proportion of urban population living in slums2

Some of the indicators listed below are monitored separately for the least developed countries (LDCs), Africa, landlocked developing countries, and small island developing states.

(Includes a commitment to good governance, development, and poverty reduction—both nationally and internationally.)

Target 8.B

Target 8.C

Target 8.D

Target 8.E

Target 8.F

Official development assistance (ODA) 8.1 Net ODA, total and to the least developed countries, as percentage of OECD/DAC donors’ gross national income 8.2 Proportion of total bilateral, sector-allocable ODA of OECD/DAC donors to basic social services (basic education, primary health care, nutrition, safe water, and Address the special needs of the least developed sanitation) countries 8.3 Proportion of bilateral official development assistance of OECD/DAC donors that is untied (Includes tariff and quota-free access for the least 8.4 ODA received in landlocked developing countries as a developed countries’ exports; enhanced program of proportion of their gross national incomes debt relief for heavily indebted poor countries (HIPC) and cancellation of official bilateral debt; and more 8.5 ODA received in small island developing states as a proportion of their gross national incomes generous ODA for countries committed to poverty reduction.) Market access Address the special needs of landlocked 8.6 Proportion of total developed country imports (by value developing countries and small island developing and excluding arms) from developing countries and least states (through the Programme of Action for developed countries, admitted free of duty the Sustainable Development of Small Island 8.7 Average tariffs imposed by developed countries on Developing States and the outcome of the 22nd agricultural products and textiles and clothing from special session of the General Assembly) developing countries 8.8 Agricultural support estimate for OECD countries as a percentage of their GDP 8.9 Proportion of ODA provided to help build trade capacity Deal comprehensively with the debt problems of developing countries through national and Debt sustainability international measures in order to make debt 8.10 Total number of countries that have reached their HIPC sustainable in the long term decision points and number that have reached their HIPC completion points (cumulative) 8.11 Debt relief committed under HIPC Initiative and Multilateral Debt Relief Initiative (MDRI) 8.12 Debt service as a percentage of exports of goods and services In cooperation with pharmaceutical companies, 8.13 Proportion of population with access to affordable provide access to affordable essential drugs in essential drugs on a sustainable basis developing countries In cooperation with the private sector, make 8.14 Telephone lines per 100 population available the benefits of new technologies, 8.15 Cellular subscribers per 100 population especially information and communications 8.16 Internet users per 100 population

1. Where available, indicators based on national poverty lines should be used for monitoring country poverty trends. 2. The proportion of people living in slums is measured by a proxy, represented by the urban population living in households with at least one of these characteristics: lack of access to improved water supply, lack of access to improved sanitation, overcrowding (3 or more persons per room), and dwellings made of nondurable material. 2011 World Development Indicators

9

1.1

Size of the economy Population

millions 2009

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

10

30 3 35 18 40 3 22 8 9 162 10 11 9 10 4 2 194 8 16 8 15 20 34 4 11 17 1,331 7 46 66 4 5 21 4 11 10 6 10 14 83 6 5 1 83 5 63c 1 2 4 82 24 11 14 10 2 10 7

Surface area

Population density

thousand sq. km 2009

people per sq. km 2009

$ billions 2009

Rank 2009

$ 2009

Rank 2009

$ billions 2009

Per capita $ 2009

46 115 15 15 15 108 3 101 106 1,246 48 356 81 9 74 3 23 70 58 323 84 41 4 7 9 23 143 6,721 41 29 11 90 66 79 105 136 130 209 55 83 297 50 32 83 18 114c 6 171 61 235 105 88 131 41 57 364 67

9.1 12.6 154.2 69.4 304.1 9.5 957.5 388.5 42.5 93.5 53.7 488.4 6.7 16.1 17.7 12.2 1,564.2 46.0 8.0 1.2 9.7 23.2 1,416.4 2.0 6.7 160.7 4,856.2 221.1 227.8 10.6 7.7 28.7 22.5 61.0 62.2 181.6 326.5 45.9 54.1 172.1 20.8 1.6 18.9 27.2 245.3 2,750.9 10.9 0.7 11.1d 3,476.1 28.4 327.7 37.2 3.8 0.8 .. 13.5

125 114 49 63 29 124 15 25 76 57 68 19 138 105 103 117 8 73 133 186 123 93 10 177 139 48 3 37 36 121 135 86 95 66 65 43 28 74 67 45 100 180 102 89 33 5 120 196 118 4 87 27 81 162 194

310 4,000 4,420 3,750 7,550 3,100 43,770 46,450 4,840 580 5,560 45,270 750 1,630 4,700 6,260 8,070 6,060 510 150 650 1,190 41,980 450 600 9,470 3,650 31,570 4,990 160 2,080 6,260 1,070 13,770 5,550 17,310 59,060 4,550 3,970b 2,070 3,370 320 14,060 330 45,940 42,620 7,370 440 2,530 d 42,450 1,190 e 29,040 2,650 370 510 ..f 1,800

207 116 112 123 85 131 23 17 106 189 100 20 182 155 107 92 83 95 190 213 185 162 28 195 187 75 125 40 103 211 147 92 168 65 98 57 9 110 118 148 127 207 63 206 19 25 86 196 140 26 162 42 138 202 190

25.1a 27.3 283.2a 96.1 567.5 16.7 842.3 321.3 79.2 250.6 123.1 395.0 13.5 41.9 33.0 25.0 1,968.0 100.6 18.4 3.3 27.0 42.8 1,257.7 3.3 13.0 227.7 9,170.1 311.9 392.5 19.6 11.2 50.0a 34.5 85.1 .. 251.1 214.4 81.9a 110.4 471.2 39.6a 2.9a 25.6 77.3 188.3 2,191.2 18.4 2.3 20.6d 3,017.3 36.6 325.0 64.1a 9.5 1.7 .. 27.7a

860 a 8,640 8,110 a 5,190 14,090 5,410 38,510 38,410 9,020 1,550 12,740 36,610 1,510 4,250 8,770 12,840 10,160 13,260 1,170 390 1,820 2,190 37,280 750 1,160 13,420 6,890 44,540 8,600 300 3,040 10,930a 1,640 19,200 .. 23,940 38,780 8,110 a 8,100 5,680 6,420a 580 a 19,120 930 35,280 33,950 12,450 1,330 4,700 d 36,850 1,530 28,800 4,570 a 940 1,060 .. 3,710 a

652 29 2,382 1,247 2,780 30 7,741 84 87 144 208 31 113 1,099 51 582 8,515 111 274 28 181 475 9,985 623 1,284 756 9,600 1 1,142 2,345 342 51 322 57 110 79 43 49 256 1,001 21 118 45 1,104 338 549 c 268 11 70 357 239 132 109 246 36 28 112

2011 World Development Indicators

Gross national income, Atlas method

111

Gross national income per capita, Atlas method

153

Purchasing power parity gross national income

Gross domestic product

Rank 2009

% growth 2008–09

Per capita % growth 2008–09

201 106 110 131 76 128 24 25 101 181 88 32 183 146 105 87 98 84 193 211 176 169 29 207 194 81 119 18 107 212 157 95 179 65

40.8 2.5 2.1 0.7 0.9 –14.4 1.3 –3.9 9.3 5.7 1.4 –2.8 3.8 3.4 –2.9 –3.7 –0.6 –4.9 3.5 3.5 –1.9 2.0 –2.5 2.4 –1.6 –1.5 9.1 –2.8 0.8 2.7 7.6 –1.5 3.6 –5.8 4.3 –4.2 –4.9 3.5 0.4 4.6 –3.5 3.6 –14.1 8.7 –8.0 –2.6 –1.0 4.6 –3.9d –4.7 4.7 –2.0 0.6 –0.3 3.0 2.9 –1.9

37.1 2.1 0.6 –1.9 –0.1 –14.6 –0.8 –4.2 8.0 4.3 1.6 –3.5 0.6 1.6 –2.7 –5.1 –1.5 –4.5 0.1 0.6 –3.5 –0.3 –3.7 0.5 –4.2 –2.5 8.5 –3.1 –0.6 0.0 5.6 –2.8 1.2 –5.8 4.3 –4.8 –5.5 2.0 –0.7 2.8 –4.0 0.6 –14.1 5.9 –8.4 –3.2 –2.7 1.8 –4.0d –4.5 2.5 –2.4 –1.9 –2.6 0.7 1.3 –3.8

59 23 110 112 126 121 210 66 200 34 36 89 186 137 31 182 46 139 199 196 148

Population

millions 2009

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

10 1,155 230 73 31 4 7 60 3 128 6 16 40 24 49 2 3 5 6 2 4 2 4 6 3 2 20 15 27 13 3 1 107 4 3 32 23 50 2 29 17 4 6 15 155 5 3 170 3 7 6 29 92 38 11 4 1

Surface area

Population density

thousand sq. km 2009

people per sq. km 2009

93 3,287 1,905 1,745 438 70 22 301 11 378 89 2,725 580 121 100 11 18 200 237 65 10 30 111 1,760 65 26 587 118 331 1,240 1,031 2 1,964 34 1,564 447 799 677 824 147 42 268 130 1,267 924 324 310 796 75 463 407 1,285 300 313 92 9 12

112 389 127 45 72 65 344 205 249 350 67 6 70 199 503 166 157 28 27 36 413 68 41 4 53 81 34 162 84 11 3 628 55 110 2 72 29 77 3 205 490 16 48 12 170 16 9 220 46 15 16 23 308 125 116 447 122

Gross national income, Atlas method

Gross national income per capita, Atlas method

Purchasing power parity gross national income

$ billions 2009

Rank 2009

$ 2009

Rank 2009

$ billions 2009

Per capita $ 2009

130.1 1,405.7 471.0 330.6 69.7 197.1 192.0 2,114.5 12.4 4,857.2 23.7 110.0 30.3 .. 966.6 5.9 117.0 4.6 5.6 27.9 34.1 2.0 0.7 77.2 38.1 9.0 8.5 4.4 201.8 8.9 3.3 9.2 962.1 5.6g 4.4 89.9h 10.0 .. 9.3 13.0 801.1 124.3 5.7 5.2 184.7 408.5 49.8 169.8 22.7 7.9 14.3 122.4 164.6 467.6 232.9 .. ..

51 11 20 26 62 39 40 7 116 2 92 55 84

12,980 1,220 2,050 4,530 2,210 44,280 25,790 35,110 4,590 38,080 3,980 b 6,920 760 ..f 19,830 3,240 43,930 870 880 12,390 8,060 980 b 160 12,020 11,410 4,400 430 290 7,350 680 990 7,250 8,960 1,560 g 1,630 2,770 h 440 ..f 4,270 440 48,460 28,810 1,000 340 1,190 84,640 17,890 1,000 6,570 1,180 2,250 4,200 1,790 12,260 21,910 ..i ..i

66 160 149 111 146 22 46 35 109 32 117 89 181

191.3 3,786.3 855.0 836.5 105.0 147.0 201.0 1,919.2 19.5a 4,265.3 34.1 164.0 62.5 .. 1,328.0 .. 143.5 11.7 13.9 39.7 56.6 3.7 1.2 105.3a 57.8 22.2 19.5 11.9 376.6 15.4 6.4 16.9 1,506.3 10.7g 8.9 143.1h 20.1 .. 13.8 34.7 657.0 120.0 14.6a 10.3 321.0 267.5 68.3 454.7 42.1a 15.2a 28.1 236.7 325.6 697.9 256.1 .. ..

19,090 3,280 3,720 11,470 3,330 33,040 27,010 31,870 7,230a 33,440 5,730 10,320 1,570 .. 27,240 .. 53,890 2,200 2,200 17,610 13,400 1,800 290 16,400a 17,310 10,880 990 780 13,710 1,190 1,940 13,270 14,020 3,010 g 3,330 4,400 h 880 .. 6,350 1,180 39,740 27,790 2,540 a 680 2,070 55,420 24,530 2,680 12,180 a 2,260a 4,430 8,120 3,540 18,290 24,080 .. ..

13 143 50 153 146 88 82 175 197 61 80 128 131 156 38 129 166 127 14 145 157 58 122 126 113 16 53 144 148 42 24 69 46 94 134 108 54 47 21 35

54 129 10 179 178 68 84 175 211 71 72 113 200 210 87 184 174 88 78 157 155 136 196 114 196 15 43 171 204 162 3 56 171 91 165 145 115 154 69 51

WORLD VIEW

1.1

Size of the economy

Gross domestic product

Rank 2009

% growth 2008–09

Per capita % growth 2008–09

67 154 147 94 151 38 52 41 117 37 125 97 180

–6.3 9.1 4.5 1.8 4.2 –7.1 0.8 –5.0 –3.0 –5.2 2.3 1.2 2.6 .. 0.2 4.0 4.4 2.3 6.4 –18.0 9.0 0.9 4.6 2.1 –15.0 –0.7 –3.7 7.6 –1.7 4.3 –1.1 2.1 –6.5 –6.5g –1.6 4.9h 6.3 .. –0.8 4.7 –4.0 –0.4 –5.6 1.0 5.6 –1.6 12.8 3.6 2.4 4.5 –3.8 0.9 1.1 1.7 –2.6 .. 8.6

–6.2 7.7 3.4 0.5 1.6 –7.6 –1.0 –5.7 –3.5 –5.1 –0.1 –0.2 –0.1 .. –0.1 3.4 1.9 1.5 4.5 –17.6 8.2 0.0 0.3 0.1 –14.6 –0.8 –6.2 4.7 –3.3 1.9 –3.3 1.6 –7.5 –6.4g –2.7 3.6h 4.0 .. –2.7 2.8 –4.5 –1.5 –6.9 –2.9 3.2 –2.8 10.4 1.4 0.8 2.1 –5.5 –0.3 –0.7 1.6 –2.7 .. –1.3

51 6 167 167 71 82 178 213 74 72 96 197 206 78 189 173 83 77 158 151 143 201 122 191 22 48 163 209 170 8 54 162 91 166 142 109 149 69 57

2011 World Development Indicators

11

1.1

Size of the economy Population

millions 2009

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

21 142 10 25 13 7 6 5 5 2 9 49 46 20 42 1 9 8 21 7 44 68 1 7 1 10 75 5 33 46 5 62 307 3 28 28 87 4 24 13 13 6,775 s 846 4,813 3,811 1,002 5,659 1,944 404 572 331 1,568 840 1,117 327

Surface area

Population density

thousand sq. km 2009

people per sq. km 2009

238 17,098 26 2,000 j 197 88 72 1 49 20 638 1,219 505 66 2,506 17 450 41 185 143 947 513 15 57 5 164 784 488 241 604 84 244 9,832 176 447 912 331 6 528 753 391 134,123 s 17,838 80,558 31,898 48,659 98,396 16,302 23,549 20,394 8,778 5,131 24,242 35,727 2,583

93 9 405 13 65 83 80 7,125 113 101 15 41 92 324 18 69 23 193 115 50 49 133 76 122 261 67 97 11 166 79 55 256 34 19 65 32 281 672 45 17 32 52 w 49 61 124 21 59 123 18 28 38 329 36 33 128

Gross national income, Atlas method

Gross national income per capita, Atlas method

Purchasing power parity gross national income

$ billions 2009

Rank 2009

$ 2009

Rank 2009

$ billions 2009

178.9 1,324.4 4.9 436.9 13.1 43.9 1.9 185.7 87.4 48.1 .. 284.3 1,476.2 40.4 51.5 2.9 454.4 505.8 50.9 4.8 21.4k 254.7 2.7 2.9 22.4 38.9 652.4 17.5 15.2 128.9 .. 2,558.1 14,233.5 30.2 30.6 286.4 87.7 .. 25.0 12.5 4.6 59,162.8 t 431.0 16,346.7 8,845.9 7,515.1 16,792.6 6,148.6 2,745.8 4,011.3 1,190.2 1,735.4 944.2 42,417.7 12,723.2

44 12 150 23 112 75 178 41 60 72

8,330 9,340 490 17,210 1,040 6,000 340 37,220 16,130 23,520 ..f 5,760 32,120 1,990 1,220 2,470 48,840 65,430 2,410 700 500k 3,760 2,460 440 16,700 3,720 8,720 3,420 460 2,800 ..i 41,370 46,360 9,010 1,100 10,090 1,000 b ..l 1,060 960 360 8,732 w 509 3,397 2,321 7,502 2,968 3,163 6,793 7,007 3,597 1,107 1,125 37,990 38,872

81 76 193 58 170 96 204 33 60 49

312.4 2,599.4 11.3 609.8 22.7 85.6 4.5 248.3 119.8 54.1 .. 495.6 1,447.2 95.8 84.1 5.7 353.9 364.1 97.3 13.5 57.9 k 517.5 5.2a 5.6 33.4 a 81.4 1,009.8 35.7a 39.0 284.4 .. 2,217.4 14,011.0 43.1 80.9 a 346.9 243.6 .. 55.0 16.5 .. 71,774.4 t 1,032.5 30,653.8 18,229.1 12,461.9 31,684.3 11,712.8 5,097.0 5,888.7 2,617.6 4,658.7 1,722.2 40,433.9 11,127.6

31 9 77 70 167 22 18 71 151 97 32 169 168 96 78 17 104 106 52 6 1 85 83 30 59 90 115 154

97 39 151 160 143 14 8 144 183 192 122 141 196 59 124 79 126 194 135 29 18 77 167 74 171 169 176 203

Per capita $ 2009

14,540 18,330 1,130 24,020 1,810 11,700 790 49,780 22,110 26,470 .. 10,050 31,490 4,720 1,990 4,790 38,050 47,100 4,620 1,950 1,360k 7,640 4,730 a 850 24,970a 7,810 13,500 6,980a 1,190 6,180 .. 35,860 45,640 12,900 2,910a 12,220 2,790 .. 2,330 1,280 .. 10,594 w 1,220 6,370 4,784 12,440 5,599 6,026 12,609 10,286 7,911 2,972 2,051 36,213 33,997

Rank 2009

75 68 195 58 177 93 205 11 63 53 99 43 136 171 134 28 14 138 172 184 115 133 203 55 113 80 118 189 123 33 16 86 159 90 161 165 187

Gross domestic product

% growth 2008–09

–8.5 –7.9 4.1 0.6 2.2 –3.0 4.0 –1.3 –6.2 –7.8 .. –1.8 –3.6 3.5 4.5 1.2 –5.1 –1.9 4.0 3.4 6.0k –2.2 1.9 2.5 –3.0 3.1 –4.7 8.0 7.1 –15.1 –0.7 –4.9 –2.6 2.9 8.1 –3.3 5.3 .. 3.8 6.4 5.7 –1.9 w 4.6 2.6 7.1 –2.6 2.7 7.4 –5.8 –1.9 3.4 8.1 1.7 –3.3 –4.1

Per capita % growth 2008–09

–8.4 –7.8 1.2 –1.7 –0.4 –2.6 1.5 –4.2 –6.4 –8.8 .. –2.8 –4.5 2.8 2.2 –0.3 –6.0 –3.0 1.5 1.7 3.0k –2.8 –1.3 0.0 –3.4 2.1 –5.8 6.6 3.6 –14.6 –3.2 –5.6 –3.5 2.5 6.3 –4.8 4.0 .. 0.8 3.8 5.2 –3.0 w 2.4 1.5 5.9 –3.4 1.4 6.6 –6.1 –3.0 1.6 6.5 –0.7 –3.9 –4.5

a. Based on regression; others are extrapolated from the 2005 International Comparison Program benchmark estimates. b. Included in the aggregates for lower middle-income economies based on earlier data. c. Excludes the French overseas departments of French Guiana, Guadeloupe, Martinique, and Réunion. d. Excludes Abkhazia and South Ossetia. e. Included in the aggregates for low-income economies based on earlier data. f. Estimated to be low income ($995 or less). g. Excludes Transnistria. h. Includes Former Spanish Sahara. i. Estimated to be high income ($12,196 or more). j. Provisional estimate. k. Covers mainland Tanzania only. l. Estimated to be lower middle income ($996–$3,945).

12

2011 World Development Indicators

About the data

1.1

WORLD VIEW

Size of the economy Definitions

Population, land area, income, and output are basic

conventional price indexes allow comparison of real

• Population is based on the de facto definition of

measures of the size of an economy. They also

values over time.

population, which counts all residents regardless of

provide a broad indication of actual and potential

PPP rates are calculated by simultaneously com-

legal status or citizenship—except for refugees not

resources. Population, land area, income (as mea-

paring the prices of similar goods and services

permanently settled in the country of asylum, who

sured by gross national income, GNI), and output

among a large number of countries. In the most

are generally considered part of the population of

(as measured by gross domestic product, GDP) are

recent round of price surveys conducted by the Inter-

their country of origin. The values shown are midyear

therefore used throughout World Development Indica-

national Comparison Program (ICP), 146 countries

estimates. See also table 2.1. • Surface area is

tors to normalize other indicators.

and territories participated in the data collection,

a country’s total area, including areas under inland

Population estimates are generally based on

including China for the first time, India for the first

bodies of water and some coastal waterways. • Pop-

extrapolations from the most recent national cen-

time since 1985, and almost all African countries.

ulation density is midyear population divided by land

sus. For further discussion of the measurement of

The PPP conversion factors presented in the table

area in square kilometers. • Gross national income

population and population growth, see About the data

come from three sources. For 45 high- and upper

(GNI) is the sum of value added by all resident pro-

for table 2.1.

middle-income countries conversion factors are

ducers plus any product taxes (less subsidies) not

The surface area of an economy includes inland

provided by Eurostat and the Organisation for Eco-

included in the valuation of output plus net receipts

bodies of water and some coastal waterways. Sur-

nomic Co-operation and Development (OECD), with

of primary income (compensation of employees and

face area thus differs from land area, which excludes

PPP estimates for 34 European countries incorpo-

property income) from abroad. Data are in current

bodies of water, and from gross area, which may

rating new price data collected since 2005. For the

U.S. dollars converted using the World Bank Atlas

include offshore territorial waters. Land area is par-

remaining 2005 ICP countries the PPP estimates are

method (see Statistical methods). • GNI per capita is

ticularly important for understanding an economy’s

extrapolated from the 2005 ICP benchmark results,

GNI divided by midyear population. GNI per capita in

agricultural capacity and the environmental effects

which account for relative price changes between

U.S. dollars is converted using the World Bank Atlas

of human activity. (For measures of land area and

each economy and the United States. For countries

method. • Purchasing power parity (PPP) GNI is GNI

data on rural population density, land use, and agri-

that did not participate in the 2005 ICP round, the

converted to international dollars using PPP rates. An

cultural productivity, see tables 3.1–3.3.) Innova-

PPP estimates are imputed using a statistical model.

international dollar has the same purchasing power

tions in satellite mapping and computer databases

More information on the results of the 2005 ICP

over GNI that a U.S. dollar has in the United States.

have resulted in more precise measurements of land and water areas.

is available at www.worldbank.org/data/icp.

• Gross domestic product (GDP) is the sum of value

All 213 economies shown in World Development

added by all resident producers plus any product

GNI measures total domestic and foreign value

Indicators are ranked by size, including those that

taxes (less subsidies) not included in the valuation

added claimed by residents. GNI comprises GDP

appear in table 1.6. The ranks are shown only in

of output. Growth is calculated from constant price

plus net receipts of primary income (compensation

table 1.1. No rank is shown for economies for which

GDP data in local currency. • GDP per capita is GDP

of employees and property income) from nonresident

numerical estimates of GNI per capita are not pub-

divided by midyear population.

sources. The World Bank uses GNI per capita in U.S.

lished. Economies with missing data are included in

dollars to classify countries for analytical purposes

the ranking at their approximate level, so that the rel-

and to determine borrowing eligibility. For definitions

ative order of other economies remains consistent.

of the income groups in World Development Indicators, see Users guide. For discussion of the usefulness of national income and output as measures of productivity or welfare, see About the data for tables 4.1 and 4.2.

Data sources

When calculating GNI in U.S. dollars from GNI

Population estimates are prepared by World Bank

reported in national currencies, the World Bank fol-

staff from a variety of sources (see Data sources

lows the World Bank Atlas conversion method, using

for table 2.1). Data on surface and land area are

a three-year average of exchange rates to smooth

from the Food and Agriculture Organization (see

the effects of transitory fluctuations in exchange

Data sources for table 3.1). GNI, GNI per capita,

rates. (For further discussion of the World Bank Atlas

GDP growth, and GDP per capita growth are esti-

method, see Statistical methods.)

mated by World Bank staff based on national

Because exchange rates do not always refl ect

accounts data collected by World Bank staff during

differences in price levels between countries,

economic missions or reported by national statis-

the table also converts GNI and GNI per capita

tical offices to other international organizations

estimates into international dollars using purchas-

such as the OECD. PPP conversion factors are

ing power parity (PPP) rates. PPP rates provide

estimates by Eurostat/OECD and by World Bank

a standard measure allowing comparison of real

staff based on data collected by the ICP.

levels of expenditure between countries, just as

2011 World Development Indicators

13

1.2

Millennium Development Goals: eradicating poverty and saving lives Eradicate extreme poverty and hunger Share of poorest quintile Vulnerable in national employment consumption Unpaid family workers and or income own-account workers % % of total employment 1995– 2009a,b 1990 2008

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

14

9.0 8.1 6.9 2.0 d 4.1d 8.8 .. 8.6 8.0 9.4 9.2 8.5 6.9 2.8 6.7 .. 3.3 5.0 7.0 9.0 6.6 5.6 7.2 5.2 6.3 8.6 5.7 5.3 2.5 5.5 5.0 4.2 5.6 8.1 .. 10.2 8.3 4.4 4.2 9.0 4.3 .. 6.8 9.3 9.6 7.2 6.1 4.8 5.3 8.5 5.2 6.7 3.4 6.4 7.2 2.5 2.0

2011 World Development Indicators

.. .. .. .. .. .. 10 .. .. .. .. 16 .. 40 e .. .. 29 e .. .. .. .. .. .. .. 94 .. .. 6 28e .. .. 25 .. .. .. 7 7 39 36 e 28e 35 .. 2e .. .. 11 48 .. .. .. .. 40 e .. .. .. .. 49 e

.. .. .. .. 20 e .. 9 9 53 .. .. 10 .. .. .. .. 27 9 .. .. .. .. 10 e .. .. 25 .. 7e 41 .. .. 20 .. 22 f .. 13 5 42 34e 25 36 .. 6e 52e 9 6 .. .. 62 7 .. 27 .. .. .. .. ..

Prevalence of malnutrition Underweight % of children under age 5 1990

2004–09a

.. .. 9.2 .. .. .. .. .. .. 61.5 .. .. .. 9.7 .. .. .. .. 29.6 30.2 .. 18.0 .. .. .. .. 12.6 .. 8.8 .. 21.1 2.5 .. .. .. 0.9 .. 8.4 .. 10.5 11.1 36.9 .. .. .. .. .. .. .. .. 24.1 .. 27.8 .. .. 23.7 15.8

32.9 6.6 3.7 .. 2.3 4.2 .. .. 8.4 41.3 1.3 .. 20.2 4.5 1.6 .. 2.2 1.6 26.0 .. 28.8 16.6 .. .. 33.9 0.5 4.5 .. 5.1 28.2 11.8 .. 16.7 1.0 .. .. .. 3.4 6.2 6.8 .. .. .. 34.6 .. .. .. 15.8 2.3 1.1 14.3 .. .. 20.8 17.4 18.9 8.6

Achieve universal primary education

Promote gender equality

Reduce child mortality

Primary completion rate %

Ratio of girls to boys enrollments in primary and secondary education %

Under-fi ve mortality rate per 1,000

1991

2009c

1991

2009c

1990

2009

28 .. 80 33 .. .. .. .. 95 41 94 79 22 71 .. 90 93 90 20 46 .. 53 .. 28 18 .. 107 102 73 48 54 79 42 .. 99 92 98 .. .. .. 65 .. .. 23 97 106 62 45 .. 100 64 99 .. 17 .. 27 64

.. 90 91 .. 102 98 .. 99 92 61 96 86 62 99 .. 95 .. 90 43 52 83 73 .. 38 33 95 .. 93 115 56 74 96 46 100 98 95 101 90 103 95 89 48 100 55 98 .. .. 79 107 104 83 101 80 62 .. .. 90

54 96 83 .. .. .. 100 95 100 75 .. 101 .. .. .. 109 .. 99 .. 82 .. 83 99 61 41 100 86 .. 108 70 89 101 .. 103 106 98 101 .. 100 81 101 82 103 68 109 102 96 65 98 99 78 99 87 45 55 .. 104

62 100 .. .. 105 103 97 97 102 108 101 98 .. 99 102 100 103 97 86 93 90 86 .. 69 64 99 105 102 105 77 .. 102 .. 102 99 101 102 97 103 .. 98 77 101 88 102 100 .. 102 96 98 95 97 94 77 .. .. 107

250 51 61 258 28 56 9 9 98 148 24 10 184 122 23 60 56 18 201 189 117 148 8 175 201 22 46 .. 35 199 104 18 152 13 14 12 9 62 53 90 62 150 17 210 7 9 93 153 47 9 120 11 76 231 240 152 55

199 15 32 161 14 22 5 4 34 52 12 5 118 51 14 57 21 10 166 166 88 154 6 171 209 9 19 .. 19 199 128 11 119 5 6 4 4 32 24 21 17 55 6 104 3 4 69 103 29 4 69 3 40 142 193 87 30

Eradicate extreme poverty and hunger Share of poorest quintile Vulnerable in national employment consumption Unpaid family workers and or income own-account workers % % of total employment 1995– 2009a,b 1990 2008

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

8.4 8.1 7.6 6.4 .. 7.4 5.7 6.5 5.2 .. 7.2 8.7 4.7 .. 7.9 .. .. 8.8 7.6 6.8 .. 3.0 6.4 .. 6.6 5.4 6.2 7.0 4.5 6.5 6.2 .. 3.9 6.8 7.1 6.5 5.2 .. .. 6.1 7.6 6.4 3.8 8.3 5.1 9.6 .. 9.0 3.6 4.5 3.8 3.9 5.6 7.6 5.8 .. 3.9

7e .. .. .. .. 20 .. 27 42 19 .. .. .. .. .. .. .. .. .. .. .. 38 .. .. .. .. 84 .. 29 .. .. 12 26 .. .. .. .. .. .. .. 8 13 .. .. .. .. .. .. 34 .. 23 e 36 e .. 28e 25e .. ..

7 .. 63 43 .. 12 7 19 35 11 .. .. .. .. 25 .. .. 47 .. 7 .. .. .. .. 9 22 .. .. 22 .. .. 17 30 32 .. 51 .. .. .. .. 9 12 45 .. .. 6 .. 62 28 .. 47 40e 45e 19 19 .. ..

Prevalence of malnutrition Underweight % of children under age 5 1990

2004–09a

2.3 59.5 31.0 .. 10.4 .. .. .. 4.0 .. 4.8 .. 20.1 .. .. .. .. .. 39.8 .. .. 13.8 .. .. .. .. 35.5 24.4 22.1 29.0 43.3 .. 13.9 .. 10.8 8.1 .. 28.8 21.5 .. .. .. 9.6 41.0 35.1 .. 21.4 39.0 .. .. 2.8 8.8 29.8 .. .. .. ..

.. 43.5 17.5g .. 7.1 .. .. .. 2.2 .. 1.9 4.9 16.4 20.6 .. .. 1.7 2.7 31.6 .. 4.2 16.6 20.4 5.6 .. 1.8 36.8 15.5 .. 27.9 16.7 .. 3.4 3.2 5.3 9.9 .. .. 17.5 38.8 .. .. 4.3 39.9 26.7 .. .. .. .. 18.1 .. 5.4 .. .. .. .. ..

1.2

WORLD VIEW

Millennium Development Goals: eradicating poverty and saving lives Achieve universal primary education

Promote gender equality

Reduce child mortality

Primary completion rate %

Ratio of girls to boys enrollments in primary and secondary education %

Under-fi ve mortality rate per 1,000

1991

2009c

1991

2009c

1990

2009

82 .. 93 88 58 103 .. 98 94 102 101 .. .. .. 99 .. 57 .. 41 .. .. 59 .. .. .. 98 36 31 91 .. 33 115 88 .. .. 48 26 .. 74 51 .. .. 42 17 .. 100 74 .. 86 46 68 .. 88 96 .. .. 71

95 95 109 101 64 99 99 104 89 101 100 106 .. .. 99 .. 93 94 75 95 85 70 58 .. 92 92 79 59 97 59 64 89 104 93 93 80 57 99 87 .. .. .. 75 40 79 98 80 61 102 .. 94 101 94 96 .. .. 108

100 73 93 85 79 104 105 100 103 101 101 .. .. .. 99 .. 100 102 77 101 101 124 .. .. 96 99 96 82 101 58 71 102 97 105 109 70 71 95 106 59 97 100 119 53 77 102 89 48 99 80 98 96 99 101 103 .. 98

98 92 98 97 81 103 101 99 100 100 102 99 95 .. 97 .. 101 101 87 100 104 107 .. .. 100 98 97 100 103 78 103 101 102 101 103 88 88 100 104 .. 98 103 102 75 85 99 97 82 101 .. 100 99 102 99 100 102 120

17 118 86 73 53 9 11 10 33 6 39 60 99 45 9 .. 17 75 157 16 40 93 247 36 15 36 167 218 18 250 129 24 45 37 101 89 232 118 73 142 8 11 68 305 212 9 48 130 31 91 42 78 59 17 15 .. 19

6 66 39 31 44 4 4 4 31 3 25 29 84 33 5 .. 10 37 59 8 12 84 112 19 6 11 58 110 6 191 117 17 17 17 29 38 142 71 48 48 4 6 26 160 138 3 12 87 23 68 23 21 33 7 4 .. 11

2011 World Development Indicators

15

1.2

Millennium Development Goals: eradicating poverty and saving lives Eradicate extreme poverty and hunger Share of poorest quintile Vulnerable in national employment consumption Unpaid family workers and or income own-account workers % % of total employment 1995– 2009a,b 1990 2008

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

8.1 6.0 4.2 .. 6.2 9.1 6.1 5.0 8.8 8.2 .. 3.1 7.0 6.9 .. 4.5 9.1 7.6 7.7 9.3 6.8 3.9 9.0 5.4 .. 5.9 5.7 6.0 5.8 9.4 .. 6.1 5.4 5.6 7.1 4.9 7.3 .. 7.2 3.6 4.6

27e 1 .. .. 83 .. .. 8 .. 12 e .. .. 22e .. .. .. .. 9 .. .. .. 70 .. .. 22 .. .. .. .. .. .. 10 .. .. .. .. .. .. .. 65 .. .. w .. .. .. .. .. .. .. .. .. .. .. .. ..

31 6 .. .. .. 23 .. 10 11 11 .. 3 12 41e .. .. 7 10 .. .. 88 e 53 .. .. .. .. 35 .. .. .. .. 11 .. 25 e .. 30 .. 36 .. .. .. .. w .. .. .. 26 .. .. 19 30 37 .. .. 12 11

Prevalence of malnutrition Underweight % of children under age 5 1990

5.0 .. 24.3 .. 19.0 .. 25.4 .. .. .. .. .. .. 29.3 31.8 .. .. .. 11.5 .. 25.1 16.3 .. 21.2 4.7 8.5 8.7 .. 19.7 .. .. .. .. 6.5 .. 6.7 40.7 .. 29.6 21.2 8.0 .. w .. 31.7 33.5 .. 32.5 18.0 .. .. .. 57.2 .. .. ..

2004–09a

.. .. 18.0 5.3 14.5 1.8 21.3 .. .. .. 32.8 .. .. 21.6 31.7 6.1 .. .. 10.0 14.9 16.7 7.0 .. 22.3 .. 3.3 3.5 .. 16.4 .. .. .. 1.3 6.0 4.4 3.7 20.2 2.2 .. 14.9 14.0 21.3 w 27.7 20.8 24.0 .. 22.4 8.8 .. 3.8 6.8 42.5 24.7 .. ..

Achieve universal primary education

Promote gender equality

Reduce child mortality

Primary completion rate %

Ratio of girls to boys enrollments in primary and secondary education %

Under-fi ve mortality rate per 1,000

1991

2009c

1991

2009c

1990

2009

96 .. 50 .. 39 .. .. .. 95 95 .. 76 104 101 .. 61 96 53 89 .. 55 .. .. 35 102 74 90 .. .. 92 103 .. .. 94 .. 81 .. .. .. .. 97 79 w 44 83 82 88 78 101 92 84 .. 62 51 .. 101

96 95 54 93 57 96 88 .. 96 96 .. 93 100 97 57 72 94 94 112 98 102 .. 80 61 93 93 93 .. 72 95 99 .. 95 106 92 95 .. 82 61 87 .. 88 w 63 92 90 100 87 99 96 101 95 79 64 98 ..

99 105 95 .. 69 .. 64 .. 102 103 .. 104 104 102 78 .. 102 97 85 .. 97 99 .. 59 101 86 81 .. 77 102 104 102 100 .. .. 105 .. .. .. .. 92 87 w 80 85 81 98 84 89 98 99 80 69 82 100 ..

99 98 100 91 95 101 84 .. 100 99 53 99 103 .. 89 92 99 97 97 91 96 103 .. 75 101 103 93 .. 99 99 100 101 100 104 99 102 .. 104 .. 96 97 96 w 91 97 95 101 96 102 97 102 96 91 88 99 ..

32 27 171 43 151 29 285 8 15 10 180 62 9 28 124 92 7 8 36 117 162 32 184 150 34 50 84 99 184 21 17 10 11 24 74 32 55 43 125 179 81 92 w 171 85 93 51 100 55 52 52 76 125 181 12 9

12 12 111 21 93 7 192 3 7 3 180 62 4 15 108 73 3 4 16 61 108 14 56 98 35 21 20 45 128 15 7 6 8 13 36 18 24 30 66 141 90 61 w 118 51 57 22 66 26 21 23 33 71 130 7 4

a. Data are for the most recent year available. b. See table 2.9 for survey year and whether share is based on income or consumption expenditure. c. Provisional data. d. Covers urban areas only. e. Limited coverage. f. Data are for 2009. g. Data are for 2010.

16

2011 World Development Indicators

About the data

1.2

WORLD VIEW

Millennium Development Goals: eradicating poverty and saving lives Definitions

Tables 1.2–1.4 present indicators for 17 of the 21

nutrients, and undernourished mothers who give

• Share of poorest quintile in national consump-

targets specified by the Millennium Development

birth to underweight children.

tion or income is the share of the poorest 20 per-

Goals. Each of the eight goals includes one or more

Progress toward universal primary education is

cent of the population in consumption or, in some

targets, and each target has several associated

measured by the primary completion rate. Because

cases, income. • Vulnerable employment is the sum

indicators for monitoring progress toward the target.

many school systems do not record school comple-

of unpaid family workers and own-account workers

Most of the targets are set as a value of a specific

tion on a consistent basis, it is estimated from the

as a percentage of total employment. • Prevalence

indicator to be attained by a certain date. In some

gross enrollment rate in the final grade of primary

of malnutrition is the percentage of children under

cases the target value is set relative to a level in

education, adjusted for repetition. Offi cial enroll-

age 5 whose weight for age is more than two stan-

1990. In others it is set at an absolute level. Some

ments sometimes differ signifi cantly from atten-

dard deviations below the median for the interna-

of the targets for goals 7 and 8 have not yet been

dance, and even school systems with high average

tional reference population ages 0–59 months. The

quantified.

enrollment ratios may have poor completion rates.

data are based on the new international child growth

The indicators in this table relate to goals 1–4.

Eliminating gender disparities in education would

standards for infants and young children, called the

Goal 1 has three targets between 1990 and 2015:

help increase the status and capabilities of women.

Child Growth Standards, released in 2006 by the

to halve the proportion of people whose income is

The ratio of female to male enrollments in primary

World Health Organization. • Primary completion

less than $1.25 a day, to achieve full and productive

and secondary education provides an imperfect mea-

rate is the percentage of students completing the

employment and decent work for all, and to halve the

sure of the relative accessibility of schooling for girls.

last year of primary education. It is calculated as

proportion of people who suffer from hunger. Esti-

The targets for reducing under-five mortality rates

the total number of students in the last grade of

mates of poverty rates are in tables 2.7 and 2.8.

are among the most challenging. Under-five mortal-

primary education, minus the number of repeaters

The indicator shown here, the share of the poorest

ity rates are harmonized estimates produced by a

in that grade, divided by the total number of children

quintile in national consumption or income, is a dis-

weighted least squares regression model and are

of official graduation age. • Ratio of girls to boys

tributional measure. Countries with more unequal

available at regular intervals for most countries.

enrollments in primary and secondary education

distributions of consumption (or income) have a

Most of the 60 indicators relating to the Millennium

is the ratio of the female to male gross enrollment

higher rate of poverty for a given average income.

Development Goals can be found in World Develop-

rate in primary and secondary education. • Under-

Vulnerable employment measures the portion of the

ment Indicators. Table 1.2a shows where to find the

ﬁve mortality rate is the probability that a newborn

labor force that receives the lowest wages and least

indicators for the first four goals. For more informa-

baby will die before reaching age five, if subject to

security in employment. No single indicator captures

tion about data collection methods and limitations,

current age-specific mortality rates. The probability

the concept of suffering from hunger. Child malnutri-

see About the data for the tables listed there. For

is expressed as a rate per 1,000.

tion is a symptom of inadequate food supply, lack

information about the indicators for goals 5–8, see

of essential nutrients, illnesses that deplete these

About the data for tables 1.3 and 1.4.

1.2a

Location of indicators for Millennium Development Goals 1–4 Goal 1. Eradicate extreme poverty and hunger

Table

1.1 Proportion of population below $1.25 a day

2.8

1.2 Poverty gap ratio

2.7, 2.8

1.3 Share of poorest quintile in national consumption

1.2, 2.9

1.4 Growth rate of GDP per person employed

2.4

1.5 Employment to population ratio

2.4

1.6 Proportion of employed people living below $1 per day

—

1.7 Proportion of own-account and unpaid family workers in total employment

1.2, 2.4

1.8 Prevalence of underweight in children under age five

1.2, 2.20

1.9 Proportion of population below minimum level of dietary energy consumption

2.20

Data sources

Goal 2. Achieve universal primary education 2.1 Net enrollment ratio in primary education

2.12

2.2 Proportion of pupils starting grade 1 who reach last grade of primary

2.13

2.3 Literacy rate of 15- to 24-year-olds

2.14

The indicators here and throughout this book have been compiled by World Bank staff from primary and secondary sources. Efforts have been made

Goal 3. Promote gender equality and empower women 3.1 Ratio of girls to boys in primary, secondary, and tertiary education

1.2, 2.12*

to harmonize the data series used to compile this

3.2 Share of women in wage employment in the nonagricultural sector

1.5, 2.3*

table with those published on the United Nations

3.3 Proportion of seats held by women in national parliament

1.5

Goal 4. Reduce child mortality

Millennium Development Goals Web site (www.

4.1 Under-fi ve mortality rate

1.2, 2.22

un.org/millenniumgoals), but some differences in

4.2 Infant mortality rate

2.22

timing, sources, and definitions remain. For more

4.3 Proportion of one-year-old children immunized against measles

2.18

information see the data sources for the indica-

— No data are available in the World Development Indicators database. * Table shows information on related indicators.

tors listed in table 1.2a.

2011 World Development Indicators

17

1.3

Millennium Development Goals: protecting our common environment Improve maternal health Maternal mortality ratio Modeled estimate per 100,000 live births 2008

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

18

1,400 31 120 610 70 29 8 5 38 340 15 5 410 180 9 190 58 13 560 970 290 600 12 850 1,200 26 38 .. 85 670 580 44 470 14 53 8 5 100 140 82 110 280 12 470 8 8 260 400 48 7 350 2 110 680 1,000 300 110

Combat HIV/AIDS and other diseases

Contraceptive prevalence rate % of married women ages 15–49 1990 2004–09b

.. .. 47 .. .. .. .. .. .. 40 .. 78 .. 30 .. 33 59 .. .. .. .. 16 .. .. .. 56 85 86 66 8 .. .. .. .. .. 78 78 56 53 47 47 .. .. 4 77 81 .. 12 .. 75 13 .. .. .. .. 10 47

2011 World Development Indicators

15 69 61 .. 78 53 .. .. 51 53 73 75 17 61 36 53 81 .. 17 9 40 29 .. 19 3 58 85 .. 78 21 44 80 13 .. 78 .. .. 73 73 60 73 .. .. 15 .. 71 .. .. 47 .. 24 .. 54 9 10 32 65

Ensure environmental sustainability

HIV Incidence prevalence of tuberculosis Carbon dioxide emissions % of per capita population per 100,000 metric tons people ages 15–49 2009 2009 1990 2007

.. .. 0.1 2.0 0.5 0.1 0.1 0.3 0.1 <0.1 0.3 0.2 1.2 0.2 .. 24.8 .. 0.1 1.2 3.3 0.5 5.3 0.2 4.7 3.4 0.4 0.1c .. 0.5 .. 3.4 0.3 3.4 <0.1 0.1 <0.1 0.2 0.9 0.4 <0.1 0.8 0.8 1.2 .. 0.1 0.4 5.2 2.0 0.1 0.1 1.8 0.1 0.8 1.3 2.5 1.9 0.8

189 15 59 298 28 73 6 11 110 225 39 9 93 140 50 694 45 41 215 348 442 182 5 327 283 11 96 82 35 372 382 10 399 25 6 9 7 70 68 19 30 99 30 359 9 6 501 269 107 5 201 5 62 318 229 238 58

0.1 2.3 3.1 0.4 3.5 1.1 17.2 7.9 6.0 0.1 9.6 10.8 0.1 0.8 1.2 1.6 1.4 8.8 0.1 0.1 0.0 0.1 16.2 0.1 0.0 2.6 2.2 4.8 1.7 0.1 0.5 1.0 0.5 3.8 3.1 13.5 9.8 1.3 1.6 1.3 0.5 .. 16.3 0.1 10.2 7.0 6.6 0.2 2.9 12.0 0.3 7.2 0.6 0.2 0.2 0.1 0.5

0.0 1.4 4.1 1.4 4.6 1.6 17.7 8.3 3.7 0.3 6.9 9.7 0.5 1.4 7.7 2.6 1.9 6.8 0.1 0.0 0.3 0.3 16.9 0.1 0.0 4.3 5.0 5.8 1.4 0.0 0.4 1.8 0.3 5.6 2.4 12.1 9.1 2.1 2.2 2.3 1.1 0.1 15.2 0.1 12.1 6.0 1.4 0.2 1.4 9.6 0.4 8.8 1.0 0.1 0.2 0.2 1.2

Proportion of species threatened with extinction % 2008

0.7 1.5 2.1 1.4 1.9 0.9 4.7 1.9 0.8 1.9 0.7 1.3 1.5 0.8 13.1 0.5 1.3 1.1 1.0 1.5 29.8 5.4 1.8 0.6 1.0 2.4 2.4 13.2 1.2 2.5 1.0 1.9 3.9 1.8 4.2 1.5 1.6 2.1 10.4 4.1 1.8 15.0 0.6 1.3 1.3 2.5 2.1 2.2 1.0 2.2 3.7 2.1 2.4 2.2 2.4 2.3 3.5

Develop a global partnership for development

Access to improved sanitation facilities % of population 1990 2008

.. .. 88 25 90 .. 100 100 .. 39 .. 100 5 19 .. 36 69 99 6 44 9 47 100 11 6 84 41 .. 68 9 .. 93 20 .. 80 100 100 73 69 72 75 9 .. 4 100 100 .. .. 96 100 7 97 65 9 .. 26 44

37 98 95 57 90 90 100 100 45 53 93 100 12 25 95 60 80 100 11 46 29 47 100 34 9 96 55 .. 74 23 30 95 23 99 91 98 100 83 92 94 87 14 95 12 100 100 33 67 95 100 13 98 81 19 21 17 71

Internet users per 100 peoplea 2009

3.4 41.2 13.5 3.3 30.4 6.8 72.0 73.5 42.0 0.4 45.9 75.2 2.2 11.2 37.7 6.2 39.2 44.8 1.1 0.8 0.5 3.8 77.7 0.5 1.7 34.0 28.8 61.4 45.5 0.6 6.7 34.5 4.6 50.4 14.3 63.7 85.9 26.8 15.1 20.0 14.4 4.9 72.3 0.5 83.9 71.3 6.7 7.6 30.5 79.5 5.4 44.1 16.3 0.9 2.3 10.0 9.8

Improve maternal health Maternal mortality ratio Modeled estimate per 100,000 live births 2008

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

13 230 240 30 75 3 7 5 89 6 59 45 530 250 18 .. 9 81 580 20 26 530 990 64 13 9 440 510 31 830 550 36 85 32 65 110 550 240 180 380 9 14 100 820 840 7 20 260 71 250 95 98 94 6 7 18 8

Combat HIV/AIDS and other diseases

Contraceptive prevalence rate % of married women ages 15–49 1990 2004–09b

.. 43 50 49 14 60 68 .. 55 58 40 .. 27 62 79 .. .. .. .. .. .. 23 .. .. .. .. 17 13 50 .. 3 75 .. .. .. 42 .. 17 29 23 76 .. .. 4 6 74 9 15 .. .. 48 59 36 49 .. .. ..

.. 54 57 79 50 89 .. .. .. 54 59 51 46 .. 80 .. .. 48 38 .. 58 47 11 .. .. 14 40 41 .. 8 9 .. 73 68 55 63 16 41 55 48 69 .. 72 11 15 88 .. 30 .. 32 79 73 51 .. 67 .. ..

Ensure environmental sustainability

HIV Incidence prevalence of tuberculosis Carbon dioxide emissions % of per capita population per 100,000 metric tons people ages 15–49 2009 2009 1990 2007

<0.1 0.3 0.2 0.2 .. 0.2 0.2 0.3 1.7 <0.1 .. 0.1 6.3 .. <0.1 .. .. 0.3 0.2 0.7 0.1 23.6 1.5 .. 0.1 .. 0.2 11.0 0.5 1.0 0.7 1.0 0.3 0.4 <0.1 0.1 11.5 0.6 13.1 0.4 0.2 0.1 0.2 0.8 3.6 0.1 0.1 0.1 0.9 0.9 0.3 0.4 <0.1 0.1 0.6 .. 0.1

16 168 189 19 64 9 5 6 7 21 6 163 305 345 90 .. 35 159 89 45 15 634 288 40 71 23 261 304 83 324 330 22 17 178 224 92 409 404 727 163 8 8 44 181 295 6 13 231 48 250 47 113 280 24 30 2 49

1.3

6.1 0.8 0.8 4.2 2.8 8.6 7.2 7.5 3.3 9.3 3.3 15.9 0.2 12.1 5.6 .. 19.2 2.4 0.1 5.1 3.1 .. 0.2 9.2 6.0 5.6 0.1 0.1 3.1 0.0 1.3 1.4 4.3 4.8 4.5 0.9 0.1 0.1 0.0 0.0 11.0 6.9 0.6 0.1 0.5 7.4 5.6 0.6 1.3 0.5 0.5 1.0 0.7 9.1 4.5 .. 25.2

5.6 1.4 1.8 7.0 3.3 10.2 9.3 7.7 5.2 9.8 3.8 14.7 0.3 3.0 10.4 .. 32.3 1.2 0.3 3.4 3.2 .. 0.2 9.3 4.5 5.5 0.1 0.1 7.3 0.0 0.6 3.1 4.5 1.3 4.0 1.5 0.1 0.3 1.5 0.1 10.6 7.7 0.8 0.1 0.6 9.1 13.7 1.0 2.2 0.5 0.7 1.5 0.8 8.3 5.5 .. 55.4

Proportion of species threatened with extinction % 2008

1.8 3.3 3.4 1.0 11.0 1.8 4.3 2.2 7.7 4.9 3.4 1.1 3.9 1.3 1.7 .. 6.3 0.8 1.2 1.4 1.2 0.6 3.8 1.6 0.9 0.9 6.4 3.3 6.9 1.0 2.9 24.3 3.2 1.3 1.1 1.9 2.9 2.7 2.1 1.1 1.3 5.1 1.3 1.0 4.3 1.5 4.2 1.7 2.9 3.6 0.5 2.8 6.6 1.2 2.8 3.6 ..

Develop a global partnership for development

Access to improved sanitation facilities % of population 1990 2008

100 18 33 83 .. 99 100 .. 83 100 .. 96 26 .. 100 .. 100 .. .. .. .. 32 11 97 .. .. 8 42 84 26 16 91 66 .. .. 53 11 .. 25 11 100 .. 43 5 37 100 85 28 58 47 37 54 58 .. 92 .. 100

WORLD VIEW

Millennium Development Goals: protecting our common environment

100 31 52 .. 73 99 100 .. 83 100 98 97 31 .. 100 .. 100 93 53 78 .. 29 17 97 .. 89 11 56 96 36 26 91 85 79 50 69 17 81 33 31 100 .. 52 9 32 100 .. 45 69 45 70 68 76 90 100 .. 100

2011 World Development Indicators

Internet users per 100 peoplea 2009

61.6 5.3 8.7 38.3 1.0 68.4 49.7 48.5 58.6 77.7 29.3 33.4 10.0 0.0 80.9 .. 39.4 41.2 4.7 66.7 23.7 3.7 0.5 5.5 58.8 51.8 1.6 4.7 57.6 1.9 2.3 22.7 26.5 35.9 13.1 32.2 2.7 0.2 5.9 2.1 90.0 83.4 3.5 0.8 28.4 91.8 43.5 12.0 27.8 1.9 15.8 27.7 6.5 58.8 48.6 25.2 28.3

19

1.3

Millennium Development Goals: protecting our common environment Improve maternal health Maternal mortality ratio Modeled estimate per 100,000 live births 2008

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

27 39 540 24 410 8 970 9 6 18 1,200 410 6 39 750 420 5 10 46 64 790 48 370 350 55 60 23 77 430 26 10 12 24 27 30 68 56 .. 210 470 790 260 w 580 200 230 82 290 89 32 86 88 290 650 15 7

Combat HIV/AIDS and other diseases

Contraceptive prevalence rate % of married women ages 15–49 1990 2004–09b

.. 34 21 .. .. .. .. 65 74 .. 1 57 .. .. 9 20 .. .. .. .. 10 .. .. 34 .. 50 63 .. 5 .. .. .. 71 .. .. .. 53 .. 10 15 43 57 w 23 58 60 52 54 75 .. .. 42 40 15 70 ..

70 80 36 24 12 41 8 .. .. .. 15 .. 66 68 8 51 .. .. 58 37 26 77 22d 17 43 60 73 48 24 67 .. .. .. 78 65 .. 80 50 28 41 65 61 w 33 66 63 75 61 77 69 75 62 51 21 .. ..

Ensure environmental sustainability

HIV Incidence prevalence of tuberculosis Carbon dioxide emissions % of per capita population per 100,000 metric tons people ages 15–49 2009 2009 1990 2007

0.1 1.0 2.9 .. 0.9 0.1 1.6 0.1 <0.1 <0.1 0.7 17.8 0.4 <0.1 1.1 25.9 0.1 0.4 .. 0.2 5.6 1.3 .. 3.2 1.5 <0.1 <0.1 .. 6.5 1.1 .. 0.2 0.6 0.5 0.1 .. 0.4 .. .. 13.5 14.3 0.8 w 2.7 0.6 0.4 1.4 0.9 0.2 0.6 0.5 0.1 0.3 5.4 0.3 0.3

125 106 376 18 282 21 644 36 9 12 285 971 17 66 119 1,257 6 5 21 202 183 137 498 446 23 24 29 67 293 101 4 12 4 22 128 33 200 19 54 433 742 137 w 294 138 147 101 161 136 89 45 39 180 342 14 9

6.8 13.9 0.1 13.2 0.4 .. 0.1 15.4 8.6 6.2 0.0 9.5 5.9 0.2 0.2 0.5 6.0 6.4 2.9 3.9 0.1 1.7 .. 0.2 13.9 1.6 2.7 7.2 0.0 11.7 29.3 10.0 19.5 1.3 5.3 6.2 0.3 .. 0.8 0.3 1.5 4.3e w 0.7 2.6 1.6 6.1 2.4 1.9 10.7 2.3 2.5 0.7 0.9 11.9 8.6

4.4 10.8 0.1 16.6 0.5 .. 0.2 11.8 6.8 7.5 0.1 9.0 8.0 0.6 0.3 0.9 5.4 5.0 3.5 1.1 0.1 4.1 0.2 0.2 27.9 2.3 4.0 9.2 0.1 6.8 31.0 8.8 19.3 1.9 4.3 6.0 1.3 0.6 1.0 0.2 0.8 4.6e w 0.3 3.3 2.8 5.3 2.9 4.0 7.2 2.7 3.7 1.2 0.8 12.5 8.2

Proportion of species threatened with extinction % 2008

1.6 1.3 1.6 3.8 2.2 .. 3.2 9.7 1.1 2.1 3.2 1.6 3.8 14.0 2.4 0.8 1.4 1.4 2.0 0.8 5.1 3.4 .. 1.2 1.7 2.1 1.4 10.7 2.5 1.1 14.1 2.8 5.7 2.6 1.0 1.1 3.5 .. 12.6 0.7 0.9

Develop a global partnership for development

Access to improved sanitation facilities % of population 1990 2008

71 87 23 .. 38 .. .. 99 100 100 .. 69 100 70 34 .. 100 100 83 .. 24 80 .. 13 93 74 84 98 39 95 97 100 100 94 84 82 35 .. 18 46 43 52 w 23 45 37 78 43 42 87 69 73 22 27 100 100

72 87 54 .. 51 92 13 100 100 100 23 77 100 91 34 55 100 100 96 94 24 96 50 12 92 85 90 98 48 95 97 100 100 100 100 .. 75 89 52 49 44 61 w 35 57 50 84 54 59 89 79 84 36 31 99 100

Internet users per 100 peoplea 2009

36.2 42.1 4.5 38.6 7.4 56.1 0.3 73.3 75.0 63.6 1.2 9.0 61.2 8.7 9.9 7.6 90.3 70.9 18.7 10.1 1.5 25.8 .. 5.4 36.2 33.5 35.3 1.6 9.8 33.3 82.2 83.2 78.1 55.5 16.9 31.2 27.5 8.8 1.8 6.3 11.4 27.1 w 2.7 20.9 17.2 34.6 18.1 24.1 36.4 31.5 21.5 5.5 8.8 72.3 67.3

a. Data are from the International Telecommunication Union’s (ITU) World Telecommunication Development Report database. Please cite ITU for third-party use of these data. b. Data are for the most recent year available. c. Includes Hong Kong SAR, China. d. Data are for 2010. e. Includes emissions not allocated to specific countries.

20

2011 World Development Indicators

About the data

The Millennium Development Goals address concerns common to all economies. Diseases and environmental degradation do not respect national boundaries. Epidemic diseases, wherever they occur, pose a threat to people everywhere. And environmental damage in one location may affect the well-being of plants, animals, and humans far away. The indicators in the table relate to goals 5, 6, and 7 and the targets of goal 8 that address access to new technologies. For the other targets of goal 8, see table 1.4. The target of achieving universal access to reproductive health has been added to goal 5 to address the importance of family planning and health services in improving maternal health and preventing maternal death. Women with multiple pregnancies are more likely to die in childbirth. Access to contraception is an important way to limit and space births. Measuring disease prevalence or incidence can be difficult. Most developing economies lack reporting systems for monitoring diseases. Estimates are often derived from survey data and report data from sentinel sites, extrapolated to the general population. Tracking diseases such as HIV/AIDS, which has a long latency

1.3

WORLD VIEW

Millennium Development Goals: protecting our common environment Definitions

between contraction of the virus and the appearance of symptoms, or malaria, which has periods of dormancy, can be particularly difficult. The table shows the estimated prevalence of HIV among adults ages 15–49. Prevalence among older populations can be affected by life-prolonging treatment. The incidence of tuberculosis is based on case notifications and estimates of cases detected in the population. Carbon dioxide emissions are the primary source of greenhouse gases, which contribute to global warming, threatening human and natural habitats. In recognition of the vulnerability of animal and plant species, a new target of reducing biodiversity loss has been added to goal 7. Access to reliable supplies of safe drinking water and sanitary disposal of excreta are two of the most important means of improving human health and protecting the environment. Improved sanitation facilities prevent human, animal, and insect contact with excreta. Internet use includes narrowband and broadband Internet. Narrowband is often limited to basic applications; broadband is essential to promote e-business, e-learning, e-government, and e-health.

• Maternal mortality ratio is the number of women who die from pregnancy-related causes during pregnancy and childbirth, per 100,000 live births. Data are from various years and adjusted to a common 2008 base year. The values are modeled estimates (see About the data for table 2.19). • Contraceptive prevalence rate is the percentage of women ages 15–49 married or in union who are practicing, or whose sexual partners are practicing, any form of contraception. • HIV prevalence is the percentage of people ages 15–49 who are infected with HIV. • Incidence of tuberculosis is the estimated number of new tuberculosis cases (pulmonary, smear positive, and extrapulmonary). • Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include emissions produced during consumption of solid, liquid, and gas fuels and gas flaring (see table 3.8). • Proportion of species threatened with extinction is the total number of threatened mammal (excluding whales and porpoises), bird, and higher native, vascular plant species as a percentage of the total number of known species of the same categories.

1.3a

Location of indicators for Millennium Development Goals 5–7

• Access to improved sanitation facilities is the percentage of the population with at least adequate

Goal 5. Improve maternal health 5.1 Maternal mortality ratio 5.2 Proportion of births attended by skilled health personnel 5.3 Contraceptive prevalence rate 5.4 Adolescent fertility rate 5.5 Antenatal care coverage 5.6 Unmet need for family planning Goal 6. Combat HIV/AIDS, malaria, and other diseases 6.1 HIV prevalence among pregnant women ages 15–24 6.2 Condom use at last high-risk sex 6.3 Proportion of population ages 15–24 with comprehensive, correct knowledge of HIV/AIDS 6.4 Ratio of school attendance of orphans to school attendance of nonorphans ages 10–14 6.5 Proportion of population with advanced HIV infection with access to antiretroviral drugs 6.6 Incidence and death rates associated with malaria 6.7 Proportion of children under age 5 sleeping under insecticide-treated bednets 6.8 Proportion of children under age 5 with fever who are treated with appropriate antimalarial drugs 6.9 Incidence, prevalence, and death rates associated with tuberculosis 6.10 Proportion of tuberculosis cases detected and cured under directly observed treatment short course Goal 7. Ensure environmental sustainability 7.1 Proportion of land area covered by forest 7.2 Carbon dioxide emissions, total, per capita and per $1 purchasing power parity GDP 7.3 Consumption of ozone-depleting substances 7.4 Proportion of fish stocks within safe biological limits 7.5 Proportion of total water resources used 7.6 Proportion of terrestrial and marine areas protected 7.7 Proportion of species threatened with extinction 7.8 Proportion of population using an improved drinking water source 7.9 Proportion of population using an improved sanitation facility Proportion of urban population living in slums

Table 1.3, 2.19 2.19 1.3, 2.19 2.19 1.5, 2.19 2.19

access to excreta disposal facilities (private or shared, but not public) that can effectively prevent human, animal, and insect contact with excreta (facilities do not have to include treatment to render sewage outflows innocuous). Improved facilities range from simple but protected pit latrines to flush toilets with a sewerage connection. To be effective,

1.3*, 2.21* 2.21* —

facilities must be correctly constructed and properly maintained. • Internet users are people with access to the worldwide network.

— — — 2.18 2.18 1.3, 2.21 2.18

Data sources

3.1

The indicators here and throughout this book have

3.8 3.9* — 3.5 — 1.3 1.3, 2.18, 3.5 1.3, 2.18, 3.11 —

— No data are available in the World Development Indicators database. * Table shows information on related indicators.

been compiled by World Bank staff from primary and secondary sources. Efforts have been made to harmonize the data series used to compile this table with those published on the United Nations Millennium Development Goals Web site (www. un.org/millenniumgoals), but some differences in timing, sources, and definitions remain. For more information see the data sources for the indicators listed in tables 1.3a and 1.4a.

2011 World Development Indicators

21

1.4

Millennium Development Goals: overcoming obstacles

Development Assistance Committee members Ofﬁcial development assistance (ODA) by donor For basic social services a Net disbursements % of total sector% of donor allocable ODA GNI commitments 2009 2009

Australia Canada European Union Austria Belgium Denmark Finland France Germany Greece Ireland Italy Luxembourg Netherlands Portugal Spain Sweden United Kingdom Japan Korea, Rep.c New Zealandc Norway Switzerland United States

0.29 0.30

14.5 25.5

0.30 0.55 0.88 0.54 0.46 0.35 0.19 0.54 0.16 1.04 0.82 0.23 0.46 1.12 0.52 0.18 0.10 0.28 1.06 0.45 0.21

6.3 12.7 21.3 5.8 8.8 8.7 11.2 32.1 12.9 35.4 11.9 3.6 24.2 10.8 21.4 18.6 6.7 27.7 21.9 9.5 31.7

Least developed countries’ access to high-income markets Goods (excluding arms) admitted free of tariffs % of exports from least developed countries 2002 2008

Support to agriculture

Average tariff on exports of least developed countries % Agricultural products 2002 2008

Textiles

Clothing

2002

2008

2002

2008

% of GDP 2009b

95.9 67.2 97.0

100.0 100.0 98.7

0.2 0.3 1.8

0.0 0.1 0.9

5.1 5.7 0.1

0.0 0.2 0.1

19.7 17.9 1.2

0.0 1.7 1.2

0.15 0.75 0.84

33.2 14.6 98.0 97.9 93.4 61.7

99.6 57.7 98.2 99.9 100.0 83.8

4.8 26.1 3.1 3.8 5.1 6.3

1.4 28.5 0.0 18.0 0.1 5.8

2.8 11.4 0.3 3.1 0.0 6.6

2.6 4.0 0.0 0.0 0.0 5.7

0.1 12.5 0.3 1.3 0.0 12.5

0.1 3.7 0.0 1.0 0.0 11.3

1.11 2.44 0.20 1.07 1.37 0.87

Heavily indebted poor countries (HIPCs) HIPC HIPC HIPC decision completion Initiative pointd pointd assistance

Afghanistan Benin Boliviae Burkina Fasoe,f Burundi Cameroon Central African Republic Chad Comoros Congo, Dem. Rep. Congo, Rep. Côte d’Ivoire Ethiopiaf Gambia, The Ghana Guinea Guinea-Bissau Guyanae

Jul. 2007 Jul. 2000 Feb. 2000 Jul. 2000 Aug. 2005 Oct. 2000 Sep. 2007 May 2001 Jun. 2010 Jul. 2003 Mar. 2006 Mar. 2009 Nov. 2001 Dec. 2000 Feb. 2002 Dec. 2000 Dec. 2000 Nov. 2000

Jan. 2010 Mar. 2003 Jun. 2001 Apr. 2002 Jan. 2009 Apr. 2006 Jun. 2009 Floating Floating Jul. 2010 Jan. 2010 Floating Apr. 2004 Dec. 2007 Jul. 2004 Floating Dec. 2010 Dec. 2003

HIPC HIPC HIPC decision completion Initiative pointd pointd assistance

MDRI assistance

MDRI assistance

end-2009 net present value

end-2009 net present value

$ millions

$ millions

654 385 1,949 812 1,009 1,861 675 241 151 9,493 1,906 3,245 2,735 98 3,091 801 746 897

20 754 1,953 764 58 646 435 .. .. 515 120 .. 1,862 232 2,570 .. 77 493

Haiti Honduras Liberia Madagascar Malawif Malie Mauritania Mozambiquee Nicaragua Niger f Rwandaf São Tomé & Principef Senegal Sierra Leone Tanzania Togo Uganda e Zambia

Nov. 2006 Jul. 2000 Mar. 2008 Dec. 2000 Dec. 2000 Sep. 2000 Feb. 2000 Apr. 2000 Dec. 2000 Dec. 2000 Dec. 2000 Dec. 2000 Jun. 2000 Mar. 2002 Apr. 2000 Nov. 2008 Feb. 2000 Dec. 2000

Jun. 2009 Apr. 2005 Jun. 2010 Oct. 2004 Aug. 2006 Mar. 2003 Jun. 2002 Sep. 2001 Jan. 2004 Apr. 2004 Apr. 2005 Mar. 2007 Apr. 2004 Dec. 2006 Nov. 2001 Dec. 2010 May 2000 Apr. 2005

164 816 2,958 1,228 1,379 792 913 3,147 4,861 947 956 172 717 919 2,977 305 1,509 3,672

665 1,893 243 1,598 898 1,308 558 1,322 1,191 651 283 34 1,661 465 2,517 463 2,245 1,962

a. Includes primary education, basic life skills for youth, adult and early childhood education, basic health care, basic health infrastructure, basic nutrition, infectious disease control, health education, health personnel development, population policy and administrative management, reproductive health care, family planning, sexually transmitted disease control including HIV/AIDS, personnel development for population and reproductive health, basic drinking water supply and basic sanitation, and multisector aid for basic social services. b. Provisional data. c. Calculated by World Bank staff using the World Integrated Trade Solution based on the United Nations Conference on Trade and Development’s Trade Analysis and Information Systems database. d. Refers to the Enhanced HIPC Initiative. e. Also reached completion point under the original HIPC Initiative. The assistance includes original debt relief. f. Assistance includes topping up at completion point.

22

2011 World Development Indicators

About the data

1.4

WORLD VIEW

Millennium Development Goals: overcoming obstacles Definitions

Achieving the Millennium Development Goals

lines with “international peaks”). The averages in

• Ofﬁcial development assistance (ODA) net dis-

requires an open, rule-based global economy in

the table include ad valorem duties and equivalents.

bursements are grants and loans (net of repayments of

which all countries, rich and poor, participate. Many

Subsidies to agricultural producers and exporters

principal) that meet the DAC definition of ODA and are

poor countries, lacking the resources to finance

in OECD countries are another barrier to developing

made to countries on the DAC list of recipients. • ODA

development, burdened by unsustainable debt, and

economies’ exports. Agricultural subsidies in OECD

unable to compete globally, need assistance from

economies are estimated at $384 billion in 2009.

for basic social services is aid commitments by DAC donors for basic education, primary health care, nutrition, population policies and programs, reproductive

rich countries. For goal 8—develop a global partner-

The Debt Initiative for Heavily Indebted Poor Coun-

ship for development—many indicators therefore

tries (HIPCs), an important step in placing debt relief

monitor the actions of members of the Organisa-

within the framework of poverty reduction, is the first

tion for Economic Co-operation and Development’s

comprehensive approach to reducing the external

(OECD) Development Assistance Committee (DAC).

debt of the world’s poorest, most heavily indebted

the effectively applied rates for all products subject to

Official development assistance (ODA) has risen

countries. A 1999 review led to an enhancement of

tariffs. • Agricultural products are plant and animal

in recent years as a share of donor countries’ gross

the framework. In 2005, to further reduce the debt

products, including tree crops but excluding timber and

national income (GNI), but the poorest economies

of HIPCs and provide resources for meeting the Mil-

fish products. • Textiles and clothing are natural and

need additional assistance to achieve the Millen-

lennium Development Goals, the Multilateral Debt

synthetic fibers and fabrics and articles of clothing

nium Development Goals. In 2009 total net ODA from

Relief Initiative (MDRI), proposed by the Group of

made from them. • Support to agriculture is the value

OECD DAC members rose 0.7 percent in real terms

Eight countries, was launched.

of gross transfers from taxpayers and consumers aris-

to $119.6 billion, representing 0.31 percent of DAC members’ combined gross national income.

health, and water and sanitation services. • Goods admitted free of tariffs are exports of goods (excluding arms) from least developed countries admitted without tariff. • Average tariff is the unweighted average of

Under the MDRI four multilateral institutions—the

ing from policy measures, net of associated budgetary

International Development Association (IDA), Inter-

receipts, regardless of their objectives and impacts on farm production and income or consumption of farm

One important action that high-income economies

national Monetary Fund (IMF), African Development

can take is to reduce barriers to exports from low-

Fund (AfDF), and Inter-American Development Bank

and middle- income economies. The European Union

(IDB)—provide 100 percent debt relief on eligible

has begun to eliminate tariffs on exports of “every-

debts due to them from countries having completed

thing but arms” from least developed countries, and

the HIPC Initiative process. Data in the table refer

the United States offers special concessions to Sub-

to status as of March 2011 and might not show

Saharan African exports. However, these programs

countries that have since reached the decision or

completion point is the date when a country success-

still have many restrictions.

completion point. Debt relief under the HIPC Initia-

fully completes the key structural reforms agreed on

Average tariffs in the table refl ect high-income

tive has reduced future debt payments by $59 bil-

at the decision point, including implementing a poverty

OECD member tariff schedules for exports of coun-

lion (in end-2009 net present value terms) for 36

reduction strategy. The country then receives full debt

tries designated least developed countries by the

countries that have reached the decision point. And

relief under the HIPC Initiative without further policy

United Nations. Although average tariffs have been

32 countries that have reached the completion point

conditions. • HIPC Initiative assistance is the debt

falling, averages may disguise high tariffs on specific

have received additional assistance of $30 billion (in

relief committed as of the decision point (assuming full

goods (see table 6.8 for each country’s share of tariff

end-2009 net present value terms) under the MDRI.

participation of creditors). Topping-up assistance and

products. • HIPC decision point is the date when a heavily indebted poor country with an established track record of good performance under adjustment programs supported by the IMF and the World Bank commits to additional reforms and a poverty reduction strategy and starts receiving debt relief. • HIPC

assistance provided under the original HIPC Initiative

1.4a

Location of indicators for Millennium Development Goal 8

were committed in net present value terms as of the decision point and are converted to end-2009 terms.

Goal 8. Develop a global partnership for development 8.1 Net ODA as a percentage of DAC donors’ gross national income 8.2 Proportion of ODA for basic social services 8.3 Proportion of ODA that is untied 8.4 Proportion of ODA received in landlocked countries as a percentage of GNI 8.5 Proportion of ODA received in small island developing states as a percentage of GNI 8.6 Proportion of total developed country imports (by value, excluding arms) from least developed countries admitted free of duty 8.7 Average tariffs imposed by developed countries on agricultural products and textiles and clothing from least developed countries 8.8 Agricultural support estimate for OECD countries as a percentage of GDP 8.9 Proportion of ODA provided to help build trade capacity 8.10 Number of countries reaching HIPC decision and completion points 8.11 Debt relief committed under new HIPC initiative 8.12 Debt services as a percentage of exports of goods and services 8.13 Proportion of population with access to affordable, essential drugs on a sustainable basis 8.14 Telephone lines per 100 people 8.15 Cellular subscribers per 100 people 8.16 Internet users per 100 people

Table 1.4, 6.14 1.4 6.15b — — 1.4 1.4, 6.8* 1.4 — 1.4 1.4 6.11*

• MDRI assistance is 100 percent debt relief on eligible debt from IDA, IMF, AfDF, and IDB, delivered in full to countries having reached the HIPC completion point.

Data sources Data on ODA are from the OECD. Data on goods admitted free of tariffs and average tariffs are from the World Trade Organization, in collaboration with the United Nations Conference on Trade and Development and the International Trade Centre. These data are available at www.mdg-trade. org. Data on subsidies to agriculture are from

— 1.3*, 5.11 1.3*, 5.11 5.12

— No data are available in the World Development Indicators database. * Table shows information on related indicators.

the OECD’s Producer and Consumer Support Estimates, OECD Database 1986–2009. Data on the HIPC Initiative and MDRI are from the World Bank’s Economic Policy and Debt Department.

2011 World Development Indicators

23

1.5

Women in development Female population

% of total 2009

Life expectancy at birth

years Male Female 2009 2009

Pregnant women receiving prenatal care

% 2004–09a

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire

48.2 50.6 49.5 50.7 51.0 53.4 50.3 51.2 51.1 49.4 53.5 51.0 49.5 50.1 51.9 50.0 50.8 51.7 50.1 51.0 51.1 50.0 50.5 50.9 50.3 50.5 48.1c 52.6 50.8 50.4 50.1 49.2 49.1

44 74 71 46 72 71 79 77 68 66 65 78 61 64 73 55 69 70 52 49 60 51 79 46 48 76 72c 80 70 46 53 77 57

44 80 74 50 79 77 84 83 73 68 76 84 63 68 78 55 76 77 55 52 63 52 84 49 50 82 75c 86 77 49 55 82 59

36 97 89 80 99 93 .. .. 77 51 99 .. 84 86 99 94 97 .. 85 92 83 b 82 .. 69 39 .. 91 .. 94 85 86 90 85

Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

51.8 49.9 50.9 50.4 49.8 49.9 49.7 52.8 50.8 53.9 50.3 51.0 51.4 50.0 50.4 53.0 51.0 49.3 50.4 51.3 49.5 50.5 50.6 50.0

73 77 74 77 70 72 69 67 58 70 54 77 78 60 55 68 77 56 78 67 56 47 60 70

80 81 80 81 76 78 72 76 62 80 57 83 85 62 58 75 83 58 83 74 60 50 63 75

100b 100 .. .. 99 84 74 94 .. .. 28 .. .. .. 98 94 .. 90 .. .. 88 78 85 92

24

2011 World Development Indicators

Teenage mothers

% of women ages 15–19 2004–09a

Women in wage employment in nonagricultural sector % of nonagricultural wage employment 2008

Unpaid family workers

Male Female % of male % of female employment employment 2008 2008

Female part-time employment

Women in Ratio parliaments of female to male wages in manufacturing

%

% of total 2004–09a

2004–09a

% of total seats 1990 2010

.. .. .. 29 .. 5 .. .. 6 33 .. .. 21 .. .. .. .. .. .. .. 8 28 .. .. 37 .. .. .. 21 24 27 .. .. 4

.. .. 13 .. 45 45 47 47 44 .. 56 47 .. 38 36 43 42 51 .. .. .. .. 50 .. .. 36 .. 49 48 .. .. 42 .. 45d

.. .. .. .. 0.7b .. 0.2 2.0 0.0 .. .. 0.4 .. .. 2.0 .. 4.6 0.6 .. .. .. .. 0.1 .. .. 0.9 .. 0.1b 3.2 .. .. 1.3 .. 0.9d

.. .. .. .. 1.6 b .. 0.4 2.7 0.0 .. .. 2.2 .. .. 8.9 .. 8.1 1.5 .. .. .. .. 0.2 .. .. 2.8 .. 1.1b 6.1 .. .. 2.8 .. 3.9d

.. .. .. .. 61b .. 71b 81 .. .. .. 81 .. .. .. .. .. 54 .. .. .. .. 68b .. .. 56 .. .. .. .. .. .. .. 59

.. .. .. .. .. .. 90 .. .. .. .. 86 .. .. .. 66 .. 69 .. .. .. .. .. .. .. .. .. 59 60 .. .. 70 .. 77

4 29 2 15 6 36 6 12 .. 10 .. 9 3 9 .. 5 5 21 .. .. .. 14 13 4 .. .. 21 .. 5 5 14 11 6 ..

28 16 8 39 39 9 25 28 11 19 35 39 11 25 19 8 9 21 15 32 21 14 22 10 5 14 21 .. 8 8 7 39 9 24

.. .. .. 21 19 10 .. .. .. 17 .. .. .. .. 10 .. 13 .. .. 32 .. 14 22

43 46 49 39 39 19 48 .. 52 47 51 49 .. .. 46 47 .. 42 43 .. .. .. 34

.. 0.3 0.3 2.9 4.4b 8.6 8.8 .. 0.0 b 7.8b 0.6 0.3 .. .. .. 0.4 .. 3.4 .. .. .. .. ..

.. 1.0 0.5 3.4 11.1b 32.6 9.9 .. 0.0 b 12.7b 0.4 0.9 .. .. .. 1.5 .. 9.8 .. .. .. .. ..

.. 69 62 .. .. .. .. .. 68 56b 64 80 .. .. 56 80 .. 68 .. .. .. .. ..

.. .. 87 .. .. 76 85 .. .. .. 84 82 .. .. 61 74 .. .. .. .. .. .. ..

34 .. 31 8 5 4 12 .. .. .. 32 7 13 8 .. .. .. 7 7 .. 20 .. 10

43 22 38 21 32 2 19 22 23 28 40 19 15 8 7 33 8 17 12 19 10 4 18

Female population

% of total 2009

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

52.5 48.3 50.1 49.2 49.4 49.9 50.4 51.4 51.1 51.3 48.7 52.4 50.0 50.6 50.5 .. 40.5 50.7 50.1 53.9 51.0 52.8 50.3 48.3 53.2 50.1 50.2 50.3 49.2 50.6 49.3 50.4 50.8 52.5 50.5 50.9 51.4 51.2 50.7 50.3 50.4 50.6 50.5 49.9 49.9 50.3 43.6 48.5 49.6 49.2 49.5 49.9 49.6 51.8 51.6 52.0 24.6

Life expectancy at birth

years Male Female 2009 2009

70 63 69 70 65 77 80 79 69 80 71 64 54 65 77 68 76 62 64 68 70 45 57 72 68 72 59 53 72 48 55 69 73 65 64 69 47 60 61 66 79 78 70 51 48 79 75 67 73 59 70 71 70 72 76 75 75

78 66 73 73 72 82 84 84 75 86 75 74 55 70 84 72 80 72 67 78 74 46 60 77 79 77 62 55 77 50 59 76 78 72 70 74 49 64 62 68 83 82 77 53 49 83 78 67 79 64 74 76 74 80 82 83 77

Pregnant women receiving prenatal care

% 2004–09a

.. 75 93 98 84 .. .. .. 91 .. 99 100 92 .. .. .. .. 97 35 .. 96 92 79 .. .. 94 86 92 79 70 75 .. 94 98 100 68 89 80 95 44 .. .. 90 46 58 .. .. 61 .. 79 96 94 91 .. .. .. ..

Teenage mothers

% of women ages 15–19 2004–09a

.. 16 9 .. .. .. .. .. .. .. 4 7 .. .. .. .. .. .. 17 .. .. 20 38 .. .. .. 34 34 .. 36 .. .. .. 6 .. 7 .. .. 15 19 .. .. 25 39 23 .. .. 9 .. .. 13 26 10 .. .. .. ..

Women in wage employment in nonagricultural sector % of nonagricultural wage employment 2008

48 .. 32 .. 12 49 49 44 48 42 16 50 .. .. 42 .. .. 51 .. 53 .. .. .. .. 53 42 .. .. 39 .. .. 37 39 54 51 21 .. .. .. .. 48 48 38 36 .. 49 22 13 42 .. 40 38 42 47 48 42 13

Unpaid family workers

Female part-time employment

Male Female % of male % of female employment employment 2008 2008

0.3 .. 7.8 5.4 .. 0.6 0.1 1.2 0.5 1.1 .. .. .. .. 1.2 .. .. 8.8 26.4 1.4 .. .. .. .. 1.0 7.0 .. .. 2.7 .. .. 0.9 4.9 1.3 .. 16.5 .. .. 0.9 .. 0.2 0.8 12.2 .. .. 0.2 .. 18.6 2.3 .. 10.8 4.7b 9.0 b 2.7 0.7 0.0 ..

0.5 .. 33.6 32.7 .. 0.8 0.4 2.5 2.2 7.3 .. .. .. .. 12.7 .. .. 19.3 64.2 1.2 .. .. .. .. 2.0 14.9 .. .. 8.8 .. .. 4.7 10.0 3.4 .. 51.8 .. .. 1.1 .. 0.8 1.5 9.1 .. .. 0.4 .. 61.9 4.0 .. 8.9 9.9 b 18.0 b 5.9 1.2 0.0 ..

% of total 2004–09a

65 .. .. .. .. 77 73 78 .. 70 .. .. .. .. 59 .. .. .. .. 59 .. .. .. .. 60 47 .. .. .. .. .. 44 65 .. .. .. .. .. .. .. 75 72b .. .. .. 71 .. .. 47 .. .. .. .. 68 68 .. ..

1.5

WORLD VIEW

Women in development

Women in Ratio parliaments of female to male wages in manufacturing

% 2004–09a

77 .. .. .. .. .. .. .. .. 60 61 70 .. .. 57 .. .. .. .. 77 .. .. .. .. 71 .. .. .. .. .. .. .. 70 .. 77 .. .. 89 .. .. 82 82 .. .. .. 89 .. .. 95 .. .. .. 91 .. 69 .. 142

2011 World Development Indicators

% of total seats 1990 2010

21 5 12 2 11 8 7 13 5 1 0 .. 1 21 2 .. .. .. 6 .. 0 .. .. .. .. .. 7 10 5 .. .. 7 12 .. 25 0 16 .. 7 6 21 14 15 5 .. 36 .. 10 8 0 6 6 9 14 8 .. ..

9 11 18 3 25 14 18 21 13 11 6 18 10 16 15 .. 8 26 25 22 3 24 13 8 19 33 8 21 10 10 22 19 26 24 4 11 39 .. 24 33 41 34 21 12 7 40 0 22 9 1 13 28 21 20 27 .. 0

25

1.5

Women in development Female population

% of total 2009

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

51.4 53.8 51.6 44.8 50.4 50.5 51.3 49.8 51.5 51.2 50.4 50.7 50.7 50.8 49.6 51.1 50.4 51.2 49.5 50.6 50.1 50.8 49.1 50.5 51.4 49.7 49.8 50.7 49.9 53.9 32.7 50.9 50.7 51.7 50.3 49.8 50.6 49.1 49.4 50.1 51.7 49.6 w 50.1 49.3 48.8 50.9 49.4 48.8 52.2 50.6 49.6 48.5 50.2 50.6 51.1

Life expectancy at birth

years Male Female 2009 2009

70 63 49 73 54 71 47 79 71 76 49 50 79 71 57 47 79 80 73 64 56 66 61 61 66 73 70 61 53 64 77 78 76 73 65 71 73 72 62 46 45 67 w 56 67 66 69 65 71 66 71 69 63 51 77 78

77 75 52 74 57 76 49 84 79 82 52 53 85 78 60 46 83 84 76 70 57 72 63 65 73 77 75 69 54 75 79 82 81 80 71 77 77 75 65 47 46 71 w 59 71 70 75 69 74 75 77 73 66 54 83 83

Pregnant women receiving prenatal care

% 2004–09a

94 .. 96 .. 94 98 87 .. .. .. 26 .. .. 99 64 85 .. .. 84 80 76 98 .. 84 96 96 95 99 94 99 .. .. .. 96 99 .. 91 99 47 94 93 82 w 67 85 83 95 82 91 .. 95 83 70 71 .. ..

Teenage mothers

% of women ages 15–19 2004–09a

.. .. 4 .. 18 .. 34 .. .. .. .. .. .. .. .. 23 .. .. .. .. 26 .. .. .. .. .. .. .. 25 4 .. .. .. .. .. .. .. .. .. 28 21

Women in wage employment in nonagricultural sector % of nonagricultural wage employment 2008

46 51 .. 15 .. 44 .. 46 48 47 .. 44 45 31 .. .. 50 48 16 37 31 45 .. .. .. .. 22 .. .. 55 20 52 48 46 39 42 .. 18 6 .. .. .. w .. .. .. 43 .. .. 48 41 .. .. .. 46 47

Unpaid family workers

Male Female % of male % of female employment employment 2008 2008

6.0 0.1 .. .. .. 3.1 .. 0.4b 0.1 3.2 .. 0.3 0.8 4.4 b .. .. 0.2 1.7b .. .. 9.7 14.0 .. .. .. .. 5.3 .. .. 0.4 .. 0.2 0.1 0.9 b .. 0.6 .. 6.6 .. .. .. .. w .. .. .. 3.3 .. .. 1.9 4.0 .. .. .. 0.6 0.8

a. Data are for the most recent year available. b. Limited coverage. c. Includes Taiwan, China. d. Data are for 2009.

26

2011 World Development Indicators

18.9 0.1 .. .. .. 11.9 .. 1.3b 0.2 5.4 .. 0.6 1.4 21.7b .. .. 0.3 3.2b .. .. 13.0 29.9 .. .. .. .. 37.7 .. .. 0.3 .. 0.5 0.1 3.0 b .. 1.6 .. 31.5 .. .. .. .. w .. .. .. 7.2 .. .. 5.3 7.5 .. .. .. 2.4 1.8

Female part-time employment

% of total 2004–09a

49 62 .. .. .. .. .. .. 59 57 .. .. 79 .. .. .. 64 81 .. .. .. .. .. .. .. .. 58 .. .. .. .. 76 67b 59 b .. .. .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. .. 71 78

Women in Ratio parliaments of female to male wages in manufacturing

% 2004–09a

74 .. .. .. .. .. .. 65 .. .. .. .. .. 93 .. .. 90 77 .. .. .. .. .. .. .. .. .. .. .. 71 .. 80 .. .. .. .. .. 53 .. .. .. 71 m 89 71 85 70 71 91 71 70 53 93 66 71 73

% of total seats 1990 2010

34 .. 17 .. 13 .. .. 5 .. .. 4 3 15 5 .. 4 38 14 9 .. .. 3 .. 5 17 4 1 26 12 .. 0 6 7 6 .. 10 18 .. 4 7 11 13 w .. 13 13 12 13 17 .. 12 4 6 .. 12 12

11 14 56 0 23 22 13 23 15 14 7 45 37 5 26 14 45 29 12 19 31 13 29 11 29 28 9 17 32 8 23 22 17 15 22 19 26 .. 0 14 15 19 w 19 18 17 19 18 19 15 24 9 19 20 23 26

About the data

1.5

WORLD VIEW

Women in development Definitions

Despite much progress in recent decades, gender

in non-agricultural wage employment. The indicator

• Female population is the percentage of the popu-

inequalities remain pervasive in many dimensions of

does not reveal any differences in the quality of the

lation that is female. • Life expectancy at birth is

life—worldwide. But while disparities exist through-

different types of non-agricultural wage employment,

the number of years a newborn infant would live if

out the world, they are most prevalent in developing

regarding earnings, conditions of work, or the legal

prevailing patterns of mortality at the time of its birth

countries. Gender inequalities in the allocation of

and social protection, which they offer. The indica-

were to stay the same throughout its life. • Pregnant

such resources as education, health care, nutrition,

tor cannot reflect whether women are able to reap

women receiving prenatal care are the percentage

and political voice matter because of the strong

the economic benefits of such employment, either.

of women attended at least once during pregnancy

association with well-being, productivity, and eco-

Finally it should be noted that the female employ-

by skilled health personnel for reasons related to

nomic growth. These patterns of inequality begin at

ment of any kind tends to be underreported in all

pregnancy. • Teenage mothers are the percentage of

an early age, with boys routinely receiving a larger

kinds of surveys. In addition, the employment share

women ages 15–19 who already have children or are

share of education and health spending than do girls,

of the agricultural sector, for both men and women,

currently pregnant. • Women in wage employment

for example.

is severely underreported.

in nonagricultural sector are female wage employ-

Because of biological differences girls are

Women’s wage work is important for economic

ees in the nonagricultural sector as a percentage

expected to experience lower infant and child mor-

growth and the well-being of families. But women

of total nonagricultural wage employment. • Unpaid

tality rates and to have a longer life expectancy than

often face such obstacles as restricted access to

family workers are those who work without pay in a

boys. This biological advantage may be overshad-

credit markets, capital, land, training, and educa-

market-oriented establishment or activity operated

owed, however, by gender inequalities in nutrition

tion, time constraints due to their traditional family

by a related person living in the same household.

and medical interventions and by inadequate care

responsibilities, and labor market bias and discrimi-

• Part-time employment, female is a female share

during pregnancy and delivery, so that female rates

nation. These obstacles force women to limit their

of total part-time workers. Part-time worker is an

of illness and death sometimes exceed male rates.

full participation in paid economic activities, and to

employed person whose normal hours of work are

These gender bias can be seen in the child mortal-

be less productive and to receive lower wages. More

less than those of comparable full-time workers. Defi -

ity rates (table 2.22) or life expectancy by gender.

women than men are found in unpaid family employ-

nition of part-time varies across countries. • Ratio of

Female child mortality rates that are as high as or

ment and part time employment. The gender wage

female to male wages in manufacturing is a ratio of

higher than male child mortality rates may indicate

gap in manufacturing remains an unfortunate reality

women’s wage to men’s in manufacturing. • Women

discrimination against girls.

of almost all countries of the world, even though the

in parliaments are the percentage of parliamentary

gap may not be attributed entirely to discrimination.

seats in a single or lower chamber held by women.

Having a child during the teenage years limits girls’ opportunities for better education, jobs, and income.

Women are vastly underrepresented in decision-

Pregnancy is more likely to be unintended during

making positions in government, although there is

the teenage years, and births are more likely to be

some evidence of recent improvement. Gender parity

Data on female population are from the United

premature and are associated with greater risks of

in parliamentary representation is still far from being

Nations Population Division’s World Population

complications during delivery and of death. In many

realized. In 2010 women accounted for 19 percent

Prospects: The 2008 Revision, and data on life

countries maternal mortality (tables 1.3 and 2.19) is

of parliamentarians worldwide, compared with 9 per-

expectancy for more than half the countries in the

a leading cause of death among women of reproduc-

cent in 1987. Without representation at this level, it

table (most of them developing countries) are from

tive age, although most of them are preventable.

is difficult for women to influence policy.

its World Population Prospects: The 2008 Revision,

Data sources

Women in wage employment in nonagricultural sec-

For information on other aspects of gender, see

with additional data from census reports, other

tor shows the extent that women have access to paid

tables 1.2 (Millennium Development Goals: eradicat-

statistical publications from national statistical

employment, which will affect their integration into

ing poverty and saving lives), 1.3 (Millennium Devel-

offices, Eurostat’s Demographic Statistics, the

the monetary economy. It also indicates the degree

opment Goals: protecting our common environment),

Secretariat of the Pacific Community’s Statistics

that labour markets are open to women in industry

2.3 (Employment by economic activity), 2.4 (Decent

and Demography Programme, and the U.S. Bureau

and services sectors which affects not only equal

work and productive employment), 2.5 (Unemploy-

of the Census International Data Base. Data on

employment opportunity for women, but also eco-

ment), 2.6 (Children at work), 2.10 (Assessing vulner-

pregnant women receiving prenatal care are from

nomic efficiency through flexibility of the labor market

ability and security), 2.13 (Education efficiency), 2.14

UNICEF’s The State of the World’s Children 2010

and the economy’s capacity to adapt to changes over

(Education completion and outcomes), 2.15 (Educa-

time. In many developing countries, non-agricultural

tion gaps by income and gender), 2.19 (Reproductive

based on household surveys including DemoData sources graphic and Health Surveys by Macro International

wage employment represents only a small portion

health), 2.21 (Health risk factors and future chal-

and Multiple Indicator Cluster Surveys by UNICEF.

of total employment. As a result the contribution of

lenges), and 2.22 (Mortality).

Data on teenage mothers are from Demographic

women to the national economy is underestimated

and Health Surveys by Macro International. Data

and therefore misrepresented. The indicator is dif-

on labor force, employment and wage are from the

ficult to interpret, unless additional information is

International Labour Organization’s Key Indicators

available on the share of women in total employ-

of the Labour Market, 6th edition. Data on women in

ment, which would allow an assessment to be made

parliaments are from the Inter-Parliamentary Union.

of whether women are under- or over-represented

2011 World Development Indicators

27

1.6

Key indicators for other economies Population Surface Population area density

Gross national income

thousands 2009

thousand sq. km 2009

people per sq. km 2009

American Samoa

67

0.2

336

..

Andorra

85

0.5

181

3,447

41,130

Antigua and Barbuda

88

0.4

199

1,062

12,130

592

..

Life Adult expectancy literacy at birth rate

Carbon dioxide emissions

Purchasing power parity

Atlas method $ millions 2009

Gross domestic product

Per capita $ 2009

..b

..d

$ millions 2009

Per capita $ 2009

% growth 2008–09

Per capita % growth 2008–09

years 2009

% ages 15 thousand and older metric tons 2007 2005–09a

..

..

..

..

..

..

..

..

..

3.6

1.6

..

..

539

–8.5

–9.5

..

99

436

..

..

75

98

2,396

1,548 c ..

17,670 c ..

Aruba

107

0.2

Bahamas, The

342

13.9

34

7,136

21,390

..

..

2.8

1.5

74

..

2,147

Bahrain

791

0.8

1,041

19,712

25,420

26,130

33,690

6.3

4.1

76

91

22,446 1,345

Barbados

256

0.4

595

..

Belize

333

23.0

15

1,205

64

0.1

1,288

..

Bhutan

697

38.4

18

1,405

Brunei Darussalam

400

5.8

76

..

Cape Verde

506

4.0

125

1,520

55

0.3

229

..

Bermuda

Cayman Islands

..d 3,740 ..d 2,020

.. 1,929c ..

.. 5,990c ..

..

..

77

..

0.0

–3.4

77

..

425

–8.1

–8.4

79

..

513

3,692

5,290

7.4

5.8

67

53

579

..d 19,706

51,200

0.6

–1.3

78

95

7,599

1,783

3,530

2.8

1.4

71

84

308

..

..

..

..

..

99

539

3,010 ..d

Channel Islands

150

0.2

789

10,242

68,610

..

..

5.9

5.7

79

..

..

Comoros

659

1.9

354

531

810

779

1,180

1.8

–0.6

66

74

121

Cyprus

871

9.3

94

24,400e

30,290 e

–1.0e

–1.9 e

80

98

8,193

Djibouti

864

23.2

37

1,106

1,280

2,480

5.0

3.2

56

..

487

74

0.8

98

360

4,900

8,460 c

–0.8

–1.3

..

..

121

676

28.1

24

8,398

12,420

4,793

49

1.4

35

..

Fiji

849

18.3

46

3,259

French Polynesia

Dominica Equatorial Guinea Faeroe Islands

30,480e 24,250 e 2,140 623c 13,069

19,330

–5.4

–7.8

51

93

..

..

..

..

80

..

696

3,840 f

3,850

4,530

–3.0

–3.6

69

..

1,458

..d

269

4.0

74

..

..d

..

..

..

..

75

..

806

Gibraltar

31

0.0

3,105

..

..d

..

..

..

..

..

..

407

Greenland

56

410.5

..

..

–5.4

–5.0

68

..

520

Grenada

104

0.3

–6.8

–7.1

75

..

242

Guam

178

..

..

76

..

..

Guyana

3.3

3.4

68

..

1,506

Iceland Isle of Man

0g

1,467

26,160

306

580

5,580

0.5

329

..

762

215.0

4

2,026

2,660

319

103.0

3

13,858

43,430

10,478

32,840

–6.5

–7.0

81

..

2,338

80

0.6

141

3,972

49,310

..

..

7.5

7.4

..

..

..

About the data

..d

802c .. 2,491c

7,710c .. 3,270 c

Definitions

The table shows data for economies with populations

• Population is based on the de facto definition of

included in the valuation of output plus net receipts

between 30,000 and 1 million and for smaller econo-

population, which counts all residents regardless of

of primary income (compensation of employees

mies if they are members of the World Bank. Where

legal status or citizenship—except for refugees not

and property income) from abroad. Data are in cur-

data on gross national income (GNI) per capita are

permanently settled in the country of asylum, who

rent U.S. dollars converted using the World Bank

not available, the estimated range is given. For more

are generally considered part of the population of

Atlas method (see Statistical methods). • Purchasing

information on the calculation of GNI and purchasing

their country of origin. The values shown are midyear

power parity (PPP) GNI is GNI converted to interna-

power parity (PPP) conversion factors, see About the

estimates. For more information, see About the data

tional dollars using PPP rates. An international dollar

data for table 1.1. Additional data for the economies

for table 2.1. • Surface area is a country’s total

has the same purchasing power over GNI that a U.S.

in the table are available on the World Development

area, including areas under inland bodies of water

dollar has in the United States. • GNI per capita is

Indicators CD-ROM or in WDI Online.

and some coastal waterways. • Population density

GNI divided by midyear population. • Gross domes-

is midyear population divided by land area in square

tic product (GDP) is the sum of value added by all

kilometers. • Gross national income (GNI), Atlas

resident producers plus any product taxes (less sub-

method, is the sum of value added by all resident

sidies) not included in the valuation of output. Growth

producers plus any product taxes (less subsidies) not

is calculated from constant price GDP data in local

28

2011 World Development Indicators

Population Surface Population area density

Gross national income

Gross domestic product

1.6

Life Adult expectancy literacy at birth rate

WORLD VIEW

Key indicators for other economies

Carbon dioxide emissions

Purchasing power parity

Atlas method thousands 2009

thousand sq. km 2009

people per sq. km 2009

Kiribati

98

0.8

121

180

1,830

–0.7

–2.2

..

..

Liechtenstein

36

0.2

224

4,906

136,630

..

..

–1.2

–1.9

83

..

..

Luxembourg

498

2.6

192

38,188

76,710

29,669

59,590

–4.1

–5.8

80

..

10,834

Macao SAR, China

538

0.0

19,213

21,275

39,550

30,874

57,390

1.3

–0.9

81

93

1,554

Maldives

309

0.3

1,031

1,229

1,625

5,250

–3.0

–4.4

72

98

898

Malta

415

0.3

1,297

7,621

18,360

9,616

23,170

–2.1

–2.8

80

92

2,722

3,060

..

..

0.0

–2.2

..

..

99

..

..

..

..

76

..

..

–1.5

–1.8

69

..

62 ..

Marshall Islands

$ millions 2009

Per capita $ 2009

3,970h

61

0.2

339

186

Mayotte

197

0.4

531

..

Micronesia, Fed. Sts.

111

0.7

158

277

2,500

Monaco

..b

$ millions 2009

Per capita $ 2009

324 c

3,310 c

359 c

3,240 c

% growth 2008–09

Per capita % growth 2008–09

years 2009

% ages 15 thousand and older metric tons 2007 2005–09a

33

33

0.0

16,406

6,483

197,590

..

..

–2.6

–2.9

..

..

Montenegro

624

13.8

46

4,149

6,650

8,183

13,110

–5.7

–6.0

74

..

..

Netherlands Antilles

198

0.8

248

..

..d

..

..

..

..

76

96

6,232

New Caledonia

250

18.6

14

..

..d

..

..

..

..

77

96

2,847

Northern Mariana Islands

87

0.5

189

..

..d

..

..

..

..

..

..

..

Palau

20

0.5

44

127

6,220

..

..

–2.1

–2.7

..

..

213

179

2.8

63

508

2,840

–5.5

–5.5

72

99

161

31

0.1

524

1,572

50,670

..

..

1.9

0.4

83

..

..

163

1.0

170

185

1,130

301

1,850

4.0

2.4

66

88

128

Samoa San Marino Sao Tome and Principe Seychelles Solomon Islands St. Kitts and Nevis

763c

4,270 c

88

0.5

191

746

8,480

1,477c

16,790 c

–7.6

–8.7

74

92

623

523

28.9

19

477

910

974 c

1,860 c

–2.2

–4.5

67

..

198

50

0.3

191

503

10,150

676 c

13,640c

–8.0

–8.8

..

..

249

8,860 c

–3.8

–4.9

..

..

381

St. Lucia St. Vincent and the Grenadines Suriname

172

0.6

282

894

5,190

1,525c

109

0.4

280

560

5,130

964 c

8,830c

–2.8

–2.8

72

..

202

520

163.8

3

2,454

4,760

3,469c

6,730c

5.1

4.2

69

95

2,437

Tonga

104

0.8

144

339

3,260

475c

4,570 c

–0.4

–0.8

72

99

176

..

..

..

..

158

Turks and Caicos Islands

33

1.0

35

..

..d

..

0.0

..

..

..i

Vanuatu

240

12.2

20

627

2,620

Virgin Islands (U.S.)

110

0.4

314

..

Tuvalu

..d

.. .. 1,028 c ..

.. .. 4,290 c ..

..

..

..

..

..

4.0

1.4

71

81

103

..

..

79

..

..

a. Data are for the most recent year available. b. Estimated to be upper middle income ($3,946–$12,195). c. Based on regression; others are extrapolated from the 2005 International Comparison Program benchmark estimates. d. Estimated to be high income ($12,196 or more). e. Data are for the area controlled by the government of the Republic of Cyprus. f. Included in the aggregates for upper middle-income economies based on earlier data. g. Less than 0.5. h. Included in the aggregates for lower middle-income economies based on earlier data. i. Estimated to be lower middle income ($996–$3,945).

currency. • GDP per capita is GDP divided by midyear population. • Life expectancy at birth is the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life. • Adult literacy rate is the percentage of adults ages 15 and older who can,

Data sources

with understanding, read and write a short, simple

The indicators here and throughout the book

statement about their everyday life. • Carbon dioxide

are compiled by World Bank staff from primary

emissions are those stemming from the burning of

and secondary sources. More information about

fossil fuels and the manufacture of cement. They

the indicators and their sources can be found in

include carbon dioxide produced during consumption

the About the data, Definitions, and Data sources

of solid, liquid, and gas fuels and gas flaring.

entries that accompany each table in subsequent sections.

2011 World Development Indicators

29

Text figures, tables, and boxes

PEOPLE

Introduction

S

ustainable development is about improving the quality of peoples’ lives and expanding their abilities to shape their futures. This generally calls for higher per capita incomes, but also for human capital development through improvements in health and education. Although developing countries have made large investments in human capital, good health and basic education remain elusive to many. This limits people’s ability to take advantage of employment opportunities and work their way out of poverty. The tables in this section review the achievements countries have made in improving the welfare of their people. They show the levels of poverty prevalent in countries, the distribution of income, and the prevalence of child labour—which while it reduces household poverty, is always at the expense of children’s education and future human capital. The section also looks at investments in health and education and their impact on the worst aspects of nonincome poverty by reducing hunger and malnutrition, lowering mortality rates, and improving education outcomes. This year’s national and international poverty estimates were prepared by the World Bank’s Global Poverty Working Group, recently established by the Poverty Board. The results of their work are evident in tables 2.7–2.9. The baseline database, with estimates for 231 data points (country and year combinations) covering 104 countries, was updated to include estimates for 577 data points covering 115 countries. Because of space restrictions in the printed edition, this report cannot include estimates for all countries. Thus, it includes only countries

for which estimates are available since 2000. But the full range of these poverty estimates can be accessed through the Bank’s Open Data Initiative (data.worldbank.org), and the entire database of $1.25 and $2 a day purchasing power parity poverty rate and poverty gap estimates will also be available through PovcalNet. In addition, several new indicators have been added to existing tables. Data on children’s learning assessment, from the Programme for International Student Assessment, have been added to table 2.14, and the lifetime risk of maternal death has been added to table 2.19. The new maternal mortality ratio, estimated by the Inter-Agency group, is now available in a consistent time series for the first time, and data for 1990 and the most recent year are presented in table 2.19. The entire time series can be accessed through data.worldbank.org; regional and income group aggregates for maternal mortality ratios are in figures 2a and 2b. The next sections look at civil registration, highlighting the problems countries face in planning for

Maternal mortality ratios have declined in all developing country regions since 1990

2a

2

Maternal mortality ratios have declined fastest among low- and lower middle-income countries but remain high

Maternal mortality ratio by region Maternal mortality ratio, modeled estimates (per 100,000 live births)

Maternal mortality ratio by income group Maternal mortality ratio, modeled estimates (per 100,000 live births)

1,000

1,000

2b

Sub-Saharan Africa 750

750 South Asia 500

Latin America and Caribbean

250

Low income 500

Middle East and North Africa

East Asia and Pacific

Lower middle income 250 Europe and Central Asia

Upper middle income 0

0 1990

1995

2000

2005

2008

Source: WHO, UNICEF, UNFPA, World Bank. Trends in Maternal Mortality: 1990–2008.

High income 1990

1995

2000

2005

2008

Source: WHO, UNICEF, UNFPA, World Bank. Trends in Maternal Mortality: 1990–2008.

2011 World Development Indicators

31

the welfare of their people. Countries need to know, at a minimum, how many people are born and die each year. In most developing countries this is not easy. The discussion highlights the obstacles countries must surmount in recording births and deaths and the interim measures they have adopted, and it indicates the way forward for countries and their development partners.

Civil registration, the missing pillar In 2009 the births of 50 million children went unrecorded. They entered the world with no proof of age, citizenship, or parentage. That same year 40 million people died unnoted except by family or friends. There are no records of where they died, when they died, and more importantly how they died. In most high-income countries these vital events (births and deaths) are recorded by civil registration systems, which also record marriages, adoptions, and divorces. But in many developing countries registration systems are incomplete or absent. In South Asia only 1 percent of the population is covered by complete vital registration records (at least 90 percent coverage for births and deaths), and in SubSaharan Africa only 2 percent (UN, Population and Vital Statistics Report, 2011). Lacking effective registration systems, countries must rely on infrequent and expensive censuses and surveys to estimate the vital statistics needed to support the core functions of government and to plan for the future. A state-of-the-art statistical system has three pillars: censuses and surveys, administrative records, and civil registration, each with an important and complementary role. Censuses give benchmark estimates that provide a base for and a check on vital statistics, and surveys 2c

The births of many children in Asia and Africa go unregistered Children under age 5 whose births are unregistered, 2007 (percent) 75

50

25

0 CEE/CISa

East Asia & Pacificb

Latin America & Caribbean

Middle East & North Africa

a. Central and Eastern Europe and Commonwealth of Independent States. b. Excludes China. Source: UNICEF Childinfo (www.childinfo.org/birth_registration_progress.html).

32

2011 World Development Indicators

South Asia

Sub-Saharan Africa

provide detailed characteristics of the population recorded by censuses and civil registration systems. Administrative records from health and education systems add further information to manage those services and—combined with census, survey, and vital statistics —are used to plan for future needs. Civil registration has two functions: administrative —providing legal documentation that protects identities, citizenship, property, and other economic, social, and human rights—and statistical—providing regular, frequent, and timely information on the dynamics of population growth, size, and distribution and on records of births and deaths by age, sex, and cause at the national and subnational levels. Vital statistics from civil registration systems are essential for planning basic social services and infrastructure development and for understanding and monitoring health status and health issues in the country. A complete civil registration system has three strengths: it costs less than conducting a census or survey, data are based on a record of events rather than recall, and information can be made available at low cost. In a well functioning civil registration system a family member or caretaker reports births and deaths at the registration office in the local area and receives appropriate legal documentation. Medical certification of death from a health care provider identifies the cause of death. To be considered complete, civil registration systems must collect information on at least 90 percent of vital events. Systems in most developing country regions fall well short of that standard. So today, most people in Africa and South Asia are born and die without a trace in any legal record or official statistic (figure 2c), causing a vicious cycle. These are the regions where most premature deaths occur and where the need for robust information for planning is most critical. Roughly half the countries claim to have complete registration of births and deaths (UN, Population and Vital Statistics Report, 2011), leaving nearly 40 percent of births and 70 percent of deaths unregistered (WHO 2007). In many countries vital events are unreported or only partially reported for certain areas, ages, or populations for a variety of reasons. People may not know their responsibility to register events or where to register. They may choose not to register because of the distance to the registration offices or for cultural reasons.

PEOPLE

Or they cannot afford the registration costs. Data from Nigeria show that most unregistered births are found among the rural poor, for whom a significant barrier may be the distance to the nearest registration facility, and among poorly educated mothers (figures 2d–2f). Where many infants die young, parents may be reluctant to go through the formalities of registration until they have some confidence in the child’s survival or need a birth certificate for administrative purposes. In many cultures, especially in Western Africa, a child’s death before age 2 is generally not registered. In Burkina Faso, for example, there are different words to express or describe death. The word for infant death among the Mossi is lebame, which translates literally to “s/he went back,” which is different from kiime, which is used for a teenager or adult who has died (private conversation). Reporting is lower for deaths than for births because people perceive death as a private, sad event and because there are fewer incentives associated with registering a death, especially where formal inheritance is rare. Such recording lapses have consequences for data quality. Even where there is complete registration, births and deaths may be recorded as need arises, rather than when they occur, reducing the timeliness and relevance of data. Not all administrative levels have the same capacity to maintain registers, resulting in omissions that may be difficult to quantify and therefore rectify, since underregistration cannot be assumed to be uniform across the population. Correct information on cause of death is critical for guiding policies and priorities for the health system. Routine data from civil registration in the United Kingdom helped identify the causal association between smoking and lung cancer in the 1950s. But even when deaths are recorded, age or cause of death may be misreported or miscoded. Correct reporting of cause of death is particularly difficult in developing countries, where many deaths occur at home without medical care or certification. In Myanmar only 10 percent of deaths occur in the hospital (Mahar 2010). More than two-thirds of people live in countries where cause of death statistics are partially reported and therefore of limited use or where deaths are not reported at all (table 2g; Mahapatra and others 2007). Because of the lack of reliable vital statistics from civil registration systems, the long-term social, economic, and demographic

impact of major diseases in developing countries can be estimated using only models or intuition and educated guesses rather than facts (Cooper and others 1998). Without data on the cause of death, verbal autopsy (an interview with caregivers or family members after a death to establish probable cause of death) can be used. In Tanzania several districts implemented sentinel demographic surveillance systems that provided routine monitoring of vital events and data for cause of death derived from a validated set of core verbal autopsy procedures. District councils used this information In Nigeria, children’s births are more likely to be unregistered in rural areas . . .

2d

Registered births, by area, Nigeria 2007 (percent) 50 40 30 20 10 0 Urban

Rural

Source: Multiple Indicator Cluster Survey 2007.

2e

. . . in poor households . . . Registered births, by wealth quintile, Nigeria 2007 (percent) 60

40

20

0 Poorest

Secondary

Middle

Fourth

Richest

Source: Multiple Indicator Cluster Survey 2007.

2f

. . . and where the mother has a lower education level Registered births, by mother’s education, Nigeria 2007 (percent) 60

40

20

0 Nonstandard curriculum

None

Primary

Secondary

Source: Multiple Indicator Cluster Survey 2007.

2011 World Development Indicators

33

Most people live in countries with low-quality cause of death statistics

2g

Classification of countries based on the quality of cause of death statistics reported to the World Health Organization, 2007 Quality

Number of countries

Percent of global population

High

31

13

Medium

50

15

Low

26

7

Limited use

17

41

No report Total

68

24

192

100

Source: Mahapatra and others 2007.

to identify disease burdens, set priorities, and allocate resources (Setel 2007). But verbal autopsy is often limited to small areas, such as sample vital registration and demographic surveillance systems, because it is expensive, and accuracy depends on family members’ knowledge of events leading to the death, the skill of interviewers, and the competence of physicians who do the diagnosis and coding.

Why civil registration fails to develop Good civil registration systems require longterm political commitment, a supportive legal framework, allocation of roles and responsibilities among stakeholders, mobilization of financial and human resources, and most critically, the trust of citizens (AbouZahr and others 2007). Although establishing civil registration systems takes time, there is no substitute in the long run. But when civil registration systems lack a sponsor or key stakeholder, or citizens lack incentives to participate, and when high initial costs deter investments, civil registration fails to take root. No single blueprint for establishing and maintaining civil registration systems ensures the availability of timely and sound vital statistics. Each country faces different challenges, and strategies must be tailored accordingly. Some obstacles to a viable civil registration system can be removed only through long-term social and economic development. These generally relate to geography and population distribution, with widely dispersed populations requiring transportation to registration centers. And a largely illiterate population may be unaware of the need to comply with the law or be unmotivated to do so.

34

2011 World Development Indicators

Other obstacles relate to the need for human and physical infrastructure to set up and maintain a civil registration system. While technical assistance and development grants can finance fixed costs and provide initial staff training, countries need to finance recurring costs to run a civil registration system effi ciently. Because many developing countries have enormous economic and social development needs, this would claim low priority. A first and inexpensive step is adequate legislation. But while most countries have legislation requiring registration of vital events, many have not established organizational arrangements to direct, coordinate, and supervise the operation.

Interim approaches Because of the time and expense of building complete civil registration systems, many countries have adopted alternative approaches to measure and monitor vital events and related sociodemographic information. But as dependence on these measures (often intended as interim) grows, national authorities have fewer incentives to invest in complete civil registration systems (figure 2h; Setel and others 2007). These alternative approaches—notably censuses, demographic household surveys, sample registration systems with verbal autopsies, demographic surveillance sites, and facility -based information—effectively fill data gaps with up-to-date information in many developing countries. Figure 2i illustrates the high underreporting of deaths in the civil registration system in the Philippines, based on calculations by the Inter-agency Group for Child Mortality Estimation, using surveys and other sources of mortality data. More countries used surveys for mortality statistics, but civil registration did not expand 2h Collection and reporting of data for mortality by sources in 57 low-income countries, 1980–2004 (number of countries) 50

Surveys

40 30 20 10 Civil register 0 1980–84

1985–89

1990–94

Source: Boerma and Stansfield 2007.

1995–99

2000–04

PEOPLE

These interim approaches also produce supplemental information that is not collected through civil registration, such as socioeconomic information, risk factors, and health status. But these approaches are not a complete or permanent solution. Censuses and surveys are expensive, and developing countries often require international technical and financial assistance. They must be repeated regularly to yield useful data. And they must be supplemented or adjusted to produce satisfactory estimates. Burkina Faso, which has partial coverage of civil registration (birth registration coverage is 60 percent), has conducted four censuses (1975, 1985, 1996, 2006), five Demographic and Health Surveys (1991, 1993, 1998, 2003, 2010), two Multiple Indicator Cluster Surveys (1996, 2006), and a migration and urbanization survey (1993).

How to build a good civil registration system Over the years, international and development agencies have tried to identify the strengths and weaknesses of national civil registration systems and assess the quality of the data they produce. In 2001 the United Nations updated the Principles and Recommendations for a Vital Statistics System, fi rst published in 1973, to offer best practice guidelines for establishing a civil registration system and producing timely, complete, and accurate statistics. Regional initiatives by the United Nations include the 1994 African Workshop on Strategies for Accelerating the Improvement of Civil Registration and Vital Statistics Systems. In 2005 the World Health Organization (WHO) established the Health Metrics Network, which recommends an integrated approach for developing health information systems, including civil registration. Some 85 countries have used the network’s Framework and Standards for Country Health Information Systems, which

Estimates of infant mortality in the Philippines differ by source

2i

Infant mortality rate (per 1,000 live births) 80 70

Estimate by Inter-Agency Group for Child Mortality Estimation

60 50 40 30

World Health Organization vital registration

20 10 1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

2009

Note: Dotted lines are Demographic and Health Surveys, World Fertility Surveys, and Family Planning Surveys for various years. Source: Inter-agency Group for Child Mortality Estimation (www.childmortality.org).

aims to ensure consistency and comparability of statistics across countries and over time. Used correctly, these principles and guidelines improve data quality, as in Chile and Tanzania (Setel and others 2007), but in reality few countries have pursued or attained most recommendations. The WHO’s International Classification of Diseases and Related Health Problems has improved the comparability of cause of death data. Still, there are substantial differences in interpretation and application of these codes. In 2007 only 31 of 192 WHO member countries (13 percent of the world’s population) reported reliable cause-of-death statistics to the WHO, most of them high-income countries (WHO 2007).

International support The international community can continue its strong supportive rule by setting standards and guidelines for collecting and validating systems and data, publicizing the importance of civil registration, and providing comprehensive and integrated technical and financial assistance. Since no single UN agency has a clear mandate for guidance and technical support for civil registration, good coordination is key.

2011 World Development Indicators

35

Tables

2.1

Population dynamics Population

Average annual population growth

Population age composition

Dependency ratio

%

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland Franceb Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

36

1990

millions 2009

2015

18.6 3.3 25.3 10.7 32.5 3.5 17.1 7.7 7.2 115.6 10.2 10.0 4.8 6.7 4.3 1.4 149.6 8.7 8.8 5.7 9.7 12.2 27.8 2.9 6.1 13.2 1,135.2 5.7 33.2 37.0 2.4 3.1 12.6 4.8 10.6 10.4 5.1 7.4 10.3 57.8 5.3 3.2 1.6 48.3 5.0 56.7 0.9 0.9 5.5 79.4 15.0 10.2 8.9 6.1 1.0 7.1 4.9

29.8 3.2 34.9 18.5 40.3 3.1 21.9 8.4 8.8 162.2 9.7 10.8 8.9 9.9 3.8 1.9 193.7 7.6 15.8 8.3 14.8 19.5 33.7 4.4 11.2 17.0 1,331.5 7.0 45.7 66.0 3.7 4.6 21.1 4.4 11.2 10.5 5.5 10.1 13.6 83.0 6.2 5.1 1.3 82.8 5.3 62.6 1.5 1.7 4.3 81.9 23.8 11.3 14.0 10.1 1.6 10.0 7.5

35.0 3.3 38.1 21.7 42.4 3.1 23.4 8.4 9.4 176.3 9.4 11.0 10.6 10.8 3.7 2.1 202.4 7.3 19.0 9.4 16.4 22.2 35.7 4.9 13.1 17.9 1,377.7 7.3 49.3 77.4 4.2 4.9 24.2 4.4 11.2 10.6 5.6 10.8 14.6 91.7 6.4 6.0 1.3 96.2 5.4 63.9 1.6 2.0 4.1 80.6 26.6 11.4 16.2 11.8 1.8 10.7 8.4

2011 World Development Indicators

% 1990–2009 2009–15

2.5 –0.2 1.7 2.9 1.1 –0.7 1.3 0.4 1.1 1.8 –0.3 0.4 3.3 2.1 –0.7 1.9 1.4 –0.7 3.1 2.0 2.2 2.5 1.0 2.2 3.2 1.3 0.8 1.1 1.7 3.0 2.2 2.1 2.7 –0.4 0.3 0.1 0.4 1.7 1.5 1.9 0.8 2.5 –0.8 2.8 0.4 0.5 2.4 3.4 –1.3 0.2 2.4 0.6 2.4 2.6 2.4 1.8 2.2

2.7 0.5 1.4 2.6 0.9 0.2 1.2 0.1 1.1 1.4 –0.4 0.3 2.9 1.6 –0.2 1.3 0.7 –0.6 3.1 2.1 1.7 2.1 0.9 1.8 2.6 0.9 0.6 0.8 1.3 2.6 2.3 1.3 2.3 –0.2 0.0 0.2 0.2 1.1 1.1 1.7 0.6 2.8 –0.1 2.5 0.3 0.3 1.8 2.5 –0.7 –0.3 1.8 0.2 2.4 2.7 2.3 1.1 1.9

Ages 0–14 2009

Ages 15–64 2009

46 24 27 45 25 20 19 15 24 31 15 17 43 36 15 33 26 13 46 38 33 41 17 41 46 23 20a 12 29 47 40 26 41 15 18 14 18 31 31 32 32 42 15 44 17 18 36 42 17 14 38 14 42 43 43 36 37

52 67 68 53 64 68 67 68 69 65 72 66 54 59 71 63 67 69 52 59 63 56 70 55 51 68 72a 75 65 51 56 68 55 68 70 71 65 63 62 63 61 56 68 53 67 65 60 55 69 66 58 68 54 54 54 59 58

Ages 65+ 2009

2 10 5 2 11 11 14 17 7 4 14 17 3 5 14 4 7 17 2 3 3 4 14 4 3 9 8a 13 5 3 4 6 4 17 12 15 16 6 7 5 7 2 17 3 17 17 4 3 14 20 4 18 4 3 3 4 4

% of working-age population Young Old 2009 2009

89 35 40 86 39 30 28 22 35 49 21 25 80 61 22 53 39 19 90 65 53 74 24 73 89 33 28 a 16 45 92 73 38 73 22 25 20 28 50 50 51 53 74 22 82 25 28 61 77 24 20 66 21 78 79 79 61 64

4 14 7 5 16 16 20 26 10 6 19 26 6 8 20 6 10 25 4 5 6 6 20 7 6 13 11a 17 8 5 7 9 7 25 17 21 25 10 10 7 12 4 25 6 25 26 7 5 21 31 6 27 8 6 6 7 7

Crude death rate

Crude birth rate

per 1,000 people 2009

per 1,000 people 2009

19 6 5 16 8 9 6 9 6 6 14 10 9 7 10 12 6 14 13 14 8 14 7 17 16 5 7 6 6 17 13 4 11 12 7 10 10 6 5 6 7 8 12 12 9 9 10 11 12 10 11 10 6 11 17 9 5

46 15 21 42 17 15 14 9 17 21 12 12 39 27 9 24 16 11 47 34 25 36 11 35 45 15 12 12 20 44 34 16 34 10 10 11 11 22 20 24 20 36 12 38 11 13 27 36 12 8 32 11 32 39 41 27 27

Population

Average annual population growth

Population age composition

Dependency ratio

%

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

1990

millions 2009

2015

10.4 849.5 177.4 54.4 18.9 3.5 4.7 56.7 2.4 123.5 3.2 16.3 23.4 20.1 42.9 1.9 2.1 4.4 4.2 2.7 3.0 1.6 2.2 4.4 3.7 1.9 11.3 9.5 18.1 8.7 2.0 1.1 83.2 4.4 2.2 24.8 13.5 40.8 1.4 19.1 15.0 3.4 4.1 7.9 97.3 4.2 1.8 108.0 2.4 4.1 4.2 21.8 62.4 38.1 9.9 3.5 0.5

10.0 1,155.3 230.0 72.9 31.5 4.5 7.4 60.2 2.7 127.6 6.0 15.9 39.8 23.9 48.7 1.8 2.8 5.3 6.3 2.3 4.2 2.1 4.0 6.4 3.3 2.0 19.6 15.3 27.5 13.0 3.3 1.3 107.4 3.6 2.7 32.0 22.9 50.0 2.2 29.3 16.5 4.3 5.7 15.3 154.7 4.8 2.8 169.7 3.5 6.7 6.3 29.2 92.0 38.1 10.6 4.0 1.4

9.9 1,246.9 247.5 78.6 36.3 4.8 8.2 60.8 2.8 125.3 6.8 16.9 46.4 24.4 49.3 1.9 3.2 5.7 7.0 2.2 4.4 2.2 4.8 7.2 3.2 2.0 22.8 18.0 30.0 15.4 3.7 1.3 113.1 3.5 2.9 34.3 25.9 53.0 2.4 32.5 16.8 4.6 6.3 19.1 178.7 5.1 3.2 193.5 3.8 7.7 7.0 31.2 102.7 38.0 10.7 4.0 1.6

% 1990–2009 2009–15

–0.2 1.6 1.4 1.5 2.7 1.3 2.5 0.3 0.6 0.2 3.3 –0.2 2.8 0.9 0.7 –0.2 1.4 1.0 2.1 –0.9 1.8 1.3 3.2 2.0 –0.5 0.4 2.9 2.5 2.2 2.1 2.7 1.0 1.3 –1.0 1.0 1.3 2.8 1.1 2.2 2.3 0.5 1.2 1.7 3.5 2.4 0.7 2.3 2.4 1.9 2.6 2.1 1.5 2.0 0.0 0.4 0.6 5.8 c

–0.2 1.3 1.2 1.2 2.4 1.1 1.6 0.1 0.4 –0.3 2.2 1.0 2.6 0.3 0.2 0.6 2.1 1.3 1.8 –0.5 0.8 0.8 3.2 1.8 –0.7 0.0 2.5 2.7 1.5 2.8 2.1 0.4 0.9 –0.7 1.1 1.2 2.1 1.0 1.7 1.7 0.3 1.0 1.4 3.7 2.4 0.8 1.9 2.2 1.5 2.2 1.6 1.1 1.8 –0.1 0.0 0.3 2.4

Ages 0–14 2009

Ages 15–64 2009

Ages 65+ 2009

15 31 27 24 41 21 28 14 29 13 34 24 43 22 17 .. 23 29 38 14 25 39 43 30 15 18 43 46 29 44 39 23 28 17 26 28 44 27 37 37 18 20 35 50 43 19 31 37 29 40 34 30 34 15 15 20 16

69 64 67 71 56 68 62 66 63 65 62 69 55 69 73 .. 74 65 59 69 67 56 54 66 69 70 54 51 66 54 58 70 65 72 70 66 53 68 60 59 67 67 60 48 54 66 66 59 64 58 61 64 62 72 67 66 83

16 5 6 5 3 11 10 20 8 22 4 7 3 10 11 .. 2 5 4 17 7 5 3 4 16 12 3 3 5 2 3 7 6 11 4 5 3 5 4 4 15 13 5 2 3 15 3 4 7 2 5 6 4 13 18 14 1

% of working-age population Young Old 2009 2009

22 49 40 34 74 30 45 22 47 21 56 34 78 32 23 .. 31 45 64 20 38 69 79 46 22 26 79 91 45 83 68 32 44 23 37 43 83 40 62 62 26 31 58 104 78 29 48 63 46 69 56 48 55 21 23 31 19

24 8 9 7 6 16 16 31 12 34 6 10 5 14 15 .. 3 8 6 25 11 8 6 6 23 17 6 6 7 4 5 10 10 15 6 8 6 8 6 7 22 19 7 4 6 22 5 7 10 4 8 9 7 19 26 21 1

PEOPLE

2.1

Population dynamics

Crude death rate

Crude birth rate

per 1,000 people 2009

per 1,000 people 2009

13 7 6 6 6 7 5 10 7 9 4 9 11 10 5 7 2 7 7 13 7 17 10 4 13 9 9 12 5 15 10 7 5 13 7 6 16 10 8 6 8 7 5 15 16 9 3 7 5 8 6 5 5 10 10 8 2

10 22 18 19 31 17 22 10 16 9 25 22 38 14 10 19 17 25 27 10 16 29 38 23 11 11 35 40 20 42 33 12 18 12 19 20 38 20 27 25 11 15 24 53 39 13 22 30 20 31 24 21 24 11 9 12 12

2011 World Development Indicators

37

2.1

Population dynamics Population

Average annual population growth

Population age composition

Dependency ratio

%

1990

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

23.2 148.3 7.2 16.3 7.5 7.6 4.1 3.0 5.3 2.0 6.6 35.2 38.8 17.1 27.1 0.9 8.6 6.7 12.7 5.3 25.5 56.7 0.7 3.9 1.2 8.2 56.1 3.7 17.7 51.9 1.9 57.2 249.6 3.1 20.5 19.8 66.2 2.0 12.3 7.9 10.5 5,278.9 s 547.3 3,751.3 2,930.9 820.3 4,298.6 1,599.6 392.4 435.6 227.4 1,128.7 514.9 980.4 301.6

millions 2009

21.5 141.9 10.0 25.4 12.5 7.3 5.7 5.0 5.4 2.0 9.1 49.3 46.0 20.3 42.3 1.2 9.3 7.7 21.1 7.0 43.7 67.8 1.1 6.6 1.3 10.4 74.8 5.1 32.7 46.0 4.6 61.8 307.0 3.3 27.8 28.4 87.3 4.0 23.6 12.9 12.5 6,775.2 s 846.1 4,812.5 3,810.8 1,001.7 5,658.7 1,943.8 404.2 572.5 330.9 1,567.7 839.6 1,116.6 327.3

2015

21.0 139.0 11.7 28.6 14.5 7.2 6.6 5.4 5.4 2.1 10.7 51.1 47.9 21.2 47.7 1.3 9.6 7.9 24.1 7.8 52.1 69.9 1.4 7.6 1.4 11.1 79.9 5.5 39.7 44.4 5.2 63.8 323.5 3.4 30.2 31.0 92.8 4.8 27.8 15.0 14.0 7,241.9 s 962.6 5,131.2 4,084.9 1,046.3 6,093.8 2,035.8 409.0 606.9 366.1 1,706.5 969.5 1,148.0 332.3

% 1990–2009 2009–15

–0.4 –0.2 1.8 2.3 2.7 –0.2 1.8 2.6 0.1 0.1 1.7 1.8 0.9 0.9 2.3 1.7 0.4 0.7 2.7 1.4 2.8 0.9 2.2 2.7 0.5 1.3 1.5 1.7 3.2 –0.6 4.7 0.4 1.1 0.4 1.6 1.9 1.5 3.8 3.4 2.6 0.9 1.3 w 2.3 1.3 1.4 1.1 1.4 1.0 0.2 1.4 2.0 1.7 2.6 0.7 0.4

–0.4 –0.3 2.7 2.0 2.4 –0.3 2.3 1.2 0.1 0.3 2.7 0.6 0.7 0.7 2.0 1.4 0.5 0.4 2.2 1.8 2.9 0.5 3.3 2.3 0.3 1.1 1.1 1.2 3.2 –0.6 2.0 0.5 0.9 0.2 1.4 1.5 1.0 2.8 2.7 2.4 1.9 1.1 w 2.1 1.1 1.2 0.7 1.2 0.8 0.2 1.0 1.7 1.4 2.4 0.5 0.3

Ages 0–14 2009

15 15 42 32 44 18 d 43 16 15 14 45 31 15 24 39 39 17 15 35 37 45 22 45 40 21 23 27 29 49 14 19 17 20 23 29 30 26 45 44 46 40 27 w 39 27 28 25 29 23 19 28 31 32 43 17 15

Ages 15–64 2009

70 72 55 65 54 68d 55 74 73 70 52 65 68 68 57 57 65 68 62 59 52 71 52 57 73 70 67 66 49 70 80 66 67 63 66 65 68 52 54 51 56 65 w 57 66 66 68 65 70 70 65 64 63 54 67 66

Ages 65+ 2009

15 13 2 3 2 14 d 2 10 12 16 3 4 17 7 4 3 18 17 3 4 3 8 3 4 7 7 6 4 3 16 1 16 13 14 4 5 6 3 2 3 4 8w 4 6 6 8 6 7 11 7 4 5 3 15 18

% of working-age population Young Old 2009 2009

22 21 77 50 81 26d 79 22 21 20 86 47 22 36 68 69 25 23 57 62 86 31 86 71 28 33 40 45 101 20 24 26 30 36 44 46 38 86 81 91 71 42 w 69 41 42 36 45 32 28 43 48 51 78 26 23

21 18 5 5 4 21d 3 13 17 23 5 7 25 11 6 6 28 25 5 6 6 11 6 6 9 10 9 6 5 22 1 25 19 22 7 8 9 6 4 6 7 12 w 6 10 9 11 9 11 16 10 7 7 6 23 27

Crude death rate

Crude birth rate

per 1,000 people 2009

per 1,000 people 2009

12 14 14 4 11 14 15 4 10 9 16 15 8 5 10 15 10 8 3 6 11 9 8 8 8 6 6 8 12 15 2 9 8 9 5 5 5 3 7 17 15 8w 11 8 8 8 8 7 11 6 6 7 14 8 9

10 12 41 24 38 10 40 10 11 11 44 22 11 19 31 30 12 10 27 28 41 14 40 32 15 18 18 22 46 11 14 13 14 15 22 21 17 35 36 42 30 20 w 34 19 20 17 21 14 15 18 24 24 38 12 10

a. Includes Taiwan, China. b. Excludes the French overseas departments of French Guiana, Guadeloupe, Martinique, and Réunion. c. Increase is due to a surge in the number of migrants since 2004. d. Includes Kosovo.

38

2011 World Development Indicators

About the data

2.1

PEOPLE

Population dynamics Definitions

Population estimates are usually based on national

Dependency ratios capture variations in the propor-

• Population is based on the de facto definition of popu-

population censuses. Estimates for the years before

tions of children, elderly people, and working-age peo-

lation, which counts all residents regardless of legal sta-

and after the census are interpolations or extrapola-

ple in the population that imply the dependency burden

tus or citizenship—except for refugees not permanently

tions based on demographic models. Errors and under-

that the working-age population bears in relation to

settled in the country of asylum, who are generally con-

counting occur even in high income countries; in devel-

children and the elderly. But dependency ratios show

sidered part of the population of their country of origin.

oping countries errors may be substantial because

only the age composition of a population, not economic

The values shown are midyear estimates for 1990 and

of limits in the transport, communications, and other

dependency. Some children and elderly people are part

2009 and projections for 2015. • Average annual popu-

resources required to conduct and analyze a full census.

of the labor force, and many working-age people are not.

lation growth is the exponential change for the period

The quality and reliability of official demographic

Vital rates are based on data from birth and death

indicated. See Statistical methods for more information.

data are also affected by public trust in the govern-

registration systems, censuses, and sample surveys

• Population age composition is the percentage of the

ment, government commitment to full and accurate

by national statistical offices and other organiza-

total population that is in specific age groups. • Depen-

enumeration, confidentiality and protection against

tions, or on demographic analysis. Data for 2009

dency ratio is the ratio of dependents—people younger

misuse of census data, and census agencies’ inde-

for most high-income countries are provisional esti-

than 15 or older than 64—to the working age popula-

pendence from political influence. Moreover, compara-

mates based on vital registers. The estimates for

tion—those ages 15–64. • Crude death rate and crude

bility of population indicators is limited by differences

many countries are projections based on extrapo-

birth rate are the number of deaths and the number of

in the concepts, definitions, collection procedures,

lations of levels and trends from earlier years or

live births occurring during the year, per 1,000 people,

and estimation methods used by national statistical

interpolations of population estimates and projec-

estimated at midyear. Subtracting the crude death rate

agencies and other organizations that collect the data.

tions from the United Nations Population Division.

from the crude birth rate provides the rate of natural

Of the 155 economies in the table and the 55 econo-

Vital registers are the preferred source for these

increase, which is equal to the population growth rate in

mies in table 1.6, 180 (about 86 percent) conducted a

data, but in many developing countries systems for

census during the 2000 census round (1995–2004).

registering births and deaths are absent or incomplete

As of January 2011, 119 countries have completed

because of deficiencies in the coverage of events or

a census for the 2010 census round (2005–14).

geographic areas. Many developing countries carry out

The currentness of a census and the availability of

special household surveys that ask respondents about

complementary data from surveys or registration

recent births and deaths. Estimates derived in this

systems are objective ways to judge demographic

way are subject to sampling errors and recall errors.

data quality. Some European countries’ registration

The United Nations Statistics Division monitors the

systems offer complete information on population in

completeness of vital registration systems. Progress

the absence of a census. See table 2.17 and Primary

has been made over the past 60 years in some coun-

data documentation for the most recent census or

tries. But many countries still have deficiencies in civil

survey year and for the completeness of registration.

registration systems. For example, only 60 percent of

the absence of migration.

Current population estimates for developing coun-

countries and areas register at least 90 percent of

tries that lack recent census data and pre- and post-

births, and only 47 percent register at least 90 percent

census estimates for countries with census data are

of deaths. Some of the most populous developing coun-

Data sources

provided by the United Nations Population Division and

tries—Bangladesh, Brazil, India, Indonesia, Nigeria,

The World Bank’s population estimates are compiled

other agencies. The cohort component method—a

Pakistan—lack complete vital registration systems.

and produced by its Development Data Group in con-

standard method for estimating and projecting popu-

International migration is the only other factor

sultation with its Human Development Network, oper-

lation— requires fertility, mortality, and net migration

besides birth and death rates that directly deter-

ational staff, and country offices. The United Nations

data, often collected from sample surveys, which can

mines a country’s population growth. From 1990 to

Population Division’s World Population Prospects: The

be small or limited in coverage. Population estimates

2005 the number of migrants in high-income coun-

2008 Revision is a source of the demographic data for

are from demographic modeling and so are susceptible

tries rose 40 million. About 195 million people (3

more than half the countries, most of them developing

to biases and errors from shortcomings in the model

percent of the world population) live outside their

countries, and the source of data on age composi-

and in the data. Because the five-year age group is the

home country. Estimating migration is difficult. At

tion and dependency ratios for all countries. Other

cohort unit and five-year period data are used, interpo-

any time many people are located outside their

important sources are census reports and other sta-

lations to obtain annual data or single age structure

home country as tourists, workers, or refugees or

tistical publications from national statistical offices;

may not reflect actual events or age composition.

for other reasons. Standards for the duration and

household surveys conducted by national agencies,

The growth rate of the total population conceals

purpose of international moves that qualify as migra-

Macro International, and the U.S. Centers for Disease

age-group differences in growth rates. In many

tion vary, and estimates require information on flows

Control and Prevention; Eurostat’s Demographic Sta-

developing countries the once rapidly growing under-

into and out of countries that is difficult to collect.

tistics; Secretariat of the Pacific Community, Statistics

15 population is shrinking. Previously high fertility

and Demography Programme; and U.S. Bureau of the

rates and declining mortality rates are now reflected

Census, International Data Base.

in the larger share of the working-age population.

2011 World Development Indicators

39

2.2

Labor force structure Labor force participation rate

Labor force

% ages 15 and older Male

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

40

Female

1990

2009

84 74 75 90 78 78 76 70 74 89 75 61 89 82 67 82 85 63 91 90 84 83 76 87 81 77 85 80 78 85 84 84 88 69 73 71 75 85 78 74 83 84 77 91 72 65 83 86 78 73 73 67 88 90 81 81 88

85 70 80 88 78 75 72 68 67 83 67 61 78 82 68 81 82 61 91 88 86 81 73 87 78 73 80 69 78 86 83 80 82 60 67 68 71 80 78 75 77 83 69 90 65 62 81 85 74 67 75 65 88 89 84 83 80

2011 World Development Indicators

Ages 15 and older average annual % growth

Total millions

1990

2009

1990

2009

32 51 23 74 43 61 52 43 59 61 60 36 57 59 53 64 45 55 77 91 78 48 58 69 65 32 73 47 29 53 59 33 43 47 36 52 62 43 33 27 41 55 63 72 59 46 63 71 60 45 70 36 39 79 59 57 41

33 49 37 75 52 60 58 53 60 59 55 47 67 62 55 72 60 48 78 91 74 54 63 72 63 42 67 52 41 57 63 45 51 46 41 49 60 51 47 22 46 63 55 81 57 51 70 71 55 53 74 43 48 79 60 58 40

5.9 1.4 7.0 4.6 13.5 1.7 8.5 3.5 3.1 49.5 5.3 3.9 1.9 2.8 2.0 0.5 62.6 4.1 3.9 2.8 4.3 4.4 14.7 1.3 2.4 5.0 643.9 2.9 11.2 13.4 1.0 1.2 4.7 2.2 4.4 4.9 2.9 2.9 3.5 16.8 1.9 1.2 0.8 21.5 2.6 25.0 0.4 0.4 2.8 38.8 6.0 4.2 3.1 2.9 0.4 2.8 1.7

9.6 1.4 14.8 8.3 19.6 1.6 11.5 4.3 4.2 78.6 5.0 4.8 3.7 4.5 1.9 1.0 101.5 3.6 7.1 4.6 7.8 7.7 19.1 2.1 4.3 7.5 783.2 3.7 19.0 24.9 1.6 2.1 8.4 2.0 5.0 5.2 3.0 4.5 5.9 27.4 2.5 2.2 0.7 40.0 2.7 28.7 0.7 0.8 2.3 42.3 11.0 5.2 5.5 4.8 0.7 4.5 2.8

Female % of labor force

1990–2009

1990

2009

2.5 0.2 3.9 3.1 1.9 –0.2 1.6 1.0 1.5 2.4 –0.3 1.0 3.5 2.6 0.0 3.2 2.5 –0.7 3.2 2.6 3.1 3.0 1.4 2.5 3.1 2.1 1.0 1.4 2.8 3.3 2.6 3.2 3.1 –0.4 0.6 0.3 0.1 2.3 2.7 2.6 1.4 3.2 –1.0 3.3 0.2 0.7 3.1 3.4 –1.2 0.5 3.2 1.1 3.0 2.7 2.4 2.5 2.6

26.2 39.9 23.4 46.3 36.9 46.3 41.3 40.9 46.8 39.9 48.9 39.0 41.1 43.1 45.2 45.5 35.1 47.9 48.0 52.5 52.8 37.5 44.1 45.6 45.6 30.5 44.8 36.3 28.2 39.9 42.1 27.4 30.1 42.7 33.0 44.4 46.1 33.2 29.5 26.6 35.2 41.4 49.5 45.1 47.1 43.3 44.2 46.2 46.9 40.7 48.9 36.2 31.0 46.8 43.0 43.0 32.3

26.6 42.5 31.6 46.9 41.6 49.6 45.4 45.5 49.5 41.2 49.5 44.9 46.2 43.8 47.1 47.4 43.7 46.1 47.1 52.6 48.3 40.1 47.0 46.5 45.2 37.2 44.6 46.3 35.8 40.6 43.6 35.5 36.9 45.8 38.1 43.2 46.9 38.8 38.0 23.0 41.9 44.5 49.1 47.9 48.1 46.8 46.7 46.2 46.8 45.6 49.1 40.5 37.9 46.9 42.4 42.3 33.9

Labor force participation rate

Labor force

% ages 15 and older Male

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

2009

65 84 81 80 73 71 64 66 80 77 71 78 90 80 73 .. 82 74 83 77 72 83 78 75 74 68 89 80 80 68 82 81 84 74 77 81 88 89 64 85 70 74 85 91 76 73 80 85 79 74 87 75 83 72 73 61 94

59 81 86 73 69 73 63 61 74 72 74 76 88 78 72 .. 83 79 79 70 72 78 76 79 62 65 89 79 79 67 81 75 81 53 78 80 87 85 63 80 73 76 78 88 73 71 77 85 81 74 87 76 79 62 69 58 93

Ages 15 and older average annual % growth

Total millions

Female

1990

2.2

PEOPLE

Labor force structure

1990

2009

1990

2009

46 34 50 22 11 35 42 35 65 50 15 62 75 55 47 .. 36 58 80 63 20 68 65 15 59 46 83 76 43 37 53 38 34 61 63 25 85 71 48 52 43 54 39 27 36 57 19 14 39 71 47 49 48 55 49 31 40

43 33 52 32 14 54 52 38 56 48 23 66 76 55 50 .. 45 55 78 54 22 71 67 25 50 43 84 75 44 38 59 41 43 47 68 26 85 63 52 63 60 62 47 39 39 63 25 22 48 72 57 58 49 46 56 36 50

4.5 317.8 74.9 15.5 4.3 1.3 1.7 23.7 1.1 63.9 0.7 7.8 9.8 10.0 19.2 .. 0.9 1.8 1.9 1.4 0.9 0.7 0.8 1.2 1.9 0.8 5.4 3.9 7.0 2.5 0.7 0.4 29.9 2.1 0.9 7.8 6.3 20.7 0.4 7.5 6.9 1.7 1.4 2.3 29.4 2.2 0.6 31.0 0.9 1.8 1.7 8.3 24.1 18.1 4.7 1.2 0.3

4.3 457.5 115.6 29.2 7.7 2.2 3.1 25.4 1.2 65.8 1.9 8.6 18.7 12.4 24.7 .. 1.5 2.5 3.1 1.2 1.5 0.9 1.6 2.4 1.6 0.9 9.7 6.3 12.0 3.8 1.4 0.6 47.2 1.5 1.4 12.0 11.0 27.0 0.8 13.3 9.0 2.4 2.3 4.8 50.0 2.6 1.1 58.1 1.6 3.0 3.0 13.6 38.8 17.4 5.6 1.5 1.0

Female % of labor force

1990–2009

1990

2009

–0.3 1.9 2.3 3.3 3.0 2.7 3.1 0.4 0.5 0.2 5.0 0.5 3.4 1.1 1.3 .. 2.8 1.7 2.5 –1.0 2.8 1.9 3.4 3.7 –1.0 0.6 3.1 2.5 2.8 2.2 3.3 1.3 2.4 –1.8 2.5 2.2 3.0 1.4 3.0 3.0 1.4 1.7 2.8 3.8 2.8 0.9 3.4 3.3 2.8 2.8 3.1 2.6 2.5 –0.2 0.9 1.2 6.9

44.5 27.1 38.4 20.1 13.1 33.9 40.6 36.5 46.6 40.7 16.2 47.0 46.0 42.6 39.7 .. 22.4 46.1 49.8 49.6 23.3 51.7 46.7 14.8 48.1 40.7 48.4 50.7 34.5 36.1 39.8 32.1 30.0 48.7 45.6 23.7 53.2 45.3 44.9 38.0 38.8 43.0 32.3 24.7 33.0 44.7 13.7 12.7 32.4 46.9 34.9 39.7 36.5 45.4 42.4 35.8 13.5

45.1 27.6 38.1 29.8 16.7 43.0 46.5 40.5 44.9 41.6 23.0 49.8 46.7 42.7 41.9 .. 25.0 42.3 50.4 48.3 25.0 52.4 47.6 22.5 48.7 40.1 49.2 49.8 35.4 37.3 42.0 36.1 36.2 49.9 47.4 25.8 52.0 44.2 46.5 45.4 45.7 46.1 38.7 31.6 35.1 47.7 18.8 19.4 37.4 48.9 39.4 43.6 38.6 45.0 46.9 40.8 11.9

2011 World Development Indicators

41

2.2

Labor force structure Labor force participation rate

Labor force

% ages 15 and older Male 1990

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

42

73 76 89 80 90 .. 68 79 72 59 84 62 67 79 79 81 72 81 81 80 91 87 82 87 76 76 81 72 91 71 92 74 76 76 68 81 82 66 74 79 80 81 w 86 82 83 78 83 84 75 82 77 85 82 73 69

2011 World Development Indicators

Female 2009

60 69 85 74 89 .. 68 76 69 65 85 63 69 75 74 75 69 74 80 78 91 81 83 86 78 71 70 74 91 65 92 70 72 76 71 80 76 68 74 79 74 78 w 84 79 80 75 80 80 69 80 75 82 81 70 65

1990

60 60 87 15 62 .. 66 51 59 47 58 36 34 37 27 45 63 57 18 59 87 75 58 56 39 21 34 58 81 56 25 52 57 48 53 36 74 11 16 61 67 52 w 65 52 54 45 53 69 56 40 22 35 57 49 42

Ages 15 and older average annual % growth

Total millions 2009

45 58 87 17 65 .. 65 54 51 53 57 47 49 34 31 53 61 61 21 57 86 66 59 64 55 26 24 62 78 52 42 55 58 54 58 52 68 17 20 60 60 52 w 66 50 50 48 52 64 50 52 26 35 61 52 49

1990

11.8 76.8 3.2 5.0 3.0 .. 1.6 1.6 2.6 0.8 2.6 10.4 15.6 6.8 8.0 0.3 4.7 3.8 3.3 2.1 12.3 32.1 0.3 1.5 0.5 2.4 20.7 1.4 7.9 25.5 1.0 29.0 129.2 1.4 7.3 7.2 31.1 0.4 2.6 3.0 4.1 2,342.6 t 232.9 1,646.7 1,317.1 329.6 1,879.5 853.5 180.3 169.1 63.3 418.8 194.6 463.0 135.2

2009

9.5 75.9 5.0 8.6 5.4 .. 2.1 2.7 2.7 1.0 3.5 18.8 22.9 8.3 13.5 0.5 5.0 4.4 6.9 2.9 21.4 38.7 0.4 3.0 0.7 3.8 25.6 2.4 14.1 23.0 2.9 31.8 159.0 1.7 12.7 13.1 46.6 1.0 6.2 4.8 5.0 3,175.8 t 384.5 2,244.8 1,786.5 458.2 2,629.2 1,090.7 187.2 269.3 115.2 625.9 341.0 546.6 158.5

1990–2009

–1.1 –0.1 2.3 2.8 3.0 .. 1.6 2.9 0.3 1.2 1.6 3.1 2.0 1.1 2.7 2.7 0.3 0.7 4.0 1.8 2.9 1.0 1.8 3.5 2.3 2.4 1.1 2.9 3.0 –0.5 5.8 0.5 1.1 0.9 2.9 3.2 2.1 4.4 4.5 2.5 1.0 1.6 w 2.6 1.6 1.6 1.7 1.8 1.3 0.2 2.4 3.2 2.1 3.0 0.9 0.8

Female % of labor force 1990

2009

46.3 48.6 52.1 11.5 40.8 .. 50.9 39.1 46.8 46.8 41.8 37.5 34.8 31.8 26.0 41.2 47.7 42.9 18.3 43.3 49.8 47.0 40.4 40.1 35.0 21.6 29.7 46.1 47.7 49.2 9.8 43.2 44.4 40.8 45.5 30.5 50.7 13.8 18.0 44.3 46.3 39.4 w 43.8 38.1 38.2 37.6 38.8 44.2 45.8 33.8 22.0 27.8 42.0 41.6 39.8

45.0 50.1 52.8 14.9 43.3 .. 51.4 41.5 44.7 46.2 40.9 43.7 42.8 32.4 29.5 43.4 47.4 46.8 20.9 43.9 49.4 46.1 40.9 43.5 43.3 26.7 25.7 47.1 46.5 49.0 15.7 45.7 46.0 44.1 45.9 39.3 48.6 19.0 21.1 43.4 47.5 40.1 w 44.6 38.4 37.7 40.8 39.3 43.9 45.5 40.5 25.7 29.0 43.6 43.9 44.4

About the data

2.2

PEOPLE

Labor force structure Definitions

The labor force is the supply of labor available for pro-

information on source, reference period, or defini-

• Labor force participation rate is the proportion

ducing goods and services in an economy. It includes

tion, consult the original source.

of the population ages 15 and older that engages

people who are currently employed and people who

The labor force participation rates in the table are

actively in the labor market, either by working or

are unemployed but seeking work as well as first-time

from the ILO’s Key Indicators of the Labour Market,

looking for work during a reference period. • Total

job-seekers. Not everyone who works is included,

6th edition, database. These harmonized estimates

labor force is people ages 15 and older who engage

however. Unpaid workers, family workers, and stu-

use strict data selection criteria and enhanced

actively in the labor market, either by working or look-

dents are often omitted, and some countries do not

methods to ensure comparability across countries

ing for work during a reference period. It includes

count members of the armed forces. Labor force size

and over time, including collection and tabulation

both the employed and the unemployed. • Average

tends to vary during the year as seasonal workers

methodologies and methods applied to such country-

annual percentage growth of the labor force is cal-

enter and leave.

specific factors as military service requirements.

culated using the exponential endpoint method (see

Data on the labor force are compiled by the Inter-

Estimates are based mainly on labor force surveys,

Statistical methods for more information). • Female

national Labour Organization (ILO) from labor force

with other sources (population censuses and nation-

labor force as a percentage of the labor force shows

surveys, censuses, establishment censuses and

ally reported estimates) used only when no survey

the extent to which women are active in the labor

surveys, and administrative records such as employ-

data are available.

force.

ment exchange registers and unemployment insur-

The labor force estimates in the table were calcu-

ance schemes. For some countries a combination

lated by applying labor force participation rates from

of these sources is used. Labor force surveys are

the ILO database to World Bank population estimates

the most comprehensive source for internationally

to create a series consistent with these population

comparable labor force data. They can cover all

estimates. This procedure sometimes results in

noninstitutionalized civilians, all branches and sec-

labor force estimates that differ slightly from those

tors of the economy, and all categories of workers,

in the ILO’s Yearbook of Labour Statistics and its

including people holding multiple jobs. By contrast,

database Key Indicators of the Labour Market.

labor force data from population censuses are often

Estimates of women in the labor force and employ-

based on a limited number of questions on the eco-

ment are generally lower than those of men and are

nomic characteristics of individuals, with little scope

not comparable internationally, reflecting that demo-

to probe. The resulting data often differ from labor

graphic, social, legal, and cultural trends and norms

force survey data and vary considerably by country,

determine whether women’s activities are regarded

depending on the census scope and coverage. Estab-

as economic. In many countries many women work

lishment censuses and surveys provide data only

on farms or in other family enterprises without pay,

on the employed population, not unemployed work-

and others work in or near their homes, mixing work

ers, workers in small establishments, or workers in

and family activities during the day.

the informal sector (ILO, Key Indicators of the Labour Market 2001–2002). The reference period of a census or survey is another important source of differences: in some countries data refer to people’s status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In developing countries, where the household is often the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working. Differing definitions of employment age also affect

Data sources

comparability. For most countries the working age is

Data on labor force participation rates are from

15 and older, but in some countries children younger

the ILO’s Key Indicators of the Labour Market, 6th

than 15 work full- or part-time and are included in the

edition, database. Labor force numbers were cal-

estimates. Similarly, some countries have an upper

culated by World Bank staff, applying labor force

age limit. As a result, calculations may systemati-

participation rates from the ILO database to popu-

cally over- or underestimate actual rates. For further

lation estimates.

2011 World Development Indicators

43

2.3

Employment by economic activity Agriculture

Male % of male employment 1990–92a 2005–08a

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

44

.. .. .. .. 0 b,c .. 6 6 .. 54 .. 3 .. .. .. .. 31c .. .. .. .. .. 6c .. .. 24 .. 1c .. .. .. 32 .. .. .. .. 7 26 10 c 35 48 .. 23 .. 11 7 .. .. .. 4 66 20 .. .. .. 76 53c

.. .. .. .. 1c 46 4 6 40 42 15 2 .. .. .. 35 23 9 .. .. .. .. 3c .. .. 16 .. 0b,c 27 .. .. 18 .. 13d 25 4 4 21 11c 28 29 .. 5 9c,d 6 4 .. .. 51 3 .. 11 44 .. .. .. 51c

2011 World Development Indicators

Industry

Female % of female employment 1990–92a

.. .. .. .. 0b,c .. 4 8 .. 85 .. 2 .. .. .. .. 25c .. .. .. .. .. 2c .. .. 6 .. 0b,c .. .. .. 5 .. .. .. .. 3 3 2c 52 15 .. 13 .. 6 5 .. .. .. 4 59 26 .. .. .. 50 6c

2005–08a

.. .. .. .. 0 b,c 46 2 6 38 68 9 1 .. .. .. 24 15 6 .. .. .. .. 2c .. .. 6 .. 0 b,c 6 .. .. 5 .. 15 d 9 2 1 2 4c 43 5 .. 2 10 c,d 3 2 .. .. 57 2 .. 12 16 .. .. .. 13 c

Male % of male employment 1990–92a

.. .. .. .. 40c .. 32 47 .. 16 .. 41 .. .. .. .. 27c .. .. .. .. .. 31c .. .. 32 .. 37c .. .. .. 27 .. .. .. .. 37 23 29c 25 23 .. 42 .. 38 39 .. .. .. 50 10 29 .. .. .. 9 18 c

2005–08a

.. .. .. .. 33c 21 31 37 17 15 33 36 .. .. .. 19 28 42 .. .. .. .. 32c .. .. 31 .. 21c 22 .. .. 28 .. 39d 22 51 32 26 28c 26 26 .. 48 25c,d 39 34 .. .. 17 41 .. 30 24 .. .. .. 20c

Services

Female % of female employment 1990–92a

.. .. .. .. 18 c .. 12 20 .. 9 .. 16 .. .. .. .. 10 c .. .. .. .. .. 11c .. .. 15 .. 27c .. .. .. 25 .. .. .. .. 16 21 17c 10 23 .. 30 .. 15 17 .. .. .. 24 10 17 .. .. .. 9 25c

2005–08a

.. .. .. .. 11c 10 9 12 9 13 24 11 .. .. .. 11 13 29 .. .. .. .. 11c .. .. 11 .. 6c 16 .. .. 13 .. 15 d 12 27 12 14 13 c 6 19 .. 23 20 c,d 11 11 .. .. 4 16 .. 9 21 .. .. .. 23 c

Male % of male employment 1990–92a

.. .. .. .. 59 c .. 61 46 .. 25 .. 56 .. .. .. .. 43c .. .. .. .. .. 64c .. .. 45 .. 63c .. .. .. 41 .. .. .. .. 56 52 62c 41 29 .. 36 .. 51 54 .. .. .. 46 23 51 .. .. .. 13 29c

2005–08a

.. .. .. .. 66c 33 64 57 44 43 37 61 .. .. .. 46 50 49 .. .. .. .. 65c .. .. 53 .. 78c 51 .. .. 54 .. 48d 54 45 64 53 61c 46 45 .. 46 76c,d 54 61 .. .. 33 56 .. 59 32 .. .. .. 29c

Female % of female employment 1990–92a

.. .. .. .. 81c .. 84 72 .. 2 .. 81 .. .. .. .. 65c .. .. .. .. .. 87c .. .. 79 .. 73c .. .. .. 69 .. .. .. .. 82 76 81c 37 63 .. 57 .. 78 78 .. .. .. 73 32 57 .. .. .. 38 69c

2005–08a

.. .. .. .. 89 c 45 89 82 53 19 64 88 .. .. .. 65 72 65 .. .. .. .. 88 c .. .. 84 .. 94 c 78 .. .. 82 .. 69 d 79 71 86 84 83 c 51 76 .. 75 64 c,d 86 86 .. .. 39 83 .. 79 63 .. .. .. 63c

Agriculture

Male % of male employment 1990–92a 2005–08a

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

19 .. 54 .. .. 19 5 8 36 6 .. .. .. .. 14 .. .. .. .. .. .. .. .. .. .. .. .. .. 23 .. .. 15 34 .. .. .. .. .. 45 75 5 13c .. .. .. 7 .. 45 35 .. .. 1c 53c .. 10 5 ..

6 .. 41 21 .. 9 3 5 26 4 .. .. .. .. 7 .. .. 37 .. 10 .. .. .. .. 10 19 82 .. 18 .. .. 10 19 36 41 35 .. .. 23 .. 3 9 42 .. .. 4 .. 36 21 .. 33 12c 42d 15c 11 2 4

Industry

Female % of female employment 1990–92a

13 .. 57 .. .. 3 2 9 16 7 .. .. .. .. 18 .. .. .. .. .. .. .. .. .. .. .. .. .. 20 .. .. 13 11 .. .. .. .. .. 52 91 2 8c .. .. .. 3 .. 69 3 .. .. 0b,c 32c .. 13 0b ..

Male % of male employment

PEOPLE

2.3

Employment by economic activity

Services

Female % of female employment

Male % of male employment

Female % of female employment

2005–08a

1990–92a

2005–08a

1990–92a

2005–08a

1990–92a

2005–08a

1990–92a

2005–08a

2 .. 41 33 .. 2 1 3 8 4 .. .. .. .. 8 .. .. 35 .. 6 .. .. .. .. 6 17 83 .. 10 .. .. 8 4 30 35 60 .. .. 8 .. 2 5 8 .. .. 1 .. 72 3 .. 24 6c 23 d 14 c 12 0b 0

43 .. 15 .. .. 33 38 41 25 40 .. .. .. .. 40 .. .. .. .. .. .. .. .. .. .. .. .. .. 31 .. .. 36 25 .. .. .. .. .. 21 4 33 31c .. .. .. 34 .. 20 20 .. .. 30 c 17c .. 39 27 ..

42 .. 21 33 .. 38 32 39 27 35 .. .. .. .. 33 .. .. 26 .. 40 .. .. .. .. 41 33 5 .. 32 .. .. 36 31 25 21 24 .. .. 24 .. 27 32 20 .. .. 33 .. 23 25 .. 24 41c 18d 41c 40 26 48

29 .. 13 .. .. 18 15 23 12 27 .. .. .. .. 28 .. .. .. .. .. .. .. .. .. .. .. .. .. 32 .. .. 48 19 .. .. .. .. .. 8 1 10 13c .. .. .. 10 .. 15 11 .. .. 13 c 14 c .. 24 19 ..

21 .. 15 29 .. 10 11 16 5 17 .. .. .. .. 16 .. .. 11 .. 17 .. .. .. .. 19 29 2 .. 23 .. .. 26 18 12 15 15 .. .. 9 .. 8 10 18 .. .. 8 .. 13 10 .. 9 43 c 10 d 18 c 17 10 4

38 .. 31 .. .. 48 57 52 39 54 .. .. .. .. 46 .. .. .. .. .. .. .. .. .. .. .. .. .. 46 .. .. 48 41 .. .. .. .. .. 34 20 60 56c .. .. .. 58 .. 35 45 .. .. 69c 29c .. 51 67 ..

52 .. 38 47 .. 53 65 57 47 59 .. .. .. .. 60 .. .. 37 .. 49 .. .. .. .. 49 48 13 .. 51 .. .. 54 50 39 39 41 .. .. 24 .. 63 58 38 .. .. 63 .. 41 54 .. 43 46c 41d 44c 49 72 48

58 .. 31 .. .. 78 83 68 72 65 .. .. .. .. 54 .. .. .. .. .. .. .. .. .. .. .. .. .. 48 .. .. 39 70 .. .. .. .. .. 40 8 81 79c .. .. .. 86 .. 16 85 .. .. 87c 55c .. 63 80 ..

77 .. 44 38 .. 88 88 81 87 77 .. .. .. .. 76 .. .. 54 .. 77 .. .. .. .. 75 54 16 .. 67 .. .. 66 77 58 50 25 .. .. 63 .. 85 85 73 .. .. 90 .. 15 87 .. 68 51c 68 d 68 c 71 89 96

2011 World Development Indicators

45

2.3

Employment by economic activity Agriculture

Male % of male employment 1990–92a 2005–08a

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

29 .. .. .. .. .. .. 1 .. .. .. .. 11 .. .. .. 5c 5 23 .. .. 59 .. .. 15 .. 33 .. .. .. .. 3 4 .. .. 17 .. .. 44 47 .. .. w .. .. .. .. .. .. .. .. .. .. .. 6 7

27 11 .. 5d 34 22 .. 2 6 10c .. 5d 6 28c .. .. 3c 5 .. .. 71 43 .. .. 6 .. 18d .. .. .. 6 2 2 16c .. 13 .. 11 .. .. .. .. w .. .. .. 17 .. .. 18 20 .. .. .. 4 5

Industry

Female % of female employment 1990–92a

2005–08a

38 .. .. .. .. .. .. 0b .. .. .. .. 8 .. .. .. 2c 4 54 .. .. 62 .. .. 6 .. 72 .. .. .. .. 1 1 .. .. 2 .. .. 83 56 .. .. w .. .. .. .. .. .. .. .. .. .. .. 5 6

30 7 .. 0 b,d 33 20 .. 1 2 10 c .. 3d 3 37c .. .. 1c 3 .. .. 78 40 .. .. 2 .. 42d .. .. .. 0b 1 1 5c .. 2 .. 36 .. .. .. . w. .. .. .. 12 .. .. 18 9 .. .. .. 3 3

Male % of male employment 1990–92a

44 .. .. .. .. .. .. 36 .. .. .. .. 41 .. .. .. 40c 39 28 .. .. 17 .. .. 34 .. 26 .. .. .. .. 41 34 .. .. 32 .. .. 14 15 .. .. w .. .. .. .. .. .. .. .. .. .. .. 38 42

2005–08a

38 38 .. 23d 20 37 .. 26 52 44c .. 31d 40 26c .. .. 33c 34 .. .. 7 22 .. .. 41 .. 21d .. .. .. 45 32 30 29c .. 30 .. 27 .. .. .. .. w .. .. .. 32 .. .. 34 29 .. .. .. 34 38

Note: Data across sectors may not sum to 100 percent because of workers not classified by sector. a. Data are for the most recent year available. b. Less than 0.5. c. Limited coverage. d. Data are for 2009.

46

2011 World Development Indicators

Services

Female % of female employment 1990–92a

30 .. .. .. .. .. .. 32 .. .. .. .. 17 .. .. .. 12c 15 8 .. .. 13 .. .. 14 .. 11 .. .. .. .. 16 14 .. .. 16 .. .. 2 3 .. .. w .. .. .. .. .. .. .. .. .. .. .. 19 20

2005–08a

24 20 .. 2d 5 20 .. 18 24 23 c .. 13 d 11 27c .. .. 9c 12 .. .. 3 19 .. .. 16 .. 15 d .. .. .. 6 9 9 13 c .. 12 .. 10 .. .. .. .. w .. .. .. 20 .. .. 20 16 .. .. .. 13 13

Male % of male employment 1990–92a

28 .. .. .. .. .. .. 63 .. .. .. .. 49 .. .. .. 55c 57 49 .. .. 24 .. .. 51 .. 41 .. .. .. .. 55 62 .. .. 52 .. .. 38 22 .. .. w .. .. .. .. .. .. .. .. .. .. .. 55 50

2005–08a

35 51 .. 72d 33 42 .. 72 43 45c .. 57d 55 41c .. .. 64c 62 .. .. 22 35 .. .. 52 .. 53d .. .. .. 49 66 68 56c .. 56 .. 61 .. .. .. .. w .. .. .. 50 .. .. 48 51 .. .. .. 61 57

Female % of female employment 1990–92a

33 .. .. .. .. .. .. 68 .. .. .. .. 75 .. .. .. 86 c 81 38 .. .. 25 .. .. 80 .. 17 .. .. .. .. 82 85 .. .. 82 .. .. 13 18 .. .. w .. .. .. .. .. .. .. .. .. .. .. 76 73

2005–08a

46 73 .. 98 d 42 61 .. 82 74 65 c .. 79 d 86 34 c .. .. 90 c 86 .. .. 19 41 .. .. 82 .. 43 d .. .. .. 92 90 90 83 c .. 86 .. 53 .. .. .. .. w .. .. .. 68 .. .. 63 75 .. .. .. 84 83

About the data

2.3

PEOPLE

Employment by economic activity Definitions

The International Labour Organization (ILO) classi-

Such broad classification may obscure fundamental

• Agriculture corresponds to division 1 (ISIC revi-

fies economic activity using the International Stan-

shifts within countries’ industrial patterns. A slight

sion 2) or tabulation categories A and B (ISIC revi-

dard Industrial Classification (ISIC) of All Economic

majority of countries report economic activity accord-

sion 3) and includes hunting, forestry, and fishing.

Activities, revision 2 (1968) and revision 3 (1990).

ing to the ISIC revision 2 instead of revision 3. The

• Industry corresponds to divisions 2–5 (ISIC revi-

Because this classification is based on where work

use of one classification or the other should not have

sion 2) or tabulation categories C–F (ISIC revision

is performed (industry) rather than type of work per-

a significant impact on the information for the three

3) and includes mining and quarrying (including oil

formed (occupation), all of an enterprise’s employees

broad sectors presented in the table.

production), manufacturing, construction, and public

are classified under the same industry, regardless

The distribution of economic wealth in the world

utilities (electricity, gas, and water). • Services corre-

of their trade or occupation. The categories should

remains strongly correlated with employment by

spond to divisions 6–9 (ISIC revision 2) or tabulation

sum to 100 percent. Where they do not, the differ-

economic activity. The wealthier economies are

categories G–P (ISIC revision 3) and include whole-

ences are due to workers who cannot be classified

those with the largest share of total employment in

sale and retail trade and restaurants and hotels;

by economic activity.

services, whereas the poorer economies are largely

transport, storage, and communications; financing,

agriculture based.

insurance, real estate, and business services; and

Data on employment are drawn from labor force surveys, household surveys, official estimates, cen-

The distribution of economic activity by gender

suses and administrative records of social insurance

reveals some clear patterns. Men still make up the

schemes, and establishment surveys when no other

majority of people employed in all three sectors, but

information is available. The concept of employment

the gender gap is biggest in industry. Employment in

generally refers to people above a certain age who

agriculture is also male-dominated, although not as

worked, or who held a job, during a reference period.

much as industry. Segregating one sex in a narrow

Employment data include both full-time and part-time

range of occupations significantly reduces economic

workers.

efficiency by reducing labor market flexibility and thus

There are many differences in how countries define

the economy’s ability to adapt to change. This seg-

and measure employment status, particularly mem-

regation is particularly harmful for women, who have

bers of the armed forces, self-employed workers, and

a much narrower range of labor market choices and

unpaid family workers. Where members of the armed

lower levels of pay than men. But it is also detri-

forces are included, they are allocated to the service

mental to men when job losses are concentrated

sector, causing that sector to be somewhat over-

in industries dominated by men and job growth is

stated relative to the service sector in economies

centered in service occupations, where women have

where they are excluded. Where data are obtained

better chances, as has been the recent experience

from establishment surveys, data cover only employ-

in many countries.

ees; thus self-employed and unpaid family workers

There are several explanations for the rising impor-

are excluded. In such cases the employment share

tance of service jobs for women. Many service jobs—

of the agricultural sector is severely underreported.

such as nursing and social and clerical work—are

Caution should be also used where the data refer

considered “feminine” because of a perceived simi-

only to urban areas, which record little or no agricul-

larity to women’s traditional roles. Women often do

tural work. Moreover, the age group and area covered

not receive the training needed to take advantage of

could differ by country or change over time within a

changing employment opportunities. And the greater

country. For detailed information on breaks in series,

availability of part-time work in service industries

consult the original source.

may lure more women, although it is unclear whether

Countries also take different approaches to the

community, social, and personal services.

this is a cause or an effect.

treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries. The ILO reports data by major divisions of the ISIC

Data sources

revision 2 or revision 3. In the table the reported

Data on employment are from the ILO’s Key Indica-

divisions or categories are aggregated into three

tors of the Labour Market, 6th edition, database.

broad groups: agriculture, industry, and services.

2011 World Development Indicators

47

2.4

Decent work and productive employment Employment to population ratio

Gross enrollment ratio, secondary

Vulnerable employment

Labor productivity

Unpaid family workers and own-account workers Total

Youth

% ages 15 and older

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

48

% ages 15–24

% of relevant age group

1991

2008

1991

2008

1991

2009a

54 49 39 77 53 38 56 52 57 74 58 44 70 61 42 47 56 45 82 85 77 59 58 73 67 51 75 62 52 68 66 56 63 50 52 58 59 44 52 43 59 66 61 71 57 47 58 73 57 54 68 44 55 82 66 56 59

55 46 49 76 57 38 59 55 60 68 52 47 72 71 42 46 64 46 82 84 75 59 61 73 70 50 71 57 62 67 65 57 60 46 54 54 60 53 61 43 54 66 55 81 55 48 58 72 54 52 65 48 62 81 67 55 56

45 37 25 71 42 24 58 61 38 66 40 31 64 48 17 34 54 27 77 74 66 37 57 59 51 34 71 54 38 60 49 48 52 27 40 48 65 28 39 22 42 60 43 64 45 28 37 59 28 58 40 31 50 75 57 37 49

47 36 31 69 36 25 64 53 39 56 35 27 59 49 18 27 53 27 74 73 68 33 61 58 50 24 55 38 43 62 46 43 45 29 32 29 61 34 40 23 39 54 29 74 44 29 33 55 22 44 40 28 52 73 63 47 43

16 89 60 12 74 .. 132 102 88 18 93 101 .. .. .. 49 .. 98 7 5 25 26 101 12 6 97 41 .. 53 21 46 45 .. 83 94 91 109 .. 55 69 38 11 100 14 116 100 40 19 95 98 35 94 23 11 5 .. 33

44 72 .. .. 85 93 149 100 99 42 95 108 .. 81 91 82 101 89 20 21 40 41 .. 14 24 90 78 82 95 37 .. 96 .. 90 90 95 119 77 81 .. 64 32 99 34 110 113 .. 51 108 102 57 102 57 37 .. .. 65

2011 World Development Indicators

Male % of male employment

Female % of female employment

1990

2008

1990

2008

.. .. .. .. .. .. 12 .. .. .. .. 17 .. 32b .. .. 29b .. .. .. .. .. .. .. .. .. .. .. 30 b .. .. 26 .. .. .. .. 7 42 33b .. .. .. 2b .. .. 11 .. .. .. .. .. .. .. .. .. .. 48 b

.. .. .. .. 22b .. 11 9 41 .. .. 11 .. .. .. .. 30 10 .. .. .. .. 12b .. .. 25 .. 10 b 41 .. .. 20 .. 23c .. 15 7 49 29 b 20 29 .. 8b 48b 11 7 .. .. .. 7 .. 27 .. .. .. .. ..

.. .. .. .. .. .. 9 .. .. .. .. 15 .. 50b .. .. 30b .. .. .. .. .. .. .. .. .. .. .. 26 b .. .. 21 .. .. .. .. 6 30 41b .. .. .. 3b .. .. 10 .. .. .. .. .. .. .. .. .. .. 50b

.. .. .. .. 17b .. 7 9 66 .. .. 9 .. .. .. .. 24 8 .. .. .. .. 9b .. .. 24 .. 4b 41 .. .. 20 .. 20 c .. 9 3 30 41b 44 44 .. 4b 56 b 7 5 .. .. .. 6 .. 27 .. .. .. .. ..

GDP per person employed % growth 1990–92

.. –17.5 –4.0 –5.0 9.0 –24.8 3.3 0.7 –12.6 1.9 –4.0 1.6 .. 2.6 –14.8 .. –0.3 3.1 1.3 .. 4.0 –6.7 0.8 .. .. 6.6 6.8 5.3 –0.7 –12.9 .. 2.4 –3.6 –7.7 .. –5.2 2.5 0.7 –0.1 2.1 .. .. –9.4 –8.4 1.4 1.4 .. .. –25.3 3.7 2.8 2.4 1.0 .. .. .. ..

2005–08

.. 6.1 –0.7 14.6 3.7 12.2 0.7 0.4 21.4 4.0 8.7 0.7 .. 1.8 1.6 .. 3.2 3.0 1.3 .. 6.5 1.0 0.2 .. .. 0.2 10.6 3.0 4.8 2.9 .. 1.9 –0.7 2.8 .. 3.4 –0.7 5.4 0.5 4.4 .. .. 2.4 7.4 1.5 0.6 .. .. 10.1 0.9 3.7 2.4 1.4 .. .. .. ..

Employment to population ratio

Gross enrollment ratio, secondary

Vulnerable employment

Labor productivity

Unpaid family workers and own-account workers Total

Youth

% ages 15 and older

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

% ages 15–24

% of relevant age group

1991

2008

1991

2008

1991

2009a

48 58 63 46 37 44 45 43 61 61 36 63 73 62 59 .. 62 58 80 58 44 48 66 45 54 37 79 72 60 49 67 56 57 58 50 46 80 74 45 60 51 55 57 59 53 58 53 48 50 70 61 53 59 53 58 37 73

45 56 62 49 37 58 50 44 56 54 38 64 73 64 58 .. 65 58 78 55 46 54 66 49 50 35 83 72 61 47 47 54 57 45 52 46 78 74 43 62 59 63 58 60 52 62 51 52 59 70 73 69 60 48 56 41 77

37 46 46 33 27 38 25 30 40 43 25 46 62 46 36 .. 29 41 74 43 31 40 57 28 36 17 65 48 47 40 54 45 50 39 39 40 67 62 24 52 55 55 46 50 29 49 30 38 33 57 51 34 42 31 53 21 35

20 40 41 36 23 44 27 25 29 40 20 42 59 39 28 .. 30 40 64 35 29 40 57 27 18 13 71 49 45 35 23 37 42 17 35 35 66 53 14 46 67 56 48 52 24 56 29 44 40 54 58 53 39 27 35 29 47

86 46 46 53 40 100 92 79 70 97 82 98 .. .. 91 .. 53 100 21 92 61 24 .. .. 92 76 19 17 57 7 13 55 54 90 82 36 7 23 43 34 120 92 43 7 24 103 45 23 62 12 31 67 70 87 66 .. 84

97 60 79 83 51 115 90 101 91 101 88 99 59 .. 97 .. 90 84 44 98 82 45 .. .. 99 84 32 30 69 38 24 87 90 88 92 56 23 53 66 .. 121 119 68 12 30 112 91 33 73 .. 67 89 82 100 104 84 85

Male % of male employment

2.4

PEOPLE

Decent work and productive employment

Female % of female employment

1990

2008

1990

2008

8b .. .. .. .. 25 .. 29 46 15 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 31 .. .. 13 29 .. .. .. .. .. .. .. 7 15 .. .. .. .. .. .. 44 .. 17b 30 b .. .. 22 .. ..

8 .. 60 40 .. 17 9 21 38 10 .. .. .. .. 23 .. .. 47 .. 8 .. .. .. .. 11 24 .. .. 23 .. .. 18 28 35 .. 46 .. .. .. .. 10 14 45 .. .. 8 .. 58 30 .. 45 33b 44b 20 18 .. ..

7b .. .. .. .. 9 .. 24 37 26 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 25 .. .. 7 15 .. .. .. .. .. .. .. 10 10 .. .. .. .. .. .. 19 .. 31b 46b .. .. 30 .. ..

6 .. 68 56 .. 5 5 15 31 12 .. .. .. .. 28 .. .. 47 .. 6 .. .. .. .. 8 20 .. .. 21 .. .. 15 32 30 .. 65 .. .. .. .. 8 10 46 .. .. 3 .. 75 24 .. 50 47b 47b 18 19 .. ..

GDP per person employed % growth 1990–92

0.3 1.0 6.2 6.5 –33.6 2.4 0.0 0.6 0.7 0.7 –5.5 –15.1 –3.9 .. 5.0 .. –0.2 –13.1 .. –19.6 .. .. .. .. –13.9 –5.6 –5.9 –1.9 6.0 0.4 .. .. 1.0 –22.0 .. –1.7 –3.0 2.0 .. .. 0.4 0.5 .. –5.7 –2.9 3.9 0.2 6.5 .. .. .. –0.8 –3.3 2.8 2.2 .. 0.1

2011 World Development Indicators

2005–08

2.0 5.9 3.8 1.8 1.9 0.7 1.3 –0.3 –2.2 1.2 2.5 4.8 2.5 .. 3.1 .. 3.2 4.3 .. 2.9 .. .. .. .. 5.2 1.2 2.2 5.6 3.1 1.9 .. .. 1.0 6.9 .. 2.8 5.5 5.8 .. .. 1.0 –0.3 .. 2.3 3.3 –1.1 3.7 2.5 .. .. .. 0.2 3.9 1.9 0.9 .. 13.3

49

2.4

Decent work and productive employment Employment to population ratio

Gross enrollment ratio, secondary

Vulnerable employment

Labor productivity

Unpaid family workers and own-account workers Total

Youth

% ages 15 and older 1991

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

56 57 87 50 67 49 d 64 64 55 55 66 39 41 51 46 54 62 65 47 54 87 77 64 66 45 41 53 56 82 57 71 56 59 53 54 51 75 30 38 57 70 62 w 71 63 65 53 63 73 55 55 43 59 64 55 48

2008

48 57 80 48 66 44 d 65 62 53 54 67 41 49 55 47 50 58 61 45 55 78 72 67 65 61 41 42 58 83 54 76 56 59 56 58 61 69 30 39 61 65 60 w 70 61 62 56 62 69 53 61 45 57 64 55 50

% ages 15–24 1991

42 34 79 26 60 28 d 38 56 43 38 59 19 36 31 29 34 59 69 38 36 79 70 51 58 33 29 48 35 73 37 43 66 56 42 36 35 75 19 23 40 48 52 w 60 52 55 41 53 67 38 46 29 48 50 47 41

% of relevant age group

2008

24 33 64 13 55 21d 42 38 30 32 58 15 37 36 23 26 45 63 32 38 70 46 58 53 46 22 31 34 75 34 46 56 51 39 39 40 51 15 22 46 50 45 w 58 42 44 38 45 51 33 45 29 42 49 43 37

a. Provisional data. b. Limited coverage. c. Data are for 2009. d. Includes Montenegro.

50

2011 World Development Indicators

1991

2009a

92 93 18 .. 15 .. 16 .. 88 89 .. 69 105 72 20 49 90 98 48 102 5 31 .. 20 82 45 48 .. 10 94 68 87 92 84 99 56 35 .. .. 21 49 50 w 26 47 42 67 44 41 85 57 54 37 22 91 ..

92 85 27 97 30 91 35 .. 92 97 8 94 120 .. 38 53 103 96 75 84 27 76 51 41 89 92 82 .. 27 94 95 99 94 88 104 82 .. 87 .. 49 .. 67 w 38 68 63 88 63 74 89 89 73 52 34 100 ..

Male % of male employment 1990

21 1 .. .. 77 .. .. 10 .. .. .. .. 20b .. .. .. .. 8 .. .. .. 67 .. .. 22 .. .. .. .. .. .. 13 .. .. .. .. .. .. .. 56 .. .. w .. .. .. .. .. .. .. .. .. .. .. .. ..

2008

31 6 .. .. .. 25 .. 12 14 12 .. 2 13 39b .. .. 9 10 .. .. 82b 51 .. .. .. .. 30 .. .. .. .. 14 .. 26b .. 28 .. 34 .. .. .. .. w .. .. .. 26 .. .. 19 30 33 .. .. 13 12

Female % of female employment 1990

33 1 .. .. 91 .. .. 6 .. .. .. .. 24b .. .. .. .. 11 .. .. .. 74 .. .. 21 .. .. .. .. .. .. 6 .. .. .. .. .. .. .. 81 .. .. w .. .. .. .. .. .. .. .. .. .. .. .. ..

2008

32 6 .. .. .. 20 .. 7 6 10 .. 3 10 44b .. .. 4 11 .. .. 93 b 56 .. .. .. .. 49 .. .. .. .. 7 .. 24b .. 33 .. 44 .. .. .. .. w .. .. .. 26 .. .. 19 30 52 .. .. 11 9

GDP per person employed % growth 1990–92

2005–08

–9.3 –7.9 .. 4.9 –1.0 .. .. 1.5 –0.8 –2.3 .. –4.5 2.4 5.5 –1.3 .. 1.9 –0.6 6.5 –20.4 –2.4 6.8 .. .. –3.5 2.6 1.0 –13.0 –1.1 –7.9 –3.9 2.0 1.7 5.2 –7.8 4.5 4.6 .. 0.9 –2.5 –4.7 0.7 w –3.2 1.3 3.2 –2.3 1.1 6.5 –9.1 1.8 1.4 3.1 –5.3 2.3 2.4

6.5 6.4 .. 0.7 0.9 .. .. –1.8 6.1 3.0 .. 3.7 0.7 9.3 7.5 .. 0.6 1.0 0.3 6.3 4.5 2.7 .. .. 5.4 2.7 2.6 7.9 6.1 5.9 0.7 2.2 1.4 4.9 5.9 4.3 5.6 .. –0.8 3.9 –7.7 3.1 w 4.4 6.2 7.4 3.6 6.1 8.7 5.8 2.6 2.2 5.5 4.1 1.2 0.7

About the data

2.4

PEOPLE

Decent work and productive employment Definitions

Four targets were added to the UN Millennium Dec-

Data on employment by status are drawn from

• Employment to population ratio is the proportion

laration at the 2005 World Summit High-Level Ple-

labor force surveys and household surveys, supple-

of a country’s population that is employed. People

nary Meeting of the 60th Session of the UN General

mented by offi cial estimates and censuses for a

ages 15 and older are generally considered the

Assembly. One was full and productive employment

small group of countries. The labor force survey is

working-age population. People ages 15–24 are

and decent work for all, which is seen as the main

the most comprehensive source for internationally

generally considered the youth population. • Gross

route for people to escape poverty. The four indi-

comparable employment, but there are still some

enrollment ratio, secondary, is the ratio of total

cators for this target have an economic focus, and

limitations for comparing data across countries and

enrollment in secondary education, regardless of

three of them are presented in the table.

over time even within a country. Information from

age, to the population of the age group that officially

The employment to population ratio indicates how

labor force surveys is not always consistent in what

corresponds to secondary education. • Vulnerable

efficiently an economy provides jobs for people who

is included in employment. For example, informa-

employment is unpaid family workers and own-

want to work. A high ratio means that a large pro-

tion provided by the Organisation for Economic

account workers as a percentage of total employ-

portion of the population is employed. But a lower

Co-operation and Development relates only to civil-

ment. • Labor productiv ity is the growth rate

employment to population ratio can be seen as a

ian employment, which can result in an underesti-

of gross domestic product (GDP) divided by the num-

positive sign, especially for young people, if it is

mation of “employees” and “workers not classified

ber of people engaged in the production of goods

caused by an increase in their education. This indi-

by status,” especially in countries with large armed

and services.

cator has a gender bias because women who do not

forces. While the categories of unpaid family work-

consider their work employment or who are not per-

ers and self-employed workers, which include own-

ceived as working tend to be undercounted. This bias

account workers, would not be affected, their relative

has different effects across countries and reflects

shares would be. Geographic coverage is another

demographic, social, legal, and cultural trends and

factor that can limit cross-country comparisons. The

norms.

employment by status data for many Latin Ameri-

Comparability of employment ratios across coun-

can countries covers urban areas only. Similarly, in

tries is also affected by variations in definitions of

some countries in Sub-Saharan Africa, where limited

employment and population (see About the data for

information is available anyway, the members of pro-

table 2.3). The biggest difference results from the

ducer cooperatives are usually excluded from the

age range used to define labor force activity. The

self-employed category. For detailed information on

population base for employment ratios can also vary

definitions and coverage, consult the original source.

(see table 2.1). Most countries use the resident,

Labor productivity is used to assess a country’s

noninstitutionalized population of working age living

economic ability to create and sustain decent

in private households, which excludes members of

employment opportunities with fair and equitable

the armed forces and individuals residing in men-

remuneration. Productivity increases obtained

tal, penal, or other types of institutions. But some

through investment, trade, technological progress, or

countries include members of the armed forces in

changes in work organization can increase social pro-

the population base of their employment ratio while

tection and reduce poverty, which in turn reduce vul-

excluding them from employment data (International

nerable employment and working poverty. Productiv-

Labour Organization, Key Indicators of the Labour

ity increases do not guarantee these improvements,

Market, 6th edition).

but without them—and the economic growth they

The proportion of unpaid family workers and

bring—improvements are highly unlikely. For compa-

own-account workers in total employment is derived

rability of individual sectors labor productivity is esti-

from information on status in employment. Each

mated according to national accounts conventions.

status group faces different economic risks, and

However, there are still significant limitations on the

unpaid family workers and own-account workers

availability of reliable data. Information on consis-

are the most vulnerable—and therefore the most

tent series of output in both national currencies and

likely to fall into poverty. They are the least likely to

purchasing power parity dollars is not easily avail-

have formal work arrangements, are the least likely

able, especially in developing countries, because the

Data on employment to population ratio, vulner-

to have social protection and safety nets to guard

definition, coverage, and methodology are not always

able employment, and labor productivity are from

against economic shocks, and often are incapable of

consistent across countries. For example, countries

the ILO’s Key Indicators of the Labour Market,

generating sufficient savings to offset these shocks.

employ different methodologies for estimating the

6th edition, database. Data on gross enrollment

A high proportion of unpaid family workers in a coun-

missing values for the nonmarket service sectors

ratios are from the United Nations Educational,

try indicates weak development, little job growth, and

and use different definitions of the informal sector.

Scientific, and Cultural Organization Institute for

often a large rural economy.

Data sources

Statistics.

2011 World Development Indicators

51

2.5

Unemployment Unemployment

Total % of total labor force 1990–92a 2006–09a

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

52

.. .. 23.0 .. 6.7b .. 10.8 3.6 .. 1.9 .. 6.7 1.5 5.5b 17.6 13.8 6.4b .. .. 0.5 .. .. 11.2b .. .. 4.4 2.3b 2.0 b 9.5b .. .. 4.1 6.7 11.1 .. 2.3 9.0 20.7 8.9b .. 7.9b .. 3.7b 1.3 11.6 10.2 .. .. .. 6.6 4.7 7.8 .. .. .. 12.7 3.2b

.. 12.7 11.3 .. 8.6b 28.6b 5.6b 4.8 6.1 .. .. 7.9 .. 5.2b 23.9 17.6b 8.3 6.8 .. .. .. 2.9 8.3b .. .. 9.7 4.3 5.2b 12.0 .. .. 4.9 .. 9.1 1.6 6.7 6.0 14.2 6.5 9.4 5.9 .. 13.7 20.5b 8.2 9.1 .. .. 16.5 7.7 .. 9.5 1.8 .. .. .. 2.9b

2011 World Development Indicators

Male % of male labor force 1990–92a 2006–09a

.. .. 24.2 .. 6.4b .. 11.4 3.5 .. 2.0 .. 4.8 2.2 5.5b 15.5 11.7 5.4b .. .. 0.7 .. .. 12.0b .. .. 3.9 .. 2.0 b 6.8 b .. .. 3.5 .. 11.1 .. 2.4 8.3 12.0 6.0b .. 8.4b .. 3.9b 1.1 13.3 8.1 .. .. .. 5.3 3.7 4.9 .. .. .. 11.9 3.3b

.. .. 11.0 .. 7.8 b 21.9 b 5.7b 5.0 7.1 .. .. 7.7 .. 4.5b 21.8 15.3 b 6.1 7.0 .. .. .. 2.5 9.4b .. .. 9.1 .. 6.0 b 9.3 .. .. 4.1 .. 8.0 1.4 5.8 6.5 8.5 5.2 5.2 7.5 .. 17.0 12.1b 8.9 8.9 .. .. 16.8 8.1 .. 6.9 1.5 .. .. .. 2.9b

Female % of female labor force 1990–92a 2006–09a

.. .. 20.3 .. 7.0 b .. 10.0 3.8 .. 1.9 .. 9.5 0.6 5.6b 21.6 17.2 7.9b .. .. 0.3 .. .. 10.2b .. .. 5.3 .. 1.9b 13.0 b .. .. 5.4 .. 11.2 .. 2.1 9.9 35.2 13.2b .. 7.2b .. 3.5b 1.6 9.6 12.8 .. .. .. 8.4 5.5 12.9 .. .. .. 13.8 3.0 b

.. .. 10.1 .. 9.8 b 35.0 b 5.4b 4.5 4.9 .. .. 8.1 .. 6.0 b 27.1 19.9b 11.0 6.6 .. .. .. 3.3 7.0 b .. .. 10.7 .. 4.3b 15.8 .. .. 6.2 .. 10.2 2.0 7.7 5.4 22.8 8.4 22.9 3.6 .. 10.8 29.9 b 7.5 9.3 .. .. 16.1 7.3 .. 13.1 2.4 .. .. .. 2.9b

Long-term unemployment

Unemployment by educational attainment

% of total unemployment Total Male Female 2006–09a 2006–09a 2006–09a

% of total unemployment Primary Secondary Tertiary 2006–09a 2006–09a 2006–09a

.. .. .. .. .. .. 14.7b 20.3 .. .. .. 44.2 .. .. .. .. .. 43.3 .. .. .. .. 7.8 b .. .. .. .. .. .. .. .. .. .. 56.2 .. 31.2 9.1 .. .. .. .. .. 27.4 .. 16.6 35.4 .. .. .. 45.5 .. 40.8 .. .. .. .. ..

.. .. .. .. .. .. 15.0 b 19.7 .. .. .. 43.5 .. .. .. .. .. 40.7 .. .. .. .. 8.1b .. .. .. .. .. .. .. .. .. .. 50.8 .. 29.0 8.9 .. .. .. .. .. 26.8 .. 18.2 35.6 .. .. .. 44.4 .. 34.4 .. .. .. .. ..

.. .. .. .. .. .. 14.4b 21.0 .. .. .. 45.0 .. .. .. .. .. 46.4 .. .. .. .. 7.4b .. .. .. .. .. .. .. .. .. .. 61.0 .. 33.4 9.4 .. .. .. .. .. 28.4 .. 14.7 35.3 .. .. .. 47.0 .. 45.6 .. .. .. .. ..

.. .. .. .. 48.1b 5.2 48.0 37.9b 6.3 .. 10.8 42.1 .. .. 95.7 .. 51.6 41.8 .. .. .. .. 27.7b .. .. 17.8 .. 40.8b 76.6 .. .. 65.2 .. 16.0 43.0 26.8 35.9 35.0 74.0b .. .. .. 23.1b .. 35.5 39.9 .. .. 5.1b 33.1 .. 29.3b .. .. .. .. ..

.. .. .. .. 36.7b 83.0 34.1 52.1b 78.9 .. 38.6 38.2 .. .. .. .. 33.6 49.7 .. .. .. .. 41.1b .. .. 58.5 .. 41.4b .. .. .. 27.3 .. 70.4 52.4 68.8 35.1 44.5 .. .. .. .. 57.8b .. 45.9 39.6 .. .. 52.5b 56.3 .. 48.4b .. .. .. .. ..

.. .. .. .. 15.3b 11.9 17.9 10.0 b 14.9 .. 50.6 19.7 .. .. 4.0 .. 3.6 8.6 .. .. .. .. 31.2b .. .. 23.5 .. 16.6b 20.6 .. .. 6.4 .. 11.6 4.6 4.3 23.0 16.4 23.6b .. .. .. 16.6b .. 18.6 19.9 .. .. 42.3b 10.6 .. 21.8b .. .. .. .. ..

Unemployment

Total % of total labor force 1990–92a 2006–09a

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

9.9 .. 2.8 11.1 .. 15.0 11.2 9.3 15.4 2.2 .. .. .. .. 2.5b .. .. .. .. .. .. .. .. .. .. .. .. .. 3.7 .. .. 3.3 3.1 .. .. 16.0b .. 6.0 19.0 .. 5.6 10.6 b 14.4 .. .. 5.9 .. 5.2 14.7 7.7 5.0b 9.4b 8.6b 13.3 4.1b 16.9 ..

10.0 .. 7.9 10.5 17.5 11.7 7.6 7.8 11.4 5.0 12.9 6.6 .. .. 3.6b 45.4 .. 8.2 .. 17.1 9.0 .. 5.6 .. 13.7 32.2 .. .. 3.7 .. .. 7.3 5.2 6.4 .. 10.0 .. .. 37.6 .. 3.4 6.1b 5.0 .. .. 3.2 .. 5.0 5.9 .. 5.6 6.8 b 7.5 8.2 9.5 13.4 0.5

Male % of male labor force 1990–92a 2006–09a

11.0 .. 2.7 9.5 .. 14.9 9.2 6.7 9.4 2.1 .. .. .. .. 2.8 b .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 3.2 2.7 .. .. 13.0 b .. 4.7 20.0 .. 4.0 11.4b 11.3 .. .. 6.6 .. 3.8 10.8 9.0 6.0 b 7.5b 7.9b 12.2 3.5b 19.1 ..

10.3 .. 7.5 9.1 16.2 14.7 7.6 6.8 8.5 5.3 10.3 5.6 .. .. 4.1b 40.7 .. 7.3 .. 20.4 8.6 .. 6.8 .. 17.1 31.7 .. .. 3.2 .. .. 4.4 5.4 7.8 .. 9.8 .. .. 32.5 .. 3.4 6.1b 4.9 .. .. 3.6 .. 4.0 4.6 .. 4.4 5.4b 7.5 7.8 8.9 14.9 0.2

Female % of female labor force 1990–92a 2006–09a

8.7 .. 3.0 24.4 .. 15.2 13.9 13.9 22.2 2.2 .. .. .. .. 2.1b .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 3.6 4.0 .. .. 25.3b .. 8.8 19.0 .. 7.8 9.7b 19.5 .. .. 5.1 .. 14.0 22.3 5.9 3.7b 12.5b 9.9b 14.7 5.0 b 13.3 ..

9.7 .. 8.5 16.8 22.5 8.0 7.6 9.3 14.8 4.7 24.1 7.5 .. .. 3.0 b 56.4 .. 9.4 .. 14.0 10.1 .. 4.2 .. 10.4 33.0 .. .. 3.7 .. .. 12.3 4.8 4.9 .. 10.5 .. .. 43.0 .. 3.5 6.1b 5.1 .. .. 2.6 .. 8.7 7.9 .. 7.5 8.3b 7.4 8.7 10.1 11.6 2.6

2.5

PEOPLE

Unemployment Long-term unemployment

Unemployment by educational attainment

% of total unemployment Total Male Female 2006–09a 2006–09a 2006–09a

% of total unemployment Primary Secondary Tertiary 2006–09a 2006–09a 2006–09a

42.6 .. .. .. .. 29.0 28.6 44.4 .. 28.5 .. .. .. .. 0.5 81.7 .. .. .. 26.7 .. .. .. .. 23.2 81.6 .. .. .. .. .. .. 1.9 .. .. .. .. .. .. .. 24.8 6.3 b .. .. .. 7.7 .. .. .. .. .. .. .. 25.2 44.2 .. ..

42.4 .. .. .. .. 32.1 32.3 42.0 .. 34.8 .. .. .. .. 0.6 82.8 .. .. .. 27.1 .. .. .. .. 21.0 82.2 .. .. .. .. .. .. 1.8 .. .. .. .. .. .. .. 23.7 6.3 b .. .. .. 7.5 .. .. .. .. .. .. .. 23.3 40.8 .. ..

42.8 .. .. .. .. 21.7 25.0 46.9 .. 18.8 .. .. .. .. 0.3 79.8 .. .. .. 26.0 .. .. .. .. 26.8 80.6 .. .. .. .. .. .. 2.1 .. .. .. .. .. .. .. 26.1 6.4b .. .. .. 8.0 .. .. .. .. .. .. .. 27.3 47.5 .. ..

33.1b .. 44.4 .. .. 39.8 12.2 46.5 9.7 67.2 .. .. .. .. 15.2 64.0 19.4 13.3 .. 24.3b .. .. .. .. 14.2b .. .. .. 13.3 .. .. 44.2 50.7 .. .. .. .. .. .. .. 41.3 30.6 72.8 .. .. 25.4 .. 14.3 36.0 .. 49.9 30.0b 13.8 16.4b 68.1b .. ..

58.7b .. 40.7 .. .. 37.2 12.8 40.6 4.3 .. .. .. .. .. 49.7 46.0 41.4 77.1 .. 59.9b .. .. .. .. 70.4b .. .. .. 61.6 .. .. 48.5 24.5 .. .. .. .. .. .. .. 39.7 38.8 2.1 .. .. 49.2 .. 11.4 39.6 .. 38.0 31.9 b 45.2 73.2b 15.4b .. ..

2011 World Development Indicators

8.1b .. 9.6 .. .. 18.2 72.5 11.3 8.4 32.8 .. .. .. .. 35.2 15.0 9.6 9.6 .. 14.6b .. .. .. .. 15.4b .. .. .. 25.1 .. .. 6.4 22.9 .. .. .. .. .. .. .. 17.0 26.9 18.0 .. .. 20.6 .. 26.0 24.0 .. 9.9 37.6b 41.1 10.4b 13.2b .. ..

53

2.5

Unemployment Unemployment

Total % of total labor force 1990–92a 2006–09a

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

.. 5.2 0.3 .. .. .. .. 2.7b .. 7.1 .. .. 18.1 14.2b .. .. 5.7 2.8 6.8 .. 3.6b 1.4 .. .. 19.6 .. 8.5 .. 1.0 .. .. 9.7 7.5 b 9.0 b .. 7.7 .. .. .. 18.9 .. .. w .. .. .. 6.7 .. 2.5 .. 6.6 .. .. .. 7.5 9.1

6.9 8.2 .. 5.4 10.0 16.6 .. 5.9 12.1 5.9 .. 23.8 18.0 7.6 .. .. 8.3 4.1 8.4 .. 4.3 1.2 .. .. 5.3 14.2 14.0 .. .. 8.8 4.0 7.7 9.3b 7.3 .. 7.6 2.4 24.5 15.0 .. .. .. w .. .. .. 9.1 .. 4.6 9.2 7.9 10.6 .. .. 8.1 9.4

Male % of male labor force 1990–92a 2006–09a

.. 5.2 0.6 .. .. .. .. 2.7b .. 8.1 .. .. 13.9 .. .. .. 6.7 2.3 5.2 .. 2.8 b 1.3 .. .. 17.0 .. 8.8 .. 1.3 .. .. 11.5 7.9b 6.8 b .. 8.2 .. .. .. 16.3 .. .. w .. .. .. 6.4 .. .. .. 5.4 .. .. .. 7.1 7.2

a. Data are for the most recent year available. b. Limited coverage.

54

2011 World Development Indicators

7.7 8.4 .. 3.5 7.9 15.3 .. 5.4 11.4 5.9 .. 22.0 17.7 7.2 .. .. 8.6 3.7 5.2 .. 2.8 1.2 .. .. 3.5 .. 13.9 .. .. 6.6 2.0 8.8 10.3b 5.3 .. 7.2 .. 17.7 11.5 .. .. .. w .. .. .. 8.5 .. .. 9.9 6.6 8.9 .. .. 8.4 9.2

Female % of female labor force 1990–92a 2006–09a

.. 5.2 0.2 .. .. .. .. 2.6b .. 6.0 .. .. 25.8 .. .. .. 4.6 3.5 14.0 .. 4.3b 1.5 .. .. 23.9 .. 7.8 .. 0.6 .. .. 7.3 7.0 b 11.8 b .. 6.8 .. .. .. 22.4 .. .. w .. .. .. 7.4 .. .. .. 8.4 .. .. .. 8.0 11.9

5.8 7.9 .. 15.9 13.6 18.4 .. 6.5 12.9 5.8 .. 25.9 18.4 8.1 .. .. 8.0 4.5 25.7 .. 5.8 1.1 .. .. 6.2 .. 14.3 .. .. 6.1 12.0 6.4 8.1b 9.7 .. 8.1 .. 38.6 40.9 .. .. .. w .. .. .. 10.3 .. .. 8.6 9.8 16.7 .. .. 7.7 9.6

Long-term unemployment

Unemployment by educational attainment

% of total unemployment Total Male Female 2006–09a 2006–09a 2006–09a

% of total unemployment Primary Secondary Tertiary 2006–09a 2006–09a 2006–09a

31.6 35.7 .. .. .. 71.1 .. .. 50.9 30.1 .. 14.4 30.2 .. .. .. 12.8 30.0 .. .. .. .. .. .. .. .. 25.3 .. .. .. .. 24.6 16.3b .. .. .. .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. .. 24.8 38.2

32.2 33.3 .. .. .. 70.1 .. .. 47.8 28.3 .. .. 26.9 .. .. .. 13.1 26.4 .. .. .. .. .. .. .. .. 22.6 .. .. .. .. 26.5 16.4b .. .. .. .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. .. 25.3 36.7

30.6 38.4 .. .. .. 72.1 .. .. 54.4 32.1 .. .. 34.4 .. .. .. 12.4 33.6 .. .. .. .. .. .. .. .. 32.2 .. .. .. .. 21.5 16.1b .. .. .. .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. .. 23.8 39.8

25.8 13.7 .. 7.5 40.2 20.3 .. 31.0 29.2 25.0b .. 36.2 54.8b 45.4b .. .. 32.2b 28.8 .. 66.5 .. 40.5 .. .. .. .. 52.3 .. .. 8.5 .. 37.3 18.7 59.1b .. .. .. 54.3 .. .. .. .. w .. .. .. 43.4 .. .. 26.7 50.8 .. .. .. 33.9 41.3

66.3 54.2 .. 48.6 6.9 68.4 .. 25.6 65.3 60.4b .. 56.3 23.6b 22.0 b .. .. 46.0b 53.2 .. 28.8 .. 45.5 .. .. .. .. 28.2 .. .. 52.2 .. 47.7 35.5 27.0 b .. .. .. 14.2 .. .. .. .. w .. .. .. 40.9 .. .. 50.2 34.9 .. .. .. 43.7 43.0

6.1 32.1 .. 43.6 2.5 11.2 .. 43.2 5.3 12.5b .. 4.5 20.4b 32.6 b .. .. 17.1b 17.9 .. 4.6 .. 0.1 .. .. .. .. 12.7 .. .. 39.3 .. 14.3 45.7 13.8 b .. .. .. 23.5 .. .. .. .. w .. .. .. 14.3 .. .. 24.1 12.3 .. .. .. 25.7 14.9

About the data

2.5

PEOPLE

Unemployment Definitions

Unemployment and total employment are the broad-

generate statistics that are more comparable inter-

• Unemployment is the share of the labor force with-

est indicators of economic activity as reflected by

nationally. But the age group, geographic coverage,

out work but available for and seeking employment.

the labor market. The International Labour Organiza-

and collection methods could differ by country or

Definitions of labor force and unemployment may

tion (ILO) defines the unemployed as members of the

change over time within a country. For detailed infor-

differ by country (see About the data). • Long-term

economically active population who are without work

mation, consult the original source.

unemployment is the number of people with continu-

but available for and seeking work, including people

Women tend to be excluded from the unemploy-

ous periods of unemployment extending for a year or

who have lost their jobs or who have voluntarily left

ment count for various reasons. Women suffer more

longer, expressed as a percentage of the total unem-

work. Some unemployment is unavoidable. At any

from discrimination and from structural, social, and

ployed. • Unemployment by educational attainment

time some workers are temporarily unemployed—

cultural barriers that impede them from seeking

is the unemployed by level of educational attainment

between jobs as employers look for the right workers

work. Also, women are often responsible for the

as a percentage of the total unemployed. The levels

and workers search for better jobs. Such unemploy-

care of children and the elderly and for household

of educational attainment accord with the ISCED97

ment, often called frictional unemployment, results

affairs. They may not be available for work during

of the United Nations Educational, Scientific, and

from the normal operation of labor markets.

the short reference period, as they need to make

Cultural Organization.

Changes in unemployment over time may reflect

arrangements before starting work. Furthermore,

changes in the demand for and supply of labor; they

women are considered to be employed when they

may also refl ect changes in reporting practices.

are working part-time or in temporary jobs, despite

Paradoxically, low unemployment rates can disguise

the instability of these jobs or their active search for

substantial poverty in a country, while high unemploy-

more secure employment.

ment rates can occur in countries with a high level of

Long-term unemployment is measured by the

economic development and low rates of poverty. In

length of time that an unemployed person has been

countries without unemployment or welfare benefits

without work and looking for a job. The data in the

people eke out a living in vulnerable employment. In

table are from labor force surveys. The underlying

countries with well developed safety nets workers

assumption is that shorter periods of joblessness

can afford to wait for suitable or desirable jobs. But

are of less concern, especially when the unem-

high and sustained unemployment indicates serious

ployed are covered by unemployment benefi ts or

inefficiencies in resource allocation.

similar forms of support. The length of time that a

The ILO definition of unemployment notwithstand-

person has been unemployed is difficult to measure,

ing, reference periods, the criteria for people consid-

because the ability to recall that time diminishes as

ered to be seeking work, and the treatment of people

the period of joblessness extends. Women’s long-

temporarily laid off or seeking work for the first time

term unemployment is likely to be lower in countries

vary across countries. In many developing countries

where women constitute a large share of the unpaid

it is especially difficult to measure employment and

family workforce.

unemployment in agriculture. The timing of a survey,

Unemployment by level of educational attainment

for example, can maximize the effects of seasonal

provides insights into the relation between the edu-

unemployment in agriculture. And informal sector

cational attainment of workers and unemployment

employment is difficult to quantify where informal

and may be used to draw inferences about changes

activities are not tracked.

in employment demand. Information on educational

Data on unemployment are drawn from labor force

attainment is the best available indicator of skill

sample surveys and general household sample

levels of the labor force. Besides the limitations to

surveys, censuses, and offi cial estimates, which

comparability raised for measuring unemployment,

are generally based on information from different

the different ways of classifying the education level

sources and can be combined in many ways. Admin-

may also cause inconsistency. Education level is

istrative records, such as social insurance statistics

supposed to be classifi ed according to Interna-

and employment office statistics, are not included

tional Standard Classifi cation of Education 1997

in the table because of their limitations in cover-

(ISCED97). For more information on ISCED97, see

age. Labor force surveys generally yield the most

About the data for table 2.11.

comprehensive data because they include groups not covered in other unemployment statistics, particularly people seeking work for the first time. These

Data sources

surveys generally use a definition of unemployment

Data on unemployment are from the ILO’s Key Indi-

that follows the international recommendations more

cators of the Labour Market, 6th edition, database.

closely than that used by other sources and therefore

2011 World Development Indicators

55

2.6

Children at work Survey year

% of children ages 7–14 in employment

% of children ages 7–14

Afghanistan Albania Algeria Angolab Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodiad Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep.d Congo, Rep Costa Ricad Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republicd Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

56

2005 2001 2004

2005 2006 2005 2006 2008 2006 2008 2006 2005 2003/04 2007 2000 2004 2003

2007 2000 2005 2004 2006

2005 2006 2005 2007

2005

2005 2006 2006 2006 1994 2006 2005 2007

Employment by economic activitya

Status in employmenta

% of children ages 7–14 in employment

% of children ages 7–14 in employment SelfUnpaid employed Wage family

Children in employment

Total

Male

Female

Work only

Study and work

.. 25.0

.. 18.8 .. 30.0 15.7 .. .. .. 5.8 25.7 12.1 .. 72.8 33.0 11.7 .. 6.9 .. 49.0 12.5 49.6 43.5 .. 66.5 64.4 5.1 .. .. 5.3 39.9 29.9 8.1 47.7 .. .. .. .. 9.0 16.9 11.5 10.1 .. .. 64.3 .. .. .. 33.9 33.6 .. 49.9 .. 24.5 47.2 52.8 37.3 13.3

.. 22.0 .. 30.1 9.8 .. .. .. 4.5 6.4 11.2 .. 76.1 31.1 9.5 .. 3.5 .. 34.5 11.0 48.1 43.4 .. 67.6 56.2 3.1 .. .. 2.3 39.8 30.2 3.5 43.6 .. .. .. .. 2.7 11.6 4.3 3.8 .. .. 47.1 .. .. .. 52.3 29.9 .. 48.0 .. 11.7 49.5 48.1 29.6 4.1

.. 6.7 .. 26.6 4.8 .. .. .. 6.3 37.8 0.0 .. 36.1 5.2 0.1 .. 4.8 .. 67.7 38.9 13.8 21.9 .. 54.9 49.1 3.2 .. .. 24.8 35.7 9.9 44.6 46.8 .. .. .. .. 6.2 21.0 21.0 24.9 .. .. 69.4 .. .. .. 32.1 1.0 .. 18.7 .. 28.4 98.6 36.4 17.7 45.1

.. 93.3 .. 73.4 95.2 .. .. .. 93.7 62.2 100.0 .. 63.9 94.8 99.9 .. 95.2 .. 32.3 61.1 86.2 78.1 .. 45.1 50.9 96.8 .. .. 75.2 64.3 90.1 55.4 53.2 .. .. .. .. 93.8 79.0 79.0 75.1 .. .. 30.6 .. .. .. 67.9 99.0 .. 81.3 .. 71.6 1.4 63.6 82.3 54.9

30.1 12.9 .. .. .. 5.2 16.2 11.7 .. 74.4 32.1 10.6 .. 5.2 .. 42.1 11.7 48.9 43.4 .. 67.0 60.4 4.1 .. .. 3.9 39.8 30.1 5.7 45.7 .. .. .. .. 5.8 14.3 7.9 7.1 .. .. 56.0 .. .. .. 43.5 31.8 .. 48.9 .. 18.2 48.3 50.5 33.4 8.7

2011 World Development Indicators

Agriculture Manufacturing

.. .. .. .. .. .. .. .. 91.7 .. .. .. .. 73.2 .. .. 54..7 .. 70.9 .. 82.3 88.5 .. .. .. 24.1 .. .. 41.2 .. .. 40.3 .. .. .. .. .. 18.5 69.3 .. 50.1 .. .. 94.6 .. .. .. .. .. .. .. .. 63.7 .. .. .. 61.6

.. .. .. .. .. .. .. .. 0.7 .. .. .. .. 6.1 .. .. 7.6 .. 1.4 .. 4.2 3.1 .. .. .. 6.9 .. .. 10.8 .. .. 9.5 .. .. .. .. .. 9.8 6.3 .. 13.3 .. .. 1.5 .. .. .. .. .. .. .. .. 9.7 .. .. .. 10.4

Services

.. .. .. .. .. .. .. .. 7.4 .. .. .. .. 19.2 .. .. 34.6 .. 24.9 .. 12.9 8.2 .. .. .. 66.9 .. .. 46.1 .. .. 49.0 .. .. .. .. .. 57.5 22.8 .. 35.2 .. .. 3.7 .. .. .. .. .. .. .. .. 24.7 .. .. .. 25.1

.. .. .. .. 34.2 .. .. .. 4.1 .. .. .. 0.9 .. .. 5.5 .. 1.9 .. 6.0 2.5 .. .. .. .. .. .. 22.7 .. .. 15.8 .. .. .. .. .. 23.8 3.6 2.2 .. .. 1.7 .. .. .. .. .. .. .. .. 2.0 .. .. .. 3.5

.. 1.4 .. 6.2 8.1 .. .. .. 3.8 17.0 9.2 .. .. 9.2 1.6 .. 24.7 .. 2.2 25.9 4.1 9.5 .. 2.0 1.8 .. .. .. 29.1 6.6 4.2 57.7 2.4 .. .. .. .. 19.5 15.2 11.4 23.6 .. .. 2.4 .. .. .. 1.1 4.3 .. 6.1 .. 18.8 .. 4.0 1.8 23.0

.. 94.5 .. 80.1 56.2 .. .. .. 92.1 77.8 78.8 .. .. 89.9 92.1 .. 69.8c .. 95.8 68.6 89.4 87.6 .. 56.4 77.2 .. .. .. 45.6 76.7 84.5 26.6 88.0 .. .. .. .. 56.2e 81.2 87.4 74.2 .. .. 95.8 .. .. .. 87.3 77.0 .. 76.2 .. 79.2 .. 87.7 79.4 73.5

Survey year

Children in employment

% of children ages 7–14 in employment

% of children ages 7–14

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexicof Moldova Mongolia Morocco Mozambiqued Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguayc Peru Philippines Poland Portugal Puerto Rico Qatar

2004/05 2000 2006

2005

2006 2000

2006

2002 2007

2005 2007 2006 2006

2009 2000 2006/07 1998/99 1996 1999 1999

2005 2006

2008 2005 2007 2001 2001

Total

Male

Female

.. 4.2 8.9 .. 14.7 .. .. .. 9.8 .. .. 3.6 37.7 .. .. .. .. 5.2 .. .. .. 2.6 37.4 .. .. 11.8 26.0 40.3 .. 49.5 .. .. 12.2 33.5 10.1 13.2 1.8 .. 15.4 47.2 .. .. 10.1 47.1 .. .. .. .. 8.9 .. 15.3 42.2 13.3 .. 3.6 .. ..

.. 4.2 8.8 .. 17.9 .. .. .. 11.3 .. .. 4.4 40.1 .. .. .. .. 5.8 .. .. .. 4.0 37.8 .. .. 14.8 27.7 41.3 .. 55.0 .. .. 16.5 34.1 11.4 13.5 1.9 .. 16.2 42.2 .. .. 16.2 49.2 .. .. .. .. 12.1 .. 22.6 44.8 16.3 .. 4.6 .. ..

.. 4.2 9.1 .. 11.3 .. .. .. 8.3 .. .. 2.8 35.2 .. .. .. .. 4.6 .. .. .. 1.3 37.1 .. .. 8.6 24.2 39.4 .. 44.1 .. .. 7.6 32.8 8.6 12.8 1.7 .. 14.7 52.4 .. .. 3.9 45.0 .. .. .. .. 5.4 .. 7.7 39.5 10.0 .. 2.6 .. ..

Work only

.. 84.9 24.9 .. 32.4 .. .. .. 2.5 .. .. 1.6 14.1 .. .. .. .. 7.9 .. .. .. 74.4 45.0 .. .. 2.8 40.9 10.5 .. 59.5 .. .. 22.6 3.8 16.4 93.2 100.0 .. 9.5 35.6 .. .. 30.8 66.5 .. .. .. .. 14.6 .. 24.2 4.0 14.8 .. 3.6 .. ..

Study and work

.. 15.2 75.1 .. 67.6 .. .. .. 97.5 .. .. 98.4 85.9 .. .. .. .. 92.1 .. .. .. 25.6 55.0 .. .. 97.2 59.1 89.5 .. 40.5 .. .. 77.4 96.2 83.6 6.8 0.0 .. 90.5 64.4 .. .. 69.2 33.5 .. .. .. .. 85.4 .. 75.7 96.0 85.2 .. 96.4 .. ..

2.6

PEOPLE

Children at work Employment by economic activitya

Status in employmenta

% of children ages 7–14 in employment

% of children ages 7–14 in employment SelfUnpaid employed Wage family

Agriculture Manufacturing

.. 69.4 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 58.0 .. .. .. .. 87.6 .. .. .. .. .. 38.2 .. 91.3 60.6 .. .. 91.5 87.0 .. .. 70.5 .. .. .. .. .. 73.3 .. 60.8 62.6 64.3 .. 48.5 .. ..

.. 16.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 0.0 .. .. .. .. 2.9 .. .. .. .. .. 11.7 .. 0.3 8.3 .. .. 0.4 1.4 .. .. 9.7 .. .. .. .. .. 2.9 .. 6.2 5.0 4.1 .. 11.2 .. ..

Services

.. 12.4 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 10.4 .. .. .. .. 8.2 .. .. .. .. .. 47.0 .. 6.3 10.1 .. .. 8.0 11.1 .. .. 19.3 .. .. .. .. .. 22.9 .. 32.1 31.1 30.6 .. 33.3 .. ..

.. 7.1 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 3.7 .. .. .. .. 0.1 .. .. .. .. .. 2.7 .. 5.1 2.1 .. .. 0.1 4.2 .. .. 1.2 4.8 .. .. .. .. 12.6 .. 9.3 3.8 4.1 .. .. .. ..

.. 6.8 17.8 .. 7.0 .. .. .. 16.3 .. .. 4.0 .. .. .. .. .. 3.7 .. .. .. 36.6 1.7 .. .. 3.9 10.0 6.7 .. 1.6 .. .. 34.3 2.9 0.1 10.0 .. .. 4.5 3.3 .. .. 13.8 74.5 .. .. .. .. 11.3 .. 24.8 7.6 22.8 .. .. .. ..

2011 World Development Indicators

.. 59.3 75.8e .. 85.3 .. .. .. 74.9 .. .. 75.0 .. .. .. .. .. 81.9 .. .. .. 59.7c 79.3 .. .. 89.5 89.9 75.5 .. 80.4 .. .. 63.1 82.0 94.7 81.7 .. .. 95.0 92.4 .. .. 85.0 c .. .. .. .. 76.1c .. 65.8 88.6 73.1 .. .. .. ..

57

2.6

Children at work Survey year

Children in employment

% of children ages 7–14 in employment

% of children ages 7–14

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudang Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzaniah Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkeyi Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RBd Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe

2000 2008 2005 2005 2007

2006 1999 1999 2000 2000

2006 2005 2005/06 2005 2006 2000 2006 2005/06 2005

2005 2006 2006 2006 2008 1999

Total

Male

Female

Work only

Study and work

1.4 .. 7.5 .. 18.5 6.9 14.9 .. .. .. 43.5 27.7 .. 17.0 19.1 11.2 .. .. 6.6 8.9 31.1 15.1 .. 38.7 3.9 .. 2.6 .. 38.2 17.3 .. .. .. .. 5.1 5.1 21.3 .. 18.3 34.4 14.3

1.7 .. 8.0 .. 24.4 7.2 14.9 .. .. .. 45.5 29.0 .. 20.4 21.5 11.4 .. .. 8.8 8.7 35.0 15.7 .. 39.8 5.2 .. 3.3 .. 39.8 18.0 .. .. .. .. 5.3 6.9 21.0 .. 20.7 35.4 15.3

1.1 .. 7.0 .. 12.6 6.6 14.9 .. .. .. 41.5 26.4 .. 13.4 16.8 10.9 .. .. 4.3 9.1 27.1 14.4 .. 37.4 2.8 .. 1.8 .. 36.5 16.6 .. .. .. .. 4.9 3.3 21.6 .. 15.9 33.3 13.3

20.7 .. 18.5 .. 61.9 2.1 57.7 .. .. .. 53.5 5.1 .. 5.4 55.9 14.0 .. .. 34.6 9.0 28.2 4.2 .. 29.8 12.8 .. 38.8 .. 7.7 0.1 .. .. .. .. 1.0 19.8 11.9 .. 30.9 18.6 12.0

79.3 .. 81.5 .. 38.1 97.9 42.3 .. .. .. 46.5 94.9 .. 94.6 44.1 86.0 .. .. 65.4 91.0 71.8 95.8 .. 70.2 87.2 .. 61.2 .. 92.3 99.9 .. .. .. .. 99.0 80.2 88.1 .. 69.1 81.4 88.0

Employment by economic activitya

Status in employmenta

% of children ages 7–14 in employment

% of children ages 7–14 in employment SelfUnpaid employed Wage family

Agriculture Manufacturing

97.1 .. 85.5 .. 79.1 .. 83.8 .. .. .. .. .. .. 71.2 .. .. .. .. .. .. 85.3 .. .. 82.9 .. .. 57.1 .. 95.5 .. .. .. .. .. .. 32.3 .. .. .. 91.9 ..

0.0 .. 0.7 .. 5.0 .. 0.8 .. .. .. .. .. .. 13.1 .. .. .. .. .. .. 0.7 .. .. 1.3 .. .. 14.3 .. 1.4 .. .. .. .. .. .. 7.2 .. .. .. 0.7 ..

Services

2.3 .. 10.5 .. 14.0 .. 13.4 .. .. .. .. .. .. 15.0 .. .. .. .. .. .. 14.0 .. .. 15.1 .. .. 27.1 .. 3.0 .. .. .. .. .. .. 55.7 .. .. .. 7.0 ..

4.5 .. 14.8 .. 6.3 .. 9.7 .. .. .. .. 7.1 .. 2.9 .. .. .. .. .. .. 56.3 .. .. 5.0 .. .. 2.1 .. 1.4 .. .. .. .. .. .. 31.6 .. .. .. 2.9 3.4

.. .. 12.8 .. 4.4 5.2 0.9 .. .. .. 1.6 7.1 .. 8.3 7.3 10.4 .. .. 21.5 24.2 0.9 13.5 .. 1.6 29.8 .. 34.1 .. 1.5 3.1 .. .. .. .. 3.8 33.1 5.9 .. 6.1 3.9 28.4

92.9 e .. 72.3 .. 84.1 89.4 87.8 .. .. .. 94.8 85.8 .. 88.0 81.3 85.9 .. .. 68.8 71.3 42.8 e 80.0 .. 93.4 64.9 63.8 .. 97.1 79.3 .. .. .. .. 78.6 35.3 91.2 .. 86.1 93.1 68.2

a. Shares may not sum to 100 percent because of a residual category not included in the table. b. Covers only Angola-secured territory. c. Refers to unpaid workers, regardless of whether they are family workers. d. Covers children ages 10–14. e. Refers to family workers, regardless of whether they are paid. f. Covers children ages 12–14. g. Northern Sudan only. h. Refers mainly to work on own shamba. i. Estimates are for children ages 6–14.

58

2011 World Development Indicators

About the data

2.6

PEOPLE

Children at work Definitions

The data in the table refer to children’s work in the

data on children in employment and in the sampling

• Survey year is the year in which the underlying

sense of “economic activity”—that is, children in

design underlying the surveys. Differences exist

data were collected. • Children in employment are

employment, a broader concept than child labor

not only across different household surveys in the

children involved in any economic activity for at least

(see ILO 2009a for details on this distinction).

same country but also across the same type of sur-

one hour in the reference week of the survey. • Work

In line with the definition of economic activity

vey carried out in different countries, so estimates

only refers to children who are employed and not

adopted by the 13th International Conference of

of working children are not fully comparable across

attending school. • Study and work refer to children

Labour Statisticians, the threshold for classifying a

countries.

attending school in combination with employment.

person as employed is to have been engaged at least

The table aggregates the distribution of children in

• Employment by economic activity is the distribu-

one hour in any activity during the reference period

employment by the industrial categories of the Inter-

tion of children in employment by the major industrial

relating to the production of goods and services

national Standard Industrial Classifi cation (ISIC):

categories (ISIC revision 2 or revision 3). • Agricul-

set by the 1993 UN System of National Accounts.

agriculture, manufacturing, and services. A residual

ture corresponds to division 1 (ISIC revision 2) or

Children seeking work are thus excluded. Economic

category—which includes mining and quarrying;

categories A and B (ISIC revision 3) and includes

activity covers all market production and certain non-

electricity, gas, and water; construction; extraterri-

agriculture and hunting, forestry and logging, and

market production, including production of goods for

torial organization; and other inadequately defined

fishing. • Manufacturing corresponds to division 3

own use. It excludes unpaid household services (com-

activities—is not presented. Both ISIC revision 2 and

(ISIC revision 2) or category D (ISIC revision 3). • Ser-

monly called “household chores”)—that is, the pro-

revision 3 are used, depending on the country’s codi-

vices correspond to divisions 6–9 (ISIC revision

duction of domestic and personal services by house-

fication for describing economic activity. This does

2) or categories G–P (ISIC revision 3) and include

hold members for own-household consumption.

not affect the definition of the groups in the table.

wholesale and retail trade, hotels and restaurants,

Data are from household surveys conducted by

The table also aggregates the distribution of

transport, financial intermediation, real estate, pub-

the International Labor Organization (ILO), the United

children in employment by status in employment,

lic administration, education, health and social work,

Nations Children’s Fund (UNICEF), the World Bank,

based on the International Classification of Status in

other community services, and private household

and national statistical offices. The surveys yield data

Employment (1993), which shows the distribution in

activity. • Self-employed workers are people whose

on education, employment, health, expenditure, and

employment by three major categories: selfemployed

remuneration depends directly on the profits derived

consumption indicators related to children’s work.

workers, wage workers (also known as employees),

from the goods and services they produce, with or

Household survey data generally include information

and unpaid family workers. A residual category—

without other employees, and include employers,

on work type—for example, whether a child is working

which includes those not classifiable by status—is

own-account workers, and members of produc-

for payment in cash or in kind or is involved in unpaid

not presented.

ers cooperatives. • Wage workers (also known as

work, working for someone who is not a member of the

In most countries more boys are involved in employ-

employees) are people who hold explicit (written or

household, or involved in any type of family work (on the

ment or the gender difference is small. However, girls

oral) or implicit employment contracts that provide

farm or in a business). Country surveys define the ages

are often more present in hidden or under-reported

basic remuneration that does not depend directly on

for child labor as 5–17. The data in the table have been

forms of employment such as domestic service, and

the revenue of the unit for which they work. • Unpaid

recalculated to present statistics for children ages 7–14.

in almost all societies girls bear greater responsibil-

family workers are people who work without pay in a

Although efforts are made to harmonize the defini-

ity for household chores in their own homes, work

market-oriented establishment operated by a related

tion of employment and the questions on employ-

that lies outside the System of National Accounts

person living in the same household.

ment in survey questionnaires, signifi cant differ-

production boundary and is thus not considered in

ences remain in the survey instruments that collect

estimates of children’s employment.

The largest sector for child labor remains agriculture, and the majority of children work as unpaid family members Child labor by sector (% of children ages 5–17), 2004–08 Not defined Industry 7% 7%

Child labor by status in employment (% of children ages 5–17), 2004–08

Self-employment 5%

Data sources Data on children at work are estimates produced

2.6a

by the Understanding Children’s Work project based on household survey data sets made available by the ILO’s International Programme on the Elimination of Child Labour under its Statistical

Not defined 6%

Monitoring Programme on Child Labour, UNICEF under its Multiple Indicator Cluster Survey program, the World Bank under its Living Standards

Service 26%

Agriculture 60%

Paid employment 21%

Measurement Study program, and national staUnpaid family workers 68%

tistical offices. Information on how the data were collected and some indication of their reliability can be found at www.ilo.org/public/english/ standards/ipec/simpoc/, www.childinfo.org, and www.worldbank.org/lsms. Detailed country statis-

Source: Accelerating Action Against Child Labour, ILO, Geneva 2010.

tics can be found at www.ucw-project.org.

2011 World Development Indicators

59

2.7

Poverty rates at national poverty lines Population below national poverty linea

Survey year b

Afghanistanc Albaniac Angola Argentina Armeniac Azerbaijanc Bangladesh Belarus Benin Bhutan Bolivia Bosnia and Herzegovinac Botswana Brazil Bulgariac Burkina Faso Burundi Cambodiac Cameroon Cape Verde Central African Republic Chad Chile China Colombia Comoros Congo, Dem. Rep. Congo, Rep. Costa Rica Croatia Côte d’Ivoirec Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Ethiopia Fiji Gabon Gambia, Thec Georgiac Ghana Guatemala Guinea Guinea-Bissau Haiti Honduras India Indonesia Iraq Jamaica Jordan Kazakhstanc Kenya Kosovo c Kyrgyz Republicc Lao PDRc Latvia c

60

2005 2008 e 2008 2001 2000 2008

2006e 2004 1993 2008e 1997

2004

2006e 2004 e 2008 e

2008e 2002 2002 2005e 2008 e 2005 2007e,f 1999 2003

1998 2000 e

2008 e,f 1994 2009 2006e 2002 2001 2005 2003 2003 2002

Rural %

.. 24.2 .. .. 22.9 42.5 52.3 .. .. .. 76.5 22.0 40.4 .. .. .. .. 37.8 .. .. .. .. 12.3 2.8 65.2 .. .. .. 22.2 .. 45.8 60.2 59.7 26.8 43.8 45.4 40.0 .. .. .. 49.6 74.5 .. .. .. 64.1 37.3 17.4 .. .. 18.7 23.2 .. 37.2 57.5 .. 11.6

2011 World Development Indicators

Urban %

National %

.. 11.2 .. 15.3 23.8 55.7 35.2 .. .. .. 50.3 11.3 24.7 .. .. .. .. 17.6 .. .. .. .. 13.9 .. 39.8 .. .. .. 19.5 .. 32.3 49.9 22.6 10.1 29.8 36.9 28.0 .. .. .. 19.4 27.1 .. .. .. 55.0 32.4 10.7 .. .. 12.9 13.0 .. 30.3 35.7 .. ..

.. 18.5 .. .. 23.5 49.6 48.9 6.1 .. .. 59.9 17.7 32.9 22.6 36.0 .. .. 34.7 .. .. .. .. 13.7 .. 46.0 .. .. .. 20.7 11.2 40.2 53.5 35.1 19.6 34.6 44.2 35.0 .. .. .. 39.5 56.2 .. .. .. 59.6 36.0 14.2 .. 14.3 14.2 17.6 .. 34.8 49.9 33.5 7.5

Survey year b

2008 d 2008 2000d 2009 e 2009 2008 2005 2009 2003d 2007d 2007e 2007 2003 2009 e 2001 2003d 2006 d 2007 2007d 2007d 2008 d 2003d 2009e 2005 e 2009e 2004 d 2005 2005 2009 e 2004 2008 2006 e 2009 e 2008 2008 e,f 2004 2009 2005 2003 d 2007 2006 2006 e 2007d 2002 2001e 2009 e,f 2005 2010 2007 2007e 2006 2002 2005d 2006 2005 2008 2004

Poverty gap at national poverty linea

Rural %

Urban %

37.5 14.6 .. .. 25.5 18.5 43.8 .. 46.0 30.9 77.3 17.8 44.8 .. .. 52.4 68.9 34.5 55.0 44.3 69.4 58.6 12.9 2.5 64.3 48.7 75.7 57.7 .. .. 54.2 57.1 57.5 30.0 49.0 39.3 43.3 44.6 67.8 29.7 39.2 70.5 63.0 69.1 88.0 64.4 28.3 16.6 39.3 .. 19.0 21.7 49.1 49.2 50.8 31.7 12.7

29.0 10.1 62.3 13.2 26.9 14.8 28.4 .. 29.0 1.7 50.9 8.2 19.4 .. .. 19.2 34.0 11.8 12.2 13.2 49.6 24.6 15.5 .. 39.6 34.5 61.5 .. .. .. 29.4 45.3 25.0 10.6 35.7 35.1 18.6 29.8 39.6 18.3 10.8 30.0 30.5 51.6 45.0 52.8 25.7 9.9 16.1 .. 12.0 10.2 33.7 37.4 29.8 17.4 ..

National %

36.0 12.4 .. .. 26.5 15.8 40.0 5.4 39.0 23.2 60.1 14.0 30.6 21.4 12.8 46.4 66.9 30.1 39.9 26.6 62.0 55.0 15.1 .. 45.5 44.8 71.3 50.1 21.7 11.1 42.7 49.4 36.0 22.0 40.0 38.9 31.0 32.7 58.0 23.6 28.5 51.0 53.0 64.7 77.0 58.8 27.5 13.3 22.9 9.9 13.0 15.4 45.9 45.0 43.1 27.6 5.9

Survey year b

2008 d 2008

2009 2008 2005 2003 d 2007d

2003 2001 2003d 2006d 2007 2007d 2007d 2008 d 2003d

2004 d 2005 2005 2004 2008

2004 2009 2005 2003d 2007 2006 2007d 2002

2010 2007 2006 2002 2005d 2006 2005 2004

Rural %

Urban %

8.3 2.6 .. .. .. .. 9.8 .. 14.0 8.1 .. .. 18.4 .. .. 17.6 24.2 8.3 17.5 14.3 35.0 23.3 .. .. .. 17.8 34.9 20.6 .. .. 20.3 .. .. .. .. 8.5 14.8 16.0 30.5 9.2 13.5 .. 22.0 27.8 .. .. .. 2.8 9.0 .. .. 4.5 17.5 14.3 12.0 .. ..

6.2 1.9 .. .. .. .. 6.5 .. 8.0 0.4 .. .. 6.5 .. .. 5.1 10.3 2.8 2.8 3.3 29.8 7.4 .. .. .. 12.1 26.2 .. .. .. 9.5 .. .. .. .. 7.7 5.4 8.5 14.8 5.3 3.1 .. 7.7 16.9 .. .. .. 1.6 2.7 .. .. 2.0 11.4 11.3 7.0 .. ..

National %

7.9 2.3 .. .. 4.9 2.0 9.0 .. 12.0 6.1 .. .. 11.7 .. 4.2 15.3 23.4 7.2 12.3 8.1 33.1 21.6 .. .. .. 16.3 32.2 18.9 .. 2.6 15.3 .. .. .. .. 8.3 10.1 10.0 25.1 7.2 9.6 .. 17.6 25.0 .. .. .. 2.2 4.5 .. 2.8 3.1 16.3 13.3 10.0 .. 1.2

Population below national poverty linea

Lesothoc Liberia c Macedonia, FYRc Madagascar Malawi Malaysiac Mali Mauritania Mexico Moldovac Mongolia Montenegro Morocco Mozambique Namibia Nepal Nicaragua Niger Nigeria Pakistan Panama Paraguay Peru Philippines Polandc Romaniac Russian Federationc Rwanda São Tomé and Príncipe Senegalc Serbiac Sierra Leone South Africa Sri Lanka Swaziland Tajikistanc Tanzania Thailand Timor-Leste Togo Turkey Uganda Ukrainec Uruguay Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe

Survey year b

Rural %

1994

68.9 .. 21.2 77.3 58.1 7.1 .. .. 54.7 .. .. 12.0 .. 55.3 .. 43.3 67.8 .. .. 28.1 62.7 48.8 59.8 .. .. 23.5 22.7 .. .. .. 13.9 .. .. 24.7 .. 54.4 38.6 11.5 .. .. 34.6 34.2 18.1 29.4 .. 20.4 .. 42.5 77.3 ..

2005 2004 1998 2007

2006 e 2004 2007 2002 1996 2001e

2005 2003 2008e 2008 2006 2001 2005 2005

2006 2000 2002 2007 2000 2008 2001 2008 2005 2004 2007e 2008 e 2006 2007 1998 2004

2.7

PEOPLE

Poverty rates at national poverty lines

Poverty gap at national poverty linea

Urban %

National %

Survey year b

Rural %

Urban %

National %

36.7 .. 19.8 53.7 18.5 2.0 .. .. 35.6 .. .. 5.5 .. 51.5 .. 21.6 30.1 .. .. 14.9 20.0 30.2 23.5 .. .. 8.1 8.1 .. .. .. 5.2 .. .. 7.9 .. 49.3 23.1 3.0 .. .. 9.4 13.7 12.0 25.5 .. 3.9 .. 32.3 29.1 ..

66.6 .. 20.4 72.1 54.1 3.6 .. .. 42.6 26.5 .. 8.0 .. 54.1 .. 41.8 45.8 .. .. 23.9 36.8 37.9 36.2 26.4 15.6 15.1 11.9 .. .. .. 9.0 .. 38.0 22.7 .. 53.1 35.6 9.0 39.7 .. 17.1 31.1 14.0 26.0 32.6 16.0 31.2 40.1 58.4 ..

2003 2007 2006 2005 2004 2009 2006 d 2000 d 2008 e 2005 2008 d 2008 2001 2008 2003d 2004 2005e 2007d 2004 d 2006 2008 2009 e 2009 2009 2002 2006 2006 2006d 2001 2005 d 2007 2003d 2005 2007 2001d 2009 2007 2009 2007 2006 2009 2009 2005 2008 e 2009 e 2008 2009 2005 2006 2003 d

60.5 67.7 21.3 73.5 55.9 8.2 57.6 61.2 60.8 .. 46.6 8.9 25.1 56.9 49.0 34.6 67.9 63.9 63.8 27.0 59.8 49.8 60.3 .. .. 22.3 21.2 64.2 64.9 61.9 9.8 78.5 .. 15.7 75.0 49.2 37.4 10.4 .. 74.3 38.7 27.2 11.3 22.2 .. 18.7 .. 40.1 76.8 ..

41.5 55.1 17.7 52.0 25.4 1.7 25.5 25.4 39.8 .. 26.9 2.4 7.6 49.6 17.0 9.6 29.1 36.7 43.1 13.1 17.7 24.7 21.1 .. .. 6.8 7.4 23.2 45.0 35.1 4.3 47.0 .. 6.7 49.0 41.8 21.8 3.0 .. 36.8 8.9 9.1 6.3 20.3 .. 3.3 .. 20.7 26.7 ..

56.6 63.8 19.0 68.7 52.4 3.8 47.4 46.3 47.4 29.0 35.2 4.9 15.3 54.7 38.0 30.9 46.2 59.5 54.7 22.3 32.7 35.1 34.8 26.5 16.6 13.8 11.1 58.5 53.8 50.8 6.6 66.4 23.0 15.2 69.2 47.2 33.4 8.1 49.9 61.7 18.1 24.5 7.9 20.5 29.0 14.5 21.9 34.8 59.3 72.0

Survey year b

2007 2006 2005 2004 2009 2006 d 2000d

2008 d 2008 2008 2003d 2004 2007d 2004 d

2009 2006 2006 2006d 2001 2005d 2007 2003 d 2005 2007 2001d 2007

2006 2009 2005

2008 2009 2005 2006

Rural %

Urban %

.. 26.3 7.7 28.9 19.2 1.8 .. 24.1 .. .. 13.4 1.4 .. 22.2 16.0 8.5 .. 21.2 26.6 .. .. .. .. .. .. 5.3 5.5 26.0 24.7 21.5 2.0 34.6 .. 3.2 37.0 .. 11.0 .. .. 29.3 .. 7.6 2.3 .. .. 4.6 .. 10.6 38.8 ..

.. 20.2 6.9 19.3 7.1 0.3 .. 6.3 .. .. 7.7 0.6 .. 19.1 6.0 2.2 .. 11.3 16.2 .. .. .. .. .. .. 1.4 1.7 8.0 14.9 9.3 0.8 16.3 .. 1.3 20.0 .. 6.5 .. .. 10.3 .. 1.8 1.1 .. .. 0.5 .. 4.5 9.4 ..

National %

.. 24.4 7.2 26.8 17.8 0.8 16.7 17.0 .. .. 10.1 0.9 .. 21.2 13.0 7.5 .. 19.6 22.8 .. .. .. .. 2.7 .. 3.2 2.7 24.0 19.2 16.4 1.3 27.5 7.0 3.1 32.9 .. 9.9 .. .. 22.9 .. 6.8 1.5 .. .. 3.5 4.9 8.9 28.5 ..

a. Based on per capita consumption estimated from household survey data, unless otherwise noted. b. Refers to the year in which the underlying household survey data were collected; in cases for which the data collection period bridged two calender years, the year in which most of the data were collected is reported. c. World Bank estimates. d. Estimates based on survey data from earlier year(s) are available, but are not comparable with the most recent year reported here; these are available online at http://data.worldbank.org. e. Based on income per capita estimated from household survey data. f. Measured as a share of households.

2011 World Development Indicators

61

2.7

Poverty rates at national poverty lines

About the data

Definitions

Estimates of poverty rates and gaps at national pov-

As with any indicator measured from household

• Survey year is the year in which the underlying

erty lines are useful for comparing poverty across

surveys, data quality issues can affect the precision

household survey data were collected; when the data

time within but not across countries. Table 2.8 shows

of poverty estimates and their comparability over

collection period bridged two calendar years, the year

poverty indicators at international poverty lines that

time. These include selective survey nonresponse,

in which most of the data were collected is reported.

allow for comparisons across countries.

seasonality effects, differences in the number of

• Population below national poverty line is the per-

For countries with an active poverty monitoring pro-

income or consumption items in the questionnaire,

centage of the rural, urban, and national population

gram, the World Bank—in collaboration with national

and the time period over which respondents are

living below the corresponding rural, urban, national

institutions, other development agencies, and civil

asked to recall their expenditures.

poverty line, based on consumption estimated from

society—periodically prepares poverty assessments

household survey data, unless otherwise noted.

and other analytical reports to assess the extent

National poverty lines

• Poverty gap at national poverty line is the mean

and causes of poverty. These reports review levels

National poverty lines are the benchmark for esti-

shortfall from the rural, urban, or national poverty

and changes in poverty indicators over time and

mating poverty indicators that are consistent with

line (counting the nonpoor as having zero shortfall)

across regions within countries, assess the impact

the country’s specific economic and social circum-

as a percentage of the corresponding rural, urban,

of growth and public policy on poverty and inequal-

stances. National poverty lines reflect local percep-

or national poverty line, based on consumption esti-

ity, review the adequacy of monitoring and evalua-

tions of the level and composition of consumption or

mated from household survey data, unless otherwise

tion, and contain detailed technical overviews of

income needed to be nonpoor. The perceived bound-

noted. This measure reflects the depth of poverty as

the underlying household survey data and poverty

ary between poor and nonpoor typically rises with the

well as its incidence.

measurement methods used. The reports are a key

average income of a country and thus does not pro-

source of comprehensive information on poverty indi-

vide a uniform measure for comparing poverty rates

cators at national poverty lines and generally feed

across countries. While poverty rates at national

into country-owned processes to reduce poverty,

poverty lines should not be used for comparing pov-

build in-country capacity, and support joint work.

erty rates across countries, they are appropriate for

An increasing number of countries have their own national programs to monitor and disseminate

guiding and monitoring the results of country-specific national poverty reduction strategies.

official poverty estimates at national poverty lines

Almost all national poverty lines are anchored to

along with well documented household survey data

the cost of a food bundle—based on the prevailing

sources and estimation methodology. Estimates

national diet of the poor—that provides adequate

from national poverty monitoring programs and the

nutrition for good health and normal activity, plus

underlying methods used are periodically reviewed by

an allowance for nonfood spending. National poverty

the World Bank and included in the table.

lines must be adjusted for inflation between survey

The complete online database of poverty estimates

years to remain constant in real terms and thus allow

at national poverty lines (available at http://data.

for meaningful comparisons of poverty over time.

worldbank.org) is regularly updated and may con-

Because diets and consumption baskets change

tain more recent data or revisions not incorporated

over time, countries periodically recalculate the pov-

in the table. It is maintained by the Global Poverty

erty line based on new survey data. In such cases

Working Group, a team of poverty experts from the

the new poverty lines should be deflated to obtain

Poverty Reduction and Equity Network, the Develop-

comparable poverty estimates from earlier years.

ment Research Group, and the Development Data

The table reports indicators based on the two most

Group, which recently updated the database to cover

recent years for which survey data is available. Coun-

115 countries and more than 575 sets of poverty

tries for which the most recent indicators reported

Data sources

estimates at national poverty lines for 1974−2010.

are not comparable to those based on survey data

Poverty rates at national poverty lines are com-

from an earlier year are footnoted in the table.

piled by the Global Poverty Working Group, based

Data quality

on data from World Bank’s country poverty assess-

Poverty estimates at national poverty lines are com-

ments and analytical reports as well as country

puted from household survey data collected from

Poverty Reduction Strategies and official poverty

nationally representative samples of households.

estimates. Further documentation of the data,

These data must contain sufficiently detailed infor-

measurement methods and tools, and research,

mation to compute a comprehensive estimate of

as well as poverty assessments and analytical

total household income or consumption (including

reports, are available at http://data.worldbank.

consumption or income from own production), from

org, www.worldbank.org/poverty, and http://econ.

which it is possible to construct a correctly weighted

worldbank.org.

distribution of per capita consumption or income.

62

2011 World Development Indicators

Population below International poverty linea

International poverty line in local currency

Albania Algeria Angola Argentina Armenia Azerbaijan Bangladesh Belarus Belize Benin Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Cape Verde Central African Republic Chad Chile China Colombia Comoros Congo, Dem. Rep. Congo, Rep. Costa Rica Croatia Czech Republic Côte d’Ivoire Djibouti Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Estonia Ethiopia Gabon Gambia, The Georgia Ghana Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hungary India Indonesia Iraq Jamaica Jordan Kazakhstan

$1.25 a day

$2 a day

2005

2005

75.5 48.4 c 88.1 1.7 245.2 2,170.9 31.9 949.5 1.8 c 344.0 23.1 3.2 1.1 4.2 2.0 0.9 303.0 558.8 2,019.1 368.1 97.7 384.3 409.5 484.2 5.1 g 1,489.7 368.0 395.3 469.5 348.7c 5.6 19.0 407.3 134.8 25.5c 0.6 2.5 6.0 c 11.0 3.4 554.7 12.9 1.0 5,594.8 5.7c 1,849.5 355.3 131.5c 24.2c 12.1c 171.9 19.5i 5,241.0i 799.8 54.2c 0.6 81.2

120.8 77.5c 141.0 2.7 392.4 3,473.5 51.0 1,519.2 2.9c 550.4 36.9 5.1 1.7 6.8 3.1 1.5 484.8 894.1 3,230.6 589.0 156.3 614.9 655.1 774.7 8.2g 2,383.5 588.8 632.5 751.1 557.9 c 8.9 30.4 651.6 215.6 40.8c 1.0 4.0 9.6c 17.7 5.5 887.5 20.7 1.6 8,951.6 9.1c 2,959.1 568.6 210.3c 38.7c 19.3c 275.0 31.2i 8,385.7i 1,279.7 86.7c 1.0 129.9

2.8

PEOPLE

Poverty rates at international poverty lines

Survey year b

2005 1988 2006d,e 2003 2005 2000 f 2005 1995 2005e 2004 1986 2008 e 2003 1998 1998 2004 2001 1993 2006 e 2002h 2003e

2005e 2005 1993e 2002 1996 2006e 2007e 2000 2005e 2003 2000 1998 2005 1998 2002e 2003 1993 1993e 2006e 2004 1994h 2005h 2002 2003 2003

Population below $1.25 a day %

Poverty gap at $1.25 a day %

Population below $2 a day %

Poverty gap at $2 a day %

Survey year b

<2 6.6 .. 2.8 10.6 <2 57.8 <2 14.0 .. .. 19.6 <2 35.6 4.3 <2 70.0 86.4 40.2 32.8 .. 82.8 .. <2 28.4 15.4 .. .. .. 2.4 <2 <2 23.3 4.8 4.0 4.7 <2 11.0 <2 55.6 .. 66.7 13.4 39.1 16.9 70.1 52.1 5.8 .. 18.2 <2 49.4 21.4 .. <2 <2 3.1

<0.5 1.8 .. 0.6 1.9 <0.5 17.3 <0.5 5.4 .. .. 9.7 <0.5 13.8 1.4 <0.5 30.2 47.3 11.3 10.2 .. 57.0 .. <0.5 8.7 6.1 .. .. .. <0.5 <0.5 <0.5 6.8 1.6 0.7 1.2 <0.5 4.8 <0.5 16.2 .. 34.7 4.4 14.4 6.5 32.2 20.6 2.6 .. 8.2 <0.5 14.4 4.6 .. <0.5 <0.5 <0.5

7.9 23.8 .. 8.0 43.5 <2 85.4 <2 23.6 .. .. 30.4 <2 54.7 10.4 2.4 87.6 95.4 68.2 57.7 .. 90.8 .. 2.4 51.1 26.3 .. .. .. 8.6 <2 <2 46.8 15.1 13.5 12.8 19.4 20.5 2.7 86.4 .. 82.0 30.4 63.3 29.8 87.2 75.7 15.0 .. 29.7 <2 81.7 53.8 .. 8.7 11.0 17.2

1.5 6.6 .. 2.4 11.3 <0.5 38.8 <0.5 10.5 .. .. 15.5 <0.5 25.8 3.6 0.9 49.1 64.1 28.0 23.7 .. 68.4 .. <0.5 20.6 10.9 .. .. .. 2.3 <0.5 <0.5 17.6 4.5 3.7 4.0 3.5 8.9 0.9 37.9 .. 50.0 10.9 28.5 12.9 50.3 37.4 5.4 .. 14.2 <0.5 35.3 17.3 .. 1.6 2.1 3.9

2008 1995 2000 d 2009d,e 2008 2008 2005f 2008 1999e 2003 2003 2007e 2007 1994 2009 e 2007 2003 2006 2007 2007 2001 2003 2003 2009e 2005h 2006e 2004 2006 2005 2009e 2008 1996e 2008 2002 2007e 2009e 2005 2008e 2004 2005 2005 2003 2008 2006 2006e 2007 2002 1998 e 2001e 2007e 2007 2005h 2009h 2007 2004 2006 2007

Population below $1.25 a day %

<2 6.8 54.3 <2 <2 <2 49.6 <2 12.1 47.3 26.2 14.0 <2 31.2 3.8 <2 56.5 81.3 28.3 9.6 20.6 62.4 61.9 <2 15.9 16.0 46.1 59.2 54.1 <2 <2 <2 23.8 18.8 4.3 5.1 <2 5.1 <2 39.0 4.8 34.3 14.7 30.0 12.7 43.8 48.8 7.7 54.9 23.2 <2 41.6 18.7 4.0 <2 <2 <2

Poverty gap at $1.25 a day %

<0.5 1.4 29.9 <0.5 <0.5 <0.5 13.1 <0.5 4.7 15.7 7.0 5.8 <0.5 11.0 1.1 <0.5 20.3 36.4 6.1 1.2 5.9 28.3 25.6 <0.5 4.0 5.7 20.8 25.3 22.8 <0.5 <0.5 <0.5 7.5 5.3 0.9 1.6 <0.5 1.1 <0.5 9.6 0.9 12.1 4.6 10.5 3.8 15.2 16.5 3.9 28.2 11.3 <0.5 10.8 3.6 0.6 <0.5 <0.5 <0.5

Population below $2 a day %

Poverty gap at $2 a day %

4.3 23.6 70.2 <2 12.4 7.8 81.3 <2 23.9 75.3 49.5 24.7 <2 49.4 9.9 7.3 81.2 93.5 56.5 30.8 40.3 81.9 83.3 <2 36.3 27.9 65.0 79.6 74.4 4.8 <2 <2 46.0 41.2 13.6 13.4 18.5 15.2 <2 77.6 19.6 56.7 32.6 53.6 25.7 70.0 77.9 16.8 72.2 35.6 <2 75.6 50.7 25.3 5.9 3.5 <2

0.9 6.5 42.4 <0.5 2.3 1.5 33.8 <0.5 9.7 33.5 18.8 10.9 <0.5 22.3 3.2 1.5 39.3 56.1 20.2 8.4 14.9 45.3 43.9 <0.5 12.2 11.9 34.2 42.4 38.8 0.9 <0.5 <0.5 17.9 14.6 3.9 4.4 3.5 4.5 <0.5 28.9 5.0 24.9 11.8 22.3 9.6 31.3 34.8 6.9 41.8 18.1 <0.5 30.4 15.5 5.6 0.9 0.6 <0.5

2011 World Development Indicators

63

2.8

Poverty rates at international poverty lines Population below International poverty linea

International poverty line in local currency

Kenya Kyrgyz Republic Lao PDR Latvia Lesotho Liberia Lithuania Macedonia, FYR Madagascar Malawi Malaysia Maldives Mali Mauritania Mexico Micronesia, Fed. Sts. Moldova Mongolia Montenegro Morocco Mozambique Namibia Nepal Nicaragua Niger Nigeria Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Romania Russian Federation Rwanda São Tomé and Príncipe Senegal Serbia Seychelles Sierra Leone Slovak Republic Slovenia South Africa Sri Lanka St. Lucia Suriname Swaziland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia

64

$1.25 a day

$2 a day

2005

2005

40.9 16.2 4,677.0 0.4 4.3 0.6 2.1 29.5 945.5 71.2 2.6 12.2 362.1 157.1 9.6 0.8 c 6.0 653.1 0.6 6.9 14,532.1 6.3 33.1 9.1c 334.2 98.2 25.9 0.8 c 2.1c 2,659.7 2.1 30.2 2.7 2.1 16.7 295.9 7,953.9 372.8 42.9 5.6c 1,745.3 23.5 198.2 5.7 50.0 2.4 c 2.3c 4.7 30.8 1.2 603.1 21.8 0.6 c 352.8 5.8c 0.9

65.4 26.0 7,483.2 0.7 6.9 1.0 3.3 47.2 1,512.8 113.8 4.2 19.5 579.4 251.3 15.3 1.3c 9.7 1,045.0 1.0 11.0 23,251.4 10.1 52.9 14.6c 534.7 157.2 41.4 1.2c 3.4 c 4,255.6 3.3 48.4 4.3 3.4 26.8 473.5 12,726.3 596.5 68.6 9.0c 2,792.4 37.7 317.2 9.1 80.1 3.8 c 3.7c 7.5 49.3 1.9 964.9 34.9 1.0 c 564.5 9.2c 1.4

2011 World Development Indicators

Survey year b

1997 2004 2002 2004 1995 2004 2003 2001 1998 2004e 2001 1996 2006 2004 2001 2003 1996 2001e 2005 1996 2005 2006e 2007e 2006e 2003 2005 2005 2005 2000 2001 2000 1990 1992e 2002 1995 2002 1995 2003 2000 2004 2001 1988e 1995

Population below $1.25 a day %

19.6 21.8 44.0 <2 47.6 .. <2 <2 76.3 83.1 <2 .. 61.2 23.4 <2 .. 8.1 .. .. 6.3 74.7 .. 68.4 19.4 65.9 68.5 22.6 9.5 .. 6.5 7.9 22.0 <2 <2 <2 76.6 .. 44.2 .. <2 62.8 <2 <2 21.4 14.0 .. .. 78.6 .. 36.3 88.5 <2 52.9 .. <2 6.5

Poverty gap at $1.25 a day %

Population below $2 a day %

Poverty gap at $2 a day %

Survey year b

4.6 4.4 12.1 <0.5 26.7 .. <0.5 <0.5 41.4 46.0 <0.5 .. 25.8 7.1 <0.5 .. 1.7 .. .. 0.9 35.4 .. 26.7 6.7 28.1 32.1 4.4 3.1 .. 2.7 1.9 5.5 <0.5 <0.5 <0.5 38.2 .. 14.3 .. <0.5 44.8 <0.5 <0.5 5.2 2.6 .. .. 47.7 .. 10.3 46.8 <0.5 19.1 .. <0.5 1.3

42.7 51.9 76.9 <2 61.1 .. <2 3.2 88.8 93.5 7.8 .. 82.0 48.3 4.8 .. 29.0 .. .. 24.3 90.0 .. 88.1 37.5 85.6 86.4 60.3 17.9 .. 14.2 18.5 43.8 <2 3.4 <2 90.3 .. 71.3 .. <2 75.0 <2 <2 39.9 39.7 .. .. 89.3 .. 68.8 96.6 11.5 77.5 .. 8.6 20.4

14.7 16.8 31.1 <0.5 37.3 .. <0.5 0.7 57.2 62.3 1.4 .. 43.6 17.8 1.0 .. 7.9 .. .. 6.3 53.6 .. 46.8 14.5 46.7 49.7 18.7 7.1 .. 5.5 6.0 16.0 <0.5 0.9 <0.5 55.7 .. 31.2 .. <0.5 54.0 <0.5 <0.5 15.0 11.9 .. .. 61.7 .. 26.7 64.4 2.0 37.1 .. 1.9 5.8

2005 2007 2008 2008 2003 2007 2008 2008 2005 2004 2009e 2004 2006 2000 2008 2000 2008 2002 2008 2007 2008 1993 e 2004 2005e 2007 2004 2006 2009e 1996 2008e 2009e 2006 2008 2008 2008 2005 2001 2005 2008 2007 2003 1996e 2004 2000 2007 1995e 1999 e 2001 2004 2004 2007 2009 2007 2006 1992e 2000

Population below $1.25 a day %

19.7 <2 33.9 <2 43.4 83.7 <2 <2 67.8 73.9 <2 <2 51.4 21.2 <2 31.1 <2 15.5 <2 2.5 60.0 49.1 55.1 15.8 43.1 64.4 22.6 2.4 35.8 5.1 5.9 22.6 <2 <2 <2 76.8 28.6 33.5 <2 <2 53.4 <2 <2 26.2 7.0 20.9 15.5 62.9 <2 21.5 67.9 12.8 37.4 38.7 4.2 2.6

Poverty gap at $1.25 a day %

6.1 <0.5 9.0 <0.5 20.8 40.8 <0.5 <0.5 26.5 32.3 <0.5 <0.5 18.8 5.7 <0.5 16.3 <0.5 3.6 <0.5 0.5 25.2 24.6 19.7 5.2 11.9 29.6 4.1 <0.5 12.3 1.5 1.4 5.5 <0.5 <0.5 <0.5 40.9 8.2 10.8 <0.5 <0.5 20.3 <0.5 <0.5 8.2 1.0 7.2 5.9 29.4 <0.5 5.1 28.1 2.4 8.9 11.4 1.1 <0.5

Population below $2 a day %

Poverty gap at $2 a day %

39.9 29.4 66.0 <2 62.3 94.8 <2 4.3 89.6 90.5 2.3 12.2 77.1 44.1 8.6 44.7 12.5 38.9 <2 14.0 81.6 62.2 77.6 31.9 75.9 83.9 61.0 9.5 57.4 13.2 14.7 45.0 <2 <2 <2 89.6 57.3 60.4 <2 <2 76.1 <2 <2 42.9 29.1 40.6 27.2 81.0 16.9 50.9 87.9 26.5 72.8 69.3 13.5 12.8

15.1 5.5 24.8 <0.5 33.1 59.5 <0.5 0.7 46.9 51.8 <0.5 2.5 36.5 15.9 2.0 24.5 2.6 12.3 <0.5 3.2 42.9 36.5 37.8 12.3 30.6 46.9 18.8 2.4 25.5 4.3 4.7 16.4 <0.5 0.5 <0.5 57.2 21.6 24.7 <0.5 <0.5 37.5 <0.5 <0.5 18.3 7.4 15.5 11.7 45.8 3.3 16.8 47.5 8.3 27.0 27.9 3.9 3.0

Population below International poverty linea

International poverty line in local currency

$1.25 a day

$2 a day

2005

2005

5,961.1c 930.8 2.1 19.1 470.1c 1,563.9 7,399.9 113.8 3,537.9

Turkmenistan Uganda Ukraine Uruguay Uzbekistan Venezuela, RB Vietnam Yemen, Rep. Zambia

9,537.7c 1,489.2 3.4 30.6 752.1c 2,502.2 11,839.8 182.1 5,660.7

PEOPLE

2.8

Poverty rates at international poverty lines

Survey year b

1993e 2005 2005 2006e 2002 2005e 2006 1998 2003

Population below $1.25 a day %

63.5 51.5 <2 <2 42.3 10.0 21.5 12.9 64.6

Poverty gap at $1.25 a day %

Population below $2 a day %

25.8 19.1 <0.5 <0.5 12.4 4.5 4.6 3.0 27.1

85.7 75.6 <2 4.2 75.6 19.8 48.4 36.4 85.2

Poverty gap at $2 a day %

Survey year b

44.9 36.4 <0.5 0.6 30.6 8.4 16.2 11.1 45.8

1998 2009 2008 2009e 2003 2006e 2008 2005 2004

Population below $1.25 a day %

Poverty gap at $1.25 a day %

Population below $2 a day %

Poverty gap at $2 a day %

24.8 37.7 <2 <2 46.3 3.5 13.1 17.5 64.3

7.0 12.1 <0.5 <0.5 15.0 1.1 2.3 4.2 32.8

49.7 64.5 <2 <2 76.7 10.2 38.4 46.6 81.5

18.4 27.2 <0.5 <0.5 33.2 3.2 10.8 14.8 48.3

a. Based on nominal per capita consumption averages and distributions estimated from household survey data, unless otherwise noted. b. Refers to the year in which the underlying household survey data were collected; in cases for which the data collection period bridged two calender years, the year in which most of the data were collected is reported. c. Based on purchasing power parity (PPP) dollars imputed using regression. d. Urban areas only. e. Based on per capita income averages and distribution data estimated from household survey data. f. Adjusted by spatial consumer price index data. g. PPP conversion factor based on urban prices. h. Population-weighted average of urban and rural estimates. i. Based on benchmark national PPP estimate rescaled to account for cost-of-living differences in urban and rural areas.

Regional poverty estimates and progress toward

84 percent to 16 percent, leaving 620 million fewer

developing countries in 2005 was $2.00 a day. The

the Millennium Development Goals

people in poverty.

poverty rate for all developing countries measured

Global poverty measured at the $1.25 a day pov-

Over the same period the poverty rate in South Asia

at this line fell from nearly 70 percent in 1981 to 47

erty line has been decreasing since the 1980s. The

fell from 59 percent to 40 percent (table 2.8c). In con-

percent in 2005, but the number of people living on

share of population living on less than $1.25 a day

trast, the poverty rate fell only slightly in Sub- Saharan

less than $2.00 a day has remained nearly constant

fell 10 percentage points, to 42 percent, in 1990 and

Africa—from less than 54 percent in 1981 to more

at 2.5 billion. The largest decrease, both in number

then fell nearly 17 percentage points between 1990

than 58 percent in 1999 then down to 51 percent

and proportion, occurred in East Asia and Pacific, led

and 2005. The number of people living in extreme

in 2005. But the number of people living below the

by China. Elsewhere, the number of people living on

poverty fell from 1.9 billion in 1981 to 1.8 billion

poverty line has nearly doubled. Only East Asia and

less than $2.00 a day increased, and the number of

in 1990 to about 1.4 billion in 2005 (figure 2.8a).

Pacific is consistently on track to meet the Millennium

people living between $1.25 and $2.00 a day nearly

This substantial reduction in extreme poverty over

Development Goal target of reducing 1990 poverty

doubled, to 1.2 billion.

the past quarter century, however, disguises large

rates by half by 2015. A slight acceleration over his-

Once household survey data collected after 2005

regional differences.

torical growth rates could lift Latin America and the

in large countries—such as China and India, as well

The greatest reduction in poverty occurred in East

Caribbean and South Asia to the target. However, the

as some countries in Sub-Saharan Africa and the

Asia and Pacific, where the poverty rate declined

recent slowdown in the global economy may leave

Middle East and North Africa—become available,

from 78 percent in 1981 to 17 percent in 2005 and

these regions and many countries short of the target.

the World Bank’s Development Research Group will

the number of people living on less than $1.25 a day

Most of the people who have escaped extreme

update regional poverty estimates at international

dropped more than 750 million (figure 2.8b). Much

poverty remain very poor by the standards of mid-

poverty lines; see http://iresearch.worldbank.org/

of this decline was in China, where poverty fell from

dle- income economies. The median poverty line for

povcalnet/.

While the number of people living on less than $1.25 a day has fallen, the number living on $1.25–$2.00 a day has increased 2.8a

80

2.5

1.5 1.0

People living on less than $1.25 a day, other developing regions

People living on more than $1.25 and less than $2.00 a day, all developing regions

Sub-Saharan Africa

60

40

South Asia

People living on less than $1.25 a day, East Asia & Paciﬁc 20

0.5

2.8b

Share of population living on less than $1.25 a day, by region (percent)

People living in poverty (billions) 3.0

2.0

Poverty rates have begun to fall

People living on less than $1.25 a day, South Asia

People living on less than $1.25 a day, Sub-Saharan Africa

0 1981

1984

1987

Source: World Bank PovcalNet.

1990

1993

1996

1999

2002

2005

East Asia & Pacific

Europe & Central Asia Middle East & North Africa

Latin America & Caribbean

0 1981

1984

1987

1990

1993

1996

1999

2002

2005

Source: World Bank PovcalNet.

2011 World Development Indicators

65

2.8

Poverty rates at international poverty lines 2.8c

Regional poverty estimates Region or country

1981

1984

1987

1990

1993

1996

1999

2002

2005

People living on less than 2005 PPP $1.25 a day (millions) East Asia & Pacific China Europe & Central Asia

1,072

947

822

873

845

622

635

507

316

835

720

586

683

633

443

447

363

208

7

6

5

9

20

22

24

22

17 45

Latin America & Caribbean

47

59

57

50

47

53

55

57

Middle East & North Africa

14

12

12

10

10

11

12

10

11

548

548

569

579

559

594

589

616

596

South Asia India Sub-Saharan Africa Total

420

416

428

436

444

442

447

460

456

211

242

258

297

317

356

383

390

388

1,900

1,814

1,723

1,818

1,799

1,658

1,698

1,601

1,374

Share of people living on less than 2005 PPP $1.25 a day (percent) East Asia & Pacific China Europe & Central Asia Latin America & Caribbean Middle East & North Africa

77.7

65.5

54.2

54.7

50.8

36.0

35.5

27.6

16.8

84.0

69.4

54.0

60.2

53.7

36.4

35.6

28.4

15.9

1.7

1.3

1.1

2.0

4.3

4.6

5.1

4.6

3.7

12.9

15.3

13.7

11.3

10.1

10.9

10.9

10.7

8.2

7.9

6.1

5.7

4.3

4.1

4.1

4.2

3.6

3.6

59.4

55.6

54.2

51.7

46.9

47.1

44.1

43.8

40.3

59.8

55.5

53.6

51.3

49.4

46.6

44.8

43.9

41.6

Sub-Saharan Africa

53.4

55.8

54.5

57.6

56.9

58.8

58.4

55.0

50.9

Total

51.9

46.7

41.9

41.7

39.2

34.5

33.7

30.5

25.2

South Asia India

People living on less than 2005 PPP $2.00 a day (millions) East Asia & Pacific China

1,278

1,280

1,238

1,274

1,262

1,108

1,105

954

729

972

963

907

961

926

792

770

655

474

Europe & Central Asia

35

28

25

32

49

56

68

57

42

Latin America & Caribbean

90

110

103

96

96

107

111

114

94

Middle East & North Africa South Asia India Sub-Saharan Africa Total

46

44

47

44

48

52

52

51

51

799

836

881

926

950

1,009

1,031

1,084

1,092

609

635

669

702

735

757

783

813

828

294

328

351

393

423

471

509

536

556

2,542

2,625

2,646

2,765

2,828

2,803

2,875

2,795

2,564

Share of people living on less than 2005 PPP $2.00 a day (percent) East Asia & Pacific China Europe & Central Asia Latin America & Caribbean

92.6

88.5

81.6

79.8

75.8

64.1

61.8

51.9

38.7

97.8

92.9

83.7

84.6

78.6

65.1

61.4

51.2

36.3

8.3

6.5

5.6

6.9

10.3

11.9

14.3

12.0

8.9

24.6

28.1

24.9

21.9

20.7

22.0

21.8

21.6

17.1

Middle East & North Africa

26.7

23.1

22.7

19.7

19.8

20.2

19.0

17.6

16.9

South Asia

86.5

84.8

83.9

82.7

79.7

79.9

77.2

77.1

73.9

86.6

84.8

83.8

82.6

81.7

79.8

78.4

77.6

75.6

Sub-Saharan Africa

73.8

75.5

74.0

76.1

75.9

77.9

77.6

75.6

72.9

Total

69.4

67.7

64.3

63.4

61.6

58.3

57.1

53.3

47.0

India

Source: World Bank PovcalNet.

66

2011 World Development Indicators

2.8

PEOPLE

Poverty rates at international poverty lines About the data The World Bank produced its first global poverty esti-

The statistics reported here are based on consump-

PPP rates were designed for comparing aggregates from

mates for developing countries for World Development

tion data or, when unavailable, on income surveys.

national accounts, not for making international poverty

Report 1990: Poverty using household survey data for

Analysis of some 20 countries for which income and

comparisons. As a result, there is no certainty that an

22 countries (Ravallion, Datt, and van de Walle 1991).

consumption expenditure data were both available from

international poverty line measures the same degree

Since then there has been considerable expansion in

the same surveys found income to yield a higher mean

of need or deprivation across countries. So-called pov-

the number of countries that field household income

than consumption but also higher inequality. When pov-

erty PPPs, designed to compare the consumption of

and expenditure surveys. The World Bank’s poverty

erty measures based on consumption and income were

the poorest people in the world, might provide a better

monitoring database now includes more than 600

compared, the two effects roughly cancelled each other

basis for comparison of poverty across countries. Work

surveys representing 115 developing countries. More

out: there was no significant statistical difference.

on these measures is ongoing.

than 1.2 million randomly sampled households were

Definitions

interviewed in these surveys, representing 96 percent

International poverty lines

of the population of developing countries.

International comparisons of poverty estimates entail

• International poverty line in local currency is the

both conceptual and practical problems. Countries have

international poverty lines of $1.25 and $2.00 a day in

Data availability

different definitions of poverty, and consistent compari-

2005 prices, converted to local currency using the PPP

The number of data sets within two years of any given

sons across countries can be difficult. Local poverty

conversion factors estimated by the International Com-

year rose dramatically, from 13 between 1978 and

lines tend to have higher purchasing power in rich coun-

parison Program. • Survey year is the year in which the

1982 to 158 between 2001 and 2006. Data cover-

tries, where more generous standards are used, than

underlying household survey data were collected; when

age is improving in all regions, but the Middle East

in poor countries.

the data collection period bridged two calendar years,

and North Africa and Sub-Saharan Africa continue to

Poverty measures based on an international poverty

the year in which most of the data were collected is

lag. A complete database of estimates, maintained

line attempt to hold the real value of the poverty line con-

reported. • Population below $1.25 a day and popula-

by a team in the World Bank’s Development Research

stant across countries, as is done when making com-

tion below $2 a day are the percentages of the popula-

Group, is updated annually as new survey data

parisons over time. Since World Development Report

tion living on less than $1.25 a day and $2.00 a day at

become available, and a major reassessment of prog-

1990 the World Bank has aimed to apply a common

2005 international prices based on nominal per capita

ress against poverty is made about every three years.

standard in measuring extreme poverty, anchored to

consumption averages and distributions estimated from

The most recent estimates and a complete overview

what poverty means in the world’s poorest countries.

household survey data, unless otherwise noted. As a

of data availability by year and country are available

The welfare of people living in different countries can

result of revisions in PPP exchange rates, poverty rates

at http://iresearch.worldbank.org/povcalnet/.

be measured on a common scale by adjusting for dif-

for individual countries cannot be compared with pov-

ferences in the purchasing power of currencies. The

erty rates reported in earlier editions. • Poverty gap

Data quality

commonly used $1 a day standard, measured in 1985

is the mean shortfall from the poverty line (counting

Besides the frequency and timeliness of survey data,

international prices and adjusted to local currency using

the nonpoor as having zero shortfall), expressed as a

other data quality issues arise in measuring household

purchasing power parities (PPPs), was chosen for World

percentage of the poverty line. This measure reflects

living standards. The surveys ask detailed questions on

Development Report 1990 because it was typical of the

the depth of poverty as well as its incidence.

sources of income and how it was spent, which must

poverty lines in low-income countries at the time. Data sources

be carefully recorded by trained personnel. Income is

Early editions of World Development Indicators used

generally more difficult to measure accurately, and

PPPs from the Penn World Tables to convert values in

The poverty measures are prepared by the World

consumption comes closer to the notion of living stan-

local currency to equivalent purchasing power measured

Bank’s Development Research Group. The interna-

dards. And income can vary over time even if living

in U.S dollars. Later editions used 1993 consumption

tional poverty lines are based on nationally repre-

standards do not. But consumption data are not always

PPP estimates produced by the World Bank. Interna-

sentative primary household surveys conducted by

available: the latest estimates reported here use con-

tional poverty lines were recently revised using the new

national statistical offices or by private agencies

sumption for about two-thirds of countries.

data on PPPs compiled in the 2005 round of the Inter-

under the supervision of government or interna-

However, even similar surveys may not be strictly

national Comparison Program, along with data from an

tional agencies and obtained from government

comparable because of differences in timing or in

expanded set of household income and expenditure

statistical offices and World Bank Group country

the quality and training of enumerators. Comparisons

surveys. The new extreme poverty line is set at $1.25

departments. The World Bank Group has pre-

of countries at different levels of development also

a day in 2005 PPP terms, which represents the mean

pared an annual review of its poverty work since

pose a potential problem because of differences in

of the poverty lines found in the poorest 15 countries

1993. For details on data sources and methods

the relative importance of the consumption of nonmar-

ranked by per capita consumption. The new poverty line

used to derive the World Bank’s latest estimates,

ket goods. The local market value of all consumption

maintains the same standard for extreme poverty—

further discussion of the results, and related

in kind (including own production, particularly impor-

the poverty line typical of the poorest countries in the

publications, see http://iresearch.worldbank.org/

tant in underdeveloped rural economies) should be

world—but updates it using the latest information on

povcalnet/ and Shaohua Chen and Martin Rav-

included in total consumption expenditure, but may

the cost of living in developing countries.

allion’s “The Developing World Is Poorer Than

not be. Most survey data now include valuations for

PPP exchange rates are used to estimate global pov-

consumption or income from own production, but valu-

erty, because they take into account the local prices

ation methods vary.

of goods and services not traded internationally. But

We Thought, but No Less Successful in the Fight against Poverty” (2008).

2011 World Development Indicators

67

2.9

Distribution of income or consumption Survey year

Gini index

2008 b 2008 b 1995b 2000 b 2009 d 2008b 1994 d 2000 d 2008b 2005b 2008 b 2000 d 1999d 2003b 2007d 2007b 1994 b 2009 d 2007b 2003 b 2006b 2007b 2001b 2000 d 2003b 2003b 2009 d 2005d 1996d 2006d 2006 b 2005 b 2009 d 2008 b 2008b

29.4 34.5 35.3 58.6 45.8 30.9 35.2 29.1 33.7 31.0 27.2 33.0 54.4 38.6 57.3 36.2 61.0 53.9 45.3 39.6 33.3 44.4 44.6 32.6 43.6 39.8 22.6 41.5 43.4 58.5 44.4 47.3 50.3 41.5 33.7 .. 25.8 24.7 48.4 49.0 32.1 46.9 .. 36.0 29.8 26.9 32.7 41.5 47.3 41.3 28.3 42.8 34.3 53.7 39.4 35.5 59.5 57.7

Percentage share of income or consumptiona

Lowest 10%

Afghanistan Albania Algeria Angolac Argentinac Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Belize Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

68

1996d 1997d 2007d 2009d 2005 b 2007d 2004b 2005 b 2000 d 1995 d 2005b 2003b 2008 b 2000 d 2006b 2000d 2006 d 2007b 2002b 2001d 2007d

2011 World Development Indicators

3.8 3.5 2.8 0.6 1.5 3.7 2.0 3.3 3.4 4.3 3.8 3.4 1.2 2.9 1.0 2.7 1.3 1.2 2.0 3.0 4.1 3.0 2.4 2.6 2.1 2.6 3.1 2.4 2.0 0.9 2.3 2.1 1.7 2.2 3.3 .. 4.3 2.6 1.7 1.6 3.9 1.6 .. 2.7 4.1 4.0 2.8 2.5 2.0 2.0 3.2 1.9 2.5 1.3 2.7 2.9 0.9 0.6

Lowest 20%

9.0 8.1 6.9 2.0 4.1 8.8 5.9 8.6 8.0 9.4 9.2 8.5 3.4 6.9 2.8 6.7 3.1 3.3 5.0 7.0 9.0 6.6 5.6 7.2 5.2 6.3 8.6 5.7 5.3 2.5 5.5 5.0 4.2 5.6 8.1 .. 10.2 8.3 4.4 4.2 9.0 4.3 .. 6.8 9.3 9.6 7.2 6.1 4.8 5.3 8.5 5.2 6.7 3.4 6.4 7.2 2.5 2.0

Second 20%

Third 20%

Fourth 20%

13.1 12.1 11.5 5.7 8.9 12.8 12.0 13.3 12.1 12.6 13.8 13.0 7.2 10.9 6.4 11.3 5.8 7.2 9.1 10.6 11.9 9.4 9.3 12.7 9.4 10.4 15.5 9.8 9.4 6.0 9.2 8.4 7.8 10.1 12.2 .. 14.3 14.7 8.4 8.3 12.6 9.0 .. 11.6 13.2 14.1 12.6 10.1 8.6 10.3 13.7 9.8 11.9 7.2 10.5 11.6 5.9 6.0

16.9 15.9 16.3 10.8 14.3 16.7 17.2 17.4 16.2 16.1 17.8 16.3 11.9 15.1 11.1 16.1 9.6 11.9 13.9 14.7 15.4 13.1 13.7 17.2 14.3 15.0 20.2 14.7 13.9 10.7 13.8 13.0 12.5 14.9 16.2 .. 17.5 18.2 13.1 13.2 16.1 13.9 .. 16.2 16.8 17.5 17.2 14.6 13.2 15.2 17.8 14.8 16.8 12.0 15.1 16.0 10.5 11.3

22.3 20.9 22.8 19.7 22.2 21.9 23.6 22.9 21.7 21.1 22.9 20.8 19.1 21.2 18.8 22.7 16.4 19.5 21.0 20.6 21.0 19.2 20.5 23.0 21.7 21.8 24.7 22.0 20.7 18.7 20.9 20.5 20.1 21.8 21.6 .. 21.7 22.9 20.5 20.4 20.9 20.9 .. 22.5 21.4 22.1 22.8 21.2 20.6 22.1 23.1 21.9 23.0 19.5 21.9 22.1 18.1 20.0

Highest 20%

38.7 43.0 42.4 61.9 50.5 39.8 41.3 37.8 42.1 40.8 36.4 41.4 58.5 45.9 61.0 43.2 65.0 58.1 51.0 47.1 42.8 51.7 50.9 39.9 49.4 46.6 30.9 47.8 50.7 62.1 50.6 53.1 55.4 47.6 42.0 .. 36.2 35.8 53.6 53.9 41.5 51.9 .. 43.0 39.4 36.7 40.2 47.9 52.8 47.2 36.9 48.3 41.5 57.8 46.2 43.0 63.0 60.8

Highest 10%

24.0 29.0 26.9 44.7 33.6 25.4 25.4 23.0 27.4 26.6 21.9 28.1 43.5 31.0 45.4 27.3 51.2 42.5 35.2 32.4 28.0 37.3 35.5 24.8 33.0 30.8 16.5 31.4 34.9 46.2 34.7 37.1 39.4 31.8 27.5 .. 22.7 21.3 37.8 38.3 27.6 36.3 .. 27.7 25.6 22.6 25.1 32.7 36.9 31.3 22.1 32.5 26.0 42.4 30.3 28.0 47.8 43.8

Survey year

Gini index

Percentage share of income or consumptiona

Lowest 10%

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Maldives Mali Mauritania Mauritius Mexico Micronesia Moldova Mongolia Montenegro Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland

2007b 2005 b 2009 b 2005b 2000d 2001d 2000 d 2004b 1993d 2006b 2007b 2005 b 1998d

2007b 2008 b 2008 b 2003 b 2007b 2008b 2008b 2005b 2004b 2009d 2004b 2006b 2000 b 2008d 2000 b 2008b 2008 b 2008 b 2007b 2008b 1993d 2004b 1999d 1997d 2005 d 2007b 2004b 2000 d 2006b 2009d 1996b 2008 d 2009d 2006b 2008 b

31.2 36.8 36.8 38.3 .. 34.3 39.2 36.0 45.5 24.9 37.7 30.9 47.7 .. 31.6 .. .. 33.4 36.7 35.7 .. 52.5 52.6 .. 37.6 44.2 47.2 39.0 46.2 37.4 39.0 39.0 .. 51.7 61.1 38.0 36.5 30.0 40.9 45.6 .. 74.3 47.3 30.9 36.2 52.3 34.0 42.9 25.8 .. 32.7 52.3 50.9 52.0 48.0 44.0 34.2

2.9

PEOPLE

Distribution of income or consumption

3.5 3.6 3.3 2.6 .. 2.9 2.1 2.3 2.1 4.8 3.0 3.8 1.8 .. 2.9 .. .. 4.1 3.3 2.7 .. 1.0 2.4 .. 2.6 2.2 2.6 2.9 1.8 2.7 2.7 2.5 .. 1.5 0.4 2.9 3.0 3.6 2.7 1.9 .. 0.6 2.7 2.5 2.2 1.4 3.7 2.0 3.9 .. 4.0 1.3 1.9 1.4 1.4 2.4 3.2

Lowest 20%

8.4 8.1 7.6 6.4 .. 7.4 5.7 6.5 5.2 10.6 7.2 8.7 4.7 .. 7.9 .. .. 8.8 7.6 6.8 .. 3.0 6.4 .. 6.6 5.4 6.2 7.0 4.5 6.5 6.5 6.2 .. 3.9 1.6 6.8 7.1 8.5 6.5 5.2 .. 1.5 6.1 7.6 6.4 3.8 8.3 5.1 9.6 .. 9.0 3.6 4.5 3.8 3.9 5.6 7.6

Second 20%

Third 20%

Fourth 20%

12.9 11.3 11.3 10.9 .. 12.3 10.5 12.0 9.0 14.2 11.1 12.8 8.8 .. 13.6 .. .. 11.8 11.3 11.7 .. 7.2 11.4 .. 11.1 9.3 9.6 10.8 8.7 10.9 10.7 10.5 .. 7.9 5.2 10.9 11.2 13.1 10.5 9.5 .. 2.8 8.9 13.2 11.4 7.7 12.0 9.7 14.0 .. 12.4 7.4 7.7 7.7 8.4 9.1 12.0

16.9 14.9 15.1 15.6 .. 16.3 15.9 16.8 13.8 17.6 15.2 16.7 13.3 .. 18.0 .. .. 15.5 15.3 16.3 .. 12.5 15.7 .. 15.7 14.0 13.1 14.9 13.7 15.7 15.2 15.4 .. 12.5 10.2 15.4 15.6 17.2 14.5 13.7 .. 5.5 12.5 17.2 15.8 12.3 15.8 14.7 17.2 .. 15.8 12.2 12.1 12.4 13.6 13.7 16.3

22.0 20.4 21.1 22.2 .. 21.9 23.0 22.8 20.9 22.0 21.1 22.0 20.3 .. 23.1 .. .. 21.2 20.9 22.4 .. 21.0 21.6 .. 22.1 21.0 17.7 20.9 21.6 22.7 21.6 22.3 .. 19.4 19.1 21.7 22.1 22.4 20.6 20.1 .. 12.0 18.4 23.3 22.6 19.4 21.1 21.9 22.0 .. 20.7 20.1 19.3 19.7 21.5 21.2 22.0

Highest 20%

39.9 45.3 44.9 45.0 .. 42.0 44.9 42.0 51.2 35.7 45.4 39.9 53.0 .. 37.5 .. .. 42.8 44.8 42.9 .. 56.4 45.0 .. 44.4 50.3 53.5 46.4 51.5 44.2 46.0 45.7 .. 56.2 64.0 45.3 44.0 38.8 47.9 51.5 .. 78.3 54.2 38.7 43.8 56.9 42.8 48.6 37.2 .. 42.1 56.8 56.4 56.5 52.6 50.4 42.2

2011 World Development Indicators

Highest 10%

25.4 31.1 29.9 29.6 .. 27.2 28.8 26.8 35.6 21.7 30.7 25.2 37.8 .. 22.5 .. .. 27.9 30.3 27.6 .. 39.4 30.1 .. 29.1 34.5 41.5 31.7 34.7 28.0 30.5 29.6 .. 41.4 47.1 29.8 28.4 24.1 33.2 36.7 .. 65.0 40.4 22.9 27.8 41.8 28.5 32.4 23.4 .. 28.3 40.6 40.9 41.0 35.9 33.9 27.2

69

2.9

Distribution of income or consumption Survey year

Gini index

Percentage share of income or consumptiona

Lowest 10%

Portugal Puerto Rico Qatar Romania Russian Federation Rwanda São Tomé & Príncipe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe

1997d 2007b 2008 b 2008b 2005 b 2000 b 2005b 2008 b 2007b 2003 b 1998d 1996d 2004b 2000 b 2000d 2007b 2001b 2000 d 2000 d 2004b 2007b 2007b 2009b 2007b 2006b 1992d 2000 b 2008 b 1998b 2009b 2008 b 1999d 2000 d 2009 d 2003b 2006 d 2008b 2005 b 2004 b 1995b

38.5 .. 41.1 31.2 42.3 53.1 50.8 .. 39.2 28.2 19.0 42.5 42.5 25.8 31.2 .. 57.8 34.7 40.3 .. 50.7 25.0 33.7 35.8 29.4 37.6 53.6 31.9 34.4 40.3 40.8 39.7 40.8 44.3 27.5 .. 36.0 40.8 42.4 36.7 43.5 37.6 .. 37.7 50.7 50.1

2.0 .. 1.3 3.3 2.6 1.7 2.2 .. 2.5 3.9 4.7 2.6 1.9 3.1 3.4 .. 1.3 2.6 3.1 .. 1.8 3.6 2.9 3.4 4.0 2.8 1.6 4.0 2.0 2.1 2.4 2.1 2.5 2.4 4.1 .. 2.1 1.9 2.3 2.9 1.9 3.2 .. 2.9 1.3 1.8

Lowest 20%

5.8 .. 3.9 8.1 6.0 4.2 5.2 .. 6.2 9.1 10.8 6.1 5.0 8.8 8.2 .. 3.1 7.0 6.9 .. 4.5 9.1 7.6 7.7 9.3 6.8 3.9 9.0 5.4 5.5 5.9 5.7 6.0 5.8 9.4 .. 6.1 5.4 5.6 7.1 4.9 7.3 .. 7.2 3.6 4.6

Second 20%

Third 20%

Fourth 20%

11.0 .. .. 12.8 9.8 7.7 8.5 .. 10.6 13.5 15.7 9.7 9.4 14.9 12.8 .. 5.6 12.1 10.4 .. 8.0 14.0 12.2 11.4 13.4 11.1 7.0 12.5 10.3 10.3 10.2 10.8 10.2 9.6 13.6 .. 11.4 10.7 9.8 11.5 9.6 10.9 .. 11.3 7.8 8.1

15.5 .. .. 17.1 14.3 11.7 12.2 .. 15.3 17.5 19.9 14.0 14.6 18.6 17.0 .. 9.9 16.4 14.4 .. 12.3 17.6 16.3 15.5 16.7 15.6 11.4 16.1 15.2 15.5 14.9 15.6 14.9 13.8 17.5 .. 16.0 15.7 14.5 15.7 14.7 15.1 .. 15.3 12.8 12.2

21.9 .. .. 22.7 20.9 18.2 17.7 .. 22.0 22.5 24.2 20.9 22.0 22.9 22.6 .. 18.8 22.5 20.5 .. 19.4 22.7 22.6 21.4 21.5 21.7 19.2 21.2 22.0 22.7 21.8 22.1 21.7 20.0 22.5 .. 22.5 22.4 21.4 21.5 21.8 21.3 .. 21.0 20.6 19.3

Highest 20%

45.9 .. 52.0 39.3 48.9 58.2 56.4 .. 45.9 37.4 29.4 49.3 49.0 34.8 39.4 .. 62.7 42.0 47.8 .. 55.9 36.6 41.3 43.9 39.0 44.8 58.6 41.3 47.1 45.9 47.2 45.8 47.2 50.7 37.1 .. 44.0 45.8 48.6 44.2 49.0 45.4 .. 45.3 55.2 55.7

Highest 10%

29.8 .. 35.9 24.5 33.5 44.0 43.6 .. 30.1 22.8 15.4 33.6 32.8 20.8 24.6 .. 44.9 26.6 32.9 .. 40.8 22.2 25.9 28.9 25.2 29.6 42.6 27.0 31.3 29.9 31.6 30.3 31.8 36.1 22.6 .. 28.5 29.9 32.9 29.5 33.0 30.2 .. 30.8 38.9 40.3

a. Percentage shares by quintile may not sum to 100 percent because of rounding. b. Refers to expenditure shares by percentiles of population, ranked by per capita expenditure. c. Covers urban areas only. d. Refers to income shares by percentiles of population, ranked by per capita income.

70

2011 World Development Indicators

About the data

2.9

PEOPLE

Distribution of income or consumption Definitions

Inequality in the distribution of income is reflected

• Survey year is the year in which the underlying data

in the percentage shares of income or consumption

were collected. • Gini index measures the extent

accruing to portions of the population ranked by

to which the distribution of income (or consump-

income or consumption levels. The portions ranked

tion expenditure) among individuals or households

lowest by personal income receive the smallest

within an economy deviates from a perfectly equal

shares of total income. The Gini index provides a con-

distribution. A Lorenz curve plots the cumulative

venient summary measure of the degree of inequal-

percentages of total income received against the

ity. Data on the distribution of income or consump-

cumulative number of recipients, starting with the

tion come from nationally representative household

poorest individual. The Gini index measures the area

surveys. Where the original data from the house-

between the Lorenz curve and a hypothetical line of

hold survey were available, they have been used to

absolute equality, expressed as a percentage of the

directly calculate the income or consumption shares

maximum area under the line. Thus a Gini index of

by quintile. Otherwise, shares have been estimated

0 represents perfect equality, while an index of 100

from the best available grouped data.

implies perfect inequality. • Percentage share of

The distribution data have been adjusted for

income or consumption is the share of total income

household size, providing a more consistent measure

or consumption that accrues to subgroups of popula-

of per capita income or consumption. No adjustment

tion indicated by deciles or quintiles.

has been made for spatial differences in cost of living within countries, because the data needed for such calculations are generally unavailable. For further details on the estimation method for low- and middleincome economies, see Ravallion and Chen (1996). Because the underlying household surveys differ in method and type of data collected, the distribution data are not strictly comparable across countries. These problems are diminishing as survey methods improve and become more standardized, but achieving strict comparability is still impossible (see About the data for tables 2.7 and 2.8). Two sources of non-comparability should be noted in particular. First, the surveys can differ in many respects, including whether they use income or consumption expenditure as the living standard indicator. The distribution of income is typically more unequal than the distribution of consumption. In addition, the definitions of income used differ more often among surveys. Consumption is usually a much better welfare indicator, particularly in developing countries. Second, households differ in size (number of members) and in the extent of income sharing among members. And individuals differ in age and consumption needs. Differences among countries in these respects may bias comparisons of distribution. World Bank staff have made an effort to ensure that the data are as comparable as possible. Wher-

Data sources

ever possible, consumption has been used rather

Data on distribution are compiled by the World

than income. Income distribution and Gini indexes for

Bank’s Development Research Group using pri-

high-income economies are calculated directly from

mary household survey data obtained from govern-

the Luxembourg Income Study database, using an

ment statistical agencies and World Bank country

estimation method consistent with that applied for

departments. Data for high-income economies are

developing countries.

from the Luxembourg Income Study database.

2011 World Development Indicators

71

2.10

Assessing vulnerability and security Youth unemployment

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

72

Female-headed households

Pension contributors

Male % of male labor force ages 15–24

Female % of female labor force ages 15–24

% of total

2006–09a

2006–09a

2006–09a

Year

.. .. .. .. 19b 47b 13b 10 19 .. .. 21 .. .. 45 .. 14 18 .. .. .. .. 18b .. .. 21 .. 15b 18 .. .. 10 .. 19 3 17 12 21 12b 17 13 .. 32 20 b 22 23 .. .. 32 12 .. 19 .. .. .. .. ..

.. .. .. .. 25b 69b 10 b 9 10 .. .. 22 .. .. 52 .. 23 14 .. .. .. .. 12b .. .. 24 .. 10 b 30 .. .. 13 .. 27 4 17 10 45 18 b 48 8 .. 21 29b 19 22 .. .. 41 10 .. 34 .. .. .. .. ..

.. .. .. 25 34 .. .. .. 25 13 .. .. 23 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 19 21 .. .. .. 24 46 .. .. 35 .. .. .. .. .. .. .. .. .. .. .. .. 34 .. .. .. .. 44 26

2005 2007 2002

2011 World Development Indicators

2008 2008 2005 2005 2007 2004 2008 2005

2008 2009 2006 2008 2008 2004

2007 2004

2008 2007 2008 2008

2004

2010

2007 2007 2008 2004 2009 2008

2004

2005 2005

2006 2004 2005 2004 2005 2008 1993 2004

2008

Public expenditure on pensions

% of labor force

% of workingage population

.. 51.1 36.7 .. 41.9 39.2 92.6 96.4 35.4 2.8 93.5 94.2 .. 11.4 70.2 9.0 53.8 72.7 1.2 .. .. .. 66.9 1.5 .. 53.8 19.3 .. 31.3 .. .. 55.3 .. 82.9 .. 84.5 94.4 21.0 31.6 57.0 23.9 .. 95.2 .. 88.7 89.9 .. 2.7 29.9 88.2 9.1 85.2 20.3 1.5 1.9 .. 18.7

2.2 34.7 22.1 .. 31.3 23.9 69.6 68.7 24.7 2.1 66.8 61.6 .. 8.9 28.7 7.3 41.7 49.6 1.0 .. .. .. 53.6 1.3 .. 36.2 15.9 55.6 20.0 .. .. 37.6 .. 52.6 .. 67.3 86.9 15.2 21.1 31.0 16.2 .. 68.6 .. 67.2 61.4 .. 2.2 22.7 65.5 7.1 58.5 14.7 1.8 1.5 .. 12.6

Year

2005 2009 2002

2007 2008 2005 2005 2007 2006 2008 2005 2006 2000 2009

2004 2007

2001 2005 2004

2001

2008

2004 2006

2009

2007 2005 2000 2002 2004 2006 2001 2007 2006 2005 2005

2004 2005 2002 2005 2005

2005

% of GDP

0.5 6.1 3.2 .. 8.0 4.3 3.5 12.6 3.8 0.3 10.2 9.0 1.5 4.5 9.4 .. 12.6 9.8 .. .. .. 0.8 4.1 0.8 .. 2.9 .. .. 3.0 .. 0.9 2.4 .. 10.3 .. 8.5 5.4 0.8 2.5 4.1 1.9 0.3 10.9 0.3 8.4 12.4 .. .. 3.0 11.4 1.3 11.5 1.0 .. 2.1 .. ..

Year

2000 2007

2006

2002

2004

2006

2005

2005

2007

2003

Average pension % of average wage

.. .. .. .. 43.8 20.3 .. .. 24.3 .. 41.6 .. .. .. .. .. .. 42.9 .. .. .. .. .. .. .. 53.5 .. .. .. .. .. .. .. 32.4 .. 40.7 .. .. .. .. .. .. 35.4 .. .. .. .. .. 13.0 .. .. .. .. .. .. .. ..

Youth unemployment

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

Female-headed households

Pension contributors

Male % of male labor force ages 15–24

Female % of female labor force ages 15–24

% of total

2006–09a

2006–09a

2006–09a

Year

28 .. 22 20 .. 31 16 23 22 10 23 7 .. .. 12b .. .. 14 .. 38 22 .. 6b .. 35 53 .. .. 10 .. .. 18 10 16 .. 23 .. .. .. .. 7 16b 8 .. .. 10 .. 7 12 .. 9 13b 16 20 19 29b 1

24 .. 23 34 .. 17 14 29 33 8 46 8 .. .. 9b .. .. 16 .. 28 22 .. 4b .. 22 59 .. .. 12 .. .. 26 11 15 .. 19 .. .. .. .. 6 17b 10 .. .. 8 .. 10 21 .. 17 16b 19 21 22 22b 7

.. 14 13 .. 11 .. .. .. .. .. 10 .. .. .. .. .. .. 25 .. .. .. .. 31 .. .. 8 .. .. .. 12 .. .. .. .. 29 .. .. .. 44 23 .. .. .. 19 .. .. .. 10 .. .. .. 22 19 .. .. .. ..

2008 2006 2008 2001 2009 2005

2005 2004 2005 2006 2004 2006

2005

2006

2003 2003 2005

2004 2007 2008

2008

2000 2008 2009 2005 2007

2008 2005 2003 2008 2006 2004 2005

2008

2004 2008 2007 2005 2005

2.10

PEOPLE

Assessing vulnerability and security

Public expenditure on pensions

% of labor force

% of workingage population

92.0 10.3 11.7 35.1 16.8 88.0 .. 92.4 17.4 95.3 38.4 34.4 7.5 .. 49.5 .. .. 42.2 .. 92.4 33.1 5.7 .. 65.5 99.3 47.9 .. .. 49.0 .. .. 51.4 30.3 58.7 27.9 23.8 .. .. .. 3.4 90.7 92.7 21.7 1.9 1.9 93.2 .. 3.9 .. .. 11.6 19.1 25.0 83.8 92.0 .. ..

56.7 6.4 8.7 20.0 15.2 63.9 .. 58.4 12.6 75.0 19.9 26.5 6.5 .. 34.3 .. .. 28.9 .. 66.5 19.9 3.6 .. 38.1 68.7 30.4 .. .. 32.5 .. .. 33.6 20.6 32.1 21.3 13.6 .. .. .. 2.6 70.7 72.3 14.6 1.2 1.1 75.2 .. 2.2 .. .. 9.1 13.9 17.0 54.7 71.6 .. ..

Year

2008 2007

2000 2009 2005

2005

2005 2001 2009 2003

2005 2007 2010

2009 2003

2001 2009 2008

2005 2009 2007 2003

2006 2005 2005

2006

2005

2004

2001 2000

2009 2005

% of GDP

10.5 2.2 .. 1.1 3.9 3.4 .. 14.0 .. 8.7 2.2 3.2 1.1 .. 1.6 2.7c .. 2.7 .. 8.5 2.1 .. .. 2.1 8.9 9.4 .. .. .. .. .. .. 1.3 9.1 6.5d 1.9 .. .. .. 0.2 5.0e 4.4 e .. 0.7 .. 4.8e .. 0.5 .. .. 1.2 2.6 .. 10.0 10.2e .. ..

Year

2005

2003

2003

2005

2005 2006

2003

2007

2011 World Development Indicators

Average pension % of average wage

39.8 .. .. .. .. .. .. .. .. .. .. 24.9 .. .. .. .. .. 27.5 .. 33.1 .. .. .. .. 30.9 55.0 .. .. .. .. .. .. .. 20.9 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 47.1 .. .. ..

73

2.10

Assessing vulnerability and security Youth unemployment

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

Female-headed households

Pension contributors

Male % of male labor force ages 15–24

Female % of female labor force ages 15–24

% of total

2006–09a

2006–09a

2006–09a

Year

.. .. .. .. .. 29 .. .. .. .. .. .. .. .. 19 48 .. .. .. .. .. 30 .. .. .. .. .. .. 30 49 .. .. .. .. .. .. .. .. .. 24 38

2007 2007 2004

21 18 .. 24 12 31 .. 10 28 14 .. 45 39 17 .. .. 26 8 13 .. 7 4 .. .. 9 .. 25 .. .. .. 8 22 20 b 16 .. 12 .. 39 .. .. .. .. w .. .. .. 19 .. .. 17 12 18 .. .. 19 21

20 19 .. 46 20 41 .. 17 27 13 .. 53 36 28 .. .. 24 9 49 .. 10 5 .. .. 13 .. 25 .. .. .. 22 16 15b 25 .. 16 .. 47 .. .. .. .. w .. .. .. 23 .. .. 18 18 37 .. .. 16 21

2003 2003 2004 2008 2003 2008

2007 2005 2006

2005 2005 2008

2006 2008

2008 2004 2007

2004 2010

2005 2005 2007 2005 2008 2008 2009 2006 2006

Public expenditure on pensions

% of labor force

% of workingage population

54.8 67.0 4.6 .. 5.1 45.0 5.5 61.7 78.9 87.4 .. 6.5 69.4 24.1 .. .. 88.8 95.4 26.8 .. 4.3 23.0 .. .. 76.4 48.6 60.3 .. 10.3 65.3 .. 93.2 92.2 72.7 86.1 32.1 19.3 18.5 10.4 10.9 ..

36.4 50.0 4.1 .. 4.1 35.4 3.8 45.3 55.3 63.2 .. 3.7 48.7 14.9 .. .. 72.2 78.7 13.8 .. 4.0 18.6 .. .. 54.2 25.5 31.0 .. 9.2 52.3 .. 71.5 71.5 56.9 57.5 22.7 15.2 8.0 5.0 8.0 ..

Year

2009 2007

2003 2010

2007 2007

2006 2005 2007

2005 2005 2004 2006

2003 2008 2003 2010

2005 2005 2007 2005 2001 2009 2008 2002

% of GDP

8.3 4.7 .. .. 1.3 14.0 .. .. 9.3 e 12.7 .. 1.2 8.1e 2.0 .. .. 7.7e 6.8e 1.3 .. 0.9 .. .. .. .. 4.3 6.2 .. 0.3 17.8 .. 5.7 6.0 e 10.0e 6.5 2.7 .. 4.0 .. 1.0 2.3

Year

2005 2003

2005 2005

2006

2000

2003

2007

2007

2006

2005

Average pension % of average wage

41.5 29.2 .. .. .. .. .. .. 44.7 44.3 .. .. 58.6 .. .. .. .. 40.0 .. 25.7 .. .. .. .. .. .. 61.3 .. .. 48.3 .. .. 29.2 .. 40.0 .. .. .. .. .. ..

a. Data are for the most rec ent year available. b. Limited coverage. c. Includes only expenditure on social pensions. d. Includes old-age, survivors, disability, military, work accident or disease pensions. e. Includes only expenditures on old-age and survivors’ benefi ts.

74

2011 World Development Indicators

About the data

2.10

PEOPLE

Assessing vulnerability and security Definitions

As traditionally measured, poverty is a static con-

citizenship, residency, or income status. In contri-

• Youth unemployment is the share of the labor force

cept, and vulnerability a dynamic one. Vulnerabil-

bution-related schemes, however, eligibility is usually

ages 15–24 without work but available for and seek-

ity reflects a household’s resilience in the face of

restricted to individuals who have contributed for a

ing employment. • Female-headed households are

shocks and the likelihood that a shock will lead to a

minimum number of years. Definitional issues—relat-

the percentage of households with a female head.

decline in well-being. Thus, it depends primarily on

ing to the labor force, for example—may arise in

• Pension contributors are the share of the labor

the household’s assets and insurance mechanisms.

comparing coverage by contribution-related schemes

force or working-age population (here defined as

Because poor people have fewer assets and less

over time and across countries (for country-specific

ages 15 and older) covered by a pension scheme.

diversified sources of income than do the better-off,

information, see Hinz and others 2011). The share

• Public expenditure on pensions is all government

fluctuations in income affect them more.

of the labor force covered by a pension scheme may

expenditures on cash transfers to the elderly, the

be overstated in countries that do not try to count

disabled, and survivors and the administrative costs

informal sector workers as part of the labor force.

of these programs. • Average pension is the aver-

Enhancing security for poor people means reducing their vulnerability to such risks as ill health, providing them the means to manage risk themselves,

Public interventions and institutions can provide

age pension payment of all pensioners of the main

and strengthening market or public institutions for

services directly to poor people, although whether

pension schemes (including old-age, survivors, dis-

managing risk. Tools include microfinance programs,

these interventions and institutions work well for the

ability, military, and work accident or disease pen-

public provision of education and basic health care,

poor is debated. State action is often ineffective,

sions) divided by the average wage of all formal sec-

and old age assistance (see tables 2.11 and 2.16).

in part because governments can influence only a

tor workers.

Poor households face many risks, and vulnerability

few of the many sources of well-being and in part

is thus multidimensional. The indicators in the table

because of difficulties in delivering goods and ser-

focus on individual risks—youth unemployment,

vices. The effectiveness of public provision is further

female-headed households, income insecurity in

constrained by the fiscal resources at governments’

old age—and the extent to which publicly provided

disposal and the fact that state institutions may not

services may be capable of mitigating some of these

be responsive to the needs of poor people.

risks. Poor people face labor market risks, often hav-

The data on public pension spending cover the

ing to take up precarious, low-quality jobs and to

pension programs of the social insurance schemes

increase their household’s labor market participa-

for which contributions had previously been made.

tion by sending their children to work (see tables

In many cases noncontributory pensions or social

2.4 and 2.6). Income security is a prime concern

assistance targeted to the elderly and disabled are

for the elderly.

also included. A country’s pattern of spending is cor-

Youth unemployment is an important policy issue for many economies. Experiencing unemployment

related with its demographic structure—spending increases as the population ages.

may permanently impair a young person’s productive potential and future employment opportunities. The table presents unemployment among youth ages 15–24, but the lower age limit for young people in a country could be determined by the minimum age for leaving school, so age groups could differ across countries. Also, since this age group is likely to include school leavers, the level of youth unemployment varies considerably over the year as a result of different school opening and closing dates. The youth unemployment rate shares similar limitations on comparability as the general unemployment rate. For further information, see About the data for table 2.5 and the original source.

Data sources Data on youth unemployment are from the ILO’s

The definition of female-headed household differs

Key Indicators of the Labour Market, 6th edition,

greatly across countries, making cross-country com-

database. Data on female-headed households are

parison difficult. In some cases it is assumed that a

from Macro International Demographic and Health

woman cannot be the head of any household with an

Surveys. Data on pension contributors and pen-

adult male, because of sex-biased stereotype. Cau-

sion spending are from Hinz and others’ Interna-

tion should be used in interpreting the data.

tional Patterns of Pension Provision II: Facts and

Pension scheme coverage may be broad or

Figures of the 2000s (2011).

even universal where eligibility is determined by

2011 World Development Indicators

75

2.11

Education inputs Public expenditure per student

Primary

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile C hina Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

76

1999

2009a

.. .. 12.0 .. 12.9 .. 16.4 25.1 6.9 .. .. 18.2 12.1 14.2 .. .. 10.8 15.5 .. 14.7 5.9 .. .. .. .. 14.4 .. 12.4 15.2 .. .. 15.5 14.8 .. 27.8 11.2 24.6 7.2 4.4 .. 8.6 15.0 20.9 .. 17.4 17.3 .. .. .. .. .. 11.7 6.7 .. .. .. ..

.. .. .. .. 14.7 11.0 16.4 23.3 .. 10.7 .. 20.5 .. .. .. 12.4 17.3 23.5 29.0 21.1 .. 7.4 .. 4.5 12.7 14.7 .. 13.8 15.9 .. .. 14.6 .. 21.8 44.7 13.0 24.5 7.3 .. .. 8.5 .. 20.0 12.4 17.5 17.7 .. .. 14.5 15.7 .. .. 10.5 7.1 .. .. ..

2011 World Development Indicators

% of GDP per capita Secondary 1999 2009a

.. .. .. .. 18.2 .. 15.0 30.2 17.0 12.5 .. 23.8 24.6 11.7 .. .. 9.5 18.8 .. .. 11.5 .. .. .. .. 14.8 11.5 17.7 16.1 .. .. 21.4 42.8 .. 41.2 21.7 38.1 .. 9.6 .. 7.5 37.3 27.2 .. 25.8 28.5 .. .. .. .. .. 15.5 4.3 .. .. .. ..

.. .. .. .. 21.9 18.8 14.5 26.7 .. 14.9 .. 33.3 .. .. .. 37.6 18.0 22.3 30.2 59.4 .. 30.7 .. 16.1 24.1 16.0 .. 16.7 15.4 .. .. 14.4 .. 25.2 51.9 22.0 32.2 7.4 .. .. 9.1 .. 23.9 8.9 30.8 26.4 .. .. 15.2 21.8 .. .. 6.2 6.3 .. .. ..

Public expenditure on education

Tertiary 1999

.. .. .. .. 17.7 .. 26.6 52.1 19.1 50.7 .. 38.3 212.7 44.1 .. .. 57.1 17.9 .. 1,051.5 43.6 .. 44.0 .. .. 19.4 90.0 .. 37.7 .. .. .. 146.3 35.8 86.2 33.7 65.9 .. .. .. 8.9 429.6 31.8 .. 40.4 29.7 .. .. .. .. .. 26.2 .. .. .. .. ..

2009a

.. .. .. .. 15.6 6.8 20.2 47.6 15.6 39.8 15.0 35.3 .. .. .. 251.5 29.6 20.1 307.1 520.4 .. 35.8 .. 124.1 217.8 12.1 .. 56.2 27.4 .. .. .. 119.1 26.2 58.8 30.5 53.8 .. .. .. 13.7 .. 20.8 642.9 31.7 34.8 .. .. 11.2 .. .. .. 19.0 102.3 .. .. ..

% of GDP 2009a

.. .. 4.3 .. 4.9 3.0 4.5 5.4 2.8 2.4 4.5 6.0 3.5 .. .. 8.9 5.1 4.1 4.6 8.3 2.1 3.7 4.9 1.3 3.2 4.0 .. 4.5 4.8 .. .. 6.3 4.6 4.6 13.6 4.2 7.8 2.3 .. 3.8 3.6 .. 4.8 5.5 5.9 5.6 .. .. 3.2 4.5 .. .. 3.2 2.4 .. .. ..

Trained Primary teachers school in primary pupil–teacher education ratio

% of total government expenditure

% of total

pupils per teacher

2009a

2009a

2009a

.. .. 20.3 .. 13.5 15.0 .. 11.1 9.1 14.0 10.6 12.4 15.9 .. .. 22.0 16.1 10.0 21.8 23.4 12.4 19.2 .. 11.7 12.6 18.2 .. 24.1 14.9 .. .. 37.7 24.6 10.4 17.5 9.9 15.4 12.0 .. 11.9 13.1 .. 13.9 23.3 12.5 10.7 .. .. 7.7 10.3 .. .. .. 19.2 .. .. ..

.. .. .. .. .. .. .. .. 99.9 58.4 99.9 .. 40.4 .. .. 97.4 .. .. 86.1 91.2 99.5 .. .. .. 34.6 .. .. 95.1 100.0 93.4 .. 87.6 100.0 100.0 100.0 .. .. 83.6 82.6 .. 93.2 92.2 .. 84.6 .. .. .. .. 94.6 .. 47.6 .. .. 73.1 .. .. 36.4

43 20 23 .. 16 19 .. 12 11 44 15 11 45 24 .. 25 23 16 49 51 49 46 .. 95 61 25 18 16 29 37 64 18 42 11 9 18 .. 25 17 27 31 38 12 58 14 19 .. 34 9 13 33 10 29 44 .. .. 33

Public expenditure per student

Primary

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

1999

2009a

18.0 11.9 .. 9.1 .. 11.0 20.5 24.0 13.4 21.1 13.7 .. 21.5 .. 18.4 .. 19.2 .. 2.3 19.5 .. 34.5 .. .. .. .. 5.7 14.0 12.5 14.3 11.4 9.3 11.7 .. .. 17.2 .. .. 21.4 9.1 15.2 20.2 .. .. .. 21.8 11.2 .. 13.7 .. 13.6 7.6 12.8 .. 19.5 .. ..

24.9 .. 11.0 15.1 .. 15.7 19.4 22.6 15.8 21.7 12.7 .. .. .. 17.0 .. 10.9 .. .. 23.3 .. 22.6 5.7 .. 15.8 .. 7.1 .. 14.3 13.0 .. 9.3 13.3 42.4 16.2 16.1 .. .. 15.6 17.6 16.9 17.6 .. 28.3 .. 18.5 .. .. 7.5 .. 10.8 8.1 9.0 24.3 .. .. 9.2

% of GDP per capita Secondary 1999 2009a

19.1 24.7 .. 9.9 .. 16.8 21.9 27.7 21.0 20.9 15.8 .. 14.5 .. 15.7 .. .. .. 4.5 23.7 .. 76.7 .. .. .. .. .. 10.0 21.7 56.1 35.9 14.2 14.2 .. .. 45.1 .. 6.9 35.2 13.1 22.2 24.1 .. .. .. 30.4 21.8 .. 19.1 .. 18.5 10.8 11.0 10.9 27.5 .. ..

23.1 .. 12.5 21.0 .. 23.2 19.0 25.2 26.8 22.4 16.3 .. .. .. 22.2 .. 14.9 .. .. 24.1 .. 50.8 8.4 .. 20.1 .. 10.5 .. 12.4 32.6 .. 15.1 13.4 40.3 .. 38.7 .. .. 15.8 11.3 24.5 19.6 .. 56.6 .. 26.5 .. .. 9.9 .. 16.3 9.9 9.1 22.0 .. .. 9.8

Public expenditure on education

Tertiary 1999

34.2 95.0 .. 34.8 .. 28.6 30.9 27.6 70.4 15.1 .. .. 209.0 .. 8.4 .. .. 24.3 68.6 27.9 13.9 875.4 .. 23.9 34.2 .. .. 2,613.3 81.1 241.3 79.0 25.4 47.8 .. .. 96.2 1,412.2 28.0 152.2 141.6 47.4 40.1 .. .. .. 45.8 .. .. 33.6 .. 58.9 21.2 15.4 21.1 28.1 .. ..

2009a

23.8 .. 16.2 22.2 .. 26.2 22.7 22.1 42.4 20.1 .. 7.9 .. .. 9.0 .. .. 17.3 .. 16.3 10.2 .. .. .. 17.1 .. 132.4 .. 34.0 117.7 .. 16.7 37.0 46.1 .. 71.1 .. .. .. 55.5 40.2 28.6 .. 429.3 .. 47.3 .. .. 21.6 .. 26.0 .. 9.6 16.6 .. .. 337.7

% of GDP 2009a

5.2 .. 2.8 4.7 .. 4.9 5.9 4.3 5.8 3.5 .. 2.8 .. .. 4.2 4.3 .. 5.9 2.3 5.0 1.8 12.4 2.8 .. 4.7 .. 3.0 .. 4.1 4.4 .. 3.2 4.8 9.6 5.6 5.6 .. .. 6.4 4.6 5.3 6.1 .. 4.5 .. 6.8 .. 2.7 3.8 .. 4.0 2.7 2.8 4.9 .. .. ..

2.11

PEOPLE

Education inputs

Trained Primary teachers school in primary pupil–teacher education ratio

% of total government expenditure

% of total

pupils per teacher

2009a

2009a

2009a

10.4 .. 17.9 20.9 .. 13.8 13.1 9.0 .. 9.4 .. .. .. .. 14.8 17.4 .. 19.0 12.2 13.9 7.2 23.7 12.1 .. 13.4 .. 13.4 .. 17.2 22.3 .. 11.4 .. 21.0 14.6 25.7 .. .. 22.4 19.5 11.7 .. .. 19.3 .. 16.5 .. 11.2 .. .. 11.9 20.7 16.9 11.7 .. .. ..

.. .. .. 98.4 .. .. .. .. .. .. .. .. 96.8 .. .. .. 100.0 65.7 96.9 .. .. 57.6 40.2 .. .. .. .. .. .. 50.0 100.0 100.0 95.4 .. 100.0 100.0 71.2 98.9 95.6 66.4 .. .. 72.7 98.0 .. .. 100.0 85.2 91.5 .. .. .. .. .. .. 6.6 48.9

2011 World Development Indicators

10 .. 17 20 17 16 13 10 .. 18 .. 16 47 .. 24 .. 9 24 29 11 14 37 24 .. 13 17 48 .. 15 50 39 22 28 16 30 27 61 29 30 33 .. 15 29 39 46 .. 12 40 24 .. 26 21 34 10 11 12 11

77

2.11

Education inputs Public expenditure per student

Primary

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

1999

2009a

.. .. 11.0 .. 14.1 .. .. .. 10.2 26.3 .. 14.2 18.0 .. .. 8.5 22.5 22.7 11.2 .. .. 17.8 .. 8.5 11.5 15.6 9.8 .. .. .. 8.7 13.9 17.9 7.2 .. .. .. .. .. 7.2 12.7 .. m .. .. .. 12.0 .. .. .. 12.7 .. .. .. 18.0 17.4

20.0 .. 8.2 18.4 20.9 56.9 7.1 10.5 15.6 .. .. 15.1 19.4 .. .. 13.0 25.0 22.5 18.3 .. 22.1 24.0 27.6 13.0 9.0 .. .. .. 7.3 .. 4.9 23.0 22.0 .. .. 9.2 19.7 .. .. .. .. .. m .. .. .. 13.8 .. .. .. 12.2 .. .. .. 19.4 17.6

a. Provisional data.

78

2011 World Development Indicators

% of GDP per capita Secondary 1999 2009a

.. .. 41.9 .. .. .. .. .. 18.4 25.7 .. 20.0 24.4 .. .. 23.7 26.2 27.3 21.7 .. .. 15.9 .. 30.3 12.2 27.1 9.6 .. .. .. 11.6 23.8 22.5 9.9 .. .. .. .. .. 19.4 19.3 .. m .. .. .. 16.4 .. .. .. 13.7 .. 13.6 .. 22.5 25.1

16.6 .. 34.3 18.3 25.7 13.6 18.0 15.7 14.7 .. .. 17.7 24.1 .. .. 36.2 30.6 25.2 15.5 .. 18.8 9.1 .. 19.1 9.9 .. .. .. 21.2 .. 6.7 28.2 24.2 .. .. 8.2 17.3 .. .. .. .. .. m .. .. .. 17.0 .. .. .. 13.4 .. .. .. 23.9 24.8

Public expenditure on education

Tertiary 1999

32.6 10.9 1,206.8 .. .. .. .. .. 32.9 27.9 .. .. 19.6 .. .. 444.5 52.1 53.8 .. .. .. 36.0 .. .. 148.7 89.4 33.5 .. .. 36.5 41.4 25.6 27.0 .. .. .. .. .. .. 164.6 193.0 .. m .. .. .. .. .. 38.2 .. .. .. 90.8 .. 31.4 29.1

2009a

% of GDP 2009a

26.2 .. 222.8 .. 191.5 40.1 .. 27.3 19.5 .. .. .. 25.1 .. .. .. 38.3 46.7 .. 21.8 .. 22.3 92.7 155.2 .. 54.5 .. .. 105.4 25.1 15.5 24.4 21.7 .. .. .. 61.7 .. .. .. .. .. m .. .. .. .. .. .. .. .. .. .. .. 25.2 28.9

4.3 .. 4.1 5.6 5.8 4.7 4.3 3.0 3.6 .. .. 5.4 4.3 .. .. 7.8 6.6 5.2 4.9 3.5 6.8 4.1 16.8 4.6 .. 7.1 .. .. 3.2 5.3 1.2 5.5 5.5 .. .. 3.7 5.3 .. 5.2 1.3 .. 4.5 m 3.7 4.1 .. 4.5 .. 3.5 4.2 4.0 4.6 2.9 3.8 5.1 5.2

Trained Primary teachers school in primary pupil–teacher education ratio

% of total government expenditure

% of total

pupils per teacher

2009a

2009a

2009a

.. .. 93.9 91.5 .. 94.2 49.4 94.3 .. .. .. 87.4 .. .. 59.7 94.0 .. .. .. 88.3 100.0 .. .. 14.6 88.0 .. .. .. 89.4 99.9 100.0 .. .. .. 100.0 86.3 99.6 100.0 .. .. .. .. m 80.4 .. .. .. .. .. .. .. .. .. .. .. ..

16 17 68 11 35 16 44 19 17 17 36 31 12 23 38 32 10 .. 18 23 54 16 29 41 17 17 .. .. 49 16 16 18 14 15 17 16 20 28 .. 61 .. 24 w 46 23 23 21 26 18 17 24 23 .. 45 15 15

11.8 .. 20.4 19.3 19.0 9.3 18.1 11.6 10.5 .. .. 16.9 11.1 .. .. 21.6 12.7 16.1 16.7 18.7 27.5 20.3 15.5 17.6 .. 22.4 .. .. 15.0 20.2 23.4 11.7 14.1 .. .. .. 19.8 .. 16.0 .. .. .. m .. .. .. 13.5 .. 15.9 13.4 .. 18.0 .. .. 12.5 11.1

About the data

2.11

PEOPLE

Education inputs Definitions

Data on education are collected by the United

The primary school pupil–teacher ratio reflects the

• Public expenditure per student is public current

Nations Educational, Scientific, and Cultural Organi-

average number of pupils per teacher at the specified

and capital spending on education divided by the

zation (UNESCO) Institute for Statistics from official

level of education. It differs from the average class

number of students by level as a percentage of gross

responses to its annual education survey. The data

size because of the different practices countries

domestic product (GDP) per capita. • Public expen-

are used for monitoring, policymaking, and resource

employ, such as part-time teachers, school shifts,

diture on education is current and capital expendi-

allocation. While international standards ensure

and multigrade classes. The comparability of pupil–

tures on education by local, regional, and national

comparable datasets, data collection methods may

teacher ratios across countries is affected by the

governments, including municipalities. • Trained

vary by country and within countries over time.

definition of teachers and by differences in class size

teachers in primary education are the percentage

For most countries the data on education spend-

by grade and in the number of hours taught, as well

of primary school teachers who have received the

ing in the table refer to public spending—total gov-

as the different practices mentioned above. More-

minimum organized teacher training (pre-service or

ernment spending on education at all levels plus

over, the underlying enrollment levels are subject to

in-service) required for teaching at the specified level

subsidies provided to households and other private

a variety of reporting errors (for further discussion of

of education in their country. • Primary school pupil–

entities—and generally exclude the part of foreign

enrollment data, see About the data for table 2.12).

teacher ratio is the number of pupils enrolled in pri-

aid for education that is not included in the govern-

While the pupil–teacher ratio is often used to com-

mary school divided by the number of primary school

ment budget. The data may also exclude spending

pare the quality of schooling across countries, it is

teachers (regardless of their teaching assignment).

by religious schools, which play a significant role in

often weakly related to student learning and quality

many developing countries. Data are gathered from

of education.

ministries of education and from other ministries or agencies involved in education spending.

All education data published by the UNESCO Institute for Statistics are mapped to the International

The share of public expenditure devoted to educa-

Standard Classification of Education 1997 (ISCED

tion allows an assessment of the priority a govern-

1997). This classification system ensures the com-

ment assigns to education relative to other public

parability of education programs at the international

investments, as well as a government’s commitment

level. UNESCO developed the ISCED to facilitate

to investing in human capital development. However,

comparisons of education statistics and indicators

returns on investment to education, especially pri-

of different countries on the basis of uniform and

mary and lower secondary education, cannot be

internationally agreed definitions. First developed in

understood simply by comparing current education

the 1970s, the current version was formally adopted

indicators with national income. It takes a long time

in November 1997.

before currently enrolled children can productively

The reference years shown in the table reflect the

contribute to the national economy (Hanushek

school year for which the data are presented. In

2002).

some countries the school year spans two calendar

High-quality data on education finance are scarce.

years (for example, from September 2009 to June

Improving the quality of education finance data is a

2010); in these cases the reference year refers to

priority of the UNESCO Institute for Statistics. Addi-

the year in which the school year ended (2010 in the

tional resources are being allocated for technical

previous example).

assistance to countries in need, especially those in Sub-Saharan Africa. Interagency partnerships and collaborations with national ministries in charge of education finance data are improving, and actual expenditure data are increasingly being collected. Tracking private educational spending is still a challenge for all countries. The share of trained teachers in primary education reveals a country’s commitment to invest in the development of its human capital engaged in teaching, but it does not take into account differences in teachers’ experiences and status, teaching methods, teaching materials, and classroom conditions—all factors that affect the quality of teaching

Data sources

and learning. Some teachers without this formal

Data on education inputs are from the UNESCO

training may have acquired equivalent pedagogical

Institute for Statistics (www.uis.unesco.org).

skills through professional experience.

2011 World Development Indicators

79

2.12

Participation in education Gross enrollment ratio

Preprimary 2009a

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

80

.. 58 23 40 69 33 82 95 24 10 102 122 14 47 15 17 65 81 3 10 19 26 71 5 1 55 47 121 51 4 13 70 4 60 105 111 96 37 131 16 60 13 95 4 65 110 .. 22 63 109 70 69 29 12 .. .. 40

% of relevant age group Primary Secondary 2009a

104 119 108 128 116 99 106 100 95 95 99 103 122 107 109 109 120 101 78 147 116 114 98 89 90 106 113 104 120 90 120 110 74 94 104 103 98 106 117 100 115 48 100 102 97 110 .. 86 108 105 105 101 114 90 .. .. 116

2011 World Development Indicators

Net enrollment rate

Adjusted net enrollment rate, primary

% of relevant age group Primary Secondary

Tertiary

2009a

2009a

1991

2009 a

1999

2009a

44 72 .. .. 85 93 149 100 99 42 95 108 .. 81 91 82 90 89 20 21 40 41 .. 14 24 90 78 82 95 37 .. 96 .. 90 90 95 119 77 81 .. 64 32 99 34 110 113 .. 51 108 102 57 102 57 37 .. .. 65

4 .. 31 .. 68 50 77 55 19 8 77 63 .. 38 37 .. 38 51 3 3 10 9 .. 2 2 55 25 57 37 6 6 .. 8 51 118 58 78 .. 42 28 25 2 64 4 94 55 .. 5 25 .. 9 91 18 9 .. .. 19

28 .. 89 .. .. .. 98 90 89 64 .. 96 51 .. .. 89 .. .. 27 50 .. 69 98 53 .. .. 97 .. 71 56 .. 87 46 .. 94 .. 98 .. .. .. .. 20 .. 30 99 100 .. 50 .. 84 .. 95 .. 27 .. 21 88

.. 85 94 .. .. 84 97 .. 85 86 94 98 95 91 87 87 95 96 63 99 95 92 .. 67 .. 95 .. 94 90 .. .. .. 57 91 99 .. 95 87 97 94 94 36 94 83 96 98 .. 69 100 98 76 99 95 73 .. .. 97

.. 70 .. .. 76 86 90 .. 75 40 82 .. 18 68 .. 54 66 85 9 .. 15 .. 95 .. 7 .. .. 74 56 .. .. .. 19 81 73 81 88 38 46 71 47 17 84 12 95 94 .. 26 76 .. 33 82 24 12 10 .. ..

27 .. .. .. 79 87 88 .. 93 41 87 .. .. 69 .. 60 52 83 15 9 34 .. .. 10 .. 85 .. 75 74 .. .. .. .. .. 83 .. 90 61 59 .. 55 27 89 .. 96 98 .. 42 81 .. 46 91 40 29 .. .. ..

% of primary-schoolage children Male Female 2009a

2009a

.. 86 96 .. .. 92 97 .. 86 86 94 98 99 92 86 86 96 97 68 98 90 97 .. 77 .. 96 .. 97 93 .. .. .. 62 91 100 .. 94 96 .. 97 95 39 96 86 96 99 .. 69 96 .. 76 99 98 78 .. .. 96

.. 84 94 .. .. 94 98 .. 85 93 96 99 86 92 88 88 94 98 60 100 87 86 .. 57 .. 95 .. 100 93 .. .. .. 52 92 99 .. 97 89 .. 93 96 34 97 81 96 99 .. 74 93 .. 77 100 95 68 .. .. 96

Children out of school

thousand primary-schoolage children Male Female 2009a

.. 15 59 .. .. 5 33 .. 38 1,234 12 6 7 58 11 21 289 4 392 9 99 38 .. 78 .. 35 .. 6 155 .. .. .. 609 8 2 .. 12 23 .. 137 23 190 1 929 7 18 .. 40 6 .. 430 2 23 174 .. .. 22

2009a

.. 16 82 .. .. 3 22 .. 37 575 7 4 91 53 9 18 393 3 473 1 131 210 .. 149 .. 41 .. 0b 152 .. .. .. 774 8 2 .. 7 70 .. 324 15 202 1 1,255 7 15 .. 33 10 .. 398 0b 55 244 .. .. 9

Gross enrollment ratio

Preprimary 2009a

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

87 54 50 40 6 .. 97 100 86 89 36 52 51 .. 111 .. 76 18 22 89 77 .. 145 .. 72 23 10 .. 71 4 .. 98 114 74 59 57 .. 7 .. .. 100 94 56 3 16 95 38 .. 66 .. 109 72 49 62 81 154 53

% of relevant age group Primary Secondary 2009a

99 117 121 103 103 105 111 103 93 102 97 108 113 .. 105 .. 95 95 121 98 103 104 91 .. 96 88 160 119 95 95 104 100 114 94 110 107 114 116 112 .. 107 101 117 62 93 99 84 85 109 .. 102 109 110 97 115 91 106

2009a

97 60 79 83 51 115 90 101 91 101 88 99 59 .. 97 .. 90 84 44 98 82 45 .. .. 99 84 32 30 69 38 24 87 90 88 92 56 23 53 66 .. 121 119 68 12 30 112 91 33 73 .. 67 89 82 100 104 84 85

Net enrollment rate

% of relevant age group Primary Secondary

Tertiary 2009a

65 13 24 36 .. 58 60 67 24 58 41 41 4 .. 98 .. 29 51 13 69 53 .. .. .. 77 40 4 0 36 6 4 26 27 38 53 13 .. 11 9 .. 61 78 .. 1 .. 73 26 6 45 .. 29 .. 29 69 60 78 10

Adjusted net enrollment rate, primary

1991

2009 a

1999

2009a

.. .. 95 97 76 90 .. .. 97 100 .. .. .. .. 99 .. 47 .. 59 .. .. 72 .. .. .. .. 72 .. .. .. .. 93 98 .. .. 56 42 .. 82 .. 95 100 70 23 .. 100 69 .. 92 65 94 86 96 .. 98 .. 89

90 91 95 99 88 97 97 98 80 100 89 89 83 .. 99 .. 88 84 93c .. 90 73 .. .. 92 86 98 91 94 73 76 94 98 88 90 90 91 .. 89 .. 99 99 92 54 61 99 77 66 97 .. 87 94 92 95 99 .. 93

82 .. 50 .. 30 84 86 88 83 99 79 87 33 .. 97 .. 89 .. 26 .. .. 17 20 .. 90 79 .. 29 65 .. 14 67 56 79 58 30 3 31 39 .. 91 90 35 6 .. 96 65 .. 59 .. 46 62 50 90 82 .. 74

91 .. 69 .. 43 88 86 95 77 98 82 89 50 .. 95 .. 80 79 36 .. 75 29 .. .. 92 .. 26 25 68 30 16 .. 72 80 82 .. 15 50 54 .. 88 .. 45 9 26 96 82 33 66 .. 59 71 61 94 88 .. 77

% of primary-schoolage children Male Female 2009a

2009a

95 91 .. .. 93 96 97 100 82 .. 93 89 83 .. 100 .. 94 91 84 .. 92 71 .. .. 96 91 99 89 94 84 74 93 99 91 99 92 93 .. 88 .. 99 99 93 60 66 99 82 72 98 .. 88 97 91 95 99 .. 98

95 88 .. .. 82 98 98 99 79 .. 94 90 84 .. 98 .. 93 91 81 .. 90 76 .. .. 96 92 100 94 94 70 79 95 100 90 99 88 88 .. 92 .. 99 100 94 48 60 99 81 60 97 .. 88 98 93 95 99 .. 98

2.12

PEOPLE

Participation in education

Children out of school

thousand primary-schoolage children Male Female 2009a

9 5,543 .. .. 176 9 13 5 31 .. 30 52 532 .. 4 .. 6 19 65 .. 19 54 .. .. 3 6 16 152 97 165 66 4 39 8 1 154 149 .. 22 .. 4 1 29 511 4,023 3 33 3,108 4 .. 52 54 555 62 2 .. 1

2011 World Development Indicators

2009a

9 7,112 .. .. 415 5 9 15 35 .. 23 42 497 .. 31 .. 8 18 76 .. 21 45 .. .. 3 5 3 85 95 304 51 3 23 8 1 203 264 .. 14 .. 9 0b 24 637 4,626 3 34 4,191 6 .. 50 43 407 55 4 .. 1

81

2.12

Participation in education Gross enrollment ratio

Preprimary 2009a

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

73 90 17 11 12 51 5 .. 94 83 .. 64 126 .. 28 .. 102 102 9 9 33 92 .. 7 81 .. 18 .. 12 101 94 81 58 86 26 77 .. 34 .. .. .. 44 w 15 46 42 63 40 44 55 68 20 .. 17 77 110

% of relevant age group Primary Secondary 2009a

100 97 151 99 84 98 158 .. 103 97 33 101 107 97 74 108 95 103 122 102 105 91 113 115 104 107 99 .. 122 98 105 106 99 114 92 103 .. 79 85 113 .. 107 w 104 109 107 111 107 111 99 116 105 108 100 101 ..

a. Provisional data. b. Less than 0.5. c. Data are for 2010.

82

2011 World Development Indicators

2009a

92 85 27 97 30 91 35 .. 92 97 8 94 120 .. 38 53 103 96 75 84 27 76 51 41 89 92 82 .. 27 94 95 99 94 88 104 82 .. 87 .. 49 .. 67 w 38 68 63 88 63 74 89 89 73 52 34 100 ..

Net enrollment rate

% of relevant age group Primary Secondary

Tertiary 2009a

66 77 5 37 8 50 .. .. 54 87 .. .. 71 .. .. .. 71 49 .. 20 .. 45 15 5 .. 34 38 .. 4 79 30 57 83 65 10 79 .. 46 10 .. 3 26 w 6 24 19 42 21 .. 55 35 27 11 6 67 ..

Adjusted net enrollment rate, primary

1991

2009 a

73 .. .. .. 45 .. .. .. .. .. .. 90 100 .. .. 74 100 84 91 .. 51 .. .. 65 90 94 89 .. .. .. 97 97 97 91 .. .. .. .. .. .. .. .. w .. .. .. .. .. 96 90 .. .. 68 .. 95 ..

90 .. 96 86 73 94 .. .. .. 97 .. 85 100 95 .. 83 95 94 .. 97 96 90 82 94 93 98 95 .. 92 89 90 100 92 99 87 92 .. 75 73 91 .. 88 w 80 88 87 93 87 .. 92 94 89 86 75 95 ..

1999

75 .. .. .. .. .. .. .. .. 90 .. 63 88 .. .. 32 96 84 36 63 5 .. 23 20 70 63 62 .. 8 91 69 95 88 .. .. 47 59 77 32 17 40 52 w .. .. .. 67 .. .. 79 59 60 .. .. 88 ..

2009a

73 .. .. 72 .. 90 25 .. .. 91 .. 72 95 .. .. 29 99 85 69 83 .. 71 .. .. 74 71 74 .. 22 85 83 93 88 70 92 71 .. 85 .. 46 .. 59 w .. .. .. 75 55 .. 81 73 64 .. .. 90 ..

% of primary-schoolage children Male Female

Children out of school

thousand primary-schoolage children Male Female

2009a

2009a

2009a

2009a

96 .. 95 88 74 96 .. .. .. 98 .. 89 100 95 .. 82 95 99 .. 99 96 91 84 98 97 99 96 .. 91 89 98 100 93 99 91 94 .. 78 80 91 .. 91 w 83 92 91 94 90 .. 94 95 92 92 78 95 ..

97 .. 97 85 76 96 .. .. .. 97 .. 91 100 96 .. 84 94 99 .. 96 97 89 82 89 94 100 94 .. 94 90 97 100 94 99 89 94 .. 77 66 94 .. 89 w 79 90 88 94 88 .. 94 95 89 88 75 96 ..

16 .. 38 205 262 5 .. .. .. 1 .. 385 1 45 .. 19 16 3 .. 2 160 281 15 10 2 6 147 .. 310 89 2 5 944 1 101 108 .. 57 395 112 ..

14 .. 22 244 232 6 .. .. .. 1 .. 331 3 36 .. 18 17 1 .. 15 107 305 17 56 4 0b 214 .. 213 81 4 1 770 2 119 96 .. 55 641 78 ..

About the data

2.12

PEOPLE

Participation in education Definitions

• Gross enrollment ratio is the ratio of total enroll-

School enrollment data are reported to the United

children of primary age enrolled in preprimary edu-

Nations Educational, Scientific, and Cultural Organi-

cation) are compiled from administrative data. Large

ment, regardless of age, to the population of the age

zation (UNESCO) Institute for Statistics by national

numbers of children out of school create pressure

group that officially corresponds to the level of educa-

education authorities and statistical offices. Enroll-

to enroll children and provide classrooms, teachers,

tion shown. • Preprimary education (ISCED O) refers

ment indicators help monitor whether a country is on

and educational materials, a task made difficult in

to programs at the initial stage of organized instruc-

track to achieve the Millennium Development Goal of

many countries by limited education budgets. How-

tion, designed primarily to introduce very young chil-

universal primary education by 2015, and whether

ever, getting children into school is a high priority for

dren, usually from age 3, to a school-type environment

an education system has the capacity to meet the

countries and crucial for achieving the Millennium

and to provide a bridge between the home and school.

needs of universal primary education.

Development Goal of universal primary education.

On completing these programs, children continue their

Enrollment indicators are based on annual school

In 2006 the UNESCO Institute for Statistics

education at the primary level. • Primary education

surveys but do not necessarily reflect actual atten-

changed its convention for citing the reference year.

(ISCED 1) refers to programs normally designed to

dance or dropout rates during the year. Also, the

For more information, see About the data for table

give students a sound basic education in reading,

length of primary education differs across coun-

2.11.

writing, and mathematics along with an elementary

tries and can influence enrollment rates and ratios,

understanding of other subjects such as history,

although the International Standard Classification of

geography, natural science, social science, art, and

Education (ISCED) tries to minimize the difference.

music. Religious instruction may also be featured. It

A shorter duration for primary education tends to

is sometimes called elementary education. • Sec-

increase the ratio; a longer one to decrease it (in

ondary education refers to programs of lower (ISCED

part because older children are more at risk of drop-

2) and upper (ISCED 3) secondary education. Lower

ping out).

secondary education continues the basic programs

Over- or under-age enrollments are frequent, par-

of the primary level, but the teaching is typically more

ticularly when parents prefer children to start school

subject focused, requiring more specialized teachers

at other than the official age. Age at enrollment may

for each subject area. In upper secondary educa-

be inaccurately estimated or misstated, especially

tion, instruction is often organized even more along

in communities where registration of births is not

subject lines, and teachers typically need a higher or

strictly enforced.

more subject-specific qualification. • Tertiary educa-

Population data used to calculate population-

tion refers to a wide range of programs with more

based indicators are drawn from the United Nations

advanced educational content. The first stage of ter-

Population Division. Using a single source for popula-

tiary education (ISECD 5) refers to theoretically based

tion data standardizes definitions, estimations, and

programs intended to provide sufficient qualifications

interpolation methods, ensuring a consistent meth-

to enter advanced research programs or professions

odology across countries and minimizing potential

with high-skill requirements and programs that are

enumeration problems in national censuses.

practical, technical, or occupationally specific. The

Gross enrollment ratios indicate the capacity of

second stage of tertiary education (ISCED 6) refers

each level of the education system, but a high ratio

to programs devoted to advanced study and original

may reflect a substantial number of over-age children

research and leading to the award of an advanced

enrolled in each grade because of repetition or late

research qualification. • Net enrollment rate is the

entry, rather than a successful education system.

ratio of total enrollment of children of official school

The net enrollment rate excludes over- and under-

age to the population of the age group that offi -

age students and more accurately captures the sys-

cially corresponds to the level of education shown.

tem’s coverage and internal efficiency. Differences

• Adjusted net enrollment rate, primary, is the ratio

between the gross enrollment ratio and net enroll-

of total enrollment of children of official school age

ment rate show the incidence of over- and under-age

for primary education who are enrolled in primary or

enrollments.

secondary education to the total primary school-age

The adjusted net enrollment rate in primary educa-

population. • Children out of school are the number

tion captures primary-school-age children who have

of primary-school-age children not enrolled in primary

progressed to secondary education faster than their

or secondary school.

peers and who would not be counted in the traditional net enrollment rate.

Data sources

Data on children out of school (primary-school-

Data on participation in education are from

age children not enrolled in primary or secondary

the UNESCO Institute for Statistics, www.uis.

school—dropouts, children never enrolled, and

unesco.org.

2011 World Development Indicators

83

2.13

Education efficiency Gross intake ratio in ﬁrst grade of primary education

Cohort survival rate

Repeaters in primary education

Transition rate to secondary education

% of grade 1 students Reaching grade 5

% of relevant age group

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

84

Male 2009a

Female

129 89 101 .. 111 86 .. 104 95 101 97 97 161 114 89 114 .. 107 90 152 158 134 .. 110 131 101 94 117 118 119 115 98 77 95 100 109 98 109 119 98 123 45 102 158 100 .. .. 91 107 100 109 102 123 106 .. .. 126

93 82 99 .. 111 89 .. 100 94 105 102 98 152 113 92 112 .. 108 83 146 157 117 .. 86 98 98 98 124 114 106 112 96 67 94 102 107 99 90 124 96 119 39 102 141 98 .. .. 96 112 99 111 103 121 96 .. .. 122

2009a

2011 World Development Indicators

Reaching last grade of primary education

Male

Female

Male

Female

% of enrollment Male Female

% Male

Female

1991

2008 a

1991

2008 a

2008a

2008a

2009a

2009a

2008a

2008a

89 .. 82 .. .. .. 98 .. .. .. .. 87 30 57 .. 73 .. .. 61 66 .. 67 .. 52 43 .. .. .. 53 66 66 70 68 .. .. .. 98 .. .. .. 54 .. .. .. 96 .. 47 59 .. .. 72 .. .. 43 .. 47 50

.. .. 94 .. 95 .. .. .. .. 67 .. 90 .. 86 .. .. .. .. 73c 62 68 76 .. 58 .. 96 .. 100 82 78 75 95 66 .. 96 99 100 .. 80 .. 78 74 99 43 99 .. .. 71 96 .. 80 98 71 72 .. .. 75

89 .. 79 .. .. .. 99 .. .. .. .. 90 31 51 .. 81 .. .. 58 61 .. 66 .. 39 22 .. .. .. 59 55 68 73 61 .. .. .. 99 .. .. .. 57 .. .. .. 97 .. 46 53 .. .. 65 .. .. 35 .. 46 43

.. .. 95 .. 98 .. .. .. .. 66 .. 92 .. 85 .. .. .. .. 78 c 68 71 79 .. 48 .. 97 .. 100 89 77 79 97 66 .. 96 99 99 .. 83 .. 82 72 98 49 100 .. .. 72 95 .. 78 97 70 64 .. .. 80

.. .. 91 .. 93 98 .. 96 100 67 99 86 .. 85 .. .. .. 93 61c 56 60 68 .. 51 .. .. .. 100 82 78 71 93 62 97 96 99 99 .. 79 .. 74 74 99 35 99 .. .. 68 95 95 75 98 65 68 .. .. 74

.. .. 95 .. 97 97 .. 99 97 66 99 88 .. 82 .. .. .. 94 67c 64 63 69 .. 41 .. .. .. 100 89 73 71 96 59 99 95 99 99 .. 82 .. 78 72 98 41 100 .. .. 72 94 96 71 97 64 57 .. .. 79

.. 2 13 .. 7 0b .. 0 0b 14 0b 4 14 1 0b 6 .. 2 11 32 10 15 .. 24 22 3 0b 1 2 15 21 6 19 0b 1 1 0 9 6 4 7 14 1 6 1 .. .. 6 0b 1 7 1 13 15 .. .. 6

.. 1 8 .. 5 0b .. 0 0b 13 0b 3 14 1 0b 4 .. 1 11 32 8 14 .. 24 24 2 0b 1 2 16 19 4 19 0b 0b 1 0 5 5 2 5 13 0b 5 0b .. .. 5 0b 1 6 1 11 16 .. .. 5

.. .. 90 .. 93 100 .. 100 100 .. 100 100 .. 96 .. 98 .. 95 56 c 48 80 42 .. 45 64 86 .. 100 100 83 65 97 47 100 99 99 95 88 81 .. 92 85 97 84 100 .. .. 83 99 99 91 .. 93 50 .. .. 82

.. .. 92 .. 96 98 .. 99 98 .. 100 99 .. 94 .. 97 .. 95 51c 23 81 45 .. 45 65 100 .. 100 100 76 62 91 45 99 98 99 98 92 77 .. 92 81 99 87 100 .. .. 83 99 99 92 .. 90 40 .. .. 86

Gross intake ratio in ﬁrst grade of primary education

Cohort survival rate

Repeaters in primary education

2.13

PEOPLE

Education efficiency

Transition rate to secondary education

% of grade 1 students Reaching grade 5

% of relevant age group

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

Male 2009a

Female

Male

2009a

1991

2008 a

1991

103 132 125 100 105 99 96 102 90 102 99 105 .. .. 106 .. 95 97 124 104 100 106 117 .. 97 92 198 136 89 102 112 99 122 94 147 107 163 140 98 .. 101 .. 158 97 102 97 88 111 105 .. 101 100 139 .. 107 97 103

103 124 122 100 103 101 98 101 86 102 99 106 .. .. 104 .. 93 97 115 105 105 98 107 .. 94 93 196 144 89 89 119 99 122 93 142 106 156 135 99 .. 101 .. 148 83 83 99 86 96 103 .. 97 100 130 .. 103 94 108

.. .. .. 75 75 .. .. .. 92 100 93 .. .. .. 92 .. .. .. 34 .. .. 53 .. .. .. .. 31 37 86 48 52 .. 81 .. .. 70 42 .. 52 44 .. 96 39 68 .. 99 77 .. .. 55 58 .. .. .. .. .. 98

.. 67 83 94 .. 98 100 99 .. 100 .. .. .. .. 98 .. 95 .. 66 98 94 56 64 .. .. .. 48 51 96 88 48 96 93 .. 94 84 56c 70 90 60 99 .. 48 66c .. 99 .. 61 88 .. 82 87 75 .. .. .. 92

.. .. .. 67 70 .. .. .. 94 100 89 .. .. .. 92 .. .. .. 32 .. .. 77 .. .. .. .. 31 33 87 42 47 .. 82 .. .. 64 34 .. 57 32 .. 95 48 65 .. 100 78 .. .. 52 60 .. .. .. .. .. 99

Reaching last grade of primary education Female

% of enrollment Male Female

Male

Female

2008 a

2008a

2008a

2009a

.. 70 89 94 .. 100 98 100 .. 100 .. .. .. .. 99 .. 96 .. 68 94 96 69 56 .. .. .. 50 50 97 85 51 99 95 .. 95 85 51c 69 93 64 100 .. 55 62c .. 100 .. 60 91 .. 85 88 82 .. .. .. 99

99 67 77 94 .. .. 99 99 .. 100 .. 98 c .. .. 98 .. 95 96 66 97 90 38 49 .. 98 98 48 42 96 81 40 94 90 95 94 78 37c 70 80 60 .. .. 45 63c .. 99 .. 61 86 .. 77 82 71 .. .. .. 91

99 70 83 95 .. .. 98 100 .. 100 .. 99 c .. .. 99 .. 96 97 68 94 93 56 43 .. 98 97 50 42 96 77 42 98 93 96 95 78 34c 69 85 64 .. .. 52 60 c .. 99 .. 60 88 .. 81 84 80 .. .. .. 97

2 3 4 2 19 1 2 0b 3 0 1 0b .. .. 0b .. 1 0b 15d 5 11 23 6 .. 1 0b 21 19 .. 13 2 4 4 0b 0b 13 7 0b 18 17 .. .. 13 5 .. .. 1 3 6 .. 5 7 3 2 .. .. 0b

% Male

Female

2009a

2008a

2008a

1 3 3 2 14 1 1 0b 3 0 1 0b .. .. 0b .. 1 0b 13d 2 7 16 7 .. 1 0b 20 18 .. 14 2 3 3 0b 0b 9 7 0b 14 17 .. .. 9 5 .. .. 2 3 4 .. 3 7 2 1 .. .. 0b

99 81 91 96 .. .. 71 100 .. .. 99 100 c .. .. 100 .. 99 99 80 92 84 68 64 .. 99 99 57 75 100 72 38 64 94 99 96 80 52c 74 80 81 .. .. .. 56 c 44 100 .. 73 96 .. 88 94 100 .. .. .. 100

99 81 93 97 .. .. 70 100 .. .. 98 100 c .. .. 100 .. 100 100 77 97 89 66 60 .. 99 100 55 74 99 68 31 75 93 98 99 78 55c 73 83 81 .. .. .. 62c 44 100 .. 72 97 .. 89 93 98 .. .. .. 100

2011 World Development Indicators

85

2.13

Education efficiency Gross intake ratio in ﬁrst grade of primary education

Cohort survival rate

Repeaters in primary education

Transition rate to secondary education

% of grade 1 students Reaching grade 5

% of relevant age group

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

Male 2009a

Female

101 .. 194 102 96 95 201 .. 100 97 .. 92 105 92 86 105 104 93 117 106 99 .. 142 105 102 106 101 .. 140 100 113 .. 103 101 94 101 .. 77 110 116 .. 114 w 133 114 115 .. 115 105 .. .. 104 126 121 102 102

99 .. 189 101 102 94 182 .. 99 97 .. 87 106 93 76 101 103 96 113 101 100 .. 134 102 100 107 98 .. 143 100 113 .. 109 111 91 98 .. 77 98 119 .. 110 w 126 110 110 .. 111 107 .. .. 101 117 113 104 101

2009a

Male 1991

.. .. 49 80 78 .. .. .. .. .. .. 61 .. 97 .. 58 99 72 87 .. 69 .. .. 55 98 76 93 .. .. .. 78 .. .. 98 .. 69 .. .. .. .. 70 .. w .. .. .. .. .. .. .. .. .. .. .. .. ..

Female

2011 World Development Indicators

Male

Female

2008 a

1991

2008 a

2008a

2008a

.. .. 46 99 69 .. .. 99 .. .. .. .. 99 88 89 75 100 .. .. .. 79 .. 72 80 97 96 94 .. 57 .. 97 .. .. 93 .. 92 .. .. .. 71 .. .. w .. .. .. .. .. .. .. .. .. 68 .. .. ..

.. .. 51 76 68 .. .. .. .. .. .. 67 .. 98 .. 64 99 72 85 .. 71 .. .. 38 99 70 92 .. .. .. 80 .. .. 100 .. 80 .. .. .. .. 72 .. w .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. 51 93 71 .. .. 99 .. .. .. .. 100 89 100 86 100 .. .. .. 83 .. 80 71 95 96 94 .. 58 .. 97 .. .. 96 .. 96 .. .. .. 70 .. .. w .. .. .. .. .. .. .. .. .. 70 .. .. ..

93 .. .. 98 56 99 .. 99 97 .. .. .. 99 88 86 70 100 .. 93 .. 71 .. 68 76 93 94 94 .. 54 96 97 .. .. 93 98 89 .. 99 .. 55 .. .. w .. .. .. .. .. .. .. .. .. 68 .. .. 98

94 .. .. 91 59 97 .. 99 98 .. .. .. 100 89 98 74 100 .. 94 .. 77 .. 78 62 93 95 94 .. 53 98 97 .. .. 96 99 95 .. 97 .. 52 .. .. w .. .. .. .. .. .. .. .. .. 70 .. .. 99

a. Provisional data. b. Less than 0.5. c. Data are for 2009. d. Data are for 2010.

86

Reaching last grade of primary education

% of enrollment Male Female 2009a

2 .. 15 4 8 1 10 0b 3 1 .. 8 3 1 4 21 0 2 9 0b 2 12 21 23 7 10 2 .. 14 0b 2 0 0 8 0b 4 .. 0 6 6 .. 5w 11 4 4 .. 5 1 .. .. 9 4 10 1 2

2009a

1 .. 14 4 7 1 10 0b 3 0b .. 8 2 1 4 15 0 1 7 0b 2 6 18 22 5 6 2 .. 14 0b 2 0 0 5 0b 3 .. 0 5 6 .. 4w 11 3 3 .. 4 1 .. .. 5 4 10 1 1

% Male

Female

2008a

2008a

97 .. .. 93 62 100 .. 86 97 .. .. 90 .. 95 90 .. 100 99 94 98 40 85 86 66 86 79 .. .. 58 100 98 .. .. 81 100 97 .. 97 .. 66 .. .. w .. .. .. .. .. .. .. .. .. 80 66 .. ..

97 .. .. 100 57 99 .. 92 97 .. .. 91 .. 97 98 .. 100 100 96 98 32 89 88 58 92 86 .. .. 55 100 99 .. .. 93 99 97 .. 97 .. 67 .. .. w .. .. .. .. .. .. .. .. .. 80 65 .. ..

About the data

2.13

PEOPLE

Education efficiency Definitions

The United Nations Educational, Scientific, and Cul-

data on repeaters by grade for the most recent of

• Gross intake ratio in ﬁrst grade of primary edu-

tural Organization (UNESCO) Institute for Statistics

those two years to reflect current patterns of grade

cation is the number of new entrants in grade 1,

calculates indicators of students’ progress through

transition. Rates approaching 100 percent indicate

regardless of age, expressed as a percentage of the

school. These indicators measure an education sys-

high retention and low dropout levels.

population of the official school age. • Cohort sur-

tem’s success in reaching students, efficiently mov-

Data on repeaters are often used to indicate an

vival rate is the percentage of children enrolled in

ing students from one grade to the next, and trans-

education system’s internal efficiency. Repeaters not

the first grade of primary education who eventually

mitting knowledge at a particular level of education.

only increase the cost of education for the family

reach grade 5 or the last grade of primary education.

The gross intake ratio to the first grade of primary

and the school system, but also use limited school

The estimate is based on the reconstructed cohort

education indicates the level of access to primary

resources. Country policies on repetition and promo-

method (see About the data). • Repeaters in primary

education and the education system’s capacity to

tion differ. In some cases the number of repeaters

education are the number of students enrolled in the

provide access to primary education. A low gross

is controlled because of limited capacity. In other

same grade as in the previous year as a percentage

intake ratio in grade 1 reflects the fact that many chil-

cases the number of repeaters is almost 0 because

of all students enrolled in primary school. • Transi-

dren do not enter primary school even though school

of automatic promotion—suggesting a system that

tion rate to secondary education is the number of

attendance, at least through the primary level, is

is highly efficient but that may not be endowing stu-

new entrants to the first grade of secondary edu-

mandatory in most countries. Because the gross

dents with enough cognitive skills.

cation (general programs only) in a given year as a

intake ratio includes all new entrants regardless of

The transition rate from primary to secondary

percentage of the number of pupils enrolled in the

age, it can exceed 100 percent in some situations,

school conveys the degree of access or transition

final grade of primary education in the previous year.

such as immediately after fees have been abolished

between the two levels. As completing primary edu-

or when the number of reenrolled children is large.

cation is a prerequisite for participating in lower

The indicator is not calculated when new entrants

secondary school, growing numbers of primary

and repeaters are not correctly distinguished in

completers will inevitably create pressure for more

grade 1.

available places at the secondary level. A low transi-

The survival rate to grade 5 and to the last grade

tion rate can signal such problems as an inadequate

of primary education shows the percentage of stu-

examination and promotion system or insufficient

dents entering primary school who are expected to

secondary school capacity. The quality of data on

reach the specified grade. It measures an education

the transition rate is affected when new entrants and

system’s holding power and internal efficiency. Sur-

repeaters are not correctly distinguished in the first

vival rates are calculated based on the reconstructed

grade of secondary school. Students who interrupt

cohort method, which uses data on enrollment by

their studies after completing primary school could

grade for the two most recent consecutive years and

also affect data quality. In 2006 the UNESCO Institute for Statistics

There are more overage children among the poor in primary school in Zambia

Percent of total enrollment 100

Overage children Underage children

changed its convention for citing the reference year.

2.13a

For more information, see About the data for table 2.11.

On-time Children

75

50

25

0 Poorest wealth quintile

Middle

Richest wealth quintile

Data sources Data on education efficiency are from the UNESCO

Source: World Bank, EdStats.

Institute for Statistics, www.uis.unesco.org.

2011 World Development Indicators

87

2.14

Education completion and outcomes

Total

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

88

1991

2009a

28 .. 80 33 100 105 .. .. 95 41 94 79 22 71 .. 90 93 90 20 46 45 53 .. 28 18 .. 107 102 73 48 54 79 42 85 99 92 98 61 91 .. 65 18 .. 23 97 106 62 45 .. 100 64 99 .. 17 5 27 64

.. 90 91 .. 102 98 .. 99 92 61 96 86 62 99 .. 95 .. 90 43 52 83 73 .. 38 33 95 .. 93 115 56 74 96 46 100 98 95 101 90 103 95 89 48 100 55 98 .. .. 79 107 104 83 101 80 62 .. .. 90

2011 World Development Indicators

Primary completion rate

Youth literacy rate

% of relevant age group

% ages 15–24

Male 1991 2009a

41 .. 86 .. .. .. .. .. 96 .. 95 76 30 78 .. 83 .. 88 25 49 .. 57 .. 37 29 .. .. .. 70 61 59 77 53 .. .. 91 98 .. 91 .. 64 21 .. 28 98 .. 59 56 .. 99 71 99 .. 24 7 29 67

.. 90 90 .. 100 96 .. 99 92 58 93 84 71 99 .. 93 .. 91 46 54 83 80 .. 47 42 101 .. 92 113 66 77 95 54 99 98 95 100 90 101 97 88 52 100 57 99 .. .. 76 110 103 85 102 83 71 .. .. 87

Adult literacy PISA rate mathematics literacy

% ages 15 and older

Female 1991 2009a

Male 1990 2005–09b

Female 1990 2005–09b

14 .. 73 .. .. .. .. .. 94 .. 95 82 14 64 .. 98 .. 92 15 43 .. 49 .. 20 7 .. .. .. 76 36 49 81 32 .. .. 93 98 .. 92 .. 66 15 .. 18 97 .. 65 34 .. 100 56 98 .. 9 3 26 61

.. .. 86 .. .. 100 .. .. .. .. 100 .. .. .. .. .. .. .. .. 59 .. .. .. 63

.. .. 62 .. .. 100 .. .. .. .. 100 .. .. .. .. .. .. .. .. 48 .. .. .. 35

.. 89 91 .. 104 100 .. 98 91 63 92 88 53 98 .. 97 .. 89 40 51 84 67 .. 29 24 88 .. 93 117 46 72 97 39 100 98 95 101 89 104 93 91 43 101 53 97 .. .. 83 104 104 81 101 77 53 .. .. 93

26 .. 97 .. .. .. .. .. 60 .. .. .. .. .. 97 71 .. .. 100 .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. 99 94 81 99 100 .. .. 100 74 100 .. 65 99 100 94 97 98 47 77 89 89 .. 72 54 99 99 .. 97 73 87 98 72 100 100 .. .. 95 97 88 95 92 100 56 .. .. 99 71 100 .. 81 99 89 68 78 .. 93

9 .. 91 .. .. .. .. .. 38 .. .. .. .. .. 96 54 .. .. 100 .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. 99 89 66 99 100 .. .. 100 77 100 .. 43 99 100 97 99 97 33 76 86 77 .. 57 39 99 99 .. 98 62 78 99 61 100 100 .. .. 97 97 82 95 86 100 33 .. .. 97 60 100 .. 79 99 84 54 64 .. 95

Total 2005–09 b

Mean score 2009

.. 96 73 70 98 100 .. .. 100 56 100 .. 42 91 98 84 90 98 29 67 78 71 .. 55 34 99 94 .. 93 67 .. 96 55 99 100 .. .. 88 84 66 84 67 100 30 .. .. 88 46 100 .. 67 97 74 39 52 49 84

.. 377 .. .. 388 .. 514 496 431 .. .. 515 .. .. .. .. 386 428 .. .. .. .. 527 .. .. 421 .. 555 381 .. .. .. .. 460 .. 493 503 .. .. .. .. .. 512 .. 541 497 .. .. .. 513 .. 466 .. .. .. .. ..

Total 1991

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

82 64 93 88 58 103 .. 98 94 102 101 103 .. .. 99 .. 57 .. 41 .. .. 59 .. .. .. 98 36 31 91 9 33 115 88 .. .. 48 26 .. 74 51 .. .. 42 17 .. 100 74 .. 86 46 68 .. 88 96 .. .. 71

Primary completion rate

Youth literacy rate

% of relevant age group

% ages 15–24

Adult literacy PISA rate mathematics literacy

% ages 15 and older

2009a

Male 1991 2009a

Female 1991 2009a

Male 1990 2005–09b

Female 1990 2005–09b

95 95 109 101 64 99 99 104 89 101 100 106 .. .. 99 .. 93 94 75 95 85 70 58 .. 92 92 79 59 97 59 64 89 104 93 93 80 57 99 87 .. .. .. 75 40 79 98 80 61 102 .. 94 101 94 96 .. .. 108

89 76 .. 93 63 103 .. 98 90 102 101 103 .. .. 99 .. 58 .. 46 .. .. 42 .. .. .. .. 35 35 91 12 39 115 91 .. .. 57 32 .. 67 70 .. .. 43 21 .. 100 78 .. 86 51 68 .. 85 .. .. .. 71

90 52 .. 82 52 103 .. 97 98 102 101 103 .. .. 100 .. 56 .. 36 .. .. 76 .. .. .. .. 37 27 91 7 26 115 92 .. .. 39 21 .. 81 41 .. .. 53 13 .. 100 70 .. 86 42 69 .. 86 .. .. .. 72

.. .. 97 85 .. .. .. .. .. .. .. 100 .. .. .. .. 91 .. .. 100 .. .. .. .. 100 .. .. 70 .. .. .. 91 96 100 .. .. .. .. .. .. .. .. .. .. .. .. .. .. 95 .. .. .. 96 .. .. 92 89

.. .. 95 66 .. .. .. .. .. .. .. 100 .. .. .. .. 84 .. .. 100 .. .. .. .. 100 .. .. 49 .. .. .. 92 95 100 .. .. .. .. .. .. .. .. .. .. .. .. .. .. 95 .. .. .. 97 .. .. 94 91

97 95 109 101 73 99 99 104 88 100 99 106 .. .. 100 .. 94 94 78 97 83 60 63 .. 92 91 79 58 97 67 63 89 104 94 94 84 63 98 83 .. .. .. 71 47 84 98 80 68 102 .. 93 101 91 .. .. .. 109

94 94 110 101 54 99 100 104 90 101 100 106 .. .. 97 .. 93 95 71 93 87 81 53 .. 92 93 79 60 97 52 66 90 105 91 92 77 51 100 91 .. .. .. 78 34 74 97 79 54 101 .. 95 101 97 .. .. .. 106

99 88 100 99 85 .. .. 100 92 .. 99 100 92 100 .. .. 99 100 89 100 98 86 70 100 100 99 66 87 98 47 71 96 99 99 95 87 78 96 91 87 .. .. 85 52 78 .. 98 79 97 65 99 98 97 100 100 87 98

2.14

PEOPLE

Education completion and outcomes

99 74 99 99 80 .. .. 100 98 .. 99 100 94 100 .. .. 99 100 79 100 99 98 81 100 100 99 64 86 99 31 64 98 98 100 97 72 64 95 95 77 .. .. 89 23 65 .. 98 61 96 70 99 97 98 100 100 88 98

Total 2005–09 b

Mean score 2009

99 63 92 85 78 .. .. 99 86 .. 92 100 87 100 .. .. 94 99 73 100 90 90 59 89 100 97 64 74 92 26 57 88 93 98 97 56 55 92 89 59 .. .. 78 29 61 .. 87 56 94 60 95 90 95 100 95 90 95

490 .. 371 .. .. 487 447 483 .. 529 387 405 .. .. 546 .. .. 331 .. 482 .. .. .. .. 477 .. .. .. .. .. .. .. 419 .. .. .. .. .. .. .. 526 519 .. .. .. 498 .. .. 360 .. .. 365 .. 495 487 .. 368

2011 World Development Indicators

89

2.14

Education completion and outcomes Primary completion rate

Youth literacy rate

% of relevant age group

% ages 15–24

Total 1991

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

96 92 50 .. 39 .. .. .. 95 95 .. 76 104 101 .. 61 96 53 89 .. 55 .. .. 35 102 74 90 .. .. 92 103 .. .. 94 80 81 .. .. .. .. 97 79 w 44 83 82 88 78 101 92 84 .. 62 51 .. 101

2011 World Development Indicators

% ages 15 and older

2009a

Male 1991 2009a

Female 1991 2009a

Male 1990 2005–09b

Female 1990 2005–09b

96 95 54 93 57 96 88 .. 96 96 .. 93 100 97 57 72 94 94 112 98 102 .. 80 61 93 93 93 .. 72 95 99 .. 95 106 92 95 .. 82 61 87 .. 88 w 63 92 90 100 87 99 96 101 95 79 64 98 ..

96 92 51 .. 48 .. .. .. 95 .. .. 72 104 101 .. 57 96 53 94 .. 56 .. .. 48 99 79 93 .. .. 99 104 .. .. 91 .. 76 .. .. .. .. 99 86 w .. 89 89 89 85 105 93 84 .. 75 57 .. 100

96 93 50 .. 31 .. .. .. 96 .. .. 80 103 101 .. 64 96 54 84 .. 55 .. .. 22 105 70 86 .. .. 99 103 .. .. 96 .. 86 .. .. .. .. 96 75 w .. 77 74 88 73 97 92 85 .. 52 47 .. 100

.. 100 .. .. 49 .. .. 99 .. .. .. .. .. .. .. 83 .. .. .. 100 86 .. .. .. 99 .. 97 .. .. .. 81 .. .. 98 .. 95 94 .. .. 67 .. 87 w 66 88 87 94 86 96 99 91 84 71 73 99 ..

.. 100 .. .. 28 .. .. 99 .. .. .. .. .. .. .. 84 .. .. .. 100 78 .. .. .. 99 .. 88 .. .. .. 85 .. .. 99 .. 96 93 .. .. 66 .. 78 w 52 78 74 92 75 91 98 92 67 47 58 99 ..

96 .. 52 95 56 97 101 .. 96 97 .. 93 100 97 53 75 95 93 113 97 102 .. 80 71 93 93 95 .. 72 98 100 .. 94 104 93 94 .. 82 72 92 .. 90 w 66 93 92 100 89 98 97 100 97 82 69 98 ..

a. Provisional data. b. Data are for the most recent year available. c. Data are for 2010.

90

Adult literacy PISA rate mathematics literacy

96 .. 56 90 57 96 75 .. 96 96 .. 94 100 98 47 69 94 95 111 93 102 .. 79 52 93 93 92 .. 73 99 98 .. 97 108 91 96 .. 81 49 82 .. 87 w 60 91 89 100 85 100 95 102 92 76 60 98 ..

97 100 77 99 74 .. 68 100 .. 100 .. 97 100 97 89 92 .. .. 96 100 78 98 .. 85 100 98 99 100 90c 100 94 .. .. 98 100 98 97 99 96 82 98 92 w 76 94 93 98 91 99 99 97 93 85 77 99 ..

98 100 77 97 56 .. 48 100 .. 100 .. 98 100 99 83 95 .. .. 93 100 76 98 .. 68 100 96 97 100 85c 100 97 .. .. 100 100 99 96 99 72 67 99 87 w 69 88 86 97 85 99 99 97 87 72 67 99 ..

Total 2005–09 b

Mean score 2009

98 100 71 86 50 .. 41 95 .. 100 .. 89 98 91 70 87 .. .. 84 100 73 94 51 57 99 78 91 100 73c 100 90 .. .. 98 99 95 93 95 62 71 92 84 w 62 83 80 92 80 94 98 91 74 61 62 98 ..

427 468 .. .. .. 442 .. 562 497 501 .. .. 483 .. .. .. 494 534 .. .. .. 419 .. .. 414 371 445 .. .. .. .. 492 487 427 .. .. .. .. .. .. ..

About the data

2.14

PEOPLE

Education completion and outcomes Definitions

Many governments publish statistics that indicate

Many countries estimate the number of literate

• Primary completion rate is approximated by the

how their education systems are working and devel-

people from self-reported data. Some use educa-

gross intake ratio to last grade of primary educa-

oping—statistics on enrollment and such efficiency

tional attainment data as a proxy but apply different

tion, which is the total number of new entrants in

indicators as repetition rates, pupil–teacher ratios,

lengths of school attendance or levels of completion.

the last grade of primary education, regardless of

and cohort progression. The World Bank and the

Because definitions and methodologies of data col-

age, expressed as a percentage of the population

United Nations Educational, Scientific, and Cultural

lection differ across countries, data should be used

at the entrance age to the last grade of primary.

Organization (UNESCO) Institute for Statistics jointly

cautiously.

• Youth literacy rate is the percentage of the popula-

developed the primary completion rate indicator.

The reported literacy data are compiled by the

tion ages 15–24 that can, with understanding, both

Increasingly used as a core indicator of an educa-

UNESCO Institute for Statistics based on national cen-

read and write a short simple statement on their

tion system’s performance, it reflects an education

suses and household surveys during 1985–2009. For

everyday life. • Adult literacy rate is the percentage

system’s coverage and the educational attainment

countries without recent literacy data, the UNESCO

of the population ages 15 and older that can, with

of students. The indicator is a key measure of edu-

Institute for Statistics estimates literacy rates with

understanding, both read and write a short simple

cation outcome at the primary level and of progress

the Global Age-specific Literacy Projections Model

statement on their everyday life. • PISA mathemat-

toward the Millennium Development Goals and the

(GALP). For detailed information on sources, defini-

ics literacy is the country’s mean mathematics

Education for All initiative. However, a high primary

tions, and methodology, consult www.uis.unesco.org.

score from the Programme for International Student

completion rate does not necessarily mean high lev-

Literacy statistics for most countries cover the

els of student learning. The primary completion rate reflects the primary

younger ages or are confined to age ranges that tend

cycle as defined by the International Standard Classi-

to inflate literacy rates. The youth literacy rate for

fication of Education (ISCED 97), ranging from three or

ages 15–24 reflects recent progress in education: it

four years of primary education (in a very small num-

measures the accumulated outcomes of primary edu-

ber of countries) to five or six years (in most coun-

cation over the previous 10 years or so by indicating

tries) and seven (in a small number of countries).

the proportion of people who have passed through

The table shows the primary completion rate, also

the primary education system and acquired basic

called the gross intake ratio to last grade of primary

literacy and numeracy skills. Generally, literacy also

education. It is the total number of new entrants in

encompasses numeracy, the ability to make simple

the last grade of primary education, regardless of age,

arithmetic calculations.

expressed as a percentage of the population at the

In many countries national assessments enable

entrance age to the last grade of primary education.

ministries of education to monitor progress in learn-

Data limitations preclude adjusting for students who

ing outcomes. Of the handful of internationally or

drop out during the final year of primary education.

regionally comparable assessments, one of the

Thus, this rate is a proxy that should be taken as an

largest is the Programme for International Student

upper estimate of the actual primary completion rate.

Assessment (PISA). Coordinated by the Organisation

There are many reasons why the primary comple-

for Economic Co-operation and Development (OECD),

tion rate can exceed 100 percent. The numerator

it measures the knowledge and skills of 15-year-olds,

may include late entrants and overage children

the age at which students in most countries are near-

who have repeated one or more grades of primary

ing the end of their compulsory time in school. The

education as well as children who entered school

assessment tests reading, mathematical, and sci-

early, while the denominator is the number of chil-

entific literacy in terms of general competencies—

dren at the entrance age to the last grade of primary

that is, how well students can apply the knowledge

education.

and skills they have learned at school to real-life

Basic student outcomes include achievements in reading and mathematics judged against established

Assessment (PISA).

population ages 15 and older, but some include

challenges. It does not test how well a student has mastered a school’s specific curriculum.

standards. The UNESCO Institute for Statistics has

The table presents the mean PISA mathematical

established literacy as an outcome indicator based

literacy score, as demonstrated through students’

on an internationally agreed definition. The literacy

ability to analyze, reason, and communicate effec-

rate is the percentage of the population who can,

tively while posing, solving, and interpreting math-

with understanding, both read and write a short,

ematical problems that involve quantitative, spatial,

simple statement about their everyday life. In prac-

probabilistic, or other mathematical concepts. The

tice, literacy is diffi cult to measure. To estimate

average score in 2009 was 496. Because the figures

literacy using such a definition requires census or

are derived from samples, the scores reflect a small

survey measurements under controlled conditions.

measure of statistical uncertainty.

Data sources Data on education completion and outcomes are from the UNESCO Institute for Statistics. Data on PISA mathematics literacy are from the OECD.

2011 World Development Indicators

91

2.15

Education gaps by income and gender Survey year

Armenia Azerbaijan Bangladesh Belize Benin Bolivia Burundi Cambodia Cameroon Colombia Côte d’Ivoire Dominican Republic Egypt, Arab Rep. Ethiopia Georgia Ghana Guatemala Guinea Guinea-Bissau Guyana Haiti Kazakhstan Kenya Kosovo Lesotho Macedonia, FYR Madagascar Malawi Malawi Mali Mauritania Moldova Mozambique Namibia Nepal Nicaragua Niger Nigeria Panama Peru Rwanda Serbia Somalia Swaziland Syrian Arab Republic Tanzania Togo Turkey Uganda Vietnam Yemen, Rep. Zambia Zimbabwe

92

2005 2006 2006 2006 2006 2003 2005 2005 2006 2005 2006 2007 2005 2005 2006 2006 2000 2005 2006 2006 2005 2006 2003 2000 2004 2005 2003/04 2004 2006 2006 2007 2005 2003 2006 2001 2001 2006 2003 2003 2004 2005 2005 2005 2006 2006 2004 2006 2003 2006 2006 2006 2007 1999

Gross intake rate in grade 1

Gross primary participation rate

Average years of schooling

Primary completion rate

Children out of school

% of relevant age group Poorest Richest quintile quintile

% of relevant age group Poorest Richest quintile quintile

Ages 15–19 Poorest Richest quintile quintile

% of relevant age group Richest quintile Male

% of relevant age group Poorest Richest quintile quintile

93 92 144 80 67 92 201 208 108 161 51 130 107 86 90 107 176 55 135 74 177 118 134 104 169 102 250 235 234 41 67 96 128 112 184 149 50 78 125 121 274 90 13 147 110 123 115 108 180 99 66 135 106

2011 World Development Indicators

80 118 147 89 107 95 191 151 75 84 77 112 97 124 104 121 124 119 184 76 188 101 125 119 111 190 153 145 207 98 96 84 143 104 141 106 90 101 116 90 195 98 44 117 149 123 148 111 144 100 109 123 111

106 100 96 106 61 108 91 113 93 127 57 113 95 47 101 81 81 52 94 105 87 106 92 95 116 89 118 98 133 46 62 99 75 118 109 85 35 70 108 118 131 98 8 117 102 82 99 97 107 108 50 105 144

102 108 105 113 114 129 144 134 116 99 110 107 99 112 103 117 114 121 166 101 159 103 106 104 124 97 145 122 169 110 116 95 143 109 139 105 89 108 102 96 151 100 93 114 107 119 128 97 124 100 101 112 144

9 9 8 8 6 6 4 5 6 6 5 7 9 3 15 5 4 5 4 10 4 9 6 9 5 8 3 5 5 5 5 9 3 7 5 4 4 7 7 7 3 9 8 6 7 5 6 6 5 .. 7 5 7

10 11 13 11 8 9 7 8 14 10 8 11 12 6 14 8 8 7 7 10 7 9 9 11 8 10 8 8 7 8 9 12 6 10 8 9 7 10 11 11 5 10 10 9 8 7 7 7 8 .. 10 9 10

Poorest quintile

119 94 65 59 31 76 20 42 43 94 47 69 84 14 102 62 15 32 34 109 31 102 40 82 36 120 42 24 30 36 17 97 13 81 49 34 31 48 100 106 31 86 2 69 92 32 40 95 27 99 25 50 36

116 109 97 130 95 98 70 121 111 109 127 109 92 90 102 88 80 93 125 118 136 115 76 94 122 119 141 81 80 79 89 100 100 109 96 124 71 71 94 99 88 96 58 110 93 108 82 85 68 104 103 101 80

113 103 83 107 67 90 44 88 90 100 88 88 92 46 106 93 34 76 80 91 73 102 71 98 69 133 77 47 49 55 48 96 57 94 69 78 60 70 105 100 48 94 26 85 93 58 67 100 50 96 84 88 51

Female

112 105 86 72 52 81 39 85 74 103 71 106 88 33 104 86 36 48 54 112 82 97 72 83 85 78 77 35 52 41 52 98 43 90 62 83 30 54 88 97 42 89 20 98 92 60 56 81 42 103 31 73 57

2 20 12 5 57 22 5 37 3 11 4 12 12 74 2 22 7 60 12 2 69 0 38 1 18 0 33 23 0 67 2 2 46 11 33 40 74 52 1 6 13 1 87 17 0 44 1 20 25 3 2 22 22

1 11 6 7 12 5 3 13 2 2 3 4 1 30 1 12 3 16 11 1 24 1 11 4 3 0 3 4 0 20 2 1 7 2 6 4 28 6 1 1 8 0 46 4 0 15 1 5 7 2 2 3 8

About the data

2.15

PEOPLE

Education gaps by income and gender Definitions

The data in the table describe basic information on

exclusion. To that extent the index provides only a

• Survey year is the year in which the underlying

school participation and educational attainment

partial view of the multidimensional concepts of pov-

data were collected. • Gross intake rate in grade 1

by individuals in different socioeconomic groups

erty, inequality, and inequity.

is the number of students in the first grade of pri-

within countries. The data are from Demographic

Creating one index that includes all asset indica-

mary education regardless of age as a percentage

and Health Surveys (DHS) conducted by Macro

tors limits the types of analysis that can be per-

of the population of the offi cial primary school

International with the support of the U.S. Agency for

formed. In particular, the use of a unified index does

entrance age. These data may differ from those in

International Development, Multiple Indicator Clus-

not permit a disaggregated analysis to examine

table 2.13. • Gross primary participation rate is

ter Surveys (MICS) conducted by the United Nations

which asset indicators have a more or less important

the ratio of total students attending primary school

Children’s Fund (UNICEF), and Living Standards

association with education status. In addition, some

regardless of age to the population of the age group

Measurement Study conducted by the World Bank

asset indicators may reflect household wealth better

that offi cially corresponds to primary education.

Development Economics Research Group. These

in some countries than in others—or reflect differ-

• Average years of schooling are the years of for-

large-scale household sample surveys, conducted

ent degrees of wealth in different countries. Taking

mal schooling received, on average, by youths and

periodically in developing countries, collect infor-

such information into account and creating country

adults ages 15–19. • Primary completion rate is

mation on a large number of health, nutrition, and

specific asset indexes with country-specific choices

the total number of students regardless of age in the

population measures as well as on respondents’

of asset indicators might produce a more effective

last grade of primary school, minus the number of

social, demographic, and economic characteristics

and accurate index for each country. The asset index

repeaters in that grade, divided by the total number

using detailed questionnaires. The data presented

used in the table does not have this flexibility.

of children of official graduation age. These data dif-

here draw on responses to individual and household

The analysis was carried out for around 80 coun-

fer from those in table 2.14 because the source is

tries. The table only shows the estimates for the

different. • Children out of school are the number

Typically, those surveys collect basic information

poorest and richest quintiles, gender, and latest

of children in the official primary school ages who

on educational attainment and enrollment levels

data; the full set of estimates for all indicators, other

are not attending primary or secondary education,

from every household member ages 5 or 6 and older

subgroups including urban and rural areas, and older

expressed as a percentage of children of the official

as part of the household’s socioeconomic charac-

data are available in the country reports (see Data

primary school ages. Children in the official primary

teristics. The surveys are not intended for the col-

sources). The data in the table differ from data for

school age, who are attending pre-primary education,

lection of detailed education data. As a result, the

similar indicators in preceding tables either because

are considered out-of-school. These data differ from

education section of the surveys does not replace

the indicator refers to a period a few years preceding

those in table 2.12 because the source is different.

education flows, nor are as detailed as, for instance,

the survey date or because the indicator definition

the health section for the case of the DHS and MICS.

or methodology is different. Findings should be used

Still, the education data are very useful for providing

with caution because of measurement error inherent

micro-level information on education that cannot be

in the use of survey data.

questionnaires.

obtained from administrative data, such as information on children not attending school. Socioeconomic status as displayed in the table is based on a household’s assets, including ownership of consumer items, features of the household’s dwelling, and other characteristics related to wealth. Each household asset on which information was collected was assigned a weight generated through principalcomponent analysis which was then used to create break-points defining wealth quintiles, expressed as quintiles of individuals in the population.

Data sources Data on education gaps by income and gender are

The selection of the asset index for defining socio-

from an analysis of Demographic and Health Sur-

economic status was based on pragmatic rather than

veys by Macro International, Multiple Indicators

conceptual considerations: Demographic and Health

Cluster surveys by UNICEF, and Living Standards

Surveys do not collect consumption data but do have

Measurement Study by World Bank, and these

detailed information on households’ ownership of

sources are analyzed by the EdStats team of the

consumer goods and access to a variety of goods

World Bank Human Development Network Edu-

and services. Like income or consumption, the asset

cation using ADePT Education. Country reports,

index defines disparities primarily in economic terms.

further updates, and ADePT Education software

It therefore excludes other possibilities of disparities

are available at www.worldbank.org/education/

among groups, such as those based on gender, edu-

edstats/.

cation, ethnic background, or other facets of social

2011 World Development Indicators

93

2.16

Health systems Health expenditure

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

94

Health workers

Outpatient visits

PPP $

per 1,000 people Nurses and Physicians midwives

per 1,000 people

per capita

2008

2008

2004–09a

2004–09a

2000–09a

47b 281 272 148c 695 143 4,180 d 5,201 240 17 351 5,243 32 75 506 530 721 482 37 19 c 43 65c 4,445 20 49 762 146 .. 317 13 81 618 61 1,230 672 1,469 6,133 261 216 97 217 10 c 1,074 14 4,481 4,966 264 c 27 258 4,720 55 3,110 184 21 17c 40 121

57b 569 437 183c 1,062 224 3,365d 4,150 395 44 688 4,096 61 187 937 1,053 875 974 82 50 c 118 117c 3,867 32 86 1,088 265 .. 517 23 108 1,059 88 1,553 495 1,830 3,814 465 466 261 410 18 c 1,325 37 3,299 3,851 384 c 75 433 3,922 114 3,010 308 58 32c 69 248

0.2 1.1 1.2 0.1 3.2 3.7 3.0 4.7 3.8 0.3 5.1 3.0 0.1 .. 1.4 0.3 1.7 3.6 0.1 0.0 0.2 0.2 1.9 0.1 0.0 1.3 1.4 .. 1.4 0.1 0.1 .. 0.1 2.7 6.4 3.6 3.4 .. .. 2.8 1.6 0.1 3.4 0.0 2.7 3.5 0.3 0.0 4.5 3.5 0.1 6.0 .. 0.1 0.0 .. ..

Total % of GDP

Public % of total

Out of pocket % of total

External resources % of total

$

2008

2008

2008

2008

7.4b 6.8 5.4 3.3c 8.4 3.8 8.5d 10.5 4.3 3.3 5.6 11.1 4.1 4.4 10.3 7.6 8.4 7.1 5.9 13.0c 5.7 5.3c 9.8 4.3 6.4 7.5 4.3 .. 5.9 7.3 2.7 9.4 5.4 7.8 12.0 7.1 9.9 5.7 5.3 4.8 6.0 3.1c 6.1 4.3 8.8 11.2 2.6 c 5.5 8.7 10.5 7.8 10.1 6.5 5.5 6.0c 6.1 6.3

21.5b 39.4 86.1 85.0c 62.6 44.5 65.4 d 73.7 19.3 31.4 72.2 66.8 51.7 63.1 58.2 78.2 44.0 57.8 59.1 40.0 c 23.8 22.7c 69.5 39.3 50.6 44.0 47.3 .. 83.9 54.2 49.9 66.9 16.9 84.9 95.5 80.1 80.1 37.1 42.3 42.2 59.6 44.9c 77.8 51.9 70.7 75.9 43.7c 48.1 30.9 74.6 50.0 60.9 35.7 13.6 26.0c 22.1 58.6

77.7b 58.6 13.2 15.0 c 22.2 51.8 17.9d 15.1 73.3 66.2 19.9 20.5 44.7 30.1 41.8 7.2 31.9 36.5 38.1 38.1c 64.4 73.5c 15.5 57.7 47.8 36.5 43.5 .. 7.9 39.2 50.1 29.3 75.6 14.5 4.1 15.7 13.6 41.8 50.4 56.5 35.8 55.1c 19.7 38.5 18.5 7.4 56.3c 25.1 66.5 11.8 39.4 37.0 57.4 85.9 40.7c 47.4 34.5

17.3b 2.1 0.0 3.0 c 0.0 10.4 0.0 d 0.0 0.6 5.8 0.2 0.0 17.7 9.1 1.3 4.2 0.0 0.0 29.2 34.5c 17.1 5.5c 0.0 31.5 5.3 0.0 0.2 .. 0.1 18.8 4.7 0.1 5.9 0.0 0.2 0.0 0.0 1.6 1.1 0.6 3.5 60.8 c 1.5 40.7 0.0 0.0 2.3 c 38.0 10.5 0.0 14.0 0.0 1.8 10.1 77.3c 34.7 10.4

2011 World Development Indicators

Hospital beds

Per capita

2004–09a

0.5 4.0 2.0 1.4 0.5 4.9 9.6 7.8 8.4 0.3 12.6 0.3 0.8 .. 4.7 2.8 6.5 4.7 0.7 0.2 0.8 1.6 10.1 0.4 0.3 .. 1.4 .. .. 0.5 0.8 .. 0.5 5.6 8.6 8.6 14.5 .. .. 3.5 0.4 0.6 6.8 0.2 15.5 8.9 5.0 0.6 3.9 10.8 1.1 3.7 .. 0.0 0.6 .. ..

0.4 2.9 1.7 0.8 4.0 4.1 3.8 7.7 7.9 0.4 11.2 6.6 0.5 1.1 3.0 1.8 2.4 6.5 0.9 0.7 0.1 1.5 3.4 1.2 0.4 2.1 4.1 .. 1.0 0.8 1.6 1.2 0.4 5.5 5.9 7.2 3.6 1.0 1.5 1.7 1.1 1.2 5.7 0.2 6.5 7.1 1.3 1.1 3.3 8.2 0.9 4.8 0.6 0.3 1.0 1.3 0.8

.. 1.5 .. .. .. 2.8 6.2 6.7 4.6 .. 13.2 7.0 .. .. 3.3 .. .. .. .. .. .. .. 6.3 .. .. .. .. .. .. .. .. .. .. 6.4 .. 15.0 4.1 .. .. .. .. .. 6.9 .. 4.3 6.9 .. .. 2.2 7.0 .. .. .. .. .. .. ..

Health expenditure

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

Health workers

Total % of GDP

Public % of total

Out of pocket % of total

External resources % of total

$

2008

2008

2008

2008

2008

68.9 32.4 54.4 42.4 70.2c,e 76.9 58.4 76.3 50.4 80.5 62.7f 58.5 36.3 .. 53.9 .. 76.3 48.4 17.6 60.0 48.3 63.3 33.0 70.3c 68.3 68.2 70.2 59.4 44.1 47.1 61.4 c 34.8 46.9 50.6g 81.4 36.3 75.2 8.8 54.6 37.7 75.3 80.2 54.6 57.7 36.7c 78.6 76.4 32.3 69.3 80.1 40.1 59.4 34.7 67.4 67.4 .. 79.8

23.9 50.3 32.1 55.6 29.8 c,e 14.4 30.5 20.2 35.2 14.5 30.8f 41.0 49.2 .. 35.0 .. 21.7 45.0 62.6 38.7 40.7 25.3 35.0 29.7c 26.8 31.6 20.2 11.6 40.9 52.6 38.6 c 57.8 49.3 48.3g 14.6 55.0 7.0 87.1 8.1 45.1 5.7 14.0 41.8 40.7 60.4 c 15.5 14.4 53.7 25.7 8.2 52.8 30.6 53.9 22.4 22.1 .. 14.8

0.0 1.6 1.7 0.0 8.2c,e 0.0 0.0 0.0 1.5 0.0 1.8f 0.2 26.8 .. 0.0 .. 0.0 12.6 16.1 0.0 4.8 19.3 47.0 0.1c 1.1 1.8 16.1 87.0 0.0 22.2 27.4c 2.0 0.0 4.7g 7.5 0.2 80.8 10.7 21.4 11.0 0.0 0.0 10.3 26.3 4.6c 0.0 0.0 4.8 0.2 20.6 1.6 0.8 1.5 0.0 0.0 .. 0.0

7.2 4.2 2.3 5.5 3.2c,e 8.7 7.6 8.7 4.8 8.3 9.4f 3.9 4.2 .. 6.5 .. 2.0 5.7 4.0 6.6 8.5 7.6 11.9 3.0 c 6.6 6.8 4.4 6.5 4.3 5.6 2.6c 5.5 5.9 10.7g 3.8 5.3 4.7 2.0 6.9 6.0 9.9 9.7 9.4 5.9 5.2c 8.5 2.1 2.6 7.2 3.2 6.0 4.5 3.7 7.0 10.6 .. 2.1

Hospital beds

Outpatient visits

PPP $

per 1,000 people Nurses and Physicians midwives

per 1,000 people

per capita

2008

2004–09a

2004–09a

2000–09a

Per capita

1,119 45 51 254 109c,e 5,253 2,093 3,343 256 3,190 325f 333 33 .. 1,245 .. 990 54 34 979 604 60 26 458c 931 328 22 18 353 39 27c 402 588 181 g 73 149 21 10 284 24 5,243 2,917 105 21 73c 8,019 454 22 493 39 161 200 68 971 2,434 .. 1,775

2.16

1,506 122 91 613 107c,e 3,796 2,093 2,836 364 2,817 496f 444 66 .. 1,806 .. 932 123 84 1,206 1,009 119 46 502c 1,318 738 46 50 621 65 54c 681 837 320 g 131 231 39 23 440 66 4,233 2,655 251 40 113c 5,207 593 62 924 70 281 381 129 1,271 2,578 .. 1,689

3.1 0.6 0.3 0.9 0.7 3.2 3.6 4.2 .. 2.1 2.5 3.8 0.1 .. 2.0 .. 1.8 2.3 0.3 3.0 3.5 .. 0.0 1.9 3.7 2.5 0.2 0.0 0.9 0.0 0.1 1.1 2.9 2.7 2.8 0.6 0.0 0.5 0.4 0.2 3.9 2.4 .. 0.0 0.4 4.1 1.9 0.8 .. 0.1 .. 0.9 1.2 2.1 3.8 .. 2.8

2004–09a

6.3 1.3 2.0 1.6 1.4 15.7 6.2 6.5 .. 4.1 4.0 7.8 .. .. 5.3 .. 4.6 5.7 1.0 4.8 2.2 .. 0.3 6.8 7.3 4.3 0.3 0.3 2.7 0.3 0.7 3.7 4.0 6.7 3.5 0.9 0.3 0.8 2.8 0.5 0.2 10.9 .. 0.1 1.6 14.8 4.1 0.6 .. 0.5 .. 1.3 6.0 5.7 5.3 .. 7.4

7.0 0.9 .. 1.4 1.3 5.2 5.8 3.7 1.7 13.8 1.8 7.6 1.4 .. 12.3 .. 1.8 5.1 1.2 6.4 3.5 1.3 0.7 3.7 6.8 4.6 0.3 1.1 1.8 0.6 0.4 3.3 1.6 6.1 5.9 1.1 0.8 0.6 2.7 5.0 4.3 .. 0.9 0.3 0.5 3.5 1.9 0.6 2.2 .. 1.3 1.5 0.5 6.6 3.4 .. 1.4

2011 World Development Indicators

PEOPLE

Health systems

12.9 .. .. .. .. .. 7.1 6.1 .. 14.4 .. 6.7 .. .. .. .. .. 3.6 .. 5.5 .. .. .. .. 6.6 6.0 0.5 .. .. .. .. .. 2.5 6.0 .. .. .. .. .. .. 5.4 4.4 .. .. .. .. .. .. .. .. .. .. .. 6.1 3.9 .. ..

95

2.16

Health systems Health expenditure

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

Health workers

Total % of GDP

Public % of total

Out of pocket % of total

External resources % of total

$

2008

2008

2008

2008

2008

78.9 64.3 47.8 68.2 55.4 62.5 6.5 34.1 67.1 68.6 .. 39.7 69.7 43.7 33.1 60.8 78.1 59.1 38.8 27.7 71.9 74.3 73.4 24.5 48.9 54.1 73.1 49.1c 17.4 55.9 67.1 82.6 47.8 63.1 50.5 44.9 38.5 .. 30.1 62.0 .. 60.5 w 41.9 51.4 45.5 55.4 51.2 48.2 65.4 50.3 53.0 32.6 42.9 62.2 73.7

17.6 29.1 23.2 17.0 35.0 35.5 83.7 62.1 24.9 12.8 .. 17.9 20.7 48.8 64.1 16.6 15.6 30.8 61.2 68.8 18.3 17.5 6.8 63.5 41.8 40.0 17.4 50.9 c 54.0 40.9 21.7 11.1 12.7 12.1 48.5 49.3 55.5 .. 68.9 28.3 .. 17.9 w 47.9 37.0 45.0 31.4 37.2 42.2 28.2 34.3 44.3 51.5 36.5 14.2 14.2

0.0 0.0 42.6 0.0 11.4 0.4 17.0 0.0 0.0 0.0 .. 1.2 0.0 1.8 4.3 11.1 0.0 0.0 0.5 10.5 59.2 0.3 21.8 14.1 0.3 0.5 0.0 0.3c 27.9 0.4 0.0 0.0 0.0 0.2 2.4 0.0 1.7 .. 4.6 38.4 .. 0.2 w 24.2 0.6 1.1 0.2 1.1 0.5 0.3 0.2 1.0 2.4 9.3 0.0 0.0

5.4 4.8 9.4 3.6 5.7 10.0 13.3 3.3 8.0 8.3 .. 8.2 9.0 4.1 6.9 5.8 9.4 10.7 3.1 5.0 4.5 4.1 13.8 5.9 4.7 6.2 6.1 2.2c 8.4 6.8 2.5 8.7 15.2 7.8 4.9 5.4 7.2 .. 5.3 5.9 .. 9.4 w 5.3 5.3 4.3 6.3 5.3 4.2 5.4 7.2 5.0 4.0 6.1 11.0 10.0

Outpatient visits

PPP $

per 1,000 people Nurses and Physicians midwives

per 1,000 people

per capita

2008

2004–09a

2004–09a

2000–09a

Per capita

517 568 45 676 62 499 47 1,404 1,395 2,238 .. 459 3,132 83 97 141 4,858 6,988 71 37 22 164 71 38 908 248 623 82c 44 268 1,427 3,771 7,164 725 51 597 76 .. 67 68 .. 857 w 25 186 95 531 163 125 448 542 176 40 74 4,455 4,132

Hospital beds

840 985 102 831 102 867 104 1,833 1,849 2,420 .. 843 2,941 187 147 287 3,622 4,815 123 95 57 328 126 70 1,237 501 845 146c 112 502 868 3,222 7,164 982 134 683 201 .. 137 80 .. 901 w 55 314 188 792 277 231 738 733 350 106 132 4,136 3,458

1.9 4.3 0.0 0.9 0.1 2.0 0.0 1.8 3.0 2.5 0.0 0.8 3.7 0.5 0.3 0.2 3.6 4.1 1.5 2.0 0.0 0.3 0.1 0.1 1.2 1.2 1.6 2.4 0.1 3.1 1.9 2.7 2.7 3.7 2.6 .. 1.2 .. 0.3 0.1 0.2 1.4 w 0.2 1.3 1.0 2.3 1.1 1.2 3.2 2.2 1.5 0.6 0.2 2.9 3.8

2004–09a

4.2 8.5 0.5 2.1 0.4 4.4 0.2 5.9 6.6 8.2 0.1 4.1 5.2 1.9 0.8 6.3 11.6 16.0 1.9 5.0 0.2 1.5 2.2 0.3 3.6 3.3 1.9 4.5 1.3 8.5 4.1 10.3 9.8 5.6 10.8 .. 1.0 .. 0.7 0.7 0.7 3.0 w 0.5 2.3 1.7 4.8 2.0 1.7 6.8 4.8 2.2 1.1 1.0 7.9 7.5

6.5 9.7 1.6 2.2 0.3 5.4 0.4 3.1 6.6 4.7 .. 2.8 3.2 3.1 0.7 2.1 .. 5.3 1.5 5.4 1.1 .. .. 0.9 2.5 2.1 2.4 4.1 0.4 8.7 1.9 3.4 3.1 2.9 4.8 1.3 2.9 .. 0.7 1.9 3.0 2.9 w .. 2.4 1.9 4.5 2.3 4.0 7.3 .. 1.6 0.9 .. 6.1 5.8

5.6 9.0 .. .. .. .. .. .. 12.5 6.6 .. .. 9.5 .. .. .. 2.8 .. .. 8.3 .. .. .. .. .. .. 3.1 3.7 .. 10.8 .. 4.9 9.0 .. 8.7 .. .. .. .. .. .. .. w .. .. .. .. .. .. 7.6 .. .. .. .. 8.5 6.8

a. Data are for the most recent year available. b. GDP includes measures of illicit activities such as opium production. Government expenditures include external assistance (external budget). c. Derived from incomplete data. d. Excludes expenditure in residential facilities for care of the aged. e. Excludes northern Iraq. f. Includes contributions from the United Nations Relief and Works Agency for Palestine. g. Excludes Transdniestria.

96

2011 World Development Indicators

About the data

2.16

PEOPLE

Health systems Definitions

Health systems—the combined arrangements of

this reason, data for this indicator should not be

• Total health expenditure is the sum of public and

institutions and actions whose primary purpose

compared across editions.

private health expenditure. It covers the provision

is to promote, restore, or maintain health (World

External resources for health are disbursements

of health services (preventive and curative), family

Health Organization, World Health Report 2000)—

to recipient countries as reported by donors, lagged

planning and nutrition activities, and emergency aid

are increasingly being recognized as key to com-

one year to account for the delay between disburse-

for health but excludes provision of water and sani-

bating disease and improving the health status of

ment and expenditure. Disbursement data are not

tation. • Public health expenditure is recurrent and

populations. The World Bank’s Healthy Develop-

available before 2002, so commitments are used.

capital spending from central and local governments,

ment: Strategy for Health, Nutrition, and Population

Except where a reliable full national health account

external borrowing and grants (including donations

Results emphasizes the need to strengthen health

study has been done, most data are from the Organ-

from international agencies and nongovernmental

systems, which are weak in many countries, in order

isation for Economic Co-operation and Development

organizations), and social (or compulsory) health

to increase the effectiveness of programs aimed at

Development Assistance Committee’s Creditor

insurance funds. • Out-of-pocket health expendi-

reducing specific diseases and further reduce mor-

Reporting System database, which compiles data

ture is the percentage of total expenditure that is

bidity and mortality (World Bank 2007). To evaluate

from government expenditure accounts, government

direct household outlays, including gratuities and

health systems, the World Health Organization (WHO)

records on external assistance, routine surveys of

in-kind payments, for health practitioners and phar-

has recommended that key components—such as

external financing assistance, and special services.

maceutical suppliers, therapeutic appliances, and

financing, service delivery, workforce, governance,

Because of the variety of sources, care should be

other goods and services whose primary intent is

and information—be monitored using several key

taken in interpreting the data.

to restore or enhance health. • External resources

indicators (WHO 2008b). The data in the table are

In countries where the fiscal year spans two cal-

for health are funds or services in kind that are pro-

a subset of the first four indicators. Monitoring

endar years, expenditure data have been allocated

vided by entities not part of the country in ques-

health systems allows the effectiveness, efficiency,

to the later year (for example, 2008 data cover fis-

tion. The resources may come from international

and equity of different health system models to be

cal year 2007/08). Many low-income countries use

organizations, other countries through bilateral

compared. Health system data also help identify

Demographic and Health Surveys or Multiple Indica-

arrangements, or foreign nongovernmental orga-

weaknesses and strengths and areas that need

tor Cluster Surveys funded by donors to obtain health

nizations and are part of public and private health

investment, such as additional health facilities,

system data.

better health information systems, or better trained human resources.

expenditure. • Health expenditure per capita is

Data on health worker (physicians, nurses, and

total health expenditure divided by population in

midwives) density show the availability of medical

U.S. dollars and in international dollars converted

Health expenditure data are broken down into pub-

personnel. The WHO estimates that at least 2.5

using 2005 purchasing power parity (PPP) rates from

lic and private expenditures. In general, low-income

physicians, nurses, and midwives per 1,000 people

the World Bank’s International Comparison Project.

economies have a higher share of private health

are needed to provide adequate coverage with pri-

• Physicians include generalist and specialist medi-

expenditure than do middle- and high-income coun-

mary care interventions associated with achieving

cal practitioners. • Nurses and midwives include pro-

tries, and out-of-pocket expenditure (direct payments

the Millennium Development Goals (WHO, World

fessional nurses and midwives, auxiliary nurses and

by households to providers) makes up the largest

Health Report 2006). The WHO compiles data from

midwives, enrolled nurses and midwives, and other

proportion of private expenditure. High out-of-pocket

household and labor force surveys, censuses, and

personnel, such as dental nurses and primary care

expenditures may discourage people from access-

administrative records. Data comparability is limited

nurses. • Hospital beds are inpatient beds for both

ing preventive or curative care and can impoverish

by differences in definitions and training of medical

acute and chronic care available in public, private,

households that cannot afford needed care. Health

personnel varies. In addition, human resources tend

general, and specialized hospitals and rehabilita-

financing data are collected through national health

to be concentrated in urban areas, so that average

tion centers. • Outpatient visits per capita are the

accounts, which systematically, comprehensively,

densities do not provide a full picture of health per-

number of visits to health care facilities per capita,

and consistently monitoring health system resource

sonnel available to the entire population.

including repeat visits.

flows. To establish a national health account, coun-

Availability and use of health services, shown by

tries must define the boundaries of the health system

hospital beds per 1,000 people and outpatient visits

and classify health expenditure information along

per capita, reflect both demand- and supply-side fac-

several dimensions, including sources of financing,

tors. In the absence of a consistent definition these

Data sources

providers of health services, functional use of health

are crude indicators of the extent of physical, finan-

Data on health expenditures are from the WHO’s

expenditures, and beneficiaries of expenditures. The

cial, and other barriers to health care.

National Health Account database (latest updates

accounting system can then provide an accurate pic-

are available at www.who.int/nha/), supple-

ture of resource envelopes and financial flows and

mented by country data. Data on physicians, and

allow analysis of the equity and efficiency of financing

nurses and midwives, are from WHO’s Global Atlas

to inform policy.

of the Health Workforce. For the latest updates and

This year’s table presents out-of-pocket expendi-

metadata, see http://apps.who.int/globalatlas/.

ture as a percentage of total health expenditure; pre-

Data on hospital beds and outpatient visits are

vious editions presented out-of-pocket expenditure

from the WHO, supplemented by country data.

as a percentage of private health expenditure. For

2011 World Development Indicators

97

2.17

Health information Year last national health account completed

Number of national health accounts completed

Year of last health survey

Year of last census

Completeness

%

1995–2009

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

98

2009 2003 1997 2009 2007 2008 2008 2008 2008 2007 2009 2002 2006 2007 2008 2007 1995 2009

2008 2007 2003 2009 2005 2003 2008

2008 2007 2008 2008 2008 2009 2008 2008 2008 2008 2004 2009 2008 2002 2008

2006 2005

2011 World Development Indicators

0 3 3 0 1 6 13 14 0 13 0 6 4 13 6 3 7 6 6 1 0 1 15 0 0 5 13 0 9 7 1 2 2 0 0 14 13 8 7 3 14 0 10 4 14 14 0 3 9 14 1 0 14 0 0 1 3

2001–11

2003 2008/09 2006 2006/07 2005

2006 2007 2005 2006 2008 2006 2000 1996 2006 2005 2005 2006 2006 2004

2005 2010 2009 1993 2006 2006 1993 2007 2004 2008 2008 2002 2005

2000 2005/06 2005 2008 2002 2005 2010 2005/06 2005/06

2001 2008 2010 2001 2006 2001 2009 2001 2009 2001 2002 2001 2001 2010 2001 2006 2008 2008 2005 2006 2003 2009 2002 2010 2006 2006 2007 2000 2001 2002 2001 2001 2010 2010 2006 2007 2000 2007 2010 2006 2003 2003 2002 2010 2001 2002 2009 2003 2001

Birth registration 2004–09a

.. 99 99 .. 91 96 .. .. 94 10 .. .. 60 .. 100 72 91 .. 64 60 66 70 .. 49 9 99 .. .. 90 31 81 .. 55 .. 100 .. .. 78 85 99 99 .. .. 7 .. .. .. 55 92 .. 71 .. .. 43 39 81 94

Infant death reporting 2004–09a

.. 28 .. .. 100 38 100 90 24 .. 55 100 .. .. 54 35 48 79 29 .. 0 .. 100 .. .. 100 .. 66 52 .. .. 90 .. 75 99 84 97 1 58 47 36 .. 68 .. 84 95 .. .. 54 96 95 78 62 .. .. .. 100

Total death reporting 2004–09a

.. 76 90 .. 100 100 96 100 100 .. 96 97 .. 30 92 47 87 100 88 .. 100 .. 98 .. .. 100 99 91 71 .. .. 98 .. 100 100 94 97 54 86 97 75 .. 94 88 98 100 .. .. 83 99 .. 95 93 .. .. .. 99

Year last national health account completed

Number of national health accounts completed

Year of last health survey

Year of last census

2.17

PEOPLE

Health information

Completeness

%

1995–2009

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

2008 2004 2008 2007 2008 2006 2008 2000 2007 2008 2007 2006 2008

2009 2007 2005 2008 2008 2007 2006 2006 2004 2004 2009 2003 2006 2006 2007 2008 2005 2008 2008 2008 2006 2005 2008 1998 2006 2003 2000 2008 2005 2007 2008 2007

14 2 8 4 0 14 1 4 1 13 5 1 2 0 14 0 0 5 0 5 4 0 1 0 7 0 2 5 10 6 0 2 15 0 5 3 4 10 11 5 14 14 14 4 8 12 1 1 1 3 13 11 13 14 8 0 0

2001–11

2005/06 2007 2000 2006

2005 2009 2006 2008/09 2010

1996 2005/06 2006 2000 2009/10 2009 2000 2005 2008/09 2006 2006 2007 1995 2005 2005 2006 2009 2000 2006/07 2006

2006/07 2006 2008 1995 2006/07 2003 1996 2004 2008 2008

1996

2001 2001 2010 2006 2006 2009 2001 2001 2010 2004 2009 2009 2008 2005 2010 2009 2005 2000 2006 2008 2006 2001 2002 2008 2010 2009 2000 2000 2010 2004 2010 2004 2007 2001 2001 2001 2006 2005 2001 2006 2001 2010 2010 2000 2002 2007 2010 2002 2001 2010 2010

Birth registration 2004–09a

.. 41 53 .. 95 .. .. .. 89 .. .. 99 60 .. .. .. .. 94 72 .. .. 26 4 .. .. 94 75 .. .. 53 56 .. .. .. 98 .. 31 .. 67 35 .. .. .. 32 30 .. .. 27 .. .. .. 93 .. .. .. .. ..

Infant death reporting 2004–09a

Total death reporting 2004–09a

84 .. .. .. 100 75 90 99 76 88 .. 95 37 43 80 .. 100 78 .. 79 .. .. .. .. 68 87 .. .. 62 .. .. 80 89 62 60 .. .. 56 .. .. 84 100 66 .. .. 97 100 85 70 .. 34 41 39 95 85 100 95

2011 World Development Indicators

97 .. .. 99 100 99 99 98 68 98 76 82 39 91 92 .. 100 95 .. 96 72 .. .. .. 95 99 .. 75 100 .. .. 97 100 89 96 .. .. 55 100 .. 97 98 68 .. 1 100 97 84 88 .. 71 70 100 100 95 95 77

99

2.17

Health information Year last national health account completed

Number of national health accounts completed

Year of last health survey

Year of last census

Completeness

%

1995–2009

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe

2006 2007 2006 2005 2009 2006 2008 2008 1998 2008 2006 2008 2008 2009 2008 2006 2007 2002 2000 2005 2005 2006 2008 2008 2009 2008

2007 2007 2006 2001

a. Data are for the most recent year available.

100

2011 World Development Indicators

9 13 5 0 2 7 3 0 12 14 0 3 14 12 1 0 8 15 0 2 3 13 0 1 1 5 8 0 6 6 0 12 15 13 0 0 10 1 4 11 3

2001–11

1999 1996 2007 2007 2008/09 2005/06 2008 2005

2006 2003 2006/07 2006 2006/07

2006 2005 2007/08 2005/06 2009 2006 2006 2006 2003 2006 2009/10 2007

2009 2006 2000 2006 2006 2006 2007 2005/06

2002 2010 2002 2010 2002 2002 2004 2010 2001 2002 2001 2001 2001 2008 2007 2010 2004 2010 2002 2010 2010 2010 2000 2004 2000 2002 2001 2010 2001 2010 2004 2001 2009 2007 2004 2000 2002

Birth registration 2004–09a

.. .. 82 .. 55 99 51 .. .. .. 3 92 .. 97 33 30 .. .. 95 88 22 99 .. 78 96 .. 94 96 21 100 .. .. .. .. 100 .. 88 96 22 14 74

Infant death reporting 2004–09a

76 80 .. 94 .. 38 .. 93 93 72 .. 81 99 63 .. .. 83 100 .. 19 .. 86 .. .. 50 .. 56 .. .. 90 75 100 100 78 .. 62 72 31 .. .. ..

Total death reporting 2004–09a

96 95 .. 100 .. 90 .. 72 98 96 .. 81 100 91 .. .. 99 99 100 69 .. 65 .. .. 94 98 100 .. .. 100 100 95 100 100 .. 84 83 66 15 .. ..

About the data

2.17

PEOPLE

Health information Definitions

According to the World Health Organization (WHO),

the institutional frameworks needed to ensure data

• Year last national health account completed is the

health information systems are crucial for moni-

quality, including independence, transparency, and

latest year for which the health expenditure data are

toring and evaluating health systems, which are

access. Benchmarks include the availability of inde-

available using the national health account approach.

increasingly recognized as important for combating

pendent coordination mechanisms and micro- and

• Number of national health accounts completed is

disease and improving health status. Health informa-

meta-data (WHO 2008a).

the number of national health accounts completed

tion systems underpin decisionmaking through four

The indicators in the table are all related to data

between 1995 and 2008. • Year of last health sur-

data functions: generation, compilation, analysis and

generation, including the years the last national

vey is the latest year the national survey that collects

synthesis, and communication and use. The health

health account, last health survey, and latest popu-

health information was conducted. • Year of last cen-

information system collects data from the health sec-

lation census were completed. Frequency of data col-

sus is the latest year a census was conducted in the

tor and other relevant sectors; analyzes the data and

lection, a benchmark of data generation, is shown

last 10 years. • Completeness of birth registration is

ensures their overall quality, relevance, and timeli-

as the number of years for which a national health

the percentage of children under age 5 whose births

ness; and converts data into information for health-

account was completed between 1995 and 2009.

were registered at the time of the survey. The numera-

related decisionmaking (WHO 2008b).

National health account data may be collected

tor of completeness of birth registration includes chil-

Numerous indicators have been proposed to

using different approaches such as Organisation for

dren whose birth certificate was seen by the interviewer

assess a country’s health information system.

Economic Co-operation and Development (OECD)

or whose mother or caretaker says the birth has been

They can be grouped into two broad types: indica-

System of Health Accounts, WHO National Health

registered. • Completeness of infant death reporting

tors related to data generation using core sources

Account producers guide approach, local national

is the number of infant deaths reported by national

and methods (health surveys, civil registration, cen-

health accounting methods, or Pan American

statistical authorities to the United Nations Statistics

suses, facility reporting, health system resource

Health Organization/WHO satellite health accounts

Division’s Demographic Yearbook divided by the number

tracking) and indicators related to capacity for

approach.

of infant deaths estimated by the United Nations Popu-

data synthesis, analysis, and validation. Indicators

Indicators related to data generation include com-

lation Division. • Completeness of total death report-

related to data generation reflect a country’s capac-

pleteness of birth registration, infant death report-

ing is the number of total deaths from civil registration

ity to collect relevant data at suitable intervals using

ing, and total death reporting.

system reported by national statistical authorities to

the most appropriate data sources. Benchmarks

the United Nations Statistics Division’s Demographic

include periodicity, timeliness, contents, and avail-

Yearbook divided by the number of total deaths esti-

ability. Indicators related to capacity for synthesis,

mated by the United Nations Population Division.

analysis, and validation measure the dimensions of Data sources Data on year last national health account completed South Asia has the highest number of unregistered births

2.17a

and number of national health accounts completed were compiled by staff in the World Health Organization’s Health Financing Department and the World

Number of unregistered births, 2007 (millions)

Bank’s Health, Nutrition, and Population Unit using data on the health expenditures reported by the

Latin America and Caribbean 1.3

CEE/CIS 0.4

Middle East and North Africa 2.4

WHO and OECD and consultation with colleagues from countries and other international organizations. Data on year of last health survey are from Macro

East Asia and Pacific, excluding China 3.5

International and the United Nations Children’s Eastern and Southern Africa 9.7

Fund (UNICEF). Data on year of last census are from South Asia 24.3

West and Central Africa 9.8

United Nations Statistics Division’s 2011 World Population and Housing Census Program (http:// unstats.un.org/unsd/demographic/sources/census/2010_PHC/default.htm.) Data on completeness of birth registration are compiled by UNICEF in State of the World’s Children 2010 based mostly on household surveys and ministry of health data. Data used to calculate completeness of infant death reporting

Too many people, especially poor, are never counted. They are born, live, and die uncounted and ignored. Around 50 million, or 40 percent of children born in 2007, have not been registered. Source: United Nations Children’s Fund Childinfo.

and total death reporting are from the United Nations Statistics Division’s Population and Vital Statistics Report and the United Nations Population Division’s World Population Prospects: The 2008 Revision.

2011 World Development Indicators

101

2.18

Disease prevention coverage and quality Access to an improved water source

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

102

Access to improved sanitation facilities

% of population 1990 2008

% of population 1990 2008

.. .. 94 36 94 .. 100 100 70 78 100 100 56 70 .. 93 88 100 41 70 35 50 100 58 38 90 67 .. 88 45 .. 93 76 .. 82 100 100 88 72 90 74 43 98 17 100 100 .. 74 81 100 54 96 82 52 .. 47 72

.. .. 88 25 90 .. 100 100 .. 39 .. 100 5 19 .. 36 69 99 6 44 9 47 100 11 6 84 41 .. 68 9 .. 93 20 .. 80 100 100 73 69 72 75 9 .. 4 100 100 .. .. 96 100 7 97 65 9 .. 26 44

48 97 83 50 97 96 100 100 80 80 100 100 75 86 99 95 97 100 76 72 61 74 100 67 50 96 89 .. 92 46 71 97 80 99 94 100 100 86 94 99 87 61 98 38 100 100 87 92 98 100 82 100 94 71 61 63 86

2011 World Development Indicators

37 98 95 57 90 90 100 100 45 53 93 100 12 25 95 60 80 100 11 46 29 47 100 34 9 96 55 .. 74 23 30 95 23 99 91 98 100 83 92 94 87 14 95 12 100 100 33 67 95 100 13 98 81 19 21 17 71

Child immunization rate

% of children ages 12–23 monthsb Measles DTP3 2009 2009

76 97 88 77 99 96 94 83 67 89 99 94 72 86 93 94 99 96 75 91 92 74 93 62 23 96 94 .. 95 76 76 81 67 98 96 98 84 79 66 95 95 95 95 75 98 90 55 96 83 96 93 99 92 51 76 59 99

83 98 93 73 94 93 92 83 73 94 96 99 83 85 90 96 99 94 82 92 94 80 80 54 23 97 97 .. 92 77 91 86 81 96 96 99 89 82 75 97 91 99 95 79 99 99 45 98 88 93 94 99 92 57 68 59 98

Children with acute respiratory infection taken to health provider

Children with diarrhea who received oral rehydration and continuous feeding

Children sleeping under treated netsa

Children with fever receiving antimalarial drugs

% of children under age 5 with ARI 2004–09 c

% of children under age 5 with diarrhea 2004–09 c

% of children under age 5 2004–09c

% of children under age 5 with fever 2004–09c

.. 70 53 .. .. 36 .. .. 33 37 90 .. 36 51 91 .. 50 .. 39 38 48 35 .. 32 12 .. .. .. 62 42 48 .. 35 .. .. .. .. 70 .. 73 67 .. .. 19 .. .. .. 69 74 .. 51 .. .. 42 57 31 56

.. 63 24 .. .. 59 .. .. 31 68 54 .. 42 .. 53 .. .. .. 42 23 50 22 .. 47 27 .. .. .. 39 42 39 .. 45 .. .. .. .. 55 .. 19 .. .. .. 15 .. .. .. 38 37 .. 45 .. .. 38 25 43 49

.. .. .. 17.7 .. .. .. .. .. .. .. .. 20.1 .. .. .. .. .. 9.6 8.3 4.2 13.1 .. 15.1 .. .. .. .. .. 5.8 6.1 .. 3.0 .. .. .. .. .. .. .. .. .. .. 33.1 .. .. .. 49.0 .. .. 28.2 .. .. 4.5 39.0 .. ..

.. .. .. 29.3 .. .. .. .. .. .. .. .. 54.0 .. .. .. .. .. 48.0 30.0 0.2 57.8 .. 57.0 53.0 .. .. .. .. 29.8 48.0 .. 36.0 .. .. .. .. 0.6 .. .. .. .. .. 9.5 .. .. .. 62.6 .. .. 43.0 .. .. 43.5 45.7 5.1 0.5

Tuberculosis

Treatment success rate

Case detection rate

% of new registered cases

% of new estimated cases

2008

2009

88 91 90 70 44 73 80 47 56 91 71 76 89 84 92 65 71 85 76 90 95 76 78 71 54 72 94 68 76 87 76 89 76 58 88 68 41 75 78 89 91 76 60 84 72 .. 53 84 73 68 86 .. 83 78 70 82 85

48 94 100 75 67 70 89 48 75 44 140 88 47 64 91 62 86 86 14 25 60 70 93 60 26 130 75 89 70 46 69 93 27 76 120 70 79 60 51 63 92 58 89 50 110 77 42 47 100 91 31 92 33 26 59 60 68

Access to an improved water source

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

Access to improved sanitation facilities

% of population 1990 2008

% of population 1990 2008

96 72 71 91 81 100 100 100 93 100 97 96 43 100 .. .. 99 .. .. 99 100 61 58 54 .. .. 31 40 88 29 30 99 85 .. 58 74 36 57 64 76 100 100 74 35 47 100 80 86 84 41 52 75 84 100 96 .. 100

100 18 33 83 .. 99 100 .. 83 100 .. 96 26 .. 100 .. 100 .. .. .. .. 32 11 97 .. .. 8 42 84 26 16 91 66 .. .. 53 11 .. 25 11 100 .. 43 5 37 100 85 28 58 47 37 54 58 .. 92 .. 100

100 88 80 .. 79 100 100 100 94 100 96 95 59 100 98 .. 99 90 57 99 100 85 68 .. .. 100 41 80 100 56 49 99 94 90 76 81 47 71 92 88 100 100 85 48 58 100 88 90 93 40 86 82 91 100 99 .. 100

100 31 52 .. 73 99 100 .. 83 100 98 97 31 .. 100 .. 100 93 53 78 .. 29 17 97 .. 89 11 56 96 36 26 91 85 79 50 69 17 81 33 31 100 .. 52 9 32 100 .. 45 69 45 70 68 76 90 100 .. 100

Child immunization rate

% of children ages 12–23 monthsb Measles DTP3 2009 2009

99 71 82 99 69 89 96 91 88 94 95 99 74 98 93 .. 97 99 59 96 53 85 64 98 96 96 64 92 95 71 59 99 95 90 94 98 77 87 76 79 96 89 99 73 41 92 97 80 85 58 91 91 88 98 95 .. 99

99 66 82 99 65 93 93 96 90 98 98 98 75 93 94 .. 98 95 57 95 74 83 64 98 98 96 78 93 95 74 64 99 89 85 95 99 76 90 83 82 97 92 98 70 42 92 98 85 84 64 92 93 87 99 96 .. 99

Children with acute respiratory infection taken to health provider

Children with diarrhea who received oral rehydration and continuous feeding

Children sleeping under treated netsa

Children with fever receiving antimalarial drugs

% of children under age 5 with ARI 2004–09 c

% of children under age 5 with diarrhea 2004–09 c

% of children under age 5 2004–09c

% of children under age 5 with fever 2004–09c

.. 69 66 .. 82 .. .. .. 75 .. 75 71 56 93 .. .. .. 62 32 .. .. 66 62 .. .. 93 42 52 .. 38 45 .. .. 60 63 38 65 .. 72 43 .. .. .. 47 45 .. .. 69 .. 63 .. 72 50 .. .. .. ..

.. 33 54 .. 64 .. .. .. 39 .. 32 48 .. .. .. .. .. 22 49 .. .. 53 47 .. .. 45 47 27 .. 38 32 .. .. 48 47 46 47 .. 48 37 .. .. .. 34 25 .. .. 37 .. .. .. 60 60 .. .. .. ..

.. .. 3.3 .. .. .. .. .. .. .. .. .. 46.1 .. .. .. .. .. 40.5 .. .. .. 26.4 .. .. .. 45.8 24.7 .. 27.1 2.1 .. .. .. .. .. 22.8 .. 10.5 .. .. .. .. 42.8 5.5 .. .. .. .. .. .. .. .. .. .. .. ..

.. 8.2 0.8 .. .. .. .. .. .. .. .. .. 23.2 .. .. .. .. .. 8.2 .. .. .. 67.2 .. .. .. 19.7 24.9 .. 31.7 20.7 .. .. .. .. .. 36.7 .. 9.8 0.1 .. .. .. 33.0 33.2 .. .. 3.3 .. .. .. .. 0.0 .. .. .. ..

2.18

PEOPLE

Disease prevention coverage and quality

Tuberculosis

Treatment success rate

Case detection rate

% of new registered cases

% of new estimated cases

2008

2009

53 87 91 83 88 76 81 .. 64 48 84 64 85 89 84 .. 80 84 93 33 77 73 79 69 82 89 81 87 78 82 68 87 85 62 87 85 84 85 82 89 85 73 89 81 78 84 98 90 79 64 81 82 88 74 87 63 73

82 67 67 74 48 89 89 66 78 89 100 80 85 93 89 .. 89 66 68 94 78 93 52 82 81 98 44 49 76 16 24 41 99 68 75 93 46 64 76 73 89 89 90 36 19 91 89 63 94 73 78 97 57 84 86 89 89

2011 World Development Indicators

103

2.18

Disease prevention coverage and quality Access to an improved water source

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

Access to improved sanitation facilities

% of population 1990 2008

% of population 1990 2008

.. 93 68 89 61 .. .. 100 .. 100 .. 83 100 67 65 .. 100 100 85 .. 55 91 .. 49 88 81 85 .. 43 .. 100 100 99 96 90 90 58 .. .. 49 78 77 w 55 74 70 89 72 69 91 85 87 74 49 99 100

71 87 23 .. 38 .. .. 99 100 100 .. 69 100 70 34 .. 100 100 83 .. 24 80 .. 13 93 74 84 98 39 95 97 100 100 94 84 82 35 .. 18 46 43 52 w 23 45 37 78 43 42 87 69 73 22 27 100 100

.. 96 65 .. 69 99 49 100 100 99 30 91 100 90 57 69 100 100 89 70 54 98 69 60 94 94 99 .. 67 98 100 100 99 100 87 .. 94 91 62 60 82 87 w 64 88 86 95 84 88 95 93 87 87 60 100 100

72 87 54 .. 51 92 13 100 100 100 23 77 100 91 34 55 100 100 96 94 24 96 50 12 92 85 90 98 48 95 97 100 100 100 100 .. 75 89 52 49 44 61 w 35 57 50 84 54 59 89 79 84 36 31 99 100

Child immunization rate

% of children ages 12–23 monthsb Measles DTP3 2009 2009

97 98 92 98 79 95 71 95 99 95 24 62 98 96 82 95 97 90 81 89 91 98 70 84 94 98 97 99 68 94 92 86 92 94 95 83 97 .. 58 85 76 82 w 78 82 79 93 81 91 96 93 87 75 68 93 94

97 98 97 98 86 95 75 97 99 96 31 69 96 97 84 95 98 95 80 93 85 99 72 89 90 99 96 96 64 90 92 93 95 95 98 83 96 .. 66 81 73 82 w 80 81 79 93 81 93 95 92 88 72 70 95 96

Children with acute respiratory infection taken to health provider

Children with diarrhea who received oral rehydration and continuous feeding

Children sleeping under treated netsa

Children with fever receiving antimalarial drugs

% of children under age 5 with ARI 2004–09 c

% of children under age 5 with diarrhea 2004–09 c

% of children under age 5 2004–09c

% of children under age 5 with fever 2004–09c

.. .. 55.7 .. 29.2 .. 25.8 .. .. .. 11.4 .. .. 2.9 27.6 0.6 .. .. .. 1.3 63.8 d .. .. 38.4 .. .. .. .. 9.7 .. .. .. .. .. .. .. 5.0 .. .. 41.1 17.3 .. w .. .. .. .. .. .. .. .. .. .. 20.2 .. ..

.. .. 5.6 .. 9.1 .. 30.1 .. .. .. 7.9 .. .. 0.3 54.2 0.6 .. .. .. 1.9 59.1d .. .. 47.7 .. .. .. .. 61.3 .. .. .. .. .. .. .. 2.6 .. .. 43.3 23.6 .. w 30.6 .. .. .. .. .. .. .. .. 7.2 34.4 .. ..

.. .. 28 .. 47 93 46 .. .. .. 13 .. .. 58 90 73 .. .. 77 64 59 84 71 23 74 59 .. 83 73 .. .. .. .. .. 68 .. 83 .. .. 68 25 .. w 45 .. .. .. .. .. .. .. .. 67 .. .. ..

.. .. 24 .. 43 71 57 .. .. .. 7 .. .. 67 56 22 .. .. 34 22 53 46 .. 22 32 62 22 25 39 .. .. .. .. .. 28 .. 65 .. 48 56 35 .. w 39 .. .. .. .. .. .. .. .. 37 33 .. ..

Tuberculosis

Treatment success rate

Case detection rate

% of new registered cases

% of new estimated cases

2008

2009

37 57 87 61 84 86 86 81 93 80 81 76 .. 85 81 68 87 .. 86 82 88 82 85 79 67 86 92 83 70 62 68 78 85 83 81 83 92 94 85 88 74 86 w 86 .. 89 72 .. 92 67 77 86 88 79 69 ..

79 84 19 89 31 89 31 89 89 80 42 74 89 70 52 67 89 89 88 44 77 69 84 10 89 86 77 92 44 78 61 94 89 96 50 68 54 4 67 80 46 62 w 50 .. 63 79 .. 70 78 73 78 64 48 87 ..

a. For malaria prevention only. b. Refers to children who were immunized before 12 months or in some cases at any time before the survey (12–23 months). c. Data are for the most recent year available. d. Data are for 2010.

104

2011 World Development Indicators

About the data

2.18

PEOPLE

Disease prevention coverage and quality Definitions

People’s health is influenced by the environment

the use of oral rehydration therapy have changed over

• Access to an improved water source refers to

in which they live. Lack of clean water and basic

time based on scientific progress, so it is difficult

people with access to at least 20 liters of water

sanitation is the main reason diseases transmitted

to accurately compare use rates across countries.

a person a day from an improved source, such as

by feces are so common in developing countries.

Until the current recommended method for home

piped water into a dwelling, public tap, tubewell,

Access to drinking water from an improved source

management of diarrhea is adopted and applied in

protected dug well, and rainwater collection, within

and access to improved sanitation do not ensure

all countries, the data should be used with caution.

1 kilometer of the dwelling. • Access to improved

safety or adequacy, as these characteristics are

Also, the prevalence of diarrhea may vary by season.

sanitation facilities refers to people with at least

not tested at the time of the surveys. But improved

Since country surveys are administered at different

adequate access to excreta disposal facilities that

drinking water technologies and improved sanitation

times, data comparability is further affected.

can effectively prevent human, animal, and insect

facilities are more likely than those characterized

Malaria is endemic to the poorest countries in the

contact with excreta. Improved facilities range from

as unimproved to provide safe drinking water and to

world, mainly in tropical and subtropical regions of

protected pit latrines to flush toilets. • Child immu-

prevent contact with human excreta. The data are

Africa, Asia, and the Americas. Insecticide-treated

nization rate refers to children ages 12–23 months

derived by the Joint Monitoring Programme (JMP)

nets, properly used and maintained, are one of the

who, before 12 months or at any time before the

of the World Health Organization (WHO) and United

most important malaria-preventive strategies to limit

survey, had received one dose of measles vaccine

Nations Children’s Fund (UNICEF) based on national

human-mosquito contact.

and three doses of diphtheria, pertussis (whooping

censuses and nationally representative household

Prompt and effective treatment of malaria is a criti-

cough), and tetanus (DTP3) vaccine. • Children with

surveys. The coverage rates for water and sanita-

cal element of malaria control. It is vital that suffer-

acute respiratory infection (ARI) taken to health

tion are based on information from service users

ers, especially children under age 5, start treatment

provider are children under age 5 with ARI in the

on the facilities their households actually use rather

within 24 hours of the onset of symptoms, to pre-

two weeks before the survey who were taken to an

than on information from service providers, which

vent progression—often rapid—to severe malaria

appropriate health provider. • Children with diarrhea

may include nonfunctioning systems. While the esti-

and death.

who received oral rehydration and continuous feed-

mates are based on use, the JMP reports use as

Data on the success rate of tuberculosis treatment

ing are children under age 5 with diarrhea in the two

access, because access is the term used in the Mil-

are provided for countries that have submitted data

weeks before the survey who received either oral

lennium Development Goal target for drinking water

to the WHO. The treatment success rate for tuber-

rehydration therapy or increased fluids, with con-

and sanitation.

culosis provides a useful indicator of the quality of

tinuous feeding. • Children sleeping under treated

Governments in developing countries usually

health services. A low rate suggests that infectious

nets are children under age 5 who slept under an

finance immunization against measles and diphthe-

patients may not be receiving adequate treatment.

insecticide-treated net to prevent malaria the night

ria, pertussis (whooping cough), and tetanus (DTP)

An important complement to the tuberculosis treat-

before the survey. • Children with fever receiving

as part of the basic public health package. In many

ment success rate is the case detection rate, which

antimalarial drugs are children under age 5 who were

developing countries lack of precise information on

indicates whether there is adequate coverage by the

ill with fever in the two weeks before the survey and

the size of the cohort of one-year-old children makes

recommended case detection and treatment strat-

received any appropriate (locally defined) antimalarial

immunization coverage diffi cult to estimate from

egy. Uncertainty bounds for the case detection rate,

drugs. • Tuberculosis treatment success rate is new

program statistics. The data shown here are based

not shown in the table, are available at http://data.

registered infectious tuberculosis cases that were

on an assessment of national immunization cover-

worldbank.org or the original source.

cured or that completed a full course of treatment as

age rates by the WHO and UNICEF. The assessment

Editions before 2010 included the tuberculosis

a percentage of smear-positive cases registered for

considered both administrative data from service

detection rates by DOTS, the internationally rec-

treatment outcome evaluation. • Tuberculosis case

providers and household survey data on children’s

ommended strategy for tuberculosis control. This

detection rate is newly identified tuberculosis cases

immunization histories. Based on the data available,

year’s edition, like last year’s, shows the tuberculo-

(including relapses) as a percentage of estimated

consideration of potential biases, and contributions

sis detection rate for all detection methods, so data

incident cases (case detection, all forms).

of local experts, the most likely true level of immuni-

on the case detection rate cannot be compared with

zation coverage was determined for each year. Acute

data in previous editions.

respiratory infection continues to be a leading cause

For indicators that are from household surveys, the

of death among young children, killing about 2 million

year in the table refers to the survey year. For more

children under age 5 in developing countries each

information, consult the original sources.

year. Data are drawn mostly from household health surveys in which mothers report on number of episodes and treatment for acute respiratory infection. Since 1990 diarrhea-related deaths among children have declined tremendously. Most diarrhea-related deaths are due to dehydration, and many of these deaths can be prevented with the use of oral rehydra-

Data sources Data on access to water and sanitation are from the WHO and UNICEF’s Progress on Sanitation and Drinking Water (2010). Data on immunization are from WHO and UNICEF estimates (www.who.int/ immunization_monitoring). Data on children with ARI, with diarrhea, sleeping under treated nets, and receiving antimalarial drugs are from UNICEF’s State of the World’s Children 2010, Childinfo, and Demographic and Health Surveys by Macro International. Data on tuberculosis are from the WHO’s Global Tuberculosis Control: A Short Update to the 2010 Report.

tion salts at home. However, recommendations for

2011 World Development Indicators

105

2.19

Reproductive health Total fertility rate

births per woman 1990 2009

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

106

8.0 2.9 4.7 7.2 3.0 2.5 1.9 1.5 2.7 4.4 1.9 1.6 6.7 4.9 1.7 4.7 2.8 1.8 6.8 6.6 5.8 5.9 1.8 5.8 6.7 2.6 2.3 1.3 3.1 7.1 5.4 3.2 6.3 1.6 1.8 1.9 1.7 3.5 3.7 4.6 4.0 6.2 2.0 7.1 1.8 1.8 5.2 6.1 2.2 1.5 5.6 1.4 5.6 6.7 5.9 5.4 5.1

6.5 1.9 2.3 5.6 2.2 1.7 1.9 1.4 2.3 2.3 1.5 1.9 5.4 3.4 1.2 2.8 1.8 1.6 5.8 4.5 2.9 4.5 1.6 4.7 6.1 1.9 1.8 1.0 2.4 5.9 4.3 1.9 4.5 1.5 1.5 1.5 1.8 2.6 2.5 2.8 2.3 4.5 1.6 5.2 1.9 2.0 3.2 5.0 1.6 1.4 3.9 1.5 4.0 5.3 5.7 3.4 3.2

2011 World Development Indicators

Adolescent Unmet Contraceptive Pregnant fertility women need for prevalence rate receiving contraception rate prenatal care births per 1,000 women ages 15–19 2009

117 14 7 121 56 35 14 12 33 68 20 7 108 76 15 50 74 40 125 18 37 122 12 96 155 59 10 6 72 191 106 67 122 14 46 10 6 107 82 37 81 62 20 94 11 6 85 87 44 7 61 8 104 147 125 45 90

% of married women ages 15–49 2004–09a

.. .. 11 .. .. 13 .. .. 23 17 .. .. 30 .. 23 .. .. .. 31 .. 25 3 .. .. 21 .. .. .. 6 24 16 .. 29 .. 8 .. .. 11 .. 9 .. .. .. 34 .. .. .. .. .. .. 35 .. .. 21 25 38 17

any method % of married women ages 15–49 2004–09a

% 2004–09a

15 69 61 .. 78 53 .. .. 51 53 73 75 17 61 36 53 81 .. 17 9 51b 29 .. 19 3 58 85 .. 78 21 44 80 13 .. 78 .. .. 73 73 60 73 .. .. 15 .. 71 .. .. 47 .. 24 .. 54 9 10 32 65

36 97 89 80 99 93 .. .. 77 51 99 .. 84 86 99 94 97 .. 85 92 83b 82 .. 69 39 .. 91 .. 94 85 86 90 85 100b 100 .. .. 99 84 74 94 .. .. 28 .. .. .. 98 94 .. 90 .. .. 88 78 85 92

Births attended by skilled health staff

Maternal mortality ratio

% of total 1990 2004–09a

National estimates Modeled estimates 2004–09a 1990 2008

Probability 1 woman in: 2008

.. 21 .. .. 40 27 .. .. 26 348 3 .. 397 310 3 198 75 6 307 615 461 669 .. 543 1,099 18 34 .. 76 549 781 27 543 13 b 47 6 .. 159 60 55 59 .. 7 673 .. .. .. .. 14 .. 451 .. 133 980 405 630 ..

11 1,700 340 29 600 1,900 7,400 14,300 1,200 110 5,100 10,900 43 150 9,300 180 860 5,800 28 25 110 35 5,600 27 14 2,000 1,500 .. 460 24 39 1,100 44 5,200 1,400 8,500 10,900 320 270 380 350 72 5,300 40 7,600 6,600 110 49 1,300 11,100 66 31,800 210 26 18 93 240

Lifetime risk of maternal death

per 100,000 live births

.. .. 77 .. 96 .. 100 .. .. .. .. .. .. 43 97 77 72 .. .. .. .. 58 .. .. .. .. 50 .. 82 .. .. 98 .. 100 .. .. .. 93 .. 37 52 .. .. .. .. .. .. 44 .. .. 40 .. .. 31 .. 23 45

24 99 95 47 95 100 .. .. 88 24 100 .. 74 71 100 95 97 100 54 34 71b 63 100 44 14 100 99 100 96 74 83 99 57 100 b 100 100 .. 98 98 79 96 .. 100 6 .. .. .. 57 98 100 57 .. 51 46 39 26 67

1,700 48 250 1,000 72 51 10 10 64 870 37 7 790 510 18 83 120 24 770 1,200 690 680 6 880 1,300 56 110 .. 140 900 460 35 690 8 63 15 7 220 230 220 200 930 48 990 7 13 260 750 58 13 630 6 140 1,200 1,200 670 210

1,400 31 120 610 70 29 8 5 38 340 15 5 410 180 9 190 58 13 560 970 290 600 12 850 1,200 26 38 .. 85 670 580 44 470 14 53 8 5 100 140 82 110 280 12 470 8 8 260 400 48 7 350 2 110 680 1,000 300 110

Total fertility rate

births per woman 1990 2009

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

1.8 4.0 3.1 4.8 6.0 2.1 2.8 1.3 2.9 1.5 5.5 2.7 6.0 2.4 1.6 3.9 3.5 3.7 6.0 2.0 3.1 4.9 6.5 4.8 2.0 2.1 6.3 7.0 3.7 6.7 5.9 2.3 3.4 2.4 4.2 4.0 6.2 3.4 5.2 5.2 1.6 2.2 4.8 7.9 6.6 1.9 6.6 6.1 3.0 4.8 4.5 3.8 4.3 2.0 1.4 2.2 4.4

1.3 2.7 2.1 1.8 3.9 2.1 3.0 1.4 2.4 1.4 3.4 2.6 4.9 1.9 1.3 2.3 2.2 2.8 3.4 1.3 1.8 3.3 5.8 2.6 1.5 1.4 4.6 5.5 2.5 6.5 4.4 1.5 2.1 1.5 2.0 2.3 5.0 2.3 3.3 2.8 1.8 2.1 2.7 7.1 5.6 2.0 3.0 3.9 2.5 4.0 3.0 2.5 3.0 1.4 1.3 1.7 2.4

Adolescent Unmet Contraceptive Pregnant women fertility need for prevalence receiving rate contraception rate prenatal care births per 1,000 women ages 15–19 2009

19 64 37 17 80 15 14 5 75 5 24 29 101 0 6 .. 13 32 34 14 16 69 136 3 20 21 127 127 12 155 82 41 63 33 15 19 139 18 67 91 4 21 111 152 118 8 10 42 80 50 69 52 43 13 15 50 15

% of married women ages 15–49 2004–09a

.. 13 9 .. .. .. .. .. .. .. 11 .. .. .. .. .. .. 1 .. .. .. 31 36 .. .. 34 24 28 .. 31 25 .. .. 7 14 10 .. .. 7 25 .. .. 8 16 .. .. .. 25 .. .. .. 8 22 .. .. .. ..

any method % of married women ages 15–49 2004–09a

% 2004–09a

.. 54 57 79 50 89 .. .. .. 54 59 51 46 .. 80 .. .. 48 38 .. 58 47 11 .. .. 14 40 41 .. 8 9 .. 73 68 55 63 16 41 55 48 69 .. 72 11 15 88 .. 30 .. 32 79 73 51 .. 67 .. ..

.. 75 93 98 84 .. .. .. 91 .. 99 100 92 .. .. .. .. 97 35 .. 96 92 79 .. .. 94 86 92 79 70 75 .. 94 98 100 68 89 80 95 44 .. .. 90 46 58 .. .. 61 .. 79 96 94 91 .. .. .. ..

2.19

Births attended by skilled health staff

Maternal mortality ratio

% of total 1990 2004–09a

National estimates Modeled estimates 2004–09a 1990 2008

PEOPLE

Reproductive health

Lifetime risk of maternal death

per 100,000 live births

.. .. 32 .. 54 .. .. .. 79 100 87 .. 50 .. 98 .. .. .. .. .. .. .. .. .. .. .. 57 55 .. .. 40 91 .. .. .. 31 .. .. 68 7 .. .. .. 15 33 100 .. 19 .. .. 66 80 .. .. 98 .. ..

100 53 75 97 80 .. .. .. 95 100 99 100 44 .. .. .. .. 98 20 100 98 62 46 .. 100 100 44 54 99 49 61 99 93 100 99 63 55 64 81 19 .. .. 74 33 39 .. 99 39 92 53 82 83 62 100 .. 100 ..

17 254 228 25 84 .. .. .. .. .. 19 37 488 77 .. .. .. 55 405 8 .. 762 994 .. 9 4 498 807 29 464 686 .. 63 38 81 132 .. 316 449 281 .. .. 77 648 545 .. 17 276 60 733 118 .. 162 5 .. .. ..

23 570 620 150 93 6 12 10 66 12 110 78 380 270 18 .. 10 77 1,200 57 52 370 1,100 100 34 16 710 910 56 1,200 780 72 93 62 130 270 1,000 420 180 870 10 18 190 1,400 1,100 9 49 490 86 340 130 250 180 17 15 29 15

13 230 240 30 75 3 7 5 89 6 59 45 530 250 18 .. 9 81 580 20 26 530 990 64 13 9 440 510 31 830 550 36 85 32 65 110 550 240 180 380 9 14 100 820 840 7 20 260 71 250 95 98 94 6 7 18 8

2011 World Development Indicators

Probability 1 woman in: 2008

5,500 140 190 1,500 300 17,800 5,100 15,200 450 12,200 510 950 38 230 4,700 .. 4,500 450 49 3,600 2,000 62 20 540 5,800 7,300 45 36 1,200 22 41 1,600 500 2,000 730 360 37 180 160 80 7,100 3,800 300 16 23 7,600 1,600 93 520 94 310 370 320 13,300 9,800 3,000 4,400

107

2.19

Reproductive health Total fertility rate

births per woman 1990 2009

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

1.8 1.9 6.8 5.8 6.7 1.8 5.5 1.9 2.1 1.5 6.6 3.7 1.3 2.5 6.0 5.7 2.1 1.6 5.5 5.2 6.2 2.1 5.3 6.3 2.4 3.5 3.1 4.3 7.1 1.8 4.4 1.8 2.1 2.5 4.1 3.4 3.7 6.4 8.1 6.5 5.2 3.3 w 5.6 3.3 3.4 3.0 3.6 2.6 2.3 3.2 4.9 4.3 6.3 1.8 1.5

1.4 1.6 5.3 3.0 4.7 1.4 5.2 1.2 1.4 1.5 6.4 2.5 1.4 2.3 4.1 3.5 1.9 1.5 3.1 3.4 5.5 1.8 6.4 4.2 1.6 2.1 2.1 2.4 6.3 1.5 1.9 2.0 2.1 2.0 2.7 2.5 2.0 4.9 5.1 5.7 3.4 2.5 w 4.2 2.4 2.5 2.0 2.7 1.9 1.8 2.2 2.7 2.8 5.1 1.7 1.6

Adolescent Unmet Contraceptive Pregnant fertility women need for prevalence rate receiving contraception rate prenatal care births per 1,000 women ages 15–19 2009

% of married women ages 15–49 2004–09a

29 24 35 25 97 21 124 4 20 5 69 56 12 29 53 78 7 5 55 27 128 36 52 62 34 7 36 18 142 27 15 22 33 60 13 89 16 73 64 133 61 50 w 97 46 45 49 54 17 27 71 34 63 112 18 8

.. .. 38 .. 32 29 .. .. .. .. 26 .. .. .. 6 24 .. .. 11 24 22 .. .. 41 27 .. 18 .. 41 10 .. .. .. .. 8 .. .. .. 24 27 13 .. w 25 .. .. .. .. .. .. .. .. 15 24 .. ..

a. Data are for the most recent year available. b. Data are for 2010.

108

2011 World Development Indicators

any method % of married women ages 15–49 2004–09a

70 80 36 24 12 41 8 .. .. .. 15 .. 66 68 8 51 .. .. 58 37 26 77 22b 17 43 60 73 48 24 67 .. .. .. 78 65 .. 80 50 28 41 65 61 w 33 66 63 75 61 77 69 75 62 51 21 .. ..

Births attended by skilled health staff

Maternal mortality ratio

% of total 1990 2004–09a

National estimates Modeled estimates 2004–09a 1990 2008

Probability 1 woman in: 2008

14 32b 750 14 401 6 857 .. 4 26 1,044 .. .. 39 1,107 589 .. .. .. 38 578 12 .. .. .. .. 29 15 435 16 .. .. 13 34 21 61 75 .. .. 591 555

2,700 1,900 35 1,300 46 7,500 21 10,000 13,300 4,100 14 100 11,400 1,100 32 75 11,400 7,600 610 430 23 1,200 44 67 1,100 860 1,900 500 35 3,000 4,200 4,700 2,100 1,700 1,400 540 850 .. 91 38 42 140 w 39 190 160 570 120 580 1,700 480 380 110 31 3,900 10,100

Lifetime risk of maternal death

per 100,000 live births % 2004–09a

94 .. .. .. 96 26 .. .. 94 .. 98 .. 87 .. .. .. .. .. .. 100 26 .. .. .. .. .. 99 .. 64 69 85 .. .. .. .. .. 84 .. 80 .. 76 53 98 .. .. .. 84 31 96 .. 96 69 95 .. 99 .. 94 38 99 .. .. .. .. .. .. 99 96 .. 99 .. .. .. 91 .. 99 .. 47 16 94 51 93 70 82 w 50 w 67 .. 85 46 83 41 95 .. 82 46 91 48 .. .. 95 72 83 47 70 32 71 .. .. .. .. ..

99 100 52 96 52 99 42 100 100 100 33 .. .. 99 49 69 .. 100 93 88 43 97 .. 62 98 95 95 100 42 99 .. .. .. 99 100 .. 88 99 36 47 60 65 w 41 71 66 96 64 89 97 89 80 47 44 .. ..

170 27 74 39 1,100 540 41 24 750 410 13 8 1,300 970 6 9 15 6 11 18 1,100 1,200 230 410 7 6 91 39 830 750 260 420 7 5 8 10 120 46 120 64 880 790 50 48 650 370 650 350 86 55 130 60 68 23 91 77 670 430 49 26 28 10 10 12 12 24 39 27 53 30 84 68 170 56 .. .. 540 210 390 470 390 790 400 w 260 w 850 580 350 200 400 230 120 82 440 290 200 89 69 32 140 86 210 88 610 290 870 650 15 15 11 7

2.19

PEOPLE

Reproductive health About the data Reproductive health is a state of physical and mental

estimates of maternal mortality that it produces per-

using contraception. • Contraceptive prevalence rate

well-being in relation to the reproductive system and its

tain to 12 years or so before the survey, making them

is the percentage of women married or in union ages

functions and processes. Means of achieving reproduc-

unsuitable for monitoring recent changes or observ-

15–49 who are practicing, or whose sexual partners

tive health include education and services during preg-

ing the impact of interventions. In addition, measure-

are practicing, any form of contraception. • Pregnant

nancy and childbirth, safe and effective contraception,

ment of maternal mortality is subject to many types of

women receiving prenatal care are the percentage of

and prevention and treatment of sexually transmitted

errors. Even in high-income countries with vital regis-

women attended at least once during pregnancy by

diseases. Complications of pregnancy and childbirth

tration systems, misclassification of maternal deaths

skilled health personnel for reasons related to preg-

are the leading cause of death and disability among

has been found to lead to serious underestimation.

nancy. • Births attended by skilled health staff are the

women of reproductive age in developing countries.

The national estimates of maternal mortality ratios

percentage of deliveries attended by personnel trained

Total and adolescent fertility rates are based on data

in the table are based on national surveys, vital regis-

to give the necessary care to women during pregnancy,

on registered live births from vital registration systems

tration records, and surveillance data or are derived

labor, and postpartum; to conduct deliveries on their

or, in the absence of such systems, from censuses

from community and hospital records. The modeled

own; and to care for newborns. • Maternal mortality

or sample surveys. The estimated rates are generally

estimates are based on an exercise by the World

ratio is the number of women who die from pregnancy-

considered reliable measures of fertility in the recent

Health Organization (WHO), United Nations Children’s

related causes during pregnancy and childbirth per

past. Where no empirical information on age-specific

Fund (UNICEF), United Nations Population Fund

100,000 live births. • Lifetime risk of maternal death

fertility rates is available, a model is used to estimate

(UNFPA), and World Bank. This year’s estimates of

refers to the probability that a 15-year-old girl will

the share of births to adolescents. For countries with-

maternal mortality include country-level time-series

eventually die from a maternal cause if throughout her

out vital registration systems fertility rates are gener-

data for the first time. For countries with complete

lifetime she experiences the risks of maternal death

ally based on extrapolations from trends observed in

vital registration systems with good attribution of

and the overall level of fertility and mortality that are

censuses or surveys from earlier years.

cause of death, the data are used to directly estimate

observed for a given population. Data are presented

More couples in developing countries want to limit

maternal mortality. For countries without complete

as 1 in the number of women who are likely to die from

or postpone childbearing but are not using effective

registration data but with other types of data and for

a maternal cause.

contraception. These couples have an unmet need for

countries with no empirical national data, maternal

contraception. Common reasons are lack of knowledge

mortality is estimated with a multilevel regression

about contraceptive methods and concerns about pos-

model using available national-level maternal mortal-

sible side effects. This indicator excludes women not

ity data and socioeconomic information, including

Data on total fertility are compiled from the United

exposed to the risk of unintended pregnancy because

fertility, birth attendants, and GDP. The methodol-

Nations Population Division’s World Population

of menopause, infertility, or postpartum anovulation.

ogy of this year’s interagency estimates differs from

Prospects: The 2008 Revision, census reports

Data sources

Contraceptive prevalence reflects all methods—

previous years’, so the data should not be compared

and other statistical publications from national

ineffective traditional methods as well as highly effec-

with data in previous editions. For further information

statistical offices, household surveys conducted

tive modern methods. Contraceptive prevalence rates

on methodology, see the original source.

by national agencies, Macro International, and the

are obtained mainly from household surveys, includ-

Neither set of ratios can be assumed to provide an

U.S. Centers for Disease Control and Prevention,

ing Demographic and Health Surveys, Multiple Indicator

exact estimate of maternal mortality for any of the

Eurostat’s Demographic Statistics, and the U.S.

Cluster Surveys, and contraceptive prevalence surveys

countries in the table.

Bureau of the Census International Data Base.

(see Primary data documentation for the most recent

In countries with a high risk of maternal death,

Data on adolescent fertility are from World Popu-

survey and year). Unmarried women are often excluded

many girls die before reaching reproductive age. Life-

lation Prospects: The 2008 Revision, with annual

from such surveys, which may bias the estimates.

time risk of maternal mortality refers to the prob-

data linearly interpolated by the Development

ability that a 15-year-old girl will eventually die from

Data Group. Data on women with unmet need for

a maternal cause.

contraception and contraceptive prevalence are

Good prenatal and postnatal care improves maternal health and reduces maternal and infant mortality. Indicators on use of antenatal care services, however,

For the indicators that are from household surveys,

from household surveys, including Demographic

provide no information on the content or quality of the

the year in the table refers to the survey year. For

and Health Surveys by Macro International and

services. Data on antenatal care are obtained mostly

more information, consult the original sources.

Multiple Indicator Cluster Surveys by UNICEF.

from household surveys, which ask women who have had a live birth whether and from whom they received

Data on pregnant women receiving prenatal Definitions

antenatal care. The share of births attended by skilled

care, births attended by skilled health staff, and national estimates of maternal mortality ratios are

health staff is an indicator of a health system’s ability

• Total fertility rate is the number of children that would

from UNICEF’s State of the World’s Children 2011

to provide adequate care for pregnant women.

be born to a woman if she were to live to the end of

and Childinfo and Demographic and Health Sur-

Maternal mortality ratios are generally of unknown

her childbearing years and bear children in accordance

veys by Macro International. Modeled estimates

reliability, as are many other cause-specific mortality

with current age-specific fertility rates. • Adolescent

of maternal mortality ratios and lifetime risk of

indicators. Household surveys such as Demographic

fertility rate is the number of births per 1,000 women

maternal death are from WHO, UNICEF, UNFPA

and Health Surveys attempt to measure maternal mor-

ages 15–19. • Unmet need for contraception is the

and the World Bank’s Trends in Maternal Mortal-

tality by asking respondents about survivorship of sis-

percentage of fertile, married women of reproductive

ity: 1990–2008 (2010).

ters. The main disadvantage of this method is that the

age who do not want to become pregnant and are not

2011 World Development Indicators

109

2.20

Nutrition Prevalence of undernourishment

% of population 1990–92 2005–09

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

110

.. 10 <5 67 <5 45 <5 <5 27 38 <5 <5 20 29 8 19 11 <5 14 44 38 33 <5 44 60 7 18 c .. 15 26 42 <5 15 18 6 <5 <5 28 23 <5 13 67 10 69 <5 <5 6 14 58 <5 27 <5 15 20 22 63 19

.. <5 <5 41 <5 22 <5 <5 <5 27 <5 <5 12 27 <5 25 6 10 9 62 22 21 <5 40 37 <5 10 c .. 10 69 15 <5 14 <5 <5 <5 <5 24 15 <5 9 64 <5 41 <5 <5 <5 19 <5 <5 5 <5 21 17 22 57 12

2011 World Development Indicators

Prevalence of child Prevalence Lowmalnutrition of overweight birthweight children babies

% of children under age 5 Underweight Stunting 2004–09a 2004–09a

32.9 6.6 3.7 .. 2.3 4.2 .. .. 8.4 41.3 1.3 .. 20.2 4.5 1.6 .. 2.2 1.6 26.0 .. 28.8 16.6 .. .. 33.9 0.5 4.5 .. 5.1 28.2 11.8 .. 16.7 1.0 .. .. .. 3.4 6.2 6.8 .. .. .. 34.6 .. .. .. 15.8 2.3 1.1 14.3 .. .. 20.8 17.4 18.9 8.6

59.3 27.0 15.9 .. 8.2 18.2 .. .. 26.8 43.2 4.5 .. 44.7 27.2 11.8 .. 7.1 8.8 35.1 .. 39.5 36.4 .. .. 44.8 2.0 11.7 .. 16.2 45.8 31.2 .. 40.1 0.6 .. .. .. 10.1 29.0 30.7 .. .. .. 50.7 .. .. .. 27.6 14.7 1.3 28.6 .. .. 40.0 47.7 29.7 29.9

% of children under age 5 2004–09a

4.6 25.2 12.9 .. 9.9 11.7 .. .. 13.9 1.1 9.7 .. 11.4 8.7 25.6 .. 7.3 13.6 7.7 .. 2.0 9.6 .. .. 4.4 9.5 5.9 .. 4.2 6.8 8.5 .. 9.0 8.1 .. .. .. 8.3 5.1 20.5 .. .. .. 5.1 .. .. .. 2.7 21.0 3.5 5.9 .. .. 5.1 17.0 3.9 5.8

Exclusive breastfeeding

% of births 2004–09a

% of children under 6 months 2004–09a

.. 7 6 .. 7 7 .. .. 10 22 4 .. 15 6 5 13 8 9 16 11 9 11 .. 13 22 6 3 .. 6 10 13 7 17 5 5 .. .. 11 10 13 .. .. .. 20 .. .. .. 20 5 .. 13 .. .. 12 24 25 10

83 39 7 .. .. 33 .. .. 12 43 9 .. 43 60 18 20 40 .. 16 45 66 21 .. 23 2 85 28 .. 47 36 19 15 4 98 26 .. .. 9 40 53 31 .. .. 49 .. .. .. 41 11 .. 63 .. 50 48 16 41 30

Consumption Vitamin A of iodized supplemensalt tation

% of households 2004–09a

28 76 61 45 .. 97 .. .. 54 84 55 .. 67 89 62 .. 96 100 34 98 73 49 .. 62 56 .. 96 .. .. 79 82 .. 84 88 88 .. .. 19 .. 79 .. .. .. 20 .. .. .. 7 100 .. 32 .. 76 41 1 3 ..

Prevalence of anemia

% % of children Children Pregnant 6–59 months under age 5 women a 2009 2004–09 2004–09a

95 .. .. 28 .. .. .. .. 79 b 91 .. .. 56 45 .. 89 .. .. 100 90 98 .. .. 87 71 .. .. .. .. 89 8 .. 88 .. .. .. .. .. .. 68 b 20 44 .. 84 .. .. 0 28 .. .. 90 .. 43 94 80 .. ..

38 31 43 .. 17 37 8 11 .. 58 27 9 78 52 27 .. 55 27 .. 56 62 68 8 .. 71 24 .. .. 28 71 66 .. 69 23 27 18 9 35 38 49 .. 70 23 75 11 8 44 .. 41 8 .. 12 .. 76 75 .. ..

61 34 43 57 31 .. 12 15 .. 39 26 13 75 37 35 21 29 30 .. 47 57 51 12 .. 60 28 .. .. 31 67 55 .. 55 28 39 22 12 40 38 34 .. 55 23 63 15 11 46 .. 42 12 .. 19 .. .. 58 50 21

Prevalence of undernourishment

% of population 1990–92 2005–09

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

<5 20 16 <5 .. <5 <5 <5 11 <5 <5 <5 33 21 <5 .. 20 17 31 <5 <5 15 30 <5 <5 11 21 43 <5 27 12 7 <5 5 28 6 59 47 32 21 <5 <5 50 37 16 <5 .. 25 18 .. 16 27 24 <5 <5 .. ..

<5 21 13 <5 .. <5 <5 <5 5 <5 5 <5 31 33 <5 .. 5 10 23 <5 <5 14 33 <5 <5 <5 25 28 <5 12 7 5 <5 6 26 <5 38 16 19 16 <5 <5 19 20 6 <5 .. 26 15 .. 11 15 15 <5 <5 .. ..

Prevalence of child Prevalence Lowmalnutrition of overweight birthweight children babies

% of children under age 5 Underweight Stunting 2004–09a 2004–09a

.. 43.5 17.5d .. 7.1 .. .. .. 2.2 .. 1.9 4.9 16.4 20.6 .. .. 1.7 2.7 31.6 .. 4.2 16.6 20.4 5.6 .. 1.8 36.8 15.5 .. 27.9 16.7 .. 3.4 3.2 5.3 9.9 .. .. 17.5 38.8 .. .. 4.3 39.9 26.7 .. .. .. .. 18.1 .. 5.4 .. .. .. .. ..

.. 47.9 35.6d .. 27.5 .. .. .. 3.7 .. 8.3 17.5 35.2 43.1 .. .. 3.8 18.1 47.6 .. 16.5 45.2 39.4 21.0 .. 11.5 49.2 53.2 .. 38.5 24.2 .. 15.5 11.3 27.5 23.1 .. .. 29.6 49.3 .. .. 18.8 54.8 41.0 .. .. .. .. 43.9 .. 29.8 .. .. .. .. ..

% of children under age 5 2004–09a

.. 1.9 11.2 .. 15.0 .. .. .. 7.5 .. 6.6 14.8 5.0 .. .. .. 9.0 10.7 1.3 .. 16.7 6.8 4.2 22.4 .. 16.2 6.2 11.3 .. 4.7 2.3 .. 7.6 9.1 14.2 13.3 .. .. 4.6 0.6 .. .. 5.2 3.5 10.5 .. .. .. .. 3.4 .. 9.1 .. .. .. .. ..

Exclusive breastfeeding

2.20

Consumption Vitamin A of iodized supplemensalt tation

% of births 2004–09a

% of children under 6 months 2004–09a

% of households 2004–09a

.. 28 11d 7 15 .. .. .. 14 .. 13 6 8 .. .. .. .. 5 11 .. .. 13 14 .. .. 6 16 13 11 19 34 .. 8 6 5 .. 15 .. 16 21 .. .. 8 27 12 .. 9 32 .. 10 9 8 21 .. .. .. ..

.. 46 15d 23 25 .. .. .. 15 .. 22 17 32 65 .. .. .. 32 26 .. .. 54 29 .. .. 16 51 57 .. 38 35 .. .. 46 57 31 37 .. 24 53 .. .. 31 10 13 .. .. 37 .. 56 22 70 34 .. .. .. ..

.. 51 62d 99 28 .. .. .. .. .. .. 92 98 40 .. .. .. 76 84 .. 92 91 .. .. .. 94 53 50 .. 79 23 .. .. 60 83 21 25 93 .. .. .. .. .. 46 .. .. .. .. .. 92 94 91 45 .. .. .. ..

PEOPLE

Nutrition

Prevalence of anemia

% % of children Children Pregnant 6–59 months under age 5 women a 2009 2004–09 2004–09a

.. 66 84 .. .. .. .. .. .. .. .. .. 51 99 .. .. .. 99 88 .. .. 85 92 .. .. .. 95 95 .. 100 89 .. .. .. 95 .. 97 95 .. 95 .. .. 6 95 78 .. .. 91 .. 12 .. .. 91 .. .. .. ..

19 74 44 35 56 10 12 11 .. 11 .. .. .. .. .. .. .. .. .. 27 .. 49 .. 34 24 .. 68 73 32 .. 68 .. 24 41 21 .. .. 63 41 48 9 11 17 81 .. 6 42 .. .. 60 30 50 21 23 13 .. ..

2011 World Development Indicators

21 50 44 .. 38 15 17 15 .. 15 .. 26 .. .. 23 .. 31 34 56 25 32 25 .. 34 24 32 50 47 38 .. 53 .. 21 36 37 .. 52 50 31 42 13 18 .. 61 .. 9 .. .. .. 55 39 43 43 25 17 .. 29

111

2.20

Nutrition Prevalence of undernourishment

% of population 1990–92 2005–09

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

<5 <5 44 <5 22 <5 e 45 .. <5 <5 .. <5 <5 28 39 12 <5 <5 <5 34 28 26 39 43 11 <5 <5 9 19 <5 <5 <5 <5 5 5 10 31 10 30 35 40 17 w 38 17 19 8 19 20 7 13 7 23 31 5 5

<5 <5 34 <5 17 8e 35 .. <5 <5 .. <5 <5 19 22 18 <5 <5 <5 30 34 16 31 30 11 <5 <5 6 21 <5 <5 <5 <5 <5 11 8 11 18 31 43 30 14 w 31 13 15 6 16 11 6 9 7 22 26 5 5

Prevalence of child Prevalence Lowmalnutrition of overweight birthweight children babies

% of children under age 5 Underweight Stunting 2004–09a 2004–09a

.. .. 18.0 5.3 14.5 1.8 21.3 .. .. .. 32.8 .. .. 21.6 31.7 6.1 .. .. 10.0 14.9 16.7 7.0 .. 22.3 .. 3.3 3.5 .. 16.4 .. .. .. 1.3 6.0 4.4 3.7 20.2 2.2 .. 14.9 14.0 21.3 w 27.7 20.8 24.0 .. 22.4 8.8 .. 3.8 6.8 42.5 24.7 .. ..

.. .. 51.7 9.3 20.1 8.1 37.4 .. .. .. 42.1 .. .. 19.2 37.9 29.5 .. .. 28.6 33.1 44.4 15.7 .. 27.8 .. 9.0 15.6 .. 38.7 .. .. .. 3.9 13.9 19.6 15.6 30.5 11.8 .. 45.8 35.8 31.7 w 44.0 30.0 33.1 .. 33.3 19.0 .. 14.1 25.0 47.5 42.0 .. ..

% of children under age 5 2004–09a

.. .. 6.7 6.1 2.4 19.3 10.1 .. .. .. 4.7 .. .. 0.8 5.3 11.4 .. .. 18.7 6.7 4.9 8.0 .. 4.7 .. 8.8 9.1 .. 4.9 .. .. .. 8.0 9.4 12.8 6.1 3.0 11.4 .. 8.4 9.1 6.1 w 4.9 6.3 5.9 .. 6.0 6.6 .. 7.2 16.6 1.9 7.0 .. ..

% of births 2004–09a

8 6 6 .. 19 6 14 .. .. .. 11 .. .. 17 .. 9 .. .. 9 10 10 9 .. 12 19 5 11 4 14 4 .. .. .. 8 5 8 5 7 .. 11 11 15 w 15 15 17 8 15 6 7 8 10 27 14 .. ..

Exclusive breastfeeding

% of children under 6 months 2004–09a

16 .. 88 .. 34 15 11 .. .. .. 9 .. .. 76 34 33 .. .. 29 25 50d 5 52d 48 13 6 42 11 60 18 .. .. .. 57 26 .. 17 27 .. 61 26 37 w 44 35 34 .. 37 29 .. 44 31 46 33 .. ..

Consumption Vitamin A of iodized supplemensalt tation

% of households 2004–09a

74 .. 88 .. 41 32 58 .. .. .. 1 .. .. 92 11 80 .. .. .. 62 43 47 60 25 28 .. 69 87 96 18 .. .. .. .. 53 .. 93 86 .. .. 91 71 w 62 73 71 .. 71 87 .. 89 69 55 52 .. ..

Prevalence of anemia

% % of children Children Pregnant 6–59 months under age 5 women a 2009 2004–09 2004–09a

.. .. 94 .. 97 .. 99 .. .. .. 62 39 .. .. 84 27 .. .. .. 87 94 .. 45 100 .. .. .. .. 64 .. .. .. .. .. 65 .. 99 b .. 47b 91 77 .. w 86 .. .. .. .. .. .. .. .. 73 81 .. ..

40 27 56 33 70 .. 83 19 23 14 .. .. 13 .. 85 47 9 6 41 .. 72 .. .. 52 30 .. 33 .. 73 .. 28 .. .. 19 .. 33 .. .. 68 .. 58 .. w 66 .. .. 36 .. .. 30 38 48 71 .. .. 10

30 21 .. 32 58 .. 60 24 25 19 .. 22 18 .. 58 24 13 .. 39 45 58 .. .. 50 30 .. 40 30 64 27 28 15 6 27 .. 40 .. .. 58 .. 47 .. w 56 .. .. 31 .. .. 31 33 .. 49 .. 13 14

a. Data are for the most recent year available. b. Country’s vitamin A supplementation programs do not target children all the way up to 59 months of age. c. Includes Hong Kong SAR, China; Macao SAR, China; and Taiwan, China. d. Data are for 2010. e. Includes Montenegro.

112

2011 World Development Indicators

About the data

2.20

PEOPLE

Nutrition Definitions

Data on undernourishment are from the Food and

emerging evidence that low-birthweight babies are

• Prevalence of undernourishment is the percent-

Agriculture Organization (FAO) of the United Nations

more prone to noncommunicable diseases such as

age of the population whose dietary energy consump-

and measure food deprivation based on average

diabetes and cardiovascular diseases. Estimates of

tion is continuously below a minimum requirement

food available for human consumption per person,

low-birthweight infants are drawn mostly from hos-

for maintaining a healthy life and carrying out light

the level of inequality in access to food, and the

pital records and household surveys. Many births

physical activity with an acceptable minimum weight

minimum calories required for an average person.

in developing countries take place at home and are

for height. • Prevalence of child malnutrition is the

From a policy and program standpoint, however,

seldom recorded. A hospital birth may indicate higher

percentage of children under age 5 whose weight for

this measure has its limits. First, food insecurity

income and therefore better nutrition, or it could indi-

age (underweight) or height for age (stunting) is more

exists even where food availability is not a problem

cate a higher risk birth. The data should therefore be

than two standard deviations below the median for

because of inadequate access of poor households

used with caution.

the international reference population ages 0–59

to food. Second, food insecurity is an individual

Improved breastfeeding can save an estimated 1.3

months. Height is measured by recumbent length

or household phenomenon, and the average food

million children a year. Breast milk alone contains

for children up to two years old and by stature while

available to each person, even corrected for possible

all the nutrients, antibodies, hormones, and antioxi-

standing for older children. Data are based on the

effects of low income, is not a good predictor of food

dants an infant needs to thrive. It protects babies

WHO child growth standards released in 2006.

insecurity among the population. And third, nutrition

from diarrhea and acute respiratory infections, stimu-

• Prevalence of over weight children is the percent-

security is determined not only by food security but

lates their immune systems and response to vacci-

also by the quality of care of mothers and children

nation, and may confer cognitive benefits. The data

and the quality of the household’s health environ-

on breastfeeding are derived from national surveys.

ment (Smith and Haddad 2000).

Iodine defi ciency is the single most important

Estimates of child malnutrition, based on preva-

cause of preventable mental retardation, and it

lence of underweight and stunting, are from national

contributes significantly to the risk of stillbirth and

survey data. The proportion of underweight children

miscarriage. Widely used and inexpensive, iodized

is the most common malnutrition indicator. Being

salt is the best source of iodine, and a global cam-

even mildly underweight increases the risk of death

paign to iodize edible salt is significantly reducing

and inhibits cognitive development in children. And

the risks. The data on iodized salt are derived from

it perpetuates the problem across generations, as

household surveys.

malnourished women are more likely to have low-

Vitamin A is essential for immune system function-

birthweight babies. Stunting, or being below median

ing. Vitamin A deficiency, a leading cause of blind-

height for age, is often used as a proxy for multi-

ness, also causes a greater risk of dying from a range

faceted deprivation and as an indicator of long-term

of childhood ailments such as measles, malaria,

changes in malnutrition. Estimates of overweight

and diarrhea. Giving vitamin A to new breastfeed-

children are also from national survey data. Over-

ing mothers helps protect their children during the

weight children have become a growing concern in

first months of life. Food fortification with vitamin A

developing countries. Research shows an associa-

is being introduced in many developing countries.

tion between childhood obesity and a high preva-

Data on anemia are compiled by the WHO based

lence of diabetes, respiratory disease, high blood

mainly on nationally representative surveys, which

pressure, and psychosocial and orthopedic disorders

measured hemoglobin in the blood. WHO’s hemoglo-

(de Onis and Blössner 2000).

bin thresholds were then used to determine anemia

age of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO child growth standards released in 2006. • Low-birthweight babies are the percentage of newborns weighing less than 2.5 kilograms within the first hours of life, before significant postnatal weight loss has occurred. • Exclusive breastfeeding is the percentage of children less than six months old who were fed breast milk alone (no other liquids) in the past 24 hours. • Consumption of iodized salt is the percentage of households that use edible salt fortified with iodine. • Vitamin A supplementation is the percentage of children ages 6–59 months who received at least two doses of vitamin A in the previous year. • Prevalence of anemia, children under age 5, is the percentage of children under age 5 whose hemoglobin level is less than 110 grams per liter at sea level. • Prevalence of anemia, pregnant women, is the percentage of pregnant women whose hemoglobin level is less than 110 grams per liter at sea level.

Data sources

New international growth reference standards for

status based on age, sex, and physiological status.

infants and young children were released in 2006

Children under age 5 and pregnant women have the

Data on undernourishment are from www.fao.

by the World Health Organization (WHO) to monitor

highest risk for anemia. Data should be used with

org/faostat/foodsecurity/index_en.htm. Data

children’s nutritional status. Differences in growth

caution because surveys differ in quality, coverage,

on malnutrition and overweight children are from

to age 5 are influenced more by nutrition, feeding

age group interviewed, and treatment of missing val-

the WHO’s Global Database on Child Growth and

practices, environment, and healthcare than by

ues across countries and over time.

Malnutrition (www.who.int/nutgrowthdb). Data on

genetics or ethnicity. The previously reported data

For indicators from household surveys, the year in

low-birthweight babies, breastfeeding, iodized salt

were based on the U.S. National Center for Health

the table refers to the survey year. For more informa-

consumption, and vitamin A supplementation are

Statistics–WHO growth reference. Because of the

tion, consult the original sources.

from the United Nations Children’s Fund’s State of

change in standards, the data in this edition should

the World’s Children 2011 and Childinfo. Data on

not be compared with data in editions prior to 2008.

anemia are from the WHO’s Worldwide Prevalence

Low birthweight, which is associated with maternal

of Anemia 1993–2005 (2008c) and Integrated

malnutrition, raises the risk of infant mortality and

WHO Nutrition Global Databases.

stunts growth in infancy and childhood. There is also

2011 World Development Indicators

113

2.21

Health risk factors and future challenges Prevalence of smoking

% of adults

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

114

Male 2006

Female 2006

.. 43 26 .. 34 61 22 47 .. 43 64 30 13 34 49 .. 19 49 13 .. 55 c 9 21 .. 12 42 59 .. .. 10 9 26 11 34 e 36 35 35 15 23 24 .. 15 48 8 33 36 .. 17 57 37 7 63 24 .. .. .. ..

.. 4 0 .. 24 3 19 41 .. 1 22 24 1 26 35 .. 12 38 1 .. 20c 1 18 .. 1 31 4 .. .. 1 0 7 1 27e 28 27 30 11 5 1 .. 1 25 1 23 27 .. 1 6 26 1 39 4 .. .. .. ..

2011 World Development Indicators

Prevalence of HIVa

Incidence of Prevalence tuberculosis of diabetes

per 100,000 people

% of population ages 20–79

2009

2010

189 15 59 298 28 73 6 11 110 225 39 9 93 140 50 694 45 41 215 348 442 182 5 327 283 11 96 82 35 372 382 10 399 25 6 9 7 70 68 19 30 99 30 359 9 6 501 269 107 5 201 5 62 318 229 238 58

8.6 4.5 8.5 3.5 5.7 7.8 5.7 8.9 7.5 6.6 7.6 5.3 4.6 6.0 7.1 5.4 6.4 6.5 3.8 1.8 5.2 3.9 9.2 4.5 3.7 5.7 4.2 8.5 5.2 3.2 5.1 9.3 4.7 6.9 9.5 6.4 5.6 11.2 5.9 11.4 9.0 2.5 7.6 2.5 5.7 6.7 5.0 4.3 7.5 8.9 4.3 6.0 8.6 4.3 3.9 7.2 9.1

Total % of population ages 15–49 1990

2009

.. .. <0.1 0.5 0.3 <0.1 0.1 <0.1 <0.1 <0.1 <0.1 <0.1 0.2 0.1 .. 3.5 .. <0.1 3.9 3.9 0.5 0.6 0.1 3.1 1.1 <0.1 .. .. 0.2 .. 5.2 <0.1 2.4 <0.1 <0.1 <0.1 <0.1 0.4 0.3 <0.1 0.1 0.3 <0.1 .. <0.1 0.3 0.9 0.1 <0.1 0.1 0.3 0.1 0.1 1.1 0.3 1.3 1.1

.. .. 0.1 2.0 0.5 0.1 0.1 0.3 0.1 <0.1 0.3 0.2 1.2 0.2 .. 24.8 .. 0.1 1.2 3.3 0.5 5.3 0.2 4.7 3.4 0.4 0.1d .. 0.5 .. 3.4 0.3 3.4 <0.1 0.1 <0.1 0.2 0.9 0.4 <0.1 0.8 0.8 1.2 .. 0.1 0.4 5.2 2.0 0.1 0.1 1.8 0.1 0.8 1.3 2.5 1.9 0.8

Female % of total population with HIV 2009

.. .. 30 60 32 <43 31 29 60 30 50 31 58 32 .. 57 .. 29 60 60 63 58 21 61 59 31 .. .. 33 .. 59 29 58 <33 31 <42 27 59 31 23 34 60 31 .. <36 32 58 58 43 18 59 31 33 59 60 60 32

Condom use

Youth % of population ages 15–24 Male 2009

.. .. 0.1 0.6 0.3 <0.1 0.1 0.3 <0.1 <0.1 <0.1 <0.1 0.3 0.1 .. 5.2 .. <0.1 0.5 1.0 0.1 1.6 0.1 1.0 1.0 0.2 .. .. 0.2 .. 1.2 0.2 0.7 <0.1 0.1 <0.1 0.1 0.3 0.2 <0.1 0.4 0.2 0.3 .. 0.1 0.2 1.4 0.9 <0.1 0.1 0.5 0.1 0.5 0.4 0.8 0.6 0.3

% of population ages 15–24

Female 2009

Male 2004–09b

.. .. <0.1 1.6 0.2 <0.1 0.1 0.2 0.1 <0.1 0.1 <0.1 0.7 0.1 .. 11.8 .. <0.1 0.8 2.1 0.1 3.9 0.1 2.2 2.5 0.1 .. .. 0.1 .. 2.6 0.1 1.5 <0.1 0.1 <0.1 0.1 0.7 0.2 <0.1 0.3 0.4 0.2 .. <0.1 0.1 3.5 2.4 <0.1 <0.1 1.3 0.1 0.3 0.9 2.0 1.3 0.2

.. .. .. .. .. 68 .. .. 25 .. .. .. 39 .. .. .. .. .. .. .. 31 52 .. .. 18 .. .. .. .. 16 36 .. .. .. .. .. .. 58 .. .. .. .. .. 18 .. .. .. .. .. .. .. .. .. 35 .. 42 ..

Female 2004–09 b

.. .. .. .. .. 5 .. .. 1 .. .. .. 10 .. .. .. .. .. .. .. 3 24 .. .. 2 .. .. .. 24 26 16 .. .. .. .. .. .. 19 .. .. .. .. .. 2 .. .. .. .. .. .. .. .. .. 10 .. 37 7

Prevalence of smoking

% of adults

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

Male 2006

Female 2006

45 28 66f 24 29 34 31 34 18 42 59 43 23 58 53 .. 36 46 60 53 31 .. 10 .. 50 .. .. 17 49 13 24 34 36 45 46 27 19 40 22 30 33 22 .. .. 8 30 20 30 .. .. 33 .. 50 30 34 .. ..

35 1 5f 2 3 28 18 19 8 13 10 9 1 .. 6 .. 4 2 13 24 7 .. .. .. 22 .. .. 2 2 1 1 1 12 5 6 0 1 13 8 28 28 20 .. .. 0 30 0 3 .. .. 14 .. 11 38 15 .. ..

Prevalence of HIVa

Incidence of Prevalence tuberculosis of diabetes

per 100,000 people

% of population ages 20–79

2009

2010

16 168 189 19 64 9 5 6 7 21 6 163 305 345 90 .. 35 159 89 45 15 634 288 40 71 23 261 304 83 324 330 22 17 178 224 92 409 404 727 163 8 8 44 181 295 6 13 231 48 250 47 113 280 24 30 2 49

6.4 7.8 4.8 8.0 10.2 5.2 6.5 5.9 10.6 5.0 10.1 5.8 3.5 5.3 7.9 .. 14.6 5.2 5.6 7.6 7.8 3.9 4.7 9.0 7.6 6.9 3.2 2.3 11.6 4.2 4.8 16.2 10.8 7.6 1.6 8.3 4.0 3.2 4.4 3.9 5.3 5.2 10.0 3.9 4.7 3.6 13.4 9.1 9.6 3.0 4.9 6.2 7.7 7.6 9.7 10.6 15.4

Total % of population ages 15–49 1990

2009

0.1 0.1 <0.1 <0.1 .. <0.1 <0.1 0.3 2.1 <0.1 .. <0.1 3.9 .. <0.1 .. .. <0.1 <0.1 <0.1 <0.1 0.8 0.3 .. <0.1 .. 0.2 7.2 0.1 0.4 0.2 <0.1 0.4 <0.1 <0.1 <0.1 1.2 0.2 1.6 0.2 0.1 0.1 <0.1 0.1 1.3 <0.1 <0.1 <0.1 0.2 <0.1 <0.1 0.4 <0.1 <0.1 0.1 .. <0.1

<0.1 0.3 0.2 0.2 .. 0.2 0.2 0.3 1.7 <0.1 .. 0.1 6.3 .. <0.1 .. .. 0.3 0.2 0.7 0.1 23.6 1.5 .. 0.1 .. 0.2 11.0 0.5 1.0 0.7 1.0 0.3 0.4 <0.1 0.1 11.5 0.6 13.1 0.4 0.2 0.1 0.2 0.8 3.6 0.1 0.1 0.1 0.9 0.9 0.3 0.4 <0.1 0.1 0.6 .. 0.1

Female % of total population with HIV 2009

<33 39 30 29 .. 29 29 33 33 34 .. 60 59 .. 31 .. .. 29 42 30 31 62 61 .. <33 .. 31 59 11 62 31 29 27 42 <29 32 61 35 59 33 30 <37 31 53 59 30 <33 29 31 58 31 25 30 31 31 .. <50

2.21

PEOPLE

Health risk factors and future challenges

Condom use

Youth % of population ages 15–24 Male 2009

<0.1 0.1 0.1 <0.1 .. 0.1 0.1 <0.1 1.0 <0.1 .. 0.1 1.8 .. <0.1 .. .. 0.1 0.1 0.2 0.1 5.4 0.3 .. <0.1 .. 0.1 3.1 0.1 0.2 0.4 0.3 0.2 0.1 <0.1 0.1 3.1 0.3 2.3 0.2 0.1 <0.1 0.1 0.2 1.2 <0.1 <0.1 0.1 0.4 0.3 0.2 0.2 <0.1 <0.1 0.3 .. <0.1

% of population ages 15–24

Female 2009

Male 2004–09b

<0.1 0.1 <0.1 <0.1 .. 0.1 <0.1 <0.1 0.7 <0.1 .. 0.2 4.1 .. <0.1 .. .. 0.1 0.2 0.1 <0.1 14.2 0.7 .. <0.1 .. 0.1 6.8 <0.1 0.5 0.3 0.2 0.1 0.1 <0.1 0.1 8.6 0.3 5.8 0.1 <0.1 <0.1 0.1 0.5 2.9 <0.1 <0.1 <0.1 0.3 0.8 0.1 0.1 <0.1 <0.1 0.2 .. <0.1

.. 15 .. .. .. .. .. .. 74 .. .. .. 64 .. .. .. .. .. .. .. .. 44 19 .. .. .. 6 32 .. 29 .. .. .. 55 .. .. .. .. 78 24 .. .. .. 14 50 .. .. .. .. .. .. .. .. .. .. .. ..

2011 World Development Indicators

Female 2004–09 b

.. 6 .. .. .. .. .. .. 66 .. .. .. 40 .. .. .. .. .. .. .. .. 26 9 .. .. .. 3 9 .. 4 .. .. .. 22 .. .. .. .. 55 8 .. .. .. 1 36 .. .. .. .. .. .. .. .. .. .. .. ..

115

2.21

Health risk factors and future challenges Prevalence of smoking

% of adults Male 2006

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

46 60 g .. 22 13 40 .. 34 41 32 .. 27 37 27 25 21 17 32 40 .. 20 40 .. .. .. 53 48 e .. 17 65 24 26 25 39 23 32 41 .. 28 17 28 39 w 28 42 43 38 40 56 58 27 28 30 14 33 37

Female 2006

24 22g .. 3 1 27 .. 5 20 21 .. 8 27 0 2 2 23 23 .. .. 2 2 .. .. .. 6 15e .. 2 24 2 24 19 29 3 27 2 .. 6 2 2 8w 4 6 3 16 6 4 22 15 2 2 2 21 25

Prevalence of HIVa

Incidence of Prevalence tuberculosis of diabetes

Total % of population ages 15–49

Female % of total population with HIV

Condom use

Youth % of population ages 15–24

per 100,000 people

% of population ages 20–79

2009

2010

1990

2009

2009

Male 2009

Female 2009

125 106 376 18 282 21 644 36 9 12 285 971 17 66 119 1,257 6 5 21 202 183 137 498 446 23 24 29 67 293 101 4 12 4 22 128 33 200 19 54 433 742 137 w 294 138 147 101 161 136 89 45 39 180 342 14 9

6.9 7.6 1.6 16.8 4.7 6.9 4.4 10.2 6.4 7.7 3.0 4.5 6.6 10.9 4.2 4.2 5.2 8.9 10.8 5.0 3.2 7.1 3.5 4.3 11.7 9.3 8.0 5.3 2.2 7.6 18.7 3.6 10.3 5.7 5.2 6.5 3.5 8.6 3.0 4.0 4.1 6.4 w 4.4 6.3 6.0 7.5 6.1 4.6 7.3 7.4 9.1 7.8 3.8 7.9 7.1

<0.1 <0.1 5.2 .. 0.2 0.1 <0.1 <0.1 <0.1 <0.1 0.1 0.7 0.4 <0.1 0.1 2.3 0.1 0.2 .. <0.1 4.8 1.0 .. 0.6 0.2 <0.1 <0.1 .. 10.2 0.1 .. 0.1 0.5 0.1 <0.1 .. <0.1 .. .. 12.7 10.1 0.3 2.0 0.2 0.2 0.3 0.3 0.1 0.1 0.4 0.1 0.1 2.4 0.2 0.2

0.1 1.0 2.9 .. 0.9 0.1 1.6 0.1 <0.1 <0.1 0.7 17.8 0.4 <0.1 1.1 25.9 0.1 0.4 .. 0.2 5.6 1.3 .. 3.2 1.5 <0.1 <0.1 .. 6.5 1.1 .. 0.2 0.6 0.5 0.1 .. 0.4 .. .. 13.5 14.3 0.8 w 2.7 0.6 0.4 1.4 0.9 0.2 0.6 0.5 0.1 0.3 5.4 0.3 0.3

30 49 61 .. 59 24 60 30 <17 <29 47 62 24 <32 58 58 31 32 .. 30 59 40 .. 59 33 <37 30 .. 58 49 .. 31 25 32 29 .. 30 .. .. 57 60 37 w 46 .. .. 36 39 .. 42 .. 28 36 58 28 27

0.1 0.2 1.3 .. 0.3 0.1 0.6 <0.1 <0.1 <0.1 0.4 4.5 0.2 <0.1 0.5 6.5 <0.1 0.2 .. <0.1 1.7 .. .. 0.9 1.0 <0.1 <0.1 .. 2.3 0.2 .. 0.2 0.3 0.3 <0.1 .. 0.1 .. .. 4.2 3.3 0.4 w 0.9 .. .. 0.5 .. 0.1 0.1 0.2 0.1 0.1 1.5 0.2 0.1

<0.1 0.3 1.9 .. 0.7 0.1 1.5 <0.1 <0.1 <0.1 0.6 13.6 0.1 <0.1 1.3 15.6 <0.1 0.1 .. <0.1 3.9 .. .. 2.2 0.7 <0.1 <0.1 .. 4.8 0.3 .. 0.1 0.2 0.2 <0.1 .. 0.1 .. .. 8.9 6.9 0.7 w 2.0 .. .. 1.2 .. 0.1 0.2 0.2 0.1 0.1 3.8 0.1 0.1

% of population ages 15–24 Male 2004–09b

.. .. 19 .. 48 .. 20 .. .. .. .. .. .. .. .. 66 .. .. .. .. 36 .. .. .. .. .. .. .. 36 64 .. .. .. .. .. .. 16 .. .. 39 52 .. w .. .. .. .. .. .. .. .. .. 15 36 .. ..

Female 2004–09 b

.. .. 5 .. 5 .. 9 .. .. .. .. .. .. .. .. 44 .. .. .. .. 13 .. .. .. .. .. .. .. 13 43 .. .. .. .. .. .. 8 .. .. 17 9 .. w .. .. .. .. .. .. .. .. .. 6 19 .. ..

a. See plausible bounds in the database and original source. b. Data are for the most recent year available. c. Data are for 2010. d. Includes Hong Kong SAR, China. e. Data are for 2008. f. Data are for 2007. g. Data are for 2009.

116

2011 World Development Indicators

About the data

2.21

PEOPLE

Health risk factors and future challenges Definitions

The limited availability of data on health status is a

many developing countries most new infections

• Prevalence of smoking is the adjusted and age-

major constraint in assessing the health situation in

occur in young adults, with young women especially

standardized prevalence estimate of smoking among

developing countries. Surveillance data are lacking

vulnerable.

adults. The age range varies but in most countries is

for many major public health concerns. Estimates

Data on HIV are from the Joint United Nations

18 and older or 15 and older. • Incidence of tuber-

of prevalence and incidence are available for some

Programme on HIV/AIDS (UNAIDS) Global Report:

culosis is the number of new and relapse cases of

diseases but are often unreliable and incomplete.

UNAIDS Report Global AIDS Epidemic 2010. Changes

tuberculosis (all types) per 100,000 people. • Preva-

National health authorities differ widely in capacity

in procedures and assumptions for estimating the

lence of diabetes refers to the percentage of people

and willingness to collect or report information. To

data and better coordination with countries have

ages 20–79 who have type 1 or type 2 diabetes.

compensate for this and improve reliability and inter-

resulted in improved estimates of HIV and AIDS. For

• Prevalence of HIV is the percentage of people who

national comparability, the World Health Organiza-

example, improved software was used to model the

are infected with HIV. Total and youth rates are per-

tion (WHO) prepares estimates in accordance with

course of HIV epidemics and their impacts, making

centages of the relevant age group. Female rate is as

epidemiological models and statistical standards.

full use of information on HIV prevalence trends from

a percentage of the total population living with HIV.

Smoking is the most common form of tobacco use

surveillance data as well as survey data. The soft-

• Condom use is the percentage of the population

and the prevalence of smoking is therefore a good

ware explicitly includes the effect of antiretroviral

ages 15–24 who used a condom at last intercourse

measure of the tobacco epidemic (Corrao and others

therapy (ART) when calculating HIV incidence and

in the last 12 months.

2000). Tobacco use causes heart and other vascular

models reducted infectivity among people receiv-

diseases and cancers of the lung and other organs.

ing ART, which is having an increasing impact on

Given the long delay between starting to smoke and

HIV prevalence, with HIV-positive people living lon-

the onset of disease, the health impact of smoking

ger lives. The software also allows for changes in

in developing countries will increase rapidly only in

urbanization over time—important because preva-

the next few decades. Because the data present a

lence is higher in urban areas and because many

one-time estimate, with no information on intensity

countries have seen rapid urbanization over the past

or duration of smoking, and because the definition of

two decades.

adult varies, the data should be used with caution.

The estimates include plausible bounds, not shown

Tuberculosis is one of the main causes of adult

in the table, which reflect the certainty associated

deaths from a single infectious agent in develop-

with each of the estimates. The bounds are avail-

ing countries. In developed countries tuberculosis

able at http://data.worldbank.org or from the original

has reemerged largely as a result of cases among

source.

immigrants. Since tuberculosis incidence cannot

Data on condom use are from household surveys

be directly measured, estimates are obtained by

and refer to condom use at last intercourse. How-

eliciting expert opinion or are derived from mea-

ever, condoms are not as effective at preventing the

surements of prevalence or mortality. These esti-

transmission of HIV unless used consistently. Some

mates include uncertainty intervals, which are not

surveys have asked directly about consistent use,

shown in the table, which are available at http://

but the question is subject to recall and other biases.

data.worldbank.org or from the original source.

Caution should be used in interpreting the data.

Diabetes, an important cause of ill health and a

For indicators from household surveys, the year in

risk factor for other diseases in developed countries,

the table refers to the survey year. For more informa-

is spreading rapidly in developing countries. Highest

tion, consult the original sources.

among the elderly, prevalence rates are rising among younger and productive populations in developing

Data sources

countries. Economic development has led to the

Data on smoking are from the WHO’s Report on

spread of Western lifestyles and diet to develop-

the Global Tobacco Epidemic 2009: Implementing

ing countries, resulting in a substantial increase in

Smoke-Free Environments. Data on tuberculosis

diabetes. Without effective prevention and control

are from the WHO’s Global Tuberculosis Control

programs, diabetes will likely continue to increase.

Report 2010. Data on diabetes are from the Inter-

Data are estimated based on sample surveys.

national Diabetes Federation’s Diabetes Atlas,

Adult HIV prevalence rates reflect the rate of HIV

3rd edition. Data on prevalence of HIV are from

infection in each country’s population. Low national

UNAIDS’s Global Report: UNAIDS Report on the

prevalence rates can be misleading, however. They

Global AIDS Epidemic 2010. Data on condom use

often disguise epidemics that are initially concen-

are from Demographic and Health Surveys by

trated in certain localities or population groups and

Macro International.

threaten to spill over into the wider population. In

2011 World Development Indicators

117

2.22 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland Franced Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

118

Mortality Life expectancy at birth

Infant mortality rate

Under-ﬁve mortality rate

Child mortality rate

Adult mortality rate

Survival to age 65

years

per 1,000 live births 1990 2009

per 1,000 1990 2009

per 1,000 Male Female 2004–09a,b 2004–09a,b

per 1,000 Male Female 2005–09a 2005–09a

% of cohort Male Female 2009 2009

1990

2009

41 72 67 42 72 68 77 76 65 54 71 76 54 59 67 64 66 72 47 46 55 55 77 49 51 74 68 c 77 68 48 59 76 58 72 75 71 75 68 69 63 66 48 69 47 75 77 61 51 70 75 57 77 62 48 44 55 66

44 77 73 48 76 74 82 80 70 67 70 81 62 66 75 55 73 73 53 51 62 51 81 47 49 79 73c 83 73 48 54 79 58 76 79 77 79 73 75 70 71 60 75 56 80 81 61 56 72 80 57 80 71 58 48 61 72

2011 World Development Indicators

167 41 51 153 25 48 8 8 78 102 20 9 111 84 21 46 46 14 110 114 85 91 7 115 120 18 37 .. 28 126 67 16 105 11 10 10 8 48 41 66 48 92 13 124 6 7 68 104 41 7 76 9 57 137 142 105 43

134 14 29 98 13 20 4 3 30 41 11 4 75 40 13 43 17 8 91 101 68 95 5 112 124 7 17 .. 16 126 81 10 83 5 4 3 3 27 20 18 15 39 4 67 3 3 52 78 26 4 47 3 33 88 115 64 25

250 51 61 258 28 56 9 9 98 148 24 10 184 122 23 60 56 18 201 189 117 148 8 175 201 22 46 .. 35 199 104 18 152 13 14 12 9 62 53 90 62 150 17 210 7 9 93 153 47 9 120 11 76 231 240 152 55

199 15 32 161 14 22 5 4 34 52 12 5 118 51 14 57 21 10 166 166 88 154 6 171 209 9 19 .. 19 199 128 11 119 5 6 4 4 32 24 21 17 55 6 104 3 4 69 103 29 4 69 3 40 142 193 87 30

.. 3 .. .. .. 8 .. .. 9 16 .. .. 64 18 .. .. .. .. .. 65 20 73 .. 74 96 .. .. .. 4 70 49 .. .. 1 .. .. .. 6 5 5 .. .. .. 56 .. .. .. 46 5 .. 38 .. .. 89 110 33 8

.. 1 .. .. .. 3 .. .. 5 20 .. .. 65 20 .. .. .. .. .. 65 20 72 .. 82 101 .. .. .. 3 64 43 .. .. 1 .. .. .. 4 5 5 .. .. .. 56 .. .. .. 39 4 .. 28 .. .. 86 88 36 9

435 98 118 406 163 162 82 99 178 206 330 108 207 232 132 487 226 213 331 382 288 401 92 452 358 129 147 198 397 373 111 305 144 108 143 107 206 164 161 285 374 283 334 129 121 317 324 195 102 323 92 232 252 398 284 170 75

409 51 98 350 75 79 47 50 108 172 115 62 170 172 61 505 118 91 277 346 218 398 55 426 317 64 88 92 348 350 59 271 57 68 65 67 134 86 105 121 281 92 293 57 55 276 264 77 54 286 37 127 195 347 223 119 33

34 82 78 37 75 73 88 85 69 66 54 85 62 64 79 42 67 72 46 42 56 43 87 36 42 81 76 72 38 46 82 53 77 83 79 83 70 76 72 63 46 64 49 84 85 56 48 70 85 51 86 68 55 39 57 73 88

36 90 82 44 87 85 93 93 79 71 83 92 67 72 89 43 81 87 52 47 64 45 92 41 47 90 83 84 44 50 90 59 90 89 90 89 79 86 80 81 58 87 54 93 93 61 55 84 92 56 94 80 63 45 64 80 94

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

2.22

Life expectancy at birth

Infant mortality rate

Under-ﬁve mortality rate

Child mortality rate

Adult mortality rate

Survival to age 65

years

per 1,000 live births 1990 2009

per 1,000 1990 2009

per 1,000 Male Female 2004–09a,b 2004–09a,b

per 1,000 Male Female 2005–09a 2005–09a

% of cohort Male Female 2009 2009

1990

2009

69 58 62 65 65 75 77 77 71 79 67 68 60 70 71 68 75 68 54 69 69 59 49 68 71 71 51 49 70 43 56 69 71 67 61 64 43 59 62 54 77 75 64 42 45 77 70 61 72 55 68 66 65 71 74 75 70

74 64 71 72 68 80 82 81 72 83 73 68 55 67 80 70 78 67 65 73 72 45 59 75 73 74 61 54 75 49 57 73 75 69 67 72 48 62 62 67 81 80 73 52 48 81 76 67 76 61 72 73 72 76 79 79 76

15 84 56 55 42 8 10 8 28 5 32 51 64 23 8 .. 14 63 108 12 33 74 165 32 12 32 102 129 16 139 81 21 36 30 73 69 155 84 49 99 7 9 52 144 126 7 37 101 25 67 34 62 41 15 12 .. 17

5 50 30 26 35 4 3 3 26 2 22 26 55 26 5 .. 8 32 46 7 11 61 80 17 5 10 41 69 6 101 74 15 15 15 24 33 96 54 34 39 4 5 22 76 86 3 9 71 16 52 19 19 26 6 3 .. 10

17 118 86 73 53 9 11 10 33 6 39 60 99 45 9 .. 17 75 157 16 40 93 247 36 15 36 167 218 18 250 129 24 45 37 101 89 232 118 73 142 8 11 68 305 212 9 48 130 31 91 42 78 59 17 15 .. 19

6 66 39 31 44 4 4 4 31 3 25 29 84 33 5 .. 10 37 59 8 12 84 112 19 6 11 58 110 6 191 117 17 17 17 29 38 142 71 48 48 4 6 26 160 138 3 12 87 23 68 23 21 33 7 4 .. 11

.. 9 13 .. 6 .. .. .. 5 .. 3 5 27 .. .. .. .. 8 .. .. .. 22 62 .. .. 2 30 52 .. 117 53 .. .. 7 11 9 .. .. 24 21 .. .. .. 138 91 .. .. 14 .. .. .. 13 10 .. .. .. ..

.. 12 12 .. 7 .. .. .. 6 .. 7 4 25 .. .. .. .. 4 .. .. .. 19 64 .. .. 1 31 54 .. 114 44 .. .. 4 10 11 .. .. 19 18 .. .. .. 135 93 .. .. 22 .. .. .. 4 9 .. .. .. ..

250 256 162 142 211 88 86 82 221 86 159 400 392 169 105 .. 84 257 222 311 150 666 251 144 346 132 266 434 147 386 304 230 137 279 284 144 489 250 346 196 81 87 201 344 404 82 96 162 136 344 170 162 153 209 124 130 109

104 170 113 96 105 56 48 43 116 43 109 151 403 117 41 .. 51 122 180 114 98 633 206 89 116 79 216 395 84 355 236 114 76 125 180 94 469 188 334 171 59 58 113 295 380 50 71 131 72 251 123 100 99 80 53 52 100

68 59 72 75 66 87 87 86 70 88 74 47 47 67 83 .. 85 61 63 64 74 25 56 75 60 77 57 44 76 39 50 67 79 60 58 74 36 58 55 67 87 87 71 44 40 88 83 68 79 50 73 74 74 73 83 80 82

2011 World Development Indicators

PEOPLE

Mortality

86 68 81 82 81 92 93 94 81 95 82 76 48 77 93 .. 90 78 70 86 83 29 63 84 86 85 63 49 85 42 59 81 87 78 71 83 40 66 59 71 92 91 81 49 42 92 87 72 87 61 80 83 83 89 92 91 83

119

2.22

Mortality Life expectancy at birth

Infant mortality rate

Under-ﬁve mortality rate

Child mortality rate

Adult mortality rate

Survival to age 65

years

per 1,000 live births 1990 2009

per 1,000 1990 2009

per 1,000 Male Female 2004–09a,b 2004–09a,b

per 1,000 Male Female 2005–09a 2005–09a

% of cohort Male Female 2009 2009

1990

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

70 69 33 68 52 71 40 74 71 73 45 61 77 70 53 60 78 77 68 63 51 69 46 58 69 70 65 63 48 70 73 76 75 73 67 71 65 68 54 51 61 65 w 52 64 63 68 63 67 68 68 64 58 50 75 76

2009

73 69 51 73 56 74 48 81 75 79 50 52 82 74 58 46 81 82 74 67 56 69 62 63 70 74 72 65 53 69 78 80 79 76 68 74 75 74 63 46 45 69 w 57 69 68 72 67 72 70 74 71 64 53 80 81

25 23 103 35 73 25 166 6 13 9 109 48 8 23 78 67 6 7 30 91 99 27 138 89 30 40 69 81 111 18 15 8 9 21 61 27 39 35 88 108 54 64 w 108 61 66 41 70 41 43 42 57 89 109 10 8

10 11 70 18 51 6 123 2 6 2 109 43 4 13 69 52 2 4 14 52 68 12 48 64 31 18 19 42 79 13 7 5 7 11 32 15 20 25 51 86 56 43 w 76 38 43 19 47 21 19 19 27 55 81 6 3

32 27 171 43 151 29 285 8 15 10 180 62 9 28 124 92 7 8 36 117 162 32 184 150 34 50 84 99 184 21 17 10 11 24 74 32 55 43 125 179 81 92 w 171 85 93 51 100 55 52 52 76 125 181 12 9

12 12 111 21 93 7 192 3 7 3 180 62 4 15 108 73 3 4 16 61 108 14 56 98 35 21 20 45 128 15 7 6 8 13 36 18 24 30 66 141 90 61 w 118 51 57 22 66 26 21 23 33 71 130 7 4

.. .. 69 3 43 4 67 .. .. .. 53 .. .. .. 38 32 .. .. 5 18 56 .. .. 55 5 .. 6 .. 75 4 .. .. .. .. 11 .. 5 3 10 66 21 .. w 52 .. .. .. .. .. .. .. .. 11 68 .. ..

.. .. 55 4 39 3 61 .. .. .. 54 .. .. .. 30 30 .. .. 3 13 52 .. .. 43 8 .. 6 .. 62 1 .. .. .. .. 7 .. 4 3 11 55 21 .. w 49 .. .. .. .. .. .. .. .. 15 65 .. ..

192 396 397 137 325 153e 498 80 195 149 368 575 106 192 302 605 78 78 120 208 369 291 259 238 236 122 149 298 401 385 76 100 141 139 237 175 134 125 247 528 687 213 w 312 201 201 201 216 158 286 190 155 242 390 120 107

82 147 351 88 266 82e 464 41 73 57 315 517 44 76 257 638 48 46 81 137 355 170 224 197 139 70 83 151 399 142 63 61 81 63 135 91 88 90 198 518 664 151 w 275 134 136 122 153 99 123 103 104 169 358 63 52

70 47 40 76 48 75 30 86 72 81 42 32 86 71 53 30 88 88 79 64 49 63 58 61 63 78 74 55 44 53 86 86 84 77 62 74 78 78 60 33 24 68 w 52 67 67 67 65 74 59 72 73 61 44 84 85

86 78 47 85 55 86 34 93 88 92 47 41 94 86 59 29 93 93 86 74 52 77 63 68 78 87 84 73 47 80 89 91 89 89 75 84 85 84 67 35 27 77 w 58 77 75 81 74 82 80 83 81 69 48 91 93

a. Data are for the most recent year available. b. Refers to a survey year. Values were estimated directly from surveys and cover the 5 or 10 years preceding the survey. c. Includes Taiwan, China. d. Excludes the French overseas departments of French Guiana, Guadeloupe, Martinique, and Réunion. e. Includes Kosovo.

120

2011 World Development Indicators

About the data

2.22

PEOPLE

Mortality Definitions

Mortality rates for different age groups (infants, chil-

weighted least squares method to fit a regression

• Life expectancy at birth is the number of years

dren, and adults) and overall mortality indicators (life

line to the relationship between mortality rates and

a newborn infant would live if prevailing patterns of

expectancy at birth or survival to a given age) are

their reference dates and then extrapolate the trend

mortality at the time of its birth were to stay the

important indicators of health status in a country.

to the present. (For further discussion of childhood

same throughout its life. • Infant mortality rate is

Because data on the incidence and prevalence of

mortality estimates, see UNICEF, WHO, World Bank,

the number of infants dying before reaching one year

diseases are frequently unavailable, mortality rates

and United Nations Population Division 2010; for a

of age, per 1,000 live births in a given year. • Under-

are often used to identify vulnerable populations.

graphic presentation and detailed background data,

ﬁve mortality rate is the probability per 1,000 that a

And they are among the indicators most frequently

see www.childmortality.org.)

newborn baby will die before reaching age 5, if sub-

Infant and child mortality rates are higher for boys

ject to current age-specific mortality rates. • Child

than for girls in countries in which parental gender

mortality rate is the probability per 1,000 of dying

The main sources of mortality data are vital reg-

preferences are insignificant. Child mortality cap-

between ages 1 and 5—that is, the probability of a

istration systems and direct or indirect estimates

tures the effect of gender discrimination better than

1-year-old dying before reaching age 5—if subject to

based on sample surveys or censuses. A “complete”

infant mortality does, as malnutrition and medical

current age-specific mortality rates. • Adult mortal-

vital registration system—covering at least 90 per-

interventions are more important in this age group.

ity rate is the probability per 1,000 of dying between

cent of vital events in the population—is the best

Where female child mortality is higher, as in some

the ages of 15 and 60—that is, the probability of a

source of age-specific mortality data. Where reliable

countries in South Asia, girls probably have unequal

15-year-old dying before reaching age 60—if subject

age-specific mortality data are available, life expec-

access to resources. Child mortality rates in the

to current age-specific mortality rates between those

tancy at birth is directly estimated from the life table

table are not compatible with infant mortality and

ages. • Survival to age 65 refers to the percent-

constructed from age-specific mortality data.

under-five mortality rates because of differences in

age of a hypothetical cohort of newborn infants that

But complete vital registration systems are fairly

methodology and reference year. Child mortality data

would survive to age 65, if subject to current age-

uncommon in developing countries. Thus estimates

were estimated directly from surveys and cover the

specific mortality rates.

must be obtained from sample surveys or derived

10 years preceding the survey. In addition to esti-

by applying indirect estimation techniques to reg-

mates from Demographic Health Surveys, estimates

istration, census, or survey data (see table 2.17

derived from Multiple Indicator Cluster Surveys have

and Primary data documentation). Survey data are

been added to the table; they cover the 5 years pre-

Data on infant and under-five mortality are from

subject to recall error, and surveys estimating infant

ceding the survey.

Levels and Trends in Child Mortality, Report 2010

used to compare socioeconomic development across countries.

Data sources

deaths require large samples because households

Rates for adult mortality and survival to age 65

by the Inter-agency Group for Child Mortality Esti-

in which a birth has occurred during a given year

come from life tables. Adult mortality rates increased

mation, covered in About the data, based mainly

cannot ordinarily be preselected for sampling. Indi-

notably in a dozen countries in Sub-Saharan Africa

on household surveys, censuses, and vital regis-

rect estimates rely on model life tables that may be

between 1995–2000 and 2000–05 and in several

tration data, supplemented by the World Bank’s

inappropriate for the population concerned. Because

countries in Europe and Central Asia during the first

Human Development Network estimates based

life expectancy at birth is estimated using infant mor-

half of the 1990s. In Sub-Saharan Africa the increase

on vital registration and sample registration data.

tality data and model life tables for many develop-

stems from AIDS-related mortality and affects both

Data on child mortality are from Demographic and

ing countries, similar reliability issues arise for this

sexes, though women are more affected. In Europe

Health Surveys by Macro International and World

indicator. Extrapolations based on outdated surveys

and Central Asia the causes are more diverse (high

Bank calculations based on infant and under-five

may not be reliable for monitoring changes in health

prevalence of smoking, high-fat diet, excessive alco-

mortality from Multiple Indicator Cluster Surveys

status or for comparative analytical work.

hol use, stressful conditions related to the economic

by UNICEF. Data on survival to age 65 and most

transition) and affect men more.

data on adult mortality are linear interpolations of

Estimates of infant and under-five mortality tend to vary by source and method for a given time and place.

The percentage of a hypothetical cohort surviv-

five-year data from World Population Prospects: The

Years for available estimates also vary by country,

ing to age 65 reflects both child and adult mortality

2008 Revision. Remaining data on adult mortality

making comparison across countries and over time

rates. Like life expectancy, it is a synthetic mea-

are from the Human Mortality Database by the Uni-

difficult. To make infant and under-five mortality esti-

sure based on current age-specific mortality rates.

versity of California, Berkeley, and the Max Planck

mates comparable and to ensure consistency across

It shows that even in countries where mortality is

Institute for Demographic Research (www.mortal-

estimates by different agencies, the Inter-agency

high, a certain share of the current birth cohort will

ity.org). Data on life expectancy at birth are World

Group for Child Mortality Estimation, comprising the

live well beyond the life expectancy at birth, while in

Bank calculations based on male and female data

United Nations Children’s Fund (UNICEF), the United

low-mortality countries close to 90 percent will reach

from World Population Prospects: The 2008 Revi-

Nations Population Division, the World Health Organi-

at least age 65.

sion (for more than half of countries, most of them

zation (WHO), the World Bank, and other universities

Annual data series from the United Nations are

developing countries), census reports and other

and research institutes, developed and adopted a

interpolated based on five-year estimates and thus

statistical publications from national statistical

statistical method that uses all available informa-

may not reflect actual events.

offices, Eurostat’s Demographic Statistics, and the

tion to reconcile differences. The method uses the

U.S. Bureau of the Census International Data Base.

2011 World Development Indicators

121 Text figures, tables, and boxes

ENVIRONMENT

Introduction

Environmental sustainability

T

he United Nations Conference on the Human Environment, held in Stockholm in 1972, drew worldwide attention to the growing impact of human activity on the environment and to the need for sustainable management of environmental resources. Twenty years later the United Nations Conference on Environment and Development in Rio de Janeiro adopted a comprehensive plan of action for a sustainable future. That plan later became part of the Millennium Declaration, with some of the more important targets included in Millennium Development Goal 7: ensuring environmental sustainability. Understanding climate change is a central issue for environmental sustainability and for development policy. Public policy should help people cope with new or worsened risks, facilitate investments in clean energy technologies, and adapt land and water management to better protect a threatened natural environment while feeding an expanding and more prosperous population. The World Bank Group plays a key role in financing climate change adaptation and mitigation. Since 1999 it has led in forming carbon markets, which are now directing funds toward clean low-carbon development. At the UN Climate Change Conference in Copenhagen in 2009, it launched the Carbon Partnership Facility, the latest addition in a family of carbon funds and facilities. The facility assists developing countries in pursuing low-carbon growth and in accelerating reductions of greenhouse gas emissions; it uses carbon finance innovatively to leverage capital for both public and private investment in clean technologies. At the UN Framework Convention on Climate Change conference in Cancun in 2010, the World Bank joined global leaders and policymakers in the Roadmap for Action: Agriculture, Food Security, and Climate Change, which outlines concrete actions linking agricultural investments and policies with the transition to climatesmart growth. It highlights a “triple-win” approach: increasing farm productivity and incomes, making agriculture more resilient to climate change, and making agriculture part of the solution to climate change by sequestering more carbon in the soil and biomass.

Environmental indicators Monitoring progress toward the environment targets of the Millennium Development Goals and measuring the complexity of environmental phenomena require new measurement frameworks and new data. This year’s Environment section of World Development Indicators includes a new table on natural resource rents that measures human dependence on environmental assets. And in recognition of the mainstreaming of green accounting, the data on adjusted net savings — gross savings adjusted for capital depreciation, resource depletion, pollution damage, and human capital investment— have been moved to the Economy section (table 4.11), joining a new table showing corresponding adjustments to national income (table 4.10). Together these tables provide a clearer picture of the impact of the environment on the long-term sustainability of economic growth. Other indicators in this section describe land use, agriculture and food production, forests and biodiversity, water resources, energy use and effi ciency, urbanization, environmental impacts, government commitments, and threatened species. Where possible, the indicators come from international sources to facilitate cross- country comparison. Important to keep in mind is that country coverage may be uneven, ecosystems span national boundaries, and natural resource use may differ locally, regionally, and globally. For example, greenhouse gas emissions and climate change may be measured globally, but their effects are also manifested locally, shaping people’s lives and opportunities.

3

2011 World Development Indicators

123

Measuring dependence on environmental assets Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. The extraction or harvesting of natural resources can produce substantial rents — revenues above the cost of extracting them—which are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the world price of units of specifi c commodities and subtracting estimates of the average unit costs of extraction or harvesting. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity, as a share of gross national income (GNI). Table 3.16 presents data on rents from oil, gas, coal, and other mineral production and from forests as a share of GNI. In some countries those rents, especially from fossil fuels The 10 countries with the highest natural resource rents are primarily oil and gas producers Natural resource rents, 2009 (percent of GNI)

Forest

3a Minerals

Coal

Natural gas

Oil

75

and minerals, account for 30–50 percent of GNI (figure 3a) — almost 70 percent in Iraq. Rents from nonrenewable resources —fossil fuels and minerals — as well as rents from overharvesting of forests indicate the liquidation of a country’s capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future. For resource-rich countries—where resource rents are at least 5 percent of GNI—transforming nonrenewable natural capital into other forms of wealth is a major development challenge. Figure 3b plots adjusted net savings— net national savings plus education expenditure, minus energy depletion, mineral depletion, net forest depletion, and carbon dioxide and particulate emissions damage — against energy and mineral rents for resource-rich countries. Countries with negative adjusted net savings, such as Angola and Republic of Congo, are depleting natural capital without replacing it and becoming poorer over time. Countries with positive adjusted net savings, such as Botswana and China, are adding to wealth and well-being and reducing natural resource depletion by investing in other types of capital. (See About the data for tables 4.10 and 4.11.)

50

Mainstreaming environmental and wealth accounting in country statistical systems

25

0 Iraq

Congo, Rep.

Libya

Saudi Arabia

Gabon

Azerbaijan Turkmenistan

Oman

Angola

Chad

Source: Table 3.16.

Countries with negative adjusted net savings are depleting natural capital without replacing it and are becoming poorer

3b

Adjusted net savings in resource-rich countries, 2008 (percent of GNI) 50 China Botswana 25 0 –25 Angola Congo, Rep.

–50 0

25

50

75

Energy and mineral rents (percent of GNI) Note: The underlying data were produced as part of a long-term World Bank project on measuring sustainable development. Estimates of natural resource rents are used in calculating comprehensive wealth and adjusted net savings, which are now in tables 4.10 and 4.11. For further discussion of wealth accounting, see The Changing Wealth of Nations (World Bank 2011). Source: World Development Indicators data files.

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2011 World Development Indicators

There has been considerable effort over the past 20 years to develop statistical methods for environmental accounting (a broad framework that includes natural capital accounting) under the aegis of the United Nations Statistical Commission. The commission established the London Group on Environmental Accounting and later a high-level body, the UN Committee of Experts on Environmental and Economic Accounting, to develop methodological guidelines. In 2003 the United Nations and other international organizations produced the Handbook of National Accounting: Integrated Environmental and Economic Accounting (UN and others 2003). It is currently under revision and will become part of the statistical standard, like the System of National Accounts, which establishes methodology for national accounts. Other institutions and individual scholars have also done work on wealth accounting over

ENVIRONMENT

the past 20 years. Official statistical offices in more than 30 countries have institutionalized wealth accounting, and 16 of them regularly compile at least one type of natural resource asset account. The majority of countries focus on mineral and energy assets, but some, notably Australia and Norway, construct more comprehensive accounts for natural capital. National statistical offices, the academic community, and nongovernmental organizations have produced empirical work on natural capital accounting nationally, regionally, and locally. Together, these studies have deepened our knowledge of wealth accounting, leading to better understanding of the prospects for growth

and poverty reduction, especially in resourcerich countries. Stiglitz, Sen, and Fitoussi (2009) offer further support for the comprehensive wealth approach to sustainable development. They propose ways to modify and extend conventional national accounts to provide a more accurate and useful guide for policy. An important part of the proposed changes, to better reflect the sustainability of economies, is comprehensive wealth. They recommend compiling accounts for all assets (natural, human-made, and human capital) and changes in those assets, which correspond to the components of adjusted net savings.

2011 World Development Indicators

125

Tables

3.1

Rural population and land use Rural population

% of total 1990 2009

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

126

82 64 48 63 13 33 15 34 46 80 34 4 66 44 61 58 25 34 86 94 87 59 23 63 79 17 73 1 32 72 46 49 60 46 27 25 15 45 45 57 51 84 29 87 39 26 31 62 45 27 64 41 59 72 72 72 60

76 53 34 42 8 36 11 33 48 72 26 3 58 34 52 40 14 29 80 89 78 42 20 61 73 11 56 0 25 65 38 36 51 43 24 27 13 30 34 57 39 79 31 83 36 22 15 43 47 26 49 39 51 65 70 52 52

2011 World Development Indicators

average annual % growth 1990–2009

2.1 –1.2 –0.1 0.8 –1.6 –0.2 –0.1 0.2 1.3 1.2 –1.7 –1.3 2.7 0.6 –1.5 –0.1 –1.7 –1.6 2.7 1.7 1.6 0.7 0.1 2.0 2.8 –0.7 –0.5 .. 0.5 2.5 1.2 0.5 1.8 –0.8 –0.2 0.4 –0.4 –0.4 0.0 2.0 –0.6 2.1 –0.5 2.5 0.1 –0.2 –1.5 1.5 –1.0 0.0 1.1 0.2 1.6 2.1 2.3 0.1 1.5

Land area

Land use

% of land area thousand sq. km 2009

Forest area 1990 2010

Permanent cropland 1990 2008

Arable land 1990 2008

652.2 27.4 2,381.7 1,246.7 2,736.7 28.5 7,682.3 82.5 82.6 130.2 202.9 30.3 110.6 1,083.3 51.2 566.7 8,459.4 108.6 273.6 25.7 176.5 472.7 9,093.5 623.0 1,259.2 743.5 9,327.5 1.0 1,109.5 2,267.1 341.5 51.1 318.0 56.0 106.4 77.3 42.4 48.3 248.4 995.5 20.7 101.0 42.4 1,000.0 303.9 547.7 257.7 10.0 69.5 348.6 227.5 128.9 107.2 245.7 28.1 27.6 111.9

2.1 28.8 0.7 48.9 12.7 12.2 20.1 45.8 11.2 11.5 38.4 22.4 52.1 58.0 43.2 24.2 68.0 30.1 25.0 11.3 73.3 51.4 34.1 37.2 10.4 20.5 16.8 .. 56.3 70.7 66.5 50.2 32.1 33.1 19.2 34.0 10.5 40.8 49.9 0.0 18.2 16.0 49.3 15.1 71.9 26.5 85.4 44.2 40.0 30.8 32.7 25.6 44.3 29.6 78.8 4.2 72.7

0.2 4.6 0.2 0.4 0.4 2.1 0.0 1.0 3.7 2.5 0.9 0.5a 0.9 0.1 2.9 0.0 0.8 2.7 0.2 14.0 0.6 2.6 0.7 0.1 0.0 0.3 0.8 .. 1.5 0.5 0.1 4.9 11.0 2.0 4.2 3.1 0.2 9.3 4.8 0.4 12.5 0.0 0.3 0.5 0.0 2.2 0.6 0.5 4.8 1.3 6.6 8.3 4.5 2.0 4.2 11.6 3.2

12.1 21.1 3.0 2.3 9.6 14.9 6.2 17.3 20.5 70.0 30.0 23.3a 14.6 1.9 16.6 0.7 6.0 34.9 12.9 36.2 20.9 12.6 5.0 3.1 2.6 3.8 13.3 .. 3.0 2.9 1.4 5.1 7.6 21.7 31.6 41.1 60.4 18.6 5.8 2.3 26.5 4.9 26.3 10.0 7.4 32.9 1.1 18.2 11.4 34.3 11.9 22.5 12.1 3.3 8.9 28.3 13.1

2.1 28.3 0.6 46.9 10.7 9.2 19.4 47.1 11.3 11.1 42.5 22.4 41.2 52.8 42.7 20.0 61.4 36.2 20.6 6.7 57.2 42.1 34.1 36.3 9.2 21.8 22.2 .. 54.5 68.0 65.6 51.0 32.7 34.3 27.0 34.4 12.8 40.8 39.7 0.1 13.9 15.2 52.3 12.3 72.9 29.1 85.4 48.0 39.5 31.8 21.7 30.3 34.1 26.6 71.9 3.7 46.4

0.2 3.2 0.4 0.2 0.4 1.9 0.0 0.8 2.8 6.1 0.6 0.8 2.7 0.2 1.8 0.0 0.9 1.7 0.2 15.2 0.9 2.5 0.8 0.1 0.0 0.6 1.5 .. 1.5 0.3 0.2 5.9 13.4 1.5 3.8 3.1 0.2 10.3 5.1 0.8 11.1 0.0 0.2 0.9 0.0 2.0 0.6 0.5 1.7 0.6 12.5 8.7 8.8 2.8 8.9 10.9 3.7

11.9 22.3 3.1 2.7 11.7 15.8 5.7 16.7 22.5 60.7 27.2 27.9 23.1 3.3 19.7 0.4 7.2 28.2 23.0 35.0 22.1 12.6 5.0 3.1 3.4 1.7 11.6 .. 1.6 3.0 1.4 3.9 8.8 15.4 33.5 39.2 56.6 16.6 5.0 2.8 33.1 6.6 14.1 13.6 7.4 33.3 1.3 39.0 6.7 34.2 19.3 16.3 12.4 9.8 10.7 36.3 9.1

Arable land hectares per 100 people 1990 2008

42.6 17.6 28.0 27.2 81.2 1.5 280.7 18.5 0.8 7.9 0.5 0.2 33.7 31.5 3.5 31.1 33.9 44.2 39.9 16.4 38.1 48.6 163.7 65.6 53.6 21.2 10.9 .. 10.0 18.0 19.6 8.4 19.3 2.4 32.0 32.1 49.8 12.2 15.6 4.0 10.3 0.1 3.5 1.4 45.5 31.7 31.8 20.3 1.0 15.1 18.0 28.5 14.6 13.1 24.5 11.0 29.8

26.9 19.4 21.8 18.9 80.2 14.6 205.4 16.5 21.4 4.9 57.0 7.9 29.4 37.1 26.7 13.0 31.8 40.2 41.4 11.1 26.8 31.2 135.4 44.5 39.4 7.5 8.2 .. 4.1 10.4 13.6 4.4 13.6 19.4 31.9 29.0 43.7 8.0 9.2 3.4 11.2 13.6 44.6 16.9 42.4 29.3 22.4 23.5 10.9 14.5 18.8 18.7 9.7 24.4 19.0 10.1 13.9

Rural population

% of total 1990 2009

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

34 75 69 44 30 43 10 33 51 37 28 44 82 42 26 .. 2 62 85 31 17 86 55 24 32 42 76 88 50 77 60 56 29 53 43 52 79 75 72 91 31 15 48 85 65 28 34 69 46 85 51 31 51 39 52 28 8

32 70 47 31 34 38 8 32 47 33 22 42 78 37 18 .. 2 64 68 32 13 74 39 22 33 33 70 81 29 67 59 58 23 59 43 44 62 67 63 82 18 13 43 83 51 23 28 63 26 88 39 29 34 39 40 1 4

average annual % growth 1990–2009

–0.5 1.3 –0.6 –0.3 3.2 0.7 1.7 0.1 0.2 –0.4 2.1 –0.4 2.5 0.3 –1.2 .. 0.3 1.1 1.0 –0.7 0.4 0.5 1.4 1.6 –0.5 –1.0 2.5 2.0 –0.7 1.5 2.5 1.1 0.1 –0.5 0.9 0.5 1.5 0.4 1.5 1.7 –2.5 0.5 1.2 3.4 1.2 –0.5 1.3 1.9 –1.1 2.7 0.7 1.1 –0.1 0.0 –1.0 –15.0 2.7

Land area

3.1

ENVIRONMENT

Rural population and land use Land use

% of land area thousand sq. km 2009

Forest area 1990 2010

Permanent cropland 1990 2008

Arable land 1990 2008

89.6 2,973.2 1,811.6 1,628.6 437.4 68.9 21.6 294.1 10.8 364.5 88.2 2,699.7 569.1 120.4 96.9 10.9b 17.8 191.8 230.8 62.2 10.2 30.4 96.3 1,759.5 62.7 25.2 581.5 94.1 328.6 1,220.2 1,030.7 2.0 1,944.0 32.9 1,553.6 446.3 786.4 653.5 823.3 143.4 33.8 263.3 120.3 1,266.7 910.8 305.5 309.5 770.9 74.3 452.9 397.3 1,280.0 298.2 304.2 91.5 8.9 11.6

20.0 21.5 65.4 6.8 1.8 6.7 6.1 25.8 31.9 68.4 1.1 1.3 6.5 68.1 64.5 .. 0.2 4.4 75.0 51.1 12.8 1.3 51.2 0.1 31.0 35.9 23.5 41.4 68.1 11.5 0.4 19.2 36.2 9.7 8.1 11.3 55.2 60.0 10.6 33.7 10.2 29.3 37.5 1.5 18.9 30.0 0.0 3.3 51.0 69.6 53.3 54.8 22.0 29.2 36.4 32.4 0.0

2.6 2.2 6.5 0.8 0.7 0.0 4.1 10.1 9.2 1.3 0.8 0.1 0.8 1.5 1.6 .. 0.1 0.4 0.3 0.4 11.9 0.1 1.6 0.2 0.7 2.2 1.0 1.4 16.0 0.1 0.0 3.0 1.0 12.8 0.0 1.6 0.3 0.8 0.0 0.5 0.9 0.2 1.6 0.0 2.8 0.0 0.1 0.6 2.1 1.2 0.2 0.3 14.8 1.1 8.5 5.6 0.1

56.2 54.8 11.2 9.3 13.3 15.1 15.9 30.6 11.0 13.1 2.0 13.0 8.8 19.0 19.8 .. 0.2 6.9 3.5 27.2 17.9 10.4 3.6 1.0 46.0 23.8 4.7 23.9 5.2 1.7 0.4 49.3 12.5 52.8 0.9 19.5 4.4 14.6 0.8 16.0 26.0 10.0 10.8 8.7 32.4 2.8 0.1 26.6 6.7 0.4 5.3 2.7 18.4 47.3 25.6 7.3 0.9

22.6 23.0 52.1 6.8 1.9 10.7 7.1 31.1 31.1 68.5 1.1 1.2 6.1 47.1 64.2 .. 0.3 5.0 68.2 53.9 13.4 1.4 44.9 0.1 34.5 39.6 21.6 34.4 62.3 10.2 0.2 17.2 33.3 11.7 7.0 11.5 49.6 48.6 8.9 25.4 10.8 31.4 25.9 1.0 9.9 32.9 0.0 2.2 43.7 63.4 44.3 53.1 25.7 30.7 37.8 62.2 0.0

2.2 3.8 8.3 1.1 0.6 0.0 3.6 9.0 10.2 0.9 0.9 0.0 0.9 1.7 1.9 .. 0.2 0.4 0.4 0.1 13.9 0.1 2.3 0.2 0.4 1.4 1.0 1.3 17.6 0.1 0.0 2.0 1.4 9.2 0.0 2.1 0.3 1.7 0.0 0.8 1.0 0.3 1.9 0.0 3.3 0.0 0.1 1.1 2.0 1.4 0.3 0.6 16.8 1.3 6.4 4.2 0.3

51.0 53.2 12.1 10.5 11.9 16.0 13.9 24.2 11.5 11.8 1.7 8.4 9.3 22.4 16.0 27.6 0.6 6.7 5.4 18.8 14.1 11.7 4.2 1.0 29.7 17.1 5.1 37.2 5.5 4.0 0.4 42.9 12.8 55.4 0.5 18.0 5.7 16.2 1.0 16.4 31.6 1.7 15.8 11.4 41.2 2.8 0.2 26.4 7.4 0.6 10.6 2.9 17.8 41.3 11.5 6.8 1.1

Arable land hectares per 100 people 1990 2008

48.7 19.2 11.4 27.9 30.7 29.7 7.4 15.9 5.0 3.9 5.6 0.3 21.3 11.4 4.6 .. 0.2 1.2 19.0 2.0 6.2 19.8 16.2 41.4 1.5 3.0 24.1 23.8 9.4 23.7 20.1 9.5 29.2 39.7 61.8 35.1 25.5 23.4 46.6 12.0 5.9 76.7 31.4 139.6 30.3 20.3 1.9 19.0 20.7 4.6 49.7 16.1 8.8 37.7 23.7 1.8 2.1

2011 World Development Indicators

45.6 13.9 9.7 23.7 16.9 24.9 4.1 11.9 4.7 3.4 2.6 144.8 13.7 11.3 3.2 16.8 0.4 24.2 20.1 51.6 3.4 17.3 10.5 27.8 55.4 21.2 15.4 23.6 6.7 38.2 12.4 6.9 23.3 50.1 32.2 25.5 20.1 21.4 37.6 8.2 6.5 10.6 33.5 98.6 24.8 17.7 2.0 12.2 16.1 4.1 67.3 12.7 5.9 33.0 9.9 1.5 1.0

127

3.1

Rural population and land use Rural population

% of total 1990 2009

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

47 27 95 23 61 50 67 0 44 50 70 48 25 83 73 77 17 27 51 68 81 71 79 70 92 42 41 55 89 33 21 11 25 11 60 16 80 32 79 61 71 57 w 78 62 70 33 64 71 37 29 48 75 72 27 29

46 27 81 18 57 48 62 0 43 52 63 39 23 85 56 75 15 27 45 74 74 66 72 57 86 33 31 51 87 32 22 10 18 8 63 6 72 28 69 64 62 50 w 71 52 59 25 55 55 36 21 42 70 63 23 27

average annual % growth 1990–2009

Land area

2011 World Development Indicators

Forest area 1990 2010

Permanent cropland 1990 2008

Arable land 1990 2008

Arable land hectares per 100 people 1990 2008

27.8 49.4 12.9 0.5 48.6 26.2 43.5 3.0 40.0 59.0 13.2 6.8 27.7 37.5 32.1 27.4 66.5 28.8 2.0 2.9 46.8 38.3 65.0 12.6 47.0 4.1 12.6 8.8 24.1 16.0 2.9 10.8 32.4 5.3 7.2 59.0 28.8 1.5 1.0 71.0 57.3 32.1 w 31.9 33.9 26.2 38.8 33.5 29.0 38.4 51.6 2.4 16.6 31.3 28.1 33.6

2.6 0.1 12.4 0.0 0.2 .. 1.9 1.5 1.0 1.8 0.0 0.7 9.7 15.9 0.0 0.7 0.0 0.6 4.0 0.9 1.1 6.1 3.9 1.7 6.8 12.5 3.9 0.1 9.4 1.9 0.2 0.3 0.2 0.3 0.9 0.9 3.2 19.1 0.2 0.0 0.3 1.1 w 0.7 1.4 1.8 1.0 1.2 2.2 0.4 0.9 0.8 1.8 0.8 0.7 4.8

41.2 8.1 35.7 1.7 16.1 .. 6.8 1.5 32.5 9.9 1.6 11.1 30.7 14.4 5.4 10.5 6.9 10.3 26.6 6.1 10.2 34.2 7.4 38.6 7.0 18.7 32.0 2.9 25.4 57.6 0.4 27.4 20.3 7.2 10.5 3.2 16.4 18.1 2.9 3.1 7.5 9.0 w 6.4 8.4 14.9 4.2 8.1 12.1 2.1 6.6 5.9 42.6 6.2 11.8 26.7

40.7 0.0 12.3 20.9 41.0 4.5 11.9 0.0 31.0 1.8 15.5 38.2 39.5 5.3 47.2 20.8 33.2 6.1 38.4 1.0 35.4 30.9 14.9 53.5 3.0 35.7 43.9 36.7 28.2 0.1 1.9 11.6 74.4 40.6 21.8 14.3 8.1 .. 12.4 29.0 27.6 22.2 w 20.1 17.7 15.8 24.5 18.0 12.0 12.1 30.3 22.4 18.0 28.4 41.5 22.1

% of land area thousand sq. km 2009

–0.5 229.9 –0.1 16,376.9 1.0 24.7 0.8 2,000.0 c 2.4 192.5 –0.4 88.4 1.3 71.6 .. 0.7 0.1 48.1 0.3 20.1 1.1 627.3 0.7 1,214.5 0.5 499.1 1.0 62.7 0.9 2,376.0 1.5 17.2 –0.1 410.3 0.7 40.0 2.0 183.6 1.8 140.0 2.4 885.8 0.6 510.9 1.8 14.9 1.7 54.4 0.2 5.1 0.0 155.4 0.0 769.6 1.4 469.9 3.1 197.1 –0.8 579.3 5.0 83.6 –0.2 241.9 –0.6 9,147.4 –1.6 175.0 1.9 425.4 –2.9 882.1 0.9 310.1 3.0 6.0 2.7 528.0 2.9 743.4 0.2 386.9 0.6 w 129,561.8 s 1.8 17,303.9 0.4 78,352.9 0.5 30,841.8 –0.3 47,511.0 0.7 95,656.7 –0.3 15,853.7 0.0 22,686.7 –0.3 20,116.2 1.3 8,643.6 1.4 4,771.2 1.9 23,585.4 –0.3 33,905.1 –0.1 2,552.0

a. Includes Luxembourg. b. Data are from national sources. c. Provisional estimate.

128

Land use

28.6 49.4 17.6 0.5 44.0 30.7 38.1 2.9 40.2 62.2 10.8 4.7 36.4 29.7 29.4 32.7 68.7 31.0 2.7 2.9 37.7 37.1 49.9 5.3 44.1 6.5 14.7 8.8 15.2 16.8 3.8 11.9 33.2 10.0 7.7 52.5 44.5 1.5 1.0 66.5 40.4 31.1 w 28.2 32.8 25.9 37.2 31.9 29.6 38.6 47.0 2.4 17.1 28.0 28.9 37.3

1.6 0.1 11.3 0.1 0.3 3.4 1.9 0.3 0.5 1.3 0.0 0.8 9.6 15.1 0.1 0.8 0.0 0.6 5.3 1.0 1.5 7.1 4.4 3.1 4.3 14.2 3.8 0.1 11.4 1.6 2.4 0.2 0.3 0.2 0.8 0.7 10.0 19.5 0.6 0.0 0.3 1.1 w 0.9 1.4 2.4 0.7 1.3 3.1 0.4 1.0 1.0 2.9 1.0 0.7 4.2

37.9 7.4 52.3 1.7 18.2 37.4 25.1 0.7 28.7 9.0 1.6 11.9 25.0 19.9 8.7 10.3 6.4 10.2 25.6 5.3 10.8 29.8 10.8 45.2 4.9 18.2 28.0 3.9 28.7 56.1 0.8 24.8 18.6 9.4 10.1 3.1 20.3 16.8 2.4 3.2 9.6 10.7 w 8.6 11.0 16.0 7.8 10.6 11.3 10.4 7.4 6.0 41.5 8.5 10.9 24.4

40.5 85.7 13.3 13.9 28.7 44.9 32.3 0.0 25.6 9.0 11.2 29.7 27.4 6.2 50.1 15.2 28.5 5.3 22.8 10.8 22.6 22.6 14.6 38.1 1.9 27.5 29.2 36.7 17.8 70.2 1.4 9.8 56.0 49.2 15.7 9.7 7.3 2.8 5.6 18.7 29.9 20.7 w 17.9 18.2 13.1 37.4 18.2 9.3 58.8 26.4 16.0 12.8 24.4 34.0 18.9

About the data

3.1

ENVIRONMENT

Rural population and land use Deﬁnitions

With more than 3 billion people, including 70 percent

Satellite images show land use that differs from

• Rural population is calculated as the difference

of the world’s poor people, living in rural areas, ade-

that of ground-based measures in area under cultiva-

between the total population and the urban popula-

quate indicators to monitor progress in rural areas

tion and type of land use. Moreover, land use data

tion (see Definitions for tables 2.1 and 3.11). • Land

are essential. However, few indicators are disaggre-

in some countries (India is an example) are based

area is a country’s total area, excluding area under

gated between rural and urban areas (for some that

on reporting systems designed for collecting tax rev-

inland water bodies and national claims to the con-

are, see tables 2.7, 3.5, and 3.11). The table shows

enue. With land taxes no longer a major source of

tinental shelf and to exclusive economic zones. In

indicators of rural population and land use. Rural

government revenue, the quality and coverage of land

most cases the definition of inland water bodies

population is approximated as the midyear nonurban

use data have declined. Data on forest area may be

includes major rivers and lakes. (See table 1.1 for

population. While a practical means of identifying the

particularly unreliable because of irregular surveys

the total surface area of countries.) Variations from

rural population, it is not precise (see box 3.1a for

and differences in definitions (see About the data

year to year may be due to updated or revised data

further discussion).

for table 3.4). The forest area statistics released by

rather than to change in area. • Land use is a coun-

The data in the table show that land use patterns

FAO between 1948 and 1963 were based mostly on

try’s total area, excluding area under inland water

are changing. They also indicate major differences

data from country questionnaires. Remote sensing,

bodies and national claims to the continental shelf

in resource endowments and uses among countries.

statistical modeling, and expert analysis of country

and to exclusive economic zones. In most cases defi -

True comparability of the data is limited, however,

surveys have been applied since 1980 to improve

nitions of inland water bodies includes major rivers

by variations in definitions, statistical methods, and

the forest coverage estimates. FAO’s Global Forest

and lakes. (See table 1.1 for the total surface area of

quality of data. Countries use different definitions of

Resources Assessment 2010 covers 233 countries

countries.) Variations from year to year may be due

rural and urban population and land use. The Food

and is the most comprehensive assessment of for-

to updated or revised data rather than to change in

and Agriculture Organization of the United Nations

ests, forestry, and the benefits of forest resources

area. • Forest area is land under natural or planted

(FAO), the primary compiler of the data, occasion-

in both scope and number of countries and people

stands of trees of at least 5 meters in situ, whether

ally adjusts its definitions of land use categories

involved. It examines status and trends for about 90

productive or not, and excludes tree stands in agricul-

and revises earlier data. Because the data reflect

variables on the extent, condition, uses, and values

tural production systems (for example, in fruit planta-

changes in reporting procedures as well as actual

of forests and other wooded land.

tions and agroforestry systems) and trees in urban

changes in land use, apparent trends should be inter-

parks and gardens. • Permanent cropland is land

preted cautiously.

cultivated with crops that occupy the land for long periods and need not be replanted after each har-

What is rural? Urban?

3.1a

vest, such as cocoa, coffee, and rubber. Land under

The rural population identified in table 3.1 is approximated as the difference between total population

flowering shrubs, fruit trees, nut trees, and vines

and urban population, calculated using the urban share reported by the United Nations Population

is included, but land under trees grown for wood or

Division. There is no universal standard for distinguishing rural from urban areas, and any urban-rural

timber is not. • Arable land is land defined by the FAO

dichotomy is an oversimplification (see About the data for table 3.11). The two distinct images—isolated

as under temporary crops (double-cropped areas are

farm, thriving metropolis—represent poles on a continuum. Life changes along a variety of dimensions,

counted once), temporary meadows for mowing or

moving from the most remote forest outpost through fields and pastures, past tiny hamlets, through

pasture, land under market or kitchen gardens, and

small towns with weekly farm markets, into intensively cultivated areas near large towns and small cities,

land temporarily fallow. Land abandoned as a result

eventually reaching the center of a megacity. Along the way access to infrastructure, social services, and

of shifting cultivation is excluded.

nonfarm employment increase, and with them population density and income. Because rurality has many dimensions, for policy purposes the rural-urban dichotomy presented in tables 3.1, 3.5, and 3.11 is inadequate. A 2005 World Bank Policy Research Paper proposes an operational definition of rurality based on population density and distance to large cities (Chomitz, Buys, and Thomas 2005). The report argues that these criteria are important gradients along which economic behavior and appropriate development

Data sources

interventions vary substantially. Where population densities are low, markets of all kinds are thin, and the

Data on urban population shares used to estimate

unit cost of delivering most social services and many types of infrastructure is high. Where large urban

rural population are from the United Nations Popu-

areas are distant, farm-gate or factory-gate prices of outputs will be low and input prices will be high, and

lation Division’s World Urbanization Prospects: The

it will be difficult to recruit skilled people to public service or private enterprises. Thus, low population

2009 Revision, and data on total population are

density and remoteness together define a set of rural areas that face special development challenges.

World Bank estimates. Data on land area, perma-

Using these criteria and the Gridded Population of the World (CIESIN 2005), the authors’ estimates of

nent cropland, and arable land are from the FAO’s

the rural population for Latin America and the Caribbean differ substantially from those in table 3.1. Their

electronic files. The FAO gathers these data from

estimates range from 13 percent of the population, based on a population density of less than 20 people

national agencies through annual questionnaires

per square kilometer, to 64 percent, based on a population density of more than 500 people per square

and by analyzing the results of national agricultural

kilometer. Taking remoteness into account, the estimated rural population would be 13–52 percent. The

censuses. Data on forest area are from the FAO’s

estimate for Latin America and the Caribbean in table 3.1 is 21 percent.

Global Forest Resources Assessment 2010.

2011 World Development Indicators

129

3.2

Agricultural inputs Agricultural landa

% of land area 1990 2008

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

130

58 41 16 46 47 41 60 42 53 77 46 44 b 21 33 43 46 29 56 35 83 25 19 7 8 38 21 57 .. 41 10 31 45 60 43 63 55 66 53 28 3 68 73 32 31 8 56 20 64 46 52 55 72 40 49 51 58 30

% irrigated 2008

58 43 17 46 49 61 54 38 58 71 44 45 31 34 42 46 31 48 45 85 31 19 7 8 39 21 56 .. 38 10 31 35 64 23 62 55 63 52 30 4 75 75 19 35 8 53 20 66 36 49 69 36 39 56 58 65 28

2011 World Development Indicators

5.8 10.0 2.1 .. .. 8.9 0.4 1.4 30.0 62.0 0.7 1.7 .. .. .. 0.0 .. 1.4 .. .. .. .. .. .. .. 5.6 10.2 .. .. .. .. .. .. 0.7 .. 0.2 9.5 .. 10.2 .. 2.1 .. .. 0.5 2.8 5.4 .. .. 4.0 .. .. 27.4 .. .. .. .. ..

Average annual precipitation

Land under cereal production

thousand hectares

millimeters 2008

1990

327 1,485 89 1,010 591 562 534 1,110 447 2,666 618 847 1,039 1,146 1,028 416 1,782 608 748 1,274 1,904 1,604 537 1,343 322 1,522 .. .. 2,612 1,543 1,646 2,926 1,348 1,113 1,335 677 703 1,410 2,087 51 1,724 384 626 848 536 867 1,831 836 1,026 700 1,187 652 1,996 1,651 1,577 1,440 1,976

2,253.0 321.0 2,366.0 775.1 9,015.0 162.8 13,428.8 948.4 627.0 11,140.6 2,603.0 368.2b 643.9 582.5 304.1 205.1 18,512.4 2,055.3 2,528.9 217.5 1,900.0 657.6 21,547.9 110.5 1,075.4 823.5 93,555.2 .. 1,742.8 1,863.6 9.6 92.6 1,400.0 592.7 230.5 1,613.6 1,570.3 122.2 802.2 2,283.4 425.4 329.3 453.7 4,040.3 1,212.6 9,060.4 14.4 90.0 248.5 6,944.9 853.0 1,470.4 718.5 729.6 109.3 351.5 465.1

2009

3,188.0 146.2 3,176.3 1,752.1 8,031.6 169.3 19,805.6 838.0 1,113.8 12,032.5 2,418.0 345.0 976.1 897.0 295.8 85.7 20,220.4 1,829.2 4,178.6 222.0 2,888.0 1,223.3 14,863.2 264.3 2,486.7 567.5 88,592.8 .. 1,186.0 1,977.3 27.5 74.4 853.5 562.7 419.9 1,544.4 1,497.7 158.2 819.1 3,129.8 363.0 492.3 316.4 8,748.0 1,133.1 9,388.2 20.5 295.2 193.8 6,908.4 1,570.7 1,174.8 855.9 1,863.0 152.6 437.0 382.6

Fertilizer consumption

% of fertilizer production 2008

146.9 .. 226.4 .. 305.9 .. 161.3 .. .. 141.1 22.0 .. .. .. .. .. 313.5 68.4 .. .. .. .. 25.1 .. .. 102.8 99.2 .. 278.7 .. .. .. .. 70.7 554.4 117.1 .. .. .. 68.5 .. .. 65.4 .. 77.4 153.8 .. .. 13.0 55.7 .. 472.4 .. .. .. .. ..

kilograms per hectare of arable land 2008

3.2 38.4 6.8 8.3 38.8 18.1 33.9 109.6 20.9 164.5 237.4 .. 0.0 5.5 11.9 .. 165.7 81.8 3.9 2.2 22.7 8.6 56.9 .. .. 588.8 468.0 .. 492.4 1.0 1.1 707.5 18.9 387.6 39.7 135.1 128.3 .. 214.1 723.6 118.4 0.0 100.3 7.7 134.2 146.1 14.1 2.6 37.1 160.4 6.4 143.8 92.0 1.5 .. .. 107.7

Agricultural employment

Agricultural machinery

% of total employment 1990 2008

Tractors per 100 sq. km of arable land 1990 2008

.. .. .. 5.1 0.4 .. 5.6 7.9 30.9 64.9 .. 3.1 .. 1.2 .. .. 22.8 18.5 .. .. .. .. 4.1 .. 83.0 19.3 53.4 0.9 1.4 .. .. 25.9 .. .. 24.9 7.7 5.5 20.3 7.5 39.0 10.2 .. 21.0 .. 8.8 5.6 41.6 64.7 .. 4.1 62.0 23.9 12.9 .. .. 65.6 50.1

.. 58.0 .. .. 0.8 46.2 3.4 5.6 38.7 48.1 .. 1.8 .. .. .. 29.9 19.3 7.5 .. .. .. .. 2.5 .. .. 12.3 .. 0.2 18.4 .. .. 13.2 .. 12.8 18.7 3.3 2.7 14.5 8.3 31.2 18.9 .. 3.7 8.6 4.5 3.0 .. .. 53.4 2.2 .. 11.4 33.2 .. .. .. 39.2

0.2 212.4 129.1 .. 100.2 345.5 .. 2,373.7 194.8 2.4 206.9 1,523.3b 1.0 24.8 235.3 140.5 143.8 135.8 2.4 1.8 3.3 0.9 164.8 .. .. 127.6 66.6 .. 96.8 .. .. .. 19.9 35.2 226.2 264.6 634.7 25.9 54.2 249.6 .. 5.0 455.3 .. 916.5 800.0 .. .. 295.6 1,309.4 7.1 744.2 .. 45.0 0.8 2.6 30.9

1.2 121.9 139.6 .. .. 327.8 .. 2,390.3 116.1 3.9 89.8 1,127.1 .. .. .. 134.8 129.2 173.5 .. .. 11.8 .. 162.5 .. .. 425.9 277.1 .. .. .. .. .. .. .. 203.2 276.4 486.3 .. .. 372.1 .. .. 604.7 .. 784.7 635.3 .. .. 594.0 646.0 4.5 1,196.9 .. .. .. .. ..

Agricultural landa

% of land area 1990 2008

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Romania

72 61 25 38 23 82 27 57 44 16 12 82 47 21 22 .. 8 53 7 41 59 76 26 9 54 51 62 45 22 26 38 56 53 78 81 68 61 16 47 29 59 61 33 26 79 3 3 34 29 2 43 17 37 62 43 49 64

64 60 27 30 22 61 23 46 43 13 11 77 48 25 19 52 8 56 10 29 67 78 27 9 43 42 70 58 24 32 38 48 53 76 75 67 62 18 47 29 57 43 43 34 86 3 6 34 30 2 51 17 40 53 38 21 59

% irrigated 2008

1.4 .. 16.3 19.0 .. .. .. 19.2 .. 35.1 9.5 .. 0.1 .. 51.6 .. .. 9.3 .. 0.0 19.9 .. .. .. .. 2.7 2.2 .. .. .. .. 21.4 5.2 9.1 .. 4.4 .. 24.8 .. 27.7 10.6 .. .. .. .. 5.4 .. 73.0 .. .. .. .. .. 0.5 12.0 8.5 1.9

Average annual precipitation

millimeters 2008

589 1,083 2,702 228 216 1,118 435 832 2,051 1,668 111 250 630 1,054 1,274 .. 121 533 1,834 641 661 788 2,391 56 656 619 1,513 1,181 2,875 282 92 2,041 752 450 241 346 1,032 2,091 285 1,500 778 1,732 2,391 151 1,150 1,414 125 494 2,692 3,142 1,130 1,738 2,348 600 854 2,054 637

Land under cereal production

thousand hectares 1990

2,778.6 102,536.5 13,660.5 9,468.1 3,256.3 298.9 113.8 4,413.4 2.1 2,471.5 105.8 22,152.4 1,785.5 1,605.0 1,441.0 .. 0.5 578.0 687.0 696.7 41.2 233.5 175.0 404.1 1,134.0 235.2 1,326.9 1,425.3 700.7 2,438.7 118.9 0.6 10,543.1 675.6 654.1 5,603.3 1,549.5 5,221.4 214.2 3,045.2 195.3 172.5 320.0 6,882.3 15,400.0 356.4 2.4 11,864.1 184.6 1.7 393.7 683.7 7,138.5 8,530.9 760.0 0.5 5,704.1

2009

2,883.6 99,880.0 17,044.2 9,095.5 2,141.4 293.5 79.1 3,453.8 1.5 1,936.2 48.3 16,575.0 2,329.0 1,265.5 1,018.5 .. 1.4 612.4 1,048.9 540.9 67.9 179.2 190.0 342.9 1,103.5 178.9 1,476.5 1,780.1 678.6 3,988.4 242.9 0.1 10,182.4 881.6 252.4 5,316.7 1,892.0 8,912.0 307.2 3,418.0 220.8 162.7 438.2 9,929.1 18,899.0 305.9 5.0 13,689.0 144.3 3.3 1,344.8 1,286.1 7,216.3 8,582.8 305.9 0.3 5,265.5

Fertilizer consumption

% of fertilizer production 2008

227.2 190.6 117.6 148.1 132.8 .. 2.6 264.9 .. 135.7 3.1 38.1 .. .. 134.0 .. 3.2 .. .. .. 8.6 .. .. 17.2 13.5 .. .. 3,197.3 242.6 .. .. .. 319.7 .. .. 40.0 .. 1,515.4 .. .. 17.9 320.3 .. .. 1,929.1 24.4 2.4 115.6 .. .. .. .. 973.8 100.0 190.1 .. 42.1

kilograms per hectare of arable land 2008

94.3 153.5 189.1 90.9 43.8 480.3 252.6 156.0 51.3 278.2 337.4 3.1 33.3 .. 479.5 .. 1,250.9 19.0 .. 124.3 56.2 .. .. 27.3 79.1 56.2 4.3 1.7 929.9 9.0 .. 210.1 44.7 12.5 8.2 53.8 0.0 3.3 0.3 7.7 269.1 1,721.0 32.3 0.4 13.3 219.0 395.0 163.3 35.3 78.6 66.8 81.6 131.2 190.4 236.5 .. 45.6

3.2

ENVIRONMENT

Agricultural inputs Agricultural employment

Agricultural machinery

% of total employment 1990 2008

Tractors per 100 sq. km of arable land 1990 2008

18.2 .. 55.9 .. .. 15.1 4.1 8.8 27.3 7.2 6.6 .. .. .. 17.9 .. 1.3 32.7 .. .. .. .. .. .. .. .. .. .. 26.0 .. .. 16.7 22.6 33.8 39.5 3.9 .. 69.7 48.2 81.2 4.5 10.6 39.3 .. .. 6.4 9.3 51.1 29.1 .. 1.9 1.2 45.2 25.2 17.9 3.6 29.1

4.5 .. 41.2 22.8 .. 5.6 1.6 3.8 18.2 4.2 .. .. .. .. 7.4 .. .. 36.3 .. 7.7 .. .. .. .. 7.7 18.2 82.0 .. 14.8 .. .. 9.1 13.5 32.8 40.6 43.3 .. .. .. .. 2.7 7.2 29.1 .. .. 2.8 .. 43.6 14.7 .. 29.5 9.3 36.1 14.7 11.5 1.1 28.7

97.7 60.7 2.2 141.5 65.8 1,623.4 798.8 1,586.5 .. 4,492.9 340.4 62.0 20.0 .. 211.0 .. 220.0 189.4 .. 363.7 174.9 57.7 .. 184.3 256.0 730.3 4.9 .. 152.9 10.2 8.4 .. 123.5 310.1 80.3 45.0 .. 13.6 .. 21.9 2,073.1 .. 20.0 0.2 4.7 1,779.1 41.1 129.7 102.0 59.4 71.6 36.3 65.2 823.6 563.1 438.5 140.6

2011 World Development Indicators

261.9 .. .. 182.8 .. 1,476.4 705.3 .. .. 4,382.4 366.8 17.7 .. .. 1,632.5 .. 95.6 191.1 .. 501.4 .. .. .. .. 631.6 1,243.8 .. .. .. 2.7 9.8 .. 97.2 197.5 38.0 .. .. 10.9 .. 122.9 1,301.5 .. .. .. 6.6 1,539.1 .. 204.8 .. .. 61.5 .. .. 1,246.0 1,397.7 525.0 200.4

131

3.2

Agricultural inputs Agricultural landa

% of land area 1990 2008

Russian Federation Rwanda Qatar Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

14 76 5 .. 46 .. 39 3 51 28 70 80 61 37 52 72 8 40 73 32 38 42 21 59 15 56 52 69 61 72 3 75 47 85 65 25 21 62 45 27 34 37 w 35 37 49 30 37 49 28 34 24 55 42 39 51

% irrigated 2008

13 82 6 .. 48 57 58 1 40 25 70 82 56 42 58 71 8 39 76 34 39 38 25 67 11 64 51 69 66 71 7 73 45 85 63 24 32 61 45 30 41 38 w 37 38 50 30 38 48 28 35 23 55 45 37 44

2.0 .. .. .. 0.7 0.5 .. .. 1.3 0.8 .. .. 11.9 .. 1.3 .. .. .. 9.8 15.0 .. .. .. .. .. 4.0 13.3 .. .. 5.3 .. .. .. 1.2 .. .. .. 4.6 3.3 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

Average annual precipitation

millimeters 2008

460 1,212 74 59 686 .. 2,526 2,497 824 1,162 282 495 636 1,712 416 788 624 1,537 252 691 1,071 1,622 .. 1,168 2,200 207 593 161 1,180 565 78 1,220 715 1,265 206 1,875 1,821 402 167 1,020 657

Land under cereal production

thousand hectares 1990

59,541.3 254.1 1.1 974.6 1,229.0 .. 468.6 .. 836.6 112.5 732.5 6,162.9 7,551.4 869.8 3,734.6 85.7 1,285.2 211.9 4,134.4 266.5 2,629.3 10,536.9 82.6 648.0 5.9 1,445.7 13,640.1 331.3 1,055.0 12,542.3 1.3 3,657.3 65,700.0 514.6 1,225.3 753.9 6,474.6 0.0 844.9 895.2 1,576.1 708,090.3 s 63,834.3 482,334.4 307,764.8 174,569.6 546,168.7 142,232.3 127,839.3 47,401.7 29,953.1 131,803.8 66,938.5 161,921.6 34,697.5

2009

2011 World Development Indicators

% of fertilizer production 2008

kilograms per hectare of arable land 2008

41,715.7 11.9 15.9 368.2 .. 8.3 2.0 0.3 276.9 466.4 14.5 75.2 1,647.2 53.7 2.4 1,919.0 252.9 115.2 1,111.4 .. .. 1,918.9 .. .. 768.7 81.1 130.0 101.9 .. 283.6 470.2 .. .. 3,318.7 262.4 49.7 6,043.3 84.8 106.5 1,017.9 2,961.4 284.3 9,453.8 .. 3.6 48.7 .. .. 1,032.1 414.7 142.1 153.0 .. 226.3 2,774.1 184.5 88.0 409.5 452.5 0.0 5,087.0 .. 6.0 12,282.7 1,462.8 130.9 103.4 .. .. 826.7 .. 4.9 2.1 17.8 2,337.2 876.6 8.7 32.1 11,955.9 242.3 88.7 970.3 .. .. 1,826.0 .. 3.4 15,114.8 40.7 32.8 0.0 7.6 336.3 3,173.0 133.5 208.2 58,001.4 96.2 103.1 1,049.4 1,492.7 118.3 1,607.5 .. .. 1,237.1 74.5 232.9 8,528.5 218.6 286.6 35.0 .. .. 672.8 .. 14.3 1,062.5 .. 50.1 2,236.8 164.7 27.9 708,451.8 s 94.9 w 119.4 w 92,275.5 221.9 16.7 468,186.3 103.8 139.6 321,963.9 115.0 191.8 146,222.5 76.9 70.7 560,461.8 104.8 123.1 148,824.2 108.5 .. 104,480.3 26.9 33.3 50,290.2 279.7 111.8 27,642.2 57.6 95.3 133,310.2 176.9 148.0 95,914.7 578.1 11.6 147,990.0 73.6 109.3 31,367.7 95.1 150.5

a. Includes permanent pastures, arable land, and land under permanent crops. b. Includes Luxembourg.

132

Fertilizer consumption

Agricultural employment

Agricultural machinery

% of total employment 1990 2008

Tractors per 100 sq. km of arable land 1990 2008

13.9 90.1 .. .. .. .. .. 0.4 .. 10.7 .. .. 11.5 47.8 .. .. 3.4 4.2 26.5 44.7 .. 63.3 .. .. 12.3 25.8 46.9 .. .. 19.8 .. 2.1 2.9 0.0 .. 13.4 .. .. 52.6 49.8 .. .. w .. .. .. 20.8 .. 53.6 22.9 18.7 .. .. .. 6.5 7.2

9.0 97.8 .. 1.0 3.0 84.0 4.7 19.2 33.7 1.6 20.8 .. .. 4.1 1.1 .. 4.0 197.5 10.2 .. .. 16.0 8.8 107.9 4.3 483.1 31.3 .. .. 7.2 .. 228.9 2.2 601.9 3.9 2,783.1 .. 128.1 .. 415.4 74.6 8.2 41.7 33.0 .. 8.5 .. 0.5 4.3 .. .. 82.4 26.2 279.8 .. 464.7 .. .. 16.7 153.3 4.9 51.4 1.4 760.6 1.4 238.4 11.0 260.4 .. .. 8.7 .. .. 47.0 15.6 442.2 .. 39.0 .. 27.2 .. 60.1 .. w 186.4 w .. 15.7 .. 91.7 .. 66.0 14.9 125.1 .. 85.0 .. 53.2 16.3 132.3 15.8 120.9 .. 114.6 .. 62.2 .. 20.2 3.5 474.9 3.8 977.3

30.0 .. 56.2 .. .. 17.7 .. .. 154.7 .. 12.0 .. 824.4 .. 12.4 87.1 592.4 2,597.2 233.9 216.1 .. .. .. 0.5 .. 142.6 488.5 .. .. 103.3 .. .. 257.6 222.4 .. .. .. 767.9 .. .. .. .. w .. .. .. .. .. .. 111.0 .. 190.2 119.9 .. .. 811.1

3.2

ENVIRONMENT

Agricultural inputs About the data Agriculture is still a major sector in many economies,

land as share of total agricultural land area and data

(July–June). Previous editions of World Development

and agricultural activities provide developing coun-

on average precipitation to illustrate how countries

Indicators reported data on a crop year basis, but

tries with food and revenue. But agricultural activi-

obtain water for agricultural use.

this edition uses the calendar year, as adopted by the FAO. Caution should thus be used when compar-

ties also can degrade natural resources. Poor farming

The data here and in table 3.3 are collected by

practices can cause soil erosion and loss of soil fertil-

the Food and Agriculture Organization of the United

ity. Efforts to increase productivity by using chemical

Nations (FAO) through annual questionnaires. The

fertilizers, pesticides, and intensive irrigation have

FAO tries to impose standard definitions and report-

environmental costs and health impacts. Excessive

ing methods, but complete consistency across

• Agricultural land is permanent pastures, arable,

use of chemical fertilizers can alter the chemistry of

countries and over time is not possible. Thus, data

and land under permanent crops. Permanent pasture

soil. Pesticide poisoning is common in developing

on agricultural land in different climates may not

is land used for five or more years for forage, includ-

countries. And salinization of irrigated land dimin-

be comparable. For example, permanent pastures

ing natural and cultivated crops. Arable land includes

ishes soil fertility. Thus, inappropriate use of inputs

are quite different in nature and intensity in African

land defined by the FAO as land under temporary

for agricultural production has far-reaching effects.

countries and dry Middle Eastern countries. Data

crops (double-cropped areas are counted once),

ing data over time. Deﬁnitions

The table provides indicators of major inputs to

on agricul-tural employment, in particular, should

temporary meadows for mowing or for pasture, land

agricultural production: land, fertilizer, labor, and

be used with caution. In many countries much agri-

under market or kitchen gardens, and land tempo-

machinery. There is no single correct mix of inputs:

cultural employment is informal and unrecorded,

rarily fallow. Land abandoned as a result of shift-

appropriate levels and application rates vary by coun-

including substantial work performed by women

ing cultivation is excluded. Land under permanent

try and over time and depend on the type of crops, the

and children. To address some of these concerns,

crops is land cultivated with crops that occupy the

climate and soils, and the production process used.

this indicator is heavily footnoted in the database in

land for long periods and need not be replanted after

sources, definition, and coverage.

each harvest, such as cocoa, coffee, and rubber.

The agriculture sector is the most water-intensive sector, and water delivery in agriculture is increas-

Fertilizer consumption measures the quantity of

Land under flowering shrubs, fruit trees, nut trees,

ingly important. The table shows irrigated agricultural

plant nutrients. Consumption is calculated as pro-

and vines is included, but land under trees grown

duction plus imports minus exports. Because some

for wood or timber is not. • Irrigated land refers to

chemical compounds used for fertilizers have other

areas purposely provided with water, including land

industrial applications, the consumption data may

irrigated by controlled flooding. • Average annual

overstate the quantity available for crops. Fertilizer

precipitation is the long-term average in depth

consumption as a share of production shows the

(over space and time) of annual precipitation in the

agriculture sector’s vulnerability to import and energy

country. Precipitation is defined as any kind of water

price fluctuation. The FAO recently revised the time

that falls from clouds as a liquid or a solid. • Land

series for fertilizer consumption and irrigation for

under cereal production refers to harvested areas,

2002 onward, but recent data are not available for

although some countries report only sown or culti-

all countries. FAO collects fertilizer statistics for pro-

vated area. • Fertilizer consumption is the quantity

duction, imports, exports, and consumption through

of plant nutrients applied to arable land. Fertilizer

the new FAO fertilizer resources questionnaire. In

products cover nitrogen, potash, and phosphate

the previous release, the data were based on total

fertilizers (including ground rock phosphate). Tradi-

consumption of fertilizers, but the data in the recent

tional nutrients—animal and plant manures—are

release are based on the nutrients in fertilizers.

not included. • Fertilizer production is fertilizer

Some countries compile fertilizer data on a calendar

consumption, exports, and nonfertilizer use of fertil-

year basis, while others do so on a crop year basis

izer products minus fertilizer imports. • Agricultural

Nearly 40 percent of land globally is devoted to agriculture

3.2a

Total land area in 2008: 130 million sq. km

Permanent pastures 26%

Other 31%

Arable land 11% Forests 31% Permanent crops 1% Note: Agricultural land includes permanent pastures, arable land, and land under permanent crops. Source: Tables 3.1 and 3.2.

employment is employment in agriculture, forestry,

Rainfed agriculture plays a signiﬁcant role in Sub-Saharan agriculture where about 95 percent of cropland depends on precipitation, 2008

3.2b

hunting, and fishing (see table 2.3). • Agricultural machinery refers to wheel and crawler tractors (excluding garden tractors) in use in agriculture at

Sierra Leone Liberia Mauritius Gabon Guinea Congo, Rep. Cameroon Guinea-Bissau Congo, Dem. Rep. Madagascar

the end of the calendar year specified or during the first quarter of the following year.

Data sources 0

500

1,000

1,500

2,000

Average annual precipitation (millimeters per year)

2,500

3,000

Data on agricultural inputs are from electronic files that the FAO makes available to the World Bank and from the FAO web site (www.fao.org).

Source: Table 3.2.

2011 World Development Indicators

133

3.3

Agricultural output and productivity Crop production index

1999–2001 = 100 1990 2009

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

134

96.0 107.0 66.0 56.0 64.0 106.0 58.0 97.0 136.0 74.0 110.0 72.0 a 53.0 62.0 107.0 102.0 74.0 160.0 62.0 109.0 66.0 69.0 90.0 75.0 59.0 74.0 68.0 .. 91.0 122.0 81.0 68.0 71.0 80.0 119.0 .. 113.0 118.0 75.0 66.0 95.0 .. 121.0 .. 116.0 96.0 81.0 55.0 122.0 86.0 43.0 75.0 73.0 71.0 72.0 111.0 100.0

2011 World Development Indicators

176.0 140.0 196.0 250.0 101.0 195.0 88.0 108.0 152.0 130.0 161.0 113.0 110.0 126.0 125.0 120.0 149.0 103.0 144.0 108.0 202.0 117.0 119.0 110.0 118.0 113.0 130.0 .. 120.0 97.0 116.0 118.0 109.0 96.0 82.0 98.0 113.0 111.0 114.0 136.0 102.0 154.0 127.0 153.0 115.0 101.0 104.0 114.0 61.0 102.0 156.0 76.0 138.0 133.0 120.0 110.0 153.0

Food production index

1999–2001 = 100 1990 2009

77.0 82.0 69.0 61.0 72.0 108.0 68.0 89.0 107.0 72.0 135.0 85.0 a 58.0 72.0 119.0 109.0 65.0 151.0 62.0 109.0 64.0 72.0 84.0 68.0 64.0 71.0 59.0 .. 81.0 119.0 79.0 68.0 73.0 100.0 127.0 .. 100.0 101.0 67.0 64.0 85.0 .. 180.0 .. 113.0 97.0 91.0 60.0 99.0 102.0 46.0 84.0 72.0 72.0 73.0 101.0 90.0

127.0 115.0 163.0 198.0 106.0 191.0 95.0 97.0 151.0 132.0 157.0 96.0 116.0 133.0 138.0 113.0 148.0 76.0 136.0 110.0 184.0 120.0 119.0 123.0 125.0 120.0 133.0 .. 128.0 98.0 123.0 126.0 120.0 104.0 83.0 99.0 107.0 131.0 126.0 139.0 116.0 126.0 134.0 151.0 104.0 98.0 103.0 117.0 66.0 103.0 155.0 83.0 141.0 133.0 122.0 112.0 145.0

Livestock production index

1999–2001 = 100 1990 2009

63.0 65.0 80.0 72.0 90.0 108.0 82.0 90.0 102.0 70.0 146.0 88.0 a 88.0 80.0 119.0 110.0 58.0 165.0 65.0 131.0 59.0 83.0 76.0 64.0 82.0 66.0 45.0 .. 79.0 102.0 73.0 77.0 90.0 126.0 152.0 .. 86.0 74.0 61.0 62.0 74.0 .. 192.0 .. 111.0 95.0 84.0 95.0 78.0 115.0 89.0 105.0 81.0 56.0 78.0 65.0 66.0

89.0 92.0 121.0 92.0 108.0 177.0 95.0 94.0 152.0 137.0 150.0 91.0 135.0 139.0 167.0 112.0 142.0 63.0 132.0 118.0 109.0 105.0 105.0 132.0 120.0 134.0 133.0 .. 140.0 96.0 157.0 126.0 132.0 129.0 109.0 90.0 104.0 151.0 140.0 134.0 131.0 101.0 118.0 140.0 100.0 94.0 100.0 132.0 67.0 105.0 127.0 99.0 118.0 167.0 128.0 111.0 133.0

Cereal yield

Agricultural productivity

kilograms per hectare 1990 2009

Agriculture value added per worker 2000 $ 1990 2009

1,201 2,794 688 321 2,232 1,843 1,716 5,577 2,113 2,491 2,741 5,755a 848 1,361 3,553 265 1,755 3,954 600 1,349 1,362 1,241 2,636 807 559 3,620 4,323 .. 2,475 800 624 3,097 887 3,975 2,342 .. 6,118 3,996 1,724 5,703 1,939 .. 1,304 .. 3,543 6,083 1,643 1,004 1,998 5,411 989 3,036 1,998 1,455 1,531 1,027 1,468

1,983 4,315 1,654 588 3,167 2,230 1,764 6,136 2,607 3,890 3,372 9,632 1,330 2,089 4,539 465 3,526 3,413 1,035 1,313 2,947 1,574 3,301 948 812 5,472 5,460 .. 4,017 772 776 3,770 1,724 6,117 2,069 5,074 6,810 4,246 2,974 7,635 2,727 938 2,761 1,652 3,760 7,460 1,663 1,053 1,917 7,201 1,660 4,103 1,624 1,711 1,422 961 1,752

.. 764 1,703 200 6,702 1,607 20,150 13,413 1,000 251 2,042 .. 422 681 .. 770 1,625 3,983 113 116 .. 419 28,898 321 168 3,453 263 .. 3,122 209 .. 2,984 653 5,546 4,117 .. 14,588 1,925 2,105 1,737 1,742 .. 3,288 .. 17,163 21,423 1,216 272 2,359 13,669 .. 6,707 2,243 156 .. .. 1,180

.. .. 2,184 313 9,987 5,049 29,257 24,715 1,342 435 5,184 42,035 .. 733 14,299 597 3,760 10,227 .. .. 411 730 44,752 .. .. 6,618 525 .. 2,861 168 .. 5,232 926 15,137 3,647 5,687 45,905 4,579 1,766 3,024 2,778 66 3,207 215 43,813 58,070 1,869 275 1,872 31,659 .. 10,779 2,783 225 .. .. 1,958

Crop production index

1999–2001 = 100 1990 2009

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

119.0 78.0 80.0 71.0 105.0 91.0 112.0 88.0 83.0 115.0 102.0 163.0 79.0 111.0 88.0 .. 57.0 68.0 65.0 129.0 99.0 101.0 71.0 78.0 79.0 108.0 93.0 54.0 74.0 68.0 60.0 108.0 81.0 135.0 293.0 100.0 70.0 60.0 77.0 75.0 92.0 78.0 70.0 64.0 60.0 143.0 59.0 77.0 116.0 76.0 93.0 51.0 87.0 124.0 110.0 153.0 51.0

102.0 116.0 145.0 117.0 83.0 84.0 108.0 91.0 95.0 89.0 157.0 150.0 107.0 107.0 96.0 .. 122.0 108.0 157.0 147.0 96.0 72.0 115.0 102.0 138.0 112.0 115.0 141.0 141.0 162.0 116.0 95.0 111.0 100.0 270.0 142.0 130.0 151.0 140.0 135.0 100.0 109.0 117.0 210.0 134.0 90.0 95.0 125.0 114.0 112.0 122.0 145.0 131.0 99.0 86.0 105.0 121.0

Food production index

1999–2001 = 100 1990 2009

123.0 75.0 80.0 69.0 111.0 94.0 90.0 90.0 75.0 109.0 81.0 163.0 83.0 104.0 79.0 .. 53.0 78.0 60.0 222.0 92.0 91.0 88.0 77.0 157.0 108.0 91.0 47.0 67.0 79.0 86.0 95.0 74.0 159.0 101.0 93.0 68.0 61.0 98.0 76.0 102.0 74.0 63.0 61.0 60.0 112.0 57.0 67.0 91.0 77.0 76.0 55.0 78.0 119.0 101.0 125.0 64.0

100.0 119.0 146.0 124.0 92.0 90.0 122.0 95.0 100.0 95.0 156.0 145.0 126.0 112.0 101.0 .. 114.0 103.0 148.0 138.0 111.0 72.0 131.0 109.0 138.0 115.0 114.0 129.0 144.0 183.0 116.0 106.0 117.0 105.0 110.0 140.0 102.0 162.0 101.0 130.0 94.0 115.0 135.0 186.0 135.0 95.0 102.0 132.0 116.0 119.0 136.0 153.0 131.0 111.0 95.0 94.0 77.0

Livestock production index

1999–2001 = 100 1990 2009

140.0 70.0 81.0 64.0 140.0 93.0 69.0 94.0 64.0 106.0 52.0 178.0 89.0 124.0 62.0 .. 63.0 110.0 57.0 274.0 64.0 86.0 91.0 77.0 185.0 101.0 99.0 78.0 71.0 94.0 91.0 57.0 68.0 197.0 94.0 81.0 47.0 51.0 101.0 78.0 101.0 77.0 59.0 56.0 70.0 101.0 65.0 64.0 67.0 79.0 81.0 65.0 57.0 123.0 82.0 119.0 77.0

85.0 133.0 157.0 142.0 128.0 93.0 130.0 103.0 110.0 101.0 139.0 141.0 147.0 133.0 106.0 .. 102.0 105.0 126.0 126.0 149.0 78.0 127.0 116.0 116.0 115.0 111.0 153.0 133.0 153.0 115.0 138.0 123.0 98.0 101.0 128.0 89.0 248.0 90.0 120.0 101.0 114.0 149.0 153.0 121.0 95.0 122.0 135.0 116.0 127.0 134.0 158.0 134.0 110.0 103.0 91.0 46.0

3.3

ENVIRONMENT

Agricultural output and productivity Cereal yield

Agricultural productivity

kilograms per hectare 1990 2009

Agriculture value added per worker 2000 $ 1990 2009

4,521 1,891 3,800 1,445 1,061 6,577 3,484 3,945 1,116 5,846 1,220 1,338 1,562 3,926 5,853 .. 3,653 2,772 2,268 1,641 1,878 1,036 1,029 674 1,938 2,652 1,945 992 2,740 726 870 4,193 2,424 2,928 1,098 1,120 474 2,762 457 1,920 6,959 5,034 1,524 310 1,148 4,399 2,160 1,766 1,867 2,395 1,979 2,601 2,065 3,284 1,878 1,080 2,897

4,713 2,471 4,813 2,291 1,222 6,798 3,250 5,035 1,253 5,920 1,044 1,254 1,204 3,698 7,073 .. 2,679 3,034 3,808 3,075 2,828 421 1,553 623 3,450 3,387 2,291 1,599 3,750 1,588 873 7,895 3,111 2,417 1,552 1,003 846 3,585 465 2,374 9,032 6,922 1,872 489 1,598 3,094 3,358 2,803 2,735 3,727 2,358 3,910 3,229 3,475 3,455 1,897 3,820

4,232 362 512 1,906 .. .. .. 10,410 2,224 20,934 2,077 1,781 400 .. 5,338 .. .. 684 387 1,896 .. 260 .. .. .. 2,413 214 89 3,850 406 653 3,446 2,275 1,349 1,241 1,806 132 .. 1,267 247 23,593 19,782 .. 235 .. 17,454 1,037 739 2,303 563 1,657 907 911 1,605 4,495 .. ..

2011 World Development Indicators

10,948 468 734 3,061 .. 13,573 .. 29,498 2,716 52,062 3,030 2,033 334 .. 19,105 .. .. 1,041 516 3,636 41,037 207 .. .. 5,369 5,811 192 162 6,529 523 408 5,556 3,364 1,531 1,888 3,306 220 .. 1,638 238 45,969 25,446 2,495 .. .. 40,666 .. 903 4,185 672 1,338 1,545 1,204 2,776 6,764 .. ..

135

3.3

Agricultural output and productivity Crop production index

Food production index

1999–2001 = 100 1990 2009

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

98.0 126.0 97.0 118.0 72.0 98.0 b 127.0 223.0 .. 82.0 144.0 86.0 91.0 90.0 49.0 112.0 126.0 112.0 66.0 123.0 91.0 76.0 88.0 71.0 120.0 93.0 87.0 98.0 77.0 131.0 16.0 101.0 86.0 66.0 107.0 77.0 57.0 .. 75.0 81.0 77.0 81.0 c w 76.2 73.6 71.9 79.2 73.8 69.9 111.3 75.8 74.7 78.0 71.1 90.7 90.9

100.0 136.0 132.0 124.0 130.0 .. 204.0 440.0 102.0 94.0 96.0 111.0 97.0 115.0 112.0 101.0 103.0 103.0 115.0 148.0 154.0 129.0 105.0 109.0 89.0 119.0 112.0 128.0 109.0 155.0 52.0 96.0 112.0 187.0 145.0 116.0 137.0 104.0 130.0 170.0 55.0 122.2 w 134.1 128.2 128.6 126.9 128.7 133.1 129.3 128.1 127.3 119.3 128.7 103.9 96.9

1999–2001 = 100 1990 2009

105.0 129.0 94.0 103.0 73.0 109.0 b 121.0 335.0 .. 76.0 101.0 87.0 88.0 91.0 51.0 106.0 110.0 104.0 71.0 134.0 86.0 77.0 94.0 74.0 90.0 81.0 88.0 60.0 78.0 147.0 19.0 105.0 82.0 74.0 93.0 73.0 59.0 .. 71.0 87.0 87.0 80.0c w 76.5 68.8 66.5 75.6 69.4 62.7 117.7 71.2 72.8 74.5 72.9 90.4 95.6

a. Includes Luxembourg. b. Includes Montenegro. c. FAO estimate.

136

2011 World Development Indicators

107.0 130.0 134.0 124.0 134.0 .. 201.0 132.0 97.0 97.0 104.0 122.0 97.0 120.0 119.0 115.0 100.0 104.0 131.0 162.0 134.0 126.0 111.0 132.0 125.0 115.0 119.0 137.0 112.0 123.0 45.0 98.0 115.0 144.0 155.0 122.0 138.0 102.0 144.0 135.0 82.0 123.0 w 134.4 130.2 130.5 129.3 130.5 135.1 126.2 131.2 131.6 122.7 130.0 106.3 97.7

Livestock production index

1999–2001 = 100 1990 2009

124.0 147.0 79.0 64.0 80.0 103.0 b 105.0 481.0 .. 76.0 95.0 93.0 77.0 88.0 58.0 108.0 100.0 103.0 74.0 196.0 75.0 74.0 89.0 85.0 72.0 57.0 91.0 64.0 79.0 170.0 54.0 105.0 81.0 83.0 99.0 73.0 50.0 .. 63.0 83.0 79.0 82.0c w 78.3 62.5 56.5 75.1 63.3 48.8 136.8 69.7 69.3 69.2 80.1 90.7 98.2

115.0 118.0 158.0 135.0 144.0 .. 144.0 105.0 80.0 97.0 105.0 130.0 103.0 123.0 123.0 140.0 93.0 106.0 143.0 168.0 104.0 111.0 114.0 137.0 149.0 110.0 125.0 129.0 120.0 101.0 125.0 99.0 108.0 127.0 137.0 128.0 157.0 93.0 165.0 106.0 107.0 120.3 w 131.0 131.5 132.6 129.4 131.4 135.2 119.1 132.6 134.7 132.9 125.1 104.1 100.0

Cereal yield

Agricultural productivity

kilograms per hectare 1990 2009

Agriculture value added per worker 2000 $ 1990 2009

3,011 1,743 1,043 4,245 795 2,926b 1,202 .. .. 3,279 793 1,877 2,485 2,965 456 1,278 4,964 5,984 750 1,020 1,506 2,009 1,608 747 2,826 1,143 2,214 2,210 1,498 2,834 2,216 6,171 4,755 2,182 1,777 2,486 3,073 .. 908 1,352 1,625 2,755c w 1,561 2,563 2,696 2,103 2,429 3,795 2,596 2,089 1,471 1,926 1,033 4,138 4,490

2,825 2,279 1,097 5,212 1,135 4,626 989 .. 4,335 5,266 417 4,395 2,957 3,722 587 560 5,086 6,579 1,707 2,250 1,224 2,954 1,276 1,136 2,659 1,401 2,808 2,974 1,539 3,004 2,000 7,008 7,238 4,047 4,578 3,826 5,075 1,684 1,003 2,068 313 3,514 w 1,966 3,210 3,446 2,690 3,005 4,843 2,471 3,282 2,352 2,628 1,302 5,439 5,822

2,351 1,917 172 7,863 252 .. .. .. .. 13,217 .. 2,290 8,947 678 501 1,025 23,307 20,451 2,613 370 220 446 .. 351 1,825 2,736 2,175 1,272 177 1,232 9,042 21,400 18,523 6,166 1,427 4,443 225 .. 428 212 270 803 w 236 489 360 2,270 460 313 2,188 2,227 1,760 372 304 14,116 11,982

8,993 3,031 .. 20,431 245 .. .. 49,867 9,728 67,838 .. 3,641 21,831 926 922 1,176 51,057 26,726 4,717 542 283 708 .. .. 1,502 3,602 3,491 2,930 203 2,461 .. 26,370 49,512 9,064 2,584 7,941 356 .. .. 216 141 998 w 278 777 604 3,683 704 570 3,182 3,436 2,896 495 318 25,066 26,730

About the data

3.3

ENVIRONMENT

Agricultural output and productivity Deﬁnitions

The agricultural production indexes in the table are

production. These prices, expressed in international

• Crop production index is agricultural production

prepared by the Food and Agriculture Organization of

dollars (equivalent in purchasing power to the U.S.

for each period relative to the average over the base

the United Nations (FAO). The FAO obtains data from

dollar), are derived using a Geary-Khamis formula

period 1999–2001. It includes all crops except fod-

official and semiofficial reports of crop yields, area

applied to agricultural outputs (see Inter-Secretariat

der crops. The regional and income group aggregates

under production, and livestock numbers. If data are

Working Group on National Accounts 1993, sections

for the FAO’s production indexes are calculated from

unavailable, the FAO makes estimates. The indexes

16.93–96). This method assigns a single price to

the underlying values in international dollars, normal-

are calculated using the Laspeyres formula: produc-

each commodity so that, for example, one metric ton

ized to the average over the base period 1999–2001.

tion quantities of each commodity are weighted by

of wheat has the same price regardless of where it

• Food production index covers food crops that are

average international commodity prices in the base

was produced. The use of international prices elimi-

considered edible and that contain nutrients. Cof-

period and summed for each year. Because the FAO’s

nates fluctuations in the value of output due to transi-

fee and tea are excluded because, although edible,

indexes are based on the concept of agriculture as a

tory movements of nominal exchange rates unrelated

they have no nutritive value. • Livestock production

single enterprise, estimates of the amounts retained

to the purchasing power of the domestic currency.

index includes meat and milk from all sources, dairy

for seed and feed are subtracted from the produc-

Data on cereal yield may be affected by a variety

products such as cheese, and eggs, honey, raw silk,

tion data to avoid double counting. The aggregates

of reporting and timing differences. Millet and sor-

wool, and hides and skins. • Cereal yield, measured

represent production available for any use except as

ghum, which are grown as feed for livestock and poul-

in kilograms per hectare of harvested land, includes

seed and feed and presented as “net”. The FAO’s

try in Europe and North America, are used as food

wheat, rice, maize, barley, oats, rye, millet, sorghum,

indexes may differ from those from other sources

in Africa, Asia, and countries of the former Soviet

buckwheat, and mixed grains. Production data on

because of differences in coverage, weights, con-

Union. So some cereal crops are excluded from the

cereals refer to crops harvested for dry grain only.

cepts, time periods, calculation methods, and use

data for some countries and included elsewhere,

Cereal crops harvested for hay or harvested green for

of international prices.

depending on their use.

food, feed, or silage, and those used for grazing, are

To facilitate cross-country comparisons, the

excluded. The FAO allocates production data to the

FAO uses international commodity prices to value

calendar year in which the bulk of the harvest took place. But most of a crop harvested near the end of

The food production index has increased steadily since early 1960, and the index for low-income economies has been higher than the world average since early 2000

a year will be used in the following year. • Agricul-

3.3a

tural productivity is the ratio of agricultural value added, measured in 2000 U.S. dollars, to the num-

Index (1999–2001=100)

ber of workers in agriculture. Agricultural productivity is measured by value added per unit of input. (For

160 World

Middle income

120

further discussion of the calculation of value added in national accounts, see About the data for tables

90

4.1 and 4.2.) Agricultural value added includes that High income

Low income

from forestry and fishing. Thus interpretations of land

60

productivity should be made with caution. 30 0 1990

1995

2000

2005

2008

Source: Table 3.3.

Cereal yield in Sub-Saharan Africa increased between 1990 and 2009 but still is the lowest among the regions

3.3b

6 Kilograms per hectare (thousands)

1990

2009

5 4

Data sources 3

Data on agricultural production indexes, cereal yield, and agricultural employment are from elec-

2

tronic files that the FAO makes available to the 1

World Bank. The files may contain more recent information than published versions. Data on agri-

0 East Asia &

Source: Table 3.3.

Europe & Central Asia

Latin America & Caribbean

Middle East & North Africa

South Asia

Sub-Saharan Africa

cultural value added are from the World Bank’s national accounts files.

2011 World Development Indicators

137

3.4

Deforestation and biodiversity Forest area

Average annual deforestationa

thousand sq. km

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

138

1990

2010

14 8 17 610 348 3 1,545 38 9 15 78 7 58 628 22 137 5,748 33 68 3 129 243 3,101 232 131 153 1,571 .. 625 1,604 227 26 102 19 21 26 4 20 138 0 4 16 21 151 219 145 220 4 28 107 74 33 47 73 22 1 81

14 8 15 585 294 3 1,493 39 9 14 86 7 46 572 22 114 5,195 39 56 2 101 199 3,101 226 115 162 2,069 .. 605 1,541 224 26 104 19 29 27 5 20 99 1 3 15 22 123 222 160 220 5 27 111 49 39 37 65 20 1 52

2011 World Development Indicators

% 1990–2000 2000–10

0.00 0.26 0.54 0.21 0.88 1.31 –0.03 –0.16 0.00 0.18 –0.62 0.15 1.29 0.44 0.11 0.90 0.51 –0.14 0.91 3.71 1.14 0.94 0.00 0.13 0.62 –0.37 –1.20 .. 0.16 0.20 0.08 0.76 –0.10 –0.19 –1.70 –0.03 –0.89 0.00 1.53 –2.98 1.26 0.28 –0.71 0.97 –0.26 –0.55 0.00 –0.42 0.04 –0.31 1.99 –0.88 1.20 0.51 0.44 0.62 2.38

0.00 –0.09 0.57 0.21 0.80 1.48 0.37 –0.13 0.00 0.18 –0.42 –0.16 1.03 0.49 0.00 0.99 0.49 –1.53 1.00 1.40 1.33 1.04 0.00 0.13 0.66 –0.25 –1.57 .. 0.17 0.20 0.06 –0.92 –0.07 –0.18 –1.66 –0.08 –1.13 0.00 1.81 –1.72 1.45 0.28 0.12 1.08 0.14 –0.38 0.00 –0.40 0.09 0.00 2.08 –0.81 1.39 0.53 0.47 0.76 2.06

Threatened species

GEF beneﬁts index for biodiversity

Terrestrial protected areas

Marine protected areas

% of total land area

% of territorial waters

Mammals

Birds

Fish

Higher plantsb

0–100 (no biodiversity to maximum biodiversity)

2010

2010

2010

2010

2008

11 3 14 15 37 9 55 3 7 34 4 3 11 20 4 7 80 7 9 10 37 39 12 8 13 20 74 2 51 30 11 9 24 7 14 2 2 6 43 17 5 10 1 32 1 9 14 10 10 6 16 10 16 22 12 5 7

13 6 11 21 50 10 52 8 15 29 4 2 5 33 6 9 123 12 6 10 24 16 15 7 9 34 85 17 91 34 3 19 14 10 17 6 2 14 71 10 5 10 3 23 4 7 5 6 10 6 9 11 10 13 3 13 9

5 38 33 37 36 3 100 11 10 19 2 10 27 0 31 2 80 18 4 17 28 110 32 3 1 19 97 11 50 81 45 46 43 56 30 2 14 17 49 36 12 18 4 14 5 40 59 21 9 21 42 73 20 63 30 17 22

2 0 15 33 44 1 67 4 0 16 0 1 14 72 1 0 387 0 3 2 30 378 2 17 2 41 453 6 227 83 37 116 106 3 166 4 3 30 1,837 2 27 3 0 26 1 15 120 4 0 12 118 13 82 22 4 29 113

3.4 0.2 2.9 8.3 17.7 0.2 87.7 0.3 0.8 1.4 0.0 0.0 0.2 12.5 0.4 1.4 100.0 0.8 0.3 0.3 3.5 12.5 21.5 1.5 2.2 15.3 66.6 .. 51.5 19.9 3.6 9.7 3.4 0.6 12.5 0.1 0.2 6.0 29.3 2.9 0.9 0.8 0.1 8.4 0.2 5.3 3.0 0.1 0.6 0.6 1.9 2.8 8.0 2.3 0.6 5.2 7.2

1990

0.4 4.3 6.3 12.4 4.6 6.9 7.4 20.1 6.2 1.5 6.5 0.6 23.8 8.5 0.5 30.3 10.8 1.9 13.3 3.8 0.0 7.0 6.0 14.4 9.4 16.0 13.5 41.1 20.3 10.0 5.4 18.7 22.6 7.1 4.3 13.7 4.8 22.1 21.6 1.9 0.6 4.9 19.6 17.7 4.2 10.1 4.2 1.5 2.8 31.8 13.9 5.7 26.0 6.8 7.6 0.3 13.6

2009

0.4 9.8 6.3 12.4 5.4 8.0 10.5 22.9 7.1 1.6 7.3 0.9 23.8 18.2 0.6 30.9 28.0 9.1 13.9 4.8 24.0 9.2 8.0 14.7 9.4 16.5 16.6 41.8 20.4 10.0 9.4 20.9 22.6 7.3 6.2 15.1 5.0 22.1 25.1 5.9 0.8 5.0 20.0 18.4 9.1 15.1 14.9 1.5 3.7 40.5 14.0 13.8 30.6 6.8 16.1 0.3 18.2

1990

2009

.. 0.1 0.2 0.1 0.8 .. 10.9 .. .. 0.4 .. 0.0 0.0 .. 0.7 .. 11.4 0.1 .. .. 0.0 0.4 0.8 .. .. 3.4 0.4 0.0 3.7 3.7 0.0 12.1 0.1 1.2 1.3 .. 3.7 30.4 0.1 4.4 3.2 0.0 26.1 .. 3.5 1.1 0.2 0.1 0.2 35.7 0.0 0.6 0.3 0.0 2.7 0.0 0.0

.. 1.5 0.3 0.1 1.1 .. 28.3 .. .. 0.8 .. 0.0 0.0 .. 0.7 .. 20.1 3.0 .. .. 0.9 0.4 1.2 .. .. 3.7 1.4 0.0 5.9 4.3 2.1 12.3 0.1 1.2 2.7 .. 3.8 30.4 13.0 9.3 3.2 0.0 26.1 .. 5.0 3.4 7.1 0.1 0.4 36.3 0.0 2.5 12.5 0.0 45.8 0.0 1.9

Forest area

Average annual deforestationa

thousand sq. km

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

1990

2010

18 639 1,185 111 8 5 1 76 3 250 1 34 37 82 64 .. 0 8 173 32 1 0 49 2 19 9 137 39 224 141 4 0 703 3 125 50 434 392 88 48 3 77 45 19 172 91 0 25 38 315 212 702 66 89 33 3 0

20 684 944 111 8 7 2 91 3 250 1 33 35 57 62 5c 0 10 158 34 1 0 43 2 22 10 126 32 205 125 2 0 648 4 109 51 390 318 73 36 4 83 31 12 90 101 0 17 33 287 176 680 77 93 35 6 0

% 1990–2000 2000–10

–0.57 –0.22 1.75 0.00 –0.17 –3.16 –1.49 –0.98 0.12 0.03 0.00 0.17 0.35 1.67 0.13 .. –5.24 –0.26 0.46 –0.21 0.00 –0.49 0.63 0.00 –0.38 –0.49 0.42 0.88 0.36 0.58 2.66 0.00 0.52 –0.16 0.67 0.06 0.52 1.17 0.87 2.09 –0.43 –0.69 1.67 3.74 2.68 –0.19 0.00 1.76 1.18 0.45 0.88 0.14 –0.80 –0.20 –0.28 –4.92 0.00

–0.62 –0.46 0.51 0.00 –0.09 –1.53 –0.07 –0.90 0.12 –0.04 0.00 0.17 0.33 2.00 0.11 .. –1.84 –1.07 0.48 –0.34 –0.45 –0.47 0.67 0.00 –0.67 –0.41 0.44 0.97 0.54 0.61 2.66 1.08 0.30 –1.77 0.72 –0.22 0.54 0.93 0.96 0.70 –0.14 0.00 2.01 0.98 3.67 –0.79 0.00 2.24 0.36 0.48 0.96 0.18 –0.74 –0.30 –0.10 –1.75 0.00

Threatened species

3.4

ENVIRONMENT

Deforestation and biodiversity GEF beneﬁts index for biodiversity

Terrestrial protected areas

Marine protected areas

% of total land area

% of territorial waters

Mammals

Birds

Fish

Higher plantsb

0–100 (no biodiversity to maximum biodiversity)

2010

2010

2010

2010

2008

2 94 183 16 13 5 15 7 5 28 13 16 28 9 9 .. 6 6 45 1 10 2 19 12 3 5 63 7 70 12 15 6 99 4 11 18 12 45 12 31 4 9 6 12 27 7 9 23 15 39 8 54 39 5 11 3 2

9 78 119 21 18 1 13 8 10 40 10 21 30 22 30 .. 9 12 22 3 7 7 11 4 4 10 35 14 45 7 9 11 55 9 21 10 23 41 24 33 2 70 11 6 13 2 10 26 17 37 27 96 72 6 9 8 5

8 122 138 29 11 18 35 42 17 59 13 14 66 12 17 .. 11 3 23 5 21 1 52 21 5 14 83 101 60 3 30 12 150 9 1 45 52 33 25 8 12 21 26 4 56 18 24 33 36 41 0 19 65 6 47 15 11

1 255 393 1 0 1 0 27 209 15 1 16 129 6 3 .. 0 14 22 0 1 4 47 2 0 0 280 14 692 6 0 88 255 0 0 31 52 42 26 7 0 21 43 2 172 2 6 2 202 143 10 274 222 4 21 53 0

0.2 39.9 81.0 7.3 1.6 0.6 0.8 3.8 4.4 36.0 0.4 5.1 8.8 0.7 1.7 .. 0.1 1.1 5.0 0.0 0.2 0.3 2.6 1.6 0.0 0.2 29.2 3.5 13.9 1.5 1.3 3.3 68.7 0.0 4.2 3.5 7.2 10.0 5.2 2.1 0.2 20.2 3.3 0.9 6.0 1.3 3.7 4.9 10.9 25.4 2.8 33.4 32.3 0.5 5.5 4.0 0.1

1990

4.6 5.0 10.0 5.2 0.1 0.6 17.2 5.0 10.2 13.2 8.4 2.4 11.5 3.9 2.2 .. 1.6 6.4 0.8 6.4 0.5 0.5 18.1 0.1 1.4 4.2 2.1 15.0 16.9 2.3 0.5 1.7 2.4 0.9 4.1 1.2 14.8 3.1 14.4 7.7 11.0 25.0 15.4 6.8 11.6 4.7 0.0 10.3 17.2 1.9 2.9 4.7 8.7 15.3 5.9 10.1 0.0

2009

5.1 5.3 14.1 7.1 0.1 1.0 18.7 9.9 18.9 16.3 9.4 2.5 11.6 4.0 2.4 .. 1.6 6.9 16.3 17.8 0.5 0.5 18.1 0.1 4.5 4.8 2.9 15.0 17.9 2.4 0.5 4.5 11.1 1.4 13.4 1.5 15.8 6.3 14.5 17.0 12.4 25.8 36.7 6.8 12.8 14.4 10.7 10.3 18.7 3.1 5.4 13.6 10.9 21.8 5.9 10.1 0.7

1990

2009

.. 1.5 0.5 1.3 0.0 0.1 1.0 0.5 0.2 2.0 0.0 .. 5.1 0.1 5.0 .. 0.0 .. .. 4.6 0.0 .. 0.0 0.0 0.8 .. 0.0 .. 1.1 .. 32.1 0.3 1.9 .. .. 0.7 1.8 0.3 0.5 .. 13.5 0.4 0.7 .. 0.2 1.0 0.0 1.8 3.1 0.3 .. 2.8 0.2 3.8 1.8 1.5 0.0

.. 1.7 1.9 1.9 0.0 0.1 1.0 16.7 4.2 5.6 20.8 .. 10.4 0.1 5.3 .. 0.0 .. .. 6.6 0.1 .. 0.0 0.0 2.7 .. 0.1 .. 1.6 .. 32.1 0.3 16.7 .. .. 1.2 3.3 0.3 0.5 .. 21.2 7.1 20.1 .. 0.2 2.3 1.3 1.8 4.0 0.3 .. 2.8 1.5 4.5 1.8 1.6 0.3

2011 World Development Indicators

139

3.4

Deforestation and biodiversity Forest area

Average annual deforestationa

thousand sq. km 1990

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

2010

% 1990–2000 2000–10

64 66 0.01 8,090 8,091 0.00 3 4 –0.79 10 10 0.00 93 85 0.49 23 27 –0.62 31 27 0.65 0 0 0.00 19 19 0.01 12 13 –0.37 83 67 0.97 82 57 1.67 138 182 –2.09 24 19 1.20 764 699 0.80 5 6 –0.93 273 282 –0.04 12 12 –0.37 4 5 –1.51 4 4 –0.05 415 334 1.02 195 190 0.28 10 7 1.22 7 3 3.37 2 2 0.29 6 10 –2.67 97 113 –0.47 41 41 0.00 48 30 2.03 93 97 –0.25 2 3 –2.38 26 29 –0.68 2,963 3,040 –0.13 9 17 –4.38 30 33 –0.54 520 463 0.57 94 138 –2.28 0 0 0.00 5 5 0.00 528 495 0.32 222 156 1.58 41,582 s 40,204 s 0.20 w 4,881 0.63 5,524 26,552 25,660 0.24 8,103 7,996 0.26 18,449 17,664 0.23 32,076 30,541 0.31 4,602 4,698 0.17 8,703 8,750 –0.02 10,389 9,460 0.48 207 211 –0.08 795 817 0.01 7,379 6,605 0.58 9,506 9,663 –0.13 838 930 –0.73

–0.32 0.00 –2.37 0.00 0.49 –0.98 0.69 0.00 –0.06 –0.16 1.07 2.00 –0.68 1.12 0.08 –0.84 –0.29 –0.38 –1.29 0.00 1.13 0.02 1.40 5.13 0.35 –1.86 –1.11 0.00 2.55 –0.20 –0.22 –0.31 –0.13 –2.13 –0.20 0.60 –1.64 0.00 0.00 0.33 1.88 0.13 w 0.61 0.10 –0.13 0.20 0.18 –0.38 –0.03 0.45 –0.13 –0.27 0.52 –0.03 –0.31

Threatened species

2011 World Development Indicators

Terrestrial protected areas

Marine protected areas

% of total land area

% of territorial waters

Mammals

Birds

Fish

Higher plantsb

0–100 (no biodiversity to maximum biodiversity)

2010

2010

2010

2010

2008

7 12 18 1 32 18 35 8 20 12 9 4 9 14 22 3 16 9 41 9 6 11 11 1 17 10 45 48 11 17 25 57 3 7 5 2 4 4 26 0 15 11 26 21 24 39 81 97 16 15 62 55 30 14 41 283 15 14 17 18 5 9 4 11 1 3 11 3 2 2 9 3 16 13 33 3 8 9 5 14 35 42 172 298 57 45 72 91 4 7 5 0 11 3 24 10 2 2 19 1 13 7 31 7 17 15 67 5 9 15 11 3 22 19 61 41 11 12 21 1 7 10 13 0 5 2 41 14 37 74 177 245 11 23 35 1 10 15 7 15 32 27 34 70 54 40 46 146 3 8 0 0 9 14 21 159 9 14 20 9 9 13 3 16 1,131 s 1,240 s 1,851 s 8,724 s

a. Negative values indicate an increase in forest area. b. Flowering plants. c. National sources.

140

GEF beneﬁts index for biodiversity

0.7 34.1 0.9 3.2 1.0 0.2 1.3 0.1 0.1 0.2 6.1 20.7 6.8 7.9 5.1 0.1 0.3 0.2 0.9 0.7 14.8 8.0 0.6 0.3 2.2 0.5 6.2 1.8 2.8 0.5 0.2 3.5 94.2 1.2 1.1 25.3 12.1 .. 3.2 3.8 1.9

1990

2.8 8.2 9.9 7.6 24.1 3.0 5.0 5.0 19.5 7.5 0.6 6.5 7.7 19.6 4.7 3.0 7.1 14.5 0.3 1.9 26.5 14.2 0.0 11.3 30.5 1.3 1.7 3.0 7.3 1.8 0.3 21.8 14.8 0.3 2.1 39.3 4.4 .. 0.0 36.0 18.0 9.1 w 10.0 8.6 8.8 8.4 8.9 10.8 6.6 10.5 3.1 5.5 11.0 9.9 11.1

2009

7.1 9.0 10.0 31.3 24.1 6.0 5.0 5.4 23.5 12.1 0.6 6.9 8.6 20.8 4.9 3.0 11.3 22.8 0.6 4.1 27.7 19.6 6.0 11.3 31.2 1.3 1.9 3.0 9.7 3.5 5.6 24.4 14.8 0.3 2.3 53.7 6.2 .. 0.5 36.0 28.0 12.5 w 11.2 12.4 11.5 13.0 12.2 14.9 7.4 20.8 4.0 6.1 11.7 13.4 15.4

1990

2009

1.5 3.1 .. 0.6 5.8 .. 0.0 0.0 .. 0.0 0.0 0.7 0.6 0.1 0.0 .. 3.7 .. 0.0 .. 3.7 4.0 0.0 0.0 0.2 1.1 2.4 .. .. 4.1 0.3 4.7 18.3 0.2 .. 7.0 0.3 .. 0.0 .. .. 4.8 w .. 2.9 0.8 4.1 3.2 0.5 3.1 6.7 0.9 1.5 3.2 8.7 6.5

33.2 9.1 .. 3.4 12.4 .. 0.0 1.6 .. 0.6 0.0 6.5 3.4 1.1 0.0 .. 5.3 .. 0.6 .. 10.0 4.3 6.7 0.0 2.8 1.2 2.4 .. .. 4.9 2.6 5.2 24.7 0.2 .. 15.3 2.1 .. 1.9 .. .. 9.2 w .. 6.6 2.0 9.4 6.6 1.5 8.8 13.1 2.0 1.7 4.7 15.1 10.1

About the data

3.4

ENVIRONMENT

Deforestation and biodiversity Deﬁnitions

As threats to biodiversity mount, the international com-

ecoregions, and threatened ecoregions. To combine

• Forest area is land spanning more than 0.5 hectares

munity is increasingly focusing on conserving diversity.

these dimensions into one measure, the indicator

with trees higher than 5 meters and a canopy cover

Deforestation is a major cause of loss of biodiversity,

uses dimensional weights that reflect the consensus

of more than 10 percent or with trees able to reach

and habitat conservation is vital for stemming this

of conservation scientists at the GEF, IUCN, WWF Inter-

these thresholds in situ. It does not include land that

loss. Conservation efforts have focused on protecting

national, and other nongovernmental organizations.

is predominantly under agricultural or urban land use.

areas of high biodiversity. The Food and Agriculture

The World Conservation Monitoring Centre (WCMC)

• Average annual deforestation is the permanent con-

Organization of the United Nations (FAO) Global Forest

compiles data on protected areas, numbers of certain

version of natural forest area to other uses, including

Resources Assessment 2010 provides detailed informa-

species, and numbers of those species under threat

agriculture, ranching, settlements, and infrastruc-

tion on forest cover in 2010 and adjusted estimates

from various sources. Because of differences in defini-

ture. Deforested areas do not include areas logged

of forest cover in 1990 and 2000. The current survey

tions, reporting practices, and reporting periods, cross-

but intended for regeneration or areas degraded by

uses a uniform definition of forest. Because of space

country comparability is limited. Nationally protected

fuelwood gathering, acid precipitation, or forest fires.

limitations, the table does not break down forest cover

areas are defined using the six IUCN management cat-

• Threatened species are the number of species clas-

between natural forest and plantation, a breakdown

egories for areas of at least 1,000 hectares: scientific

sified by the IUCN as endangered, vulnerable, rare,

the FAO provides for developing countries. Thus the

reserves and strict nature reserves with limited public

indeterminate, out of danger, or insufficiently known.

deforestation data in the table may underestimate the

access; national parks of national or international sig-

Mammals exclude whales and porpoises. Birds are

rate at which natural forest is disappearing in some

nificance and not materially affected by human activ-

listed for the country where their breeding or wintering

countries.

ity; natural monuments and natural landscapes with

ranges are located. Plants are native vascular plant

The number of threatened species is an important

unique aspects; managed nature reserves and wildlife

species. • GEF beneﬁts index for biodiversity is a

measure of the immediate need for conservation in

sanctuaries; protected landscapes (which may include

composite index of relative biodiversity potential based

an area. Global analyses of the status of threatened

cultural landscapes); and areas managed mainly for the

on the species represented in each country and their

species have been carried out for few groups of organ-

sustainable use of natural systems to ensure long-term

threat status and diversity of habitat types. The index

isms. Only for mammals, birds, and amphibians has the

protection and maintenance of biological diversity. The

has been normalized from 0 (no biodiversity potential)

status of virtually all known species been assessed.

data in the table cover these six categories as well as

to 100 (maximum biodiversity potential). • Nationally

Threatened species are defined using the International

terrestrial protected areas that are not assigned to a

protected areas are totally or partially protected areas

Union for Conservation of Nature’s (IUCN) classifica-

category by the IUCN. Designating an area as protected

of at least 1,000 hectares that are designated as sci-

tion: endangered (in danger of extinction and unlikely

does not mean that protection is in force. And for small

entific reserves with limited public access, national

to survive if causal factors continue operating) and vul-

countries that only have protected areas smaller than

parks, natural monuments, nature reserves or wildlife

nerable (likely to move into the endangered category in

1,000 hectares, the size limit in the definition leads to

sanctuaries, and protected landscapes. Terrestrial

the near future if causal factors continue operating).

an underestimate of protected areas.

protected areas exclude marine areas, unclassified

The Global Environment Facility’s (GEF) benefits

Due to variations in consistency and methods of

areas, littoral (intertidal) areas, and sites protected

index for biodiversity is a comprehensive indicator

collection, data quality is highly variable across coun-

under local or provincial law. Marine protected areas

of national biodiversity status and is used to guide

tries. Some countries update their information more

are areas of intertidal or subtidal terrain—and overly-

its biodiversity priorities. For each country the biodi-

frequently than others, some have more accurate data

ing water and associated flora and fauna and histori-

versity indicator incorporates the best available and

on extent of coverage, and many underreport the num-

cal and cultural features—that have been reserved to

comparable information in four relevant dimensions:

ber or extent of protected areas.

protect part or the entire enclosed environment.

represented species, threatened species, represented

3.4a

At least 33 percent of assessed species are estimated to be threatened

Data sources Data on forest area are from the FAO’s Global Forest Resources Assessment 2010 and the FAO’s data web

Species (thousands)

site. Data on species are from the electronic files

80

of the United Nations Environment Programme and WCMC, the 2010 IUCN Red List of Threatened Spe-

60

cies, and Froese and Pauly’s (2008) FishBase database. The GEF benefits index for biodiversity is from

Assessed

40

Kiran Dev Pandey, Piet Buys, Ken Chomitz, and David Wheeler’s, “Biodiversity Conservation Indicators: New Threatened

20

Tools for Priority Setting at the Global Environment Facility” (2006). Data on protected areas are from the United Nations Environment Programme and WCMC,

0 2000

2002

2004

Source: International Union for Conservation of Nature.

2006

2008

2010

based on data from national authorities and national legislation and international agreements.

2011 World Development Indicators

141

3.5

Freshwater Internal renewable freshwater resourcesa

Flows billion cu. m 2007

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

142

55 27 11 148 276 9 492 55 8 105 37 12 10 304 36 2 5,418 21 13 10 121 273 2,850 141 15 884 2,813 .. 2,112 900 222 112 77 38 38 13 6 21 432 2 18 3 13 122 107 200 164 3 58 107 30 58 109 226 16 13 96

Per capita cu. m 2007

1,946 8,588 332 8,431 6,989 2,952 23,348 6,626 946 666 3,834 1,129 1,227 31,868 9,395 1,268 28,498 2,742 849 1,283 8,417 14,630 86,426 33,119 1,412 53,137 2,134 .. 47,611 14,395 62,516 25,209 3,819 8,499 3,402 1,272 1,099 2,139 32,379 22 2,907 586 9,475 1,551 20,232 3,229 115,340 1,857 13,339 1,301 1,325 5,182 8,177 23,505 10,383 1,338 13,372

2011 World Development Indicators

Annual freshwater withdrawals

billion cu. m 2007b

23.3 1.7 6.1 0.4 29.2 3.0 23.9 2.1 12.2 79.4 2.8 0.0 0.1 1.4 0.0 0.2 59.3 10.5 0.8 0.3 4.1 1.0 46.0 0.0 0.2 12.6 554.1 .. 10.7 0.4 0.0 2.7 0.9 0.0 8.2 2.6 1.3 3.4 17.0 68.3 1.3 0.6 0.2 5.6 2.5 31.8 0.1 0.0 1.6 47.1 1.0 7.8 2.0 1.5 0.2 1.0 0.9

% of internal resources 2007b

42.3 6.4 54.0 0.2 10.6 32.5 4.9 3.8 150.5 75.6 7.5 .. 1.3 0.5 .. 8.1 1.1 50.0 6.4 2.9 3.4 0.4 1.6 0.0 1.5 1.4 22.4 .. 0.5 0.0 0.0 2.4 1.2 .. 21.5 19.6 21.2 16.1 3.9 3,794.4 7.2 20.8 1.2 4.6 2.3 22.4 0.1 1.0 2.8 44.0 3.2 13.4 1.8 0.7 1.1 7.6 0.9

% for agriculture 2007b

98 62 65 60 74 66 75 1 76 96 30 .. 45 81 .. 41 62 19 86 77 98 74 12 4 83 64 65 .. 46 31 9 53 65 .. 69 2 43 66 82 86 59 95 5 94 3 12 42 65 65 20 66 80 80 90 82 94 80

Water productivity

% for industry 2007b

0 11 13 17 9 4 10 64 19 1 47 .. 23 7 .. 18 18 78 1 6 0 8 69 16 0 25 23 .. 4 17 22 17 12 .. 12 57 25 2 5 6 16 0 38 0 84 69 8 12 13 68 10 3 13 2 5 1 12

% for domestic 2007b

2 27 22 23 17 30 15 35 4 3 23 .. 32 13 .. 41 20 3 13 17 1 18 20 80 17 11 12 .. 50 53 70 29 24 .. 19 41 32 32 12 8 25 5 57 6 14 18 50 23 22 12 24 16 6 8 13 5 8

GDP/water use 2000 $ per cu. m 2007b

.. 3 12 61 13 1 22 105 1 1 8 .. 23 7 .. 41 14 2 5 3 2 13 19 39 13 8 4 .. 13 16 89 9 11 .. 6 30 141 10 1 2 13 1 64 2 62 38 49 19 3 44 7 22 12 3 1 4 12

Access to an improved water source

% of rural population 2008

39 98 79 38 80 93 100 100 71 78 99 100 69 67 98 90 84 100 72 71 56 51 99 51 44 75 82 .. 73 28 34 91 68 97 89 100 100 84 88 98 76 57 97 26 100 100 41 86 96 100 74 99 90 61 51 55 77

% of urban population 2008

78 96 85 60 98 98 100 100 88 85 100 100 84 96 100 99 99 100 95 83 81 92 100 92 67 99 98 .. 99 80 95 100 93 100 96 100 100 87 97 100 94 74 99 98 100 100 95 96 100 100 90 100 98 89 83 71 95

Internal renewable freshwater resourcesa

Flows billion cu. m 2007

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

6 1,276 2,019 129 35 49 1 183 9 430 1 75 21 67 65 .. 0 46 190 17 5 5 200 1 16 5 337 16 580 60 0c 3 409 1 35 29 100 1003 6 198 11 327 190 4 221 382 1 55 147 801 94 1,616 479 54 38 7 0

Per capita cu. m 2007

597 1,134 8,987 1,809 1,175 11,246 104 3,074 3,514 3,365 120 4,871 548 2,824 1,338 .. 0 8,873 31,256 7,355 1,153 2,574 55,138 97 4,610 2,647 18,114 1,118 21,841 4,835 127 2,182 3,885 273 13,326 929 4,586 20,415 2,949 7,007 671 77,336 33,912 248 1,496 81,119 514 338 44,094 124,716 15,343 56,685 5,399 1,406 3,582 1,801 45

Annual freshwater withdrawals

billion cu. m 2007b

7.6 40.4 82.8 93.3 66.0 .. 2.0 44.4 0.4 88.4 0.9 35.0 2.7 9.0 18.6 .. 0.5 10.1 3.0 0.3 1.3 0.1 0.1 4.3 0.3 0.0 15.0 1.0 9.0 6.5 1.7 0.7 78.2 2.3 0.4 12.6 0.6 33.2 0.3 10.2 7.9 2.1 1.3 2.2 8.0 2.2 1.3 169.4 0.8 0.1 0.5 20.1 28.5 16.2 11.3 0.0 0.4

% of internal resources 2007b

127.3 51.2 2.9 72.6 187.5 .. 260.5 24.3 4.4 20.6 138.0 46.4 13.2 13.5 28.7 .. .. 21.7 1.6 1.8 27.3 1.0 0.1 721.0 1.7 .. 4.4 6.3 1.6 10.9 425.0 26.4 19.1 231.0 1.3 43.4 0.6 3.8 4.9 5.1 72.2 0.6 0.7 62.3 3.6 0.6 94.4 308.0 0.6 0.0 0.5 1.2 6.0 30.2 29.6 .. 870.6

Water productivity

% for agriculture 2007b

% for industry 2007b

% for domestic 2007b

32 91 82 92 79 .. 58 45 49 62 65 82 79 55 48 .. 54 94 90 13 60 20 55 83 7 .. 96 80 62 90 88 68 77 33 52 87 87 89 71 96 34 42 83 95 69 11 88 96 28 1 71 82 74 8 78 .. 59

59 2 7 1 15 .. 6 37 17 18 4 17 4 25 16 .. 2 3 6 33 11 40 18 3 15 .. 2 5 21 1 3 3 5 58 27 3 2 1 5 1 60 9 2 0 10 67 1 2 5 42 8 10 9 79 12 .. 2

9 7 12 7 7 .. 36 18 34 20 31 2 17 20 36 .. 44 3 4 53 29 40 27 14 78 .. 3 15 17 9 9 30 17 10 20 10 11 10 24 3 6 48 15 4 21 23 10 2 67 56 20 8 17 13 10 .. 39

GDP/water use 2000 $ per cu. m 2007b

8 1 1.2 2 0 125 79 27 25 59 14 1 6 .. 40 .. 67 0 1 48 17 18 5 11 73 .. 0 2 15 1 1 8 9 1 4 4 12 .. 19 1 55 31 4 1 9 90 20 1 21 59 18 4 4 14 11 .. 90

3.5

ENVIRONMENT

Freshwater

Access to an improved water source

% of rural population 2008

100 84 71 .. 55 100 100 100 89 100 91 90 52 100 88 .. 99 85 51 96 100 81 51 .. .. 99 29 77 99 44 47 99 87 85 49 60 29 69 88 87 100 100 68 39 42 100 77 87 83 33 66 61 87 100 100 .. 100

2011 World Development Indicators

% of urban population 2008

100 96 89 98 91 100 100 100 98 100 98 99 83 100 100 .. 99 99 72 100 100 97 79 .. .. 100 71 95 100 81 52 100 96 96 97 98 77 75 99 93 100 100 98 96 75 100 92 95 97 87 99 90 93 100 99 .. 100

143

3.5

Freshwater Internal renewable freshwater resourcesa

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

Flows billion cu. m 2007

Per capita cu. m 2007

42 4,313 10 2 26 44c 160 1 13 19 6 45 111 50 30 3 171 40 7 66 84 210 12 4 4 227 1 39 53 0 145 2,818 59 16 722 367 1 2 80 12 43,464 s 4,418 29,421 11,728 17,694 33,839 9,454 5,059 13,425 225 1,819 3,858 9,624 955

1,963 30,350 1,005 99 2,169 5,419c 29,518 131 2,334 9,251 687 928 2,478 2,499 742 2,293 18,692 5,350 349 9,855 2,035 3,135 1,825 2,891 410 3,109 273 1,273 1,142 34 2,378 9,344 17,750 608 26,287 4,304 212 94 6,513 985 6,616 w 5,452 6,271 3,155 18,142 6,150 4,940 12,911 24,001 714 1,194 4,826 9,017 2,932

Annual freshwater withdrawals

billion cu. m 2007b

23.2 76.7 0.2 23.7 2.2 0.0 c 0.4 0.0 0.0 0.0 3.3 12.5 35.6 12.6 37.3 1.0 3.0 2.6 16.7 12.0 5.2 87.1 0.2 0.3 2.6 40.1 24.7 0.0 37.5 4.0 9.5 477.8 3.2 58.3 8.4 71.4 0.4 3.4 1.7 4.2 3,850.0 s 240.9 2,672.1 2,103.9 568.2 2,913.0 959.0 351.9 264.9 275.6 941.1 120.5 937.0 200.2

% of internal resources 2007b

54.8 1.8 1.6 986.1 8.6 .. 0.2 .. .. .. 55.0 27.9 32.0 25.2 124.4 39.5 1.7 6.4 238.4 18.0 6.2 41.5 1.5 8.1 62.9 17.7 1,812.5 .. 70.7 2,665.3 6.6 17.1 5.3 357.0 1.2 19.5 .. 161.9 2.2 34.3 9.0 w 5.6 9.1 17.9 3.2 8.6 10.2 7.0 2.0 122.3 51.7 3.2 10.5 22.0

% for agriculture 2007b

57 18 68 88 93 .. 92 .. .. .. 99 63 68 95 97 97 9 2 88 92 89 95 45 6 82 74 98 40 52 83 3 40 96 93 47 68 45 90 76 79 70 w 93 78 81 65 79 74 63 71 86 90 87 42 38

Water productivity

Access to an improved water source

% for industry 2007b

% for domestic 2007b

GDP/water use 2000 $ per cu. m 2007b

% of rural population 2008

% of urban population 2008

34 63 8 3 3 .. 3 .. .. .. 0 6 19 2 1 1 54 74 4 5 0 2 2 26 4 11 1 16 35 2 75 46 1 2 7 24 7 2 7 7 20 w 2 14 12 19 13 20 27 10 6 4 3 43 48

9 19 24 9 4 .. 5 .. .. .. 0 31 13 2 3 2 37 24 9 4 10 2 53 68 14 15 2 43 12 15 22 14 3 5 46 8 48 8 17 14 10 w 5 9 7 15 8 7 10 19 8 6 10 15 15

2 5 19 10 3 .. 4 .. .. .. .. 14 21 2 1 2 103 111 2 0 3 2 9 46 10 9 0 .. 1 28 185 24 8 0 19 1 .. 4 3 1 10 w 1 3 2 7 3 3 3 10 2 1 4 32 34

.. 89 62 .. 52 98 26 .. 100 99 9 78 100 88 52 61 100 100 84 61 45 98 41 93 84 96 72 64 97 100 100 94 100 81 75 92 91 57 46 72 78 w 56 81 81 86 76 81 89 80 80 83 47 98 100

.. 98 77 97 92 99 86 100 100 100 67 99 100 98 64 92 100 100 94 94 80 99 87 98 99 100 97 91 98 100 100 100 100 98 94 99 91 72 87 99 96 w 85 95 94 98 94 96 98 97 95 95 82 100 100

a. Excludes river flows from other countries because of data unreliability. b. Data are for the most recent year available (see Primary data documentation). c. Includes Kosovo and Montenegro.

144

2011 World Development Indicators

About the data

3.5

ENVIRONMENT

Freshwater Deﬁnitions

The data on freshwater resources are based on

to variations in collection and estimation methods.

• Internal renewable freshwater resources are

estimates of runoff into rivers and recharge of

In addition, inflows and outflows are estimated at

the average annual flows of rivers and groundwater

groundwater. These estimates are based on differ-

different times and at different levels of quality and

from rainfall in the country. Natural incoming flows

ent sources and refer to different years, so cross-

precision, requiring caution in interpreting the data,

originating outside a country’s borders are excluded.

country comparisons should be made with caution.

particularly for water-short countries, notably in the

Overlapping water resources between surface run-

Because the data are collected intermittently, they

Middle East and North Africa.

off and groundwater recharge are also deducted.

may hide signifi cant variations in total renewable

Water productivity is an indication only of the

• Renewable internal freshwater resources per

water resources from year to year. The data also

effi ciency by which each country uses its water

capita are calculated using the World Bank’s popu-

fail to distinguish between seasonal and geographic

resources. Given the different economic structure

lation estimates (see table 2.1). • Annual freshwater

variations in water availability within countries. Data

of each country, these indicators should be used

withdrawals are total water withdrawals, not count-

for small countries and countries in arid and semiarid

carefully, taking into account the countries’ sectoral

ing evaporation losses from storage basins. With-

zones are less reliable than those for larger countries

activities and natural resource endowments.

drawals also include water from desalination plants

The data on access to an improved water source

in countries where they are a significant source. With-

Caution should also be used in comparing data

measure the percentage of the population with ready

drawals can exceed 100 percent of total renewable

on annual freshwater withdrawals, which are subject

access to water for domestic purposes. The data

resources where extraction from nonrenewable aqui-

are based on surveys and estimates provided by

fers or desalination plants is considerable or where

governments to the Joint Monitoring Programme of

water reuse is significant. Withdrawals for agriculture

the World Health Organization (WHO) and the United

and industry are total withdrawals for irrigation and

Nations Children’s Fund (UNICEF). The coverage

livestock production and for direct industrial use

rates are based on information from service users

(including for cooling thermoelectric plants). With-

on actual household use rather than on information

drawals for domestic uses include drinking water,

from service providers, which may include nonfunc-

municipal use or supply, and use for public services,

tioning systems. Access to drinking water from an

commercial establishments, and homes. • Water

improved source does not ensure that the water

productivity is calculated as GDP in constant prices

is safe or adequate, as these characteristics are

divided by annual total water withdrawal. • Access

not tested at the time of survey. While information

to an improved water source is the percentage of the

on access to an improved water source is widely

population with reasonable access to an adequate

used, it is extremely subjective, and such terms as

amount of water from an improved source, such as

safe, improved, adequate, and reasonable may have

piped water into a dwelling, plot, or yard; public tap

different meaning in different countries despite offi -

or standpipe; tubewell or borehole; protected dug

cial WHO definitions (see Definitions). Even in high-

well or spring; and rainwater collection. Unimproved

income countries treated water may not always be

sources include unprotected dug wells or springs,

safe to drink. Access to an improved water source is

carts with small tank or drum, bottled water, and

equated with connection to a supply system; it does

tanker trucks. Reasonable access is defined as the

not take into account variations in the quality and

availability of at least 20 liters a person a day from

cost (broadly defined) of the service.

a source within 1 kilometer of the dwelling.

and countries with greater rainfall.

Agriculture is still the largest user of water, accounting for some 70 3.5a percent of global withdrawals . . . Percent 100

Domestic

Agriculture

Industry

80

60

40

20

0 Low income

Lower middle income

Upper middle income

High income

World

Source: Table 3.5.

. . . and approaching 90 percent in some developing regions Percent 100

Domestic

Industry

3.5b Agriculture

80

60

Data sources Data on freshwater resources and withdrawals

40

are from the Food and Agriculture Organization of the United Nations AQUASTAT data. The GDP

20

estimates used to calculate water productivity are from the World Bank national accounts data-

0 East Europe Latin Middle Asia & America East & & Central & North Pacific Asia Caribbean Africa Source: Table 3.5.

South SubAsia Saharan Africa

base. Data on access to water are from WHO and UNICEF’s Progress on sanitation and drinking water (2010).

2011 World Development Indicators

145

3.6

Water pollution Emissions of organic water pollutants

thousand kilograms per day 1990 2007a

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

146

.. 2.4 .. .. 181.4 .. .. 90.5 41.3 250.8 .. 107.8 .. 11.3 .. 2.5 .. 124.3 .. .. 3.8 .. 300.9 .. .. .. .. .. .. .. .. .. .. 48.5 .. 177.1 84.5 88.6 28.6 206.5 .. 2.4 21.7 18.5 72.5 326.5 .. 0.8 .. 806.6 .. 50.9 .. .. .. 5.2 ..

0.2 3.6 .. .. 155.5 .. .. 84.4 20.0 303.0 .. 95.9 .. 11.5 .. 3.2 .. 102.1 .. .. .. .. 306.6 .. .. 92.5 9,428.9 .. 87.0 .. .. .. .. 42.9 .. 146.5 61.0 .. 44.7 .. .. 2.5 16.0 32.2 55.3 569.4 .. .. .. 936.2 16.0 60.8 .. .. .. .. ..

2011 World Development Indicators

Industry shares of emissions of organic water pollutants

kilograms per day per worker

% of total Stone, Food and ceramics, beverages and glass

Primary metals

Paper and pulp

Chemicals

Textiles

Wood

Other

2007a

2007a

2007a

2007a

2007a

2007a

2007a

27.9 .. .. .. 15.8 .. .. 9.3 18.5 3.0 .. 18.6 .. 13.1 .. .. .. 8.0 .. .. .. .. 10.9 .. .. 13.7 13.0 .. 17.3 .. .. .. .. 9.5 .. 10.9 13.1 .. 12.8 .. .. 9.5 7.1 10.9 8.7 15.0 .. .. .. 12.4 15.9 10.1 .. .. .. .. ..

14.1 39.8 .. .. 30.5 .. .. 12.2 19.6 7.6 .. 16.4 .. 35.4 .. 43.8 .. 17.7 .. .. .. .. 14.0 .. .. 35.1 7.4 .. 21.3 .. .. .. .. 17.6 .. 10.9 16.4 .. 46.4 .. .. 27.3 14.6 34.7 9.0 16.6 .. .. .. 11.4 18.6 23.9 .. .. .. .. ..

11.7 .. .. .. 3.5 .. .. 5.8 8.4 2.6 .. 3.1 .. 7.7 .. 0.6 .. 4.8 .. .. .. .. 2.8 .. .. 3.6 6.3 .. 5.3 .. .. .. .. 5.9 .. 6.4 4.8 .. 4.4 .. .. 9.6 5.5 8.3 4.4 3.8 .. .. .. 3.4 4.1 7.0 .. .. .. .. ..

23.3 60.2 .. .. 14.3 .. .. 4.3 11.7 79.3 .. 5.5 .. 18.4 .. 3.9 .. 26.8 .. .. .. .. 7.3 .. .. 9.1 20.6 .. 24.1 .. .. .. .. 14.5 .. 7.4 1.5 .. 12.3 .. .. 29.0 8.0 27.9 2.8 4.8 .. .. .. 2.4 10.2 14.4 .. .. .. .. ..

1990

2007a

2007a

.. 0.25 .. .. 0.21 .. .. 0.15 0.15 0.15 .. 0.17 .. 0.24 .. 0.30 .. 0.17 .. .. 0.17 .. 0.17 .. .. .. .. .. .. .. .. .. .. 0.17 .. 0.14 0.18 0.18 0.24 0.19 .. 0.19 0.15 0.23 0.19 0.11 .. 0.27 .. 0.13 .. 0.19 .. .. .. 0.20 ..

0.21 0.25 .. .. 0.23 .. .. 0.14 0.18 0.14 .. 0.17 .. 0.25 .. 0.23 .. 0.17 .. .. .. .. 0.16 .. .. 0.25 0.13 .. 0.20 .. .. .. .. 0.17 .. 0.13 0.16 .. 0.28 .. .. 0.20 0.14 0.24 0.14 0.16 .. .. .. 0.14 0.17 0.20 .. .. .. .. ..

.. .. .. .. 3.8 .. .. 5.7 8.8 0.7 .. 6.4 .. 0.9 .. .. .. 3.7 .. .. .. .. 4.3 .. .. 7.6 7.2 .. 2.3 .. .. .. .. 3.1 .. 5.4 1.4 .. 1.8 .. .. 0.2 0.4 1.4 1.0 3.2 .. .. .. 3.8 3.0 3.9 .. .. .. .. ..

19.7 .. .. .. 8.4 .. .. 7.1 3.0 2.3 .. 7.9 .. 9.8 .. 2.4 .. 4.3 .. .. .. .. 8.9 .. .. 6.3 3.9 .. 8.9 .. .. .. .. 7.2 .. 4.8 11.5 .. 7.8 .. .. 4.4 7.3 6.0 15.4 7.4 .. .. .. 7.1 3.8 9.0 .. .. .. .. ..

.. .. .. .. 2.1 .. .. 6.0 1.5 0.5 .. 2.2 .. 5.3 .. .. .. 3.0 .. .. .. .. 6.5 .. .. 6.9 1.7 .. 0.9 .. .. .. .. 4.9 .. 4.4 4.0 .. 2.2 .. .. 0.1 16.4 1.5 7.3 2.4 .. .. .. 1.9 33.3 2.8 .. .. .. .. ..

3.1 11.9 .. .. 21.6 .. .. 49.5 28.6 4.2 .. 40.0 .. 9.5 .. 50.0 .. 31.7 .. .. .. .. 45.3 .. .. 17.7 39.9 .. 19.9 .. .. .. .. 37.2 .. 49.8 47.3 .. 12.3 .. .. 20.3 40.8 9.3 51.4 46.9 .. .. .. 57.6 11.2 28.9 .. .. .. .. ..

Emissions of organic water pollutants

thousand kilograms per day 1990 2007a

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

122.1 .. 721.8 131.6 7.7 36.1 54.6 378.3 .. 1,455.0 15.0 123.5 .. .. 366.9 .. .. 28.9 4.3 39.8 14.7 .. .. .. 54.0 27.0 .. 37.2 .. .. .. 16.8 425.0 29.2 .. .. .. .. .. 26.4 142.3 46.7 .. .. .. 51.8 3.8 .. 10.3 .. 15.3 .. 169.0 446.7 140.6 .. ..

110.6 .. 883.0 160.8 7.7 28.4 52.7 479.2 .. 1,126.9 29.1 97.4 .. .. 319.6 .. .. 12.2 4.3 28.4 14.7 5.3 .. .. 42.2 20.3 92.8 32.7 208.3 .. .. 15.4 .. 18.8 8.8 74.0 .. .. .. 26.8 128.2 61.6 .. .. .. 46.9 7.6 153.7 13.7 .. 10.8 .. 144.6 359.7 87.7 .. 6.4

3.6

ENVIRONMENT

Water pollution Industry shares of emissions of organic water pollutants

kilograms per day per worker 1990

2007a

0.18 .. 0.18 0.16 0.27 0.19 0.16 0.13 .. 0.14 0.18 0.23 .. .. 0.12 .. .. 0.14 0.14 0.12 0.19 .. .. .. 0.15 0.20 .. 0.40 .. .. .. 0.16 0.18 0.44 .. .. .. .. .. 0.14 0.20 0.24 .. .. .. 0.20 0.15 .. 0.30 .. 0.20 .. 0.17 0.16 0.14 .. ..

0.15 .. 0.19 0.15 0.27 0.16 0.16 0.13 .. 0.15 0.18 0.24 .. .. 0.11 .. .. 0.20 0.14 0.18 0.19 0.13 .. .. 0.17 0.18 0.14 0.39 0.12 .. .. 0.17 .. 0.45 0.22 0.16 .. .. .. 0.16 0.19 0.23 .. .. .. 0.18 0.16 0.17 0.32 .. 0.28 .. 0.15 0.16 0.17 .. 0.12

% of total Stone, Food and ceramics, beverages and glass

Primary metals

Paper and pulp

Chemicals

Textiles

Wood

Other

2007a

2007a

2007a

2007a

2007a

2007a

2007a

2007a

10.6 .. 12.0 12.8 29.9 17.6 13.4 10.3 .. 11.2 13.7 8.9 .. .. 12.1 .. .. 8.5 3.8 5.8 6.0 0.3 .. .. 8.3 6.3 12.4 3.7 16.5 .. .. 5.9 .. .. 3.3 7.9 .. .. .. 7.2 14.1 8.6 .. .. .. 7.5 17.8 9.1 6.9 .. 16.7 .. 9.5 11.3 3.4 .. 10.5

15.2 .. 23.1 16.1 16.9 14.8 16.4 9.3 .. 15.0 20.8 18.7 .. .. 6.3 .. .. 24.2 9.2 21.1 25.5 2.6 .. .. 20.5 15.1 7.6 82.1 9.1 .. .. 14.7 .. 95.2 27.2 16.3 .. .. .. 19.2 18.2 31.1 .. .. .. 19.1 20.4 15.1 55.2 .. 42.6 .. 14.4 18.1 19.8 .. 6.5

3.7 .. 4.0 13.8 5.4 5.9 2.9 5.4 .. 3.6 11.5 9.3 .. .. 3.0 .. .. 17.5 7.5 4.4 12.9 0.8 .. .. 4.7 3.2 2.8 0.6 3.8 .. .. .. .. .. 9.5 6.5 .. .. .. 29.9 4.0 3.1 .. .. .. 4.3 20.5 4.3 4.0 .. 5.9 .. 2.7 5.5 5.2 .. 18.1

9.1 .. 29.2 11.2 9.1 0.8 7.9 13.6 .. 5.3 18.6 3.9 .. .. 9.3 .. .. 9.8 49.2 11.8 16.7 93.5 .. .. 17.6 44.7 58.9 7.5 6.6 .. .. 63.9 .. .. 41.6 43.5 .. .. .. 29.4 2.1 5.8 .. .. .. 2.0 2.4 55.6 4.7 .. 11.0 .. 21.6 10.3 16.3 .. 20.7

2.7 .. 1.4 7.1 13.1 1.3 1.6 3.5 .. 3.3 2.3 33.3 .. .. 4.2 .. .. 9.8 1.8 2.7 0.5 0.9 .. .. 0.9 5.8 0.3 .. 2.8 .. .. 0.4 .. .. 3.7 1.0 .. .. .. 1.6 3.1 2.0 .. .. .. 4.9 4.0 2.2 0.9 .. 3.1 .. 2.6 3.3 0.2 .. 3.7

6.4 .. 4.1 2.8 25.6 10.2 8.9 5.2 .. 7.0 6.1 2.3 .. .. 5.4 .. .. 6.3 2.2 7.7 7.5 0.5 .. .. 5.7 4.7 1.6 1.4 4.9 .. .. 3.6 .. 3.8 5.1 2.9 .. .. .. 3.9 13.4 12.2 .. .. .. 12.1 4.6 1.9 11.6 .. 9.3 .. 4.2 5.1 8.1 .. 6.7

3.3 .. 6.3 0.7 .. 3.8 1.2 2.9 .. 2.0 2.3 0.6 .. .. 0.9 .. .. 1.6 21.4 19.1 4.5 .. .. .. 11.4 2.9 6.3 1.1 7.8 .. .. 0.7 .. .. 5.4 2.0 .. .. .. 2.0 2.6 8.0 .. .. .. 6.0 4.0 0.4 1.6 .. 4.5 .. 2.1 4.9 8.5 .. 12.5

2011 World Development Indicators

49.0 .. 19.9 35.5 .. 45.5 47.6 49.6 .. 52.5 24.5 23.0 .. .. 58.9 .. .. 22.4 4.9 27.3 26.3 1.4 .. .. 30.8 17.3 10.0 3.6 48.5 .. .. 10.9 .. 0.9 4.1 19.9 .. .. .. 6.8 42.5 29.3 .. .. .. 44.2 26.3 11.2 15.0 .. 6.9 .. 42.9 41.5 38.5 .. 21.3

147

3.6

Water pollution Emissions of organic water pollutants

thousand kilograms per day 1990 2007a

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe

411.2 1,521.4 8.1 .. 6.1 .. .. 33.1 72.8 28.1 .. 260.5 348.0 .. .. .. 116.8 .. 59.7 29.1 .. 369.4 .. .. 7.0 .. 175.8 .. 3.3 .. .. 599.9 2,307.0 .. .. .. 141.0 .. 12.6 .. 29.3

222.1 1,381.7 8.1 106.6 6.6 .. .. 38.3 47.9 28.8 .. 229.6 378.8 266.1 38.6 .. 96.9 .. 80.4 12.8 30.3 581.4 .. .. 7.6 .. 346.4 .. 2.1 498.2 .. 521.7 1,850.8 .. .. .. 544.8 .. 46.5 .. ..

Industry shares of emissions of organic water pollutants

kilograms per day per worker 1990

2007a

0.12 0.16 0.37 .. 0.30 .. .. 0.09 0.13 0.13 .. 0.17 0.16 .. .. .. 0.15 .. 0.16 0.17 .. 0.15 .. .. 0.23 .. 0.18 .. 0.29 .. .. 0.16 0.14 .. .. .. 0.16 .. 0.24 .. 0.20

0.15 0.17 0.37 0.18 0.29 .. .. 0.09 0.14 0.13 .. 0.17 0.15 0.19 0.29 .. 0.14 .. 0.16 0.24 0.34 0.15 .. .. 0.29 .. 0.15 .. 0.23 0.19 .. 0.17 0.14 .. .. .. 0.14 .. 0.21 .. ..

% of total Stone, Food and ceramics, beverages and glass

Primary metals

Paper and pulp

Chemicals

Textiles

Wood

Other

2007a

2007a

2007a

2007a

2007a

2007a

2007a

2007a

7.1 11.6 9.0 11.6 23.8 .. .. 11.9 9.1 12.2 .. 10.6 10.8 9.0 7.0 .. 9.9 .. 7.3 2.0 8.6 12.4 .. .. 21.3 .. 8.6 .. 7.3 11.2 .. 13.5 13.1 .. .. .. 6.8 .. 7.4 .. ..

13.9 17.9 77.1 20.0 44.6 .. .. 5.3 10.7 7.7 .. 15.7 15.3 22.4 57.5 .. 8.6 .. 19.9 18.0 61.2 16.4 .. .. 39.3 .. 12.4 .. 34.8 19.7 .. 14.9 12.0 .. .. .. 12.7 .. 35.9 .. ..

4.0 8.3 4.3 10.7 3.9 .. .. 1.3 6.0 4.1 .. 5.2 7.9 6.3 14.2 .. 2.6 .. 11.3 8.9 1.9 4.7 .. .. 8.0 .. 6.6 .. 13.3 6.8 .. 3.6 3.9 .. .. .. 6.4 .. 14.6 .. ..

25.0 6.3 1.9 14.4 10.5 .. .. 2.3 5.0 10.8 .. 10.4 8.4 43.6 8.0 .. 1.2 .. 32.0 38.4 12.7 20.5 .. .. 7.7 .. 32.2 .. 17.2 5.6 .. 4.3 4.3 .. .. .. 40.2 .. 15.5 .. ..

4.5 8.4 .. 3.2 4.9 .. .. 0.5 7.9 4.6 .. 9.9 3.1 2.6 0.6 .. 5.3 .. 1.6 28.2 2.6 1.9 .. .. 4.8 .. 3.8 .. .. 13.9 .. 2.7 3.5 .. .. .. 1.4 .. .. .. ..

3.5 4.9 .. 6.9 6.3 .. .. 5.5 5.4 6.1 .. 6.6 8.0 4.3 1.9 .. 11.9 .. 1.9 2.7 4.8 4.2 .. .. 18.2 .. 3.8 .. 7.8 4.3 .. 12.5 8.1 .. .. .. 3.5 .. 2.1 .. ..

5.3 4.2 2.9 3.3 0.8 .. .. 0.5 4.2 4.9 .. 4.2 3.8 2.5 1.7 .. 5.6 .. 5.2 0.3 2.9 2.8 .. .. 8.5 .. 1.7 .. 2.3 2.1 .. 2.5 4.1 .. .. .. 3.3 .. 5.1 .. ..

36.8 38.4 4.8 30.0 5.3 .. .. 72.7 51.7 49.6 .. 37.4 42.7 9.3 9.1 .. 54.9 .. 20.9 1.8 5.3 37.2 .. .. 5.0 .. 30.9 .. 19.6 36.5 .. 46.1 51.1 .. .. .. 25.8 .. 19.4 .. ..

a. Data are derived using the United Nations Industrial Development Organization’s (UNIDO) industry database four-digit International Standard Classification (ISIC). Data in italics are for the most recent year available and are derived using UNIDO’s industry database at the three-digit ISIC.

148

2011 World Development Indicators

About the data

3.6

ENVIRONMENT

Water pollution Deﬁnitions

Emissions of organic pollutants from industrial

emissions of organic water pollutants. Such data are

• Emissions of organic water pollutants are mea-

activities are a major cause of degradation of water

fairly reliable because sampling techniques for mea-

sured as biochemical oxygen demand, or the amount

quality. Water quality and pollution levels are gener-

suring water pollution are more widely understood

of oxygen that bacteria in water will consume in

ally measured as concentration or load—the rate of

and much less expensive than those for air pollution.

breaking down waste, a standard water treatment

occurrence of a substance in an aqueous solution.

Hettige, Mani, and Wheeler (1998) used plant- and

test for the presence of organic pollutants. Emis-

Polluting substances include organic matter, metals,

sector-level information on emissions and employ-

sions per worker are total emissions divided by the

minerals, sediment, bacteria, and toxic chemicals.

ment from 13 national environmental protection

number of industrial workers. • Industry shares of

The table focuses on organic water pollution result-

agencies and sector-level information on output

emissions of organic water pollutants are emissions

ing from industrial activities. Because water pollu-

and employment from the United Nations Industrial

from manufacturing activities as defined by two-digit

tion tends to be sensitive to local conditions, the

Development Organization (UNIDO). Their economet-

divisions of the International Standard Industrial

national-level data in the table may not reflect the

ric analysis found that the ratio of BOD to employ-

Classification revision 3.

quality of water in specific locations.

ment in each industrial sector is about the same

The data in the table come from an international

across countries. This finding allowed the authors to

study of industrial emissions that may have been

estimate BOD loads across countries and over time.

the first to include data from developing countries

The estimated BOD intensities per unit of employ-

(Hettige, Mani, and Wheeler 1998). These data were

ment were multiplied by sectoral employment num-

updated through 2007 by the World Bank’s Develop-

bers from UNIDO’s industry database for 1980–98.

ment Research Group. Unlike estimates from earlier

These estimates of sectoral emissions were then

studies based on engineering or economic models,

used to calculate kilograms of emissions of organic

these estimates are based on actual measurements

water pollutants per day for each country and year.

of plant-level water pollution. The focus is on organic

The data in the table were derived by updating these

water pollution caused by organic waste, measured in

estimates through 2007.

terms of biochemical oxygen demand (BOD), because the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water’s dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD. Data on water pollution are more readily available than are other emissions data because most industrial pollution control programs start by regulating

Emissions of organic water pollutants vary among countries from 1990 to 2007 1990–98

Kilograms per day (millions)

3.6a 2000–07

3.0

2.0

1.5

Data sources 1.0

Data on water pollutants are from Hettige, Mani, and Wheeler, “Industrial Pollution in Economic

0.5

Development: Kuznets Revisited” (1998). The data were updated through 2007 by the World

0.0 United States

Russian Federation

Japan

Germany

Indonesia

Thailand

France

Vietnam

United Kingdom

Bank’s Development Research Group using the same methodology as the initial study. Data on

Note: Data are for the most recent year available during the period specified.

industrial sectoral employment are from UNIDO’s

Source: Table 3.6.

industry database.

2011 World Development Indicators

149

3.7

Energy production and use Energy production

Energy use

Alternative and nuclear energy production % of total

Total million metric tons of oil equivalent

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

150

1990

2008

.. 2.4 100.1 28.7 48.4 0.1 157.5 8.1 21.3 10.8 3.3 13.1 1.8 4.9 4.6 0.9 104.2 9.6 .. .. .. 11.0 273.8 .. .. 7.4 886.3 0.0 48.2 12.0 8.7 1.0 3.4 5.1 6.6 40.1 10.1 1.0 16.5 54.9 1.7 0.7 5.1 14.1 12.1 111.9 14.6 .. 1.8 186.2 4.4 9.2 3.4 .. .. 1.3 1.7

.. 1.2 162.0 105.8 82.9 0.8 302.1 11.0 58.6 23.4 4.0 14.5 1.8 16.8 4.3 1.0 228.1 10.2 .. .. 3.6 10.1 407.4 .. .. 9.0 1,993.3 0.1 93.6 22.7 13.2 2.7 11.4 3.9 5.1 32.8 26.6 1.7 28.5 87.5 3.0 0.5 4.2 29.6 16.6 136.6 13.5 .. 1.1 134.1 6.9 9.9 5.4 .. .. 2.0 2.1

2011 World Development Indicators

Total million metric tons of oil equivalent 1990

.. 2.7 22.2 5.9 46.1 7.7 86.2 24.8 25.8 12.7 45.5 48.3 1.7 2.8 7.0 1.3 140.2 28.6 .. .. .. 5.0 208.7 .. .. 13.8 863.0 8.7 24.2 11.8 0.8 2.0 4.3 9.0 16.5 48.8 17.3 4.1 6.0 31.8 2.5 0.9 9.6 14.9 28.4 223.9 1.2 .. 12.1 351.4 5.3 21.4 4.4 .. .. 1.6 2.4

2008

.. 2.1 37.1 11.0 76.4 3.0 130.1 33.2 13.4 27.9 28.1 58.6 3.0 5.7 6.0 2.1 248.5 19.8 .. .. 5.2 7.1 266.8 .. .. 31.4 2,116.4 14.1 30.8 22.3 1.4 4.9 10.3 9.1 12.1 44.6 19.0 8.2 10.3 70.7 4.9 0.7 5.4 31.7 35.3 266.5 2.1 .. 3.0 335.3 9.5 30.4 8.1 .. .. 2.8 4.6

average annual % growth 1990–2008

.. 2.0 2.8 3.6 2.5 –2.8 2.3 1.9 –2.7 4.6 -1.8 1.0 3.2 3.2 2.6 2.5 3.1 –1.2 .. .. 3.5 2.2 1.6 .. .. 4.8 4.9 2.5 0.7 3.9 3.0 5.0 5.1 1.4 –1.1 0.2 0.2 3.8 3.9 4.8 3.7 –2.1 –1.8 3.5 1.7 1.0 2.6 .. -6.8 -0.1 3.4 2.4 3.8 .. .. 3.6 3.7

Per capita kilograms of oil equivalent 1990

.. 809 878 552 1,418 2,171 5,053 3,214 3,609 110 4,470 4,844 346 416 1,627 933 938 3,277 .. .. .. 407 7,509 .. .. 1,049 760 1,534 730 319 326 658 343 1,884 1,558 4,705 3,374 556 583 551 463 276 6,101 308 5,692 3,946 1,275 .. 2,217 4,424 353 2,110 498 .. .. 219 486

2008

.. 664 1,078 609 1,915 974 6,071 3,988 1,540 175 2,907 5,471 347 587 1,588 1,102 1,295 2,595 .. .. 358 372 8,008 .. .. 1,871 1,598 2,026 684 346 378 1,084 499 2,047 1,076 4,282 3,460 820 767 867 796 138 4,026 393 6,635 4,279 1,431 .. 694 4,083 405 2,707 590 .. .. 281 632

Fossil fuel

Combustible renewables and waste

1990

2008

1990

2008

.. 76.5 99.9 25.5 88.7 97.2 93.9 79.2 100.0 45.5 95.6 76.0 4.8 69.1 93.9 66.1 51.2 84.3 .. .. .. 18.7 74.5 .. .. 75.1 75.5 100.0 67.4 11.2 35.0 48.3 23.3 86.5 64.3 93.2 89.6 74.8 79.1 94.0 31.4 19.3 100.0 5.5 55.5 58.1 32.0 .. 88.6 86.8 18.2 94.6 28.1 .. .. 19.7 30.0

.. 63.7 99.8 33.5 89.8 73.4 94.6 71.6 98.9 68.4 92.1 73.8 37.1 82.1 92.8 67.2 52.6 76.2 .. .. 29.7 23.9 74.9 .. .. 77.6 86.9 94.9 72.7 4.0 43.5 45.6 25.0 85.1 89.9 81.2 80.4 79.2 83.9 96.1 38.4 19.8 88.3 6.7 48.0 51.0 43.8 .. 66.6 80.1 27.8 92.8 42.9 .. .. 28.3 54.1

.. 13.6 0.1 73.5 3.7 0.1 4.6 10.0 0.0 53.9 0.4 1.6 94.2 27.2 2.3 33.4 34.1 0.6 .. .. .. 76.7 4.0 .. .. 19.3 23.2 0.6 22.8 84.7 59.5 36.6 73.5 3.5 35.6 0.0 6.6 24.4 13.8 3.3 48.2 80.7 2.0 93.9 16.1 4.9 62.9 .. 3.8 1.4 73.7 4.2 68.5 .. .. 77.8 62.9

.. 10.3 0.1 63.5 3.7 0.0 4.2 16.3 0.0 31.1 5.5 4.0 61.0 14.4 3.1 22.3 31.6 3.8 .. .. 69.6 71.0 4.5 .. .. 15.5 9.6 0.4 14.7 93.4 51.3 17.3 74.0 3.6 10.0 4.9 15.6 18.9 6.3 2.1 31.2 80.0 11.7 92.4 21.8 5.2 52.5 .. 12.7 7.0 66.8 3.4 53.3 .. .. 71.2 41.7

% of total energy use 1990

.. 9.2 0.1 1.1 7.5 1.7 1.5 11.0 0.2 0.6 0.0 23.1 0.0 3.6 3.8 0.1 13.1 13.9 .. .. .. 4.6 21.5 .. .. 5.5 1.3 0.0 9.8 4.1 5.3 14.4 2.6 3.6 0.1 6.9 0.3 0.7 7.2 2.7 20.3 0.0 0.0 0.6 20.9 38.7 5.2 .. 5.4 11.8 9.3 1.0 3.4 .. .. 2.5 8.2

2008

.. 15.9 0.1 3.0 5.9 26.6 1.2 10.8 1.4 0.5 0.0 20.4 0.0 3.4 6.5 0.0 14.3 22.3 .. .. 0.1 5.1 21.6 .. .. 6.6 3.5 0.0 13.0 2.9 2.3 37.3 1.6 5.1 0.1 16.0 3.3 1.8 9.4 1.9 30.3 0.0 0.2 0.9 21.2 45.3 3.7 .. 21.1 13.3 5.6 2.2 4.0 .. .. 0.6 4.3

Energy production

3.7

Energy use

ENVIRONMENT

Energy production and use

Alternative and nuclear energy production % of total

Total million metric tons of oil equivalent

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

1990

2008

14.6 291.8 172.2 179.8 104.9 3.5 0.4 25.3 0.5 75.2 0.2 90.5 9.0 28.9 22.6 .. 50.4 2.5 .. 1.1 0.1 .. .. 73.2 4.9 1.3 .. .. 48.8 .. .. .. 193.4 0.1 2.7 0.8 5.6 10.7 0.2 5.5 60.5 11.4 1.5 .. 150.5 119.1 38.3 34.3 0.6 .. 4.6 10.6 15.7 103.9 3.4 .. 26.6

10.5 468.3 347.0 326.9 117.7 1.5 3.3 26.9 0.5 88.7 0.3 148.2 15.1 20.8 44.7 .. 152.8 1.2 .. 1.8 0.2 .. .. 103.7 3.9 1.7 .. .. 93.1 .. .. .. 233.6 0.1 3.9 0.6 11.5 23.1 0.3 8.7 66.5 14.9 2.2 .. 226.8 219.7 63.5 63.3 0.7 .. 7.4 12.3 23.3 71.4 4.4 .. 124.8

Total million metric tons of oil equivalent 1990

28.7 318.9 103.9 68.3 18.1 10.0 11.5 146.6 2.8 439.3 3.3 72.7 10.9 33.2 93.1 .. 7.8 7.5 .. 7.9 2.2 .. .. 11.3 16.1 2.5 .. .. 22.0 .. .. .. 121.3 9.9 3.4 6.9 5.9 10.7 0.7 5.8 65.7 12.7 2.1 .. 70.6 21.0 3.9 43.0 1.5 .. 3.1 9.7 27.5 103.1 16.7 .. 6.9

2008

26.5 621.0 198.7 202.1 34.0 15.0 22.0 176.0 4.4 495.8 7.1 70.9 18.0 20.3 226.9 .. 26.3 2.9 .. 4.5 5.2 .. .. 18.2 9.2 3.1 .. .. 72.7 .. .. .. 180.6 3.2 3.2 15.0 9.3 15.7 1.8 9.8 79.7 16.9 3.5 .. 111.2 29.7 16.4 82.8 2.9 .. 4.4 14.7 41.1 97.9 24.2 .. 24.1

average annual % growth 1990–2008

0.0 3.6 3.5 6.1 3.8 2.8 3.5 1.4 2.7 0.8 4.3 –0.8 2.8 –2.1 4.8 .. 7.2 -3.8 .. -2.3 3.5 .. .. 2.2 –2.1 0.9 .. .. 6.1 .. .. .. 2.1 –5.2 –0.9 4.0 2.8 2.4 5.2 3.1 1.0 1.5 3.1 .. 2.5 1.6 6.6 3.7 3.4 .. 1.5 2.3 2.2 -0.5 2.6 .. 7.1

Per capita kilograms of oil equivalent 1990

2,762 375 586 1,256 957 2,849 2,462 2,584 1,167 3,556 1,028 4,450 467 1,649 2,171 .. 3,681 1,693 .. 2,941 755 .. .. 2,596 4,357 1,298 .. .. 1,215 .. .. .. 1,457 2,261 1,541 280 437 261 446 303 4,392 3,682 506 .. 725 4,952 2,105 398 618 .. 723 447 440 2,705 1,691 .. 14,732

2008

2,636 545 874 2,808 1,107 3,385 3,011 2,942 1,633 3,883 1,215 4,525 465 851 4,669 .. 9,637 542 .. 1,979 1,250 .. .. 2,895 2,733 1,520 .. .. 2,693 .. .. .. 1,698 867 1,193 474 416 316 823 340 4,845 3,967 621 .. 735 6,222 5,903 499 853 .. 699 510 455 2,567 2,274 .. 18,830

Fossil fuel

Combustible renewables and waste

1990

2008

1990

2008

81.5 55.7 54.3 98.2 98.6 84.6 97.2 93.4 82.6 84.5 98.2 96.9 17.5 93.1 83.8 .. 99.9 93.5 .. 81.8 93.5 .. .. 98.9 75.8 98.0 .. .. 88.8 .. .. .. 88.1 100.0 97.0 93.8 5.5 14.4 62.0 5.1 96.0 67.3 28.3 .. 19.3 51.9 100.0 52.8 58.4 .. 21.3 63.3 45.8 97.8 80.4 .. 99.9

77.8 71.1 65.6 99.4 99.4 90.2 96.6 89.9 88.5 83.0 98.0 98.8 16.2 88.9 81.2 .. 100.0 69.2 .. 64.3 95.3 .. .. 99.1 60.8 84.2 .. .. 95.1 .. .. .. 88.8 89.1 96.2 93.7 7.3 31.0 71.6 10.9 92.5 66.7 38.5 .. 18.3 58.6 100.0 61.8 75.7 .. 28.2 76.1 56.9 93.8 78.3 .. 100.0

2.3 41.9 43.3 1.0 0.1 1.1 0.0 0.6 17.1 1.1 0.1 0.2 77.9 2.9 0.8 .. 0.1 0.1 .. 8.4 4.6 .. .. 1.1 1.8 0.0 .. .. 9.7 .. .. .. 6.1 0.4 2.5 4.6 93.9 84.7 16.0 93.7 1.4 4.3 53.9 .. 80.2 4.9 0.0 43.7 28.3 .. 72.5 27.5 35.2 2.2 14.8 .. 0.1

5.8 26.3 26.7 0.5 0.1 1.8 0.0 3.0 11.1 1.4 0.1 0.2 76.9 5.1 1.3 .. 0.0 0.1 .. 24.8 2.7 .. .. 0.9 8.8 5.6 .. .. 4.1 .. .. .. 4.6 2.5 3.3 3.2 81.9 66.8 11.2 86.4 3.9 6.1 52.3 .. 81.2 4.6 0.0 34.8 12.3 .. 53.7 12.8 18.6 6.0 13.0 .. 0.0

% of total energy use 1990

12.8 2.4 2.4 0.8 1.2 0.6 3.1 3.9 0.3 14.4 1.8 0.9 4.5 4.0 15.4 .. 0.0 11.5 .. 4.9 1.9 .. .. 0.0 28.2 1.7 .. .. 1.6 .. .. .. 5.9 0.2 0.0 1.5 0.4 1.0 17.5 1.3 1.4 28.1 17.5 .. 0.5 49.6 0.0 3.6 12.7 .. 76.0 9.2 19.0 0.1 4.8 .. 0.0

2011 World Development Indicators

2008

15.2 2.4 7.7 0.2 0.1 2.0 4.8 5.1 0.4 15.6 1.6 0.9 7.0 6.0 17.5 .. 0.0 32.3 .. 6.1 1.0 .. .. 0.0 29.1 2.6 .. .. 0.9 .. .. .. 6.7 0.2 0.0 0.7 14.0 2.2 7.0 2.7 1.9 27.0 9.2 .. 0.4 40.7 0.0 3.4 11.8 .. 109.4 11.2 24.5 0.3 5.4 .. 0.0

151

3.7

Energy production and use Energy production

Energy use

Alternative and nuclear energy production % of total

Total million metric tons of oil equivalent 1990

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

40.8 1,293.1 .. 370.6 1.0 13.4 a .. 0.0 5.3 3.1 .. 114.5 34.6 4.2 8.8 .. 29.7 10.0 22.3 2.0 9.1 26.5 .. 1.1 12.6 5.7 25.8 74.9 .. 135.8 110.2 208.0 1,652.5 1.1 38.6 148.9 24.7 .. 9.4 4.9 8.6 8,840.1 t 172.8 4,796.0 2,168.7 2,627.2 4,966.5 1,226.1 1,769.6 609.0 558.6 349.5 475.6 3,892.9 476.5

2008

28.8 1,253.9 .. 579.0 1.2 9.9 .. 0.0 6.4 3.7 .. 163.0 30.4 5.1 34.9 .. 33.2 12.7 23.5 1.5 17.5 63.9 .. 2.1 40.0 7.5 29.0 68.6 .. 81.3 180.5 166.7 1,706.1 1.4 62.0 180.7 71.4 .. 15.3 6.8 8.5 12,357.7 t 264.0 7,284.5 4,001.4 3,284.5 7,544.8 2,658.9 1,772.9 922.0 856.3 573.6 810.3 4,843.0 463.1

a. Includes Kosovo and Montenegro.

152

2011 World Development Indicators

Total million metric tons of oil equivalent 1990

2008

62.3 39.4 879.2 686.8 .. .. 59.0 161.6 1.7 2.9 19.3a 16.0 .. .. 11.5 18.5 21.3 18.3 5.7 7.7 .. .. 90.9 134.5 90.1 138.8 5.5 8.9 10.6 15.4 .. .. 47.2 49.6 24.0 26.7 11.4 19.7 5.3 2.5 9.7 19.0 42.0 107.2 .. .. 1.3 2.6 6.0 19.4 4.9 9.2 52.8 98.5 19.6 18.8 .. .. 251.8 136.1 19.9 58.4 205.9 208.5 1,915.0 2,283.7 2.3 4.2 46.4 50.5 43.6 64.1 24.3 59.4 .. .. 2.5 7.5 5.4 7.4 9.3 9.5 8,569.9 t 11,899.4 t 200.3 279.9 3,864.0 6,002.2 1,993.4 3,842.7 1,871.1 2,161.8 4,049.8 6,266.2 1,139.4 2,655.4 1,577.0 1,215.0 454.0 729.2 185.5 431.3 389.2 756.8 310.5 497.4 4,544.3 5,672.5 1,059.7 1,226.5

average annual % growth 1990–2008

Per capita kilograms of oil equivalent 1990

2008

–1.9 2,683 1,830 –1.1 5,929 4,838 .. .. .. 4.8 3,631 6,514 3.5 224 234 0.2 2,550 a 2,181 .. .. .. 1.8 3,760 3,828 –0.1 4,037 3,385 2.0 2,858 3,827 .. .. .. 2.2 2,581 2,756 3.0 2,320 3,047 3.3 322 443 2.6 392 372 .. .. .. 0.4 5,514 5,379 0.6 3,581 3,491 2.8 895 957 –3.1 1,001 365 4.1 382 446 5.1 742 1,591 .. .. .. 4.3 322 397 7.6 4,899 14,557 3.7 607 889 3.6 941 1,333 1.5 5,352 3,730 .. .. .. –3.0 4,852 2,943 5.4 10,645 13,030 0.1 3,597 3,395 1.1 7,672 7,503 1.8 725 1,254 0.6 2,261 1,849 1.6 2,206 2,295 5.2 367 689 .. .. .. 6.2 204 326 1.7 683 583 –0.1 889 763 1.9 w 1,669 w 1,835 w 2.1 380 357 2.5 1,029 1,261 3.6 679 1,019 1.0 2,283 2,177 2.5 966 1,157 4.6 716 1,380 –1.1 4,038 3,030 2.5 1,044 1,290 4.7 814 1,329 3.6 348 495 2.6 676 678 1.4 4,649 5,131 1.0 3,527 3,763

Fossil fuel 1990

96.1 93.4 .. 100.0 43.2 90.6a .. 100.0 81.6 71.3 .. 86.1 77.4 24.1 17.5 .. 37.3 59.3 97.9 71.3 6.9 63.9 .. 15.0 99.2 87.0 81.8 100.0 .. 91.8 100.0 90.7 86.4 58.7 99.2 91.5 20.4 .. 97.0 15.6 44.8 81.0 w 39.8 78.9 70.1 88.2 77.4 71.5 93.0 71.2 97.2 53.8 41.2 84.2 79.8

2008

Combustible renewables and waste 1990

2008

% of total energy use 1990

79.4 1.0 10.3 1.6 90.9 1.4 0.9 5.2 .. .. .. .. 100.0 0.0 0.0 0.0 57.3 56.8 41.7 0.0 89.5 6.0a 5.0 4.2a .. .. .. .. 100.0 0.0 0.0 0.0 70.0 0.8 3.7 15.5 69.4 4.7 6.7 25.6 .. .. .. .. 87.2 11.5 10.4 2.5 81.7 4.5 4.2 18.1 43.4 71.0 52.6 4.9 31.2 81.8 68.0 0.8 .. .. .. .. 33.1 11.7 20.0 50.9 52.7 4.8 8.1 36.7 98.7 0.0 0.0 2.1 42.3 0.0 0.0 26.7 10.6 91.7 88.2 1.4 80.6 34.9 18.7 1.0 .. .. .. .. 14.3 82.8 83.1 0.6 99.9 0.8 0.1 0.0 86.3 12.9 13.6 0.1 90.6 13.7 4.9 4.6 100.0 0.0 0.0 0.3 .. .. .. .. 81.8 0.1 0.7 8.2 100.0 0.0 0.0 0.0 90.2 0.3 2.2 8.5 85.0 3.3 3.7 10.3 64.9 24.3 23.9 26.8 98.1 0.0 0.0 1.2 87.6 1.2 0.8 7.3 54.0 77.7 41.8 1.9 .. .. .. .. 99.0 3.1 1.0 0.0 7.5 74.3 81.0 12.7 26.1 50.9 65.3 4.0 81.1 w 10.1 w 9.8 w 8.7 w 29.2 56.0 66.2 4.4 81.5 16.9 13.3 4.1 79.0 27.1 16.9 2.9 86.0 6.1 6.8 5.4 79.7 18.4 15.2 4.1 83.7 26.6 12.4 1.9 89.7 1.5 1.7 5.3 72.4 19.7 16.8 9.2 98.3 1.7 1.1 1.1 68.9 43.6 28.5 2.5 39.8 56.6 57.7 2.3 82.6 2.8 3.9 12.8 75.0 3.2 5.9 16.7

2008

11.2 8.4 .. 0.0 0.7 5.4 .. 0.0 26.0 25.6 .. 2.6 14.6 4.0 0.8 .. 45.9 39.6 1.3 54.7 1.2 0.6 .. 0.3 0.0 0.1 4.6 0.0 .. 17.9 0.0 7.1 11.2 9.3 1.9 11.7 3.8 .. 0.0 11.3 3.9 9.1 w 4.4 5.2 4.2 7.0 5.2 4.0 8.7 10.8 0.6 2.5 2.5 13.3 18.6

About the data

3.7

ENVIRONMENT

Energy production and use Deﬁnitions

In developing economies growth in energy use is

Data sources). All forms of energy—primary energy

• Energy production refers to forms of primary

closely related to growth in the modern sectors—

and primary electricity—are converted into oil equiva-

energy—petroleum (crude oil, natural gas liquids,

industry, motorized transport, and urban areas—

lents. A notional thermal efficiency of 33 percent is

and oil from nonconventional sources), natural gas,

but energy use also reflects climatic, geographic,

assumed for converting nuclear electricity into oil

solid fuels (coal, lignite, and other derived fuels),

and economic factors (such as the relative price

equivalents and 100 percent efficiency for converting

and combustible renewables and waste—and pri-

of energy). Energy use has been growing rapidly in

hydroelectric power.

mary electricity, all converted into oil equivalents

low- and middle-income economies, but high-income

The IEA makes these estimates in consultation

(see About the data). • Energy use refers to the use

economies still use almost five times as much energy

with national statistical offices, oil companies, elec-

of primary energy before transformation to other

on a per capita basis.

tric utilities, and national energy experts. The IEA

end-use fuels, which is equal to indigenous produc-

Energy data are compiled by the International

occasionally revises its time series to reflect politi-

tion plus imports and stock changes, minus exports

Energy Agency (IEA). IEA data for economies that

cal changes, and energy statistics undergo contin-

and fuels supplied to ships and aircraft engaged in

are not members of the Organisation for Economic

ual changes in coverage or methodology as more

international transport (see About the data). • Fos-

Co-operation and Development (OECD) are based

detailed energy accounts become available. Breaks

sil fuel comprises coal, oil, petroleum, and natural

on national energy data adjusted to conform to

in series are therefore unavoidable.

gas products. • Combustible renewables and waste

annual questionnaires completed by OECD member

comprise solid biomass, liquid biomass, biogas,

governments.

industrial waste, and municipal waste. • Alternative

Total energy use refers to the use of primary energy

and nuclear energy production is noncarbohydrate

before transformation to other end-use fuels (such

energy that does not produce carbon dioxide when

as electricity and refined petroleum products). It

generated. It includes hydropower and nuclear, geo-

includes energy from combustible renewables and

thermal, and solar power, among others.

waste—solid biomass and animal products, gas and liquid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity. Data for combustible renewables and waste are often based on small surveys or other incomplete information and thus give only a broad impression of developments and are not strictly comparable across countries. The IEA reports include country notes that explain some of these differences (see

A person in a high-income economy uses more than 14 times as much energy on average as a person in a low3.7a income economy in 2008 Energy use per capita (thousands of kilograms of oil equivalent)

Fossil fuels are still the primary global energy source in 2008

Fossil fuel 1990

2008

6

Percent 100

Combustible renewables and waste

3.7b

Alternative and nuclear energy

80

5 4

60

3

40

Data sources

2

Data on energy production and use are from IEA 20

1

electronic files and are published in IEA’s annual publications, Energy Statistics and Balances of

0 High income

Upper middle income

Source: Table 3.7.

Middle income

Lower middle income

Low income

World

0 High income

Upper middle income

Lower middle income

Low income

World

Non-OECD Countries, Energy Statistics of OECD Countries, and Energy Balances of OECD Countries.

Source: Table 3.7.

2011 World Development Indicators

153

3.8

Energy dependency and efficiency and carbon dioxide emissions Net energy importsa

GDP per unit of energy use

% of energy use

2005 PPP $ per kilogram of oil equivalent

1990

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

154

.. 8 –351 –387 –5 98 –83 67 17 16 93 73 –7 –77 34 28 26 66 .. .. .. –120 –31 .. .. 46 –3 100 –99 –2 –997 49 22 43 60 18 42 75 –175 –72 31 19 47 5 57 50 –1,139 .. 85 47 17 57 24 .. .. 20 29

2008

.. 45 –337 –865 –9 73 –132 67 –338 16 86 75 39 –195 28 53 8 48 .. .. 30 –42 -53 .. .. 71 6 100 –204 –2 -868 45 –11 57 58 26 –40 79 –176 –24 38 20 22 7 53 49 –552 .. 64 60 27 68 33 .. .. 28 55

2011 World Development Indicators

1990

.. 4.8 7.1 5.8 5.3 1.4 4.7 8.0 1.3 6.2 1.5 5.2 3.2 7.0 .. 7.6 7.7 2.3 .. .. .. 5.1 3.6 .. .. 6.3 1.4 15.5 8.4 1.9 10.7 9.5 5.5 7.1 .. 3.5 7.5 6.7 9.4 5.8 8.0 1.9 1.7 1.8 4.1 6.3 11.8 .. 2.4 5.8 2.5 8.3 6.7 .. .. 6.4 5.5

2008

.. 11.0 6.8 8.8 6.9 5.8 5.7 9.1 5.3 7.1 4.0 6.1 3.9 6.7 4.7 11.6 7.4 4.6 .. .. 5.0 5.4 4.5 .. .. 7.2 3.6 20.0 12.0 0.8 9.6 9.6 3.1 8.5 .. 5.4 9.9 9.2 9.9 5.8 7.9 3.8 4.7 2.0 5.1 7.4 9.4 .. 6.6 8.3 3.4 10.0 7.4 .. .. 3.7 5.7

Carbon dioxide emissions

Total million metric tons 1990

2.7 7.5 78.8 4.4 112.5 3.7 292.9 60.9 44.1 15.5 98.5 107.5 0.7 5.5 4.7 2.2 208.7 76.6 0.6 0.3 0.5 1.7 449.7 0.2 0.1 34.9 2,458.7 27.6 57.3 4.1 1.2 3.0 5.8 25.0 33.3 162.6 50.4 9.6 16.8 75.9 2.6 .. 28.2 3.0 50.9 398.7 6.1 0.2 15.3 960.2 3.9 72.7 5.1 1.1 0.3 1.0 2.6

2007

0.7 4.2 140.0 24.7 183.6 5.1 373.7 68.7 31.7 43.7 66.7 103.0 3.9 13.2 29.0 5.0 368.0 51.7 1.7 0.2 4.4 6.2 556.9 0.3 0.4 71.6 6,533.0 39.9 63.4 2.4 1.6 8.1 6.4 24.8 27.0 124.9 50.0 20.7 30.0 184.5 6.7 0.6 20.5 6.5 64.1 371.5 2.0 0.4 6.0 787.3 9.8 98.0 12.9 1.4 0.3 2.4 8.8

Carbon intensity kilograms per kilogram of oil equivalent energy use

Per capita metric tons

kilograms per 2005 PPP $ of GDP

1990

2007

1990

2007

1990

2007

.. 2.8 3.6 0.8 2.4 0.5 3.4 2.5 1.9 1.2 2.6 2.2 0.4 2.0 1.0 1.7 1.5 2.7 .. .. .. 0.3 2.2 .. .. 2.5 2.8 3.1 2.4 0.3 1.5 1.5 1.3 2.8 2.0 3.3 2.9 2.3 2.8 2.4 1.1 .. 2.9 0.2 1.8 1.8 5.2 .. 1.4 2.8 0.7 3.4 1.1 .. .. 0.6 1.1

.. 2.0 3.8 2.3 2.5 1.8 3.0 2.1 2.7 1.7 2.4 1.8 1.3 2.4 5.2 2.5 1.6 2.6 .. .. 0.9 0.8 2.1 .. .. 2.3 3.3 2.9 2.2 0.1 1.3 1.7 0.6 2.7 2.7 2.7 2.5 2.6 2.5 2.7 1.4 0.8 3.6 0.3 1.8 1.4 1.1 .. 1.8 2.4 1.0 3.0 1.6 .. .. 0.9 1.9

0.1 2.3 3.1 0.4 3.5 1.2 17.2 7.9 7.0 0.1 10.9 10.8 0.1 0.8 1.6 1.6 1.4 8.8 0.1 0.1 0.0 0.1 16.2 0.1 0.0 2.6 2.2 4.8 1.7 0.1 0.5 1.0 0.5 5.2 3.1 15.7 9.8 1.3 1.6 1.3 0.5 .. 18.0 0.1 10.2 7.0 6.6 0.2 3.2 12.0 0.3 7.2 0.6 0.2 0.2 0.1 0.5

0.0 1.4 4.1 1.4 4.6 1.6 17.7 8.3 3.7 0.3 6.9 9.7 0.5 1.4 7.7 2.6 1.9 6.8 0.1 0.0 0.3 0.3 16.9 0.1 0.0 4.3 5.0 5.8 1.4 0.0 0.4 1.8 0.3 5.6 2.4 12.1 9.1 2.1 2.2 2.3 1.1 0.1 15.2 0.1 12.1 6.0 1.4 0.2 1.4 9.6 0.4 8.8 1.0 0.1 0.2 0.2 1.2

.. 0.6 0.5 0.1 0.5 0.4 0.7 0.3 1.5 0.2 1.7 0.4 0.1 0.3 .. 0.2 0.2 1.2 0.1 0.1 .. 0.1 0.6 0.1 0.0 0.4 2.0 0.2 0.3 0.2 0.1 0.2 0.2 0.4 .. 1.0 0.4 0.3 0.3 0.4 0.1 .. 1.8 0.1 0.4 0.3 0.4 0.2 0.6 0.4 0.3 0.4 0.2 0.2 0.2 0.1 0.2

0.0 0.2 0.6 0.3 0.4 0.3 0.5 0.2 0.5 0.2 0.7 0.3 0.3 0.4 1.1 0.2 0.2 0.6 0.1 0.1 0.2 0.2 0.5 0.1 0.0 0.3 0.9 0.1 0.2 0.1 0.1 0.2 0.2 0.3 .. 0.5 0.3 0.3 0.3 0.5 0.2 0.2 0.8 0.1 0.4 0.2 0.1 0.2 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.3

Net energy importsa

GDP per unit of energy use

% of energy use

2005 PPP $ per kilogram of oil equivalent

1990

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

49 8 –66 –163 –480 65 96 83 83 83 95 –24 18 13 76 .. –544 67 .. 86 94 .. .. –546 69 49 .. .. –122 .. .. .. –60 99 20 89 5 0 67 5 8 10 29 .. –113 –467 –888 20 59 .. –49 –9 43 –1 80 .. –286

2008

60 25 –75 –62 –246 90 85 85 88 82 96 –109 16 -3 80 .. –481 58 .. 60 96 .. .. –469 58 45 .. .. –28 .. .. .. -29 97 –23 96 –23 –47 82 11 16 12 39 .. –104 –640 –286 24 76 .. –69 16 43 27 82 .. –418

1990

4.4 3.3 3.6 5.0 .. 6.2 7.3 9.2 5.1 7.3 3.2 1.6 3.0 .. 5.2 .. 2.8 1.5 .. 3.4 7.5 .. .. .. 2.9 6.4 .. .. 5.5 .. .. .. 6.9 1.7 1.4 9.7 0.9 .. 9.4 2.3 6.0 5.1 3.7 .. 2.0 6.5 7.1 4.2 9.8 .. 5.5 10.0 5.4 3.0 9.6 .. ..

2008

6.8 5.1 4.2 3.7 2.9 11.6 8.5 9.6 4.4 8.1 4.2 2.3 3.1 .. 5.5 .. 4.8 3.8 .. 7.9 8.8 .. .. 5.2 6.4 5.8 .. .. 4.9 .. .. .. 7.9 3.1 2.8 8.4 1.9 .. 7.3 3.0 7.9 6.3 4.1 .. 2.6 7.9 4.0 4.7 13.8 .. 6.2 15.4 7.1 6.4 9.7 .. 4.5

3.8

ENVIRONMENT

Energy dependency and efficiency and carbon dioxide emissions Carbon dioxide emissions

Total million metric tons 1990

63.4 690.0 149.4 227.0 52.5 30.3 33.5 424.7 8.0 1,152.3 10.4 261.1 5.8 244.6 241.5 .. 40.7 11.0 0.2 13.3 9.1 .. 0.5 40.3 22.1 10.8 1.0 0.6 56.5 0.4 2.7 1.5 357.2 21.0 10.0 23.5 1.0 4.3 0.0 0.6 164.0 23.9 2.6 1.0 45.3 31.3 10.3 68.5 3.1 2.1 2.3 21.1 44.5 347.6 44.3 .. 11.8

2007

56.4 1,611.0 396.8 495.6 100.0 44.3 66.7 456.1 14.0 1,253.5 21.4 227.2 11.2 70.7 502.9 .. 86.1 6.1 1.5 7.8 13.3 .. 0.7 57.3 15.3 11.3 2.2 1.1 194.3 0.6 1.9 3.9 471.1 4.7 10.6 46.4 2.6 13.2 3.0 3.4 173.1 32.6 4.6 0.9 95.2 42.7 37.3 156.3 7.2 3.4 4.1 43.0 70.9 317.1 58.1 .. 63.0

Carbon intensity kilograms per kilogram of oil equivalent energy use

Per capita metric tons

kilograms per 2005 PPP $ of GDP

1990

2007

1990

2007

1990

2007

2.2 2.2 1.5 3.3 2.9 3.0 2.9 2.9 2.9 2.6 3.2 4.0 0.5 7.4 2.6 .. 5.2 1.6 .. 1.9 4.0 .. .. 3.6 1.5 6.4 .. .. 2.5 .. .. .. 2.9 2.4 2.9 3.4 0.2 0.4 0.0 0.1 2.5 1.8 1.3 .. 0.6 1.5 2.4 1.6 2.1 .. 0.7 2.2 1.6 3.4 2.6 .. 1.7

2.1 2.7 2.1 2.7 3.0 2.9 3.0 2.6 2.8 2.4 3.0 3.4 0.6 3.8 2.3 .. 3.4 2.1 .. 1.7 3.3 .. .. 3.2 1.7 3.7 .. .. 2.7 .. .. .. 2.6 1.4 3.4 3.2 0.3 0.8 1.9 0.4 2.2 1.9 1.3 .. 0.9 1.6 2.4 1.9 2.6 .. 1.0 3.1 1.8 3.3 2.3 .. 2.8

6.1 0.8 0.8 4.2 2.8 8.6 7.2 7.5 3.3 9.3 3.3 18.0 0.2 12.1 5.6 .. 19.2 2.8 0.1 5.6 3.1 .. 0.2 9.2 6.8 8.3 0.1 0.1 3.1 0.0 1.3 1.4 4.3 5.4 4.5 0.9 0.1 0.1 0.0 0.0 11.0 6.9 0.6 0.1 0.5 7.4 5.6 0.6 1.3 0.5 0.5 1.0 0.7 9.1 4.5 .. 25.2

5.6 1.4 1.8 7.0 3.3 10.2 9.3 7.7 5.2 9.8 3.8 14.7 0.3 3.0 10.4 .. 32.3 1.2 0.3 3.4 3.2 .. 0.2 9.3 4.5 5.5 0.1 0.1 7.3 0.0 0.6 3.1 4.5 1.3 4.0 1.5 0.1 0.3 1.5 0.1 10.6 7.7 0.8 0.1 0.6 9.1 13.7 1.0 2.2 0.5 0.7 1.5 0.8 8.3 5.5 .. 55.4

0.5 0.7 0.4 0.7 .. 0.5 0.4 0.3 0.6 0.4 1.0 2.5 0.2 .. 0.5 .. 0.6 1.1 0.1 0.6 0.5 .. 0.5 .. 0.5 1.0 0.1 0.1 0.5 0.1 0.9 0.2 0.4 1.4 2.0 0.4 0.2 .. 0.0 0.0 0.4 0.4 0.3 0.2 0.3 0.2 0.4 0.4 0.2 0.3 0.1 0.2 0.3 1.1 0.3 .. ..

0.3 0.5 0.5 0.7 1.1 0.2 0.4 0.3 0.7 0.3 0.8 1.4 0.2 .. 0.4 .. 0.7 0.6 0.1 0.2 0.3 .. 0.5 0.6 0.3 0.7 0.1 0.1 0.6 0.0 0.3 0.3 0.3 0.5 1.3 0.4 0.2 .. 0.2 0.1 0.3 0.3 0.3 0.1 0.3 0.2 0.6 0.4 0.2 0.3 0.2 0.2 0.3 0.5 0.2 .. 0.7

2011 World Development Indicators

155

3.8

Energy dependency and efficiency and carbon dioxide emissions Net energy importsa

GDP per unit of energy use

% of energy use

2005 PPP $ per kilogram of oil equivalent

1990

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

34 –47 .. –528 43 31 .. 100 75 46 .. –26 62 24 17 .. 37 59 –96 62 7 37 .. 17 –111 –16 51 –281 .. 46 –454 –1 14 49 17 –242 -2 .. –273 9 8 –3 c w 14 –24 –9 –40 –23 –8 –12 –34 –201 10 –54 15 55

2008

27 –83 .. –258 57 38 .. 100 65 53 .. –21 78 43 –127 .. 33 52 –19 40 8 40 .. 17 –106 18 71 –265 .. 40 –209 20 25 67 –23 –182 –20 .. –104 8 10 –4c w 6 –21 –4 –52 –20 0 –46 –26 –99 24 –63 15 62

Carbon dioxide emissions

Total million metric tons

1990

2008

1990

2007

2.9 2.1 .. 5.3 6.3 4.6 .. 6.2 3.1 5.7 .. 3.1 8.5 6.3 2.5 .. 4.5 9.3 3.3 3.1 2.2 5.3 .. 2.7 2.2 6.6 8.3 0.7 .. 1.7 4.8 6.6 4.2 10.1 0.9 4.3 2.5 .. 8.7 1.8 .. 4.2 w 2.6 3.0 2.4 3.7 3.0 2.0 2.2 6.9 5.7 3.5 2.8 5.3 6.6

6.4 3.1 .. 3.3 7.1 4.7 .. 12.5 6.1 7.1 .. 3.5 9.3 9.5 5.3 .. 6.4 10.9 4.4 4.8 2.6 4.7 .. 1.9 1.7 8.3 8.9 1.7 .. 2.3 4.2 10.0 5.8 9.3 1.3 5.1 3.7 .. 6.8 2.1 .. 5.5 w 3.2 4.4 4.0 5.2 4.4 3.8 3.6 7.7 4.7 5.2 3.2 6.6 8.2

158.7 2,073.5 0.7 214.9 3.2 45.3 b 0.4 46.9 44.3 12.3 0.0 333.2 227.4 3.8 5.6 0.4 51.7 42.9 37.4 21.3 2.4 95.8 .. 0.8 16.9 13.3 150.7 28.0 0.8 611.0 54.8 569.8 4,861.0 4.0 113.9 122.1 21.4 .. 10.1 2.4 15.5 22,529.9d t 357.6 9,758.0 4,772.5 4,984.4 10,115.2 3,091.2 4,214.9 1,017.3 578.7 781.4 465.1 11,669.7 2,595.7

94.1 1,536.1 0.7 402.1 5.5 53.5b 1.3 54.1 37.0 15.1 0.6 433.2 359.0 12.3 11.5 1.1 49.2 38.0 69.8 7.2 6.0 277.3 0.2 1.3 37.0 23.8 288.4 45.8 3.2 317.3 135.4 539.2 5,832.2 6.2 116.0 165.4 111.3 2.3 22.0 2.7 9.6 30,649.4d t 228.2 15,574.9 10,391.5 5,175.3 15,802.5 7,693.8 2,897.1 1,538.1 1,177.0 1,828.9 679.5 13,761.0 2,656.8

Carbon intensity kilograms per kilogram of oil equivalent energy use

Per capita metric tons

1990

2007

1990

2007

2.5 2.7 .. 3.6 1.9 1.5b .. 4.1 2.6 3.2 .. 3.7 2.5 0.7 0.5 .. 1.1 1.8 3.3 4.3 0.2 2.3 .. 0.6 2.8 2.7 2.9 1.6 .. 2.7 2.8 2.8 2.5 1.8 2.8 2.8 0.9 .. 3.3 0.5 1.7 2.6d w 2.2 2.6 2.4 2.7 2.5 2.7 2.7 2.2 3.1 2.0 1.7 2.6 2.4

2.4 2.3 .. 2.7 2.0 .. .. 2.0 2.1 2.1 .. 3.2 2.5 1.3 0.8 .. 1.0 1.5 3.6 1.9 0.3 2.7 .. 0.5 2.4 2.7 2.9 2.5 .. 2.3 2.6 2.6 2.5 2.0 2.4 2.6 2.0 .. 3.0 0.4 1.0 2.5d w 1.0 2.7 2.9 2.5 2.7 3.1 2.4 2.2 2.9 2.5 1.6 2.4 2.2

6.8 15.8 0.1 13.2 0.4 6.4b 0.1 15.4 10.4 9.1 0.0 9.5 5.9 0.2 0.2 0.5 6.0 6.4 2.9 4.5 0.1 1.7 .. 0.2 13.9 1.6 2.7 8.6 0.0 13.3 29.3 10.0 19.5 1.3 6.3 6.2 0.3 .. 0.8 0.3 1.5 4.3d w 0.7 2.6 1.6 6.1 2.4 1.9 10.7 2.3 2.5 0.7 0.9 11.9 8.6

4.4 10.8 0.1 16.6 0.5 6.3b 0.2 11.8 6.8 7.5 0.1 9.0 8.0 0.6 0.3 0.9 5.4 5.0 3.5 1.1 0.1 4.1 0.2 0.2 27.9 2.3 4.0 9.2 0.1 6.8 31.0 8.8 19.3 1.9 4.3 6.0 1.3 0.6 1.0 0.2 0.8 4.6d w 0.3 3.3 2.8 5.3 2.9 4.0 7.2 2.7 3.7 1.2 0.8 12.5 8.2

kilograms per 2005 PPP $ of GDP 1990

2007

0.9 1.2 0.1 0.7 0.3 .. 0.1 0.7 0.8 0.6 .. 1.2 0.3 0.1 0.2 0.1 0.2 0.2 1.0 1.5 0.1 0.4 .. 0.2 1.3 0.4 0.3 2.3 0.1 1.6 0.6 0.4 0.6 0.2 3.1 0.6 0.4 .. 0.5 0.2 .. 0.6d w 0.8 0.8 1.0 0.7 0.8 1.4 1.2 0.3 0.6 0.6 0.6 0.5 0.4

a. Negative values indicate that a country is a net exporter. b. Includes Kosovo and Montenegro. c. Deviation from zero is due to statistical errors and changes in stock. d. Includes emissions not allocated to specific countries.

156

2011 World Development Indicators

0.4 0.8 0.1 0.8 0.3 .. 0.3 0.2 0.4 0.3 .. 1.0 0.3 0.2 0.2 0.2 0.2 0.1 0.8 0.6 0.1 0.6 0.3 0.3 1.2 0.3 0.3 1.6 0.1 1.0 0.6 0.3 0.4 0.2 1.9 0.5 0.5 .. 0.4 0.2 .. 0.5d w 0.3 0.6 0.7 0.5 0.6 0.8 0.7 0.3 0.6 0.5 0.4 0.4 0.3

About the data

3.8

ENVIRONMENT

Energy dependency and efficiency and carbon dioxide emissions Deﬁnitions

Because commercial energy is widely traded, its pro-

elemental carbon, were converted to actual carbon

• Net energy imports are estimated as energy use

duction and use need to be distinguished. Net energy

dioxide mass by multiplying them by 3.664 (the ratio

less production, both measured in oil equivalents.

imports show the extent to which an economy’s use

of the mass of carbon to that of carbon dioxide).

• GDP per unit of energy use is the ratio of gross

exceeds its production. High-income economies are

Although estimates of global carbon dioxide emis-

domestic product (GDP) per kilogram of oil equiva-

net energy importers; middle-income economies are

sions are probably accurate within 10 percent (as cal-

lent of energy use, with GDP converted to 2005

their main suppliers.

culated from global average fuel chemistry and use),

international dollars using purchasing power parity

The ratio of gross domestic product (GDP) to energy

country estimates may have larger error bounds.

(PPP) rates. An international dollar has the same

use indicates energy efficiency. To produce compa-

Trends estimated from a consistent time series tend

purchasing power over GDP that a U.S. dollar has

rable and consistent estimates of real GDP across

to be more accurate than individual values. Each year

in the United States. Energy use refers to the use

economies relative to physical inputs to GDP—that

the CDIAC recalculates the entire time series since

of primary energy before transformation to other

is, units of energy use—GDP is converted to 2005

1949, incorporating recent findings and corrections.

end-use fuel, which is equal to indigenous produc-

international dollars using purchasing power parity

Estimates exclude fuels supplied to ships and aircraft

tion plus imports and stock changes minus exports

(PPP) rates. Differences in this ratio over time and

in international transport because of the difficulty of

and fuel supplied to ships and aircraft engaged in

across economies reflect structural changes in an

apportioning the fuels among benefiting countries.

international transport (see About the data for table

economy, changes in sectoral energy efficiency, and

The ratio of carbon dioxide per unit of energy shows

3.7). • Carbon dioxide emissions are emissions from

differences in fuel mixes.

carbon intensity, which is the amount of carbon diox-

the burning of fossil fuels and the manufacture of

Carbon dioxide emissions, largely by-products of

ide emitted as a result of using one unit of energy in

cement and include carbon dioxide produced during

energy production and use (see table 3.7), account

the process of production. The proportion of carbon

consumption of solid, liquid, and gas fuels and gas

for the largest share of greenhouse gases, which are

dioxide per unit of GDP indicates how clean produc-

flaring.

associated with global warming. Anthropogenic car-

tion processes are.

bon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced. The U.S. Department of Energy’s Carbon Dioxide Information Analysis Center (CDIAC) calculates annual anthropogenic emissions from data on fossil fuel consumption (from the United Nations Statistics Division’s World Energy Data Set) and world cement manufacturing (from the U.S. Bureau of Mines’s Cement Manufacturing Data Set). Carbon dioxide emissions, often calculated and reported as

3.8a

High-income economies depend on imported energy Net energy imports (% of energy use)

1990

2008

Low income Lower middle income Upper middle income

Data sources High income

Data on energy use are from the electronic files of the International Energy Agency. Data on car-

Euro area –60

–40

–20

0

20

Note: Negative values indicate that the income group is a net energy exporter. Source: Table 3.8.

40

60

80

bon dioxide emissions are from the CDIAC, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States.

2011 World Development Indicators

157

3.9

Trends in greenhouse gas emissions Carbon dioxide emissions

average annual % growtha % changeb 1990– 1990– 2007 2007

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

158

–7.8 2.5 3.7 10.1 2.4 1.2 1.4 1.1 –2.2 6.5 –2.6 –0.3 9.1 4.0 14.1 4.1 3.3 –2.1 5.9 –4.7 16.0 4.2 1.5 1.0 10.5 4.6 5.2 2.0 –0.3 –3.7 –0.8 5.1 1.5 1.8 –1.3 –1.1 –1.0 4.7 2.9 5.2 4.7 7.4 –2.1 4.6 1.3 –0.3 –6.2 4.2 –6.2 –1.1 4.9 2.2 6.0 1.5 –0.4 7.6 7.4

–73.3 –43.3 77.6 459.0 63.1 21.8 27.6 12.7 –36.2 181.7 –39.9 –4.2 442.1 139.6 315.2 130.2 76.3 –32.5 188.8 –41.0 884.6 254.9 23.8 27.8 162.5 105.4 165.7 44.5 10.6 –40.2 33.6 174.7 10.1 –0.8 –18.9 –23.2 –0.8 116.9 78.1 143.2 155.9 .. –27.5 115.7 26.0 –6.8 –66.6 107.7 –65.1 –18.0 149.5 34.9 154.2 31.6 13.0 141.3 240.7

2011 World Development Indicators

Methane emissions Total thousand metric tons of carbon dioxide equivalent 2005

.. 2,407 54,219 45,409 101,821 2,962 126,488 8,515 36,607 92,414 11,498 10,063 4,080 30,350 2,741 4,501 492,160 10,867 .. .. 20,215 18,518 89,338 .. .. 18,149 1,333,098 2,820 58,108 56,445 5,584 2,580 10,997 3,864 9,455 11,497 7,935 6,081 17,125 46,996 3,131 2,467 2,108 52,243 9,742 77,252 8,218 .. 4,410 67,582 8,990 7,289 8,306 .. .. 4,006 5,191

Nitrous oxide emissions

% of total % changeb From energy processes Agricultural 1990– 2005 2005 2005

.. –5.1 33.1 –8.3 –8.3 2.5 9.7 –15.0 110.7 6.5 –32.8 –21.8 –15.8 30.9 –53.5 –22.6 56.4 –24.8 .. .. 35.0 37.1 30.8 .. .. 49.8 28.5 84.0 13.5 –41.6 –10.4 –31.3 –2.2 –60.5 –21.0 –40.3 –0.5 3.8 31.2 68.8 18.0 30.9 –36.8 32.8 –2.8 –0.3 1.4 .. –12.4 –44.8 24.2 2.1 74.7 .. .. 34.9 31.5

.. 20.0 83.2 15.6 18.9 50.8 29.7 21.7 82.0 10.0 7.6 11.6 15.6 25.6 46.7 8.6 7.6 13.0 .. .. 4.9 39.1 32.2 .. .. 24.4 45.8 26.7 19.9 10.2 32.2 9.5 16.9 57.0 11.2 49.4 16.4 7.8 31.2 50.7 12.4 11.2 42.3 14.3 7.4 44.3 90.4 .. 36.1 32.1 23.3 26.3 12.4 .. .. 12.1 7.2

.. 70.8 8.2 27.9 70.6 36.7 55.1 48.6 13.6 70.5 70.9 56.7 47.8 34.1 42.4 84.1 61.1 18.9 .. .. 76.1 42.4 29.3 .. .. 39.4 38.8 0.0 68.0 23.1 31.9 67.2 17.4 33.3 62.4 33.6 65.2 63.7 57.8 31.7 53.1 73.2 30.5 72.5 20.7 47.7 1.1 .. 50.8 43.8 39.5 50.0 48.8 .. .. 56.2 78.4

Total thousand metric tons of carbon dioxide equivalent 2005

.. 1,036 4,898 38,881 49,821 580 62,966 4,448 2,633 21,386 11,680 6,571 2,902 15,092 1,196 3,081 235,987 4,227 .. .. 5,794 9,127 40,171 .. .. 8,135 467,213 422 21,288 54,643 3,566 1,334 7,364 2,851 6,356 8,878 6,290 2,255 4,571 18,996 1,377 1,189 932 30,510 7,124 49,058 482 .. 2,019 56,560 4,899 5,977 5,376 .. .. 1,438 2,865

Other greenhouse gas emissions

% of total % changeb Energy and industry Agricultural 1990– 2005 2005 2005

.. –18.7 27.5 –6.7 29.6 –27.6 –0.1 –13.5 0.4 42.1 –28.3 –27.6 –21.5 3.2 –40.8 –44.1 52.6 –55.2 .. .. 46.9 –13.3 –5.5 .. .. 57.5 48.5 –1.0 5.2 –37.3 –17.2 –26.2 –1.6 –24.5 –31.8 –10.2 –21.5 11.0 42.3 60.7 7.7 15.6 –50.7 19.4 –4.1 –30.6 57.9 .. –26.9 –23.9 –5.5 –17.1 121.2 .. .. 59.6 26.1

.. 7.1 22.6 0.4 3.9 1.2 10.3 31.0 8.3 7.5 23.1 38.1 4.0 0.7 24.7 1.4 3.4 36.0 .. .. 3.5 2.6 23.7 .. .. 16.6 12.9 38.5 4.4 2.2 1.0 4.5 2.7 36.6 15.1 53.0 18.0 7.8 3.8 8.3 8.2 3.8 21.5 5.2 42.8 24.2 10.0 .. 35.5 38.2 9.3 22.1 5.5 .. .. 6.2 3.8

.. 78.4 58.6 38.4 89.2 81.6 78.2 52.5 77.5 83.1 72.9 44.3 61.5 36.5 57.8 92.0 67.0 48.1 .. .. 66.1 75.9 58.9 .. .. 73.4 74.3 0.0 86.1 31.3 51.8 85.4 29.3 52.4 78.7 36.9 73.4 76.8 84.9 80.0 76.2 90.9 60.5 88.8 41.7 66.8 23.3 .. 56.9 52.2 70.5 58.2 56.8 .. .. 84.2 85.9

Total thousand metric tons of carbon dioxide equivalent 2005

.. 62 489 20 785 335 6,505 2,329 89 0 467 2,106 0 0 571 0 11,816 383 .. .. 0 419 21,943 .. .. 13 141,394 119 83 0 5 62 0 59 129 1,121 1,422 0 63 3,181 77 0 40 10 826 15,539 9 .. 12 31,543 15 1,842 481 .. .. 0 0

% changeb 1990– 2005

.. .. 50.0 .. –65.8 .. 33.5 46.2 –49.5 .. .. 583.8 .. .. –7.4 .. 40.5 .. .. .. .. –55.0 69.7 .. .. –29.5 1,073.0 –68.6 98.3 .. .. .. .. –93.4 .. .. 458.3 .. .. 54.5 .. .. 1,790.5 .. 724.4 57.1 .. .. .. 8.1 –97.5 –20.9 .. .. .. .. ..

Carbon dioxide emissions

average annual % growtha % changeb 1990– 1990– 2007 2007

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

–0.6 4.8 4.6 4.7 3.7 2.5 3.6 0.6 2.4 0.4 4.3 –2.1 4.6 –9.2 4.0 .. 9.0 –4.1 14.0 –4.4 3.0 .. 5.3 2.2 –3.1 –0.4 5.1 3.8 6.4 1.9 –4.6 6.2 1.6 –10.5 –0.7 3.8 5.4 6.9 42.7 8.1 0.0 2.2 4.4 –1.2 5.9 2.9 8.2 4.8 4.4 5.3 3.0 3.6 3.1 –1.0 2.0 .. 5.9

–11.0 133.5 165.5 118.3 90.5 46.1 99.0 7.4 75.3 8.8 106.2 –22.9 92.9 –71.1 108.2 .. 111.4 –51.2 554.7 –47.9 46.8 .. 39.4 42.2 –38.9 –29.3 128.3 72.5 243.6 37.4 –26.8 165.7 31.9 –80.1 5.4 97.1 159.7 208.5 .. 439.9 5.6 36.5 73.6 –4.6 110.0 36.5 260.5 128.1 131.2 57.2 82.7 103.1 59.2 –8.8 31.2 .. 435.5

Methane emissions Total thousand metric tons of carbon dioxide equivalent 2005

7,767 583,978 208,944 114,585 15,937 15,331 3,517 40,790 1,302 42,771 1,796 47,119 22,130 18,195 32,069 .. 14,380 3,591 .. 3,108 1,003 .. .. 14,682 5,516 1,403 .. .. 46,501 .. .. .. 128,209 3,372 6,067 10,573 12,843 77,211 5,057 22,142 21,259 27,635 6,018 .. 130,317 16,870 17,849 137,401 3,219 .. 15,388 17,187 51,889 70,023 12,173 .. 15,706

Nitrous oxide emissions

% of total % changeb From energy processes Agricultural 1990– 2005 2005 2005

–22.9 10.5 18.4 32.5 –45.8 14.3 83.8 –13.4 14.4 –36.5 111.5 –27.3 23.3 –15.0 2.4 .. 119.4 –38.1 .. –42.1 46.6 .. .. –34.7 –34.1 –36.5 .. .. 64.7 .. .. .. 26.3 –17.5 –25.9 15.8 18.2 –7.4 47.2 9.7 –30.4 3.6 26.3 .. 10.9 47.2 194.9 50.7 16.5 .. 2.0 22.7 28.6 –36.6 22.4 .. 387.2

29.1 15.9 25.5 70.6 58.4 11.9 18.4 14.7 11.4 8.1 25.0 66.2 16.9 58.6 19.9 .. 93.4 6.8 .. 53.6 9.7 .. .. 86.3 32.0 32.1 .. .. 69.3 .. .. .. 40.2 45.2 2.5 8.0 22.7 12.6 0.3 5.9 23.4 3.6 6.6 .. 68.9 74.6 94.1 23.7 4.0 .. 3.9 13.5 9.3 62.0 13.8 .. 96.5

33.6 64.4 46.4 18.2 18.6 76.7 31.2 39.8 50.3 71.2 21.8 25.3 65.5 23.5 38.6 .. 1.1 72.3 .. 27.7 25.5 .. .. 5.7 33.8 46.6 .. .. 12.4 .. .. .. 42.3 29.4 92.1 51.7 44.2 69.0 94.9 82.9 43.4 90.2 74.8 .. 19.8 12.6 3.0 63.5 79.2 .. 84.1 61.3 63.7 21.9 35.4 .. 0.4

Total thousand metric tons of carbon dioxide equivalent 2005

6,961 212,927 123,275 26,644 3,440 7,486 1,793 28,620 599 29,785 667 17,594 10,542 3,422 13,548 .. 650 1,510 .. 1,253 672 .. .. 1,285 2,451 599 .. .. 15,087 .. .. .. 42,514 849 3,489 5,814 9,501 30,932 3,797 4,516 14,596 12,930 3,340 .. 21,565 4,737 561 26,838 1,204 .. 9,067 7,560 12,950 30,198 5,958 .. 200

Other greenhouse gas emissions

% of total % changeb Energy and industry Agricultural 1990– 2005 2005 2005

–31.2 33.3 43.5 41.1 –9.9 –8.3 41.6 –5.4 29.5 –17.0 39.6 –46.2 14.3 –60.6 34.7 .. 157.1 –57.7 .. –58.7 79.1 .. .. 9.2 –45.7 –33.9 .. .. 13.5 .. .. .. 8.9 –51.0 –30.0 12.2 –12.7 –23.9 47.1 26.0 –10.7 23.5 10.1 .. 12.6 –3.1 82.6 46.0 18.1 .. 0.6 35.4 34.0 4.7 24.3 .. 105.1

3.9

ENVIRONMENT

Trends in greenhouse gas emissions

30.9 12.8 3.7 11.4 9.7 4.5 15.3 39.1 12.1 41.6 8.2 12.8 5.0 13.2 41.3 .. 27.7 11.2 .. 11.6 12.6 .. .. 11.2 5.0 15.9 .. .. 6.7 .. .. .. 10.6 5.5 2.2 3.0 3.4 2.6 1.1 13.0 52.5 3.5 3.3 .. 9.1 46.5 16.0 14.5 4.9 .. 1.7 2.9 9.1 33.5 22.0 .. 33.9

60.1 73.4 71.5 75.3 63.3 90.5 53.0 43.7 59.0 27.9 55.4 62.5 88.8 62.3 35.9 .. 16.9 72.6 .. 77.4 58.8 .. .. 51.9 86.0 63.9 .. .. 64.9 .. .. .. 75.2 73.5 93.2 82.6 71.4 42.9 94.3 76.8 39.5 94.2 91.7 .. 77.3 39.0 68.0 74.2 83.7 .. 82.6 81.9 73.1 57.7 43.8 .. 25.0

Total thousand metric tons of carbon dioxide equivalent 2005

1,552 8,433 1,027 2,569 86 1,151 1,981 13,968 51 53,786 112 339 0 2,794 10,221 .. 931 24 .. 890 0 .. .. 280 656 120 .. .. 994 .. .. .. 4,555 8 0 0 282 0 0 0 3,750 973 0 .. 669 5,202 175 819 0 .. 0 330 365 2,451 783 .. 0

2011 World Development Indicators

% changeb 1990– 2005

121.1 –11.9 –40.6 –2.9 –66.0 3,062.9 88.7 211.1 .. 81.1 .. .. .. .. 66.0 .. 253.9 .. .. .. .. .. .. –0.7 .. .. .. .. 66.3 .. .. .. 53.1 .. .. .. .. .. .. .. –40.9 3.4 .. .. 176.6 –39.4 .. –18.8 .. .. .. .. 125.6 360.6 606.6 .. ..

159

3.9

Trends in greenhouse gas emissions Carbon dioxide emissions

average annual % growtha % changeb 1990– 1990– 2007 2007

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

–2.9 –2.2 0.6 2.6 3.0 0.6c 7.4 0.3 –1.6 0.6 38.1 1.3 2.9 7.5 6.4 10.0 –0.5 –0.3 3.6 –6.9 4.9 5.7 .. 3.6 4.1 3.3 3.4 2.8 8.3 –4.4 5.5 –0.5 1.2 2.0 0.1 2.9 11.7 19.7 4.5 –0.2 –3.3 1.8 w –4.1 2.6 4.3 0.3 2.4 4.7 –2.0 2.4 4.2 4.8 2.2 1.0 0.2

–40.7 –34.3 4.8 87.1 72.1 –20.1c 237.7 15.4 –32.8 –17.3 .. 30.0 57.9 226.3 107.3 150.0 –4.8 –11.6 86.6 –69.9 154.7 189.6 .. 70.1 118.4 79.9 91.4 44.7 291.9 –54.0 147.3 –5.4 20.0 55.7 –9.7 35.5 420.3 .. 117.4 10.0 –37.9 36.0 w –36.2 59.6 117.7 3.8 56.2 148.9 –31.3 51.2 103.4 134.1 46.1 17.9 2.4

Methane emissions Total thousand metric tons of carbon dioxide equivalent 2005

24,331 562,801 .. 48,152 7,129 7,782 .. 2,237 3,911 3,498 .. 63,785 36,338 10,210 67,441 .. 11,311 4,748 12,458 3,898 32,024 83,257 .. 2,889 10,070 8,160 64,251 27,984 .. 70,360 23,283 65,788 548,074 19,589 39,602 61,183 82,978 .. 6,677 19,294 9,539 7,135,973 s 464,616 5,128,922 3,120,011 2,008,911 5,593,538 1,928,355 933,500 1,008,557 287,084 846,145 589,897 1,542,435 315,597

Nitrous oxide emissions

% of total % changeb From energy processes Agricultural 1990– 2005 2005 2005

–35.1 –18.3 .. 67.4 35.1 –58.7 .. 136.6 –39.7 0.6 .. 24.6 11.9 –11.2 55.5 .. 1.3 –17.1 –10.8 –9.3 24.0 5.7 .. 5.0 32.0 106.2 46.4 –5.0 .. –42.2 58.0 –44.1 –14.4 24.1 24.0 5.9 40.1 .. 73.5 –28.4 –5.7 6.2 w –4.0 13.9 18.6 7.3 12.2 24.5 –17.2 30.3 20.0 14.6 5.4 –10.8 –18.0

42.7 79.3 .. 83.6 9.9 41.5 .. 60.1 18.2 30.7 .. 45.4 10.4 5.3 7.1 .. 9.9 19.8 53.8 12.8 12.6 16.9 .. 23.5 83.9 55.6 16.0 75.2 .. 62.1 93.1 24.8 41.0 1.5 57.3 47.4 22.7 .. 17.0 6.7 11.4 37.3 w 13.6 39.0 35.1 45.0 36.9 39.2 67.5 17.3 64.7 16.1 30.5 38.9 25.9

36.0 9.1 .. 4.0 68.3 43.7 .. 1.3 39.0 32.1 .. 31.4 56.8 65.2 85.2 .. 28.1 67.6 28.1 68.6 63.2 66.0 .. 39.8 0.7 25.5 33.6 21.6 .. 23.3 2.6 38.2 34.8 94.3 33.7 40.0 63.9 .. 54.9 59.3 73.3 42.6 w 60.7 42.6 46.9 35.8 44.1 43.6 17.4 58.6 20.7 65.4 44.0 37.0 46.9

Total thousand metric tons of carbon dioxide equivalent 2005

11,537 76,121 .. 6,501 4,083 4,581 .. 1,068 3,354 1,156 .. 24,048 26,529 2,056 49,472 .. 5,865 2,415 5,509 1,378 21,647 22,304 .. 1,738 230 2,366 32,781 4,276 .. 26,097 1,169 30,565 317,153 7,017 10,003 14,935 23,030 .. 3,250 25,068 6,114 2,852,592 s 239,126 1,799,128 1,153,692 645,436 2,038,253 707,496 213,150 442,132 73,539 267,722 334,216 814,339 218,258

Other greenhouse gas emissions

% of total % changeb Energy and industry Agricultural 1990– 2005 2005 2005

–44.0 –48.7 .. 17.5 37.2 –8.8 .. 162.8 –37.1 –12.2 .. 12.9 6.5 18.0 34.9 .. –13.1 –15.5 33.4 0.2 0.8 15.1 .. –21.3 12.4 18.0 12.8 93.8 .. –51.4 78.7 –44.7 1.8 16.1 9.4 23.4 98.3 .. 57.4 –29.7 –16.1 5.8 w –16.7 18.3 30.5 1.5 12.8 38.0 –38.8 32.9 36.8 34.9 –7.6 –8.4 –17.8

32.4 27.8 .. 14.0 2.7 24.2 .. 77.6 52.0 13.2 .. 12.6 18.7 12.1 1.3 .. 26.8 20.8 9.0 1.4 2.5 21.7 .. 5.6 11.5 21.4 22.7 16.4 .. 42.8 18.3 24.8 30.6 1.4 6.2 5.0 6.1 .. 11.2 2.6 3.7 15.4 w 4.1 10.7 10.8 10.6 10.0 10.5 25.4 4.6 10.7 12.6 3.7 29.1 31.7

Total thousand metric tons of carbon dioxide equivalent 2005

56.2 746 44.3 59,673 .. .. 46.1 2,193 88.5 0 63.6 4,493 .. .. 2.8 2,532 37.7 395 70.4 473 .. .. 59.8 2,552 62.6 9,080 65.1 0 92.6 0 .. .. 60.2 2,078 59.3 2,109 78.1 0 86.9 383 78.8 0 65.5 1,104 .. .. 67.5 0 60.3 0 66.4 0 66.4 5,066 78.1 73 .. .. 45.6 693 43.6 1,075 60.0 10,403 56.4 239,517 96.9 59 84.2 608 75.2 2,468 83.0 0 .. .. 72.5 0 71.7 0 85.2 0 66.2 w 724,183 s 63.6 .. 70.8 259,893 72.3 159,984 68.1 99,909 70.0 263,401 72.2 .. 56.6 74,802 72.4 20,972 74.5 6,717 74.3 9,253 66.1 .. 56.9 460,781 55.4 84,190

% changeb 1990– 2005

–62.8 130.6 .. –10.6 .. 353.7 .. 396.0 478.0 –38.5 .. 71.1 47.7 .. .. .. 133.8 97.4 .. –86.3 .. –22.8 .. .. .. .. 96.9 .. .. 209.3 27.4 96.7 158.7 .. .. –24.0 .. .. .. .. .. 122.4 w .. 208.5 439.4 83.1 200.5 .. 112.0 23.5 20.7 –12.5 .. 93.7 37.2

a. Calculated using the least squares method, which accounts for ups and downs of all data points in the period (see Statistical methods). b. Calculated as the change in emission since 1990, which is the baseline for Kyoto Protocal requirements. c. Includes Kosovo and Montenegro.

160

2011 World Development Indicators

About the data

3.9

ENVIRONMENT

Trends in greenhouse gas emissions Deﬁnitions

Greenhouse gases—which include carbon dioxide,

compared. A kilogram of methane is 21 times as

• Carbon dioxide emissions are emissions from

methane, nitrous oxide, hydrofluorocarbons, per-

effective at trapping heat in the earth’s atmosphere

the burning of fossil fuels and the manufacture of

fluorocarbons, and sulfur hexafluoride—contribute

as a kilogram of carbon dioxide within 100 years.

cement and include carbon dioxide produced during

Nitrous oxide emissions are mainly from fossil fuel

consumption of solid, liquid, and gas fuels and gas

Carbon dioxide emissions, largely a byproduct of

combustion, fertilizers, rainforest fires, and animal

flaring. • Methane emissions are emissions from

energy production and use (see table 3.7), account

waste. Nitrous oxide is a powerful greenhouse gas,

human activities such as agriculture and from indus-

for the largest share of greenhouse gases. Anthro-

with an estimated atmospheric lifetime of 114 years,

trial methane production. • Methane emissions from

pogenic carbon dioxide emissions result primarily

compared with 12 years for methane. The per kilo-

energy processes are emissions from the produc-

from fossil fuel combustion and cement manufactur-

gram global warming potential of nitrous oxide is

tion, handling, transmission, and combustion of fos-

ing. Burning oil releases more carbon dioxide than

nearly 310 times that of carbon dioxide within 100

sil fuels and biofuels. • Agricultural methane emis-

burning natural gas, and burning coal releases even

years.

sions are emissions from animals, animal waste, rice

to climate change.

more for the same level of energy use. Cement manu-

Other greenhouse gases covered under the Kyoto

production, agricultural waste burning (nonenergy,

facturing releases about half a metric ton of carbon

Protocol are hydrofluorocarbons, perfluorocarbons,

on-site), and savannah burning. • Nitrous oxide

dioxide for each metric ton of cement produced.

and sulfur hexafluoride. Although emissions of these

emissions are emissions from agricultural biomass

Methane emissions result largely from agricultural

artificial gases are small, they are more powerful

burning, industrial activities, and livestock manage-

activities, industrial production landfills and waste-

greenhouse gases than carbon dioxide, with much

ment. • Nitrous oxide emissions from energy pro-

water treatment, and other sources such as tropi-

higher atmospheric lifetimes and high global warm-

cesses are emissions produced by the combustion

cal forest and other vegetation fires. The emissions

ing potential.

of fossil fuels and biofuels. • Agricultural nitrous

are usually expressed in carbon dioxide equivalents

For a discussion of carbon dioxide sources and

oxide emissions are emissions produced through

using the global warming potential, which allows

the methodology behind emissions calculation, see

fertilizer use (synthetic and animal manure), ani-

the effective contributions of different gases to be

About the data for table 3.8.

mal waste management, agricultural waste burning (nonenergy, on-site), and savannah burning. • Other greenhouse gas emissions include hydrofluorocar-

The six largest contributors to methane emissions account for about 50 percent of emissions

3.9a

bons, perfl uorocarbons, and sulfur hexafl uoride, which are to be curbed under the Kyoto Protocol.

Methane emissions, 2005 (million metric tons of carbon dioxide equivalent) 1,400

Hydrofluorocarbons, used as a replacement for chlorofluorocarbons, are used mainly in refrigeration and semiconductor manufacturing. Perfluorocarbons,

1,050

also used as a replacement for chlorofluorocarbons in manufacturing semiconductors, are a byproduct of

700

aluminum smelting and uranium enrichment. Sulfur 350

hexafluoride is used largely to insulate high-voltage electric power equipment.

0 China

India

Russian Federation

United States

Brazil

Indonesia

Source: Table 3.9.

The ﬁve largest contributors to nitrous oxide emissions account for about 50 percent of emissions

3.9b

Nitrous oxide emissions, 2005 (million metric tons of carbon dioxide equivalent) 500

375

Data sources Data on carbon dioxide emissions are from the

250

Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National

125

0

Laboratory, Tennessee, United States. Data on methane, nitrous oxide, and other greenhouse China

Source: Table 3.9.

United States

Brazil

India

Indonesia

gases emissions are compiled by the International Energy Agency.

2011 World Development Indicators

161

3.10

Sources of electricity Electricity production

Sources of electricitya

% of total billion kilowatt hours 1990

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

162

.. 3.2 16.1 0.8 50.7 10.4 154.3 49.3 23.2 7.7 39.5 70.3 0.0 2.1 14.6 0.9 222.8 42.1 .. .. .. 2.7 482.0 .. .. 18.4 621.2 28.9 36.4 5.7 0.5 3.5 2.0 9.2 15.0 62.3 26.0 3.7 6.3 42.3 2.2 0.1 17.4 1.2 54.4 417.2 1.0 .. 13.7 547.7 5.7 34.8 2.3 .. .. 0.6 2.3

2008

.. 3.8 40.2 4.0 121.4 5.8 257.1 64.4 23.9 35.0 35.0 83.6 0.1 6.2 13.3 0.6 463.4 44.6 .. .. 1.5 5.6 651.2 .. .. 59.7 3,456.9 38.0 56.0 7.5 0.5 9.5 5.8 12.2 17.7 83.2 36.4 15.4 18.6 131.0 6.0 0.3 10.6 3.8 77.4 570.3 2.0 .. 8.4 631.2 8.4 62.9 8.7 .. .. 0.5 6.5

2011 World Development Indicators

Coal

Natural Gas

Oil

Hydropower

Nuclear power

1990

2008

1990

2008

1990

2008

1990

2008

1990

2008

.. 0.0 0.0 0.0 1.3 0.0 78.7 14.2 0.0 0.0 0.0 28.2 0.0 0.0 71.8 88.1 2.1 50.3 .. .. .. 0.0 17.1 .. .. 38.3 71.3 98.3 10.1 0.0 0.0 0.0 0.0 6.8 0.0 76.4 90.7 1.2 0.0 0.0 0.0 0.0 85.8 0.0 18.5 8.5 0.0 .. 0.0 58.7 0.0 72.4 0.0 .. .. 0.0 0.0

.. 0.0 0.0 0.0 2.3 0.0 76.9 10.7 0.0 1.8 0.0 8.7 0.0 0.0 64.4 100.0 2.7 52.1 .. .. 0.0 0.0 17.2 .. .. 23.6 79.1 68.2 5.4 0.0 0.0 0.0 0.0 20.4 0.0 59.9 48.0 13.8 0.0 0.0 0.0 0.0 91.0 0.0 11.8 4.8 0.0 .. 0.0 46.0 0.0 53.0 13.0 .. .. 0.0 0.0

.. 0.0 93.7 0.0 39.2 16.4 9.3 15.7 0.0 84.3 58.1 7.7 0.0 37.6 0.0 0.0 0.3 7.6 .. .. .. 0.0 2.0 .. .. 2.1 0.4 0.0 12.4 0.0 0.0 0.0 0.0 20.2 0.2 0.6 2.7 0.0 0.0 39.6 0.0 0.0 5.5 0.0 8.6 0.7 16.4 .. 15.6 7.4 0.0 0.3 0.0 .. .. 0.0 0.0

.. 0.0 97.3 0.0 53.6 26.2 15.0 17.4 84.1 89.0 96.9 29.5 0.0 46.5 0.0 0.0 6.3 5.3 .. .. 0.0 7.7 6.2 .. .. 3.7 0.9 31.5 10.3 0.4 18.7 0.0 65.1 20.1 0.0 1.2 19.0 12.9 7.3 68.4 0.0 0.0 4.0 0.0 14.5 3.8 24.7 .. 15.2 13.9 0.0 21.9 0.0 .. .. 0.0 0.0

.. 10.9 5.4 13.8 9.8 68.6 2.3 3.8 97.0 4.3 41.8 1.9 100.0 5.3 7.3 11.9 2.2 2.9 .. .. .. 1.5 3.4 .. .. 9.2 7.9 1.7 1.0 0.4 0.6 2.5 33.3 31.6 91.4 0.9 3.4 88.6 21.5 36.9 6.9 100.0 8.3 11.6 3.1 2.1 11.2 .. 29.2 1.9 0.0 22.3 9.0 .. .. 20.6 1.7

.. 0.0 2.0 3.7 11.7 0.0 1.1 1.9 6.6 5.0 2.7 0.5 99.3 14.0 1.3 0.0 3.8 0.6 .. .. 96.5 15.9 1.5 .. .. 26.9 0.7 0.3 0.3 0.2 0.0 7.1 0.2 16.2 97.0 0.2 3.1 61.8 29.8 19.7 38.6 99.3 0.3 12.4 0.5 1.0 31.2 .. 0.0 1.5 25.9 15.9 26.6 .. .. 62.8 61.9

.. 89.1 0.8 86.2 35.2 15.0 9.2 63.9 3.0 11.4 0.1 0.4 0.0 55.3 20.9 0.0 92.8 4.5 .. .. .. 98.5 61.6 .. .. 48.5 20.4 0.0 75.6 99.6 99.4 97.5 66.7 41.3 0.8 1.9 0.1 9.4 78.5 23.5 73.5 0.0 0.0 88.4 20.0 12.9 72.1 .. 55.2 3.2 100.0 5.1 76.0 .. .. 76.5 98.3

.. 100.0 0.7 96.3 24.9 31.1 4.6 59.0 9.3 4.2 0.1 0.5 0.7 36.6 34.3 0.0 79.8 6.3 .. .. 3.1 76.2 58.7 .. .. 40.5 16.9 0.0 82.8 99.4 81.3 78.0 32.7 42.7 0.8 2.4 0.1 11.2 60.7 11.2 34.2 0.0 0.3 87.3 22.1 11.2 43.8 .. 84.8 3.3 74.1 5.3 42.6 .. .. 37.2 35.0

.. 0.0 0.0 0.0 14.3 0.0 0.0 0.0 0.0 0.0 0.0 60.8 0.0 0.0 0.0 0.0 1.0 34.8 .. .. .. 0.0 15.1 .. .. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 35.3 75.3 0.0 .. 0.0 27.8 0.0 0.0 0.0 .. .. 0.0 0.0

.. 0.0 0.0 0.0 6.0 42.6 0.0 0.0 0.0 0.0 0.0 54.5 0.0 0.0 0.0 0.0 3.0 35.4 .. .. 0.0 0.0 14.4 .. .. 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 31.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 29.6 77.1 0.0 .. 0.0 23.5 0.0 0.0 0.0 .. .. 0.0 0.0

Electricity production

3.10

ENVIRONMENT

Sources of electricity Sources of electricitya

% of total billion kilowatt hours 1990

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

28.4 289.4 33.3 59.1 24.0 14.2 20.9 213.1 2.5 835.5 3.6 87.4 3.2 27.7 105.4 .. 18.5 15.7 .. 6.6 1.5 .. .. 10.2 28.4 5.8 .. .. 23.0 .. .. .. 115.8 16.2 3.5 9.6 0.5 2.5 1.4 0.9 71.9 32.3 1.4 .. 13.5 121.6 4.5 37.7 2.7 .. 27.2 13.8 27.4 134.4 28.4 .. 4.8

2008

40.0 830.1 149.4 214.5 36.8 29.4 56.4 313.5 7.8 1,075.0 13.8 80.3 7.1 23.2 443.9 .. 51.7 11.9 .. 5.3 10.6 .. .. 28.7 13.3 6.3 .. .. 97.4 .. .. .. 258.9 3.6 4.1 20.8 15.1 6.6 2.1 3.1 107.6 43.8 3.4 .. 21.1 141.7 15.7 91.6 6.4 .. 55.5 32.4 60.8 155.6 45.5 .. 21.6

Coal

Natural Gas

Oil

Hydropower

Nuclear power

1990

2008

1990

2008

1990

2008

1990

2008

1990

2008

30.5 66.2 31.5 0.0 0.0 41.6 50.1 16.8 0.0 14.0 0.0 71.1 0.0 40.1 16.8 .. 0.0 13.1 .. 0.0 0.0 .. .. 0.0 0.0 89.7 .. .. 12.3 .. .. .. 6.7 30.8 92.4 23.0 13.9 1.6 1.5 0.0 38.3 2.1 0.0 .. 0.1 0.1 0.0 0.1 0.0 .. 0.0 0.0 7.0 97.5 32.1 .. 0.0

18.0 68.6 41.1 0.2 0.0 17.8 62.7 15.5 0.0 26.8 0.0 70.3 0.0 36.0 43.2 .. 0.0 3.5 .. 0.0 0.0 .. .. 0.0 0.0 83.8 .. .. 26.9 .. .. .. 8.3 0.0 96.1 56.2 0.0 0.0 31.1 0.0 24.9 11.0 0.0 .. 0.0 0.1 0.0 0.1 0.0 .. 0.0 2.7 25.9 92.2 24.6 .. 0.0

15.7 3.4 2.3 52.5 0.0 27.7 0.0 18.6 0.0 20.0 11.9 10.5 0.0 0.0 9.1 .. 45.7 23.5 .. 26.1 0.0 .. .. 0.0 23.8 0.0 .. .. 20.4 .. .. .. 12.5 42.3 0.0 0.0 0.0 39.3 0.0 0.0 50.9 17.7 0.0 .. 53.7 0.0 81.6 33.6 0.0 .. 0.0 1.7 0.0 0.1 0.0 .. 100.0

37.9 9.9 16.9 80.8 0.0 54.7 26.2 55.1 0.0 26.3 80.6 10.7 0.0 0.0 18.3 .. 30.4 6.1 .. 39.0 0.0 .. .. 41.0 15.2 0.0 .. .. 63.6 .. .. .. 50.6 95.6 0.0 13.8 0.1 35.7 0.0 0.0 58.9 24.3 0.0 .. 58.2 0.3 82.0 32.4 0.0 .. 0.0 28.0 32.2 2.0 33.4 .. 100.0

4.8 3.5 42.7 37.3 89.2 10.0 49.9 48.2 92.4 18.5 87.8 10.0 7.1 3.6 17.9 .. 54.3 0.0 .. 5.4 66.7 .. .. 100.0 14.6 1.8 .. .. 50.0 .. .. .. 53.6 25.4 7.6 64.4 23.6 10.9 3.3 0.1 4.3 0.0 39.8 .. 13.7 0.0 18.4 20.6 14.7 .. 0.0 21.5 45.3 1.2 33.1 .. 0.0

0.9 4.1 28.8 16.6 98.5 5.9 10.6 10.0 96.0 9.7 18.9 9.7 38.4 3.4 3.5 .. 69.6 0.0 .. 0.0 96.5 .. .. 59.0 4.2 2.9 .. .. 1.9 .. .. .. 19.0 0.4 3.9 24.2 0.0 3.5 1.4 0.4 1.9 0.3 64.5 .. 14.7 0.0 18.0 35.4 37.9 .. 0.0 9.0 8.0 1.5 9.1 .. 0.0

0.6 24.8 20.2 10.3 10.8 4.9 0.0 14.8 3.6 10.7 0.3 8.4 76.6 56.3 6.0 .. 0.0 63.5 .. 67.6 33.3 .. .. 0.0 1.5 8.5 .. .. 17.3 .. .. .. 20.3 1.6 0.0 12.7 62.6 48.1 95.2 99.9 0.1 71.9 28.8 .. 32.6 99.6 0.0 44.9 83.2 .. 99.9 75.8 22.1 1.1 32.3 .. 0.0

0.5 13.8 7.7 2.3 1.5 3.3 0.0 13.3 2.0 7.1 0.4 9.3 40.4 60.6 0.7 .. 0.0 90.4 .. 58.9 3.5 .. .. 0.0 3.0 13.3 .. .. 7.7 .. .. .. 15.1 2.3 0.0 4.5 99.9 60.8 67.5 99.6 0.1 51.0 15.9 .. 27.1 98.5 0.0 30.3 61.8 .. 100.0 58.7 16.2 1.4 15.0 .. 0.0

48.3 2.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 24.2 0.0 0.0 0.0 0.0 50.2 .. 0.0 0.0 .. 0.0 0.0 .. .. 0.0 60.0 0.0 .. .. 0.0 .. .. .. 2.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.9 0.0 0.0 .. 0.0 0.0 0.0 0.8 0.0 .. 0.0 0.0 0.0 0.0 0.0 .. 0.0

37.0 1.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 24.0 0.0 0.0 0.0 0.0 34.0 .. 0.0 0.0 .. 0.0 0.0 .. .. 0.0 74.2 0.0 .. .. 0.0 .. .. .. 3.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.9 0.0 0.0 .. 0.0 0.0 0.0 1.8 0.0 .. 0.0 0.0 0.0 0.0 0.0 .. 0.0

2011 World Development Indicators

163

3.10

Sources of electricity Electricity production

Sources of electricitya

% of total billion kilowatt hours 1990

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

64.3 1,082.2 .. 69.2 0.9 40.9 .. 15.7 25.5 12.4 .. 165.4 151.2 3.2 1.5 .. 146.0 55.0 11.6 18.1 1.6 44.2 .. 0.2 3.6 5.8 57.5 14.6 .. 298.6 17.1 317.8 3,202.8 7.4 56.3 59.3 8.7 .. 1.7 8.0 9.4 11,839.5 t 138.9 3,984.6 1,654.0 2,330.2 4,122.5 796.3 1,935.8 598.1 187.9 341.7 260.2 7,736.5 1,694.1

2008

65.0 1,038.4 .. 204.2 2.4 36.8 .. 41.7 28.8 16.4 .. 255.5 311.1 9.2 4.5 .. 149.9 67.1 41.0 16.1 4.4 147.4 .. 0.1 7.9 15.3 198.4 15.0 .. 192.5 86.3 385.3 4,343.8 8.8 49.4 119.3 73.0 .. 6.5 9.7 8.0 20,201.4 t 206.7 8,948.5 5,548.8 3,402.8 9,174.7 4,044.1 1,864.6 1,285.3 566.8 977.2 424.1 11,079.9 2,352.2

Coal

Natural Gas

1990

2008

1990

28.8 14.3 .. 0.0 0.0 69.1 .. 0.0 31.9 31.3 .. 94.3 40.1 0.0 0.0 .. 1.1 0.1 0.0 0.0 0.0 25.0 .. 0.0 0.0 0.0 35.1 0.0 .. 38.2 0.0 65.0 53.1 0.0 7.4 0.0 23.1 .. 0.0 0.5 53.3 37.3 w 13.2 32.4 47.7 21.6 31.8 61.0 23.1 4.0 1.2 56.1 62.2 40.2 33.7

39.8 18.9 .. 0.0 0.0 72.4 .. 0.0 17.9 32.5 .. 94.2 16.1 0.0 0.0 .. 1.1 0.0 0.0 0.0 2.7 21.4 .. 0.0 0.0 0.0 29.1 0.0 .. 35.6 0.0 32.9 49.1 0.0 4.1 0.0 20.8 .. 0.0 0.0 46.3 40.8 w 6.4 47.4 63.3 21.3 46.4 71.6 25.3 4.5 2.1 58.3 58.0 36.1 22.4

35.1 47.3 .. 48.1 2.3 3.2 .. 0.0 7.1 0.0 .. 0.0 1.0 0.0 0.0 .. 0.3 0.6 20.5 9.1 0.0 40.2 .. 0.0 99.0 63.7 17.7 95.2 .. 16.7 96.3 1.6 11.9 0.0 76.4 26.2 0.1 .. 0.0 0.0 0.0 14.6 w 9.2 22.3 11.7 29.8 21.8 3.4 36.7 9.4 36.9 8.5 2.8 10.7 8.6

Oil

Hydropower

2008

1990

2008

15.3 47.6 .. 43.1 1.7 1.1 .. 80.3 5.6 2.9 .. 0.0 39.1 0.0 0.0 .. 0.4 1.1 31.3 1.9 36.2 69.4 .. 0.0 99.6 88.7 49.7 100.0 .. 11.4 98.3 45.9 21.0 0.0 70.0 14.7 41.5 .. 0.0 0.0 0.0 21.3 w 17.5 19.8 9.9 35.8 19.7 6.7 40.2 20.7 62.5 14.6 4.4 22.5 24.0

18.4 11.5 .. 51.9 93.0 4.6 .. 100.0 6.4 7.9 .. 0.0 5.7 0.2 36.8 .. 0.9 0.7 56.0 0.0 4.9 23.5 .. 39.9 0.1 35.5 6.9 0.0 .. 16.1 3.7 10.9 4.1 5.1 4.4 11.5 15.0 .. 100.0 0.3 0.0 10.2 w 1.7 14.6 14.2 14.9 14.1 12.6 13.6 17.8 48.3 5.3 1.9 8.1 9.6

1.1 1.5 .. 56.9 85.8 0.5 .. 19.7 2.4 0.1 .. 0.1 5.8 55.1 67.6 .. 0.6 0.2 61.7 0.0 0.9 1.1 .. 24.4 0.2 10.8 3.8 0.0 .. 0.4 1.7 1.6 1.3 39.1 2.9 12.5 2.1 .. 100.0 0.3 0.3 5.1 w 5.0 6.0 5.0 7.6 6.0 2.0 2.0 13.5 29.4 7.5 3.8 4.4 3.9

2008

1990

2008

17.7 15.3 .. 0.0 0.0 23.1 .. 0.0 7.4 23.7 .. 0.6 16.8 99.8 63.2 .. 49.7 54.2 23.5 90.9 95.1 11.3 .. 60.1 0.0 0.8 40.2 4.8 .. 3.5 0.0 1.6 8.5 94.2 11.8 62.3 61.8 .. 0.0 99.2 46.7 18.0 w 53.7 23.1 20.3 25.1 24.1 21.4 14.5 64.4 12.4 27.4 15.9 14.7 11.1

26.5 15.9 .. 0.0 9.5 26.0 .. 0.0 14.0 24.5 .. 0.5 7.6 44.7 32.4 .. 46.1 53.7 7.0 98.1 60.1 4.8 .. 74.0 0.0 0.2 16.8 0.0 .. 5.9 0.0 1.3 5.9 51.4 23.0 72.8 35.6 .. 0.0 99.7 53.4 15.8 w 48.9 20.4 16.8 26.4 21.0 16.4 16.4 55.3 4.4 15.4 17.2 11.3 9.5

0.0 10.9 .. 0.0 0.0 0.0 .. 0.0 47.2 37.1 .. 5.1 35.9 0.0 0.0 .. 46.7 43.0 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 .. 25.5 0.0 20.7 19.1 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 17.0 w 0.0 6.4 5.0 7.3 6.1 0.0 11.7 2.1 0.0 1.9 3.2 22.7 35.6

17.3 15.7 .. 0.0 0.0 0.0 .. 0.0 58.1 38.3 .. 5.1 19.0 0.0 0.0 .. 42.6 41.3 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 .. 46.7 0.0 13.6 19.3 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 13.5 w 0.0 4.7 3.2 7.2 4.6 1.7 15.7 2.4 0.0 1.7 3.1 20.8 31.6

a. Shares may not sum to 100 percent because some sources of generated electricity (such as wind, solar, and geothermal) are not shown.

164

2011 World Development Indicators

Nuclear power

1990

About the data

3.10

ENVIRONMENT

Sources of electricity Deﬁnitions

Use of energy is important in improving people’s

as more detailed energy accounts have become

• Electricity production is measured at the termi-

standard of living. But electricity generation also

available. Breaks in series are therefore unavoidable.

nals of all alternator sets in a station. In addition to

can damage the environment. Whether such damage

hydropower, coal, oil, gas, and nuclear power gen-

occurs depends largely on how electricity is gener-

eration, it covers generation by geothermal, solar,

ated. For example, burning coal releases twice as

wind, and tide and wave energy as well as that from

much carbon dioxide—a major contributor to global

combustible renewables and waste. Production

warming—as does burning an equivalent amount

includes the output of electric plants designed to

of natural gas (see About the data for table 3.8).

produce electricity only, as well as that of combined

Nuclear energy does not generate carbon dioxide

heat and power plants. • Sources of electricity are

emissions, but it produces other dangerous waste

the inputs used to generate electricity: coal, gas, oil,

products. The table provides information on electric-

hydropower, and nuclear power. • Coal is all coal and

ity production by source.

brown coal, both primary (including hard coal and

The International Energy Agency (IEA) compiles

lignite-brown coal) and derived fuels (including pat-

data on energy inputs used to generate electricity.

ent fuel, coke oven coke, gas coke, coke oven gas,

IEA data for countries that are not members of the

and blast furnace gas). Peat is also included in this

Organisation for Economic Co-operation and Devel-

category. • Gas is natural gas but not natural gas

opment (OECD) are based on national energy data

liquids. • Oil is crude oil and petroleum products.

adjusted to conform to annual questionnaires com-

• Hydropower is electricity produced by hydroelectric

pleted by OECD member governments. In addition,

power plants. • Nuclear power is electricity produced

estimates are sometimes made to complete major

by nuclear power plants.

aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. It occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years More than 50 percent of electricity in Latin America is produced 3.10a by hydropower Percent

Other Hydropower

Nuclear power Natural gas

Oil Coal

Lower middle-income countries produce the majority of their power 3.10b from coal Percent

100

100

80

80

60

60

40

40

20

20

0

Other Hydropower

Nuclear power Natural gas

Oil Coal

Data sources Data on electricity production are from the IEA’s

0 East Asia Europe Latin America Middle & Pacific & Central & East Asia Caribbean & North Africa

Source: Table 3.10.

South Asia

Low income

Lower middle income

Upper middle income

Low & middle income

High income

electronic files and its annual publications Energy Statistics and Balances of Non-OECD Countries, Energy Statistics of OECD Countries, and Energy

Source: Table 3.10.

Balances of OECD Countries.

2011 World Development Indicators

165

3.11

Urbanization Urban population

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

166

millions 1990 2009

% of total population 1990 2009

3 1 13 4 28 2 15 5 4 23 7 10 2 4 2 1 112 6 1 0 1 5 21 1 1 11 311 6 23 10 1 2 5 3 8 8 4 4 6 25 3 0 1 6 3 42 1 0 3 58 5 6 4 2 0 2 2

18 36 52 37 87 68 85 66 54 20 66 96 35 56 39 42 75 66 14 6 13 41 77 37 21 83 27 100 68 28 54 51 40 54 73 75 85 55 55 44 49 16 71 13 61 74 69 38 55 73 36 59 41 28 28 29 40

7 1 23 11 37 2 19 6 5 45 7 11 4 7 2 1 167 5 3 1 3 11 27 2 3 15 586 7 34 23 2 3 10 3 8 8 5 7 9 35 4 1 1 14 3 49 1 1 2 60 12 7 7 4 0 5 4

2011 World Development Indicators

24 47 66 58 92 64 89 67 52 28 74 97 42 66 48 60 86 71 20 11 22 58 81 39 27 89 44 100 75 35 62 64 49 58 76 74 87 70 66 43 61 21 69 17 64 78 86 57 53 74 51 61 49 35 30 48 48

average annual % growth 1990–2009

4.0 1.2 2.9 5.2 1.4 -1.0 1.5 0.6 0.9 3.5 0.3 0.5 4.3 3.0 0.4 3.8 2.1 -0.3 5.0 4.8 5.2 4.3 1.3 2.4 4.6 1.7 3.3 1.1 2.2 4.2 2.8 3.3 3.9 -0.1 0.5 0.0 0.5 2.9 2.5 1.8 1.9 4.0 -1.0 4.5 0.5 0.8 3.6 5.5 -1.5 0.2 4.2 0.8 3.3 3.8 2.7 4.6 3.2

Population in urban agglomerations of more than 1 million

Population in largest city

% of total population 1990 2009

% of urban population 1990 2009

% of urban population 1990 2008

7 .. 7 15 39 33 60 20 24 8 16 17 .. 25 .. .. 35 14 6 .. 6 14 40 .. .. 35 9 100 31 13 29 24 17 .. 20 12 20 21 26 21 18 .. .. 4 17 23 .. .. 22 8 13 30 9 15 .. 16 12

38 21 14 40 37 49 25 30 45 29 24 17 30 29 24 22 13 21 44 66 50 19 18 43 38 42 3 100 21 35 53 47 42 27 27 16 24 37 28 36 37 72 43 29 28 22 62 66 41 6 22 51 22 52 53 56 29

.. .. 99 58 93 95 100 100 .. 59 .. 100 14 29 .. 58 81 100 28 41 38 65 100 21 20 91 48 .. 80 23 .. 94 38 .. 86 100 100 83 86 91 88 58 .. 21 100 100 .. .. 97 100 11 100 84 18 .. 44 68

12 .. 8 24 39 36 59 20 22 13 19 18 .. 33 .. .. 40 16 11 .. 10 19 44 .. .. 35 17 100 37 17 35 31 19 .. 19 11 21 21 33 18 25 .. .. 3 21 23 .. .. 26 8 17 29 8 16 .. 26 13

49 29 12 42 35 56 23 30 43 32 26 18 22 25 22 17 12 22 56 51 46 18 20 41 27 39 3 99 24 37 57 48 38 27 25 15 24 30 29 31 41 60 43 20 33 21 49 45 50 6 19 47 16 45 63 55 28

Access to improved sanitation facilities

60 98 98 86 91 95 100 100 51 56 91 100 24 34 99 74 87 100 33 49 67 56 100 43 23 98 58 .. 81 23 31 95 36 99 94 99 100 87 96 97 89 52 96 29 100 100 33 68 96 100 18 99 89 34 49 24 80

% of rural population 1990 2008

.. .. 77 6 73 .. 100 100 .. 34 .. 100 1 6 .. 20 35 98 2 44 5 35 99 5 2 48 38 .. 43 4 .. 91 8 .. 64 98 100 61 48 57 62 0 .. 1 100 100 .. .. 95 100 4 92 51 6 .. 19 28

30 98 88 18 77 80 100 100 39 52 97 100 4 9 92 39 37 100 6 46 18 35 99 28 4 83 52 .. 55 23 29 96 11 98 81 97 100 74 84 92 83 4 94 8 100 100 30 65 93 100 7 97 73 11 9 10 62

Urban population

millions 1990 2009

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

7 217 54 31 13 2 4 38 1 78 2 9 4 12 32 .. 2 2 1 2 2 0 1 3 2 1 3 1 9 2 1 0 59 2 1 12 3 10 0 2 10 3 2 1 34 3 1 33 1 1 2 15 30 23 5 3 0

7 345 121 50 21 3 7 41 1 85 5 9 9 15 40 .. 3 2 2 2 4 1 2 5 2 1 6 3 20 4 1 1 83 1 2 18 9 17 1 5 14 4 3 3 76 4 2 62 3 1 4 21 60 23 6 4 1

% of total population 1990 2009

66 26 31 56 70 57 90 67 49 63 72 56 18 58 74 .. 98 38 15 69 83 14 45 76 68 58 24 12 50 23 40 44 71 47 57 48 21 25 28 9 69 85 52 15 35 72 66 31 54 15 49 69 49 61 48 72 92

68 30 53 69 67 62 92 68 54 67 78 58 22 63 82 .. 98 36 32 68 87 26 61 78 67 67 30 19 71 33 41 43 78 41 57 56 38 33 37 18 82 87 57 17 49 78 72 37 74 13 61 72 66 61 60 99 96

average annual % growth 1990–2009

0.0 2.4 4.2 2.6 2.4 1.7 2.5 0.4 1.1 0.5 3.9 0.0 3.8 1.3 1.2 .. 1.5 0.8 6.0 -1.0 2.1 4.6 4.7 2.2 -0.6 1.2 4.2 5.2 4.1 3.9 2.8 0.8 1.8 -1.6 1.0 2.1 5.8 2.6 3.8 5.9 1.5 1.3 2.2 3.9 4.2 1.1 2.7 3.3 3.6 1.6 3.3 1.7 3.6 0.0 1.6 2.3 6.0

Population in urban agglomerations of more than 1 million

Population in largest city

% of total population 1990 2009

% of urban population 1990 2009

% of urban population 1990 2008

29 4 15 21 31 46 48 9 49 42 37 12 32 21 33 .. 67 38 70 49 52 50 106 26 23 40 36 24 12 37 53 30 26 32 45 22 27 29 35 23 9 30 34 35 14 22 27 22 65 32 53 39 26 7 54 60 54

100 49 58 86 .. 100 100 .. 82 100 98 96 24 .. 100 .. 100 94 .. .. 100 29 21 97 .. .. 14 50 88 36 29 93 80 .. .. 81 36 .. 66 41 100 .. 59 19 39 100 97 73 73 78 61 71 70 96 97 .. 100

19 10 10 24 26 26 56 19 .. 46 27 7 6 13 51 .. 65 .. .. .. 43 .. .. 20 .. .. 8 .. 8 9 .. .. 34 .. .. 18 6 9 .. .. 13 25 18 5 12 .. .. 16 35 .. 26 27 14 4 37 44 ..

17 13 9 24 23 24 57 17 .. 49 18 9 8 12 48 .. 80 .. .. .. 45 .. 29 17 .. .. 9 .. 9 13 .. .. 36 .. .. 19 7 11 .. .. 12 32 23 7 15 .. .. 18 39 .. 31 30 14 4 39 69 ..

25 6 8 14 27 39 47 8 40 43 23 15 39 19 25 .. 81 44 39 46 52 41 37 22 24 35 31 28 8 38 52 28 23 43 62 18 18 26 42 19 8 36 29 40 13 23 31 21 53 37 51 42 19 7 44 70 32

3.11

ENVIRONMENT

Urbanization

Access to improved sanitation facilities

100 54 67 .. 76 100 100 .. 82 100 98 97 27 .. 100 .. 100 94 86 82 100 40 25 97 .. 92 15 51 96 45 50 93 90 85 64 83 38 86 60 51 100 .. 63 34 36 100 97 72 75 71 90 81 80 96 100 .. 100

% of rural population 1990 2008

100 7 22 78 .. 98 100 .. 83 100 .. 97 27 .. 100 .. 100 .. .. .. .. 32 3 96 .. .. 6 41 81 23 8 90 30 .. .. 27 4 .. 9 8 100 88 26 2 36 100 61 8 40 42 15 16 46 .. 87 .. 100

2011 World Development Indicators

100 21 36 .. 66 98 100 .. 84 100 97 98 32 .. 100 .. 100 93 38 71 .. 25 4 96 .. 82 10 57 95 32 9 90 68 74 32 52 4 79 17 27 100 .. 37 4 28 100 .. 29 51 41 40 36 69 80 100 .. 100

167

3.11

Urbanization Urban population

millions 1990 2009

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

168

12 109 0 12 3 4 1 3 3 1 2 18 29 3 7 0 7 5 6 2 5 17 0 1 0 5 33 2 2 35 1 51 188 3 8 17 13 1 3 3 3 2,257 s 121 1,437 883 554 1,558 461 246 308 117 281 145 699 213

12 103 2 21 5 4 2 5 3 1 3 30 36 3 19 0 8 6 12 2 11 23 0 3 0 7 52 3 4 31 4 56 252 3 10 27 25 3 7 5 5 3,398 s 243 2,309 1,559 750 2,552 875 258 452 191 467 310 845 241

2011 World Development Indicators

% of total population 1990 2009

53 73 5 77 39 50 33 100 57 50 30 52 75 17 27 23 83 73 49 32 19 29 21 30 9 58 59 45 11 67 79 89 75 89 40 84 20 68 21 39 29 43 w 22 38 30 68 36 29 63 71 52 25 28 73 71

54 73 19 82 43 52 38 100 57 48 37 61 77 15 44 25 85 74 55 26 26 34 28 43 14 67 69 49 13 68 78 90 82 92 37 94 28 72 31 36 38 50 w 29 48 41 75 45 45 64 79 58 30 37 77 73

average annual % growth 1990–2009

-0.3 -0.3 8.3 2.7 3.1 0.0 2.5 2.6 0.1 -0.1 2.9 2.6 1.0 0.2 5.0 2.2 0.5 0.8 3.2 0.5 4.5 1.7 3.8 4.6 3.0 2.1 2.3 2.2 4.1 -0.5 4.7 0.5 1.5 0.6 1.1 2.5 3.2 4.1 5.5 2.0 2.3 2.2 w 3.7 2.5 3.0 1.6 2.6 3.4 0.2 2.0 2.6 2.7 4.0 1.0 0.6

Population in urban agglomerations of more than 1 million

Population in largest city

% of total population 1990 2009

% of urban population 1990 2009

9 17 .. 34 19 15 .. 99 .. .. 16 28 22 .. 9 .. 12 15 30 .. 5 10 .. 16 .. .. 23 .. 4 12 25 26 42 50 10 34 9 .. 5 10 10 17 w 8 14 11 25 13 .. 16 32 20 10 11 .. 18

9 18 .. 41 22 15 .. 95 .. .. 15 34 23 .. 12 .. 14 15 32 .. 7 10 .. 24 .. .. 28 .. 5 14 33 26 45 49 8 32 12 .. 9 11 13 20 w 11 18 15 29 17 .. 18 35 20 13 14 .. 18

17 8 57 19 48 30 39 99 .. 27 53 10 15 21 33 22 15 20 25 35 27 35 79 52 44 14 20 25 38 7 32 15 9 56 26 17 25 .. 25 24 35 17 w 33 14 11 19 16 9 14 24 26 9 27 20 16

17 10 49 23 52 29 40 95 .. 26 40 12 16 22 27 25 16 20 26 38 28 30 53 56 32 11 20 25 36 9 42 15 8 53 22 11 24 .. 30 31 34 16 w 32 13 11 19 15 7 16 22 22 12 26 19 16

Access to improved sanitation facilities

% of urban population 1990 2008

88 93 35 100 62 .. .. 99 100 100 .. 80 100 85 63 .. 100 100 94 93 27 93 .. 25 93 95 96 99 35 97 98 100 100 95 95 89 61 .. 64 62 58 77 w 39 69 58 87 67 54 94 81 90 53 43 100 100

88 93 50 100 69 96 24 100 100 100 52 84 100 88 55 61 100 100 96 95 32 95 76 24 92 96 97 99 38 97 98 100 100 100 100 .. 94 91 94 59 56 76 w 44 71 63 90 69 64 94 86 92 57 43 100 100

% of rural population 1990 2008

52 70 22 .. 22 .. .. .. 100 100 .. 58 100 67 23 .. 100 100 72 .. 23 74 .. 8 93 44 66 97 40 91 95 100 99 83 76 45 29 .. 6 36 37 35 w 19 31 28 58 29 37 75 38 57 11 21 99 99

54 70 55 .. 38 88 6 .. 99 100 6 65 100 92 18 53 100 100 95 94 21 96 40 3 92 64 75 97 49 90 95 100 99 99 100 .. 67 84 33 43 37 45 w 32 43 41 67 41 54 80 54 76 27 24 98 100

About the data

3.11

ENVIRONMENT

Urbanization Deﬁnitions

There is no consistent and universally accepted

populous nations were to change their definition of

• Urban population is the midyear population of

standard for distinguishing urban from rural areas, in

urban centers. According to China’s State Statis-

areas defined as urban in each country and reported

part because of the wide variety of situations across

tical Bureau, by the end of 1996 urban residents

to the United Nations (see About the data). • Popula-

countries (see About the data for table 3.1). Most

accounted for about 43 percent of China’s popula-

tion in urban agglomerations of more than 1 million

countries use an urban classification related to the

tion, more than double the 20 percent considered

is the percentage of a country’s population living in

size or characteristics of settlements. Some define

urban in 1994. In addition to the continuous migra-

metropolitan areas that in 2005 had a population of

urban areas based on the presence of certain infra-

tion of people from rural to urban areas, one of the

more than 1 million. • Population in largest city is

structure and services. And other countries designate

main reasons for this shift was the rapid growth in

the percentage of a country’s urban population living

urban areas based on administrative arrangements.

the hundreds of towns reclassified as cities in recent

in that country’s largest metropolitan area. • Access

years.

to improved sanitation facilities is the percentage

The population of a city or metropolitan area depends on the boundaries chosen. For example, in

Because the estimates in the table are based on

of the urban or rural population with access to at

1990 Beijing, China, contained 2.3 million people in

national definitions of what constitutes a city or met-

least adequate excreta disposal facilities (private or

87 square kilometers of “inner city” and 5.4 million

ropolitan area, cross-country comparisons should be

shared but not public) that can effectively prevent

in 158 square kilometers of “core city.” The popula-

made with caution. To estimate urban populations,

human, animal, and insect contact with excreta.

tion of “inner city and inner suburban districts” was

UN ratios of urban to total population were applied

Improved facilities range from simple but protected

6.3 million and that of “inner city, inner and outer

to the World Bank’s estimates of total population

pit latrines to flush toilets with a sewerage connec-

suburban districts, and inner and outer counties”

(see table 2.1).

tion. To be effective, facilities must be correctly con-

was 10.8 million. (Most countries use the last defini-

The table shows access to improved sanitation

tion.) For further discussion of urban-rural issues see

facilities for both urban and rural populations to

box 3.1a in About the data for table 3.1.

allow comparison of access. Definitions of access

Estimates of the world’s urban population would change significantly if China, India, and a few other

structed and properly maintained.

and urban areas vary, however, so comparisons between countries can be misleading.

Urban population is increasing in developing economies, especially in low and lower middle-income economies

3.11a

Urban population (millions) 1,600

1990

2009

1,200

800

400

0 Low income

Lower middle income

Upper middle income

High income

Source: Table 3.11.

Latin America and Caribbean has the greatest share of urban population, even greater than the high-income economies in 2009 Percent

3.11b Urban

Rural

100

Data sources Data on urban population and the population in

75

urban agglomerations and in the largest city are from the United Nations Population Division’s

50

World Urbanization Prospects: The 2009 Revision. Data on total population are World Bank

25

estimates. Data on access to sanitation are from the World Health Organization and United Nations

0 East Asia & Pacific

Europe & Central Asia

Source: Tables 3.1 and 3.11.

Latin America Middle East & & Caribbean North Africa

South Asia

Sub-Saharan Africa

High income

Children’s Fund’s Progress on sanitation and drinking water (2010).

2011 World Development Indicators

169

3.12

Urban housing conditions Census year

Household size

number of people National Urban

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

170

2001 1998 2001 2001 2001 2001 1999 2001 1999 2001 1992 2001 2001 2000 2001 1996 1990 2005 1987 2001 2003 1993 2002 2000 1993 1984 1984 2000 1998 2001 2002 2001 2001 2002 2001 1996 1992 2000 1994 2000 1999 2003 1993 2002 2001 2000 2001 2002 1996 1982 2001

.. 4.2 4.9 .. 3.6 4.1 3.8 2.4 4.7 4.8 .. 2.6 5.9 4.2 .. 4.2 3.8 2.7 6.2 4.7 5.0 5.2 2.6 5.2 5.1 3.4 3.4 .. 4.8 5.4 10.5 4.0 5.4 3.0 3.1 2.4 2.2 3.9 3.5 4.7 .. .. 2.4 4.8 2.2 2.5 5.2 8.9 3.5 2.3 5.1 3.0 4.4 6.7 .. 4.2 4.4

2011 World Development Indicators

.. 3.9 .. .. .. 4.0 .. .. 4.4 4.8 .. .. .. 4.3 .. 3.9 3.7 2.7 5.8 .. 4.9 5.1 .. 5.8 5.1 3.5 3.2 .. .. .. .. .. .. .. .. .. .. .. 3.7 .. .. .. 2.3 4.7 .. .. .. .. 3.5 .. 5.1 .. 4.7 .. .. .. ..

Overcrowding

Households living in overcrowded dwellings a % of total National Urban

.. .. .. .. 19 4 1 2 .. .. .. 0b .. 40 .. 27 .. .. 30 .. 35 67 .. 32 .. .. .. .. 27b 55 .. 22 .. .. 5 .. .. .. 30 .. 63 .. 3 .. .. .. .. .. .. .. .. 1 .. 63 .. 26 ..

.. .. .. .. .. 6 .. .. .. .. .. .. .. .. .. 47 .. .. 53 .. 32 77 .. 36b .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

Durable dwelling units

Home ownership

Buildings with durable structure % of total National Urban

Privately owned dwellings % of total National Urban

.. .. .. .. 97 93 .. .. .. 21b .. .. 26 43 .. 88 .. 79 .. .. 79 77 .. 78 .. 91 82 .. 83b .. .. 88 .. .. .. .. .. 97 81 .. 67 .. .. .. .. .. .. 18 .. .. 45 .. 67 .. .. .. 69

.. .. .. .. .. 93 .. .. .. 42b .. .. .. 58 .. 90 b .. 89 .. .. 88 .. .. 92 .. 92 .. .. .. .. .. .. .. .. .. .. .. .. 88 .. 83 .. .. 23 .. .. .. .. .. .. .. .. 80 .. .. .. 85

.. 65 b 67 .. .. 95 .. 48 74 88 b .. 67 59 70 .. 61 74 98 .. .. 58 73 64 85 .. 66 88 .. 68 b .. 76 72 .. .. .. 52 .. .. 68 b .. 70 .. .. .. 64 55 .. 68 .. 43 57 .. 81 76 .. 92 ..

.. 30 b .. .. .. 90 .. .. 62 61b .. .. .. 59 .. 47 75 98 .. .. 57 48 .. 74 .. 65 74 .. .. .. .. .. .. .. .. .. .. .. 58b .. 68 .. .. 54 .. .. .. .. .. .. .. .. 74 .. .. 68 ..

Multiunit dwellings

Vacancy rate

% of total National Urban

Unoccupied dwellings % of total National Urban

.. .. .. .. 4 1 .. .. 4 .. .. 32b .. 3b .. 1 .. .. .. .. 27 27 32 .. .. 13 .. .. 13 .. .. 2 .. .. .. 49 .. 8 9 75 3 .. 72 .. 44 .. .. .. .. .. 53 .. 2 .. .. .. ..

.. .. .. .. .. 1 .. .. 5 .. .. .. .. 5b .. .. .. .. .. .. 32 42 .. .. .. 15 .. .. .. .. .. 3 .. .. .. .. .. .. 14 .. 6 .. .. .. .. .. .. .. .. .. .. .. 4 .. .. .. ..

.. 12 19 .. 16 b .. .. .. .. .. .. .. .. 6 .. .. .. 23 .. .. .. .. 8 .. .. 11 1 .. 10 b .. .. 9 .. 12 .. 12 .. 11 12 .. 11 .. 13 .. .. 7 .. .. .. 7 5 .. 13 .. .. 9 14

.. 13 .. .. .. .. .. .. .. .. .. .. .. 4 .. .. .. 17 .. .. .. .. .. .. .. 10 .. .. .. .. .. 6 .. .. .. .. .. .. 7 .. 11 .. .. .. .. .. .. .. .. .. .. .. 11 .. .. 19 ..

Census year

Household size

number of people National Urban

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Laos Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

2001 2001 2000 1996 1997 2002 1995 2001 2001 2000 2004 1999 2000 1993 1995 1999 1995 2000 2001 1974 2001 2002 1993 1998 2000 1998 1988 2000 2005 2003 2000 1982 1997 2001 2001 2001 1995 2001 1991 1980 2003 1998 2000 1990 2002 2007 2000 1988 2001 2005

2.6 5.3 4.0 4.8 7.7 3.0 3.5 2.8 3.5 2.7 5.3 .. 4.6 3.8 4.4 .. 6.4 4.4 6.1 3.0 .. 5.0 4.8 6.4 2.6 3.6 4.9 4.4 4.5 5.6 .. 3.9 4.0 .. 4.4 5.9 4.4 .. 5.3 5.4 .. 2.8 5.3 6.4 5.0 2.7 7.1 6.8 4.1 4.5 b 4.6 3.9 4.9 3.2 2.8 2.8 ..

.. 5.3 .. 4.6 7.2 .. .. .. .. .. 5.1 .. 3.4 .. .. .. .. 3.6 6.1 2.6 .. .. .. .. .. 3.6 4.8 4.4 4.4 .. .. 3.8 3.9 .. 4.5 5.3 4.9 .. .. 4.9 .. .. .. 6.0 4.7 .. .. 6.8 .. 6.5 4.5 3.9 .. .. .. . ..

Overcrowding

Households living in overcrowded dwellings a % of total National Urban

2 77 .. 33 b .. .. .. .. .. .. 35 .. .. 23 .. .. .. .. .. 4 .. 10 b 31 .. 7 8b 64 30 .. .. .. 6 24 .. .. .. 37 .. .. .. .. 1b .. .. .. 1 .. .. 28 b .. 38b 35 .. .. .. 1 ..

.. 71 .. 26 b .. .. .. .. .. .. 34 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 57 .. .. .. .. 7 20 .. .. .. 28 .. .. .. .. .. .. .. .. .. .. .. .. .. ..b 31 .. .. .. .. ..

Durable dwelling units

Home ownership

Buildings with durable structure % of total National Urban

Privately owned dwellings % of total National Urban

.. 83 .. 72 88 .. .. .. 98 b .. .. .. 35 .. .. .. .. .. 49 88 .. .. 20 .. .. 95b .. 48 .. .. .. 91 .. .. .. .. 7 .. .. .. .. .. 79 .. .. .. .. 58 88 .. 95b .. .. .. .. .. ..

.. 81 .. 76 96 .. .. .. .. .. .. .. 72 .. .. .. .. .. 77 .. .. .. .. .. .. 95b .. 84 .. .. .. 94 .. .. .. .. 20 .. .. .. .. .. 87 .. .. .. .. 86 98b .. 98b .. .. .. .. .. ..

.. 87 .. 73 70 .. .. .. 58 b 61 64 .. 72 50 .. .. .. .. 96 58 .. 84 1 .. .. 48 b 81 86 .. .. .. 87 .. .. .. .. 92 .. .. 88 .. 65 84 77 .. 67 .. 81 80 .. 79 .. 71 .. 76 75 ..

.. 67 .. 67 66 .. .. .. .. .. 60 .. 25 .. .. .. .. .. 86 .. .. .. .. .. .. .. 59 47 .. .. .. 81 .. .. .. .. 83 .. .. .. .. .. 86 40 .. .. .. .. 66b 44 75 .. .. .. .. .. ..

Multiunit dwellings

Vacancy rate

% of total National Urban

Unoccupied dwellings % of total National Urban

.. .. .. .. 4 8b .. .. 2b 37 72 .. .. 15 .. .. 9b .. .. 74 .. 0 .. .. .. .. .. .. 10 b .. .. .. .. .. 48 .. 1 .. .. .. .. 17 0 .. .. 38 .. .. 10 b .. 1b .. 12 .. 86 .. ..

.. .. .. .. 5 .. .. .. .. .. 80 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 16b .. .. .. .. .. 56 .. 1 .. .. .. .. .. 0 .. .. .. .. .. 10 b 8 2b .. .. .. .. ..

4 6 .. .. 13 .. .. 21 .. .. .. .. 39 .. .. .. 11 .. .. 0 .. .. .. 7 .. 7b .. .. .. .. .. 7 3 .. .. .. 0 .. .. 0 .. 10 8 .. .. .. .. .. 14 .. 6b .. 1 .. .. ..

2011 World Development Indicators

ENVIRONMENT

3.12

Urban housing conditions

.. 9 .. .. 15 .. .. .. .. .. .. .. 17 .. .. .. .. .. .. .. .. .. .. .. .. 3b .. .. .. .. .. 6 2 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 6b .. .. .. .. ..

171

3.12

Urban housing conditions Census year

Household size

number of people National Urban

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela. RB Vietnam West Bank and Gaza Yemen Zambia Zimbabwe

2002 2002 2002 2004 2002 2001 1985 2000 2002 1975 2007 2001 2001 1993 1997 1990 2000 1981 2000 2002 2000

2000 1994 1990 2002 2003 2001 2005 1996 2001 1999 1997 1994 2000 1992

2.9 2.8 4.4 5.5 9.2 2.9 6.8 4.4 .. 2.8 .. 3.0 2.9 3.8 5.8 5.4 2.0 2.2 6.3 .. 4.9 3.8 .. .. 3.7 8.0 5.0 .. 4.7 .. .. .. 2.5 3.3 .. 4.4 4.6 7.1 6.7 5.3 4.8

Overcrowding

Households living in overcrowded dwellings a % of total National Urban

2.8 2.7 3.7 .. 8.0 2.2 .. .. .. 2.7 .. 2.8 .. .. 6.0 3.7 .. .. 6.0 .. 4.5 .. .. .. .. .. .. .. 3.9 .. .. 2.4 .. 3.4 .. .. 4.5 .. 6.8 5.9 4.2

a. More than two people per room. b. Data are from a previous census.

172

2011 World Development Indicators

20 7 43 .. 72 .. .. .. .. 14 .. 16 1 .. .. .. .. 1 .. .. 33b .. .. .. 9b .. .. .. .. .. .. .. 0 22b .. .. .. .. 54b .. ..

20 5 36 .. 68 .. .. .. .. 17 .. 15 .. .. .. .. .. .. .. .. 7b .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 6b .. ..

Durable dwelling units

Home ownership

Buildings with durable structure % of total National Urban

Privately owned dwellings % of total National Urban

.. .. 13 92b .. .. 34 .. .. .. .. .. .. 93b .. .. .. .. .. .. .. 93 .. .. 98 b 99 .. .. 19 .. .. .. .. .. .. .. 77 .. .. .. ..

.. .. 31 .. .. .. .. .. .. .. .. .. .. 92b .. .. .. .. .. .. .. 93 .. .. .. .. .. .. 61 .. .. .. .. .. .. .. 89 .. .. .. ..

84 .. 79 43 74 .. 68 .. .. 91 .. 43 82 70 b 86 b .. .. 34 .. .. 82b 81 .. .. 74b 71 70 .. 76 .. .. .. 74 57b .. 78 95 78 88 b 94 94

72 .. 41 .. 54 .. .. .. .. 87 .. 40 .. 58b 58b .. .. .. .. .. 43b 62 .. .. .. 89b .. .. 28 .. .. 69 .. 57b .. .. 86 .. 68b 30 30

Multiunit dwellings

Vacancy rate

% of total National Urban

Unoccupied dwellings % of total National Urban

.. 73 36 .. .. .. .. .. .. 33 .. .. .. 1 0b .. 54 77 .. .. .. 3 .. .. 17b 6 .. .. 37 .. .. .. 26 .. .. 14 .. 45 3b .. 6

.. 86 60 .. .. .. .. .. .. 56 .. .. .. 14b 1b .. .. .. .. .. .. .. .. .. .. 10 b .. .. 71 .. .. 19 .. .. .. .. .. .. 11b .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. 13 .. .. 1 .. .. .. .. 3 .. .. .. 15 .. .. .. .. .. .. .. 13 b .. 16 .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. 1b .. .. .. .. .. .. .. .. .. .. .. 12b .. .. .. .. .. .. .. 13 b .. .. .. .. .. .. ..

About the data

3.12

ENVIRONMENT

Urban housing conditions Deﬁnitions

Urbanization can yield important social benefi ts,

There is a strong demand for quantitative indi-

• Census year is the year in which the underlying

improving access to public services and the job mar-

cators that can measure housing conditions on a

data were collected. • Household size is the aver-

ket. It also leads to significant demands for services.

regular basis to monitor progress. However, data

age number of people within a household, calcu-

Inadequate living quarters and demand for housing

deficiencies and lack of rigorous quantitative analy-

lated by dividing total population by the number

and shelter are major concerns for policymakers.

sis hamper informed decisionmaking on desirable

of households in the country and in urban areas.

The unmet demand for affordable housing, along

policies to improve housing conditions. The data

• Overcrowding refers to the number of households

with urban poverty, has led to the emergence of

in the table are from housing and population cen-

living in dwellings with two or more people per room

slums in many poor countries. Improving the shel-

suses, collected using similar definitions. The table

as a percentage of total households in the country

ter situation requires a better understanding of the

will incorporate household survey data in future edi-

and in urban areas. • Durable dwelling units are

mechanisms governing housing markets and the pro-

tions. The table focuses attention on urban areas,

the number of housing units in structures made of

cesses governing housing availability. That requires

where housing conditions are typically most severe.

durable building materials (concrete, stone, cement,

good data and adequate policy-oriented analysis so

Not all the compiled indicators are presented in the

brick, asbestos, zinc, and stucco) expected to main-

that housing policy can be formulated in a global

table because of space limitations.

tain their stability for 20 years or longer under local

comparative perspective and drawn from lessons

conditions with normal maintenance and repair, tak-

learned in other countries. Housing policies and

ing into account location and environmental hazards

outcomes affect such broad socioeconomic condi-

such as floods, mudslides, and earthquakes, as a

tions as the infant mortality rate, performance in

percentage of total dwellings. • Home ownership

school, household saving, productivity levels, capital

refers to the number of privately owned dwellings as

formation, and government budget deficits. A good

a percentage of total dwellings. When the number

understanding of housing conditions thus requires

of private dwellings is not available from the census

an extensive set of indicators within a reasonable

data, the share of households that own their housing

framework.

unit is used. Privately owned and owner-occupied units are included, depending on the definition used

3.12a

Selected housing indicators for smaller economies

Census Household Overcrowding Durable Home Multiunit Vacancy year size dwelling ownership dwellings rate units Households

Antigua and Barbuda Bahamas Bahrain Barbados Belize Cape Verde Cayman Islands Equatorial Guinea Fiji Guam Isle of Man Maldives Marshall Islands Netherlands Antilles New Caledonia Northern Mariana Islands Palau Seychelles Solomon Islands St. Vincent & Grenadines Turks and Caicos Virgin Islands (UK) Western Samoa

2001 1990 2001 1990 2000 1990 1999 1993 1996 2000 2001 2000 1999 2001 1989 1995 2000 1997 1999 1991 1990 1991 1991

number of people

living in overcrowded dwellings a % of total

3.0 3.8 5.9 3.5 4.6 5.1 3.1 7.5 5.4 4.0 2.4 6.6 7.8 2.9 4.1 4.9 5.7 4.2 6.3 3.9 3.3 3.0 7.3

.. 12 .. 3 .. 28 .. 14 .. 2b 0 .. .. 24b .. 9b 8 15b 51 .. 4 2 ..

Privately Buildings owned with durable dwellings structure % of total % of total

99b 99 94b 100 93 78 100 56b 60 93 .. 93 95 99 77 99 76 97 23 98 96 99 42

65b 55 51 76 63 72 53 75 65 48 68 .. 72 60 53 33 79 78 85 71 66 40 90

in the census data. State- and community-owned units and rented, squatted, and rent-free units are excluded. • Multiunit dwellings are the number of multiunit dwellings, such as apartments, flats, condominiums, barracks, boardinghouses, orphan-

Unoccupied dwellings % of total % of total

3b 13 28 9 4 2 38 14 7 29 16 1 12 16 9 27 11 .. 1 7 11 46 47

22 14 6 9 .. .. 19 .. .. 19 .. 15 8 12 13 17 3 0 .. .. .. .. 30

ages, retirement houses, hostels, hotels, and collective dwellings, as a percentage of total dwellings. • Vacancy rate is the percentage of completed dwelling units that are currently unoccupied. It includes all vacant units, whether on the market or not (such as second homes).

Data sources Data on urban housing conditions are from

a. More than two people per room. b. Data are from a previous census. Source: National population and housing censuses.

national population and housing censuses.

2011 World Development Indicators

173

3.13

Traffic and congestion Motor vehicles

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

174

Passenger Road cars density

Road sector energy consumption

Fuel price

Particulate matter concentration

per 1,000 people

per kilometer of road

per 1,000 people

km. of road per 100 sq. km. of land area

Diesel

Urban-populationweighted PM10 micrograms per cubic meter

2008

2008

2008

2008

2008

2008

2008

2008

2010

2010

1990

2008

27 114 112 40 314 105 687 562 89 2 282 543 21 68 135 113 198 353 11 6 20 .. 605 0 6 172 37 73 58 5 26 163 20 388 38 513 477 123 63 43 84 11 477 3 534 598 .. 7 116 554 33 560 117 .. 33 .. 97

19 20 35 .. .. 42 18 42 13 2 .. 38 .. 7 23 7 18 67 2 .. 6 .. 14 0 2 36 13 248 16 .. .. 19 5 59 7 41 36 .. 19 33 .. .. 11 4 36 39 .. 3 16 71 13 54 .. .. 15 .. ..

19 84 72 8 .. 96 551 514 72 1 240 479 17 18 119 56 158 310 7 2 18 11 399 0 .. 109 27 55 41 .. 15 126 16 346 21 424 377 62 38 31 41 6 412 1 461 495 .. 5 95 502 21 443 .. .. 27 .. 69

6 63 5 .. 8 26 11 132 61 166 46 503 17 6 43 4 21 36 34 44 21 11 14 .. 3 .. 39 187 14 7 5 74 25 52 .. 166 170 .. 15 10 .. .. 128 4 23 173 3 33 29 180 24 88 .. 18 12 .. ..

.. 32 16 11 18 10 18 22 12 6 6 15 23 25 15 31 23 13 .. .. 7 10 17 .. .. 18 5 10 25 1 26 30 4 21 3 13 23 18 38 17 16 5 13 4 11 16 10 .. 19 15 12 21 23 .. .. 9 21

.. 213 173 65 346 100 1,091 877 188 11 161 827 79 149 242 340 298 335 .. .. 26 36 1,324 .. .. 345 85 204 171 3 98 320 21 432 29 553 779 144 289 145 131 7 542 16 740 666 143 .. 135 609 49 581 134 .. .. 25 135

.. 184 92 31 179 0 327 605 72 5 89 659 27 72 151 136 148 196 .. .. 14 17 336 .. .. 191 36 149 81 0 67 159 14 249 21 330 442 48 123 79 58 6 286 13 419 483 106 .. 45 309 23 192 64 .. .. 0 73

.. 24 63 30 102 64 645 199 108 2 51 134 47 48 84 186 73 78 .. .. 11 18 889 .. .. 137 45 47 70 3 27 144 6 153 5 189 312 89 151 54 67 1 239 2 284 129 31 .. 81 243 23 359 63 .. .. 23 55

1.15 1.46 0.32 0.65 0.96 1.08 1.27 1.63 0.75 1.09 1.08 1.87 1.04 0.70 1.42 0.93 1.58 1.51 1.44 1.43 1.15 1.20 1.21 1.71 1.32 1.38 1.11 1.92 1.41 1.28 1.27 1.14 1.68 1.59 1.72 1.75 2.00 1.23 0.53 0.48 0.92 2.54 1.54 0.91 1.94 1.98 1.14 0.79 1.13 1.90 0.82 2.05 0.95 0.95 .. 1.16 1.04

1.00 1.40 0.19 0.43 1.05 0.99 1.23 1.55 0.56 0.63 0.86 1.62 1.21 0.54 1.42 0.97 1.14 1.58 1.28 1.42 0.98 1.10 1.08 1.69 1.31 1.02 1.04 1.32 0.95 1.27 0.84 0.97 1.30 1.49 1.24 1.69 1.79 1.03 0.28 0.32 0.89 1.07 1.57 0.78 1.60 1.72 0.90 0.75 1.13 1.68 0.83 1.78 0.85 0.95 .. 0.89 0.92

68 92 113 111 104 481 22 39 132 237 23 30 78 113 36 93 39 108 144 68 88 122 25 60 209 92 115 .. 38 71 129 43 87 45 42 67 29 43 36 212 44 141 44 108 22 18 9 136 204 27 38 64 69 103 114 68 44

37 46 69 55 68 69 14 29 33 134 7 21 45 74 19 69 21 51 64 31 41 47 15 34 81 62 66 .. 20 40 68 32 32 27 23 18 16 16 20 97 28 71 13 59 15 13 7 62 49 16 24 32 60 53 47 35 42

2011 World Development Indicators

kilograms of oil equivalent per capita

$ per liter

% of total consumption

Total

Diesel fuel

Gasoline fuel

Super grade gasoline

Motor vehicles

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

Passenger Road cars density

Road sector energy consumption

per 1,000 people

per kilometer of road

per 1,000 people

km. of road per 100 sq. km. of land area

2008

2008

2008

2008

2008

2008

384 15 77 128 .. 534 313 673 188 593 146 197 21 .. 346 .. 507 59 21 474 .. .. 3 291 546 144 27 9 334 9 .. 159 264 139 72 71 13 7 109 5 515 733 57 5 31 575 225 11 120 9 82 55 33 495 509 642 724

20 4 40 53 .. 24 126 83 24 63 110 33 10 .. 161 .. 233 9 10 15 .. .. .. .. 23 21 10 .. 83 .. .. 99 77 39 4 38 10 13 4 .. 62 33 16 4 .. 29 12 8 30 .. .. 15 14 49 70 .. ..

304 10 43 113 .. 451 260 596 138 319 102 164 15 .. 257 .. 282 44 2 412 .. .. 2 225 498 129 8 4 298 7 .. 123 181 101 48 53 9 5 52 3 449 616 17 4 31 461 174 9 131 6 39 35 11 422 495 614 335

212 129 23 10 .. 137 82 162 202 318 9 3 11 21 105 .. 32 17 15 108 67 .. .. .. 124 54 .. 13 30 2 1 99 19 38 3 13 4 4 .. 12 328 35 16 1 21 29 17 33 18 .. .. 8 67 123 90 287 67

16 7 12 19 30 29 16 21 12 14 22 6 6 2 12 .. 14 17 .. 24 29 .. .. 19 18 13 .. .. 19 .. .. .. 28 10 13 24 4 7 33 3 15 25 13 .. 8 12 11 13 17 .. 26 29 17 15 25 .. 12

435 36 103 522 330 996 481 626 204 541 264 277 26 17 559 .. 1,343 94 .. 481 360 .. .. 542 486 194 .. .. 523 .. .. .. 472 85 157 112 18 22 274 11 708 1,004 79 .. 58 733 665 63 148 .. 181 148 76 391 579 .. 2,245

Fuel price

Particulate matter concentration

% of total consumption

Total

Diesel fuel

Gasoline fuel

Super grade gasoline

Diesel

Urban-populationweighted PM10 micrograms per cubic meter

2008

2008

2010

2010

1990

2008

254 22 31 223 185 570 155 389 0 175 105 25 16 9 278 .. 401 0 .. 293 3 .. .. 325 275 106 .. .. 193 .. .. .. 128 53 8 93 13 11 77 7 396 423 44 .. 8 437 56 40 0 .. 140 106 43 216 409 .. 1,388

149 10 67 249 129 385 300 181 190 331 150 238 9 7 152 .. 868 89 .. 163 334 .. .. 192 122 58 .. .. 310 .. .. .. 312 26 139 15 4 8 170 2 252 529 32 .. 46 271 567 9 138 .. 31 29 28 105 140 .. 756

1.67 1.15 0.79 0.10 0.78 1.78 1.85 1.87 0.98 1.60 1.04 0.71 1.33 1.51 1.52 1.63 0.23 0.85 1.26 1.48 1.13 0.97 0.98 0.17 1.59 1.52 1.52 1.71 0.59 1.42 1.16 1.55 0.81 1.21 1.11 1.23 1.11 0.80 1.06 1.18 2.13 1.47 1.09 1.07 0.44 2.12 0.31 0.86 0.85 0.94 1.28 1.41 1.05 1.57 1.85 0.65 0.19

1.61 0.82 0.51 0.02 0.56 1.69 1.87 1.69 0.98 1.37 0.73 0.51 1.27 1.40 1.35 1.60 0.21 0.79 0.97 1.49 0.77 1.07 0.96 0.13 1.42 1.27 1.26 1.54 0.56 1.25 0.99 1.23 0.72 1.08 1.04 0.88 0.86 0.80 1.09 0.91 1.71 0.97 0.99 1.16 0.77 2.01 0.38 0.92 0.77 0.90 1.01 1.10 0.84 1.50 1.58 0.78 0.19

33 111 133 86 164 23 66 41 55 42 107 42 64 180 51 .. 77 76 87 38 64 123 68 101 52 45 91 93 35 259 145 21 67 109 190 38 112 113 73 67 45 14 44 199 195 21 136 220 58 35 106 96 56 60 49 23 71

16 59 72 55 138 13 28 23 37 27 33 15 30 59 31 .. 95 26 39 13 36 46 31 76 17 20 33 35 20 112 69 18 33 36 111 27 26 46 48 32 31 12 23 96 46 16 94 109 34 18 67 51 19 35 21 21 35

kilograms of oil equivalent per capita

ENVIRONMENT

3.13

Traffic and congestion

$ per liter

2011 World Development Indicators

175

3.13

Traffic and congestion Motor vehicles

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

per 1,000 people

per kilometer of road

per 1,000 people

km. of road per 100 sq. km. of land area

2008

2008

2008

2008

219 245 4 .. 23 227 5 150 319 565 .. 159 606 61 28 89 521 567 62 38 73 .. .. 2 351 114 138 106 7 152 313 526 809 176 .. 147 13 39 35 18 106 .. w .. 42 23 .. .. 47 185 169 88 16 34 622 592

24 35 3 20 19 42 2 218 35 29 .. .. 41 13 .. 25 8 61 20 .. 3 .. .. .. .. 61 24 22 3 41 .. 77 38 .. .. .. 7 29 .. .. .. .. w .. .. 8 .. .. 16 30 .. .. 4 .. 38 ..

187 206 2 415 17 202 3 114 272 520 .. 108 486 19 20 46 464 522 27 29 4 54 .. 2 .. 76 92 80 3 138 293 462 451 151 .. 107 13 30 .. 11 91 118 w .. 36 15 129 35 33 152 118 66 10 25 432 418

83 6 53 11 8 45 .. 475 89 192 .. .. 132 148 .. 21 128 173 35 .. 9 35 .. 21 .. 12 54 .. 29 28 5 172 68 44 .. .. 48 85 14 .. 25 28 w .. 25 50 .. 22 36 8 18 12 129 .. 43 140

a. Includes Montenegro.

176

Passenger Road cars density

2011 World Development Indicators

Road sector energy consumption

Fuel price

Particulate matter concentration

% of total consumption

Total

Diesel fuel

Gasoline fuel

Super grade gasoline

Diesel

Urban-populationweighted PM10 micrograms per cubic meter

2008

2008

2008

2008

2010

2010

1990

2008

1.46 0.84 1.63 0.16 1.57 1.50 0.94 1.42 1.70 1.67 1.12 1.19 1.56 1.19 0.62 1.07 1.87 1.66 0.96 1.02 1.22 1.41 1.40 1.18 0.36 0.94 2.52 0.22 1.42 1.01 0.47 1.92 0.76 1.49 0.92 0.02 0.88 1.71 0.35 1.66 1.29 1.21 m 1.18 1.08 1.05 1.14 1.11 1.08 1.17 1.04 0.94 1.12 1.22 1.63 1.78

1.46 0.72 1.62 0.07 1.34 1.48 0.94 1.04 1.53 1.62 1.15 1.14 1.47 0.66 0.43 1.10 1.82 1.77 0.45 0.91 1.19 0.95 0.90 1.17 0.24 0.82 2.03 0.20 1.11 0.92 0.71 1.98 0.84 1.44 0.83 0.01 0.77 1.54 0.23 1.52 1.15 1.07 m 1.11 0.96 0.89 1.03 0.98 0.93 1.11 0.98 0.56 0.83 1.15 1.54 1.62

36 41 60 157 92 33 a 87 107 46 38 94 33 41 94 282 55 15 35 145 112 56 77 .. 57 135 71 76 259 33 71 281 24 30 236 145 21 123 .. 137 124 55 80 w 128 96 121 57 98 112 58 58 124 133 119 38 33

12 16 27 104 81 14 a 38 31 13 29 31 22 28 74 159 35 11 22 69 43 22 55 .. 29 105 26 37 65 12 18 87 13 19 160 40 9 53 .. 67 39 40 46 w 60 53 63 31 54 61 24 32 71 72 49 24 20

kilograms of oil equivalent per capita

12 7 .. 20 24 12 .. 13 11 26 .. 11 23 19 14 .. 16 22 20 4 6 16 .. 11 4 17 14 5 .. 6 14 19 23 21 3 24 13 .. 27 2 4 14 w 5 10 8 15 10 7 8 23 19 7 8 19 18

216 318 .. 1,279 57 251 .. 494 379 985 .. 293 703 86 54 .. 844 754 189 15 26 262 .. 45 587 151 181 191 .. 177 1,884 641 1,703 259 63 553 90 .. 90 10 27 261 w 19 129 78 320 116 97 228 302 259 36 57 964 665

136 80 .. 568 45 182 .. 305 230 628 .. 121 539 56 36 .. 410 283 118 0 19 153 .. 15 229 101 114 0 .. 55 964 335 399 163 8 81 50 .. 18 0 15 103 w 10 56 37 127 51 42 82 121 128 23 24 356 422

67 222 .. 646 10 63 .. 167 115 316 .. 161 135 25 15 .. 365 441 62 12 6 73 .. 27 327 41 31 182 .. 114 829 271 1,148 81 49 416 38 .. 62 10 11 135 w 7 61 36 156 55 50 128 136 111 9 31 526 194

$ per liter

About the data

3.13

ENVIRONMENT

Traffic and congestion Deﬁnitions

Traffic congestion in urban areas constrains eco-

associations. If they lack data or do not respond,

• Motor vehicles include cars, buses, and freight

nomic productivity, damages people’s health, and

other agencies are contacted, including road direc-

vehicles but not two-wheelers. Population fi gures

degrades the quality of life. In recent years own-

torates, ministries of transport or public works, and

refer to the midyear population in the year for

ership of passenger cars has increased, and the

central statistical offices. As a result, data quality

which data are available. Roads refer to motor-

expansion of economic activity has led to more

is uneven. Coverage of each indicator may differ

ways, highways, main or national roads, and sec-

goods and services being transported by road over

across countries because of different definitions.

ondary or regional roads. A motorway is a road

greater distances (see table 5.10). These devel-

Comparability is also limited when time series data

designed and built for motor traffi c that sepa-

opments have increased demand for roads and

are reported. The IRF is taking steps to improve the

rates the traffi c fl owing in opposite directions.

vehicles, adding to urban congestion, air pollution,

quality of the data in its World Road Statistics 2010.

• Passenger cars are road motor vehicles, other than

health hazards, and traffic accidents and injuries.

Because this effort covers 2003–08 only, time

two-wheelers, intended for the carriage of passen-

The data on motor vehicles, passenger cars, and

series data may not be comparable. Another rea-

gers and designed to seat no more than nine people

road density in the table are compiled by the Interna-

son is coverage. Road density is a rough indicator of

(including the driver). • Road density is the ratio of

tional Road Federation (IRF) through questionnaires

accessibility and does not capture road width, type,

the length of the country’s total road network to the

sent to national organizations. The IRF uses a hier-

or condition. Thus comparisons over time and across

country’s land area. The road network includes all

archy of sources to gather as much information as

countries should be made with caution.

roads in the country—motorways, highways, main

possible. Primary sources are national road Biogasoline consumption as a share of total consumption is 3.13a highest in Brazil . . . Biogasoline consumption (percent of total consumption)

2000

2008

40

30

20

10

0 Brazil

France Canada Germany China

United States

Source: International Energy Agency.

. . . but the United States consumes 3.13b the most biogasoline

Road sector energy consumption includes energy

or national roads, secondary or regional roads, and

from petroleum products, natural gas, renewable and

other urban and rural roads. • Road sector energy

combustible waste, and electricity. Biodiesel and bio-

consumption is the total energy used in the road

gasoline, forms of renewable energy, are biodegrad-

sector, including energy from petroleum products,

able and emit less sulfur and carbon monoxide than

natural gas, combustible and renewable waste,

petroleum-derived ones. They can be produced from

and electricity. • Total energy consumption is the

vegetable oils, such as soybean, corn, palm, peanut,

country’s total energy consumption from all sources

or sunflower oil, and can be used directly only in a

(see table 3.7). • Gasoline is light hydrocarbon oil

modified internal combustion engine. Data are pro-

use in internal combustion engines such as motor

vided by the International Energy Agency.

vehicles, excluding aircraft. • Diesel is heavy oils

Data on fuel prices are compiled by the German

used as a fuel for internal combustion in diesel

Agency for International Cooperation (GIZ), from its

engines. • Fuel price is the pump price of super

global network, and other sources, including the

grade gasoline and of diesel fuel, converted from the

Allgemeiner Deutscher Automobile Club (for Europe)

local currency to U.S. dollars (see About the data).

and the Latin American Energy Organization for Latin

• Particulate matter concentration is fi ne sus-

America. Local prices are converted to U.S. dollars

pended particulates of less than 10 microns in diam-

using the exchange rate in the Financial Times inter-

eter (PM10) that are capable of penetrating deep

national monetary table on the survey date. When

into the respiratory tract and causing severe health

multiple exchange rates exist, the market, parallel,

damage. Data are urban-population-weighted PM10

or black market rate is used. Prices were compiled

levels in residential areas of cities with more than

in mid-November 2010, based on the crude oil price

100,000 residents. The estimates represent the

of $81 per barrel Brent.

average annual exposure level of the average urban

Considerable uncertainty surrounds estimates of

resident to outdoor particulate matter.

particulate matter concentrations, and caution should be used in interpreting them. They allow for crosscountry comparisons of the relative risk of particulate

Data sources

matter pollution facing urban residents. Major sources

Data on vehicles and road density are from the

of urban outdoor particulate matter pollution are

IRF’s electronic files and its annual World Road

traffic and industrial emissions, but nonanthropogenic

Statistics, except where noted. Data on road sector

sources such as dust storms may also be a substan-

energy consumption are from the IRF and the Inter-

tial contributor for some cities. Country technology

national Energy Agency. Data on fuel prices are

and pollution controls are important determinants of

from the GIZ’s electronic files. Data on particulate

particulate matter. Data on particulate matter for

matter concentrations are from Pandey and oth-

selected cities are in table 3.14.

ers’ “Ambient Particulate Matter Concentrations in Residential and Pollution Hotspot Areas of World Cities: New Estimates Based on the Global Model

Source: International Energy Agency.

of Ambient Particulates (GMAPS)” (2006b).

2011 World Development Indicators

177

3.14

Air pollution City

City population

thousands 2009

Argentina Australia

Austria Belgium Brazil Bulgaria Canada

Chile China

Colombia Croatia Cuba Czech Republic Denmark Ecuador Egypt, Arab Rep. Finland France Germany

Ghana Greece Hungary Iceland India

178

Buenos Aires Córdoba Melbourne Perth Sydney Vienna Brussels Rio de Janeiro São Paulo Sofia Montréal Toronto Vancouver Santiago Anshan Beijing Changchun Chengdu Chongqing Dalian Guangzhou Guiyang Harbin Jinan Kunming Lanzhou Liupanshui Nanchang Shanghai Shenyang Shenzhen Tianjin Wuhan Zhengzhou Zibo Foshan Chengdu Xi’an Bogotá Zagreb Havana Prague Copenhagen Guayaquil Quito Cairo Helsinki Paris Berlin Frankfurt Munich Accra Athens Budapest Reykjavik Ahmadabad

2011 World Development Indicators

12,988 1,479 3,813 1,578 4,395 1,693 1,892 11,836 19,960 1,192 3,750 5,377 2,197 5,883 1,632 12,214 3,504 4,869 9,348 3,252 8,735 2,125 4,224 3,186 3,062 2,243 1,221 2,648 16,344 5,074 8,847 7,759 7,582 2,914 2,396 4,876 4,869 4,704 8,262 779 b 2,140 1,162 1,174 2,634 1,801 10,902 1,107 10,410 3,438 680 b 1,334 2,269 3,252 1,705 319b 5,606

Particulate matter concentration

Sulfur dioxide

Nitrogen dioxide

Urbanpopulationweighted PM10 micrograms per micrograms per micrograms per cubic meter cubic meter cubic meter 1990 2008 2001a 2001a

159 78 17 16 27 45 33 49 57 118 24 29 17 103 132 141 117 136 194 79 99 111 121 148 111 145 94 124 115 160 89 198 125 154 117 107 136 221 51 48 47 68 30 33 44 272 24 14 30 27 27 37 69 35 23 127

104 51 11 11 18 34 23 26 30 55 15 17 10 69 75 80 66 77 110 45 56 63 69 84 63 82 53 70 65 90 50 112 71 87 66 61 77 125 27 28 26 19 17 18 24 124 17 10 18 16 16 24 34 16 14 68

.. .. .. 5 28 14 20 129 43 39 10 17 14 29 115 90 21 77 340 61 57 424 23 132 19 102 102 69 53 99 .. 82 40 63 198 .. .. .. .. 31 1 14 7 15 22 69 4 14 18 11 8 .. 34 39 5 30

.. 97 30 19 81 42 48 .. 83 122 42 43 37 81 88 122 64 74 70 100 136 53 30 45 33 104 .. 29 73 73 .. 50 43 95 43 .. .. .. .. .. 5 33 54 .. .. .. 35 57 26 45 53 .. 64 51 42 21

About the data

Indoor and outdoor air pollution places a major burden on world health. More than half the world’s people rely on dung, wood, crop waste, or coal to meet basic energy needs. Cooking and heating with these fuels on open fires or stoves without chimneys lead to indoor air pollution, which is responsible for 1.6 million deaths a year—one every 20 seconds. In many urban areas air pollution exposure is the main environmental threat to health. Long-term exposure to high levels of soot and small particles contributes to a range of health effects, including respiratory diseases, lung cancer, and heart disease. Particulate pollution, alone or with sulfur dioxide, creates an enormous burden of ill health. Sulfur dioxide and nitrogen dioxide emissions lead to deposition of acid rain and other acidic compounds over long distances, which can lead to the leaching of trace minerals and nutrients critical to trees and plants. Sulfur dioxide emissions can damage human health, particularly that of the young and old. Nitrogen dioxide is emitted by bacteria, motor vehicles, industrial activities, nitrogen fertilizers, fuel and biomass combustion, and aerobic decomposition of organic matter in soils and oceans. Where coal is the primary fuel for power plants without effective dust controls, steel mills, industrial boilers, and domestic heating, high levels of urban air pollution are common—especially particulates and sulfur dioxide. Elsewhere the worst emissions are from petroleum product combustion. Sulfur dioxide and nitrogen dioxide concentration data are based on average observed concentrations at urban monitoring sites, which not all cities have. The data on particulate matter are estimated average annual concentrations in residential areas away from air pollution “hotspots,” such as industrial districts and transport corridors. The data are from the World Bank’s Development Research Group and Environment Department estimates of annual ambient concentrations of particulate matter in cities with populations exceeding 100,000 (Pandey and others 2006b). A country’s technology and pollution controls are important determinants of particulate matter concentrations. Pollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Thus these data should be considered only a general indication of air quality, and comparisons should be made with caution. Current World Health Organization (WHO) air quality guidelines are annual mean concentrations of 20 micrograms per cubic meter for particulate matter less than 10 microns in diameter and 40 micrograms for nitrogen dioxide and daily mean concentrations of 20 micrograms per cubic meter for sulfur dioxide.

City

City population

thousands 2009

Indonesia Iran, Islamic Rep. Ireland Italy

Japan

Kenya Korea, Rep

Malaysia Mexico Netherlands New Zealand Norway Philippines Poland

Portugal Romania Russian Federation Singapore Slovak Republic South Africa

Spain Sweden Switzerland Thailand Turkey Ukraine United Kingdom

United States

Venezuela, RB

Bangalore Chennai Delhi Hyderabad Kanpur Kolkata Lucknow Mumbai Nagpur Pune Jakarta Tehran Dublin Milan Rome Turin Osaka-Kobe Tokyo Yokohama Nairobi Pusan Seoul Taegu Kuala Lumpur Mexico City Amsterdam Auckland Oslo Manila Katowice Lódz Warsaw Lisbon Bucharest Moscow Omsk Singapore Bratislava Cape Town Durban Johannesburg Barcelona Madrid Stockholm Zurich Bangkok Ankara Istanbul Kiev Birmingham London Manchester Chicago Los Angeles New York-Newark Caracas

7,079 7,416 21,720 6,627 3,298 15,294 2,815 19,695 2,556 4,898 9,121 7,190 1,084 2,962 3,357 1,662 11,325 36,507 3,654 b 3,375 3,439 9,778 2,458 1,493 19,319 1,044 1,360 875 11,449 309 b 742b 1,710 2,808 1,933 10,523 1,128 4,737 500b 3,353 2,837 3,607 5,029 5,762 1,279 1,143 6,902 3,846 10,378 2,779 2,296 8,615 2,247 9,134 12,675 19,300 3,051

Particulate matter concentration

Sulfur dioxide

Nitrogen dioxide

Urbanpopulationweighted PM10 micrograms per micrograms per micrograms per cubic meter cubic meter cubic meter 1990 2008 2001a 2001a

69 57 229 62 166 195 167 96 85 71 138 86 24 46 44 66 48 54 42 67 52 55 59 36 89 45 13 27 78 62 61 67 44 40 42 44 107 44 20 40 42 43 37 14 33 88 74 87 91 22 27 24 33 45 28 32

37 30 122 33 89 104 89 51 45 38 74 55 13 26 25 38 31 35 27 32 31 33 36 20 43 31 11 20 26 36 36 39 19 14 16 17 31 13 13 27 28 29 25 10 21 63 36 42 22 11 17 12 21 29 18 14

.. 15 24 12 15 49 26 33 6 .. .. 209 20 31 .. .. 19 18 100 .. 60 44 81 24 74 10 3 8 33 83 21 16 8 10 109 20 20 21 21 31 19 11 24 3 11 11 55 120 14 9 25 26 14 9 26 33

.. 17 41 17 14 34 25 39 13 .. .. .. .. 248 .. .. 63 68 13 .. 51 60 62 .. 130 58 20 43 .. 79 43 32 52 71 .. 34 30 27 72 .. 31 43 66 20 39 23 46 .. 51 45 77 49 57 74 79 57

3.14

ENVIRONMENT

Air pollution Deﬁnitions

• City population is the number of residents of the city or metropolitan area as defined by national authorities and reported to the United Nations. • Particulate matter concentration is fi ne suspended particulates of less than 10 microns in diameter (PM10) that are capable of penetrating deep into the respiratory tract and causing severe health damage. Data are urban-population-weighted PM10 levels in residential areas of cities with more than 100,000 residents. The estimates represent the average annual exposure level of the average urban resident to outdoor particulate matter. • Sulfur dioxide is an air pollutant produced when fossil fuels containing sulfur are burned. • Nitrogen dioxide is a poisonous, pungent gas formed when nitric oxide combines with hydrocarbons and sunlight, producing a photochemical reaction. These conditions occur in both natural and anthropogenic activities.

Data sources Data on city population are from the United Nations Population Division’s World Urbanization Prospects: The 2009 Revision. Data on particulate matter concentrations are from Kiran D. Pandey, David Wheeler, Bart Ostro, Uwe Deichman, Kirk Hamilton, and Kathrine Bolt’s “Ambient Particulate Matter Concentration in Residential and Pollution Hotspot Areas of World Cities: New Estimates Based on the Global Model of Ambient Particulates (GMAPS)” (2006). Data on sulfur dioxide and nitrogen dioxide concentrations are from the WHO’s Healthy Cities Air Management Information System and the World Resources Institute.

a. Data are for the most recent year available. b. Data are from national sources.

2011 World Development Indicators

179

3.15

Government commitment EnvironBiodiversity mental assessments, strategies strategies, or or action action plans plans

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

180

1993 2001 1992 1992

1994

1998 1991

1990

1993 1994

1988

1990

1993 1994 1999 1990

1991 1988 1994 1989 1989 1994

1990 1994 1998

1990 1994 2001

1993 1994 1988 1990 1990 1992 1991 2000

1994 1994 1993 1992 1994 1995 1998 1994 1995 1990 1992 1998

1995 1995 1988 1988

1991

1990 1989

1992

1988

1994 1994 1993 1999 1993

1988 1988 1991

2011 World Development Indicators

Participation in treatiesa

Climate changeb

Ozone layer

CFC control

2002 1995 1994 2000 1994 1994 1994 1994 1995 1994 2000 1996 1994 1995 2000 1994 1994 1995 1994 1997 1996 1995 1994 1995 1994 1995 1994

2004 d 1999d 1992d 2000 d 1990 1999d 1987d 1987 1996d 1990 d 1986e 1988 1993d 1994d 1992f 1991d 1990d 1990 d 1989 1997d 2001d 1989d 1986 1993d 1989d 1990 1989d

2004d 1999 d 1992d 2000d 1990 1999 d 1989 1989 1996 d 1990d 1988e 1988 1993d 1994 d 1992f 1991d 1990d 1990d 1989 1997d 2001d 1989d 1988 1993d 1994 1990 1991d

1995 1995 1997 1994 1995 1996 1994 1994 1994 1999 1994 1995 1996 1995 1994 1994 1994 1994 1998 1994 1994 1994 1995 1994 1996 1994 1996 1996 1996

1990d 1994 d 1994 d 1991d 1993 d 1991e 1992d 1993e 1988 1993 d 1990 d 1988 1992 2005d 1996d 1994 d 1986 1987g 1994d 1990d 1996d 1988 1989d 1988 1987d 1992d 2002d 2000 d 1993d

1993d 1994 d 1994 d 1991d 1993d 1991e 1992d 1993e 1988 1993d 1990d 1988 1992 2005d 1996 d 1994 d 1988 1988g 1994 d 1990d 1996 d 1988 1989 1988 1989d 1992d 2002d 2000 d 1993d

Law of the Seac

2003d 1996 1994 1995 2002d 1994 1995 2001 2006d 1998 1997 1995 1994f 1994 1994 1996 2005

1994 2003

1997 1996

1995 2008 1994 1994 1994f 1994 1996 2004 1994

2005d 1996 1996 1998 1994 1996 d 1994 d 1994 1995 1997 1994 1994 1996 1994

Biological diversity b

Kyoto Protocolb

2002 1994 d 1995 1998 1994 1993e 1993 1994 2000 f 1994 1993 1996 1994 1994 2002d 1995 1994 1996 1993 1997 1995d 1994 1992 1995 1994 1994 1993

2005 2005 2007 2005 2005 2008 2005 2005 2005 2005 2005 2005 2005 2007 2005 2005 2005 2005 2005 2005 2005 2005 2008 2009 2005 2005

1994 1996 1994 1994 1994 1996 1994 1993g 1993 1996 1993 1994 1994 1996 d 1994 1994 1994e 1994 1997 1994 1994 d 1993 1994 1994 1995 1993 1995 1996 1995

2005 2005 2007 2005 2007 2007 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2007 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005

CITES

1985d 2003d 1983d 1981 1976 1982d 1998d 1981 1995d 1983 1984 d 1979 2002 1977d 1975 1991d 1989 d 1988d 1997 1981d 1975 1980d 1989 d 1975 1981d 1981 1976d 1983d 1975 1994 d 2000 d 1990 d 1993f 1977 1986d 1975 1978 1987d 1994d 1992d 1989 d 1976d 1978 1989 d 1977d 1996d 1976 1975 1992d 1979 1981d 1990 d 1985 d

CCD

Stockholm Convention

1996 d 2000d 1996 1997 1997 1997 2000 1997d 1998 d 1996 2001d 1997d 1996 1996 2002d 1996 1997 2001d 1996 1997 1997 1997 1996 1996 1996 1998 1997

2004 2006 2006d 2005 2003 2004 2002 2004d 2007 2004d 2006 2004 2003 2010 2002d 2004 2004 2004 2005 2006 2009 2001 2008 2004 2005 2004

1999 2004 1997 1998 1997 2001e 1997 2000 d 1996d 1997d 1996 1996 1997d 1996 1997 1996 e 1997 1996 d 1996 1999 1996 1997 1997 1998d 1997 1996 1996 1997

2008 2005d 2007 2007 2004 2007 2007 2002 2003 2007 2004 2003 2008 2005d 2008d 2003 2002e 2004g 2007 2006 2006 2002 2003 2006 2008 2007 2008 2005

EnvironBiodiversity mental assessments, strategies strategies, or or action action plans plans

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

1995 1993 1993

1994 1993

1994 1991 1994

1992

1995 1995

1989

1988 1994 1991

1991 1988 1989

1988 1990 1988 2002 1995 1988 1994 1989 1992 1993 1994 1994 1994 1990

1994 1990 1992

1989 1993 1995

1991 1992 1994 1991 1993 1988 1989 1991

3.15

ENVIRONMENT

Government commitment Participation in treatiesa

Climate changeb

Ozone layer

CFC control

Law of the Seac

Biological diversity b

Kyoto Protocolb

1994 1994 1994 1996

1988d 1991d 1992d 1990 d

1989d 1992d 1992 1990d

2002 1995 1994

1994 1994 1994 1996 1996 1995 1994 1995 1993e 1993 1994 1994 1994 g 1994

2005 2005 2005 2005 2009 2005 2005 2005 2005 2005 2005 2009 2005 2005 2005

2002 1996 g 1996 g 1995 1994 1995 2000 2001 1996 1997d 1996 1994 1994 1995 1996 1992 1993 1995 1993 1995 1995 1995 1997 1993 1994 e 1993 1995 1995 1994 1993 1995 1994 1995 1993 1994 1993 1993 1996 1993

2005 2005 2005 2005 2007 2005 2005 2006 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005

1994 1996 1994 1995 1994 1994 1995 1994 1995 1994

1988d 1992d 1988 1993d 1988d 1989 d 1998d 1988d 1995d 1992

1988 1992 1988 1993d 1988 1989d 1998 d 1988 1995d 1992

1995 2000 1995 1995 1995 1995 2003 1999 1995 1998 1999 1994 1994 1995 1994 1994 1994 1995 1994 1996 1995 1995 1995 1994 1994 1994 1996 1995 1994 1994 1995 1994 1995 1994 1994 1994 1994 1994 1994

1992d 2000 d 1998d 1995d 1993d 1994d 1996d 1990d 1995d 1994f 1996d 1991d 1989d 1994 d 1994 d 1992d 1987 1996d 1996d 1995 1994d 1993d 1993d 1994 d 1988d 1987 1993d 1992d 1988d 1986 1999 d 1992d 1989d 1992d 1992d 1989 1991d 1990d 1988 d

1992d 2000d 1998 d 1995d 1993d 1994 d 1996 d 1990d 1995d 1994f 1996 d 1991d 1989d 1994 d 1994 d 1992d 1988 1996 d 1996 d 1995 1994 d 1993d 1993d 1994 d 1988e 1988 1993d 1992d 1988d 1988 1999 d 1992d 1989 1992d 1992d 1993 d 1991 1990d 1988

1994 1996 1995 1994 1996 1995d 1994 1996 1994 1998 2004d 1995 2007 2008 2003d 1994f 2001 1996 1994 1996 1994 1994 2007 1996 2007 1997 1996 1994 1998 1996 1996 2000 1994 1996 1994 1997 1996 1997 1994 1994 1998 1997

2005

CITES

1985d 1976 1978d 1976 2002 1979 1979 1997d 1980 1978d 2000 d 1978 1993d 2002 2004d 1997d 2003 2005d 2003d 2001d 2000d 1975 1982d 1977d 1994 d 1998 d 1975 1991d 2001d 1996d 1975 1981d 1997d 1990 d 1975 d 1984 1989d 1977d 1975 1974 1976 1976d 1978 1975d 1976 1975 1981 1989 1980

CCD

1999 d 1997 1998 1997 2010 1997 1996 1997 1998 d 1998e 1997 1997 1997 2004d 1999

Stockholm Convention

2008 2006 2006 2010

2007 2002d 2004 2007 2004 2002d 2007

1997 1997d 1996 e 2003d 1996 1996 1998 d 1996 2003d 2002d 1997 1996 1997 1996 1996 1996 1996 1999 d 1996 1997 1997 1997d 1997 1997 1996e 2000 d 1998 1996 1997 1996 1996 1997 1996 2001d 1997 1996 2000 2002 1996

2003 2005 2004 2003 2004 2004 2004 2005 2004d 2005d 2007 2002e 2004 2005 2006 2004 2002 2005 2008 2003 2003 2004 2005 2004 2008 2004d

1999

2004d

2011 World Development Indicators

2006 2006 2006 2004 2003 2002 2002d 2005d 2006 2004 2005 2009

181

3.15

Government commitment EnvironBiodiversity mental assessments, strategies strategies, or or action action plans plans

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe

1995 1999 1991

1994

1984

1991

1994 1993

1995

1994 1993 1994

1991

1999 1994

1988

1991 1994 1998

1988

1994 1999

1988

1995 1995

1994 1995

1993 1996 1994 1987

1992

Participation in treatiesa

Climate changeb

Ozone layer

CFC control

Law of the Seac

Biological diversity b

Kyoto Protocolb

1994 1995 1998 1995 1995

1993d 1986 e 2001d 1993d 1993d

1993d 1988e 2001d 1993d 1993

1996 1997

1994 1995 1996 2001g 1994

1995 1997 1994 1996 1997 1994 1994 1994 1997 1994 1994 1996 1998 1996 1995 1995 1994 1994 2004 1995 1994 1997 1996 1994 1994 1994 1994 1995 1995

2001d 1989d 1993 f 1992f 2001d 1990d 1988 d 1989 d 1993d 1992d 1986 1987 1989d 1996d 1993d 1989 d 1991d 1989d 1989d 1991d 1993 d 1988d 1986e 1989d 1987 1986 1989 d 1993 d 1988 d 1994d

2001d 1989d 1993f 1992f 2001d 1990d 1988 1989d 1993d 1992d 1988 1988 1989d 1998 d 1993d 1989 1991 1989d 1989d 1991d 1993d 1988 1988e 1989d 1988 1988 1991d 1993d 1989 1994 d

1994 1994 1996 1995f 1994 1997 1997 1994 1994

1995 1995 1994 1995 1994 1993 1994 1996 1997g 1996 2004 1995e 1996 1993 1997 1996 g 1993 1995 2000 1994

2005 2005 2005 2005 2005 2008 2007 2006 2005 2005 2011 2005 2005 2005 2005 2006 2005 2005 2006 2009 2005 2005 2005 2005 2005 2009 2005 2005 2005 2005 2005

2006d

1993 1995g 1994 1994

1996 1994 1994

1996 d 1990d 1992d

1996 d 1990d 1992d

1994 1994 1994

1996 1993 1994

1996 1994

1996

1994 1994 1994 1994

1994 1999 1997d 1994

1994 g 1995 1994 g 1996

CITES

1994 d 1992 1980 d 1996d 1977d

CCD

2005 2005 2005 2005

1991d 1999 d 1990d 1976 1974 1975 1997d 1977 1994 d

1998 2003 1999 1997 1996 2008 1997 1999 2002 2001 2002 1997 1996 1999 1996 1997 1996 1996 1997 1997 1997 2001 1996 2000 1996 1998 1996 1997 2002 1999 1997 2001 1999 1996 1998 1998

2005 2006 2009

1997d 1980 d 1981d

1997 1996 1997

1994d 1986 d 1993 2000 d 1985d 1975 1986d 1979 d 1982 1997d 1974 1974 2003d 1979 1983 1978 1984 d 1974 1996d

Stockholm Convention

a. Ratification of the treaty. b. Year the treaty entered into force in the country. c. Convention became effective November 16, 1994. d. Accession. e. Acceptance. f. Succession. g. Approval.

182

2011 World Development Indicators

2004 2002d 2003 2009 2003d 2005 2002 2004 2010d 2002 2004 2005 2006 2006 2002 2003 2005 2007 2004 2005 2004 2002d 2004 2009 2004 d 2002 2005 2004 2005 2002 2004 2006

About the data

3.15

ENVIRONMENT

Government commitment Deﬁnitions

National environmental strategies and participation

Environment and Development (the Earth Summit) in

• Environmental strategies or action plans pro-

in international treaties on environmental issues pro-

Rio de Janeiro, which produced Agenda 21—an array

vide a comprehensive analysis of conservation and

vide some evidence of government commitment to

of actions to address environmental challenges:

resource management issues that integrate envi-

sound environmental management. But the signing

• The Framework Convention on Climate Change

ronmental concerns with development. They include

of these treaties does not always imply ratification,

aims to stabilize atmospheric concentrations of

national conservation strategies, environmental

nor does it guarantee that governments will comply

greenhouse gases at levels that will prevent human

action plans, environmental management strategies,

with treaty obligations.

activities from interfering dangerously with the

and sustainable development strategies. The date

global climate.

is the year a country adopted a strategy or action

In many countries efforts to halt environmental degradation have failed, primarily because govern-

• The Vienna Convention for the Protection of the

plan. • Biodiversity assessments, strategies, or

ments have neglected to make this issue a priority, a

Ozone Layer aims to protect human health and the

action plans include biodiversity profiles (see About

reflection of competing claims on scarce resources.

environment by promoting research on the effects

the data). • Participation in treaties covers nine

To address this problem, many countries are prepar-

of changes in the ozone layer and on alternative

international treaties (see About the data). • Cli-

ing national environmental strategies—some focus-

substances (such as substitutes for chlorofluoro-

mate change refers to the Framework Convention

ing narrowly on environmental issues, and others

carbon) and technologies, monitoring the ozone

on Climate Change (signed in 1992). • Ozone layer

integrating environmental, economic, and social

layer, and taking measures to control the activities

refers to the Vienna Convention for the Protection

concerns. Among such initiatives are conservation

that produce adverse effects.

of the Ozone Layer (signed in 1985). • CFC control

strategies and environmental action plans. Some

• The Montreal Protocol for Chlorofl uorocarbon

refers to the Protocol on Substances That Deplete

countries have also prepared country environmen-

Control requires that countries help protect the

the Ozone Layer (the Montreal Protocol for Chloro-

tal profiles and biodiversity strategies and profiles.

earth from excessive ultraviolet radiation by cut-

fluorocarbon Control) (signed in 1987). • Law of

National conservation strategies—promoted by

ting chlorofluorocarbon consumption by 20 percent

the Sea refers to the United Nations Convention on

the World Conservation Union (IUCN)—provide a

over their 1986 level by 1994 and by 50 percent

the Law of the Sea (signed in 1982). • Biological

comprehensive, cross-sectoral analysis of conser-

over their 1986 level by 1999, with allowances for

diversity refers to the Convention on Biological Diver-

vation and resource management issues to help inte-

increases in consumption by developing countries.

sity (signed at the Earth Summit in 1992). • Kyoto

grate environmental concerns with the development

• The United Nations Convention on the Law of the

Protocol refers to the protocol on climate change

process. Such strategies discuss current and future

Sea, which became effective in November 1994,

adopted at the third conference of the parties to the

needs, institutional capabilities, prevailing technical

establishes a comprehensive legal regime for seas

United Nations Framework Convention on Climate

conditions, and the status of natural resources in

and oceans, establishes rules for environmental

Change in December 1997. • CITES is the Conven-

a country.

standards and enforcement provisions, and devel-

tion on International Trade in Endangered Species of

ops international rules and national legislation to

Wild Fauna and Flora, an agreement among govern-

prevent and control marine pollution.

ments to ensure that the survival of wild animals

National environmental action plans, supported by the World Bank and other development agencies, describe a country’s main environmental concerns,

• The Convention on Biological Diversity promotes

and plants is not threatened by uncontrolled exploita-

identify the principal causes of environmental prob-

conservation of biodiversity through scientifi c

tion. Adopted in 1973, it entered into force in 1975.

lems, and formulate policies and actions to deal with

and technological cooperation among countries,

• CCD is the United Nations Convention to Combat

them. These plans are a continuing process in which

access to financial and genetic resources, and

Desertification, an international convention address-

governments develop comprehensive environmental

transfer of ecologically sound technologies.

ing the problems of land degradation in the world’s

policies, recommend specific actions, and outline

But 10 years after the Earth Summit in Rio de

drylands. Adopted in 1994, it entered into force in

the investment strategies, legislation, and institu-

Janeiro the World Summit on Sustainable Develop-

1996. • Stockholm Convention is an international

tional arrangements required to implement them.

ment in Johannesburg recognized that many of the

legally binding instrument to protect human health

Biodiversity profiles—prepared by the World Con-

proposed actions had yet to materialize. To help

and the environment from persistent organic pollut-

servation Monitoring Centre and the IUCN—provide

developing countries comply with their obligations

ants. Adopted in 2001, it entered into force in 2004.

basic background on species diversity, protected

under these agreements, the Global Environment

areas, major ecosystems and habitat types, and

Facility (GEF) was created to focus on global improve-

legislative and administrative support. In an effort

ment in biodiversity, climate change, international

to establish a scientific baseline for measuring prog-

waters, and ozone layer depletion. The UNEP, United

Data on environmental strategies and participa-

ress in biodiversity conservation, the United Nations

Nations Development Programme, and World Bank

tion in international environmental treaties are

Environment Programme (UNEP) coordinates global

manage the GEF according to the policies of its gov-

from the Secretariat of the United Nations Frame-

biodiversity assessments.

erning body of country representatives. The World

work Convention on Climate Change, the Ozone

Bank is responsible for the GEF Trust Fund and chairs

Secretariat of the UNEP, the World Resources

the GEF.

Institute, the UNEP, the Center for International

To address global issues, many governments have also signed international treaties and agreements

Data sources

launched in the wake of the 1972 United Nations

Earth Science Information Network, and the

Conference on the Human Environment in Stock-

United Nations Treaty Series.

holm and the 1992 United Nations Conference on

2011 World Development Indicators

183

3.16 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

184

Contribution of natural resources to gross domestic product Total natural resources rents

Oil rents

Natural gas rents

Coal rents, hard and soft

Mineral rents

Forest rents

% of GDP

% of GDP

% of GDP

% of GDP

% of GDP

% of GDP

2009

2009

2009

2009

2009

2009

4.0 1.8 25.2 39.0 6.0 0.8 6.7 0.3 44.5 3.9 1.7 0.0 1.9 17.5 2.0 3.5 5.0 1.2 3.7 11.3 1.5 9.4 3.7 7.3 36.4 15.6 2.0 0.0 6.3 28.0 56.8 0.4 5.9 1.1 .. 0.3 1.8 0.8 15.7 10.7 0.5 1.4 0.7 5.0 0.6 0.1 45.0 3.2 0.3 0.1 8.6 0.1 1.6 10.5 3.5 0.7 1.8

0.0 1.7 15.1 38.6 3.5 0.0 0.9 0.1 39.6 0.0 1.2 0.0 0.0 4.5 0.0 0.0 2.1 0.0 0.0 0.0 0.0 6.8 2.1 0.0 33.7 0.0 1.4 0.0 5.2 3.9 52.8 0.0 3.6 0.4 .. 0.0 1.4 0.0 15.3 5.3 0.0 0.0 0.0 0.0 0.0 0.0 39.9 0.0 0.2 0.0 0.0 0.0 0.7 0.0 0.0 0.0 0.0

2011 World Development Indicators

0.0 0.0 9.7 0.1 1.9 0.0 0.8 0.1 4.9 3.2 0.1 0.0 0.0 10.3 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.3 0.6 0.0 0.0 0.1 0.2 0.0 0.5 0.0 0.0 0.0 1.0 0.5 .. 0.0 0.4 0.0 0.1 5.1 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0 1.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.2 0.4 0.0 0.6 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 2.7 0.0 1.0 0.0 0.0 0.0 0.0 0.0 .. 0.3 0.0 0.0 0.0 0.0 0.0 0.0 1.1 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.2 0.0 0.0 0.0 0.0 0.0

.. 0.0 0.2 0.0 0.5 0.8 4.9 0.0 0.0 0.0 0.0 0.0 0.0 2.2 1.5 3.4 2.4 1.0 0.0 1.2 0.0 0.1 0.6 0.0 0.0 14.8 0.3 0.0 0.5 11.6 0.0 0.1 0.0 0.0 .. 0.0 0.0 0.8 0.0 0.2 0.0 0.0 0.0 0.2 0.1 0.0 0.1 0.0 0.0 .. 6.5 0.1 0.0 5.3 0.0 0.0 0.6

4.0 0.1 0.1 0.2 0.1 0.0 0.1 0.1 0.0 0.6 0.5 0.0 1.9 0.4 0.5 0.2 0.4 0.2 3.7 10.1 1.5 2.2 0.4 7.3 2.7 0.6 0.2 0.0 0.1 12.5 3.9 0.4 1.3 0.2 .. 0.2 0.0 0.0 0.2 0.1 0.5 1.4 0.7 4.8 0.5 0.0 4.7 3.2 0.1 0.0 2.1 0.0 0.9 5.3 3.5 0.7 1.2

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

3.16

Total natural resources rents

Oil rents

Natural gas rents

Coal rents, hard and soft

Mineral rents

Forest rents

% of GDP

% of GDP

% of GDP

% of GDP

% of GDP

% of GDP

2009

2009

2009

2009

2009

2009

0.5 4.0 5.9 28.4 68.6 0.1 0.3 0.1 1.2 0.0 1.7 27.3 1.4 .. 0.0 0.0 .. 0.5 1.9 1.1 0.0 1.8 15.6 48.4 1.4 0.1 2.0 2.5 12.3 1.3 30.1 0.0 6.8 0.2 12.7 2.3 8.5 .. 0.5 5.6 1.1 2.3 2.9 1.7 23.3 13.2 40.1 4.4 0.1 32.7 1.7 8.2 1.7 0.8 0.1 .. 28.6

0.2 0.8 2.4 21.4 68.1 0.0 0.0 0.1 0.0 0.0 0.0 20.9 0.0 .. 0.0 0.0 .. 0.5 0.0 0.0 0.0 0.0 0.0 44.7 0.1 0.0 0.0 0.0 6.1 0.0 0.0 0.0 5.5 0.1 1.4 0.0 0.0 .. 0.0 0.0 0.1 0.7 0.0 0.0 20.3 9.5 32.3 0.7 0.0 0.0 0.0 0.9 0.0 0.1 0.0 .. 14.0

0.2 0.5 1.3 6.6 0.5 0.0 0.2 0.0 0.0 0.0 0.1 4.7 0.0 .. 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 0.0 3.7 0.0 0.0 0.0 0.0 5.7 0.0 0.0 0.0 0.7 0.0 0.0 0.0 5.1 .. 0.0 0.0 1.1 0.5 0.0 0.0 1.8 3.6 7.7 2.7 0.0 0.0 0.0 0.4 0.4 0.1 0.0 .. 14.6

0.1 2.2 2.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.3 0.0 .. 0.0 .. .. 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 3.9 0.0 0.0 .. 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.1 0.9 0.0 .. 0.0

0.0 1.7 1.6 0.3 0.0 0.0 0.1 0.0 1.1 .. 1.6 1.7 0.0 .. .. .. .. 0.0 0.0 0.0 0.0 0.0 0.7 0.0 0.0 0.0 0.1 0.0 0.0 0.0 29.4 0.0 0.4 0.0 11.0 2.2 0.0 .. 0.5 0.0 0.0 0.4 1.0 .. 0.0 0.0 0.0 0.0 0.0 29.7 0.0 6.8 1.1 0.4 .. .. 0.0

ENVIRONMENT

Contribution of natural resources to gross domestic product

0.1 1.0 0.5 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 1.4 .. 0.0 .. .. 0.0 1.9 1.1 0.0 1.8 14.9 0.0 1.3 0.1 1.9 2.5 0.5 1.3 0.6 0.0 0.1 0.1 0.3 0.1 3.4 .. 0.0 5.6 0.0 0.6 1.8 1.7 1.2 0.1 0.0 1.0 0.1 3.0 1.7 0.1 0.2 0.2 0.1 .. ..

2011 World Development Indicators

185

3.16 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

Contribution of natural resources to gross domestic product Total natural resources rents

Oil rents

Natural gas rents

Coal rents, hard and soft

Mineral rents

Forest rents

% of GDP

% of GDP

% of GDP

% of GDP

% of GDP

% of GDP

2009

2009

2009

2009

2009

2009

0.8 5.8 0.0 3.4 0.0 .. 0.0 0.0 0.0 0.0 .. 0.1 0.0 0.0 0.0 0.0 0.0 0.0 1.8 0.1 0.4 1.7 0.0 0.0 24.9 0.9 0.0 24.0 0.0 2.4 3.3 0.4 0.2 0.0 25.4 1.2 1.3 .. 0.3 0.0 0.0 0.6 w 0.9 1.4 0.8 2.1 1.3 0.6 3.7 0.5 4.6 0.8 0.5 0.3 0.1

0.2 1.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.1 .. 4.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.1 0.0 0.0 2.4 0.0 0.0 0.2 0.0 0.1 0.0 2.2 .. 0.0 0.1 3.2 0.5 w 0.1 1.4 2.1 0.5 1.3 2.4 0.9 0.1 0.0 1.8 1.3 0.1 0.0

.. 1.1 0.0 0.0 0.4 .. 0.6 0.0 0.0 0.0 .. 3.3 .. 0.0 0.1 .. 0.4 0.0 0.2 0.0 3.5 0.0 0.0 2.1 0.0 .. 0.1 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.5 0.1 .. 0.0 16.4 1.9 0.5 w 2.0 1.0 0.6 1.5 1.0 0.4 0.7 2.0 0.4 1.4 1.8 0.2 ..

2.0 20.7 3.3 47.2 1.8 .. 4.5 0.0 0.3 0.1 .. 4.7 0.0 0.8 16.9 2.3 0.8 0.0 14.4 0.2 6.3 3.6 0.4 4.5 35.2 4.9 0.3 41.0 5.2 3.6 20.9 1.4 0.9 0.7 28.2 15.6 8.1 .. 19.7 18.4 5.2 3.7 w 6.3 8.7 6.8 11.2 8.7 5.3 13.7 7.0 22.7 5.8 14.2 1.6 0.2

0.9 13.4 0.0 43.8 0.0 .. 0.0 0.0 0.0 0.0 .. 0.1 0.0 0.0 16.0 0.0 0.0 0.0 12.4 0.1 0.0 1.6 0.0 0.0 10.3 3.8 0.1 17.1 0.0 0.9 17.6 1.0 0.5 0.0 2.8 13.8 6.0 .. 19.4 0.0 0.0 1.9 w 0.7 4.7 2.9 6.8 4.6 1.6 8.2 4.1 17.6 0.8 8.8 0.9 0.0

Note: Components may not sum to 100 percent because of rounding.

186

2011 World Development Indicators

0.2 0.3 3.3 0.0 1.3 .. 3.8 0.0 0.3 0.1 .. 1.2 0.0 0.8 0.8 2.3 0.5 0.0 0.0 0.0 2.4 0.3 0.4 2.4 0.0 0.2 0.1 .. 5.2 0.3 .. 0.0 0.1 0.7 0.0 0.0 0.7 .. 0.0 2.0 3.3 0.2 w 2.6 0.3 0.4 0.3 0.4 0.2 0.2 0.3 0.1 1.1 1.7 0.1 0.0

3.16

ENVIRONMENT

Contribution of natural resources to gross domestic product About the data Accounting for the contribution of natural resources

the price of a commodity and the average cost of

savings measures the net additions or subtractions

to economic output is important in building an analyt-

producing it. This is done by estimating the world

from a country’s stock of tangible and intangible

ical framework for sustainable development. In some

price of units of specific commodities and subtract-

capital. This table is now included in the Economy

countries earnings from natural resources, especially

ing estimates of average unit costs of extraction

section as table 4.11 along with the closely related

from fossil fuels and minerals, account for a sizable

or harvesting costs (including a normal return on

table 4.10 “Toward a broader measure of income.”

share of GDP, and much of these come in the form of

capital). These unit rents are then multiplied by the

economic rents—revenues above the cost of extract-

physical quantities countries extract or harvest to

ing them. Natural resources give rise to economic

determine the rents for each commodity as a share

rents because they are not produced. For produced

of gross national income.

Deﬁnitions • Oil rents are the difference between the value of

goods and services competitive forces expand sup-

This definition of economic rent differs from that

crude oil production at world prices and total costs

ply until economic profits are driven to zero, but natu-

used in the System of National Accounts, where

of production. • Natural gas rents are the differ-

ral resources in fixed supply often command returns

rents are a form of property income, consisting of

ence between the value of natural gas production

well in excess of their cost of production. Rents from

payments to landowners by a tenant for the use of

at world prices and total costs of production. • Coal

nonrenewable resources—fossil fuels and miner-

the land or payments to the owners of subsoil assets

rents are the difference between the value of both

als—as well as rents from overharvesting of forests

by institutional units permitting them to extract sub-

hard and soft coal production at world prices and

indicate the liquidation of a country’s capital stock.

soil deposits.

their total costs of production. • Mineral rents are

When countries use such rents to support current

The Environment section of previous editions of the

the difference between the value of production for

consumption rather than to invest in new capital to

World Development Indicators included a table “Toward

a stock of minerals at world prices and their total

replace what is being used up, they are, in effect,

a broader measure of savings,” which showed the

costs of production. Minerals included in the calcu-

borrowing against their future.

derivation of adjusted net savings taking into account

lation are tin, gold, lead, zinc, iron, copper, nickel,

The estimates of natural resources rents shown in

consumption of fixed and natural capital and pollution

silver, bauxite, and phosphate. • Forest rents are

the table are calculated as the difference between

damage and additions to human capital. Adjusted net

roundwood harvest times the product of average prices and a region-specific rental rate (based on a number of reviews, World Bank 2011). • Total natu-

Oil dominates the contribution of natural resources in the Middle East and North Africa

3.16a

ral resources rents are the sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents,

Natural resources rents (percent of GDP)

Oil

Natural gas

Coal

Mineral

Forest

and forest rents.

25 20 15 10 5 0 East Asia & Pacific

Europe & Central Asia

Latin America & Carib.

Middle East & N. Africa

South Asia

Sub-Saharan Africa

Source: Table 3.16.

Upper middle-income countries have the highest contribution of natural resources to GDP

Natural resources rents (percent of GDP)

Oil

3.16b

Natural gas

Coal

Mineral

Forest

9.6 7.2

Data sources

4.8

Data on contributions of natural resources to

2.4

GDP are estimates based on sources and meth0.0 Low income

Source: Table 3.16.

Lower middle income

Upper middle income

High income

World

ods described in The Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium (World Bank 2011a).

2011 World Development Indicators

187

Text figures, tables, and boxes

ECONOMY

Introduction

4

R

ecently revised data now confirm that in 2009 the world economy experienced the steepest global recession since the Great Depression. World gross domestic product (GDP) contracted 1.9 percent in 2009, with high-income economies contracting 3.3 percent and developing economies expanding just 2.7 percent, down from 8.6 percent in 2008. Among developing country regions, Europe and Central Asia fared the worst, contracting 5.8 percent (figure 4a). Contrast that with East Asia and Pacific, which grew at 7.4 percent, and South Asia, at 7 percent. The global economy rebounded in 2010, with domestic demand in developing countries accounting for 46 percent of global growth. Developing economies’ contribution to global growth has been rising since 2000 and was more stable than that of high-income economies during the recent recession (figure 4b). Preliminary estimates, often revised, indicate that the world economy grew 3.9 percent—2.8 percent in high-income economies and 7 percent in developing economies (figure 4c). Revisions to GDP Revisions to GDP usually occur one to two months after the initial release, as additional data sources become available. For example, the U.S. Bureau of Economic Analysis releases three versions of quarterly GDP estimates—advance (about a month after the quarter ends), preliminary (two months after), and final (three months after). Other countries follow a similar process, although the reporting lag varies. And some countries compile GDP only annually not quarterly. The differences between GDP estimates decline with each revision, and GDP data become more stable on average (figure 4d). More significant revisions to GDP involve new methodologies and new or improved data sources and data collection practices. Countries with advanced statistical capacity comprehensively revise GDP estimates every five years. These revisions take into account the latest recommendations of the Intersecretariat Working Group on National Accounts. They may also incorporate a change in the base year used for the constant price data (rebasing). Rebasing adjusts the weights used to compute aggregate measures by selecting a new set of relative component prices in the newly chosen base year. Comprehensive revisions of GDP estimates are usually higher as improved data sources increase the coverage of the economy and new weights for growing industries more accurately reflect contributions

Differences in GDP growth among developing country regions

4a

GDP growth (percent)

2008

2009

2010a

10 5 0 –5 –10 East Asia & Pacific

Europe & Central Asia

Latin America & Caribbean

Middle East & North Africa

South Asia

Sub-Saharan Africa

a. Data are preliminary estimates. Source: World Development Indicators data files.

Developing countries are contributing more to global growth Contribution to GDP growth (percent)

4b

High-income economies

Developing economies

World GDP

6 4 2 0 –2 –4 2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010a

a. Data are preliminary estimates. Source: World Development Indicators data files.

2011 World Development Indicators

189

4c

Economies—both developing and high income—rebounded in 2010 GDP growth (percent) 10 Developing economies

5 World

0 High-income economies –5 2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010a

a. Data are preliminary estimates. Source: World Development Indicators data files.

Revisions to GDP decline over time, and GDP data become more stable on average

4d

Average difference in GDP (percent) 3

2

1

0 2000

2001

2002

2003

2004

2005

2006

2007

2008

Note: Average differences in current price GDP between World Development Indicators 2010 and 2011. Source: World Development Indicators data files.

4e

Ghana’s revised GDP was 60 percent higher in the new base year, 2006 World Development Indicators 2010

GDP ($ billions)

World Development Indicators 2011

30

20

10

0 2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Source: World Development Indicators data files.

4f

Revised data for Ghana show a larger share of services in GDP 2008 value added by industry (percent of GDP)

World Development Indicators 2010

World Development Indicators 2011

50

to the economy. This has been the case for several countries that recently undertook such revisions to their national accounts statistics. In November 2010 the Ghana Statistical Service revised Ghana’s national accounts series, increasing GDP 60 percent in 2006, the new base year (figure 4e). Of the increase, 11 percentage points are in agriculture, 6 in industry, and 44 in services (figure 4f). Other countries have made similar revisions to their national accounts, incorporating improved methodology and data sources. Namibia revised its national accounts in 2008, resulting in 10–30 percent higher GDP estimates for 2000–07. Malawi revised its national accounts in 2007, raising GDP 37 percent. São Tomé and Príncipe revised its national accounts in 2006, resulting in 47.5 percent higher GDP in the new base year 2001. For more information on countries that have recently revised their national accounts data, see Primary data documentation. Many countries do not incorporate new sources of data into national accounts data compilation until they change the base year, which is the base or pricing period for constant price calculations. Such revisions can be substantial because of the long lag between rebasing exercises. The adjustments arising from rebasing can be reduced by incorporating new data sources in a timely manner and ensuring that the accounts are rebased at least every five years. Data users should be aware that rebasing creates a break in the time series. New data sources and methodologies are usually implemented only for recent years, creating a jump in GDP between the last year of the old data and the first year of the new. For constant price GDP these breaks can be eliminated by linking the old series to the new using historical growth rates. But for nominal GDP data the break in the time series cannot be avoided unless the statistics office revises historical series backward at a detailed level.

40

Broader measures of income and savings

30 20 10 0 Agriculture Source: World Development Indicators data files.

190

2011 World Development Indicators

Industry

Services

Two tables have been added to the Economy section this year. Table 4.10 contains new measures of adjusted net national income, and table 4.11 contains measures of adjusted net savings, previously included in the Environment section. Both tables follow recommendations of

ECONOMY

the recently published The Changing Wealth of Nations (World Bank 2011a). Adjusted net savings measures the change in a country’s national wealth. It begins with gross national savings and then adjusts for consumption of fixed capital, depletion of natural resources, changes in human capital, and damages from carbon dioxide and particulate emissions. If adjusted net savings is negative, capital stocks are declining and future well-being is reduced. The report argues that the key to increasing living standards is building national wealth through investment and national savings to finance the investment. The table on adjusted net national income presents growth rates of GDP, gross national income (GNI), and adjusted net national income. GNI is more useful than GDP for measuring the economic resources available to residents of an economy because it takes into account inflows of income (profits, wages, and rents) from outside the economy, net of outflows to other economies (box 4g). Adjusted net national income goes one step further by subtracting from GNI a charge for the consumption of fixed capital (or depreciation) and the depletion of natural resources. For some countries, adjusted net national income growth rates tell a story quite different from that of the more widely used GDP growth rates.

Changes to monetary indicators The monetary indicators in table 4.15 have been revised to reflect the International Monetary Fund’s (IMF) new presentation of monetary data for countries reporting in compliance with the Monetary and Financial Statistics Manual (IMF 2000) and Monetary and Financial Statistics Compilation Guide (IMF 2008). More than 120 countries report their monetary data under

Commission on the Measurement of Economic and Social Progress

4g

Gross domestic product (GDP), the most quoted measure of economic activity, is often used as a measure of welfare. But as the Commission on the Measurement of Economic and Social Progress points out, GDP has many shortcomings as the sole measure of well-being. The commission’s report identified problems with the GDP measure itself and recommended including additional measures of the objective and subjective dimensions of well-being and measures of the sustainability of current consumption levels. The commission endorsed the adjusted net savings approach as the “relevant economic counterpart of the notion of sustainability” (Stiglitz, Sen, and Fitoussi 2009, p. 108). But it pointed out that the adjustment for environmental degradation has so far been limited mostly to carbon dioxide emissions. The report also notes the difficulties of pricing natural resources and environmental degradation. Other recommendations for improving GDP measurement include accounting more accurately for improvements in the quality of goods and services produced and the value of government services (usually based on inputs rather than on actual outputs produced).

this new presentation. A majority of these countries transmit the data on standardized report forms for the country’s monetary aggregates and for the assets and liabilities of the central bank, other depository corporations, and other financial corporations. This new presentation better classifies financial institution assets and liabilities by financial instrument, sector of the domestic economy, and residency. For many countries the new presentation provides broader institutional coverage of other depository corporations and monetary aggregates. In the new presentation, the IMF has adopted broad money as the flagship concept. Broad money consists of currency in circulation outside depository corporations, transferable deposits, and other liquid components. Table 4.15 has replaced money and quasi money with broad money. Claims on the private sector have been replaced with other claims on the domestic economy, consisting of the private sector plus state and local governments, public nonfinancial corporations, and other financial corporations. Claims on governments and other public entities have been replaced with net claims on the central government.

2011 World Development Indicators

191

Tables

4.a Albania Algeria Angola Argentinab Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Bolivia Botswana Brazil Bulgaria Cambodia Cameroon Canada Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Costa Rica Côte d’Ivoire Croatia Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Haiti Honduras Hungary India Indonesia Iran, Islamic Rep. Ireland Israel Italy Jamaica Japan Jordan

192

Recent economic performance Gross domestic product

Exports of goods and services

Imports of goods and services

GDP deﬂator

average annual % growth 2009 2010a

average annual % growth 2009 2010a

average annual % growth 2009 2010a

average annual % growth 2009 2010a

2.5 2.1 0.7 0.9 –14.4 1.3 –3.9 9.3 5.7 1.4 –2.8 3.4 –3.7 –0.6 –4.9 –1.9 2.0 –2.5 –1.5 9.1 –2.8 0.8 2.7 –1.5 3.6 –5.8 –4.2 –4.9 3.5 0.4 4.6 –3.5 –14.1 8.7 –8.0 –2.6 –1.0 4.6 –3.9 –4.7 4.7 –2.0 0.6 2.9 –1.9 –6.3 9.1 4.5 1.8 –7.1 0.8 –5.0 –3.0 –5.2 2.3

3.0 2.4 3.0 8.0 4.0 2.8 1.5 3.7 5.8 7.0 2.1 4.1 7.8 7.6 0.0 4.9 3.0 3.0 5.5 10.0 6.0 4.3 5.2 3.6 3.0 –0.8 1.7 2.1 4.4 2.3 5.1 1.3 1.0 9.0 3.0 1.6 5.1 5.0 5.5 3.5 6.6 –4.0 2.2 –8.5 2.4 0.3 9.5 5.9 1.5 –0.6 3.8 1.1 0.6 4.4 4.0

2011 World Development Indicators

5.9 –3.0 2.4 –6.4 –32.8 2.9 –16.1 2.8 0.0 –8.2 –11.4 –10.8 –28.0 –10.2 –10.3 –6.3 –4.8 –14.2 –5.6 –10.3 –10.1 –2.8 5.4 0.6 9.3 –16.2 –10.2 –9.7 –7.4 –6.4 –14.5 –16.4 –11.2 6.9 –20.5 –12.2 –4.9 2.5 –8.4 –14.3 12.6 –6.2 –6.2 9.9 –12.6 –9.1 –6.7 –9.7 8.5 –4.2 –11.9 –19.1 –10.8 –24.2 –2.7

12.7 3.0 10.0 12.8 8.5 15.0 8.2 11.0 –9.0 6.0 9.7 11.4 12.0 26.0 11.0 8.0 17.0 15.5 8.5 33.0 22.1 17.4 9.3 6.2 4.4 2.5 9.4 5.1 8.1 –2.0 11.8 9.4 5.4 11.7 6.8 6.6 7.0 5.2 11.0 10.7 8.9 0.5 9.9 –7.1 4.5 6.8 8.1 24.7 –3.0 1.7 17.8 8.0 5.7 28.7 5.2

–12.0 16.7 6.6 –19.0 –21.0 –9.0 –14.4 –5.3 –2.6 –8.6 –11.1 –10.2 –9.3 –11.5 –21.5 –4.9 –5.2 –13.9 –14.3 4.1 –8.8 –7.9 –11.9 –12.4 11.0 –20.7 –10.2 –12.5 –9.8 –8.0 –17.9 –23.3 –26.8 16.4 –18.1 –10.7 –2.8 3.8 –6.4 –9.4 –14.1 –18.6 –9.4 5.8 –26.0 –15.4 –7.3 –15.0 7.8 –9.7 –17.7 –14.5 –11.4 –16.7 –7.8

5.2 12.5 8.5 23.1 4.2 28.7 6.8 3.5 –12.5 3.4 8.2 12.3 8.9 35.1 3.0 12.6 12.0 14.6 25.5 35.0 22.5 21.4 10.8 13.1 5.0 1.5 10.4 1.0 11.8 5.0 12.0 15.2 4.0 4.4 3.5 5.2 4.8 3.1 9.0 9.1 10.5 –12.1 14.3 5.9 10.4 5.4 6.8 32.5 16.5 2.1 17.5 9.4 9.3 15.6 6.5

2.3 –9.4 –5.8 10.0 1.4 4.9 0.8 –16.8 6.5 3.9 1.1 –2.4 –5.7 5.7 4.1 5.1 –3.4 –2.1 4.2 –0.6 0.2 4.9 30.2 8.9 1.3 3.3 2.7 0.4 3.0 4.3 10.8 –1.0 –0.6 24.4 0.9 0.5 –19.0 2.4 –2.0 1.4 16.7 1.3 2.4 3.5 4.4 4.6 7.5 8.4 0.6 –3.2 5.2 2.1 6.5 –0.9 8.1

2.0 8.6 36.1 9.4 7.5 5.7 0.6 –2.7 10.7 6.4 –2.8 6.5 6.0 5.3 –0.6 3.9 3.4 2.7 6.6 1.7 0.3 4.8 21.7 7.9 1.3 1.6 1.6 5.3 7.1 4.4 11.2 3.6 –1.1 9.9 –0.4 1.4 9.1 4.8 7.4 1.6 10.6 4.8 6.4 12.6 10.5 2.7 11.5 6.2 15.0 0.6 4.6 1.6 16.7 –1.0 8.5

Current account balance

Gross international reserves

% of GDP 2009 2010a

$ millions 2010a

months of import coverage 2010a

2,496 166,989 .. 52,208 1,859 42,268 22,339 6,409 11,175 5,025 26,779 .. .. 288,575 17,223 3,787 .. 57,151 27,827 2,711,162 266,055 28,076 1,768 4,630 3,502 14,133 42,328 75,077 3,501 2,622 36,517 2,897 2,567 .. 9,547 165,852 .. .. 2,264 215,978 .. 6,352 5,949 1,282 .. 44,988 300,480 92,815 .. 2,114 70,914 158,478 2,330 1,096,069 13,388

4.5 42.0 .. 10.2 6.7 1.8 1.4 7.0 6.4 2.0 0.9 .. .. 13.9 7.6 6.0 .. 1.4 5.4 21.6 6.7 6.6 7.3 4.1 4.8 7.1 3.9 6.7 2.7 1.1 5.7 3.9 2.3 .. 1.4 2.9 .. .. 5.0 2.0 .. 0.9 5.1 5.3 .. 5.5 9.7 7.1 .. 0.2 12.0 3.5 4.0 17.6 9.5

–15.6 –10.0 –10.0 2.8 –15.7 –4.2 2.9 23.7 3.7 –13.0 0.7 4.7 –4.4 –1.5 –9.8 –8.8 –5.1 –2.9 2.6 6.0 8.3 –2.1 –13.7 –1.8 7.2 –5.3 –1.1 3.6 –4.6 –0.5 –1.8 –1.8 4.7 –7.7 2.9 –2.0 .. 8.6 –11.3 5.0 –4.6 –10.9 0.0 –3.6 –3.1 –0.5 –1.9 2.0 3.4 –2.9 3.9 –3.1 –9.3 2.8 –5.0

–12.2 4.6 –5.1 1.8 –12.7 –2.2 3.9 27.2 2.4 –14.0 0.6 8.0 –2.1 –2.7 –2.4 –8.6 –2.7 –2.0 0.6 5.5 22.0 –2.7 –17.2 –3.2 4.1 –4.4 –2.7 5.6 –5.9 –0.8 –4.1 –3.1 4.0 –8.5 3.7 –2.1 12.7 5.2 –12.1 5.9 –3.6 –8.5 –2.5 –13.6 –4.7 –0.4 –3.8 2.6 6.1 –3.6 4.9 –3.6 –7.9 3.8 –4.6

Kazakhstan Kenya Korea, Rep. Kuwait Latvia Lebanon Lithuania Malaysia Mauritius Mexico Morocco Namibia Nepal Netherlands New Zealand Nigeria Norway Oman Pakistan Panama Paraguay Peru Philippines Poland Portugal Romania Russian Federation Saudi Arabia Senegal Singapore Slovak Republic Slovenia South Africa Spain Sri Lanka Sudan Sweden Switzerland Syrian Arab Republic Tanzania Thailand Trinidad and Tobago Tunisia Turkey Uganda Ukraine United Kingdom United States Uruguay Venezuela, RB Vietnam Yemen, Rep. Zambia Zimbabwe

Gross domestic product

Exports of goods and services

Imports of goods and services

GDP deﬂator

average annual % growth 2009 2010a

average annual % growth 2009 2010a

average annual % growth 2009 2010a

average annual % growth 2009 2010a

1.2 2.6 0.2 –4.0 –18.0 9.0 –15.0 –1.7 2.1 –6.5 4.9 –0.8 4.7 –4.0 –0.4 5.6 –1.6 3.6 3.6 2.4 –3.8 0.9 1.1 1.7 –2.6 –8.5 –7.9 0.6 2.2 –1.3 –6.2 –7.8 –1.8 –3.6 3.5 4.5 –5.1 –1.9 4.0 6.0 –2.2 –3.0 3.1 –4.7 7.1 –15.1 –4.9 –2.6 2.9 –3.3 5.3 3.8 6.4 5.7

5.5 5.0 6.2 1.9 –2.2 8.0 0.4 7.4 4.2 5.2 3.5 4.2 3.3 1.7 2.2 7.6 –0.2 4.8 4.4 5.7 8.5 8.0 6.8 3.5 1.4 –1.9 3.8 3.7 4.0 17.5 3.7 1.5 2.7 –0.4 7.1 5.9 5.2 2.7 5.0 7.0 7.5 2.2 3.8 8.1 6.3 4.3 1.7 2.8 7.9 –2.3 6.7 8.0 6.4 5.7

–6.2 –7.0 –0.8 –11.1 –13.9 5.3 –14.3 –10.4 –4.8 –14.8 –13.1 –14.0 38.4 –7.9 0.4 1.1 –3.9 –0.4 –3.3 –0.9 –12.8 –2.5 –13.4 –9.1 –11.7 –11.8 –4.7 –2.8 –8.8 –10.1 8.8 –19.3 –19.5 –11.6 –12.3 –4.4 –13.3 –8.7 5.6 15.5 –12.7 –3.8 –1.6 –5.3 16.2 –25.6 –10.1 –9.5 2.5 –12.9 11.1 –16.3 21.5 5.2

13.0 12.0 28.0 –2.0 4.0 20.0 6.5 28.0 –4.0 15.5 18.4 5.3 6.4 11.7 10.5 5.9 4.6 8.0 14.1 5.3 30.1 –4.1 23.0 6.4 7.4 12.0 5.2 1.5 6.8 29.7 6.9 1.4 6.5 7.8 2.0 7.2 12.2 6.7 –2.0 5.3 21.0 3.0 13.0 6.5 3.4 9.5 7.0 15.0 15.6 3.2 25.0 43.6 20.0 10.5

–15.9 –0.2 –8.2 –17.0 –34.2 6.5 –29.4 –12.3 –4.6 –18.2 –6.0 5.3 20.2 –8.5 –14.8 7.3 –11.4 –13.0 –15.2 –5.6 –13.2 –11.9 –1.9 –14.3 –10.8 –24.6 –30.4 –8.8 –17.1 –11.7 8.4 –7.9 –17.4 –17.8 –9.1 –7.3 –13.2 –5.4 6.4 14.1 –21.8 –4.1 6.7 –14.3 25.2 –38.6 –12.3 –13.8 –8.6 –19.6 6.7 –4.7 15.6 36.0

6.0 14.5 28.0 22.0 1.6 18.5 4.2 30.0 3.9 19.4 7.6 8.3 6.8 12.7 17.5 8.2 8.1 18.0 11.2 13.1 30.3 15.3 23.8 7.5 3.6 8.5 17.5 7.5 4.0 26.7 6.0 –4.1 12.7 7.0 11.5 7.2 15.0 8.3 4.5 6.2 32.0 4.2 16.1 16.0 10.5 5.5 9.4 18.8 19.2 –3.0 32.5 14.2 12.3 6.2

4.7 6.7 3.4 –14.7 –0.7 5.8 –2.1 –6.7 1.5 4.3 1.8 6.5 12.1 –0.3 1.7 –0.6 –4.0 –26.0 20.0 4.1 –0.1 3.0 2.6 3.7 0.1 6.5 2.5 –21.6 –0.5 –1.8 0.0 1.9 7.3 0.2 5.7 –0.8 2.0 0.3 –7.6 7.4 2.0 –15.7 2.9 5.2 16.5 13.4 1.4 0.9 5.9 8.4 6.0 –4.1 12.7 25.3

6.9 4.8 –0.5 13.9 –4.9 4.5 0.0 1.5 2.1 4.9 2.2 4.3 15.1 2.9 4.4 17.0 7.9 21.2 13.4 2.4 4.8 3.2 5.6 2.4 1.0 5.5 8.0 16.7 0.7 –2.2 2.9 –0.2 5.6 0.1 8.2 13.0 0.9 0.9 10.2 8.7 –1.9 4.6 3.8 7.1 6.1 9.2 3.0 0.6 7.1 38.5 12.5 13.8 –5.8 4.2

4.a

ECONOMY

Recent economic performance Current account balance

Gross international reserves

% of GDP 2009 2010a

months of import coverage 2010a

–3.7 –5.7 5.1 25.6 8.7 –21.9 4.4 16.5 –7.9 –0.7 –5.4 1.3 –0.1 4.6 –2.9 12.5 13.1 –0.6 –2.2 –0.2 0.6 0.2 5.3 –2.2 –10.3 –4.5 4.0 6.1 –13.6 17.9 –3.2 –1.5 –4.0 –5.5 –0.5 –7.1 7.7 7.9 –4.5 –8.5 8.3 21.8 –3.1 –2.3 –2.8 –1.5 –1.7 –2.7 0.7 2.6 –7.0 –9.7 –3.2 –1.8

3.8 –5.7 3.7 25.1 1.4 –23.6 2.6 14.7 –9.4 –1.0 –3.2 –1.6 –3.0 5.6 –2.5 10.7 11.8 8.5 –3.1 –6.1 –1.8 –1.7 5.3 –3.1 –10.6 –6.3 5.1 7.8 –14.3 22.6 –0.1 –2.2 –4.1 –6.0 –3.6 –1.9 6.7 7.7 –3.9 –8.3 6.0 25.7 –4.8 –5.9 –3.6 –2.2 –2.9 –3.3 –0.6 5.9 –15.5 –0.6 –4.5 –1.3

$ millions 2010a

28,281 4,327 292,143 24,805 7,604 44,476 6,836 106,501 2,619 120,583 23,585 .. .. 46,147 15,787 .. 50,036 13,025 17,256 .. 3,962 44,215 62,324 93,472 20,937 48,048 479,222 452,391 1,911 .. 2,156 1,108 43,820 31,872 7,240 .. 48,246 269,396 .. .. 172,028 .. .. 85,959 .. 34,571 82,365 488,928 7,744 27,700 .. 5,986 2,094 ..

8.4 4.1 7.0 8.2 7.9 27.3 4.1 6.9 5.7 4.7 7.7 .. .. 1.0 4.8 .. 5.3 7.3 5.7 .. 5.0 17.1 12.1 6.3 2.9 8.0 19.2 32.9 3.9 .. 0.3 0.5 5.8 1.0 6.7 .. 2.9 14.5 .. .. 10.4 .. .. 5.3 .. 7.2 1.4 2.3 9.7 5.9 .. 10.0 5.6 ..

a. Data are preliminary estimates based on World Bank staff estimates and National Sources. b. Private analysts estimate that consumer price index inflation was considerably higher for 2007–09 and believe that GDP volume growth has been significantly higher than official reports indicate since the last quarter of 2008. Source: World Development Indicators data files, the World Bank’s Global Economic Prospects 2011, and the International Monetary Fund’s International Financial Statistics. 2011 World Development Indicators

193

4.1 Afghanistan Albania Algeria Angolaa Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benina Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China a Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. a Costa Rica Côte d’Ivoire a Croatia Cuba Czech Republic Denmark Dominican Republica Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabona Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

194

Growth of output Gross domestic product

Agriculture

Industry

Manufacturing

Services

average annual % growth 1990–2000 2000–09

average annual % growth 1990–2000 2000–09

average annual % growth 1990–2000 2000–09

average annual % growth 1990–2000 2000–09

average annual % growth 1990–2000 2000–09

.. 3.8 1.9 1.6 4.3 –1.9 3.7 2.4 –6.3 4.8 –1.6 2.2 4.8 4.0 .. 5.0 2.7 –1.1 5.5 –2.9 7.0 1.7 3.1 2.0 2.2 6.6 10.6 3.6 2.8 –4.9 1.0 5.3 3.2 0.5 –0.7 1.1 2.7 6.3 1.9 4.4 4.8 5.7 0.4 3.8 2.7 1.9 2.3 3.0 –7.1 1.8 4.3 2.2 4.2 4.4 1.2 0.5 3.2

2011 World Development Indicators

10.5 5.4 4.0 13.1 5.4 b 10.5 3.3 2.0 17.9 5.9 8.4 1.7 4.0 4.1 5.0 4.4 3.6 5.4 5.4 3.0 9.0 3.3 2.1 0.8 10.2 4.1 10.9 4.7 4.5 5.2 4.0 5.1 0.8 3.9 6.7 4.1 1.2 5.5 5.0 4.9 2.6 0.2 5.9 8.5 2.5 1.5 2.1 5.2 7.4 1.0 5.8 3.6 3.7 3.0 1.0 0.7 4.9

.. 4.3 3.6 –1.4 3.5 0.5 3.1 –0.1 –1.7 2.9 –4.0 2.7 5.8 2.9 .. –0.5 3.6 –3.9 5.9 –1.9 3.7 5.4 1.1 3.8 4.9 2.2 4.1 .. –2.7 1.4 .. 4.1 3.5 –5.5 –3.3 0.0 4.6 1.9 –1.7 3.1 1.2 1.5 –6.2 2.6 –0.3 2.0 2.0 3.3 –11.0 0.1 .. 0.5 2.8 4.3 .. .. 2.2

4.9 1.4 4.6 14.0 2.5 6.6 0.0 1.3 5.3 3.3 5.2 –1.0 4.6 3.1 4.9 1.2 3.7 –2.5 6.2 –1.5 5.7 3.4 1.4 0.3 .. 5.2 4.4 –3.3 2.5 1.7 .. 3.5 1.4 2.0 –0.9 0.1 –1.8 3.2 3.7 3.3 3.6 2.7 –2.9 7.0 2.4 0.3 1.4 3.0 0.6 –0.3 .. –1.4 2.9 6.7 .. .. 3.3

.. –0.5 1.8 4.4 3.8 –7.8 2.7 2.5 –2.1 7.3 –1.8 1.8 4.1 4.1 .. 3.7 2.4 –19.5 5.9 –4.3 14.3 –0.9 3.2 0.7 0.6 5.6 13.7 .. 1.4 –8.0 .. 6.2 6.3 –2.2 –1.0 0.2 2.5 7.1 2.6 5.1 5.1 15.0 –2.4 4.1 3.8 1.1 1.6 1.0 –8.1 –0.1 .. 1.0 4.3 4.9 .. .. 3.6

14.5 4.4 3.3 13.4 6.1 11.3 2.6 2.3 23.1 7.8 12.3 0.7 3.8 5.3 6.8 2.5 2.8 5.9 7.3 –6.2 12.0 –0.4 0.1 –0.4 .. 2.7 11.8 –2.6 4.4 8.7 .. 5.1 –0.2 4.6 2.3 5.7 –0.5 2.4 4.2 5.3 1.7 0.6 8.6 9.3 3.6 0.5 0.9 7.4 10.0 0.3 .. 1.4 2.8 4.4 .. .. 4.1

.. .. –2.1 –0.3 2.7 –4.3 1.8 2.5 –15.7 7.2 –0.7 .. 5.8 3.8 .. 4.7 2.0 .. 5.9 .. 18.6 1.4 4.5 –0.2 .. 4.4 12.9 .. –2.5 –8.7 .. 6.8 5.5 –3.5 0.8 4.3 2.2 7.0 1.5 6.3 5.2 10.6 7.3 3.9 6.4 .. 3.0 0.9 .. 0.1 .. .. 2.8 4.0 .. .. 4.0

8.7 .. 2.6 20.2 5.8 4.6 1.3 2.9 10.8 7.9 10.8 .. 2.7 4.5 7.6 4.8 2.6 6.2 6.3 .. 11.3 .. –1.6 –0.1 .. 3.2 11.4 .. 4.0 6.3 .. 4.7 –1.7 3.7 –1.5 7.0 0.4 2.7 5.3 4.7 2.1 –6.0 8.9 7.2 4.1 0.1 3.1 .. 10.9 0.8 .. 1.7 2.8 3.1 .. .. 4.6

.. 6.9 1.8 –2.2 4.5 6.4 4.2 2.5 –2.7 4.5 –0.4 2.0 4.2 4.3 .. 9.1 3.8 .. 3.9 –2.8 7.1 0.2 3.1 0.2 0.8 6.9 11.0 .. 4.1 –13.0 .. 4.7 2.0 2.2 –0.7 1.2 2.7 5.9 2.4 4.1 4.0 5.7 3.2 5.2 2.6 2.2 3.1 3.7 –0.3 2.9 .. 2.6 4.7 3.6 .. .. 3.8

13.5 8.3 5.3 12.1 4.7 12.1 3.7 2.1 10.6 6.1 5.9 2.0 3.2 3.1 4.4 5.6 3.8 6.1 5.5 10.4 9.5 6.2 3.0 –2.5 .. 4.6 11.6 5.3 4.7 11.2 .. 5.6 1.0 4.0 8.3 4.3 1.5 7.1 3.6 5.4 3.2 0.5 7.1 10.2 1.6 1.9 3.2 6.1 8.9 1.5 .. 4.7 4.4 –2.7 .. .. 6.2

Hungary India Indonesiaa Iran, Islamic Rep. Iraq Ireland Israela Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwaita Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysiaa Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmara Namibia Nepal Netherlands New Zealand Nicaragua Niger a Nigeria Norway Omana Pakistan Panama Papua New Guinea Paraguaya Peru Philippinesa Poland Portugal Puerto Rico a Qatar

Gross domestic product

Agriculture

Industry

average annual % growth 1990–2000 2000–09

average annual % growth 1990–2000 2000–09

average annual % growth 1990–2000 2000–09

1.5 5.9 4.2 3.1 .. 7.4 5.5 1.5 1.6 1.0 5.0 –4.1 2.2 .. 5.8 .. 4.9 –4.1 6.4 –1.5 5.3 4.0 4.1 .. –2.5 –0.8 2.0 3.7 7.0 4.1 2.9 5.2 3.1 –9.6 1.0 2.4 6.1 .. 4.0 4.9 3.2 3.2 3.7 2.4 2.5 3.9 4.5 3.8 4.7 3.8 2.2 4.7 3.3 4.7 2.9 4.2 ..

2.9 7.9 5.3 5.4 –0.3 3.9 3.6 0.5 1.5 1.1 6.9 8.8 4.4 .. 4.2 4.8 8.4 4.6 6.9 6.2 4.6 3.1 0.0 5.4 6.3 3.1 3.6 4.8 5.1 5.3 4.7 3.7 2.2 5.6 7.4 5.0 7.9 .. 5.3 3.7 1.7 2.5 3.3 4.3 6.6 2.1 4.5 5.2 6.9 3.4 3.4 6.0 4.9 4.4 0.8 .. 14.2

–1.9 3.2 2.0 3.2 .. 0.0 .. 2.1 –0.6 –1.3 –3.0 –8.0 1.9 .. 1.6 .. 1.0 1.5 4.8 –5.2 2.9 2.8 .. .. –0.4 0.2 1.8 8.6 0.3 2.6 –0.2 0.0 1.5 –11.2 2.5 –0.4 5.2 .. 3.8 2.5 1.8 2.9 4.7 3.0 .. 2.6 5.0 4.4 3.1 4.5 3.3 5.5 1.7 0.5 –0.6 .. ..

5.3 2.9 3.4 5.9 .. –4.6 .. –0.2 –0.7 –0.3 8.3 4.6 2.2 .. 2.0 .. .. 1.8 3.3 2.7 1.4 –2.4 .. .. 1.7 2.2 2.4 2.4 3.5 4.8 0.9 –0.8 2.0 –0.6 5.9 5.8 8.2 .. 0.5 3.1 1.5 1.8 2.7 .. .. 2.4 .. 3.5 3.5 2.2 2.3 4.1 3.6 0.8 –0.3 .. ..

3.5 6.1 5.2 2.6 .. 11.6 .. 1.0 –0.8 –0.3 5.2 –8.6 1.2 .. 6.0 .. 0.3 –10.3 11.1 –8.3 –0.2 5.5 .. .. 3.3 –2.3 2.4 2.0 8.6 6.4 3.4 5.4 3.8 –13.6 –2.5 3.2 12.3 .. 2.4 7.1 1.7 2.5 5.5 2.0 .. 3.8 3.9 4.1 6.0 5.4 0.6 5.4 3.5 7.1 3.1 .. ..

3.5 8.6 4.1 6.9 .. 4.0 .. –0.5 0.2 1.7 8.4 9.6 4.8 .. 5.4 .. .. 0.8 11.9 5.2 4.4 3.6 .. .. 9.6 3.5 4.2 5.5 3.5 4.5 5.0 1.7 1.3 –1.7 6.5 4.1 9.1 .. 6.2 2.8 0.9 1.9 3.7 .. .. –0.3 .. 6.8 5.7 4.1 1.8 6.5 4.0 5.8 –0.8 .. ..

4.1

Manufacturing

average annual % growth 1990–2000 2000–09

7.7 6.7 6.7 5.1 .. .. .. 1.6 –1.8 0.5 5.6 .. 1.3 .. 7.3 .. –0.1 –7.5 11.7 –7.3 1.9 7.9 .. .. 6.6 –5.3 2.0 0.5 9.5 –1.4 5.8 5.3 4.3 –7.1 –9.7 2.6 10.2 .. 7.4 8.9 2.6 .. 5.3 2.6 .. 1.5 6.0 3.8 2.7 4.6 1.4 3.8 3.0 9.9 2.7 .. ..

5.0 8.7 4.7 9.9 .. .. .. –1.1 –1.5 2.8 9.6 6.6 4.3 .. 6.3 .. .. –1.2 –1.9 3.1 2.2 5.7 .. .. 9.0 2.9 5.1 5.0 4.3 5.1 –1.4 0.4 1.1 1.3 7.1 3.1 7.9 .. 5.6 1.0 1.2 .. 4.8 .. .. 2.6 .. 8.7 1.5 3.8 1.2 6.2 3.9 8.5 –0.6 .. ..

ECONOMY

Growth of output

Services

average annual % growth 1990–2000 2000–09

1.3 7.7 4.0 3.8 .. 8.7 .. 1.6 3.8 1.8 5.0 1.1 3.2 .. 5.6 .. 3.5 –5.2 6.6 2.7 1.5 4.5 .. .. 5.8 0.5 2.3 1.6 8.2 3.0 4.9 6.3 2.9 0.7 0.7 3.1 5.0 .. 4.2 6.2 3.6 3.6 5.0 1.9 .. 3.8 5.0 4.4 4.5 –0.6 2.5 4.0 4.0 5.1 2.5 .. ..

2011 World Development Indicators

3.4 9.5 6.2 5.3 .. 4.4 .. 1.0 1.9 1.5 6.1 8.6 4.5 .. 3.7 .. .. 7.9 7.6 7.0 4.3 3.7 .. .. 7.4 3.0 3.6 6.5 6.4 6.5 5.5 5.7 2.6 10.5 8.7 5.0 7.0 .. 5.5 4.1 2.1 3.4 3.7 .. .. 3.0 .. 5.9 7.4 3.8 4.3 6.0 6.1 3.7 1.6 .. ..

195

4.1

Growth of output Gross domestic product

average annual % growth 1990–2000 2000–09

Romania Russian Federation Rwandaa Saudi Arabiaa Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lankaa Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzaniac Thailanda Timor-Lestea Togoa Trinidad and Tobago Tunisiaa Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnama West Bank and Gaza Yemen, Rep.a Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

–0.6 –4.7 –0.2 2.1 3.0 –4.2 –5.0 7.6 2.2 2.7 .. 2.1 2.7 5.3 5.5 3.4 2.3 1.0 5.1 –10.4 3.0 4.2 .. 3.5 3.2 4.7 3.9 –4.9 7.2 –9.3 4.8 2.8 3.6 3.3 –0.2 1.6 7.9 7.3 6.0 0.5 2.3 2.9 w 3.1 3.9 6.5 2.1 3.9 8.5 –1.8 3.2 3.8 5.5 2.5 2.7 2.1

5.6 6.0 7.6 3.8 4.3 5.0 9.5 6.5 5.8 3.8 .. 4.1 2.8 5.5 7.3 2.6 2.4 1.9 4.4 8.2 7.1 4.6 2.4 2.5 7.4 4.9 4.9 13.9 7.8 5.6 7.0 2.0 2.0 3.4 6.9 4.9 7.6 –0.9 3.9 5.4 –7.5 2.9 w 5.4 6.4 8.5 4.4 6.4 9.4 5.9 3.8 4.7 7.3 5.1 2.0 1.5

Agriculture

average annual % growth 1990–2000 2000–09

–1.9 –4.9 2.5 1.6 2.4 .. .. .. 0.2 0.4 .. 1.0 3.1 1.8 7.4 0.9 –0.8 –0.9 6.0 –6.8 3.2 1.0 .. 4.0 2.7 2.3 1.3 –4.7 3.9 –5.6 13.2 –1.3 3.8 2.6 0.5 1.2 4.3 .. 5.6 4.2 4.3 1.9 w 2.9 2.4 3.1 0.9 2.4 3.4 –2.1 2.0 2.9 3.3 3.2 1.2 1.5

7.3 2.1 .. 1.4 2.0 .. .. 2.3 5.0 –0.7 .. 1.5 –0.2 2.8 2.4 1.3 3.5 0.3 3.8 7.7 4.4 2.3 .. 2.8 –7.2 2.6 1.5 14.3 2.3 3.1 3.6 0.6 2.1 2.9 6.5 3.6 3.8 .. .. 1.2 –10.8 2.5 w 3.6 3.6 3.8 3.0 3.6 4.1 3.0 3.0 4.4 3.0 3.2 0.9 0.0

Industry

average annual % growth 1990–2000 2000–09

–1.2 –7.1 –3.8 2.2 3.8 .. .. 7.8 3.7 1.6 .. 1.0 2.3 6.9 8.5 3.2 4.6 0.3 9.2 –11.4 3.1 5.7 .. 1.8 3.2 4.6 4.7 –2.7 12.0 –12.6 3.0 1.3 3.8 1.1 –3.4 1.2 11.9 .. 8.2 –4.2 0.4 2.4 w 3.4 4.5 8.7 1.3 4.5 11.0 –4.3 3.0 4.2 6.0 1.9 1.9 1.1

6.0 4.6 .. 3.6 3.3 .. .. 5.4 10.5 4.1 .. 2.9 1.3 5.5 10.2 1.7 2.8 2.1 2.4 9.2 9.5 5.6 .. 8.1 10.2 3.6 5.4 30.3 9.5 4.6 6.0 –0.6 0.9 4.0 4.7 3.3 9.6 .. .. 9.2 –5.8 2.8 w 7.4 7.2 9.6 3.9 7.2 10.2 6.2 3.2 3.6 8.2 4.9 1.1 0.7

Manufacturing

average annual % growth 1990–2000 2000–09

.. .. –5.8 5.6 3.1 .. .. .. 9.3 1.8 .. 1.6 5.2 8.1 7.5 2.8 8.9 1.0 .. –12.6 2.8 6.9 .. 1.8 4.9 5.5 4.7 .. 13.9 –11.2 11.9 .. .. –0.1 0.7 4.5 11.2 .. 5.7 0.8 0.4 .. w 3.7 6.2 9.2 3.3 6.2 10.9 .. 2.9 4.3 6.4 2.2 .. 2.4

.. .. .. 5.9 1.4 .. .. .. 10.7 3.7 .. 3.1 –0.2 4.4 4.4 1.8 3.3 2.5 14.5 8.6 8.7 6.6 .. 7.5 9.5 3.6 5.3 .. 6.7 7.8 8.1 .. 2.4 6.2 2.3 3.6 11.3 .. .. 5.0 –6.6 4.0 w 6.4 7.6 9.8 3.6 7.6 10.2 .. 2.9 6.0 8.5 3.4 2.9 0.5

Services

average annual % growth 1990–2000 2000–09

0.9 –1.7 –0.9 2.2 3.0 .. .. 7.8 5.4 3.3 .. 3.0 2.7 5.7 1.9 3.9 1.8 1.2 1.5 –10.8 2.6 3.7 .. 3.9 3.2 5.3 4.0 –5.8 8.3 –8.1 7.2 3.5 3.6 1.5 0.4 –0.1 7.5 .. 5.0 2.5 3.0 3.2 w 2.9 4.3 6.8 3.0 4.3 8.6 0.3 3.5 3.3 6.9 2.6 3.0 2.5

5.2 7.0 .. 4.2 6.3 .. .. 6.2 2.4 4.0 .. 4.1 3.5 6.2 10.1 3.9 2.2 1.8 7.7 8.3 7.8 4.2 .. –0.7 5.3 5.9 5.3 16.0 8.5 5.8 9.5 2.9 2.3 3.4 8.5 5.9 7.5 .. .. 5.6 –4.8 2.9 w 5.9 6.6 9.3 4.5 6.6 10.0 6.3 3.9 5.5 8.7 4.8 2.2 1.9

a. Components are at producer prices. b. Private analysts estimate that consumer price index inflation was considerably higher for 2007–09 and believe that GDP volume growth has been significantly higher than official reports indicate since the last quarter of 2008. c. Covers mainland Tanzania only.

196

2011 World Development Indicators

About the data

4.1

ECONOMY

Growth of output Definitions

An economy’s growth is measured by the change in

Rebasing national accounts

• Gross domestic product (GDP) at purchaser prices

the volume of its output or in the real incomes of

When countries rebase their national accounts, they

is the sum of gross value added by all resident pro-

its residents. The 1993 United Nations System of

update the weights assigned to various components

ducers in the economy plus any product taxes (less

National Accounts (1993 SNA) offers three plausible

to better reflect current patterns of production or

subsidies) not included in the valuation of output. It

indicators for calculating growth: the volume of gross

uses of output. The new base year should represent

is calculated without deducting for depreciation of

domestic product (GDP), real gross domestic income,

normal operation of the economy—it should be a

fabricated capital assets or for depletion and degra-

and real gross national income. The volume of GDP

year without major shocks or distortions. Some

dation of natural resources. Value added is the net

is the sum of value added, measured at constant

developing countries have not rebased their national

output of an industry after adding up all outputs and

prices, by households, government, and industries

accounts for many years. Using an old base year

subtracting intermediate inputs. The industrial origin

operating in the economy.

can be misleading because implicit price and vol-

of value added is determined by the International

ume weights become progressively less relevant

Standard Industrial Classifi cation (ISIC) revision

and useful.

3. • Agriculture is the sum of gross output less

Each industry’s contribution to growth in the economy’s output is measured by growth in the industry’s value added. In principle, value added in constant

To obtain comparable series of constant price data,

the value of intermediate input used in production

prices can be estimated by measuring the quantity

the World Bank rescales GDP and value added by

for industries classified in ISIC divisions 1–5 and

of goods and services produced in a period, valu-

industrial origin to a common reference year. This

includes forestry and fishing. • Industry is the sum

ing them at an agreed set of base year prices, and

year’s World Development Indicators continues to

of gross output less the value of intermediate input

subtracting the cost of intermediate inputs, also in

use 2000 as the reference year. Because rescaling

used in production for industries classified in ISIC

constant prices. This double-deflation method, rec-

changes the implicit weights used in forming regional

divisions 10–45, which cover mining, manufactur-

ommended by the 1993 SNA and its predecessors,

and income group aggregates, aggregate growth

ing (also reported separately), construction, electric-

requires detailed information on the structure of

rates in this year’s edition are not comparable with

ity, water, and gas. • Manufacturing is the sum of

prices of inputs and outputs.

those from earlier editions with different base years.

gross output less the value of intermediate input

In many industries, however, value added is

Rescaling may result in a discrepancy between

used in production for industries classified in ISIC

extrapolated from the base year using single volume

the rescaled GDP and the sum of the rescaled com-

divisions 15–37. • Services correspond to ISIC divi-

indexes of outputs or, less commonly, inputs. Par-

ponents. Because allocating the discrepancy would

sions 50–99. This sector is derived as a residual

ticularly in the services industries, including most of

cause distortions in the growth rates, the discrep-

(from GDP less agriculture and industry) and may not

government, value added in constant prices is often

ancy is left unallocated. As a result, the weighted

properly reflect the sum of services output, including

imputed from labor inputs, such as real wages or

average of the growth rates of the components gen-

banking and financial services. For some countries

number of employees. In the absence of well defined

erally will not equal the GDP growth rate.

it includes product taxes (minus subsidies) and may also include statistical discrepancies.

measures of output, measuring the growth of services remains difficult.

Computing growth rates

Moreover, technical progress can lead to improve-

Growth rates of GDP and its components are calcu-

ments in production processes and in the quality of

lated using the least squares method and constant

goods and services that, if not properly accounted

price data in the local currency. Constant price U.S.

for, can distort measures of value added and thus

dollar series are used to calculate regional and

of growth. When inputs are used to estimate output,

income group growth rates. Local currency series are

as for nonmarket services, unmeasured technical

converted to constant U.S. dollars using an exchange

progress leads to underestimates of the volume of

rate in the common reference year. The growth rates

output. Similarly, unmeasured improvements in qual-

in the table are average annual compound growth

ity lead to underestimates of the value of output and

rates. Methods of computing growth are described

value added. The result can be underestimates of

in Statistical methods.

growth and productivity improvement and overesti-

Data sources Data on national accounts for most developing

Changes in the System of National Accounts

countries are collected from national statistical

Informal economic activities pose a particular mea-

World Development Indicators adopted the termi-

organizations and central banks by visiting and

surement problem, especially in developing coun-

nology of the 1993 SNA in 2001. Although many

resident World Bank missions. Data for high

tries, where much economic activity is unrecorded.

countries continue to compile their national accounts

income economies are from Organisation for

A complete picture of the economy requires estimat-

according to the SNA version 3 (referred to as the

Economic Co-operation and Development (OECD)

ing household outputs produced for home use, sales

1968 SNA), more and more are adopting the 1993

data files. The United Nations Statistics Division

in informal markets, barter exchanges, and illicit or

SNA. Some low-income countries still use concepts

publishes detailed national accounts for UN mem-

deliberately unreported activities. The consistency

from the even older 1953 SNA guidelines, including

ber countries in National Accounts Statistics: Main

and completeness of such estimates depend on the

valuations such as factor cost, in describing major

Aggregates and Detailed Tables and publishes

skill and methods of the compiling statisticians.

economic aggregates. Countries that use the 1993

updates in the Monthly Bulletin of Statistics.

mates of inflation.

SNA are identified in Primary data documentation.

2011 World Development Indicators

197

4.2

Structure of output Gross domestic product

Agriculture

$ millions

Afghanistan Albania Algeria Angola a Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin a Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile Chinaa Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep.a Costa Rica Côte d’Ivoirea Croatia Cuba Czech Republic Denmark Dominican Republica Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabona Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

198

Industry

% of GDP

Manufacturing

% of GDP

Services

% of GDP

% of GDP

1995

2009

1995

2009

1995

2009

1995

2009

1995

2009

.. 2,424 41,764 5,040 258,032 1,468 371,091 238,314 3,052 37,940 13,973 284,142 2,009 6,715 1,867 4,774 768,951 13,069 2,380 1,000 3,441 8,733 590,517 1,122 1,446 71,349 728,007 144,230 92,507 5,643 2,116 11,722 11,000 22,046 30,428 55,257 181,984 16,358 20,206 60,159 9,500 578 4,353 7,606 130,700 1,569,983 4,959 382 2,694 2,522,792 6,457 131,718 14,657 3,694 254 2,695 3,911

14,483 12,015 140,577 75,493 307,155 8,714 924,843 381,084 43,019 89,360 49,037 471,161 6,656 17,340 17,042 11,823 1,594,490 48,722 8,141 1,325 10,447 22,186 1,336,068 2,006 6,839 163,669 4,985,461 210,568 234,045 10,575 9,580 29,240 23,304 63,034 62,705 190,274 309,596 46,788 57,249 188,413 21,101 1,873 19,084 28,526 237,989 2,649,390 11,062 733 10,744 3,330,032 26,169 329,924 37,322 4,103 837 6,479 14,318

.. 56 11 7 6 42 3 3 27 26 17 2 34 17 21 4 6 16 35 48 50 24 3 46 36 9 20 .. 15 57 10 14 25 7 9 5 3 10 .. 17 14 21 6 57 4 3 8 30 52 1 43 9 24 19 55 .. 22

33 21 12 10 8 21 3 2 8 19 10 1 .. 14 8 3 6 6 .. .. 35 19 .. 56 14 3 10 .. 7 43 5 7 24 7 5 2 1 6 6 14 12 14 3 51 3 2 5 27 10 1 32 3 12 17 55 .. 12

.. 23 50 66 28 32 29 31 34 25 37 28 15 33 26 51 28 28 21 19 15 31 31 21 14 35 47 15 32 17 45 30 21 32 23 38 25 36 .. 32 30 17 33 10 33 25 52 13 16 32 27 21 20 29 12 .. 31

22 20 55 59 32 35 29 29 60 29 42 22 .. 36 28 40 25 30 .. .. 23 31 .. 15 49 42 46 8 34 24 71 27 25 27 20 37 22 32 23 37 27 22 29 11 28 19 54 15 21 26 19 18 28 53 13 .. 27

.. 14 12 4 18 25 15 20 13 15 31 20 9 19 11 5 19 26 15 9 10 22 18 10 11 18 34 8 16 9 8 22 15 23 15 24 17 26 .. 17 23 9 21 5 25 .. 5 6 11 23 10 .. 14 4 8 .. 18

13 20 6 6 21 16 10 19 4 18 30 14 .. 14 13 4 16 15 .. .. 15 17 .. .. 7 13 34 2 14 5 4 19 18 16 10 23 13 24 10 16 21 6 17 4 18 11 4 5 12 19 7 10 20 5 10 .. 19

.. 22 39 26 66 26 68 67 39 49 46 70 51 50 54 45 67 56 43 33 36 45 66 33 51 55 33 85 53 26 45 57 55 61 68 57 71 54 .. 51 56 62 61 33 62 72 40 57 32 67 31 70 56 52 33 .. 48

45 60 34 31 61 45 68 69 32 53 48 78 .. 50 64 57 69 64 .. .. 42 50 .. 30 38 55 43 92 58 33 24 66 50 66 75 61 77 61 71 49 60 63 68 39 69 79 41 57 69 73 49 79 59 30 32 .. 60

2011 World Development Indicators

Gross domestic product

Agriculture

$ millions

Hungary India Indonesiaa Iran, Islamic Rep. Iraq Ireland Israela Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait a Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysiaa Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmara Namibia Nepal Netherlands New Zealand Nicaragua Niger a Nigeria Norway Omana Pakistan Panama Papua New Guinea Paraguay a Peru Philippinesa Poland Portugal Puerto Ricoa Qatar

Industry

% of GDP

4.2

Manufacturing

% of GDP

ECONOMY

Structure of output

Services

% of GDP

% of GDP

1995

2009

1995

2009

1995

2009

1995

2009

1995

2009

44,656 356,299 202,132 90,829 10,114 67,061 96,065 1,126,041 5,813 5,264,380 6,727 20,374 9,046 .. 517,118 .. 27,192 1,661 1,764 5,236 11,719 814 135 25,541 7,905 4,449 3,160 1,397 88,832 2,466 1,415 4,040 286,698 1,753 1,227 32,986 2,247 .. 3,503 4,401 418,969 62,795 3,191 1,881 28,109 148,920 13,803 60,636 7,906 4,636 8,066 53,674 74,120 139,062 116,419 42,647 8,138

128,964 1,377,265 540,274 331,015 65,837 227,193 195,392 2,112,780 12,070 5,068,996 25,092 115,306 29,376 .. 832,512 5,387 148,024 4,578 5,939 26,195 34,528 1,579 876 62,360 37,206 9,221 8,590 4,727 193,093 8,996 3,024 8,589 874,810 5,405 4,202 91,375 9,790 .. 9,265 12,531 792,128 126,679 6,140 5,383 173,004 381,766 46,114 161,990 24,711 7,893 14,236 130,325 161,196 430,076 232,874 .. 98,313

7 26 17 18 9 7 .. 3 9 2 4 13 31 .. 6 .. 0 44 56 9 8 19 82 .. 11 13 27 30 13 50 37 10 6 33 41 15 35 60 12 42 3 7 23 40 .. 3 3 26 8 35 21 9 22 8 6 1 ..

4 18 16 10 .. 1 .. 2 6 1 3 6 23 .. 3 12 .. 29 35 3 5 8 61 2 4 11 29 31 10 37 21 4 4 10 24 16 31 .. 9 34 2 .. 19 .. 33 1 .. 22 6 36 19 7 15 4 2 .. ..

32 28 42 34 75 38 .. 30 37 34 29 31 16 .. 42 .. 55 20 19 30 25 43 5 .. 31 30 9 20 41 19 25 32 28 32 29 34 15 10 28 23 27 27 27 17 .. 34 46 24 18 34 23 31 32 35 28 44 ..

29 27 49 44 .. 31 .. 25 22 28 32 40 15 .. 37 20 .. 19 28 20 17 34 17 78 31 36 16 16 44 24 35 29 35 13 33 29 24 .. 33 16 24 .. 30 .. 41 40 .. 24 17 45 21 34 30 30 23 .. ..

24 18 24 12 1 30 .. 22 16 23 15 15 10 .. 28 .. 4 9 14 21 14 17 3 .. 19 23 8 16 26 8 8 23 21 26 12 19 8 7 13 10 17 18 19 6 .. 13 5 16 9 8 16 17 23 21 19 42 ..

22 15 27 11 .. 24 .. 16 9 20 20 11 9 .. 28 17 .. 13 9 10 9 17 13 4 18 23 14 10 25 3 4 19 17 13 5 16 14 .. 15 7 13 .. 20 .. .. 10 .. 17 6 6 13 14 20 16 13 .. ..

61 46 41 47 16 55 .. 66 54 64 67 56 53 .. 52 .. 45 37 25 61 68 38 13 .. 58 57 64 50 46 32 37 58 66 35 30 51 51 30 60 35 69 66 49 43 .. 63 51 50 74 31 56 60 46 57 66 55 ..

66 55 35 45 .. 68 .. 73 72 71 65 53 62 .. 61 68 .. 51 37 77 78 58 22 20 64 52 55 53 46 .. 45 67 61 77 44 55 45 .. 58 50 74 .. 51 .. 27 59 .. 54 77 20 59 59 55 66 75 .. ..

2011 World Development Indicators

199

4.2

Structure of output Gross domestic product

Agriculture

$ millions

Romania Russian Federation Rwandaa Saudi Arabiaa Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lankaa Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania b Thailanda Timor-Lestea Togoa Trinidad and Tobago Tunisiaa Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnama West Bank and Gaza Yemen, Rep.a Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

% of GDP

1995

2009

35,477 395,528 1,293 142,458 4,879 21,381 871 84,291 25,240 20,814 .. 151,113 596,751 13,030 13,830 1,699 253,680 315,940 11,397 1,232 5,255 168,019 .. 1,309 5,329 18,031 169,486 2,482 5,756 48,214 42,807 1,157,119 7,359,300 19,298 13,350 74,889 20,736 3,220 4,236 3,478 7,111 29,692,820 t 153,755 4,811,047 1,992,261 2,818,895 4,965,895 1,312,902 763,913 1,770,557 315,651 476,175 327,608 24,722,778 7,286,803

161,110 1,231,893 5,216 375,766 12,822 42,984 1,942 182,232 87,642 48,477 .. 285,366 1,460,250 41,979 54,681 3,001 406,072 491,924 52,177 4,978 21,368 263,772 558 2,855 21,204 39,561 614,603 19,947 16,043 113,545 230,252 2,174,530 14,119,000 31,511 32,104 326,133 97,180 .. 26,365 12,805 5,625 58,259,785 t 432,171 16,213,154 8,887,269 7,318,398 16,657,552 6,353,790 2,591,705 4,017,912 1,062,419 1,700,339 945,923 41,607,730 12,465,331

1995

21 7 44 6 21 .. 43 .. 6 4 .. 4 5 23 39 12 3 2 32 38 47 10 .. 38 2 11 16 17 49 15 3 2 2 9 32 6 27 .. 20 18 15 4w 37 14 21 8 15 19 14 7 16 26 18 2 3

a. Components are at producer prices. b. Covers mainland Tanzania only.

200

2011 World Development Indicators

Industry

Manufacturing

% of GDP 2009

7 5 34 3 17 13 51 .. 3 2 .. 3 3 13 30 7 2 1 21 22 29 12 .. .. 0 8 9 12 25 8 2 1 1 10 20 .. 21 .. .. 22 18 3w 26 10 13 6 10 11 8 6 11 18 13 1 2

1995

43 37 16 49 24 .. 39 35 38 35 .. 35 29 27 11 45 30 30 20 39 15 41 .. 22 47 29 33 63 14 43 52 31 26 29 28 41 29 .. 32 36 29 30 w 20 35 39 32 34 44 35 29 34 27 29 30 29

Services

% of GDP 2009

26 33 15 51 22 28 22 26 35 34 .. 31 26 30 26 49 25 27 34 24 24 43 .. .. 52 30 26 54 26 29 61 21 21 26 33 .. 40 .. .. 34 29 27 w 24 35 39 31 35 45 30 31 43 27 30 25 24

1995

29 .. 10 10 17 .. 9 27 27 26 .. 21 18 16 5 39 22 20 15 28 7 30 .. 10 9 19 23 40 7 35 10 21 19 20 12 15 15 .. 14 11 22 21 w 11 23 26 19 22 31 22 19 15 17 16 20 21

% of GDP 2009

22 15 6 10 13 .. .. 19 19 22 .. 15 13 18 7 44 16 19 13 11 10 34 .. .. 6 17 17 47 8 18 12 11 13 16 13 .. 20 .. .. 10 17 17 w 12 21 26 17 21 32 17 17 12 15 13 16 15

1995

36 56 40 45 55 .. 18 65 56 60 .. 61 66 50 51 43 66 68 48 22 38 50 .. 40 51 59 50 20 36 42 45 67 72 62 40 53 44 .. 48 46 56 65 w 43 51 40 60 51 36 51 64 50 46 53 68 68

2009

67 62 51 46 62 59 27 74 63 64 .. 66 71 58 44 43 73 72 45 54 47 45 .. .. 47 62 65 34 50 62 38 78 77 64 47 .. 39 .. .. 44 53 70 w 50 55 48 62 55 43 62 63 46 55 57 74 74

About the data

4.2

ECONOMY

Structure of output Definitions

An economy’s gross domestic product (GDP) rep-

Ideally, industrial output should be measured

• Gross domestic product (GDP) at purchaser prices

resents the sum of value added by all its produc-

through regular censuses and surveys of fi rms.

is the sum of gross value added by all resident pro-

ers. Value added is the value of the gross output of

But in most developing countries such surveys are

ducers in the economy plus any product taxes (less

producers less the value of intermediate goods and

infrequent, so earlier survey results must be extrapo-

subsidies) not included in the valuation of output.

services consumed in production, before accounting

lated using an appropriate indicator. The choice of

It is calculated without deducting for depreciation

for consumption of fixed capital in production. The

sampling unit, which may be the enterprise (where

of fabricated assets or for depletion and degrada-

United Nations System of National Accounts calls

responses may be based on financial records) or

tion of natural resources. Value added is the net

for value added to be valued at either basic prices

the establishment (where production units may be

output of an industry after adding up all outputs and

(excluding net taxes on products) or producer prices

recorded separately), also affects the quality of

subtracting intermediate inputs. The industrial origin

(including net taxes on products paid by producers

the data. Moreover, much industrial production is

of value added is determined by the International

but excluding sales or value added taxes). Both valu-

organized in unincorporated or owner-operated ven-

Standard Industrial Classifi cation (ISIC) revision

ations exclude transport charges that are invoiced

tures that are not captured by surveys aimed at the

3. • Agriculture is the sum of gross output less

separately by producers. Total GDP shown in the

formal sector. Even in large industries, where regu-

the value of intermediate input used in production

table and elsewhere in this volume is measured at

lar surveys are more likely, evasion of excise and

for industries classified in ISIC divisions 1–5 and

purchaser prices. Value added by industry is normally

other taxes and nondisclosure of income lower the

includes forestry and fishing. • Industry is the sum

measured at basic prices. When value added is mea-

estimates of value added. Such problems become

of gross output less the value of intermediate input

sured at producer prices, this is noted in Primary data

more acute as countries move from state control of

used in production for industries classified in ISIC

documentation and footnoted in the table.

industry to private enterprise, because new firms and

divisions 10–45, which cover mining, manufactur-

While GDP estimates based on the production

growing numbers of established firms fail to report.

ing (also reported separately), construction, electric-

approach are generally more reliable than estimates

In accordance with the System of National Accounts,

ity, water, and gas. • Manufacturing is the sum of

compiled from the income or expenditure side, dif-

output should include all such unreported activity

gross output less the value of intermediate input

ferent countries use different definitions, methods,

as well as the value of illegal activities and other

used in production for industries classified in ISIC

and reporting standards. World Bank staff review the

unrecorded, informal, or small-scale operations.

divisions 15–37. • Services correspond to ISIC divi-

quality of national accounts data and sometimes

Data on these activities need to be collected using

sions 50–99. This sector is derived as a residual

make adjustments to improve consistency with

techniques other than conventional surveys of firms.

(from GDP less agriculture and industry) and may not

international guidelines. Nevertheless, significant

In industries dominated by large organizations

properly reflect the sum of services output, including

discrepancies remain between international stan-

and enterprises, such as public utilities, data on

banking and financial services. For some countries

dards and actual practice. Many statistical offices,

output, employment, and wages are usually read-

it includes product taxes (minus subsidies) and may

especially those in developing countries, face severe

ily available and reasonably reliable. But in the

also include statistical discrepancies.

limitations in the resources, time, training, and bud-

services industry the many self-employed workers

gets required to produce reliable and comprehensive

and one-person businesses are sometimes difficult

series of national accounts statistics.

to locate, and they have little incentive to respond to surveys, let alone to report their full earnings.

Data problems in measuring output

Compounding these problems are the many forms

Among the difficulties faced by compilers of national

of economic activity that go unrecorded, including

accounts is the extent of unreported economic activ-

the work that women and children do for little or no

ity in the informal or secondary economy. In develop-

pay. For further discussion of the problems of using

ing countries a large share of agricultural output is

national accounts data, see Srinivasan (1994) and

either not exchanged (because it is consumed within

Heston (1994). Data sources

the household) or not exchanged for money. Dollar conversion

Data on national accounts for most developing

indirectly, using a combination of methods involv-

To produce national accounts aggregates that are

countries are collected from national statistical

ing estimates of inputs, yields, and area under cul-

measured in the same standard monetary units,

organizations and central banks by visiting and

tivation. This approach sometimes leads to crude

the value of output must be converted to a single

resident World Bank missions. Data for high

approximations that can differ from the true values

common currency. The World Bank conventionally

income economies are from Organisation for

over time and across crops for reasons other than

uses the U.S. dollar and applies the average official

Economic Co-operation and Development (OECD)

climate conditions or farming techniques. Similarly,

exchange rate reported by the International Monetary

data files. The United Nations Statistics Division

agricultural inputs that cannot easily be allocated to

Fund for the year shown. An alternative conversion

publishes detailed national accounts for UN mem-

specific outputs are frequently “netted out” using

factor is applied if the official exchange rate is judged

ber countries in National Accounts Statistics: Main

equally crude and ad hoc approximations. For further

to diverge by an exceptionally large margin from the

Aggregates and Detailed Tables and publishes

discussion of the measurement of agricultural pro-

rate effectively applied to transactions in foreign cur-

updates in the Monthly Bulletin of Statistics.

duction, see About the data for table 3.3.

rencies and traded products.

Agricultural production often must be estimated

2011 World Development Indicators

201

4.3

Structure of manufacturing Manufacturing value added

$ millions 1998 2009

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

202

.. 268 4,372 407 53,326 377 51,505 37,828 370 6,887 4,487 45,588 200 1,189 497 253 117,276 2,180 387 64 436 1,843 104,352 91 188 13,540 324,603 8,868 13,770 370 136 2,972 2,499 4,163 3,103 14,416 24,894 5,136 2,912 14,403 2,569 64 870 373 29,158 209,123 252 22 307 449,216 672 12,338 2,631 132 19 .. 826

1,632 1,995 7,315 4,586 60,116 1,213 95,726 64,124 1,927 15,472 12,638 59,032 .. 2,014 1,816 475 216,924 6,424 .. .. 1,403 3,328 172,050 .. 381 19,665 1,691,153 4,971 30,690 582 429 5,034 4,187 8,789 4,955 39,662 34,971 10,577 5,316 28,712 4,319 102 2,393 1,071 37,557 253,608 479 32 1,073 567,902 1,759 29,718 6,937 201 44 .. 2,470

2011 World Development Indicators

Food, beverages, and tobacco

Textiles and clothing

Machinery and transport equipment

Chemicals

Other manufacturinga

% of total 1998 2007

% of total 1998 2007

% of total 1998 2007

% of total 1998 2007

% of total 1998 2007

.. 20 33 .. 26 .. .. 10 .. 24 .. 13 .. 35 .. 23 20 22 .. .. 7 35 14 .. .. 32 16 12 32 .. .. 46 42 .. .. 13 19 .. 22 16 29 49 17 55 8 13 .. .. 37 8 .. 24 .. .. .. .. 42

.. 17 .. .. .. .. 19 9 18 .. .. 12 .. .. .. 22 18 16 .. .. .. .. .. .. .. 14 12 14 27 .. .. 44 .. .. .. 9 17 .. 30 .. .. 44 12 41 6 14 .. .. 34 8 .. 22 .. .. .. .. ..

.. 27 8 .. 8 .. .. 5 .. 40 .. 6 .. 5 .. 8 7 13 .. .. 87 9 4 .. .. 4 12 22 10 .. .. 8 10 .. .. 6 3 .. 3 16 28 12 15 13 2 5 .. .. 1 3 .. 12 .. .. .. .. 22

.. 22 .. .. .. .. 3 2 1 .. .. 4 .. .. .. 5 6 12 .. .. .. .. .. .. .. 2 10 12 9 .. .. 5 .. .. .. 3 2 .. 4 .. .. 19 4 9 2 3 .. .. 2 2 .. 8 .. .. .. .. ..

.. 3 .. .. 13 .. .. 24 .. 3 .. 19 .. 0 .. 15 20 18 .. .. 0 1 29 .. .. 3 15 15 5 .. .. 3 2 .. .. 23 22 .. 2 12 2 1 10 1 30 26 .. .. 12 35 .. 11 .. .. .. .. 1

.. 3 .. .. .. .. 14 28 9 .. .. 19 .. .. .. .. 21 14 .. .. .. .. .. .. .. 2 24 13 6 .. .. 3 .. .. .. 29 19 .. 3 .. .. 1 10 5 32 24 .. .. 6 36 .. 10 .. .. .. .. ..

.. 5 11 .. 15 .. .. 7 .. 11 .. 18 .. 5 .. 5 13 9 .. .. 0 6 9 .. .. 10 11 3 17 .. .. 11 12 .. .. 6 10 .. 3 21 16 6 4 7 6 12 .. .. 7 10 .. 10 .. .. .. .. 5

.. 17 .. .. .. .. 7 7 5 .. .. 23 .. .. .. .. 11 7 .. .. .. .. .. .. .. 14 11 5 13 .. .. 10 .. .. .. 6 13 .. 5 .. .. 5 4 5 6 13 .. .. 8 10 .. 6 .. .. .. .. ..

.. 46 48 .. 38 .. .. 54 .. 21 .. 44 .. 55 .. 69 40 39 .. .. 7 49 44 .. .. 52 46 49 36 .. .. 32 34 .. .. 52 46 .. 69 35 25 31 53 24 54 44 .. .. 43 44 .. 43 .. .. .. .. 30

.. 41 .. .. .. .. 58 54 66 .. .. 43 .. .. .. 73 44 50 .. .. .. .. .. .. .. 69 43 55 45 .. .. 39 .. .. .. 53 50 .. 58 .. .. 31 69 40 54 45 .. .. 50 45 .. 54 .. .. .. .. ..

Manufacturing value added

$ millions 1998 2009

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

9,959 59,562 23,857 13,607 91 26,279 .. 236,315 914 868,624 1,047 2,659 1,540 .. 85,569 .. 1,037 233 216 965 2,144 140 17 .. 1,807 645 399 216 20,774 101 100 877 82,015 238 46 6,136 422 .. 369 436 58,120 8,495 538 128 .. 16,863 654 9,131 1,135 351 1,239 8,080 14,254 30,022 19,959 22,994 ..

28,619 190,333 142,532 29,832 .. 48,709 .. 306,459 973 970,204 4,416 12,536 2,801 .. 208,142 773 .. 570 478 2,278 2,645 243 105 3,879 7,562 1,816 1,115 447 49,213 195 115 1,483 144,431 568 176 12,909 1,219 .. 1,247 807 89,029 17,968 1,086 .. .. 32,575 .. 26,290 1,490 464 1,850 16,897 32,889 61,948 26,690 .. ..

4.3

ECONOMY

Structure of manufacturing Food, beverages, and tobacco

Textiles and clothing

Machinery and transport equipment

Chemicals

Other manufacturinga

% of total 1998 2007

% of total 1998 2007

% of total 1998 2007

% of total 1998 2007

% of total 1998 2007

15 13 21 13 31 17 12 9 48 11 27 .. 46 .. 9 .. 8 .. 46 26 26 .. .. .. 27 31 31 44 10 .. .. 22 24 .. 53 34 .. .. .. 35 18 30 .. .. 30 16 17 23 52 .. .. 26 35 25 12 10 4

11 9 26 10 .. 18 10 9 .. 11 23 .. 30 .. 6 .. .. .. .. 20 .. .. .. .. 23 18 0 .. 9 .. .. 31 25 39 38 30 .. .. .. .. 18 27 .. .. .. 20 8 22 .. .. .. 30 24 16 14 9 1

7 12 18 8 15 2 6 13 7 4 6 .. 8 .. 9 .. 5 .. 22 11 10 .. .. .. 18 21 33 8 4 .. .. 51 4 .. 33 18 .. .. .. 34 2 .. .. .. 11 2 7 26 7 .. .. 10 7 7 20 4 8

3 9 13 4 .. 0 3 10 .. 2 10 .. 4 .. 5 .. .. .. .. 7 .. .. .. .. 9 17 30 .. 2 .. .. 31 3 15 17 13 .. .. .. .. 2 2 .. .. .. 1 0 29 .. .. .. 12 6 4 12 1 2

27 15 14 16 2 16 24 23 .. 33 4 .. 4 .. 35 .. 2 .. 8 8 3 .. .. .. 12 9 .. 5 8 .. .. 1 23 .. 0 4 .. .. .. 0 15 .. .. .. 7 23 2 5 .. .. .. 4 21 16 15 5 0

31 19 18 24 .. 16 22 23 .. 37 3 .. 2 .. 46 .. .. .. .. 10 .. .. .. .. 10 4 1 .. 30 .. .. 1 18 4 1 5 .. .. .. .. 19 13 .. .. .. 25 1 8 .. .. .. 2 25 20 11 9 0

11 24 13 13 23 38 12 8 19 10 21 .. 8 .. 11 .. 3 .. 3 3 6 .. .. .. 3 8 6 16 11 .. .. 4 15 .. 2 15 .. .. .. 6 13 .. .. .. 26 8 7 16 7 .. .. 10 10 7 6 62 21

10 16 11 13 .. 33 20 7 .. 11 17 .. 4 .. 8 .. .. .. .. 4 .. .. .. .. 9 6 2 .. 15 .. .. .. 19 .. 3 16 .. .. .. .. 14 .. .. .. .. 9 12 14 .. .. .. 12 8 8 6 62 17

40 37 33 49 29 28 46 47 27 42 42 .. 34 .. 36 .. 83 .. 22 52 55 .. .. .. 40 31 30 28 67 .. .. 26 32 .. 12 28 .. .. .. 26 51 70 .. .. 26 51 67 30 34 .. .. 51 28 45 46 20 67

2011 World Development Indicators

44 47 32 50 .. 33 44 51 .. 39 48 .. 62 .. 35 .. .. .. .. 60 .. .. .. .. 48 55 67 .. 44 .. .. 37 35 42 41 35 .. .. .. .. 47 58 .. .. .. 45 79 26 .. .. .. 44 38 52 63 20 80

203

4.3

Structure of manufacturing Manufacturing value added

$ millions 1998 2009

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzaniab Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

9,601 .. 223 15,492 723 .. 26 18,839 6,036 4,860 .. 23,678 103,971 2,343 957 519 48,915 51,047 1,286 255 919 34,534 9 110 552 3,660 64,408 452 545 10,578 6,532 251,809 1,440,500 3,598 1,346 17,380 4,666 .. 638 372 923 5,516,751 t 20,369 1,085,340 535,090 563,006 1,105,587 425,997 .. 334,974 49,450 78,797 43,316 4,411,013 1,250,663

31,753 161,878 335 39,128 1,490 .. .. 33,499 15,375 10,566 .. 39,014 172,433 7,618 3,515 1,114 56,948 88,054 6,686 479 1,844 89,881 .. .. 1,334 6,527 92,715 9,158 1,190 17,992 24,643 217,594 1,779,474 4,377 3,979 .. 18,099 .. .. 1,192 826 9,102,310 t 44,786 3,432,566 2,342,311 1,036,562 3,479,229 2,036,104 .. 570,166 117,926 241,774 83,017 5,603,504 1,686,936

a. Includes unallocated data. b. Covers mainland Tanzania only.

204

2011 World Development Indicators

Food, beverages, and tobacco

Textiles and clothing

Machinery and transport equipment

Chemicals

Other manufacturinga

% of total 1998 2007

% of total 1998 2007

% of total 1998 2007

% of total 1998 2007

% of total 1998 2007

29 22 75 .. 44 .. .. 4 12 10 .. 18 15 39 .. .. 8 10 .. .. 45 25 .. .. 30 17 15 .. 65 .. .. 13 13 36 .. 22 30 15 45 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

15 15 .. 19 .. .. .. 2 7 7 .. 17 15 29 .. .. 7 .. .. .. 62 16 .. .. 11 .. 12 .. .. .. .. 16 14 42 .. .. .. 27 60 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

11 3 2 .. 3 .. .. 1 7 10 .. 6 7 30 .. .. 1 3 .. .. 0 12 .. .. 1 36 18 .. 5 .. .. 5 4 9 .. 2 22 23 5 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

12 2 .. 5 .. .. .. 1 3 6 .. 3 4 29 .. .. 1 .. .. .. 8 9 .. .. 1 .. 19 .. .. .. .. 3 2 7 .. .. .. 13 9 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

14 18 .. .. 0 .. .. 52 21 17 .. 14 20 4 .. .. 37 15 .. .. 2 27 .. .. 1 3 14 .. .. .. .. 26 30 3 .. 9 11 2 0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

17 10 .. 6 .. .. .. 45 27 20 .. 14 17 0 .. .. 34 .. .. .. 1 35 .. .. 0 .. 20 .. .. .. .. 23 25 4 .. .. .. 1 0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

5 9 6 .. 29 .. .. 13 9 11 .. 11 10 7 .. .. 9 .. .. .. 7 4 .. .. 26 11 8 .. 10 .. .. 10 12 8 .. 34 7 5 2 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

5 8 .. 27 .. .. .. 32 4 14 .. 7 8 14 .. .. 13 .. .. .. 2 6 .. .. 39 .. 7 .. .. .. .. 11 15 8 .. .. .. 4 4 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

40 48 17 .. 24 .. .. 30 52 51 .. 51 47 21 .. .. 46 71 .. .. 46 32 .. .. 42 33 45 .. 20 .. .. 46 41 44 .. 41 30 55 48 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

51 65 .. 43 .. .. .. 20 59 53 .. 58 55 27 .. .. 46 .. .. .. 29 34 .. .. 49 .. 42 .. .. .. .. 47 44 39 .. .. .. 55 27 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

About the data

4.3

ECONOMY

Structure of manufacturing Definitions

The data on the distribution of manufacturing value

revision 3. Concordances matching ISIC categories

• Manufacturing value added is the sum of gross

added by industry are provided by the United Nations

to national classifi cation systems and to related

output less the value of intermediate inputs used

Industrial Development Organization (UNIDO). UNIDO

systems such as the Standard International Trade

in production for industries classified in ISIC major

obtains the data from a variety of national and inter-

Classification are available.

division D. • Food, beverages, and tobacco cor-

national sources, including the United Nations Sta-

In establishing classifi cations systems compil-

respond to ISIC divisions 15 and 16. • Textiles

tistics Division, the World Bank, the Organisation for

ers must define both the types of activities to be

and clothing correspond to ISIC divisions 17–19.

Economic Co-operation and Development, and the

described and the units whose activities are to

• Machinery and transport equipment correspond to

International Monetary Fund. To improve comparabil-

be reported. There are many possibilities, and the

ISIC divisions 29, 30, 32, 34, and 35. • Chemicals

ity over time and across countries, UNIDO supple-

choices affect how the statistics can be interpreted

correspond to ISIC division 24. • Other manufactur-

ments these data with information from industrial

and how useful they are in analyzing economic

ing is calculated as a residual. It covers wood and

censuses, statistics from national and international

behavior. The ISIC emphasizes commonalities in the

related products (ISIC division 20), paper and related

organizations, unpublished data that it collects in the

production process and is explicitly not intended to

products (ISIC divisions 21 and 22), petroleum and

field, and estimates by the UNIDO Secretariat. Nev-

measure outputs (for which there is a newly devel-

related products (ISIC division 23), basic metals and

ertheless, coverage may be incomplete, particularly

oped Central Product Classification). Nevertheless,

mineral products (ISIC division 27), fabricated metal

for the informal sector. When direct information on

the ISIC views an activity as defined by “a process

products and professional goods (ISIC division 28),

inputs and outputs is not available, estimates may

resulting in a homogeneous set of products” (United

and other industries (ISIC divisions 25, 26, 31, 33,

be used, which may result in errors in industry totals.

Nations 1990 [ISIC, series M, no. 4, rev. 3], p. 9).

36, and 37).

Moreover, countries use different reference periods

Firms typically use multiple processes to produce

(calendar or fiscal year) and valuation methods (basic

a product. For example, an automobile manufac-

or producer prices) to estimate value added. (See

turer engages in forging, welding, and painting as

About the data for table 4.2.)

well as advertising, accounting, and other service

The data on manufacturing value added in U.S. dol-

activities. Collecting data at such a detailed level

lars are from the World Bank’s national accounts files

is not practical, nor is it useful to record produc-

and may differ from those UNIDO uses to calculate

tion data at the highest level of a large, multiplant,

shares of value added by industry, in part because

multiproduct firm. The ISIC has therefore adopted as

of differences in exchange rates. Thus value added

the definition of an establishment “an enterprise or

in a particular industry estimated by applying the

part of an enterprise which independently engages in

shares to total manufacturing value added will not

one, or predominantly one, kind of economic activity

match those from UNIDO sources. Classification of

at or from one location . . . for which data are avail-

manufacturing industries in the table accords with

able . . .” (United Nations 1990, p. 25). By design,

the United Nations International Standard Industrial

this definition matches the reporting unit required

Classifi cation (ISIC) revision 3. Editions of World

for the production accounts of the United Nations

Development Indicators prior to 2008 used revision

System of National Accounts. The ISIC system is

2, first published in 1948. Revision 3 was completed

described in the United Nations’ International Stan-

in 1989, and many countries now use it. But revi-

dard Industrial Classiﬁcation of All Economic Activi-

sion 2 is still widely used for compiling cross-country

ties, Third Revision (1990). The discussion of the ISIC

data. UNIDO has converted these data to accord with

draws on Ryten (1998).

4.3a

Manufacturing continues to show strong growth in East Asia and Pacific through 2009 Value added in manufacturing (index, 1990 = 100) 600 East Asia & Pacific 500 400 South Asia 300 Latin America & Caribbean

Middle East & North Africa

Data sources

200

Data on manufacturing value added are from

100 Sub-Saharan Africa 0

the World Bank’s National Accounts files. Data used to calculate shares of industry value added

1990

1995

2000

2005

2009

are provided to the World Bank in electronic files

Manufacturing continues to be the dominant sector in East Asia and Pacific, growing an average of about

by UNIDO. The most recent published source is

10.5 percent a year between 1990 and 2009.

UNIDO’s International Yearbook of Industrial Sta-

Source: World Development Indicators data files.

tistics 2010.

2011 World Development Indicators

205

4.4 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China† Hong Kong SAR, Chinab Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras †Data for Taiwan, China

206

Structure of merchandise exports Merchandise exports

Food

Agricultural raw materials

Fuels

Ores and metals

Manufactures

$ millions 1995 2009

% of total 1995 2009

% of total 1995 2009

% of total 1995 2009

% of total 1995 2009

% of total 1995 2009

166 202 10,258 3,642 20,967 271 53,111 57,738 635 3,501 4,803 178,265 a 420 1,100 152 2,142 46,506 5,355 276 105 855 1,651 192,197 171 243 16,024 148,780 173,871 10,056 1,563 1,172 3,453 3,806 4,517 1,600 21,335 50,906 3,780 4,307 3,450 1,652 86 1,840 422 40,490 301,162 2,713 16 151 523,461 1,724 11,054 2,155 702 24 110 1,769 113,047

560 1,088 45,194 40,080 55,668 698 154,234 137,672 21,097 15,084 21,283 369,854 1,000 4,848 3,929 3,458 152,995 16,455 850 64 4,200 3,000 316,713 120 2,800 53,735 1,201,534 329,422 32,853 3,100 5,600 8,788 8,900 10,474 3,109 113,437 93,344 5,463 13,799 23,062 3,797 15 9,031 1,596 62,798 484,725 5,100 15 1,135 1,126,383 5,500 20,093 7,214 1,010 115 576 5,196 203,675

2011 World Development Indicators

.. 11 1 .. 50 11 22 4 4 10 .. 10 a 14 21 .. .. 29 18 25 91 .. 27 8 4 .. 24 8 3 31 .. 1 63 63 11 .. 6 24 19 53 10 57 .. 16 73 2 14 0 60 29 5 58 30 65 8 89 37 87 3

55 6 0 .. 50 20 14 7 4 7 11 10 .. 20 8 5 34 17 27 67 1 .. 11 .. .. 21 3 7 16 .. .. 25 48 13 .. 5 19 25 36 11 23 .. 10 77 2 12 .. 53 18 6 63 25 44 2 .. .. 54 1

.. 9 0 .. 4 5 8 3 8 3 .. 1a 75 10 .. .. 5 3 69 4 .. 28 9 20 .. 12 2 0 5 .. 8 5 20 5 .. 4 3 0 3 6 1 .. 10 13 8 1 13 1 3 1 15 4 4 1 11 0 3 2

8 3 0 .. 1 1 2 2 0 3 2 1 .. 1 6 0 4 1 60 5 1 .. 4 .. .. 5 0 2 4 .. .. 2 6 4 .. 1 2 1 4 2 1 .. 4 12 4 1 .. 1 2 1 9 3 3 5 .. .. 1 1

.. 3 95 .. 10 1 19 1 66 0 .. 3a 5 15 .. .. 1 7 0 0 .. 29 9 1 .. 0 4 0 28 .. 88 1 10 9 .. 4 3 0 36 37 0 .. 6 3 2 2 83 0 19 1 5 7 2 0 0 0 0 1

.. 12 98 .. 10 0 32 3 93 2 37 7 .. 40 13 0 9 13 0 2 0 .. 25 .. .. 1 2 4 51 .. .. 1 30 13 .. 4 8 0 50 44 3 .. 16 0 7 4 .. 0 3 2 2 9 4 2 .. .. 4 6

.. 12 1 .. 2 26 18 3 1 0 .. 4a 0 35 .. .. 10 10 0 1 .. 8 7 30 .. 48 2 1 1 .. 0 1 0 2 .. 3 1 0 0 6 3 .. 3 0 3 3 2 1 8 3 9 7 0 67 0 0 0 1

0 10 0 .. 4 47 27 3 0 0 1 3 .. 33 9 16 12 15 1 5 3 .. 7 .. .. 58 1 6 2 .. .. 1 0 4 .. 2 1 3 0 6 1 .. 2 1 4 2 .. 7 22 2 6 7 5 59 .. .. 4 2

.. 65 4 .. 34 54 30 88 20 85 .. 77a 6 19 .. .. 54 60 6 3 .. 8 63 45 .. 13 84 94 35 .. 3 25 7 74 .. 82 60 78 8 40 39 .. 65 11 83 79 2 36 41 87 13 50 28 24 0 62 9 93

18 70 2 .. 33 33 19 81 3 88 48 77 .. 6 61 78 39 53 12 21 96 .. 50 .. .. 11 94 79 28 .. .. 47 15 66 .. 87 65 70 9 37 72 .. 62 9 77 79 .. 39 55 82 19 54 43 32 .. .. 35 89

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

4.4

ECONOMY

Structure of merchandise exports Merchandise exports

Food

Agricultural raw materials

Fuels

Ores and metals

Manufactures

$ millions 1995 2009

% of total 1995 2009

% of total 1995 2009

% of total 1995 2009

% of total 1995 2009

% of total 1995 2009

12,865 30,630 45,417 18,360 496 44,705 19,046 233,766 1,427 443,116 1,769 5,250 1,878 959 125,058 .. 12,785 409 311 1,305 816 160 820 8,975 2,705 1,204 507 405 73,914 441 488 1,538 79,542 745 473 6,881 168 860 1,409 345 203,171 13,645 466 288 12,342 41,992 6,068 8,029 625 2,654 919 5,575 17,502 22,895 22,783 .. 3,651

83,778 162,613 119,481 78,113 39,500 114,587 47,935 405,777 1,316 580,719 6,366 43,196 4,421 1,550 363,534 .. 50,328 1,439 940 7,688 4,187 750 150 35,600 16,452 2,692 1,140 920 157,433 2,100 1,370 1,942 229,637 1,288 1,903 13,863 2,147 6,710 3,553 813 498,330 24,932 1,391 900 52,500 120,880 27,651 17,680 948 4,328 3,167 26,885 38,436 134,466 43,358 .. 40,500

21 19 11 4 .. 19 5 7 22 0 25 10 56 .. 2 .. 0 23 .. 14 20 .. .. 0 18 18 69 90 10 23 57 29 8 72 2 31 66 .. .. 8 20 45 75 17 2 8 5 12 75 13 44 31 13 10 7 .. 0

8 8 17 .. 0 9 3 8 27 1 17 4 44 .. 1 .. 0 24 .. 17 16 .. .. .. 19 18 29 87 11 28 12 32 7 74 2 22 23 .. 23 25 15 56 87 18 5 6 3 17 84 .. 85 23 8 11 11 .. 0

2 1 7 1 .. 1 2 1 0 1 2 3 7 .. 1 .. 0 13 .. 23 2 .. .. 0 8 5 6 2 6 75 0 1 1 2 28 3 16 .. .. 1 4 19 3 1 2 2 0 4 0 20 36 3 1 3 5 .. 0

1 1 5 .. 0 0 1 1 0 1 0 0 13 .. 1 .. 0 4 .. 10 1 .. .. .. 2 1 5 4 2 42 0 1 0 1 12 2 3 .. 0 3 3 10 1 4 1 0 0 2 1 .. 4 1 1 1 2 .. 0

3 2 25 86 .. 0 0 1 1 1 0 25 6 .. 2 .. 95 11 .. 2 0 .. .. 95 11 0 1 0 7 0 1 0 10 1 0 2 2 .. .. 0 7 2 1 0 96 47 79 1 3 38 0 5 2 8 3 .. 82

2 13 28 .. 99 1 0 4 17 2 1 71 4 .. 6 .. 93 6 .. 5 0 .. .. .. 21 1 5 0 15 6 22 0 14 0 10 2 17 .. 0 0 8 5 1 2 90 65 79 4 1 .. 0 10 2 3 5 .. 94

5 3 6 1 .. 1 1 1 6 1 24 24 3 .. 1 .. 0 13 .. 1 8 .. .. 0 5 18 7 0 1 0 42 0 3 3 60 12 2 .. .. 0 3 5 1 80 0 9 2 0 1 25 0 46 4 7 2 .. 0

1 6 9 .. 0 1 1 2 8 3 9 11 2 .. 2 .. 0 3 .. 3 8 .. .. .. 1 3 3 1 2 1 60 1 3 2 70 9 4 .. 31 5 2 3 1 69 0 5 4 1 4 .. 1 49 4 4 3 .. 0

68 74 51 9 .. 72 89 89 71 95 49 38 28 .. 93 .. 5 40 .. 58 70 .. .. 5 58 58 14 7 75 2 0 70 78 23 10 51 13 .. .. 84 63 29 21 1 1 27 14 83 20 4 19 15 42 71 83 .. 17

2011 World Development Indicators

82 67 41 .. 0 86 94 83 47 88 73 14 37 .. 90 .. 6 34 .. 61 72 .. .. .. 55 51 57 9 70 22 0 65 76 23 6 65 12 .. 45 67 56 23 10 7 4 20 10 76 10 .. 11 16 86 80 72 .. 5

207

4.4 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore b Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

Structure of merchandise exports Merchandise exports

Food

Agricultural raw materials

Fuels

Ores and metals

Manufactures

$ millions 1995 2009

% of total 1995 2009

% of total 1995 2009

% of total 1995 2009

% of total 1995 2009

% of total 1995 2009

7,910 40,633 81,095 303,388 54 193 50,040 192,296 993 2,180 .. 8,345 42 231 118,268 269,832 8,580 55,980 8,316 26,369 .. .. 62,603 27,853c 97,849 218,511 3,798 7,345 555 7,834 866 1,500 80,440 131,243 81,641 172,850 3,563 10,400 750 1,009 682 3,096 56,439 152,498 .. .. 378 800 2,455 9,126 5,475 14,445 21,637 102,129 1,880 6,595 460 2,478 13,128 39,703 28,364 175,000 237,953 352,491 584,743 1,056,043 2,106 5,386 3,430 10,735 18,457 57,595 5,449 57,096 .. .. 1,945 5,594 1,040 4,312 2,118 2,269 5,172,552 t 12,492,190 t 76,170 24,093 894,340 3,720,635 400,844 2,099,993 493,582 1,619,211 918,419 3,796,791 354,784 1,747,540 154,880 650,244 223,980 677,205 62,002 276,399 46,657 204,760 76,554 242,566 4,253,742 8,697,557 1,744,036 3,597,614

7 2 57 1 9 28 .. 4 6 4 .. 8c 15 21 44 .. 2 3 12 .. 65 19 .. 19 8 10 20 1 90 19 8 8 11 44 .. 3 30 .. 3 3 43 9w 31 14 14 15 15 11 8 20 6 17 18 8 11

7 3 42 1 30 19 .. 2 5 4 .. 10 16 26 6 21 5 4 22 .. 35 15 .. 16 3 9 11 .. 63 24 1 7 10 64 .. 0 20 .. 6 8 19 8w 25 11 9 12 11 8 8 18 .. 11 14 8 10

3 3 16 0 7 4 .. 1 4 2 .. 4c 2 4 47 .. 6 1 7 .. 23 5 .. 42 0 1 1 13 5 1 0 1 4 15 .. 0 3 .. 1 1 7 3w 10 3 3 4 3 4 3 3 1 2 7 2 2

2 2 2 0 1 2 .. 0 1 2 .. 2 1 3 1 7 4 0 1 .. 10 4 .. 9 0 0 0 .. 6 1 0 1 2 8 .. 0 3 .. 0 1 23 2w 8 2 2 2 2 2 2 2 .. 1 3 1 1

8 43 0 88 22 2 .. 7 4 1 .. 9c 2 0 0 .. 2 0 63 .. 0 1 .. 0 48 8 1 77 0 4 9 6 2 1 .. 77 18 .. 95 3 1 7w 2 12 8 15 11 6 29 15 73 1 36 6 2

6 67 0 88 24 3 .. 15 5 4 .. 11 4 0 92 1 6 3 39 .. 1 5 .. 0 79 14 4 .. 1 5 65 11 6 1 .. 96 20 .. 92 1 1 12 w 3 22 14 29 22 8 45 20 .. 11 37 9 4

3 10 12 1 12 15 .. 2 4 3 .. 8c 2 1 0 .. 3 3 1 .. 0 1 .. 32 0 2 3 1 1 7 55 3 3 1 .. 6 0 .. 1 87 12 3w 11 5 3 6 5 2 9 7 3 3 8 3 3

Note: Components may not sum to 100 percent because of unclassified trade. Exports of gold are excluded. a. Includes Luxembourg. b. Includes re-exports. c. Refers to the South African Customs Union (Botswana, Lesotho, Namibia, South Africa, and Swaziland).

208

2011 World Development Indicators

4 6 32 0 3 10 .. 1 2 3 .. 29 3 1 0 1 4 3 4 .. 25 1 .. 13 2 1 3 .. 2 6 1 3 4 0 .. 1 1 .. 0 81 22 4w 14 5 3 7 5 2 5 8 .. 5 15 3 2

78 26 14 10 48 49 .. 84 82 90 .. 44 c 78 73 6 .. 79 94 17 .. 10 73 .. 7 43 79 74 8 4 68 28 81 77 39 .. 14 44 .. 1 7 37 76 w 44 63 69 58 63 74 42 55 17 76 28 78 80

79 17 19 8 41 66 .. 74 87 87 .. 47 73 67 0 70 76 90 33 .. 25 75 .. 62 15 75 80 .. 27 63 4 72 67 26 .. 3 55 .. 2 8 34 70 w 50 59 71 48 59 80 37 51 .. 68 31 73 77

About the data

4.4

ECONOMY

Structure of merchandise exports Definitions

Data on merchandise trade are from customs

b and c are classified as re-exports. Because of dif-

• Merchandise exports are the f.o.b. value of goods

reports of goods moving into or out of an economy

ferences in reporting practices, data on exports may

provided to the rest of the world. • Food corresponds

or from reports of financial transactions related to

not be fully comparable across economies.

to the commodities in SITC sections 0 (food and live

merchandise trade recorded in the balance of pay-

The data on total exports of goods (merchandise)

animals), 1 (beverages and tobacco), and 4 (animal

ments. Because of differences in timing and defi -

are from the World Trade Organization (WTO), which

and vegetable oils and fats) and SITC division 22

nitions, trade flow estimates from customs reports

obtains data from national statistical offices and the

(oil seeds, oil nuts, and oil kernels). • Agricultural

and balance of payments may differ. Several inter-

IMF’s International Financial Statistics, supplemented

raw materials correspond to SITC section 2 (crude

national agencies process trade data, each correct-

by the Comtrade database and publications or data-

materials except fuels), excluding divisions 22, 27

ing unreported or misreported data, leading to other

bases of regional organizations, specialized agen-

(crude fertilizers and minerals excluding coal, petro-

differences.

cies, economic groups, and private sources (such as

leum, and precious stones), and 28 (metalliferous

The most detailed source of data on international

Eurostat, the Food and Agriculture Organization, and

ores and scrap). • Fuels correspond to SITC section

trade in goods is the United Nations Statistics Divi-

country reports of the Economist Intelligence Unit).

3 (mineral fuels). • Ores and metals correspond to

sion’s Commodity Trade (Comtrade) database. The

Country websites and email contact have improved

the commodities in SITC divisions 27, 28, and 68

International Monetary Fund (IMF) also collects

collection of up-to-date statistics, reducing the pro-

(nonferrous metals). • Manufactures correspond to

customs-based data on trade in goods. Exports are

portion of estimates. The WTO database now covers

the commodities in SITC sections 5 (chemicals), 6

recorded as the cost of the goods delivered to the

most major traders in Africa, Asia, and Latin America,

(basic manufactures), 7 (machinery and transport

frontier of the exporting country for shipment—the

which together with high-income countries account

equipment), and 8 (miscellaneous manufactured

free on board (f.o.b.) value. Many countries report

for nearly 95 percent of world trade. Reliability of

goods), excluding division 68.

trade data in U.S. dollars. When countries report in

data for countries in Europe and Central Asia has

local currency, the United Nations Statistics Division

also improved.

applies the average official exchange rate to the U.S. dollar for the period shown.

Export shares by major commodity group are from Comtrade. The values of total exports reported

Countries may report trade according to the gen-

here have not been fully reconciled with the esti-

eral or special system of trade. Under the general

mates from the national accounts or the balance

system exports comprise outward-moving goods that

of payments.

are (a) goods wholly or partly produced in the country;

The classification of commodity groups is based

(b) foreign goods, neither transformed nor declared

on the Standard International Trade Classification

for domestic consumption in the country, that move

(SITC) revision 3. Previous editions contained data

outward from customs storage; and (c) goods previ-

based on the SITC revision 1. Data for earlier years in

ously included as imports for domestic consumption

previous editions may differ because of this change

but subsequently exported without transformation.

in methodology. Concordance tables are available

Under the special system exports comprise cat-

to convert data reported in one system to another.

egories a and c. In some compilations categories Developing economies’ share of world merchandise exports continues to expand 1995 ($5.2 trillion)

4.4a

2009 ($12.5 trillion)

Data sources Data on merchandise exports are from the WTO. High income 82% East Asia & Pacific 7% Europe & Central Asia 3% Latin America & Caribbean 4% Middle East & N. Africa 1% South Asia 1% Sub-Saharan Africa 2%

Data on shares of exports by major commodity High income 70%

group are from Comtrade. The WTO publishes data East Asia & Pacific 14%

on world trade in its Annual Report. The IMF publishes estimates of total exports of goods in its International Financial Statistics and Direction of

Europe & Central Asia 5% Latin America & Caribbean 5% Middle East & N. Africa 2% South Asia 2% Sub-Saharan Africa 2%

Developing economies’ share of world merchandise exports increased 12 percentage points from 1995 to 2009. East Asia and the Pacifi c was the biggest gainer, capturing an additional 7 percentage points. All other developing country regions also increased their share in world trade. Source: World Development Indicators data files and World Trade Organization.

Trade Statistics, as does the United Nations Statistics Division in its Monthly Bulletin of Statistics. And the United Nations Conference on Trade and Development publishes data on the structure of exports in its Handbook of Statistics. Tariff line records of exports are compiled in the United Nations Statistics Division’s Comtrade database.

2011 World Development Indicators

209

4.5

Structure of merchandise imports Merchandise imports

$ millions 1995 2009

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China† Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras †Data for Taiwan, China

210

387 714 10,100 1,468 20,122 674 61,283 66,237 668 6,694 5,564 164,934 a 746 1,424 1,082 1,911 54,137 5,660 455 234 1,187 1,199 168,426 175 365 15,900 132,084 196,072 13,853 871 670 4,036 2,931 7,352 2,825 25,085 45,939 5,170 4,152 11,760 3,329 454 2,546 1,145 29,470 289,391 882 182 392 463,872 1,906 25,898 3,292 819 133 653 1,879 103,558

3,970 4,548 39,294 17,000 38,780 3,304 165,471 143,382 6,514 21,833 28,563 351,945 2,040 4,410 8,773 4,728 133,669 23,330 2,083 402 6,200 4,250 329,904 300 1,950 42,427 1,005,688 352,241 32,898 3,600 2,900 11,395 6,050 21,203 9,623 105,179 82,947 12,283 15,093 44,946 7,255 540 10,122 7,963 60,753 559,817 2,200 304 4,378 938,295 8,140 59,858 11,531 1,400 230 2,050 7,788 174,371

2011 World Development Indicators

Food

Agricultural raw materials

Fuels

Ores and metals

Manufactures

% of total 1995 2009

% of total 1995 2009

% of total 1995 2009

% of total 1995 2009

% of total 1995 2009

.. 34 29 .. 5 31 5 6 39 17 .. 11a 27 10 .. .. 11 8 21 21 .. 17 6 16 24 7 7 5 9 .. 21 10 21 12 .. 7 12 .. 8 28 15 .. 14 14 6 11 19 36 36 10 8 16 12 31 44 .. 13 6

18 17 16 .. 4 19 6 8 16 22 8 9 .. 9 19 13 5 10 16 13 7 .. 8 .. .. 7 5 4 10 .. .. 7 23 10 .. 6 13 14 9 17 19 .. 12 11 7 9 .. 34 15 8 15 13 14 13 .. .. 19 5

.. 1 3 .. 2 0 2 3 1 3 .. 2a 3 2 .. .. 3 3 2 2 .. 3 2 10 1 2 5 2 3 .. 1 1 1 2 .. 3 3 .. 3 7 2 .. 3 2 4 3 1 1 0 3 1 2 2 1 0 .. 1 4

0 1 1 .. 1 1 1 2 1 8 1 1 .. 1 1 1 1 1 1 1 1 .. 1 .. .. 1 3 1 1 .. .. 1 1 1 .. 1 2 1 1 3 2 .. 2 1 2 1 .. 1 1 1 1 1 1 0 .. .. 1 1

.. 2 1 .. 4 27 5 4 4 8 .. 6a 9 5 .. .. 12 34 14 11 .. 3 4 9 18 9 4 2 3 .. 20 9 19 12 .. 8 3 .. 6 1 9 .. 11 11 9 7 4 14 39 6 6 7 12 19 16 .. 12 7

24 12 1 .. 6 16 13 11 1 11 40 12 .. 11 15 13 15 20 24 2 8 .. 10 .. .. 21 13 3 4 .. .. 9 25 17 .. 9 6 21 12 11 15 .. 19 16 15 13 .. 16 18 11 14 15 19 33 .. .. 19 21

.. 1 2 .. 2 0 1 4 2 2 .. 5a 1 3 .. .. 3 4 1 1 .. 2 3 2 1 2 4 2 2 .. 1 2 1 3 .. 4 2 .. 2 3 2 .. 1 1 6 4 1 0 0 4 0 3 1 1 0 .. 1 6

0 2 1 .. 2 4 1 4 1 3 3 3 .. 1 2 2 3 7 1 1 2 .. 2 .. .. 2 14 2 2 .. .. 1 1 2 .. 3 2 1 1 8 1 .. 1 1 5 2 .. 1 2 3 1 2 1 0 .. .. 1 7

.. 61 65 .. 86 39 86 82 53 69 .. 71a 59 82 .. .. 71 48 62 64 .. 76 83 64 56 79 79 88 78 .. 58 78 57 67 .. 77 73 .. 82 61 72 .. 71 72 74 76 75 46 24 73 77 71 73 47 40 .. 74 75

17 68 80 .. 86 59 76 75 79 54 45 73 .. 78 62 70 76 59 59 81 82 .. 77 .. .. 59 64 89 82 .. .. 60 49 70 .. 78 74 63 76 60 63 .. 60 72 64 74 .. 48 64 67 69 69 64 53 .. .. 60 65

Merchandise imports

$ millions 1995 2009

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

15,465 34,707 40,630 13,882 665 32,340 29,578 205,990 2,818 335,882 3,697 3,807 2,991 1,380 135,119 .. 7,790 522 589 1,815 7,278 1,107 510 5,392 3,650 1,719 628 475 77,691 772 431 1,976 74,427 840 415 10,023 704 1,348 1,616 1,333 185,232 13,957 975 374 8,222 32,968 4,379 11,515 2,510 1,452 3,144 7,584 28,341 29,050 32,610 .. 3,398

78,175 249,590 91,749 50,375 37,000 62,507 49,278 412,721 5,064 551,960 14,075 28,409 10,207 2,080 323,085 .. 17,920 3,037 1,260 9,765 16,574 1,950 552 10,150 18,234 5,043 3,250 1,700 123,832 2,644 1,430 3,728 241,515 3,278 2,131 32,892 3,764 4,316 5,120 4,392 445,496 25,545 3,477 1,500 39,000 69,292 18,020 31,710 7,801 3,200 6,940 21,706 45,878 146,626 69,844 .. 23,000

ECONOMY

4.5

Structure of merchandise imports Food

Agricultural raw materials

Fuels

Ores and metals

Manufactures

% of total 1995 2009

% of total 1995 2009

% of total 1995 2009

% of total 1995 2009

% of total 1995 2009

6 4 9 21 .. 8 7 12 14 16 21 10 10 .. 6 .. 16 18 .. 10 21 .. .. 23 13 17 16 14 5 20 24 17 6 8 14 20 22 .. .. 12 14 7 18 32 18 7 20 18 11 .. 19 14 8 10 14 .. 9

5 4 9 .. .. 12 8 10 18 10 17 9 15 .. 5 .. 15 17 .. 17 15 .. .. .. 14 13 11 13 8 12 28 22 7 15 12 11 15 .. 14 15 11 11 18 25 12 8 11 11 12 .. 8 11 12 8 13 .. 6

3 4 6 2 .. 1 2 6 2 6 2 2 2 .. 6 .. 1 3 .. 2 2 .. .. 1 4 3 2 1 1 1 1 3 2 3 1 6 3 .. .. 3 2 1 1 1 1 3 1 6 1 .. 0 2 2 3 4 .. 1

1 2 3 .. .. 1 1 2 1 1 1 1 1 .. 2 .. 1 1 .. 1 1 .. .. .. 2 1 1 1 2 0 1 2 1 1 0 2 1 .. 1 2 1 1 1 5 1 1 1 4 0 .. 1 1 1 2 1 .. 0

12 24 8 2 .. 3 6 7 13 16 13 25 15 .. 14 .. 1 36 .. 21 9 .. .. 0 19 12 14 11 2 16 22 7 2 46 19 14 10 .. .. 12 8 5 18 13 1 3 2 16 14 .. 7 9 9 9 8 .. 1

8 34 20 .. .. 10 17 18 28 28 18 10 21 .. 28 .. 1 4 .. 16 21 .. .. .. 28 5 10 10 8 21 35 16 7 22 27 21 15 .. 14 17 13 15 22 17 1 5 5 28 17 .. 15 14 17 9 13 .. 1

4 7 4 3 .. 2 2 5 1 7 3 5 2 .. 6 .. 2 3 .. 1 2 .. .. 1 4 3 1 1 3 1 0 1 2 2 1 4 1 .. .. 3 3 3 1 3 2 6 2 3 1 .. 1 1 3 3 2 .. 2

2 6 3 .. .. 1 2 3 0 6 2 1 2 .. 7 .. 3 1 .. 2 2 .. .. .. 2 1 0 1 4 1 0 1 2 1 1 2 0 .. 1 3 2 1 0 2 2 5 3 4 1 .. 1 1 4 3 2 .. 3

75 54 73 71 .. 76 82 68 68 54 61 59 71 .. 68 .. 81 40 .. 66 66 .. .. 75 58 64 65 73 86 62 53 72 80 42 65 56 62 .. .. 46 72 83 63 51 77 81 70 57 73 .. 74 75 58 74 72 .. 87

2011 World Development Indicators

72 52 65 .. .. 68 72 65 51 52 60 80 60 .. 58 .. 81 50 .. 56 61 .. .. .. 53 62 78 74 76 65 36 59 80 61 60 63 55 .. 70 62 58 72 59 52 84 79 77 52 70 .. 76 72 67 74 62 .. 90

211

4.5

Structure of merchandise imports Merchandise imports

$ millions 1995 2009

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

10,278 54,247 60,945 191,803 236 1,227 28,091 95,567 1,412 4,713 .. 15,582 133 520 124,507 245,785 8,770 55,301 9,492 26,464 .. .. 73,172 30,546b 113,537 287,567 5,306 10,207 1,218 9,691 1,008 1,600 65,036 119,839 80,152 155,706 4,709 16,300 810 2,569 1,675 6,347 70,786 133,801 .. .. 594 1,500 1,714 6,955 7,902 19,096 35,709 140,921 1,365 6,750 1,056 4,310 15,484 45,436 23,778 140,000 267,250 481,707 770,852 1,605,296 2,867 6,907 2,750 9,023 12,649 40,597 8,155 69,949 .. .. 1,582 8,500 700 3,793 2,660 2,900 5,228,194 t 12,595,548 t 127,386 36,735 947,153 3,519,888 434,758 2,038,080 512,441 1,475,992 983,905 3,647,212 366,062 1,493,538 163,415 626,665 240,278 668,496 77,167 289,612 60,322 323,199 78,377 253,161 4,244,063 8,955,148 1,647,277 3,519,840

Food

Agricultural raw materials

Fuels

Ores and metals

Manufactures

% of total 1995 2009

% of total 1995 2009

% of total 1995 2009

% of total 1995 2009

% of total 1995 2009

8 18 19 17 25 14 .. 5 9 8 .. 7b 14 16 24 .. 7 6 17 .. 10 4 .. 18 16 13 7 24 16 8 15 10 5 10 .. 14 5 .. 29 10 6 9w 16 8 9 8 8 6 12 8 22 8 12 9 11

9 17 12 11 24 6 .. 3 7 9 .. 7 11 16 15 21 10 6 14 .. 9 6 .. 15 10 9 4 .. 13 11 7 11 5 10 .. 16 7 .. 28 6 22 8w 16 8 7 9 8 7 10 8 .. 7 11 8 10

2 1 3 1 2 4 .. 1 3 5 .. 2b 3 2 2 .. 2 2 3 .. 1 4 .. 2 1 4 6 0 3 2 0 2 2 4 .. 4 2 .. 2 2 2 3w 3 4 5 3 4 4 3 2 4 4 2 3 3

1 1 1 0 2 2 .. 0 1 3 .. 1 1 1 1 1 1 1 3 .. 1 2 .. 1 1 2 2 .. 1 1 0 1 1 2 .. 1 3 .. 1 1 0 1w 3 2 2 1 2 3 2 1 .. 2 1 1 1

21 3 12 0 30 14 .. 8 13 7 .. 8b 8 6 14 .. 6 3 1 .. 1 7 .. 30 1 7 13 3 2 48 4 4 8 10 .. 1 10 .. 8 13 9 7w 12 7 8 6 7 5 15 5 6 21 10 7 7

9 2 8 0 23 17 .. 24 12 11 .. 21 16 19 4 14 12 7 31 .. 23 19 .. 27 33 11 14 .. 19 32 1 10 17 24 .. 1 16 .. 21 14 13 15 w 16 14 18 11 14 14 14 10 .. 31 17 15 13

Note: Components may not sum to 100 percent because of unclassified trade. a. Includes Luxembourg. b. Refers to the South African Customs Union (Botswana, Lesotho, Namibia, South Africa, and Swaziland).

212

2011 World Development Indicators

4 2 3 4 1 7 .. 2 6 4 .. 2b 4 1 0 .. 4 3 1 .. 4 3 .. 1 6 3 6 2 2 3 6 3 3 1 .. 4 2 .. 1 2 2 4w 2 3 4 3 3 4 4 2 3 6 2 4 4

2 2 2 3 1 6 .. 2 2 4 .. 1 3 1 1 1 3 4 4 .. 1 4 .. 2 3 3 7 .. 1 3 5 3 2 1 .. 1 4 .. 1 13 5 3w 2 5 8 3 5 9 4 2 .. 5 2 3 3

63 45 64 76 42 60 .. 83 70 74 .. 78b 71 75 59 .. 80 85 76 .. 84 81 .. 49 76 73 68 71 78 38 75 80 79 74 .. 77 76 .. 59 72 78 75 w 66 75 72 77 75 78 57 78 66 56 73 75 73

75 76 76 36 50 69 .. 67 78 72 .. 64 68 62 78 63 70 81 47 .. 66 69 .. 55 53 75 64 .. 66 52 73 69 70 62 .. 79 70 .. 50 65 58 69 w 60 69 64 74 69 68 66 77 .. 53 66 69 68

About the data

4.5

ECONOMY

Structure of merchandise imports Definitions

Data on imports of goods are derived from the

and free trade zones. Goods transported through a

• Merchandise imports are the c.i.f. value of goods

same sources as data on exports. In principle, world

country en route to another are excluded.

purchased from the rest of the world valued in U.S.

exports and imports should be identical. Similarly,

The data on total imports of goods (merchandise)

dollars. • Food corresponds to the commodities in

exports from an economy should equal the sum of

in the table come from the World Trade Organization

SITC sections 0 (food and live animals), 1 (beverages

imports by the rest of the world from that economy.

(WTO). For further discussion of the WTO’s sources

and tobacco), and 4 (animal and vegetable oils and

But differences in timing and definitions result in dis-

and methodology, see About the data for table 4.4.

fats) and SITC division 22 (oil seeds, oil nuts, and oil

crepancies in reported values at all levels. For further

The import shares by major commodity group are

kernels). • Agricultural raw materials correspond to

discussion of indicators of merchandise trade, see

from the United Nations Statistics Division’s Com-

SITC section 2 (crude materials except fuels), exclud-

About the data for tables 4.4 and 6.2.

modity Trade (Comtrade) database. The values of

ing divisions 22, 27 (crude fertilizers and minerals

The value of imports is generally recorded as the

total imports reported here have not been fully recon-

excluding coal, petroleum, and precious stones),

cost of the goods when purchased by the importer

ciled with the estimates of imports of goods and ser-

and 28 (metalliferous ores and scrap). • Fuels cor-

plus the cost of transport and insurance to the fron-

vices from the national accounts (shown in table 4.8)

respond to SITC section 3 (mineral fuels). • Ores

tier of the importing country—the cost, insurance,

or those from the balance of payments (table 4.17).

and metals correspond to the commodities in SITC

and freight (c.i.f.) value, corresponding to the landed

The classification of commodity groups is based

divisions 27, 28, and 68 (nonferrous metals). • Man-

cost at the point of entry of foreign goods into the

on the Standard International Trade Classification

ufactures correspond to the commodities in SITC

country. A few countries, including Australia, Canada,

(SITC) revision 3. Previous editions contained data

sections 5 (chemicals), 6 (basic manufactures), 7

and the United States, collect import data on a free

based on the SITC revision 1. Data for earlier years in

(machinery and transport equipment), and 8 (miscel-

on board (f.o.b.) basis and adjust them for freight and

previous editions may differ because of this change

laneous manufactured goods), excluding division 68.

insurance costs. Many countries report trade data in

in methodology. Concordance tables are available

U.S. dollars. When countries report in local currency,

to convert data reported in one system to another.

the United Nations Statistics Division applies the average official exchange rate to the U.S. dollar for the period shown. Countries may report trade according to the general or special system of trade. Under the general system imports include goods imported for domestic consumption and imports into bonded warehouses and free trade zones. Under the special system imports comprise goods imported for domestic consumption (including transformation and repair) and withdrawals for domestic consumption from bonded warehouses

4.5a

Top 10 developing economy exporters of merchandise goods in 2009 Merchandise exports ($ billions)

1995

2009

1,500

Data sources Data on merchandise imports are from the WTO.

1,200

Data on shares of imports by major commodity group are from Comtrade. The WTO publishes data

900

on world trade in its Annual Report. The International Monetary Fund publishes estimates of total

600

imports of goods in its International Financial Statistics and Direction of Trade Statistics, as does the

300

United Nations Statistics Division in its Monthly Bulletin of Statistics. And the United Nations Con-

0 China

Russian Mexico Federation

India

Malaysia

Brazil

Thailand Indonesia

Turkey

Iran, Islamic Rep.

ference on Trade and Development publishes data on the structure of imports in its Handbook of Sta-

China continues to dominate merchandise exports among developing economies. Even when developed

tistics. Tariff line records of imports are compiled

economies are included, China ranks as the second leading merchandise exporter.

in the United Nations Statistics Division’s Com-

Source: World Development Indicators data files and World Trade Organization.

trade database.

2011 World Development Indicators

213

4.6

Structure of service exports Commercial service exports

$ millions 1995 2009

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

214

.. 94 .. 113 3,676 27 16,076 31,692 166 469 466 35,466a 159 174 457 236 6,005 1,431 38 4 103 242 25,425 0 23 3,249 18,430 33,790 1,641 .. 61 957 426 2,223 .. 6,638 15,171 1,894 687 8,262 342 49 868 310 7,334 83,108 191 38 188 73,576 139 9,528 628 17 2 98 221

.. 2,348 .. 623 10,758 580 44,513 54,080 1,670 935 3,453 79,815a 328 498 1,396 842 26,245 6,889 109 2 1,592 1,158 57,476 .. .. 8,401 128,600 86,306 4,109 .. 303 3,694 816 11,889 .. 20,278 55,346 4,864 1,130 21,302 806 .. 4,368 1,676 27,536 142,487 .. 104 1,225 226,638 1,722 37,690 1,818 67 44 327 933

2011 World Development Indicators

Transport

Travel

% of total

Insurance and ﬁnancial services

% of total

Computer, information, communications, and other commercial services

% of total

% of total

1995

2009

1995

2009

1995

2009

1995

2009

.. 19 .. 32 27 53 29 12 46 15 65 ..a 26 45 4 16 43 35 17 46 31 48 21 34 5 37 18 33 34 .. 52 14 29 32 .. 22 45 2 47 39 28 70 43 77 28 25 46 22 48 27 59 4 9 75 18 5 26

.. 11 .. 5 15 19 18 22 40 15 66 27a 4 13 20 10 15 21 19 22 12 41 15 .. .. 56 18 31 28 .. 4 8 29 9 .. 27 .. 9 31 31 34 .. 37 59 10 23 .. 19 51 23 19 50 14 22 0 .. 5

.. 69 .. 1 60 5 51 42 42 5 5 .. a 53 32 54 68 16 33 48 32 52 15 31 34 50 28 47 17 40 .. 22 71 21 61 .. 43 24 83 37 32 25 3 41 5 22 33 9 73 25 25 8 43 34 5 14 92 36

.. 78 .. 86 37 58 56 35 21 7 11 12a 72 56 49 54 20 55 57 62 74 19 24 .. .. 19 31 17 49 .. 18 49 14 76 .. 32 .. 83 59 50 40 .. 25 20 10 35 .. 60 39 15 56 39 65 4 87 96 66

.. 1 .. 9 0 7 5 4 0 0 0 ..a 7 10 3 8 17 0 0 0 0 7 11 20 2 7 10 9 6 .. 0 0 12 1 .. 1 .. 0 0 1 8 1 0 2 2 5 3 0 0 5 3 0 4 1 0 1 2

.. 0 .. 0 0 3 3 4 0 6 0 5a 2 14 1 4 7 3 2 14 0 2 11 .. .. 4 2 13 1 .. 31 0 0 1 .. 1 .. 1 0 1 4 .. 2 1 2 2 .. 0 2 7 1 2 2 9 1 0 2

.. 10 .. 59 12 41 15 42 12 80 30 ..a 14 14 39 7 24 32 35 21 18 30 37 12 44 28 24 41 19 .. 25 15 38 6 .. 34 31 15 16 28 39 27 16 16 48 37 41 5 27 44 30 52 54 18 82 2 36

.. 11 .. 9 48 21 22 38 39 72 23 55a 22 17 30 33 57 22 21 2 13 38 50 .. .. 22 49 39 23 .. 47 43 57 15 .. 40 .. 7 10 17 23 .. 36 20 78 41 .. 21 8 54 24 9 20 65 12 4 28

Commercial service exports

$ millions 1995 2009

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

5,086 6,763 5,342 533 .. 4,799 7,906 61,173 1,568 63,966 1,689 535 1,183 .. 22,133 .. 1,124 39 68 718 .. 30 .. 20 482 151 219 24 11,438 68 19 773 9,585 143 47 2,020 242 353 301 592 44,646 4,401 94 12 608 13,458 13 1,432 1,298 321 566 1,042 9,323 10,637 8,161 .. ..

18,419 90,193 13,238 .. 1,721 92,964 21,961 101,237 2,616 125,918 4,192 3,813 2,198 .. 57,304 .. 10,425 850 368 3,812 16,869 63 142 385 3,769 845 .. .. 28,727 442 .. 2,225 15,420 647 412 11,892 544 256 505 548 90,853 7,760 429 126 1,769 38,537 1,792 2,463 5,463 162 1,288 3,517 10,101 28,856 22,539 .. ..

Transport

Travel

% of total

Insurance and ﬁnancial services

% of total

4.6 Computer, information, communications, and other commercial services

% of total

1995

2009

1995

2009

8 28 1 26 .. 22 25 18 16 35 25 66 59 .. 42 .. 84 40 23 92 .. 7 .. 63 60 32 30 28 22 32 9 26 12 30 32 20 25 6 .. 9 40 35 18 3 16 63 100 58 60 11 13 32 3 29 19 .. ..

19 12 18 .. 22 4 14 13 13 25 19 57 48 .. 51 .. 30 16 8 51 2 1 10 68 56 30 .. .. 15 7 .. 15 10 39 33 18 28 51 23 7 27 19 10 9 62 41 32 44 56 9 13 21 11 30 26 .. ..

58 38 98 13 .. 46 38 47 68 5 39 23 36 .. 23 .. 11 12 76 3 .. 91 .. 12 16 14 26 72 35 37 58 56 64 40 44 64 .. 43 92 30 15 53 52 58 3 17 81 8 24 8 24 41 12 22 59 .. ..

31 12 48 .. 0 5 17 40 74 8 69 25 31 .. 16 .. 2 54 73 19 40 64 87 13 30 26 .. .. 55 62 .. 50 73 26 57 56 36 18 72 68 14 59 81 62 34 11 39 11 27 1 16 58 23 31 43 .. ..

ECONOMY

Structure of service exports

% of total

1995

2009

3 3 0 9 .. 0 0 7 1 1 0 0 1 .. 0 .. 6 0 1 2 .. 1 .. .. 1 4 2 0 0 5 0 0 7 12 5 1 .. 0 1 0 1 0 2 0 1 4 0 1 6 1 5 7 1 8 5 .. ..

1 5 2 .. 0 20 0 9 2 4 0 4 1 .. 5 .. 1 2 3 7 2 1 .. 16 1 2 .. .. 2 1 .. 4 10 1 1 2 1 .. 1 0 2 1 1 7 1 5 1 6 7 7 2 9 1 2 2 .. ..

1995

2009

31 31 2 53 .. 32 36 29 15 59 36 12 3 .. 34 .. 0 48 1 3 .. 1 .. 25 23 51 42 0 44 25 33 19 17 19 19 14 75 51 6 61 44 13 27 39 80 16 0 33 10 80 57 19 84 41 18 .. ..

49 70 32 .. 78 70 68 38 11 62 12 14 20 .. 28 .. 66 28 16 23 56 34 3 3 14 43 .. .. 28 30 .. 30 6 34 9 25 35 31 4 25 57 21 8 21 3 44 28 39 9 84 69 12 65 37 30 .. ..

2011 World Development Indicators

215

4.6

Structure of service exports Commercial service exports

$ millions 1995 2009

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

1,476 9,737 10,567 41,068 11 249 3,475 9,335 364 1,177 .. 3,478 71 53 27,234 90,690 2,378 6,259 2,016 5,999 .. .. 4,414 11,656 40,019 122,101 800 1,874 82 392 150 191 15,336 59,073 25,179 72,309 1,632 3,770 .. 142 566 1,795 14,652 29,677 .. .. 64 253 331 918 2,401 5,241 14,475 32,758 79 .. 104 854 2,846 13,324 .. .. 77,549 236,254 198,501 475,979 1,309 2,132 .. .. 1,529 1,805 2,243 5,656 265 407 141 1,085 112 241 353 .. 1,228,960 t 3,417,725 t 21,036 6,429 174,925 641,508 87,678 377,784 87,180 264,293 180,841 660,929 62,745 220,270 35,079 131,431 38,013 98,855 .. .. 10,333 97,113 12,144 35,613 1,047,874 2,755,581 425,302 1,087,280

a. Includes Luxembourg.

216

2011 World Development Indicators

Transport

Travel

% of total 1995

32 36 61 .. 15 .. 14 30 26 25 .. 24 16 42 1 18 32 15 15 .. 0 17 .. 34 59 25 12 80 18 76 .. 21 23 31 .. 38 .. 0 22 64 26 27 w 28 25 21 27 25 17 38 24 .. 32 26 27 25

Insurance and ﬁnancial services

% of total 2009

30 30 22 20 12 21 35 34 30 25 .. 12 15 46 4 4 16 8 5 50 19 19 .. 43 24 26 23 .. 4 47 .. 13 13 16 .. 39 .. 4 4 48 .. 21 w 20 21 21 22 21 17 33 18 .. 20 28 21 21

1995

40 41 22 .. 46 .. 80 28 26 54 .. 48 63 28 10 32 23 38 77 .. 89 55 .. 20 23 64 34 9 75 7 .. 26 38 47 .. 56 .. 96 35 26 51 33 w 28 45 46 43 44 49 34 51 .. 30 31 30 33

Computer, information, communications, and other commercial services

% of total 2009

13 23 70 64 46 25 48 10 37 42 .. 65 44 19 76 21 17 19 84 2 65 53 .. 16 43 53 65 .. 78 27 .. 13 25 62 .. 44 .. 66 83 41 .. 26 w 37 42 36 47 42 40 29 54 .. 13 53 22 24

% of total

1995

2009

5 1 0 .. 1 .. 0 15 5 1 .. 10 4 3 4 0 2 28 0 .. 0 1 .. 2 9 2 2 1 0 3 .. 18 4 1 .. 0 .. 0 0 0 0 5w 1 5 5 5 5 5 1 7 .. 2 6 6 4

2 4 1 13 1 1 1 12 6 2 .. 8 5 4 15 11 4 30 4 5 1 1 .. 5 25 2 3 .. 4 3 .. 28 15 4 .. 0 .. 0 0 2 .. 8w 3 4 2 5 4 1 3 7 .. 5 4 9 6

1995

23 23 18 .. 38 .. 6 27 43 21 .. 18 17 27 86 50 43 20 8 .. 11 28 .. 44 9 10 52 10 7 15 .. 35 35 21 .. 6 .. 4 43 10 23 36 w 44 27 30 25 28 31 27 18 .. 36 40 38 37

2009

56 44 8 3 40 53 15 44 26 31 .. 15 36 31 24 64 62 43 7 44 16 27 .. 36 8 18 9 .. 14 23 .. 46 47 18 .. 18 .. 30 13 9 .. 45 w 41 33 42 25 33 41 34 21 .. 62 16 48 49

About the data

4.6

ECONOMY

Structure of service exports Definitions

Balance of payments statistics, the main source of

affiliates. Another important dimension of service

• Commercial service exports are total service

information on international trade in services, have

trade not captured by conventional balance of pay-

exports minus exports of government services not

many weaknesses. Disaggregation of important

ments statistics is establishment trade—sales in

included elsewhere. • Transport covers all transport

components may be limited and varies considerably

the host country by foreign affiliates. By contrast,

services (sea, air, land, internal waterway, space,

across countries. There are inconsistencies in the

cross-border intrafirm transactions in merchandise

and pipeline) performed by residents of one economy

methods used to report items. And the recording of

may be reported as exports or imports in the balance

for those of another and involving the carriage of

major flows as net items is common (for example,

of payments.

passengers, movement of goods (freight), rental of

insurance transactions are often recorded as premi-

The data on exports of services in the table and on

carriers with crew, and related support and auxiliary

ums less claims). These factors contribute to a down-

imports of services in table 4.7, unlike those in edi-

services. Excluded are freight insurance, which is

ward bias in the value of the service trade reported

tions before 2000, include only commercial services

included in insurance services; goods procured in

in the balance of payments.

and exclude the category “government services not

ports by nonresident carriers and repairs of trans-

Efforts are being made to improve the coverage,

included elsewhere.” The data are compiled by the

port equipment, which are included in goods; repairs

quality, and consistency of these data. Eurostat and

IMF based on returns from national sources. Data on

of harbors, railway facilities, and airfield facilities,

the Organisation for Economic Co-operation and

total trade in goods and services from the IMF’s Bal-

which are included in construction services; and

Development, for example, are working together

ance of Payments database are shown in table 4.17.

rental of carriers without crew, which is included

to improve the collection of statistics on trade in

International transactions in services are defined

in other services. • Travel covers goods and ser-

services in member countries. In addition, the Inter-

by the IMF’s Balance of Payments Manual (1993) as

vices acquired from an economy by travelers in that

national Monetary Fund (IMF) has implemented

the economic output of intangible commodities that

economy for their own use during visits of less than

the new classifi cation of trade in services intro-

may be produced, transferred, and consumed at the

one year for business or personal purposes. • Insur-

duced in the fifth edition of its Balance of Payments

same time. Definitions may vary among reporting

ance and ﬁnancial services cover freight insurance

Manual (1993).

economies. Travel services include the goods and

on goods exported and other direct insurance such

Still, difficulties in capturing all the dimensions of

services consumed by travelers, such as meals,

as life insurance; financial intermediation services

international trade in services mean that the record

lodging, and transport (within the economy visited),

such as commissions, foreign exchange transac-

is likely to remain incomplete. Cross-border intrafirm

including car rental.

tions, and brokerage services; and auxiliary services

service transactions, which are usually not captured

such as financial market operational and regulatory

in the balance of payments, have increased in recent

services. • Computer, information, communica-

years. An example is transnational corporations’ use

tions, and other commercial services cover such

of mainframe computers around the clock for data

activities as international telecommunications and

processing, exploiting time zone differences between

postal and courier services; computer data; news-

their home country and the host countries of their

related service transactions between residents and nonresidents; construction services; royalties and license fees; miscellaneous business, professional,

4.6a

Top 10 developing economy exporters of commercial services in 2009 Commercial service exports ($ billions)

1995

2009

and technical services; and personal, cultural, and recreational services.

150

120

90

60

30

0 China

India

Russian Federation

Turkey

Thailand Malaysia

Brazil

Egypt, Arab Rep.

Mexico

Lebanona

The top 10 developing country exporters of commercial services accounted for almost 68 percent of developing country commercial service exports and 13 percent of world commercial service exports. a. Data are unavailable for 1995. Source: International Monetary Fund balance of payments data files.

Data sources Data on exports of commercial services are from the IMF, which publishes balance of payments data in its International Financial Statistics and Balance of Payments Statistics Yearbook.

2011 World Development Indicators

217

4.7

Structure of service imports Commercial service imports

$ millions 1995 2009

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

218

.. 98 .. 1,665 6,992 52 16,979 27,552 297 1,192 276 33,134 a 235 321 262 440 13,161 1,278 116 62 181 485 32,985 114 174 3,524 24,635 24,962 2,813 .. 690 895 1,235 1,373 .. 4,860 13,945 957 1,141 4,511 488 45 420 337 9,418 64,523 832 47 249 128,865 331 4,003 672 252 27 236 326

.. 2,215 .. 18,210 11,445 839 47,613 36,894 3,297 3,202 2,031 73,008 500 993 625 1,040 44,074 5,037 564 160 939 2,081 77,579 .. .. 9,351 158,107 44,379 6,860 .. 3,523 1,407 2,324 3,812 .. 18,887 50,912 1,733 2,556 12,765 1,231 .. 2,496 2,190 25,687 126,425 .. 83 910 253,467 2,166 19,525 2,058 288 85 736 1,077

2011 World Development Indicators

Transport

Travel

% of total

Insurance and ﬁnancial services

% of total

Computer, information, communications, and other commercial services

% of total

% of total

1995

2009

1995

2009

1995

2009

1995

2009

.. 61 .. 18 30 83 37 12 31 65 36 24 a 59 66 51 43 44 42 56 49 46 35 24 44 55 54 39 22 42 .. 19 41 50 28 .. 16 45 61 42 35 55 2 53 63 23 33 18 60 27 18 61 30 41 58 53 78 60

.. 15 .. 23 23 46 31 29 24 83 40 25 62 38 32 40 18 22 59 53 58 33 22 .. .. 52 29 34 34 .. 15 36 58 18 .. 21 .. 58 54 45 57 .. 33 67 19 26 .. 46 54 21 41 51 46 37 38 72 42

.. 7 .. 5 47 6 30 40 49 20 32 28a 15 15 31 33 26 15 20 41 5 22 31 38 15 20 15 54 31 .. 8 36 15 31 .. 34 31 18 21 28 15 7 22 8 24 25 17 30 63 47 6 33 21 8 14 15 18

.. 72 .. 1 39 39 39 29 11 8 29 25 13 29 38 22 25 35 11 39 11 17 31 .. .. 17 28 34 26 .. 5 26 15 27 .. 22 .. 20 21 20 15 .. 24 6 17 31 .. 11 20 32 27 17 35 5 54 9 27

.. 22 .. 3 7 10 7 6 1 6 4 10a 10 9 10 8 10 0 5 6 4 7 11 8 2 4 17 6 12 .. 7 5 11 3 .. 5 .. 10 6 5 11 0 5 7 5 6 9 6 8 2 6 5 9 7 5 2 2

.. 5 .. 4 5 7 3 4 3 2 4 4 5 13 4 4 8 8 17 3 5 4 12 .. .. 10 8 8 8 .. 5 9 0 5 .. 3 .. 9 6 11 15 .. 2 4 2 3 .. 8 14 4 4 8 10 9 5 1 6

.. 10 .. 75 16 1 26 43 19 10 29 38 a 16 10 8 16 21 43 20 4 45 36 34 10 29 22 29 18 15 .. 67 18 23 38 .. 45 24 11 31 32 19 93 21 22 48 36 57 4 2 33 26 33 29 26 28 6 20

.. 9 .. 72 33 8 27 38 62 8 28 47 20 19 25 34 49 35 13 6 27 45 35 .. .. 20 35 24 32 .. 75 29 27 51 .. 54 .. 13 18 24 13 .. 41 22 62 41 .. 36 12 43 28 24 9 50 4 19 25

Commercial service imports

$ millions 1995 2009

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

3,765 10,062 13,230 2,192 .. 11,252 8,131 54,613 1,073 121,547 1,385 776 900 .. 25,394 .. 3,826 193 119 225 .. 58 .. 510 457 300 277 151 14,821 412 197 630 9,021 193 87 1,350 350 233 538 305 43,618 4,571 207 120 4,398 13,052 985 2,431 1,049 642 676 1,781 6,906 7,008 6,339 .. ..

16,407 80,274 27,625 .. 7,565 104,551 16,865 114,581 1,824 146,965 3,657 9,881 1,634 .. 74,978 .. 11,297 858 114 2,260 14,301 91 141 4,323 2,883 789 .. .. 27,257 1,022 .. 1,586 21,402 678 545 5,302 1,004 547 602 771 84,625 7,825 517 599 16,127 36,504 5,555 5,844 2,118 1,915 511 4,619 8,344 23,789 14,186 .. ..

Transport

Travel

% of total

Insurance and ﬁnancial services

% of total

4.7

ECONOMY

Structure of service imports

Computer, information, communications, and other commercial services

% of total

% of total

1995

2009

1995

2009

1995

2009

1995

2009

13 57 37 43 .. 16 45 24 46 30 52 38 46 .. 38 .. 39 27 43 68 .. 75 .. 60 64 50 56 67 38 60 62 40 38 52 70 48 33 11 37 36 29 41 39 74 22 38 42 67 71 25 66 51 30 25 27 .. ..

17 44 44 .. 53 2 32 20 43 28 53 19 51 .. 31 .. 31 48 12 26 15 79 60 48 38 39 .. .. 34 63 .. 32 13 38 37 44 35 46 37 28 21 29 48 67 38 26 38 54 58 23 61 37 44 22 30 .. ..

40 10 16 11 .. 18 26 27 14 30 31 36 21 .. 25 .. 59 3 25 11 .. 23 .. 15 23 9 21 26 16 12 12 25 35 29 22 22 .. 8 17 45 27 28 19 11 21 32 5 18 12 9 20 17 6 6 33 .. ..

22 12 19 .. 10 8 17 24 12 17 29 11 14 .. 18 .. 66 31 72 35 28 15 20 37 41 13 .. .. 24 14 .. 22 33 36 39 21 21 7 18 56 25 33 28 11 25 34 16 12 16 2 25 24 29 31 27 .. ..

5 6 3 10 .. 1 3 10 9 2 6 0 10 .. 2 .. 2 4 4 7 .. 0 .. .. 1 21 4 0 0 1 1 5 12 9 0 4 2 1 9 3 3 5 3 3 3 6 5 4 9 3 12 10 2 14 9 .. ..

3 10 5 .. 27 14 2 5 11 6 8 6 8 .. 2 .. 1 2 –4 5 2 0 2 14 2 4 .. .. 4 5 .. 5 52 3 3 5 2 .. 4 4 3 4 11 4 3 4 10 4 15 12 11 11 4 6 4 .. ..

43 28 43 36 .. 65 26 39 31 38 11 25 22 .. 36 .. 0 65 28 14 .. 2 .. 25 12 21 20 7 47 27 25 30 14 10 8 26 65 81 37 16 41 26 38 12 54 24 49 10 9 63 1 22 63 55 31 .. ..

57 35 32 .. 10 76 48 50 34 50 10 64 26 .. 49 .. 2 19 20 34 55 6 17 2 19 45 .. .. 38 18 .. 40 2 24 21 30 42 47 41 12 51 34 13 18 34 37 36 30 11 63 3 28 23 41 40 .. ..

2011 World Development Indicators

219

4.7

Structure of service imports Commercial service imports

$ millions 1995 2009

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

1,801 10,154 20,205 59,241 58 503 8,670 45,540 405 1,384 .. 3,406 79 107 21,111 82,189 1,800 7,933 1,429 4,330 .. .. 5,756 14,390 22,354 86,988 1,169 2,487 150 2,684 206 539 17,112 44,373 14,899 38,867 1,358 3,127 .. 289 729 1,685 18,629 37,541 .. .. 148 358 223 271 1,245 2,812 4,654 15,607 403 .. 563 1,408 1,334 11,070 .. .. 62,524 160,036 129,227 334,311 814 1,072 .. .. 4,654 9,223 2,304 7,044 349 786 604 2,038 282 674 645 .. 1,221,691 t 3,144,723 t 29,059 9,833 218,955 749,008 109,579 443,081 109,232 304,268 228,417 777,282 82,593 272,307 35,575 139,286 52,313 127,915 19,571 62,588 15,377 93,734 24,587 88,519 992,976 2,368,417 422,763 995,810

a. Includes Luxembourg.

220

2011 World Development Indicators

Transport

Travel

% of total 1995

34 16 73 25 57 .. 17 44 17 31 .. 40 31 58 27 16 28 35 57 .. 30 42 .. 71 42 45 30 40 38 34 .. 27 32 46 .. 31 .. 28 36 79 56 31 w 51 39 42 38 40 38 30 41 45 59 40 29 25

Insurance and ﬁnancial services

% of total 2009

28 16 63 25 55 27 57 32 22 20 .. 41 20 62 51 33 16 19 58 49 36 45 .. 71 47 53 42 .. 61 32 .. 18 20 42 .. 44 .. 8 46 57 .. 25 w 58 32 38 27 33 35 30 24 47 51 42 22 23

1995

39 57 17 .. 18 .. 63 22 18 40 .. 32 20 16 29 21 32 50 37 .. 49 23 .. 12 31 20 20 18 14 16 .. 40 36 29 .. 37 .. 46 12 9 19 31 w 18 24 16 30 23 16 33 31 21 13 24 33 32

Computer, information, communications, and other commercial services

% of total 2009

15 35 14 41 13 28 12 19 26 31 .. 29 19 17 32 13 27 27 26 2 45 12 .. 5 28 15 27 .. 13 30 .. 32 24 31 .. 17 .. 68 11 6 .. 25 w 18 26 22 28 25 24 29 29 19 13 23 25 27

1995

5 0 0 3 7 .. 4 10 5 2 .. 14 7 5 0 4 1 1 6 .. 3 5 .. 4 8 6 8 7 4 7 .. 4 6 5 .. 3 .. 3 7 0 3 6w 5 9 10 8 9 10 5 10 .. 5 9 5 5

% of total 2009

7 4 1 6 11 4 9 6 14 4 .. 4 8 6 1 6 1 8 9 10 4 5 .. 10 3 10 13 .. 10 13 .. 7 21 6 .. 6 .. 1 9 11 .. 10 w 4 14 7 20 14 6 8 30 11 8 4 9 4

1995

22 26 10 72 18 .. 16 24 60 27 .. 14 41 21 44 59 38 14 6 .. 18 30 .. 12 19 28 42 35 43 43 .. 29 26 20 .. 30 .. 25 45 12 23 32 w 27 28 32 25 28 37 33 17 28 23 28 33 38

2009

51 45 22 28 21 40 22 42 37 45 .. 26 52 15 68 48 56 46 7 38 15 38 .. 14 22 23 19 .. 16 25 .. 44 35 21 .. 33 .. 23 35 26 .. 40 w 19 28 32 25 28 35 33 17 23 29 31 43 46

About the data

4.7

ECONOMY

Structure of service imports Definitions

Trade in services differs from trade in goods because

• Commercial service imports are total service

services are produced and consumed at the same

imports minus imports of government services not

time. Thus services to a traveler may be consumed

included elsewhere. • Transport covers all transport

in the producing country (for example, use of a hotel

services (sea, air, land, internal waterway, space,

room) but are classified as imports of the traveler’s

and pipeline) performed by residents of one economy

country. In other cases services may be supplied

for those of another and involving the carriage of

from a remote location; for example, insurance

passengers, movement of goods (freight), rental of

services may be supplied from one location and

carriers with crew, and related support and auxiliary

consumed in another. For further discussion of the

services. Excluded are freight insurance, which is

problems of measuring trade in services, see About

included in insurance services; goods procured in

the data for table 4.6.

ports by nonresident carriers and repairs of trans-

The data on imports of services in the table and on

port equipment, which are included in goods; repairs

exports of services in table 4.6, unlike those in edi-

of harbors, railway facilities, and airfield facilities,

tions before 2000, include only commercial services

which are included in construction services; and

and exclude the category “government services not

rental of carriers without crew, which is included

included elsewhere.” The data are compiled by the

in other services. • Travel covers goods and ser-

International Monetary Fund (IMF) based on returns

vices acquired from an economy by travelers in that

from national sources.

economy for their own use during visits of less than

International transactions in services are defined

one year for business or personal purposes. • Insur-

by the IMF’s Balance of Payments Manual (1993) as

ance and ﬁnancial services cover freight insurance

the economic output of intangible commodities that

on goods imported and other direct insurance such

may be produced, transferred, and consumed at the

as life insurance; financial intermediation services

same time. Definitions may vary among reporting

such as commissions, foreign exchange transac-

economies.

tions, and brokerage services; and auxiliary services

Travel services include the goods and services

such as financial market operational and regulatory

consumed by travelers, such as meals, lodging, and

services. • Computer, information, communica-

transport (within the economy visited), including car

tions, and other commercial services cover such

rental.

activities as international telecommunications, and postal and courier services; computer data; newsrelated service transactions between residents and nonresidents; construction services; royalties and license fees; miscellaneous business, professional,

4.7a

The mix of commercial service imports by developing economies is changing

and technical services; and personal, cultural, and recreational services.

1995 ($228 billion)

Other 28%

Insurance and financial 9%

Transport 40%

2009 ($777 billion)

Other 28%

Transport 33%

Travel 23% Insurance and ﬁnancial 14% Travel 25%

Data sources Between 1995 and 2009 developing economies’ commercial service imports more than tripled. Insur-

Data on imports of commercial services are from

ance and financial services and travel services are displacing transport as the most important services

the IMF, which publishes balance of payments

imported.

data in its International Financial Statistics and

Source: International Monetary Fund balance of payments data files.

Balance of Payments Statistics Yearbook.

2011 World Development Indicators

221

4.8 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

222

Structure of demand Household ﬁnal consumption expenditure

General government ﬁnal consumption expenditure

Gross capital formation

Exports of goods and services

Imports of goods and services

Gross savings

% of GDP 1995 2009

% of GDP 1995 2009

% of GDP 1995 2009

% of GDP 1995 2009

% of GDP 1995 2009

% of GDP 1995 2009

.. 12 26 82 10 24 18 35 28 11 50 65 20 23 20 51 7 52 14 13 31 24 37 20 22 29 20 143 15 28 65 38 42 33 13 51 38 36 26 23 22 22 68 10 37 23 59 49 26 24 24 17 19 21 12 9 44

.. 35 29 68 10 62 20 36 42 17 54 62 33 27 71 38 9 50 27 27 47 18 34 28 34 27 19 148 21 24 64 40 34 42 16 55 33 39 28 28 38 83 76 16 29 22 36 73 42 23 33 27 25 25 35 29 48

.. 87 55 34 69 109 60 56 77 83 59 54 82 76 .. 34 62 66 63 89 95 72 57 79 91 61 43 62 65 81 49 71 66 67 71 51 51 81 68 74 87 94 54 80 52 57 41 90 102 58 76 76 86 74 95 86 64

88 87 41 .. 59 82 57 54 37 77 56 52 .. 66 80 63 62 66 .. .. 74 72 59 93 79 60 35 62 64 74 42 62 72 57 54 51 49 85 69 76 92 86 53 88 54 58 41 78 83 59 82 75 86 75 83 .. 80

2011 World Development Indicators

.. 14 17 40 13 11 18 20 13 5 21 21 11 14 .. 29 21 17 25 19 6 9 21 15 7 10 14 8 15 5 13 14 11 26 24 21 25 5 13 11 9 44 26 8 23 24 12 14 11 20 12 15 6 8 6 7 9

9 10 14 .. 15 11 17 20 14 5 17 25 .. 15 23 24 22 16 .. .. 8 9 22 4 16 13 13 9 16 8 12 17 9 20 33 22 30 8 10 11 10 31 22 8 25 25 12 16 24 20 10 19 10 8 14 .. 19

.. 21 31 35 18 18 24 25 24 19 25 21 20 15 20 25 18 16 24 6 15 13 19 14 13 26 42 34 26 9 37 18 16 16 7 33 20 18 22 20 20 23 28 18 18 19 23 20 4 22 20 18 15 21 22 26 32

25 29 41 15 21 31 28 21 22 24 38 20 25 17 22 24 17 26 .. .. 21 18 21 11 34 19 48 23 23 30 25 20 11 27 11 22 17 15 32 19 13 11 19 22 18 19 28 26 12 16 20 16 13 22 23 27 20

16 29 40 52 21 12 20 51 52 19 51 73 14 36 33 34 11 48 .. .. 60 27 29 14 42 38 27 194 16 10 72 43 42 36 20 70 48 22 37 25 22 4 71 11 37 23 52 30 30 41 31 19 23 41 26 14 42

48 54 36 46 16 36 22 46 25 27 62 70 28 33 58 45 11 56 .. .. 63 31 30 22 70 30 22 187 18 22 51 42 34 39 18 64 44 30 48 32 38 20 65 29 35 25 33 50 49 36 41 29 33 45 47 44 61

.. 20 .. 78 16 –9 18 22 13 22 21 29 11 11 .. 36 16 15 29 6 6 14 18 11 12 25 42 .. 19 .. –2 15 12 11 .. 29 22 16 17 22 18 19 24 21 22 19 33 8 1 20 18 18 11 21 10 .. 27

.. 17 .. 10 23 20 21 24 45 39 25 22 11 23 13 16 15 16 .. .. 19 20 18 .. .. 22 54 31 18 .. 18 20 15 22 .. 20 22 10 24 17 11 .. 24 16 20 16 .. 19 0 21 16 3 12 8 .. .. 16

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

4.8

ECONOMY

Structure of demand Household ﬁnal consumption expenditure

General government ﬁnal consumption expenditure

Gross capital formation

Exports of goods and services

Imports of goods and services

Gross savings

% of GDP 1995 2009

% of GDP 1995 2009

% of GDP 1995 2009

% of GDP 1995 2009

% of GDP 1995 2009

% of GDP 1995 2009

68 64 62 46 .. 54 56 58 70 55 65 71 70 .. 52 .. 43 75 .. 63 103 93 .. 59 68 70 90 79 48 83 77 63 67 57 56 68 90 .. 54 75 49 59 83 86 .. 50 51 72 52 44 76 71 74 60 65 .. 32

67 56 57 45 .. 52 57 60 81 60 83 50 76 .. 54 .. 28 86 66 61 79 79 202 23 65 81 80 62 50 77 72 75 67 87 55 57 84 .. 62 81 46 60 91 .. .. 43 34 80 49 69 78 64 74 61 67 .. 21

11 11 8 16 .. 16 28 18 11 15 24 14 15 .. 11 .. 32 20 .. 24 12 35 .. 22 21 19 7 21 12 10 11 14 10 27 13 17 8 .. 30 9 24 17 11 14 .. 22 25 12 15 17 10 10 11 20 17 .. 32

9 12 10 11 .. 19 24 22 16 20 24 12 16 .. 16 18 13 23 8 21 16 50 19 9 19 18 11 21 14 10 21 15 12 22 1 18 13 .. 24 11 29 20 12 .. .. 22 15 8 10 11 12 10 11 19 21 .. 25

21 27 32 29 .. 18 25 20 29 28 33 20 22 .. 38 .. 15 18 .. 14 36 76 .. 12 21 21 11 17 44 23 20 26 20 25 32 21 27 14 22 25 21 23 22 7 .. 22 15 19 30 22 26 25 22 19 24 .. 35

22 36 31 33 .. 14 16 19 21 20 15 30 21 .. 26 28 19 22 37 19 30 31 20 28 27 24 33 25 14 22 25 21 22 27 50 36 21 .. 27 30 18 18 23 .. .. 20 30 19 25 20 16 22 15 20 20 .. 39

46 11 26 22 .. 76 29 26 51 9 52 39 33 .. 29 .. 52 29 23 43 11 24 9 29 47 33 24 30 94 21 37 59 30 49 48 27 16 1 49 25 59 29 19 17 44 38 44 17 101 61 59 13 36 23 27 72 44

81 20 24 32 .. 89 35 24 35 13 43 42 25 .. 50 14 66 50 33 42 22 51 31 67 60 44 28 30 96 26 50 48 28 37 56 29 25 .. 47 16 69 28 35 .. 36 42 59 13 77 58 47 24 32 39 28 .. 47

46 12 28 13 .. 65 37 22 61 8 73 44 39 .. 30 .. 42 42 37 45 62 128 72 22 58 43 32 48 98 36 45 61 28 58 49 34 41 2 56 35 54 28 35 24 42 32 36 19 98 44 71 18 44 21 34 97 43

80 24 21 22 .. 74 32 24 53 12 65 34 38 .. 46 54 26 81 44 43 47 112 173 27 72 67 52 38 75 36 68 59 29 73 63 39 44 .. 60 37 62 27 61 .. 27 27 38 20 61 57 52 20 31 39 36 .. 31

17 27 28 37 .. 23 13 22 25 30 29 15 23 .. 36 .. 38 8 .. 14 .. 39 .. .. 12 13 2 8 34 15 14 25 19 18 35 17 9 .. 32 21 27 18 –1 –1 .. 26 10 21 30 35 18 16 19 20 24 .. ..

2011 World Development Indicators

15 35 23 .. .. 9 20 16 13 24 10 28 15 .. 30 .. 59 14 25 29 13 28 –2 67 15 18 .. .. 31 19 .. 17 22 19 42 31 9 .. 27 38 22 16 10 .. .. 32 39 22 35 20 12 23 40 19 10 .. ..

223

4.8

Structure of demand

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzaniaa Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

Household ﬁnal consumption expenditure

General government ﬁnal consumption expenditure

Gross capital formation

Exports of goods and services

Imports of goods and services

Gross savings

% of GDP 1995 2009

% of GDP 1995 2009

% of GDP 1995 2009

% of GDP 1995 2009

% of GDP 1995 2009

% of GDP 1995 2009

68 52 97 47 80 73 88 41 52 60 .. 63 60 73 85 82 49 60 66 62 86 55 .. 77 53 63 68 44 85 55 48 63 68 73 51 69 74 98 71 72 65 61 w 81 60 55 63 60 48 61 66 63 67 69 61 57

61 54 81 38 83 74 84 43 47 55 .. 60 56 64 67 73 49 58 72 93 62 54 .. .. 49 63 72 49 76 65 46 65 71 68 56 64 66 .. .. 61 113 62 w 78 56 50 62 57 42 62 64 55 61 67 63 58

a. Covers mainland Tanzania only.

224

2011 World Development Indicators

14 19 10 24 13 23 14 8 22 19 .. 18 18 11 5 15 27 12 13 16 12 10 .. 12 12 16 11 12 11 21 16 20 15 12 22 7 8 18 14 15 18 17 w 9 14 12 15 14 13 16 15 15 10 16 17 20

15 20 15 25 9 19 14 10 20 20 .. 21 21 18 14 27 28 11 14 28 20 13 .. 9 10 13 15 10 11 19 10 23 17 13 18 13 6 .. .. 13 14 19 w 10 15 13 16 15 13 17 16 13 11 18 20 22

24 25 13 20 14 12 6 34 24 24 .. 18 22 26 14 16 17 23 27 29 20 42 .. 16 21 25 25 49 12 27 30 17 18 15 27 18 27 35 22 16 20 22 w 18 27 34 22 27 40 25 20 25 25 18 21 21

31 19 22 26 28 24 15 29 38 23 .. 19 24 25 25 17 17 20 16 22 30 22 .. .. 12 27 15 11 24 17 20 14 14 18 26 25 38 .. .. 22 2 19 w 24 28 37 20 28 40 19 20 28 33 21 17 19

28 29 5 38 31 17 19 .. 58 50 .. 23 22 36 5 60 40 36 31 66 24 42 .. 32 54 45 20 84 12 47 69 28 11 19 28 27 33 16 51 36 38 21 w 18 23 23 23 23 27 29 18 26 12 28 21 29

33 28 12 54 24 27 16 221 99 59 .. 27 23 21 15 60 49 52 34 13 23 68 .. 42 68 52 23 76 23 46 87 28 11 26 36 18 68 .. .. 36 36 24 w 23 27 29 25 27 35 30 21 38 19 30 24 36

33 26 26 28 37 24 26 .. 56 52 .. 22 22 46 10 74 33 31 38 72 42 49 .. 37 39 49 24 84 21 50 63 28 12 19 28 22 42 68 58 40 41 21 w 26 24 24 23 24 28 31 19 29 15 30 20 28

40 20 29 43 44 44 29 203 104 57 .. 28 26 28 21 76 42 41 36 56 35 58 .. 62 39 55 24 46 35 48 64 30 14 26 36 20 79 .. .. 32 65 24 w 36 26 28 24 26 30 29 21 33 24 34 24 35

19 28 20 20 8 .. –3 53 27 23 .. 17 22 20 3 16 20 30 27 .. 7 34 .. 17 27 20 22 50 13 23 .. 15 16 14 .. 21 20 12 26 9 18 22 w 17 26 33 20 26 38 23 18 .. 25 16 21 21

29 23 15 32 16 17 8 45 29 22 .. 15 20 24 12 2 24 32 14 12 21 30 .. .. 31 23 13 .. 18 16 .. 12 10 17 .. 22 29 .. .. 19 .. 19 w 24 29 40 19 29 47 19 19 .. 34 15 16 19

About the data

4.8

ECONOMY

Structure of demand Definitions

Gross domestic product (GDP) from the expenditure

1993 SNA guidelines are capital outlays on defense

• Household ﬁnal consumption expenditure is the

side is made up of household final consumption

establishments that may be used by the general pub-

market value of all goods and services, including

expenditure, general government final consumption

lic, such as schools, airfields, and hospitals, and

durable products (such as cars and computers),

expenditure, gross capital formation (private and

intangibles such as computer software and mineral

purchased by households. It excludes purchases

public investment in fixed assets, changes in inven-

exploration outlays. Data on capital formation may

of dwellings but includes imputed rent for owner-

tories, and net acquisitions of valuables), and net

be estimated from direct surveys of enterprises and

occupied dwellings. It also includes government fees

exports (exports minus imports) of goods and ser-

administrative records or based on the commodity

for permits and licenses. Expenditures of nonprofit

vices. Such expenditures are recorded in purchaser

flow method using data from production, trade, and

institutions serving households are included, even

prices and include net taxes on products.

construction activities. The quality of data on govern-

when reported separately. Household consumption

Because policymakers have tended to focus on

ment fixed capital formation depends on the quality

expenditure may include any statistical discrepancy

fostering the growth of output, and because data on

of government accounting systems (which tend to

in the use of resources relative to the supply of

production are easier to collect than data on spend-

be weak in developing countries). Measures of fixed

resources. • General government ﬁnal consump-

ing, many countries generate their primary estimate

capital formation by households and corporations—

tion expenditure is all government current expendi-

of GDP using the production approach. Moreover,

particularly capital outlays by small, unincorporated

tures for purchases of goods and services (including

many countries do not estimate all the components

enterprises—are usually unreliable.

compensation of employees). It also includes most

of national expenditures but instead derive some

Estimates of changes in inventories are rarely

expenditures on national defense and security but

of the main aggregates indirectly using GDP (based

complete but usually include the most important

excludes military expenditures with potentially wider

on the production approach) as the control total.

activities or commodities. In some countries these

public use that are part of government capital forma-

Household final consumption expenditure (private

estimates are derived as a composite residual along

tion. • Gross capital formation is outlays on addi-

consumption in the 1968 United Nations System of

with household fi nal consumption expenditure.

tions to fixed assets of the economy, net changes in

National Accounts, or SNA) is often estimated as

According to national accounts conventions, adjust-

inventories, and net acquisitions of valuables. Fixed

a residual, by subtracting all other known expendi-

ments should be made for appreciation of the value

assets include land improvements (fences, ditches,

tures from GDP. The resulting aggregate may incor-

of inventory holdings due to price changes, but this

drains); plant, machinery, and equipment purchases;

porate fairly large discrepancies. When household

is not always done. In highly inflationary economies

and construction (roads, railways, schools, buildings,

consumption is calculated separately, many of the

this element can be substantial.

and so on). Inventories are goods held to meet tem-

estimates are based on household surveys, which

Data on exports and imports are compiled from

porary or unexpected fluctuations in production or

tend to be one-year studies with limited coverage.

customs reports and balance of payments data.

sales, and “work in progress.” • Exports and imports

Thus the estimates quickly become outdated and

Although the data from the payments side provide

of goods and services are the value of all goods and

must be supplemented by estimates using price- and

reasonably reliable records of cross-border transac-

other market services provided to or received from

quantity-based statistical procedures. Complicating

tions, they may not adhere strictly to the appropriate

the rest of the world. They include the value of mer-

the issue, in many developing countries the distinc-

definitions of valuation and timing used in the bal-

chandise, freight, insurance, transport, travel, royal-

tion between cash outlays for personal business

ance of payments or correspond to the change-of-

ties, license fees, and other services (communica-

and those for household use may be blurred. World

ownership criterion. This issue has assumed greater

tion, construction, financial, information, business,

Development Indicators includes in household con-

significance with the increasing globalization of inter-

personal, government services, and so on). They

sumption the expenditures of nonprofit institutions

national business. Neither customs nor balance of

exclude compensation of employees and investment

serving households.

payments data usually capture the illegal transac-

income (factor services in the 1968 SNA) and trans-

General government final consumption expenditure

tions that occur in many countries. Goods carried

fer payments. • Gross savings are gross national

(general government consumption in the 1968 SNA)

by travelers across borders in legal but unreported

income less total consumption, plus net transfers.

includes expenditures on goods and services for

shuttle trade may further distort trade statistics.

individual consumption as well as those on services

Gross savings represent the difference between

for collective consumption. Defense expenditures,

disposable income and consumption and replace

including those on capital outlays (with certain excep-

gross domestic savings, a concept used by the World

tions), are treated as current spending.

Bank and included in World Development Indicators Data sources

Gross capital formation (gross domestic invest-

editions before 2006. The change was made to con-

ment in the 1968 SNA) consists of outlays on

form to SNA concepts and definitions. For further

Data on national accounts indicators for most

additions to the economy’s fixed assets plus net

discussion of the problems in compiling national

developing countries are collected from national

changes in the level of inventories. It is generally

accounts, see Srinivasan (1994), Heston (1994),

statistical organizations and central banks by vis-

obtained from industry reports of acquisitions and

and Ruggles (1994). For an analysis of the reliability

iting and resident World Bank missions. Data for

distinguishes only the broad categories of capital

of foreign trade and national income statistics, see

high-income economies are from Organisation for

formation. The 1993 SNA recognizes a third cat-

Morgenstern (1963).

Economic Co-operation and Development (OECD)

egory of capital formation: net acquisitions of valu-

data files.

ables. Included in gross capital formation under the

2011 World Development Indicators

225

4.9

Growth of consumption and investment Household ﬁnal consumption expenditure

General government ﬁnal consumption expenditure

Gross capital formation

Goods and services

average annual % growth average annual average annual Total Per capita Exports Imports % growth % growth 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 average annual % growth

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

226

.. 1.3 –0.1 .. 2.8 –0.5 3.2 1.7 2.0 2.6 –0.5 1.8 2.6 3.6 .. 3.9 3.7 –2.6 5.7 .. 6.0 3.1 2.6 .. 1.5 7.3 8.9 3.8 2.4 –1.1 .. 5.1 4.1 2.3 4.0 3.0 2.2 6.1 2.1 3.7 5.3 –5.0 0.6 3.6 1.8 1.6 –0.3 3.6 .. 1.9 .. 2.2 4.2 5.2 .. .. 3.0

.. 5.3 3.6 .. 4.7 8.8 3.9 1.4 14.0 4.5 11.2 1.2 2.3 3.4 .. 6.7 3.6 6.3 4.5 .. 8.2 4.5 3.4 –0.9 2.7 5.5 7.7 3.7 4.0 .. .. 4.2 .. 3.5 5.0 3.6 2.1 6.7 5.4 4.4 3.3 1.6 6.8 10.7 3.1 2.1 4.5 .. .. 0.3 .. 3.8 3.8 4.1 .. .. 5.2

2011 World Development Indicators

.. 2.2 –1.9 .. 1.5 1.1 2.0 1.4 1.0 0.6 –0.3 1.6 –0.7 1.4 .. 1.4 2.2 –2.0 2.8 .. 3.4 0.5 1.6 .. –1.7 5.6 7.7 2.0 0.6 –3.8 .. 2.5 0.9 3.0 3.5 3.0 1.8 4.2 0.3 1.7 4.1 –6.6 2.1 0.4 1.4 1.2 –3.1 –0.2 .. 1.6 .. 1.4 1.8 2.0 .. .. 0.6

.. 4.9 2.1 .. 3.7 8.7 2.4 0.9 12.9 2.8 11.7 0.6 –1.1 1.5 .. 5.2 2.4 6.9 1.1 .. 6.4 2.1 2.3 –2.7 –0.8 4.4 7.1 3.1 2.4 .. .. 2.4 .. 3.5 4.9 3.3 1.7 5.1 4.3 2.5 2.9 –2.2 7.1 7.9 2.7 1.4 2.5 .. .. 0.4 .. 3.4 1.3 2.1 .. .. 3.1

.. 14.5 3.6 .. 2.2 –1.5 2.9 2.7 –4.8 4.7 –1.9 1.6 4.4 3.6 .. 6.9 1.0 –8.0 2.9 .. 7.2 0.7 0.3 .. –8.3 3.7 9.6 3.7 10.9 –20.4 .. 2.0 0.8 1.7 –2.9 –0.9 2.4 7.0 –1.5 4.4 2.8 22.6 5.7 9.0 0.9 1.4 3.7 –2.2 .. 1.9 .. 2.1 5.1 –0.5 .. .. 2.0

.. 7.9 9.0 .. 3.6 10.9 3.2 1.6 23.0 8.8 0.0 1.6 8.3 3.5 .. 4.9 3.2 2.0 8.7 .. 11.4 2.8 2.7 –1.3 2.7 4.8 8.8 1.6 4.0 .. .. 2.7 3.1 2.9 7.6 2.2 1.8 4.9 4.2 2.7 1.5 1.2 2.2 0.7 1.6 1.7 2.1 .. .. 0.9 .. 3.1 3.0 0.3 .. .. 6.6

.. 25.8 –0.6 .. 7.4 –1.9 5.1 2.3 41.6 9.2 –7.5 2.4 12.2 8.5 .. 5.3 4.2 –5.3 3.1 .. 10.3 0.4 4.6 .. 4.0 9.3 10.8 4.8 2.1 2.6 .. 5.1 8.1 7.2 0.7 4.6 5.7 11.7 –0.6 5.8 7.1 19.1 0.5 6.5 3.2 1.8 3.0 1.9 .. 1.1 .. 4.1 6.1 0.1 .. 9.0 6.9

.. 6.1 8.8 .. 11.1 18.3 7.6 1.2 19.3 7.8 18.8 3.0 7.7 3.9 5.3 3.0 4.0 13.5 9.0 .. 14.2 4.4 4.7 –0.1 –2.4 7.7 13.9 2.2 9.8 .. .. 5.8 2.5 9.2 8.8 2.9 1.3 1.7 7.8 7.3 0.7 –1.0 14.6 11.3 2.0 1.8 5.6 .. .. –0.1 .. 1.9 0.5 –0.5 .. 1.5 3.9

.. 18.9 3.2 .. 8.7 –18.4 7.7 5.8 5.7 13.1 –4.8 5.3 1.8 4.5 .. 4.9 5.9 4.3 4.4 .. 21.7 3.2 8.7 .. 2.3 9.4 15.5 7.8 5.0 –0.5 .. 10.9 1.9 6.3 –9.0 8.7 5.0 8.3 5.3 3.5 13.4 –2.5 11.0 7.1 10.3 6.9 2.1 0.1 .. 6.0 .. 7.6 6.1 0.3 .. 10.1 1.6

.. 9.8 2.3 .. 6.3 5.0 2.2 4.7 23.0 11.5 5.7 2.8 2.7 7.7 9.0 2.8 7.1 7.9 10.9 .. 15.2 –0.4 –0.4 –3.6 33.6 5.6 20.2 9.7 5.7 6.5 .. 6.9 2.4 3.8 12.2 10.5 3.4 1.1 6.1 16.8 2.9 –6.3 6.7 10.1 4.5 1.4 –2.0 1.1 .. 5.9 .. 2.9 2.1 2.3 .. 4.4 5.1

.. 15.7 –1.0 .. 15.6 –12.7 7.6 4.8 14.1 9.7 –8.7 5.0 2.1 6.0 .. 4.9 11.6 2.9 1.9 .. 14.8 5.1 7.1 .. –1.8 11.7 16.7 8.4 9.3 –2.4 .. 9.2 8.2 4.9 –2.9 12.0 6.0 9.9 2.8 3.0 11.6 7.5 12.0 5.8 6.7 5.7 0.1 0.1 .. 5.8 .. 7.4 9.2 –1.1 .. 19.4 3.8

.. 13.7 7.8 .. 9.2 8.6 9.2 3.9 19.7 8.8 10.9 2.9 1.8 5.6 2.6 4.8 7.5 10.5 7.2 .. 14.8 3.8 3.3 –3.9 –3.7 10.5 16.9 7.8 9.7 16.3 .. 5.4 3.9 5.7 10.1 9.1 5.3 2.4 8.7 14.4 3.3 –3.7 7.4 16.5 5.1 3.3 3.8 1.3 .. 4.7 .. 2.7 2.1 0.5 .. 2.1 5.4

Household ﬁnal consumption expenditure

General government ﬁnal consumption expenditure

Gross capital formation

4.9

ECONOMY

Growth of consumption and investment

Goods and services

average annual % growth average annual average annual Total Per capita Exports Imports % growth % growth 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 average annual % growth

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

–0.1 4.8 6.6 3.2 .. 5.6 5.0 1.6 .. 1.4 4.9 –7.5 3.6 .. 4.9 .. 4.5 –4.8 .. –3.9 –0.2 1.8 .. .. 5.3 2.2 2.2 5.4 5.3 3.0 .. 5.1 3.9 9.9 .. 1.8 5.8 .. 4.8 .. 3.1 3.2 6.1 .. .. 3.5 5.4 4.9 6.4 2.5 2.6 4.0 3.7 5.2 3.0 .. ..

3.8 6.9 4.3 7.4 .. 3.7 3.4 0.6 .. 1.0 7.5 9.3 4.0 .. 3.0 .. .. 9.9 –7.8 8.0 .. 9.5 .. .. 9.7 4.8 2.2 .. 7.5 0.9 7.4 5.6 3.2 7.9 .. 4.7 6.2 .. 5.7 .. 0.6 3.4 3.7 .. .. 3.7 .. 4.6 7.2 .. 3.0 5.2 5.1 3.7 1.5 .. ..

0.1 2.9 5.0 1.6 .. 4.7 2.5 1.5 .. 1.1 1.1 –6.4 0.6 .. 3.9 .. 0.6 –5.8 .. –2.7 –1.9 0.1 .. .. 6.1 1.7 –0.8 3.2 2.6 1.0 .. 3.9 2.2 10.0 .. 0.3 2.6 .. 2.3 .. 2.5 2.0 3.9 .. .. 3.0 2.6 2.3 4.2 –0.2 0.3 2.2 1.5 5.1 2.7 .. ..

4.1 5.4 2.9 5.8 .. 1.8 1.5 –0.1 .. 0.9 5.0 8.5 1.3 .. 2.6 .. .. 9.0 –9.4 8.6 .. 8.4 .. .. 10.3 4.6 –0.7 .. 5.6 –1.5 4.5 4.7 2.1 8.2 .. 3.5 3.6 .. 3.7 .. 0.3 2.0 2.3 .. .. 2.9 .. 2.2 5.4 .. 1.1 3.9 3.1 3.7 1.1 .. ..

0.9 6.6 0.1 1.6 .. 4.1 2.7 –0.2 .. 2.9 4.7 –7.1 6.9 .. 4.7 .. –2.4 –7.2 .. 1.8 10.9 8.1 .. .. 1.9 –0.4 0.0 –4.4 4.8 3.2 .. 3.6 1.8 –12.4 .. 3.9 3.2 .. 3.3 .. 2.0 2.4 –1.5 .. .. 2.7 2.4 0.7 1.7 2.5 2.5 5.2 3.8 3.7 2.9 .. ..

1.3 5.7 8.2 3.6 .. 4.3 1.4 1.6 .. 1.6 6.7 7.8 2.3 .. 4.9 .. .. 4.2 9.7 2.1 .. 6.4 .. .. 4.3 0.0 5.5 .. 7.9 .. 3.1 3.8 0.8 5.9 .. 3.8 –4.6 .. 4.5 .. 3.2 4.1 2.7 .. .. 2.4 .. 8.3 3.6 .. 3.3 5.2 3.1 4.2 1.5 .. ..

9.6 6.9 –0.6 –0.1 .. 9.9 2.0 1.6 .. –0.8 0.3 –19.0 6.1 .. 3.4 .. 1.0 –1.1 .. –3.7 –5.8 0.2 .. .. 11.1 3.6 3.3 –8.4 5.3 0.4 .. 4.8 4.7 –15.5 .. 2.5 8.6 .. 7.3 .. 4.4 6.1 11.3 .. .. 6.0 4.0 1.8 10.4 1.9 0.7 7.4 4.1 10.6 5.9 .. ..

1.3 13.4 5.9 8.3 .. 1.5 2.3 0.3 .. –0.9 6.7 17.2 9.0 .. 3.1 .. .. 3.8 15.2 16.4 6.3 –0.5 .. .. 13.6 4.7 14.1 .. 2.1 6.2 23.8 5.3 0.4 9.8 .. 8.9 5.9 .. 9.4 .. 1.1 3.7 2.1 .. .. 5.0 .. 6.3 10.2 .. 3.0 10.5 1.3 5.9 –1.8 .. ..

9.9 12.3 5.9 1.2 .. 15.7 10.9 5.9 .. 4.3 2.6 –1.9 1.0 .. 16.0 .. –1.6 –1.6 .. 4.3 18.6 10.3 .. .. 4.9 4.2 3.8 4.0 12.0 9.9 –1.3 5.6 14.6 0.7 .. 5.9 13.1 .. 3.8 .. 7.3 5.2 9.3 .. .. 5.5 6.2 1.7 –0.4 5.1 3.1 8.5 7.8 11.3 5.7 1.6 ..

11.2 16.0 7.8 5.0 .. 4.2 5.9 0.4 .. 5.5 5.7 5.9 6.6 .. 10.6 .. .. 5.1 –7.6 7.1 10.2 10.0 .. .. 11.2 2.4 6.7 .. 5.3 6.3 –2.1 2.0 4.3 9.1 .. 6.4 16.0 .. 6.0 .. 4.1 2.2 8.3 .. .. 0.7 .. 7.1 7.8 .. 7.0 7.8 5.2 9.0 3.3 .. ..

11.4 14.4 5.7 –6.8 .. 14.5 7.6 4.4 .. 4.3 1.5 –12.7 9.4 .. 10.0 .. 0.8 –8.2 .. 7.6 –1.1 2.7 .. .. 7.5 7.5 4.1 –1.1 10.3 3.5 0.6 5.1 12.3 5.6 .. 5.1 7.6 .. 5.4 .. 7.6 6.2 12.2 .. .. 5.8 5.9 2.5 1.2 3.4 2.9 9.0 7.8 16.7 7.6 4.5 ..

2011 World Development Indicators

10.0 16.5 8.6 13.2 .. 3.9 3.8 1.2 .. 2.5 6.9 5.6 8.3 .. 8.3 .. .. 16.0 –7.2 8.0 6.3 12.2 .. .. 14.0 4.0 9.3 .. 6.1 3.9 14.1 2.3 4.7 11.1 .. 8.3 6.2 .. 9.5 .. 3.8 4.5 5.1 .. .. 5.0 .. 7.3 6.9 .. 6.0 9.5 2.9 8.0 2.7 .. ..

227

4.9

Growth of consumption and investment Household ﬁnal consumption expenditure

General government ﬁnal consumption expenditure

Gross capital formation

Goods and services

average annual % growth average annual average annual Total Per capita Exports Imports % growth % growth 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 average annual % growth

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzaniaa Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

1.3 –0.9 .. .. 2.6 .. .. .. 6.0 3.9 .. 2.9 2.4 .. 3.7 7.3 1.5 1.1 3.0 –11.8 5.1 3.7 .. 5.0 0.7 4.3 3.8 .. 6.7 –6.9 7.1 3.0 3.7 5.0 .. 0.6 5.4 5.3 3.2 2.4 .. 3.0 w 2.9 4.1 5.7 3.0 4.0 7.4 0.5 3.6 2.8 4.6 3.3 2.8 2.0

6.2 9.9 .. 5.3 5.3 3.3 .. .. 5.3 3.2 .. 4.6 2.8 .. 5.9 2.0 2.2 1.4 7.5 6.1 6.2 4.0 .. 0.5 13.3 5.3 5.3 .. 1.9 12.1 .. 2.1 2.4 2.9 .. 8.7 7.8 –1.5 .. 0.1 .. 2.7 w 4.5 5.7 6.6 5.0 5.7 6.9 7.6 4.1 5.3 6.4 4.9 2.0 1.4

a. Covers mainland Tanzania only.

228

2011 World Development Indicators

1.7 –0.7 .. .. –0.2 .. .. .. 5.8 4.0 .. 0.6 2.0 .. 1.1 4.9 1.1 0.5 0.3 –13.1 2.0 2.7 .. 2.0 0.1 2.6 2.1 .. 3.3 –6.4 1.2 2.8 2.5 4.3 .. –1.5 3.9 1.1 –0.7 –0.5 .. 1.6 w 0.5 2.6 4.1 1.8 2.4 6.1 0.4 2.0 0.6 2.6 0.6 2.1 1.6

6.7 10.3 .. 2.9 2.5 3.6 .. .. 5.2 3.0 .. 3.4 1.2 .. 3.7 1.0 1.7 0.6 4.6 4.8 3.3 3.1 .. –2.1 12.9 4.3 3.9 .. –1.3 12.9 .. 1.5 1.4 2.8 .. 6.8 6.4 –4.9 .. –2.1 .. 1.5 w 2.2 4.5 5.3 4.1 4.3 6.0 7.5 2.9 3.3 4.8 2.4 1.3 0.8

0.8 –2.2 .. .. 0.9 .. .. .. 1.8 2.2 .. 0.3 2.7 10.5 5.5 7.1 0.7 0.5 2.0 –15.7 –8.8 5.1 .. 0.0 0.3 4.1 4.6 .. 7.7 –4.1 6.8 1.0 0.7 2.3 .. 3.7 3.2 12.7 1.7 –8.1 .. 1.7 w –1.3 3.3 6.6 1.4 3.3 8.0 –0.8 1.9 3.5 5.9 0.3 1.5 1.5

4.3 2.1 .. 7.6 –0.6 4.5 .. .. 3.3 3.2 .. 5.0 5.1 .. 8.4 6.0 0.9 1.2 8.4 1.6 13.5 5.3 .. 1.3 4.3 4.4 4.0 .. 4.0 2.6 .. 2.2 2.1 1.3 .. 7.0 7.7 1.3 .. 24.9 .. 2.6 w 6.8 5.4 7.4 3.7 5.5 8.4 3.3 3.3 3.6 6.1 5.1 2.1 1.9

–5.1 –19.1 .. .. 3.5 .. .. .. 7.7 10.4 .. 4.7 3.2 6.9 22.0 –4.7 2.0 0.7 3.3 –17.6 –1.1 –4.0 .. –0.1 12.5 3.6 4.7 .. 9.2 –18.5 5.5 4.7 7.6 6.1 –2.5 11.0 19.8 9.2 11.4 3.9 .. 3.3 w 5.5 2.6 6.3 –0.5 2.7 7.8 –11.2 5.4 1.2 6.5 4.6 3.4 2.2

11.5 9.0 .. 11.4 9.6 18.3 .. .. 7.8 7.5 .. 9.1 3.3 .. 11.2 –0.3 3.3 0.4 –0.4 7.3 12.8 4.8 .. 5.9 4.2 2.9 6.9 1.6 12.0 5.1 .. 1.6 0.0 6.6 4.7 11.2 12.3 –3.0 .. 6.6 .. 3.1 w 8.7 9.9 12.2 6.3 9.9 12.4 9.1 5.0 7.4 12.4 8.5 0.8 1.2

8.1 0.8 .. .. 4.1 .. .. .. 9.6 1.7 .. 5.8 10.5 7.5 11.6 6.4 8.6 4.1 12.0 –5.3 11.7 9.5 .. 1.2 6.9 5.1 11.1 –2.4 15.4 –3.6 5.5 6.5 7.3 6.0 2.5 1.0 19.2 8.7 16.6 6.7 3.9 7.0 w 5.5 7.5 9.3 6.3 7.4 11.8 1.8 8.1 4.0 10.0 .. 6.9 6.8

9.6 7.1 .. 6.9 4.0 10.5 .. .. 11.0 9.1 .. 2.7 2.9 .. 14.3 5.2 4.6 4.7 6.5 9.5 11.6 5.8 .. 6.0 5.8 4.1 6.4 22.4 19.5 1.1 .. 3.1 4.5 7.8 4.9 –2.0 11.4 –3.1 .. 21.9 –10.7 5.9 w 9.7 10.4 14.7 5.5 10.4 14.5 7.2 5.0 7.7 14.6 .. 4.6 3.8

6.0 –6.1 .. .. 2.0 .. .. .. 12.4 5.2 .. 7.1 9.4 8.6 8.4 6.2 6.4 4.3 4.4 –6.0 4.7 4.5 .. 1.1 9.9 3.8 10.8 7.2 9.7 –6.6 6.4 6.8 9.8 9.9 –0.4 8.2 19.5 7.5 8.3 15.5 3.1 7.0 w 5.3 6.4 8.3 5.1 6.4 10.9 –2.3 10.4 0.0 11.2 5.7 7.2 6.3

13.5 15.9 .. 16.9 7.8 10.7 .. .. 9.6 8.9 .. 8.1 4.7 .. 12.0 4.7 4.2 3.6 11.3 10.6 15.9 5.7 .. 3.1 9.5 3.6 8.8 15.2 11.2 5.2 .. 3.4 3.3 6.4 4.2 13.8 13.6 –2.3 .. 15.6 –5.8 5.7 w 9.4 10.7 12.9 8.5 10.7 12.6 11.9 6.7 9.9 14.8 8.8 4.3 3.8

About the data

4.9

ECONOMY

Growth of consumption and investment Definitions

Measures of growth in consumption and capital for-

the change in government employment. Neither

• Household ﬁnal consumption expenditure is the

mation are subject to two kinds of inaccuracy. The

technique captures improvements in productivity

market value of all goods and services, including

first stems from the difficulty of measuring expendi-

or changes in the quality of government services.

durable products (such as cars and computers),

tures at current price levels, as described in About

Deflators for household consumption are usually cal-

purchased by households. It excludes purchases

the data for table 4.8. The second arises in deflat-

culated on the basis of the consumer price index.

of dwellings but includes imputed rent for owner-

ing current price data to measure volume growth,

Many countries estimate household consumption

occupied dwellings. It also includes government fees

where results depend on the relevance and reliabil-

as a residual that includes statistical discrepancies

for permits and licenses. Expenditures of nonprofit

ity of the price indexes and weights used. Measur-

associated with the estimation of other expenditure

institutions serving households are included, even

ing price changes is more difficult for investment

items, including changes in inventories; thus these

when reported separately. Household consumption

goods than for consumption goods because of the

estimates lack detailed breakdowns of household

expenditure may include any statistical discrepancy

one-time nature of many investments and because

consumption expenditures.

in the use of resources relative to the supply of

the rate of technological progress in capital goods

resources. • Household ﬁnal consumption expen-

makes capturing change in quality diffi cult. (An

diture per capita is household final consumption

example is computers—prices have fallen as qual-

expenditure divided by midyear population. • Gen-

ity has improved.) Several countries estimate capital

eral government ﬁnal consumption expenditure is

formation from the supply side, identifying capital

all government current expenditures for goods and

goods entering an economy directly from detailed

services (including compensation of employees). It

production and international trade statistics. This

also includes most expenditures on national defense

means that the price indexes used in deflating pro-

and security but excludes military expenditures with

duction and international trade, reflecting delivered

potentially wider public use that are part of govern-

or offered prices, will determine the deflator for capi-

ment capital formation. • Gross capital formation is

tal formation expenditures on the demand side.

outlays on additions to fixed assets of the economy,

Growth rates of household fi nal consumption

net changes in inventories, and net acquisitions

expenditure, household final consumption expen-

of valuables. Fixed assets include land improve-

diture per capita, general government fi nal con-

ments (fences, ditches, drains); plant, machinery,

sumption expenditure, gross capital formation, and

and equipment purchases; and construction (roads,

exports and imports of goods and services are esti-

railways, schools, buildings, and so on). Inventories

mated using constant price data. (Consumption, cap-

are goods held to meet temporary or unexpected

ital formation, and exports and imports of goods and

fluctuations in production or sales, and “work in prog-

services as shares of GDP are shown in table 4.8.)

ress.” • Exports and imports of goods and services

To obtain government consumption in constant

are the value of all goods and other market services

prices, countries may defl ate current values by

provided to or received from the rest of the world.

applying a wage (price) index or extrapolate from

They include the value of merchandise, freight, insurance, transport, travel, royalties, license fees, and

4.9a

GDP per capita is still lagging in some regions

other services (communication, construction, financial, information, business, personal, government

GDP per capita (2000 $) 5,000

services, and so on). They exclude compensation of Latin America & Caribbean

employees and investment income (factor services in the 1968 System of National Accounts) and transfer

4,000

payments. 3,000 Europe & Central Asia 2,000

Middle East & North Africa

Data sources

East Asia & Pacific

1,000

South Asia

Data on national accounts indicators for most

Sub-Saharan Africa

0 1990

1995

2000

2005

developing countries are collected from national 2009

statistical organizations and central banks by visiting and resident World Bank missions. Data for

Although GDP per capita has more than tripled in East Asia and Pacific between 1990 and 2009,

high-income economies are from Organisation for

it is still less than GDP per capita in Latin America and Carribean and in Europe and Central Asia.

Economic Co-operation and Development (OECD)

Source: World Development Indicators data files.

data files.

2011 World Development Indicators

229

4.10

Toward a broader measure of national income Gross domestic product

$ billions 2009

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

230

14.5 12.0 140.6 75.5 307.2 8.7 924.8 381.1 43.0 89.4 49.0 471.2 6.7 17.3 17.0 11.8 1,594.5 48.7 8.1 1.3 9.9 22.2 1,336.1 2.0 6.8 163.7 4,985.5 210.6 234.0 10.6 9.6 29.2 23.3 63.0 62.7 190.3 309.6 46.8 57.2 188.4 21.1 1.9 19.1 28.5 238.0 2,649.4 11.1 0.7 10.7 3,330.0 26.2 329.9 37.3 4.1 0.8 6.5 14.3

2011 World Development Indicators

Gross national income

$ billions 2009

10.6 11.9 139.6 67.5 297.7 8.9 900.7 377.1 40.3 97.5 47.9 475.0 6.6 16.7 17.4 11.3 1,562.4 46.6 8.0 1.3 9.4 22.1 1,317.3 2.0 6.1 153.4 5,028.8 216.9 224.5 9.8 6.9 28.8 22.4 60.5 61.8 178.1 318.3 45.0 56.1 188.6 20.4 1.9 18.5 28.5 238.1 2,671.2 9.5 0.7 10.6 3,377.0 25.9 320.8 36.1 3.7 0.8 .. 13.8

Adjustments

Consumption of fi xed capital % of GNI 2009

Natural resource depletion % of GNI 2009

7.7 10.5 10.5 11.7 11.8 9.7 14.4 14.3 11.5 6.8 11.1 14.0 7.9 9.5 10.4 11.5 11.8 11.7 7.4 5.5 8.1 8.6 14.2 7.2 9.9 12.6 10.2 13.6 11.3 5.9 13.6 11.3 8.8 12.9 .. 13.6 16.5 11.1 10.7 9.6 10.5 6.8 12.8 6.7 17.0 14.2 13.2 7.5 8.8 13.8 8.6 13.9 10.1 7.7 7.4 .. 9.6

3.4 1.3 16.9 29.1 4.9 0.5 5.1 0.1 32.7 2.6 0.9 0.0 1.2 11.2 .. 2.8 3.1 1.1 1.6 10.6 0.2 4.8 2.3 0.0 25.2 10.0 3.1 0.0 6.2 10.7 50.6 0.2 3.1 0.8 3.3 0.3 1.5 0.5 9.9 7.3 0.5 0.8 0.7 4.5 0.1 0.0 29.2 1.0 0.1 0.1 6.9 0.2 1.2 6.6 0.0 .. 0.4

Adjusted net national income

Gross domestic product

Gross national income

Adjusted net national income

$ billions 2009

% growth 2000–2009

% growth 2000–2009

% growth 2000–2009

9.5 10.5 101.2 40.0 248.2 8.0 725.2 322.8 22.5 88.3 42.2 408.7 6.0 13.2 .. 9.7 1,330.0 40.7 7.3 1.1 8.6 19.1 1,100.4 1.8 4.1 118.8 4,355.8 188.5 185.3 8.2 2.5 25.5 19.7 52.2 52.8 153.3 261.1 39.8 44.5 156.9 18.2 1.7 16.0 25.3 197.6 2,292.1 5.5 0.6 9.6 2,908.2 19.7 275.8 32.0 3.2 0.8 .. 12.4

.. 5.4 4.0 13.1 5.4 10.5 3.3 2.0 17.9 5.9 8.4 1.7 4.0 4.1 5.0 4.4 3.6 5.4 5.4 3.0 9.0 3.3 2.1 0.8 10.2 4.1 10.9 4.7 4.5 5.2 4.0 5.1 0.8 3.9 6.7 4.1 1.2 5.5 5.0 4.9 2.6 0.2 5.9 8.5 2.5 1.5 2.1 5.2 7.4 1.0 5.8 3.6 3.7 3.0 1.0 0.7 4.9

.. 5.8 3.7 .. 5.1 10.5 3.6 1.9 19.4 5.3 8.6 1.9 3.9 4.3 5.9 4.2 3.4 6.1 6.0 .. 9.3 2.7 1.9 –0.9 20.2 5.0 10.6 4.4 4.7 5.5 .. 4.6 0.6 4.0 6.6 4.6 0.7 5.4 4.5 5.0 2.7 1.4 6.1 8.4 2.4 1.5 2.6 5.4 .. 0.6 .. 4.1 3.8 4.1 .. .. 4.8

.. 7.3 4.9 .. 5.4 11.4 3.3 2.0 19.8 5.9 10.6 1.3 3.6 3.0 .. 3.0 3.6 4.9 5.5 .. 10.2 4.4 2.9 –1.2 –5.5 4.9 9.9 4.2 4.3 7.5 .. 4.0 0.6 5.2 6.7 4.6 2.0 5.1 5.2 2.8 2.3 4.0 6.7 10.3 1.7 1.4 3.3 3.6 .. 1.4 .. 3.0 3.2 0.9 .. .. 3.0

Gross domestic product

$ billions 2009

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

129.0 1,377.3 540.3 331.0 65.8 227.2 195.4 2,112.8 12.1 5,069.0 25.1 115.3 29.4 .. 832.5 5.4 148.0 4.6 5.9 26.2 34.5 1.6 0.9 62.4 37.2 9.2 8.6 4.7 193.1 9.0 3.0 8.6 874.8 5.4 4.2 91.4 9.8 .. 9.3 12.5 792.1 126.7 6.1 5.4 173.0 381.8 46.1 162.0 24.7 7.9 14.2 130.3 161.2 430.1 232.9 .. 98.3

Gross national income

$ billions 2009

121.2 1,369.3 478.4 328.6 61.8 184.4 190.8 2,076.3 11.4 5,228.3 25.7 103.4 29.3 .. 836.9 5.6 158.1 4.4 5.8 28.1 35.4 1.9 0.6 62.0 37.2 9.0 8.5 4.7 188.9 9.0 3.0 8.9 860.2 5.7 4.0 89.5 9.7 .. 9.2 12.8 773.9 121.4 5.9 5.3 162.9 376.4 58.1 166.4 23.1 7.8 14.0 122.6 161.1 416.1 225.1 .. ..

Adjustments

Consumption of fi xed capital % of GNI 2009

Natural resource depletion % of GNI 2009

13.0 8.6 10.9 10.7 10.1 17.7 13.6 14.0 11.2 13.5 10.3 12.7 7.4 .. 13.3 .. 5.2 8.4 8.4 11.3 11.2 6.4 8.2 11.9 12.0 10.9 7.3 7.4 11.6 7.7 8.1 10.9 11.7 8.5 9.5 10.1 7.1 .. 10.6 6.8 14.6 14.1 8.8 2.9 9.0 15.2 13.5 8.0 12.1 8.6 9.7 11.3 8.0 12.4 17.2 .. ..

0.2 4.2 6.5 17.9 45.7 0.1 0.2 0.1 0.7 0.0 1.1 22.0 1.2 .. 0.0 .. 37.0 0.5 0.0 0.3 0.0 1.4 11.0 30.5 0.2 0.1 0.2 0.9 7.9 0.0 18.8 0.0 5.4 0.2 11.1 1.4 3.8 .. 0.3 4.2 0.8 0.9 0.8 1.2 15.0 10.6 37.8 3.1 0.0 19.9 0.0 5.9 1.0 1.0 0.1 .. ..

4.10

ECONOMY

Toward a broader measure of national income Adjusted net national income

Gross domestic product

Gross national income

Adjusted net national income

$ billions 2009

% growth 2000–2009

% growth 2000–2009

% growth 2000–2009

4.0 7.8 5.1 6.2 .. 3.8 2.6 0.6 .. 0.8 6.7 9.9 4.2 .. 4.1 .. .. 4.3 6.6 6.0 3.9 0.8 .. .. 8.1 3.2 3.5 .. 4.5 5.9 3.2 3.3 2.1 5.0 .. 4.8 7.7 .. 5.7 .. 2.1 2.6 3.0 .. .. 2.2 .. 4.8 7.2 .. 3.7 6.7 4.1 4.7 1.0 .. ..

3.1 7.5 3.0 6.7 .. 2.4 4.4 0.4 .. 1.5 6.9 9.1 5.1 .. 3.3 .. .. 4.8 1.0 8.2 4.7 8.9 .. .. 9.3 2.9 2.3 .. 7.2 5.7 5.1 2.2 1.5 6.1 .. 4.4 6.3 .. 6.1 .. 1.3 2.8 2.5 .. .. 3.7 .. 4.7 6.7 .. 3.3 5.2 4.3 4.2 0.5 .. ..

105.2 1,194.1 395.3 234.7 27.4 151.8 164.4 1,784.1 10.1 4,521.8 22.8 67.6 26.8 .. 725.3 .. 91.3 4.0 5.3 24.8 31.4 1.8 0.5 35.8 32.7 8.0 7.9 4.3 152.3 8.3 2.2 7.9 713.2 5.2 3.1 79.2 8.6 .. 8.2 11.4 654.9 103.3 5.3 5.1 123.8 279.2 28.3 147.8 20.3 5.6 12.6 101.6 168.2 360.4 186.1 .. ..

2.9 7.9 5.3 5.4 –0.3 3.9 3.6 0.5 1.5 1.1 6.9 8.8 4.4 .. 4.2 4.8 8.4 4.6 6.9 6.2 4.6 3.1 0.0 5.4 6.3 3.1 3.6 4.8 5.1 5.3 4.7 3.7 2.2 5.6 7.4 5.0 7.9 .. 5.3 3.7 1.7 2.5 3.3 4.3 6.6 2.1 4.5 5.2 6.9 3.4 3.4 6.0 4.9 4.4 0.8 .. 14.2

2011 World Development Indicators

231

4.10

Toward a broader measure of national income Gross domestic product

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzaniaa Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

$ billions 2009

$ billions 2009

161.1 1,231.9 5.2 375.8 12.8 43.0 1.9 182.2 87.6 48.5 .. 285.4 1,460.3 42.0 54.7 3.0 406.1 491.9 52.2 5.0 21.4 263.8 0.6 2.9 21.2 39.6 614.6 19.9 16.0 113.5 230.3 2,174.5 14,119.0 31.5 32.1 326.1 90.1 .. 26.4 12.8 5.6 58,252.1 w 431.5 16,206.0 8,880.2 7,318.4 16,649.8 6,346.0 2,591.7 4,017.9 1,062.4 1,700.3 945.9 41,607.7 12,465.3

164.1 1,192.4 5.2 384.4 12.8 42.3 1.9 179.2 84.7 47.3 .. 279.0 1,430.2 41.5 49.3 2.9 413.4 512.3 50.9 4.9 21.4 252.0 2.9 2.8 20.7 37.3 606.9 19.2 15.7 111.1 .. 2,218.1 14,011.0 30.8 32.5 323.5 85.2 .. 24.9 11.4 5.2 57,867.2 w 433.8 16,112.0 8,952.6 7,173.2 16,558.2 6,307.5 2,521.8 3,921.9 1,192.9 1,702.0 904.2 41,369.3 12,368.9

a. Covers mainland Tanzania only.

232

Gross national income

2011 World Development Indicators

Adjustments

Consumption of fi xed capital % of GNI 2009

11.2 12.0 7.4 12.6 8.4 .. 6.8 14.1 13.0 13.5 .. 14.1 13.9 9.5 9.7 10.2 13.3 14.1 9.9 7.9 7.3 10.9 1.2 7.0 12.9 11.0 11.7 10.8 7.4 9.9 .. 13.5 14.3 12.0 8.4 12.2 8.8 .. 9.0 9.3 .. 13.1 w 7.2 10.7 9.9 11.8 10.7 10.3 11.7 11.7 10.4 8.4 10.6 14.1 14.2

Natural resource depletion % of GNI 2009

1.3 14.5 2.4 28.9 0.3 .. 2.1 0.0 0.3 0.2 .. 5.4 0.0 0.5 11.1 0.1 0.2 0.0 10.2 0.2 2.5 3.2 .. 3.6 28.2 4.6 0.2 .. 4.7 3.8 .. 1.2 0.7 0.4 17.8 9.8 7.2 .. 13.2 11.5 3.5 2.4 w 3.8 5.8 4.5 7.5 5.8 3.6 9.2 4.8 14.8 3.9 9.3 1.0 0.1

Adjusted net national income

Gross domestic product

Gross national income

Adjusted net national income

$ billions 2009

% growth 2000–2009

% growth 2000–2009

% growth 2000–2009

143.5 876.2 4.7 226.8 11.7 .. 1.7 154.0 73.4 40.8 .. 224.6 1,231.5 37.3 39.0 2.6 357.4 440.2 40.7 4.5 19.3 216.6 .. 2.5 12.2 31.5 534.7 .. 13.8 95.9 .. 1,892.3 11,909.0 27.0 24.0 252.4 71.5 .. 19.4 9.1 4.6 48,996.8 w 383.4 13,495.7 7,727.3 5,782.6 13,887.1 5,456.9 1,977.5 3,277.5 945.9 1,492.3 722.6 35,134.3 10,599.8

5.6 6.0 7.6 3.8 4.3 5.0 9.5 6.5 5.8 3.8 .. 4.1 2.8 5.5 7.3 2.6 2.4 1.9 4.4 8.2 7.1 4.6 2.4 2.5 7.4 4.9 4.9 13.9 7.8 5.6 7.0 2.0 2.0 3.4 6.9 4.9 7.6 –0.9 3.9 5.4 –7.5 2.9 w 5.4 6.4 8.5 4.4 6.4 9.4 5.9 3.8 4.7 7.3 5.1 2.0 1.5

5.4 6.1 .. 3.4 4.1 5.3 .. .. 6.1 4.7 .. 4.1 2.9 .. 7.5 3.2 2.0 2.6 4.0 7.8 6.9 4.8 .. 2.3 8.3 5.0 4.8 14.0 7.8 5.6 .. 1.8 2.2 3.7 5.0 4.6 8.0 0.2 .. 7.4 –7.2 2.8 w 5.6 6.4 8.4 4.3 6.3 9.2 5.9 3.7 4.9 7.3 4.5 1.9 1.4

7.2 8.4 .. 6.2 .. .. .. .. 5.6 4.4 .. 3.8 2.7 .. 5.7 1.7 2.5 1.5 6.3 5.5 6.4 4.4 .. 3.0 5.7 3.7 4.0 .. 7.5 8.1 .. 2.1 1.4 2.8 –6.4 8.6 7.0 .. .. 5.3 –9.0 2.6 w 5.6 6.2 7.8 4.7 6.2 8.6 6.8 3.8 5.0 7.0 4.2 1.7 1.4

About the data

4.10

ECONOMY

Toward a broader measure of national income Definitions

An economy’s growth is typically measured by the

control of institutional units. The calculation of

• Gross domestic product is the sum of value

change in the volume of its output, as shown in table

adjusted net national income, which accounts for

added by all resident producers plus any product

4.1. However the widely tracked gross domestic prod-

net forest, energy, and mineral depletion, thus

taxes (less subsidies) not included in the valu-

uct (GDP) may not always be the most relevant sum-

remains within the SNA boundaries. This point is

ation of output. • Gross national income is GDP

mary of aggregated economic performance for all

critical because it allows for comparisons across

plus net receipts of primary income (compensation

economies, such as when production occurs at the

GDP, GNI, and adjusted net national income; such

of employees and property income) from abroad.

expense of consuming capital stock. For countries

comparisons reveal the impact of natural resource

• Consumption of ﬁxed capital is the replacement

with significant exhaustible natural resources and

depletion, which is otherwise ignored by the popular

value of capital used up in production. • Natural

important foreign-investor presence, adjusted net

economic indicators.

resource depletion is the sum of net forest deple-

Adjusted net national income is particularly useful

tion, energy depletion, and mineral depletion. Net for-

in monitoring low-income, resource-rich economies,

est depletion is unit resource rents times the excess

The table presents three measures of economic

like many countries in Sub-Saharan Africa, because

of roundwood harvest over natural growth. Energy

progress: GDP, gross national income (GNI), and

such economies often see large natural resources

depletion is the ratio of the value of the stock of

adjusted net national income. GDP accounts for

depletion as well as substantial exports of resource

energy resources to the remaining reserve lifetime

all domestic production, regardless of whether the

rents to foreign mining companies. For recent years

(capped at 25 years). It covers coal, crude oil, and

income accrues to domestic or foreign institutions.

adjusted net national income gives a picture of eco-

natural gas. Mineral depletion is the ratio of the value

GNI accounts for the operation of foreign inves-

nomic growth that is strikingly different from the one

of the stock of mineral resources to the remaining

tors, who may be repatriating some of the income

provided by GDP.

reserve lifetime (capped at 25 years). It covers tin,

national income complements GDP in assessing economic progress (Hamilton and Ley 2010).

produced domestically. GNI comprises GDP plus

The key to increasing future consumption and

gold, lead, zinc, iron, copper, nickel, silver, bauxite,

net receipts of primary income from nonresident

thus the standard of living lies in increasing national

and phosphate. • Adjusted net national income is

sources. Adjusted net national income goes a step

wealth—including not only the traditional measures

GNI minus consumption of fixed capital and natural

further by subtracting from GNI a charge for the con-

of capital (such as produced and human capital),

resources depletion.

sumption of fixed capital (a calculation that yields

but also natural capital. Natural capital comprises

net national income) and for the depletion of natural

such assets as land, forests, and subsoil resources.

resources. The deduction for the depletion of natural

All three types of capital are key to sustaining eco-

resources, which covers net forest depletion, energy

nomic growth. By accounting for the consumption

depletion, and mineral depletion, reflects the decline

of fixed and natural capital depletion, adjusted net

in asset values associated with the extraction and

national income better measures the income avail-

harvest of natural resources. For more discussion

able for consumption or for investment to increase

of the estimates and methodology of produced capi-

a country’s future consumption. For a measure of

tal consumption and natural capital depletion, see

how comprehensive wealth is changing over time,

About the data in table 4.11.

see table 4.11.

The United Nations System of National Accounts

Methods of computing growth are described in Sta-

(SNA) includes nonproduced natural assets (such

tistical methods. For a detailed note on methodology,

as land, mineral resources, and forests) within the

see data.worldbank.org/.

asset boundary when they are under the effective GDP and adjusted net national income in Sub-Saharan Africa, 2000–09 (2000 $ billions)

4.10a

Data sources GNI and GDP are estimated by World Bank staff

(2000 $ billions)

based on national accounts data collected by

550

World Bank staff during economic missions or

GDP

reported by national statistical offices to other

500

international organizations such as the OECD. Data on consumption of fi xed capital are from

450

the United Nations Statistics Division’s National

Adjusted net national income

400

Accounts Statistics: Main Aggregates and Detailed Tables, extrapolated to 2009. Data on energy, min-

350

eral, and forest depletion are estimates based on sources and methods in World Bank’s The

300 2000

2002

Source: World Development Indicators data files.

2004

2006

2008

2009

Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium (2011a).

2011 World Development Indicators

233

4.11 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

234

Toward a broader measure of saving Gross savings

Consumption of ﬁxed capital

Education expenditure

Net forest depletion

Energy depletion

Mineral depletion

Carbon dioxide damage

% of GNI 2009

% of GNI 2009

% of GNI 2009

% of GNI 2009

% of GNI 2009

% of GNI 2009

% of GNI 2009

% of GNI 2009

% of GNI 2009

.. 17.6 .. 10.9 23.9 19.5 22.4 24.4 48.0 35.3 25.7 21.7 10.6 23.8 12.9 17.1 15.0 16.7 .. .. 20.3 20.4 18.0 .. .. 23.0 53.2 30.3 19.2 .. 26.2 20.8 15.4 22.6 .. 21.8 21.4 10.5 24.1 16.7 11.7 .. 24.2 16.2 19.8 16.3 .. 20.0 0.2 21.2 15.7 3.4 12.9 8.5 .. .. 16.6

7.7 10.5 10.5 11.7 11.8 9.7 14.4 14.3 11.5 6.8 11.1 14.0 7.9 9.5 10.4 11.5 11.8 11.7 7.4 5.5 8.1 8.6 14.2 7.2 9.9 12.6 10.2 13.6 11.3 5.9 13.6 11.3 8.8 12.9 .. 13.6 16.5 11.1 10.7 9.6 10.5 6.8 12.8 6.7 17.0 14.2 13.2 7.5 8.8 13.8 8.6 13.9 10.1 7.7 7.4 .. 9.6

0.0 1.3 16.7 29.1 4.5 0.0 1.9 0.1 32.7 2.1 0.9 0.0 0.0 9.7 0.7 0.3 1.6 0.4 0.0 0.0 0.0 4.7 1.9 0.0 25.2 0.1 2.9 0.0 5.9 2.9 50.6 0.0 3.1 0.7 2.4 0.3 1.5 0.0 9.8 7.0 0.0 0.0 0.7 0.0 0.0 0.0 29.1 0.0 0.1 0.1 0.0 0.1 0.4 0.0 0.0 .. 0.0

0.0 0.0 0.1 0.0 0.3 0.5 3.1 0.0 0.0 0.0 0.0 0.0 0.0 1.5 0.9 2.5 1.5 0.7 0.0 0.8 0.0 0.1 0.4 0.0 0.0 9.9 0.2 0.0 0.3 7.9 0.0 0.1 0.0 0.0 1.0 0.0 0.0 0.5 0.0 0.1 0.0 0.0 0.0 0.1 0.1 0.0 0.1 0.0 0.0 0.0 4.8 0.0 0.0 3.7 0.0 .. 0.4

0.1 0.3 0.8 0.3 0.5 0.5 0.3 0.1 1.0 0.4 1.3 0.2 0.4 0.6 1.3 0.3 0.2 0.9 0.1 0.1 0.4 0.2 0.3 0.1 0.0 0.4 1.1 0.1 0.2 0.2 0.2 0.2 0.2 0.3 0.3 0.6 0.1 0.4 0.4 0.8 0.3 0.2 0.8 0.2 0.2 0.1 0.2 0.4 0.4 0.2 0.3 0.2 0.3 0.3 0.3 .. 0.5

0.7 0.2 0.2 1.2 1.1 1.6 0.0 0.1 0.3 0.4 0.0 0.1 0.3 1.0 0.1 0.2 0.1 0.8 0.6 0.1 0.3 0.4 0.0 0.2 1.0 0.5 0.8 .. 0.1 0.5 0.7 0.1 0.3 0.2 0.1 0.0 0.0 0.0 0.0 0.5 0.1 0.3 0.0 0.2 0.0 0.0 0.0 0.4 0.7 0.0 0.0 0.3 0.1 0.5 0.6 0.4 0.2

.. 8.2 .. –29.2 10.6 9.6 1.7 15.0 5.4 27.1 16.9 13.2 4.1 6.2 .. 9.6 4.6 6.1 .. .. 13.0 6.8 5.8 .. .. 3.2 39.7 .. 5.4 .. –44.7 15.2 7.3 12.3 .. 11.3 10.7 0.4 4.4 3.1 3.7 .. 14.4 8.3 8.1 7.0 .. 12.9 –7.1 11.4 –4.7 –7.9 4.0 –4.2 .. .. 9.5

2011 World Development Indicators

.. 2.8 4.5 2.3 4.9 2.2 4.5 5.2 2.9 2.0 4.4 5.8 3.3 4.7 .. 7.4 4.8 3.8 4.3 7.1 1.6 3.1 4.7 1.3 2.3 3.6 1.8 3.0 4.0 0.9 2.5 6.2 4.3 3.9 13.6 4.0 7.4 1.9 1.4 4.4 3.3 1.6 4.4 3.7 5.5 5.0 3.1 2.1 2.8 4.3 4.7 3.3 2.9 2.3 2.3 1.5 3.5

3.4 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.6 0.0 0.0 1.2 0.0 .. 0.0 0.0 0.0 1.6 9.8 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.1 0.5 0.8 0.0 4.4 0.0 0.0 0.0 1.0 0.0 0.0 2.1 0.0 0.8 2.9 0.0 .. 0.0

Local pollution Adjusted net damage savings

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

4.11

ECONOMY

Toward a broader measure of saving Gross savings

Consumption of ﬁxed capital

Education expenditure

Net forest depletion

Energy depletion

Mineral depletion

Carbon dioxide damage

% of GNI 2009

% of GNI 2009

% of GNI 2009

% of GNI 2009

% of GNI 2009

% of GNI 2009

% of GNI 2009

% of GNI 2009

% of GNI 2009

16.0 35.2 26.2 .. .. 11.5 20.8 16.3 13.5 22.9 9.6 30.8 15.4 .. 30.1 .. 55.5 14.4 25.7 26.6 12.9 22.9 –2.7 66.8 15.1 18.8 .. .. 31.7 18.6 .. 16.2 22.1 17.8 44.7 31.8 9.2 .. 26.8 36.8 22.7 16.6 10.6 .. .. 32.6 40.3 21.5 37.4 19.7 12.3 24.0 35.0 19.2 10.4 .. ..

13.0 8.6 10.9 10.7 10.1 17.7 13.6 14.0 11.2 13.5 10.3 12.7 7.4 .. 13.3 .. 5.2 8.4 8.4 11.3 11.2 6.4 8.2 11.9 12.0 10.9 7.3 7.4 11.6 7.7 8.1 10.9 11.7 8.5 9.5 10.1 7.1 .. 10.6 6.8 14.6 14.1 8.8 2.9 9.0 15.2 13.5 8.0 12.1 8.6 9.7 11.3 8.0 12.4 17.2 .. ..

5.3 3.1 3.3 4.0 .. 5.2 5.7 4.1 6.2 3.2 5.6 4.4 6.6 .. 3.9 .. 3.2 5.2 1.1 5.6 1.6 9.4 3.1 .. 4.4 4.9 2.6 3.5 4.0 3.3 3.1 3.1 4.8 8.4 4.6 5.2 4.0 0.8 6.4 3.5 4.7 6.6 3.0 3.6 0.9 6.2 3.7 1.9 3.5 .. 3.6 2.4 2.5 4.8 5.3 .. ..

0.4 0.9 0.6 1.1 1.3 0.2 0.3 0.2 0.8 0.2 0.7 1.6 0.3 .. 0.5 0.0 0.4 1.1 0.2 0.2 0.4 0.0 1.0 0.8 0.3 1.0 0.2 0.2 0.8 0.1 0.5 0.3 0.4 0.7 2.1 0.4 0.2 .. 0.2 0.2 0.2 0.2 0.6 0.1 0.5 0.1 0.5 0.7 0.3 0.5 0.2 0.3 0.3 0.6 0.2 .. ..

0.0 0.5 0.5 0.5 2.6 0.0 0.1 0.1 0.2 0.2 0.2 0.1 0.1 0.8 0.3 .. 0.3 0.2 0.4 0.0 0.2 0.1 0.3 1.0 0.1 0.1 0.1 0.1 0.0 1.1 0.4 0.0 0.2 0.6 1.6 0.1 0.1 0.4 0.0 0.0 0.2 0.0 0.0 1.1 0.5 0.0 0.0 0.8 0.1 0.0 0.8 0.4 0.1 0.2 0.0 .. 0.1

4.5 24.1 11.0 .. .. –1.1 12.2 6.1 6.9 12.1 3.0 –1.2 13.1 .. 20.0 .. 15.7 9.4 17.8 20.4 2.7 24.4 –18.3 .. 6.0 11.6 .. .. 15.4 13.5 .. 8.0 9.1 16.2 24.9 25.0 2.0 .. 21.9 29.1 11.6 8.0 3.4 .. .. 12.8 –7.9 10.7 28.4 .. 5.2 8.6 28.0 9.7 –1.8 .. ..

0.0 0.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.2 .. 0.0 .. 0.0 0.0 0.0 0.3 0.0 1.4 10.4 0.0 0.1 0.1 0.2 0.9 0.0 0.0 0.5 0.0 0.0 0.1 0.0 0.0 0.5 .. 0.0 4.2 0.0 0.0 0.1 1.2 0.3 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.1 0.1 0.0 .. ..

0.2 2.2 5.3 17.7 45.7 0.0 0.1 0.1 0.0 0.0 0.1 20.8 0.0 .. 0.0 .. 37.0 0.5 0.0 0.0 0.0 0.0 0.0 30.4 0.1 0.0 0.0 0.0 7.9 0.0 0.0 0.0 5.1 0.1 3.8 0.0 3.2 .. 0.0 0.0 0.8 0.6 0.0 0.0 14.7 10.6 37.8 2.2 0.0 0.0 0.0 0.7 0.3 0.7 0.0 .. ..

0.0 1.1 1.2 0.2 0.0 0.0 0.1 0.0 0.7 0.0 1.0 1.2 0.0 .. 0.0 .. 0.0 0.0 0.0 0.0 0.0 0.0 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 18.3 0.0 0.3 0.0 7.3 1.4 0.0 .. 0.3 0.0 0.0 0.3 0.7 0.0 0.0 0.0 0.0 0.0 0.0 19.9 0.0 5.2 0.7 0.2 0.1 .. ..

Local pollution Adjusted net damage savings

2011 World Development Indicators

235

4.11 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzaniaa Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

Toward a broader measure of saving Gross savings

Consumption of ﬁxed capital

Education expenditure

Net forest depletion

Energy depletion

Mineral depletion

Carbon dioxide damage

% of GNI 2009

% of GNI 2009

% of GNI 2009

% of GNI 2009

% of GNI 2009

% of GNI 2009

% of GNI 2009

% of GNI 2009

3.4 3.5 3.6 7.2 5.4 4.7 3.4 2.8 3.6 4.9 .. 5.4 4.0 2.6 0.9 7.2 6.1 4.8 2.6 3.2 2.4 4.6 1.6 4.5 4.0 6.7 2.6 .. 3.0 5.9 .. 5.1 4.8 2.3 9.4 3.6 2.8 .. 4.2 1.3 6.9 4.2 w 3.2 3.2 2.4 4.1 3.2 2.1 3.6 4.4 4.3 2.9 3.6 4.6 4.5

0.0 0.0 2.4 0.0 0.0 .. 1.7 0.0 0.3 0.1 .. 0.3 0.0 0.5 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.2 .. 2.3 0.0 0.1 0.0 .. 4.7 0.0 .. 0.0 0.0 0.4 0.0 0.0 0.2 .. 0.0 0.0 0.0 0.0 w 1.4 0.1 0.2 0.0 0.1 0.0 0.0 0.0 0.0 0.9 0.6 0.0 0.0

1.4 13.8 0.0 28.9 0.0 0.4 0.0 0.0 0.0 0.1 .. 2.8 0.0 0.0 11.1 0.0 0.0 0.0 10.0 0.2 0.2 3.0 0.0 0.0 28.2 3.5 0.2 30.4 0.0 3.8 .. 1.2 0.7 0.0 17.8 9.5 7.0 .. 13.2 0.0 2.2 2.0 w 1.2 5.1 4.0 6.4 5.0 3.3 8.7 3.5 14.5 2.1 7.5 0.9 0.1

0.0 0.7 0.0 0.0 0.3 0.0 0.4 0.0 0.0 0.0 .. 2.2 0.0 0.0 0.0 0.0 0.2 0.0 0.1 0.0 2.3 0.0 0.0 1.3 0.0 1.0 0.0 0.0 0.0 0.0 .. 0.0 0.1 0.0 0.0 0.3 0.0 .. 0.0 11.5 1.3 0.3 w 1.3 0.7 0.4 1.0 0.7 0.3 0.4 1.3 0.3 0.9 1.2 0.1 0.0

0.5 1.1 0.1 0.8 0.3 0.0 0.5 0.3 0.4 0.3 .. 1.2 0.2 0.2 0.2 0.3 0.1 0.1 1.1 1.1 0.2 0.9 0.0 0.4 1.4 0.5 0.3 2.1 0.1 2.4 .. 0.2 0.3 0.2 3.2 0.4 1.1 .. 0.7 0.2 1.1 0.4 w 0.3 0.8 1.0 0.6 0.8 1.0 0.9 0.3 0.9 0.9 0.6 0.2 0.2

0.0 0.1 0.1 0.7 0.5 .. 0.8 0.4 0.0 0.1 0.4 0.1 0.2 0.2 0.5 0.0 0.0 0.1 0.7 0.3 0.1 0.2 .. 0.1 0.2 0.1 0.6 0.9 0.0 0.1 0.5 0.0 0.1 1.1 0.3 0.0 0.3 .. .. 0.2 0.2 0.2 w 0.3 0.5 0.7 0.2 0.5 0.7 0.2 0.3 0.6 0.5 0.3 0.1 0.1

28.4 23.4 15.2 31.5 16.1 17.5 8.0 45.2 29.9 22.7 .. 15.8 19.9 24.3 13.5 2.5 23.6 31.0 13.9 12.4 21.1 31.0 .. .. 34.3 24.1 13.0 .. 17.9 15.9 .. 11.9 9.8 17.5 .. 21.8 31.2 .. .. 21.3 .. 21.1 w 23.9 33.2 43.3 20.0 33.0 48.7 21.1 18.9 .. 33.6 15.4 16.5 18.6

11.2 12.0 7.4 12.6 8.4 .. 6.8 14.1 13.0 13.5 .. 14.1 13.9 9.5 9.7 10.2 13.3 14.1 9.9 7.9 7.3 10.9 1.2 7.0 12.9 11.0 11.7 10.8 7.4 9.9 .. 13.5 14.3 12.0 8.4 12.2 8.8 .. 9.0 9.3 .. 13.1 w 7.2 10.7 9.9 11.8 10.7 10.3 11.7 11.7 10.4 8.4 10.6 14.1 14.2

a. Covers mainland Tanzania only.

236

2011 World Development Indicators

Local pollution Adjusted net damage savings

% of GNI 2009

18.8 –0.8 8.8 –3.9 7.8 .. 1.2 33.0 19.8 13.6 .. 0.4 9.7 16.4 –7.1 –0.9 16.0 21.6 –14.1 6.2 13.5 20.5 .. .. –32.4 14.6 2.9 .. 8.6 5.6 .. 2.2 –0.8 6.1 .. 2.9 16.6 .. .. 1.4 .. 6.4 w .. 14.5 26.2 3.9 14.6 33.1 1.4 6.8 .. 21.6 –1.8 5.2 8.7

About the data

4.11

ECONOMY

Toward a broader measure of saving Definitions

Adjusted net savings measures the change in

of production. Natural resources give rise to rents

• Gross savings is the difference between gross

value of a specified set of assets, excluding capital

because they are not produced; in contrast, for pro-

national income and public and private consump-

gains. If a country’s net savings are positive and

duced goods and services competitive forces will

tion, plus net current transfers. • Consumption of

the accounting includes a sufficiently broad range

expand supply until economic profits are driven to

fi xed capital is the replacement value of capital

of assets, economic theory suggests that the pres-

zero. For each type of resource and each country, unit

used up in production. • Education expenditure

ent value of social welfare is increasing. Conversely,

resource rents are derived by taking the difference

is public current operating expenditures in educa-

persistently negative adjusted net savings indicate

between world prices (to reflect the social oppor-

tion, including wages and salaries and excluding

that an economy is on an unsustainable path.

tunity cost of resource extraction) and the average

capital investments in buildings and equipment.

The table shows the extent to which today’s rents

unit extraction or harvest costs (including a “normal”

• Net forest depletion is unit resource rents times

from natural resources and changes in human capital

return on capital). Unit rents are then multiplied by

the excess of roundwood harvest over natural

are balanced by net savings—that is, this genera-

the physical quantity extracted or harvested to arrive

growth. • Energy depletion is the ratio of the value

tion’s bequest to future generations.

at total rent. To estimate the value of the resource,

of the stock of energy resources to the remaining

Adjusted net savings is derived from standard

rents are assumed to be constant over the life of the

reserve lifetime (capped at 25 years). It covers coal,

national accounting measures of gross savings

resource (the El Serafy approach), and the present

crude oil, and natural gas. • Mineral depletion is the

by making four adjustments. First, estimates of

value of the rent flow is calculated using a 4 percent

ratio of the value of the stock of mineral resources to

fi xed capital consumption of produced assets are

social discount rate. For details on the estimation of

the remaining reserve lifetime (capped at 25 years).

deducted to obtain net savings. Second, current

natural wealth see World Bank (2011a).

It covers tin, gold, lead, zinc, iron, copper, nickel,

public expenditures on education are added to net

A positive net depletion figure for forest resources

silver, bauxite, and phosphate. • Carbon dioxide

savings (in standard national accounting these

implies that the harvest rate exceeds the rate of

damage is estimated at $20 per ton of carbon (the

expenditures are treated as consumption). Third,

natural growth; this is not the same as deforesta-

unit damage in 1995 U.S. dollars) times tons of

estimates of the depletion of a variety of natural

tion, which represents a change in land use (see

carbon emitted. • Particulate emissions damage

resources are deducted to reflect the decline in asset

Definitions for table 3.4). In principle, there should

is the willingness to pay to avoid illness and death

values associated with their extraction and harvest.

be an addition to savings in countries where growth

attributable to particulate emissions. • Adjusted net

And fourth, deductions are made for damages from

exceeds harvest, but empirical estimates suggest

savings is net savings plus education expenditure

carbon dioxide and particulate emissions.

that most of this net growth is in forested areas that

minus energy depletion, mineral depletion, net for-

The exercise treats public education expenditures

cannot currently be exploited economically. Because

est depletion, and carbon dioxide and particulate

as an addition to savings. However, because of the

the depletion estimates reflect only timber values,

emissions damage.

wide variability in the effectiveness of public edu-

they ignore all the external and nontimber benefits

cation expenditures, these figures cannot be con-

associated with standing forests.

strued as the value of investments in human capital.

Pollution damage from emissions of carbon dioxide

A current expenditure of $1 on education does not

is calculated as the marginal social cost per unit mul-

necessarily yield $1 of human capital. The calcula-

tiplied by the increase in the stock of carbon dioxide.

Data on gross savings are from World Bank

tion should also consider private education expen-

The unit damage figure represents the present value

national accounts data files (see table 4.8).

diture, but data are not available for a large number

of global damage to economic assets and to human

Data on consumption of fi xed capital are from

of countries.

welfare over the time the unit of pollution remains

the United Nations Statistics Division’s National

in the atmosphere.

Accounts Statistics: Main Aggregates and Detailed

While extensive, the accounting of natural

Data sources

resources depletion and pollution costs still has

Pollution damage from particulate emissions is

Tables, extrapolated to 2009. Data on educa-

some gaps. Key estimates missing on the resource

estimated by valuing the human health effects from

tion expenditure are from the United Nations

side include the value of fossil water extracted from

exposure to particulate matter pollution in urban

Educational, Scientific, and Cultural Organization

aquifers, net depletion of fish stocks, and depletion

areas. The estimates are calculated as willingness to

Institute for Statistics online database; missing

and degradation of soils. Important pollutants affect-

pay to avoid illness and death, from cardiopulmonary

data are estimated by World Bank staff. Data on

ing human health and economic assets are excluded

disease and lung cancer in adults and acute respira-

energy, mineral, and forest depletion are esti-

because no internationally comparable data are

tory infections in children, that are attributable to

mates based on sources and methods in World

widely available on damage from ground-level ozone

particulate emissions.

Bank (2011a). Data on carbon dioxide damage

or sulfur oxides.

Adjusted net savings aims to be as comprehensive

are from Fankhauser’s Valuing Climate Change:

Estimates of resource depletion are based on the

a measure as possible to provide a better under-

The Economics of the Greenhouse (1995). Data

“change in real wealth” method described in Hamil-

standing of the rate of country wealth creation or

on particulate emissions damage are from Pandey

ton and Ruta (2008), which estimates depletion as

depletion. To do so, it treats education as investment

and others’ “The Human Cost of Air Pollution: New

the ratio between the total value of the resource

and accounts for pollution damages to assets and

Estimates for Developing Countries” (2006). The

and the remaining reserve lifetime. The total value

human welfare, which goes outside the boundaries

conceptual underpinnings of the savings measure

of the resource is the present value of current and

of the United Nations System of National Accounts.

appear in Hamilton and Clemens’ “Genuine Sav-

future rents from resource extractions. An economic

For a detailed note on methodology, see data.

rent represents an excess return to a given factor

ings Rates in Developing Countries” (1999).

worldbank.org/.

2011 World Development Indicators

237

4.12

Central government finances Revenuea

Expense

Cash surplus or deﬁcit

Net incurrence of liabilities

Debt and interest payments

% of GDP

Afghanistanb Albaniab Algeria Angola Argentina Armeniab Australia Austria Azerbaijanb Bangladeshb Belarusb Belgium Beninb Bolivia Bosnia and Herzegovina Botswanab Brazilb Bulgariab Burkina Faso Burundib Cambodia Cameroonb Canadab Central African Republicb Chad Chile Chinab Hong Kong SAR, China Colombia Congo, Dem. Rep.b Congo, Rep.b Costa Rica Côte d’Ivoire Croatiab Cuba Czech Republicb Denmark Dominican Republic Ecuador b Egypt, Arab Rep.b El Salvador Eritrea Estonia Ethiopiab Finland France Gabon Gambia, Theb Georgiab Germany Ghanab Greece Guatemalab Guineab Guinea-Bissau Haiti Honduras

238

% of GDP 1995 2009

% of GDP 1995 2009

% of GDP 1995 2009

Domestic 1995 2009

1995

.. 21.2 .. .. .. .. .. 36.6 .. .. 30.0 41.5 .. .. .. 40.5 26.9 35.6 .. 19.3 .. 11.8 19.8 .. .. .. 5.4 .. .. 5.3 23.6 .. .. 36.8 .. 33.2 37.6 .. 30.9 34.8 .. .. 36.2 12.2 40.4 43.3 .. .. 12.2 29.9 17.0 35.3 8.4 11.2 .. .. ..

.. 25.6 .. .. .. .. .. 42.5 .. .. 28.7 45.7 .. .. .. 30.3 32.9 39.5 .. 23.6 .. 10.6 23.8 .. .. .. .. .. .. 8.2 29.8 .. .. 36.2 .. 32.6 41.5 .. 26.3 28.1 .. .. 32.8 12.0 49.7 47.6 .. .. 15.4 38.6 .. 44.3 7.6 12.1 .. .. ..

.. –8.9 .. .. .. .. .. –5.5 .. .. –2.7 –3.9 .. .. .. 4.9 –2.7 –5.1 .. –4.7 .. 0.2 –4.0 .. .. .. .. .. .. 0.0 –8.2 .. .. –1.1 .. –0.9 –3.7 .. 0.1 3.4 .. .. 1.6 –3.1 –7.5 –4.1 .. .. –4.3 –8.3 .. –9.1 –0.5 –4.3 .. .. ..

.. 7.4 .. .. .. .. .. .. .. .. 2.2 .. .. .. .. 0.2 .. 7.5 .. 3.1 .. –0.3 .. .. .. .. 1.6 .. .. 0.0 .. .. .. –2.3 .. –0.5 .. .. .. .. .. .. .. 1.8 8.9 .. .. .. 2.2 .. .. .. .. –0.1 .. .. ..

.. 2.1 .. .. .. .. .. .. .. .. 0.4 –0.5 .. .. .. –0.4 .. –0.8 .. 4.0 .. 0.3 .. .. .. .. .. .. .. 0.2 .. .. .. 0.7 .. –0.4 .. .. .. .. .. .. .. 2.6 0.2 .. .. .. 2.4 .. .. .. 0.4 4.5 .. .. ..

9.1 .. 36.6 .. .. 22.1 24.6 36.6 27.3 11.1 35.4 40.3 17.6 23.3 38.6 .. 23.1 32.3 14.0 .. 11.0 .. 17.4 .. .. 20.1 11.1 19.7 17.0 .. .. 24.7 18.7 34.1 .. 29.1 40.0 16.4 .. 27.0 17.5 .. 37.1 .. 39.0 40.5 .. .. 25.2 29.4 15.3 36.2 11.0 .. .. .. 20.8

2011 World Development Indicators

38.0 .. 25.0 .. .. 23.7 26.6 39.6 15.5 11.3 33.0 45.3 15.0 21.8 41.2 .. 25.6 31.6 13.0 .. 11.0 .. 19.2 .. .. 22.6 .. 18.9 19.5 .. .. 26.0 17.6 36.2 .. 37.3 42.4 16.2 .. 30.2 21.6 .. 36.8 .. 35.0 47.6 .. .. 31.0 31.7 17.9 50.7 12.6 .. .. .. 24.1

0.2 .. –4.4 .. .. –7.5 –2.4 –2.6 0.4 –1.7 0.2 –5.1 –4.5 1.2 –4.3 .. –3.5 –0.1 –4.8 .. –2.3 .. –1.9 .. .. –4.5 .. 0.6 –4.0 .. .. –3.4 0.9 –3.0 .. –6.1 –2.1 –3.8 .. –6.6 –5.0 .. –1.3 .. 4.6 –7.3 .. .. –7.8 –2.2 –5.6 –15.2 –3.2 .. .. .. –4.5

0.1 .. 5.9 .. .. 1.3 .. .. 0.0 3.1 –2.5 1.0 2.2 –0.2 3.7 .. 8.3 –0.4 4.5 .. –2.0 .. .. .. .. 0.8 0.4 1.0 5.8 .. .. .. .. 3.0 .. 2.9 .. 2.4 .. 9.9 2.0 .. .. .. –0.2 .. .. .. 1.3 3.1 2.8 .. 1.4 .. .. .. 5.0

Foreign 2009

0.8 .. 0.0 .. .. 12.3 .. .. 0.2 0.4 8.4 6.5 2.1 –0.1 3.2 .. –0.1 0.5 2.9 .. 2.3 .. .. .. .. –0.4 0.0 –0.1 0.9 .. .. .. .. 2.2 .. 1.9 .. 1.5 .. –0.2 5.9 .. .. .. –0.6 .. .. .. 3.7 –0.2 2.6 .. 1.4 .. .. .. 1.0

Total debt % of GDP 2009

Interest % of revenue 2009

.. .. .. .. .. .. 24.1 70.7 .. .. 18.1 92.4 .. .. .. .. 61.0 .. .. .. .. .. 53.2 .. .. .. .. 30.5 59.3 .. .. .. .. .. .. 31.9 41.0 .. .. 79.5 48.5 .. 9.1 .. 36.2 82.8 .. .. 34.7 47.2 .. 138.5 23.3 .. .. .. ..

0.0 .. 1.0 .. .. 2.3 3.7 7.0 0.3 21.7 2.1 8.5 2.5 8.0 1.2 .. 20.7 2.2 2.2 .. 1.3 .. 10.1 .. .. 2.8 .. 0.3 18.9 .. .. 8.8 7.1 4.8 .. 4.1 4.9 9.7 .. 15.2 12.3 .. 0.6 .. 3.2 5.4 .. .. 3.4 5.5 15.2 14.3 12.6 .. .. .. 2.9

Revenuea

Expense

Cash surplus or deﬁcit

Net incurrence of liabilities

Debt and interest payments

% of GDP

Hungary Indiab Indonesiab Iran, Islamic Rep.b Iraq Ireland Israel Italy Jamaica Japan Jordanb Kazakhstanb Kenyab Korea, Dem. Rep. Korea, Rep.b Kosovo Kuwaitb Kyrgyz Republicb Lao PDR Latviab Lebanon Lesotho b Liberiab Libya Lithuania Macedonia, FYR b Madagascar Malawi Malaysiab Mali Mauritania Mauritius Mexicob Moldovab Mongoliab Morocco b Mozambique Myanmar b Namibiab Nepalb Netherlands New Zealand Nicaraguab Niger Nigeriab Norway Omanb Pakistanb Panamab Papua New Guineab Paraguayb Perub Philippinesb Poland Portugal Puerto Rico Qatarb

% of GDP 1995 2009

% of GDP 1995 2009

43.0 12.3 15.6 24.2 .. 35.5 .. 40.4 .. .. 28.2 14.0 21.6 .. 17.8 .. 36.8 16.7 .. 25.8 .. 57.1 .. .. .. .. .. .. 23.3 .. .. .. 15.3 28.4 19.0 .. .. 6.4 31.7 10.5 41.5 .. 12.8 .. .. .. 27.8 17.2 26.1 22.7 17.2 17.4 17.7 .. 33.2 .. ..

53.2 14.4 9.5 15.8 .. 37.5 .. 48.0 .. .. 26.1 18.7 25.8 .. 14.3 .. 44.0 25.6 .. 28.3 .. 39.4 .. .. .. .. .. .. 18.7 .. .. .. 15.0 38.4 13.8 .. .. .. 35.7 .. 50.8 .. 14.2 .. .. .. 32.4 19.1 22.0 24.5 14.5 17.4 15.9 .. 37.1 .. ..

40.5 11.9 15.4 31.9 .. 30.4 34.6 38.5 27.0 .. 23.5 9.2 20.5 .. 23.1 .. 47.1 19.2 13.9 24.9 22.5 66.4 0.4 .. 28.3 34.0 14.1 .. 23.3 17.1 .. 23.5 .. 33.1 29.2 33.1 .. .. 29.2 14.5 41.0 36.1 19.1 13.6 9.7 47.2 .. 14.0 .. .. 19.0 17.2 14.6 30.1 34.7 .. 47.2

45.3 16.2 15.7 24.7 .. 43.4 40.6 44.0 41.5 .. 28.6 16.9 21.7 .. 21.9 .. 21.9 19.3 11.3 34.8 29.5 52.1 0.3 .. 38.8 31.3 11.7 .. 22.7 14.6 .. 21.6 .. 38.3 28.8 27.9 .. .. 24.1 .. 45.6 32.1 20.9 11.8 7.2 35.9 .. 16.8 .. .. 17.1 17.1 18.6 35.8 43.2 .. 19.3

% of GDP 1995 2009

–9.1 –2.2 1.7 1.1 .. –2.2 .. –7.5 .. .. 0.9 –1.8 –5.1 .. 2.4 .. –9.9 –10.8 .. –2.7 .. 5.8 .. .. .. .. .. .. 1.5 .. .. .. –0.6 –6.3 2.9 .. .. .. –5.0 .. –9.2 .. 0.6 .. .. .. –8.9 –5.3 1.5 –0.5 0.2 –1.3 –0.8 .. –5.1 .. ..

–4.0 –4.9 –1.7 0.6 .. –13.9 –4.3 –4.9 –15.9 .. –8.5 –2.0 –5.5 .. 0.0 .. 20.0 –1.4 –1.6 –6.4 –8.3 5.8 0.0 .. –9.0 –0.8 –1.9 .. –6.4 –2.1 .. 0.6 .. –5.7 –4.5 1.0 .. .. 2.0 .. –4.8 3.1 –2.3 –0.9 –1.7 10.7 .. –4.8 .. .. 0.1 –1.5 –3.9 –6.1 –8.7 .. 15.2

Domestic 1995 2009

1995

17.0 5.1 .. .. .. .. .. .. .. .. –2.5 0.8 3.9 .. –0.3 .. .. .. .. 2.4 .. 0.0 .. .. .. .. .. .. .. .. .. .. .. 3.0 1.6 .. .. .. .. 0.6 .. .. .. .. .. .. –0.1 .. .. 1.5 0.0 .. –0.5 .. –1.2 .. ..

0.2 0.0 –0.4 0.1 .. .. .. .. .. .. 6.1 2.8 –1.3 .. –0.1 .. .. .. .. 1.5 .. 7.2 .. .. .. .. .. .. .. .. .. .. 5.5 2.7 1.3 .. .. .. .. 2.5 .. .. 3.4 .. .. .. 0.0 .. .. –0.7 –0.8 3.9 –0.7 .. 4.2 .. ..

–1.9 5.6 0.9 1.4 .. .. .. .. 7.4 .. 7.6 2.8 3.0 .. 5.4 .. .. 0.5 –0.3 –2.7 11.8 –0.4 0.0 .. 1.9 –0.6 0.6 .. 6.5 –4.4 .. 3.1 .. 2.7 8.6 0.1 .. .. –0.8 3.2 .. .. .. –1.9 0.1 6.3 .. .. .. .. 1.3 0.2 1.2 1.6 3.4 .. ..

4.12

ECONOMY

Central government finances

Foreign 2009

5.8 0.2 0.4 0.0 .. .. .. .. 4.7 .. 1.2 0.5 0.1 .. –0.1 .. .. 7.7 2.1 15.1 0.3 1.6 0.0 .. 9.1 0.2 3.0 .. 0.9 2.6 .. 1.3 .. 3.3 5.2 1.7 .. .. –0.1 0.0 .. .. .. 2.4 .. –15.3 .. .. .. .. 0.1 1.1 2.0 3.6 5.9 .. ..

Total debt % of GDP 2009

Interest % of revenue 2009

81.7 53.0 28.3 .. .. 69.2 .. 118.9 115.8 157.7 57.9 9.5 .. .. .. .. .. .. .. 41.8 .. .. .. .. 33.3 .. .. .. 53.3 .. .. 38.9 .. 24.4 64.8 46.9 .. .. .. 43.7 58.3 37.9 .. .. 3.0 36.3 .. .. .. .. .. 23.6 .. 48.1 84.4 .. ..

10.6 28.5 10.9 0.6 .. 6.9 9.7 11.1 64.5 .. 8.7 2.5 10.4 .. 4.7 .. 0.0 3.3 3.2 3.8 48.7 1.3 2.1 .. 4.0 1.9 3.9 .. 9.0 1.7 .. 12.0 .. 4.0 1.6 3.1 .. .. 6.3 4.9 4.6 3.4 6.4 1.8 6.6 2.1 .. 41.7 .. .. 3.1 7.2 25.7 8.1 7.7 .. 2.1

2011 World Development Indicators

239

4.12

Central government finances Revenuea

Expense

Cash surplus or deﬁcit

Net incurrence of liabilities

Debt and interest payments

Total debt % of GDP

Interest % of revenue

2009

2009

2009

0.9 –0.2 .. .. .. 1.2 .. .. 3.0 –1.2 .. 1.0 4.8 –0.1 .. .. .. .. .. .. .. 0.0 .. –0.5 0.5 0.0 0.6 .. 1.8 4.9 .. .. 4.7 2.4 .. .. .. .. .. .. .. .. m .. 0.2 .. 0.9 .. 2.0 3.3 –0.2 0.1 0.3 .. .. 0.4

.. 8.6 .. .. .. .. .. 113.3 38.1 .. .. .. 46.5 85.0 .. .. 44.0 28.9 .. .. .. 28.6 .. .. 14.1 47.1 51.4 .. 32.7 .. .. 73.2 67.1 49.5 .. .. .. .. .. .. .. .. m .. .. .. .. .. .. .. .. .. 56.5 .. 55.7 69.9

% of GDP % of GDP

Romania Russian Federation Rwandab Saudi Arabia Senegalb Serbiab Sierra Leoneb Singaporeb Slovak Republic Sloveniab Somalia South Africa Spain Sri Lankab Sudanb Swazilandb Sweden Switzerlandb Syrian Arab Republicb Tajikistanb Tanzania Thailand Timor-Leste Togo Trinidad and Tobagob Tunisiab Turkey b Turkmenistan Ugandab Ukraineb United Arab Emiratesb United Kingdom United States Uruguay b Uzbekistan Venezuela, RB b Vietnam West Bank and Gaza Yemen, Rep.b Zambiab Zimbabweb World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

% of GDP

% of GDP

1995

2009

1995

2009

1995

.. .. 10.6 .. 15.2 .. 9.4 26.7 .. 35.8 .. .. 32.0 20.4 7.2 .. 38.6 22.6 22.9 9.3 .. .. .. .. 27.2 30.0 .. .. 10.6 .. 10.1 35.2 .. 27.6 .. 16.9 .. .. 17.3 20.0 26.7 .. w .. 14.6 10.7 .. 14.6 7.2 .. 21.2 .. 13.1 .. .. 34.9

30.9 35.4 .. .. .. 36.3 11.6 18.2 28.5 37.5 .. 28.2 22.4 14.9 .. .. 34.7 18.4 .. .. .. 18.6 .. 18.8 36.1 31.4 21.8 .. 12.4 34.5 .. 35.9 15.9 29.4 .. .. .. .. .. 17.6 .. 24.3 w .. 20.0 14.7 .. 19.8 13.4 29.3 .. 30.6 12.1 24.5 24.7 34.5

.. .. 15.0 .. .. .. .. 12.4 .. 34.3 .. .. 37.1 26.0 6.8 .. .. 25.7 .. 11.4 .. .. .. .. 25.3 28.4 .. .. .. .. 11.0 40.4 .. 27.1 .. 18.5 .. .. 19.1 21.4 32.1 .. w .. .. .. .. .. .. .. 23.3 .. 15.3 .. .. 42.3

33.8 30.9 .. .. .. 37.7 22.5 15.2 37.6 42.7 .. 33.0 30.7 19.2 .. .. .. 17.0 .. .. .. 19.6 .. 17.4 28.4 29.9 27.3 .. 13.7 40.6 .. 46.4 26.3 29.3 .. .. .. .. .. 22.9 .. 31.1 w .. .. .. .. .. .. 30.1 .. 27.3 16.0 24.2 32.2 39.8

.. .. –5.6 .. .. .. .. 19.8 .. –0.1 .. .. –5.8 –7.6 –0.4 .. .. –0.6 .. –3.3 .. .. .. .. –0.1 –2.4 .. .. .. .. 0.5 –5.5 .. –1.2 .. –2.3 .. .. –3.9 –3.1 –5.4 .. w .. .. .. .. .. .. .. –1.4 .. –2.7 .. .. –7.4

2009

–4.6 5.3 .. .. .. –2.6 –3.1 1.7 –7.3 –5.5 .. –4.9 –8.6 –6.6 .. .. .. 1.3 .. .. .. –3.0 .. –0.6 2.3 –1.7 –5.5 .. –0.9 –5.6 .. –10.9 –10.4 –1.5 .. .. .. .. .. –0.8 .. –7.1 w .. .. .. .. .. .. –0.4 .. –3.0 –4.6 –1.0 –7.7 –5.2

a. Excludes grants. b. Data were reported on a cash basis and have been adjusted to the accrual framework.

240

2011 World Development Indicators

Foreign

Domestic 1995 2009

1995

.. .. 2.9 .. .. .. 0.3 10.3 .. –0.4 .. .. .. 5.2 0.3 .. .. –0.5 .. 0.1 .. .. .. .. 2.8 0.9 .. .. .. .. .. .. .. 7.9 .. 1.1 .. .. .. 28.0 –1.4 .. m .. .. .. .. .. .. .. .. .. 3.8 .. .. ..

.. .. .. .. .. .. .. 0.0 .. 0.3 .. .. .. 3.2 .. .. .. .. .. 2.3 .. .. .. .. 2.6 2.9 .. .. .. .. .. .. .. 1.1 .. 0.1 .. .. .. 16.2 1.6 .. m .. .. .. .. .. .. .. .. .. 1.1 .. .. ..

2.4 0.8 .. .. .. 2.8 .. 13.7 2.9 12.4 .. 7.0 6.4 6.9 .. .. .. 2.0 .. .. .. 5.3 .. 2.7 –0.6 0.3 6.1 .. 1.5 6.7 .. .. 6.5 3.8 .. .. .. .. .. .. .. .. m .. 0.9 0.5 3.7 0.6 1.2 1.9 0.1 6.7 3.2 .. .. 0.8

2.0 1.3 .. .. .. 2.0 8.3 0.1 4.7 2.9 .. 8.4 6.1 31.0 .. .. .. 3.5 .. .. .. 5.8 .. 4.0 5.0 7.0 24.1 .. 7.7 3.1 .. 5.3 11.4 9.3 .. .. .. 1.1 .. 7.2 .. 5.4 m .. 7.0 6.0 7.2 4.5 5.8 2.5 9.1 7.0 21.7 .. 5.3 6.1

About the data

4.12

ECONOMY

Central government finances Definitions

Tables 4.12–4.14 present an overview of the size and

borrowing for temporary periods can also be used.

• Revenue is cash receipts from taxes, social con-

role of central governments relative to national econo-

Government excludes public corporations and quasi

tributions, and other revenues such as fines, fees,

mies. The tables are based on the concepts and recom-

corporations (such as the central bank).

rent, and income from property or sales. Grants, usu-

mendations of the second edition of the International

Units of government at many levels meet this defini-

ally considered revenue, are excluded. • Expense is

Monetary Fund’s (IMF) Government Finance Statistics

tion, from local administrative units to the national

cash payments for government operating activities in

Manual 2001. Before 2005 World Development Indica-

government, but inadequate statistical coverage pre-

providing goods and services. It includes compensa-

tors reported data derived on the basis of the 1986

cludes presenting subnational data. Although data for

tion of employees, interest and subsidies, grants,

manual’s cash-based method. The 2001 manual,

general government under the 2001 manual are avail-

social benefi ts, and other expenses such as rent

harmonized with the 1993 United Nations System of

able for a few countries, only data for the central gov-

and dividends. • Cash surplus or deﬁcit is revenue

National Accounts, recommends an accrual account-

ernment are shown to minimize disparities. Still, differ-

(including grants) minus expense, minus net acquisi-

ing method, focusing on all economic events affecting

ent accounting concepts of central government make

tion of nonfinancial assets. In editions before 2005

assets, liabilities, revenues, and expenses, not only

cross-country comparisons potentially misleading.

nonfinancial assets were included under revenue

those represented by cash transactions. It takes all

Central government can refer to consolidated or bud-

and expenditure in gross terms. This cash surplus

stocks into account, so that stock data at the end of an

getary accounting. For most countries central govern-

or deficit is close to the earlier overall budget balance

accounting period equal stock data at the beginning of

ment finance data have been consolidated into one

(still missing is lending minus repayments, which are

the period plus flows over the period. The 1986 manual

account, but for others only budgetary central gov-

included as a financing item under net acquisition

considered only the debt stock data. Further, the new

ernment accounts are available. Countries reporting

of financial assets). • Net incurrence of liabilities

manual no longer distinguishes between current and

budgetary data are noted in Primary data documenta-

is domestic financing (obtained from residents) and

capital revenue or expenditures, and it introduces the

tion. Because budgetary accounts may not include

foreign financing (obtained from nonresidents), or

concepts of nonfinancial and financial assets. Most

all central government units (such as social security

the means by which a government provides financial

countries still follow the 1986 manual, however. The

funds), they usually provide an incomplete picture.

resources to cover a budget deficit or allocates finan-

IMF has reclassified historical Government Finance Sta-

Data on government revenue and expense are col-

cial resources arising from a budget surplus. The net

tistics Yearbook data to conform to the 2001 manual’s

lected by the IMF through questionnaires to member

incurrence of liabilities should be offset by the net

format. Because of reporting differences, the reclassi-

countries and by the Organisation for Economic Co-

acquisition of financial assets (a third financing item).

fied data understate both revenue and expense.

operation and Development. Despite IMF efforts to

The difference between the cash surplus or deficit

The 2001 manual describes government’s eco-

standardize data collection, statistics are often incom-

and the three financing items is the net change in

nomic functions as the provision of goods and ser-

plete, untimely, and not comparable across countries.

the stock of cash. • Total debt is the entire stock of

vices on a nonmarket basis for collective or individual

Government finance statistics are reported in local

direct government fixed-term contractual obligations

consumption, and the redistribution of income and

currency. The indicators here are shown as percent-

to others outstanding on a particular date. It includes

wealth through transfer payments. Government

ages of GDP. Many countries report government

domestic and foreign liabilities such as currency and

activities are financed mainly by taxation and other

finance data by fiscal year; see Primary data docu-

money deposits, securities other than shares, and

income transfers, though other financing such as

mentation for information on fiscal year end by country.

loans. It is the gross amount of government liabilities reduced by the amount of equity and financial

Twenty selected economies had a central government debt to GDP ratio of 65 percent or higher

derivatives held by the government. Because debt

4.12a

Central government debt, 2009 (percent of GDP)

is a stock rather than a flow, it is measured as of a given date, usually the last day of the fiscal year. • Interest payments are interest payments on gov-

160

ernment debt—including long-term bonds, long-term loans, and other debt instruments—to domestic and 120

foreign residents.

80

Data sources Data on central government finances are from the

40

IMF’s Government Finance Statistics database. Each country’s accounts are reported using the system of common definitions and classifications us Ne Ba vis rb ad o Be s lg iu m Sr iL an ka Po r tu ga l Fr an c Hu e ng ar Eg y yp t, Mal A t a Un ra ite b R e d Ki p. ng do m Au st ria Un Ire ite lan d d St at es s

&

nd

pr Cy

in the IMF’s Government Finance Statistics Manual 2001. See these sources for complete and author-

St

.K

itt

or e

la

ap

Ic e

Si ng

Ita ly ai ca m Ja

n pa

ee Gr

Ja

ce

0

Note: Data are for the most recent year for 2005–2009. Source: International Monetary Fund, Government Finance Statistics data files, and World Development Indicators data files.

itative explanations of concepts, definitions, and data sources.

2011 World Development Indicators

241

4.13 Afghanistana Albaniaa Algeria Angola Argentina Armeniaa Australia Austria Azerbaijana Bangladesha Belarusa Belgium Benina Bolivia Bosnia and Herzegovina Botswanaa Brazila Bulgariaa Burkina Faso Burundia Cambodia Cameroona Canadaa Central African Republic a Chad Chile Chinaa Hong Kong SAR, China Colombia Congo, Dem. Rep.a Congo, Rep.a Costa Rica Côte d’Ivoire Croatiaa Cuba Czech Republica Denmark Dominican Republic Ecuador a Egypt, Arab Rep.a El Salvador Eritrea Estonia Ethiopiaa Finland France Gabon Gambia, Thea Georgiaa Germany Ghanaa Greece Guatemalaa Guineaa Guinea-Bissau Haiti Honduras

242

Central government expenses Goods and services

Compensation of employees

Interest payments

Subsidies and other transfers

Other expense

% of expense 1995 2009

% of expense 1995 2009

% of expense 1995 2009

% of expense 1995 2009

% of expense 1995 2009

.. 18 .. .. .. .. .. 5 .. .. 39 3 .. .. .. 32 5 18 .. 20 .. 17 8 .. .. .. .. .. .. 37 7 .. .. 35 .. 7 8 .. 6 18 .. .. 21 35 8 8 .. .. 52 4 .. 10 15 17 .. .. ..

2011 World Development Indicators

72 .. 11 .. .. 13 10 6 9 12 12 3 18 14 23 .. 13 9 19 .. 32 .. 8 .. .. 10 .. 26 6 .. .. 11 29 8 .. 6 9 15 .. 8 15 .. 13 .. 10 6 .. .. 19 5 16 12 15 .. .. .. 17

.. 14 .. .. .. .. .. 14 .. .. 5 7 .. .. .. 30 8 7 .. 30 .. 40 10 .. .. .. .. .. .. 58 35 .. .. 27 .. 9 12 .. 49 22 .. .. 23 40 9 23 .. .. 11 5 .. 21 50 34 .. .. ..

23 .. 34 .. .. 25 10 14 12 19 11 7 47 22 28 .. 19 19 46 .. 43 .. 12 .. .. 20 .. 22 16 .. .. 46 38 26 .. 8 13 31 .. 25 36 .. 21 .. 10 21 .. .. 17 5 40 24 29 .. .. .. 54

.. 9 .. .. .. .. .. 9 .. .. 1 18 .. .. .. 2 45 37 .. 6 .. 26 24 .. .. .. .. .. .. 1 47 .. .. 3 .. 3 14 .. 26 26 .. .. 1 15 8 6 .. .. 10 6 .. 25 12 28 .. .. ..

0 .. 1 .. .. 2 3 7 1 22 2 8 3 10 1 .. 19 2 3 .. 2 .. 9 .. .. 2 .. 0 17 .. .. 8 9 5 .. 3 5 10 .. 14 10 .. 1 .. 4 5 .. .. 3 5 16 10 11 .. .. .. 3

.. 59 .. .. .. .. .. 68 .. .. 55 71 .. .. .. 36 45 38 .. 14 .. 14 57 .. .. .. .. .. .. 2 10 .. .. 32 .. 75 59 .. .. 6 .. .. 39 18 68 59 .. .. 26 67 .. 38 18 9 .. .. ..

4 .. 45 .. .. 37 73 71 18 35 70 53 30 47 44 .. 49 64 11 .. 21 .. 69 .. .. 51 .. 17 47 .. .. 21 16 56 .. 72 17 39 .. 45 22 .. 48 .. 71 54 .. .. 49 81 28 50 33 .. .. .. 7

.. 0 .. .. .. .. .. 6 .. .. 0 2 .. .. .. 2 1 2 .. 10 .. .. 3 .. .. .. .. .. .. .. .. .. .. 3 .. 5 10 .. .. .. .. .. 4 0 11 6 .. .. .. 20 .. 8 6 1 .. .. ..

0 .. 8 .. .. 23 6 5 61 12 6 0 2 7 4 .. 0 6 21 .. 2 .. 3 .. .. 19 .. 38 15 .. .. 14 7 5 .. 11 2 5 .. 9 18 .. 4 .. 8 2 .. .. 12 4 12 7 12 .. .. .. 19

Hungary Indiaa Indonesiaa Iran, Islamic Rep.a Iraq Ireland Israel Italy Jamaica Japan Jordana Kazakhstana Kenyaa Korea, Dem. Rep. Korea, Rep.a Kosovo Kuwait a Kyrgyz Republic a Lao PDR Latviaa Lebanon Lesothoa Liberiaa Libya Lithuania Macedonia, FYRa Madagascar Malawi Malaysiaa Mali Mauritania Mauritius Mexicoa Moldovaa Mongoliaa Moroccoa Mozambique Myanmar a Namibiaa Nepala Netherlands New Zealand Nicaraguaa Niger Nigeriaa Norway Omana Pakistana Panamaa Papua New Guineaa Paraguaya Perua Philippinesa Poland Portugal Puerto Rico Qatar a

4.13

Goods and services

Compensation of employees

Interest payments

Subsidies and other transfers

Other expense

% of expense 1995 2009

% of expense 1995 2009

% of expense 1995 2009

% of expense 1995 2009

% of expense 1995 2009

8 14 22 21 .. 5 .. 4 .. .. 7 .. 15 .. 16 .. 34 32 .. 20 .. 32 .. .. .. .. .. .. 14 .. .. .. 9 10 30 .. .. .. 28 .. 5 .. 14 .. .. .. 55 .. 16 19 12 20 15 .. 9 .. ..

10 11 9 11 .. 10 27 4 6 .. 11 19 20 .. 11 .. 10 30 27 8 3 42 37 .. 10 28 15 .. 17 31 .. 12 .. 19 20 9 .. .. 20 .. 8 30 13 30 15 11 .. 22 .. .. 9 20 28 5 7 .. 25

10 10 20 56 .. 15 .. 14 .. .. 67 .. 28 .. 15 .. 33 36 .. 20 .. 45 .. .. .. .. .. .. 34 .. .. .. 19 8 12 .. .. .. 53 .. 8 .. 25 .. .. .. 30 .. 45 36 51 19 34 .. 30 .. ..

13 10 14 40 .. 23 25 15 14 .. 50 8 37 .. 10 .. 16 29 49 15 21 35 36 .. 16 17 40 .. 28 34 .. 34 .. 15 33 48 .. .. 45 .. 7 25 39 30 24 16 .. 4 .. .. 50 18 30 12 24 .. 32

17 27 17 0 .. 14 .. 24 .. .. 11 3 46 .. 3 .. 5 5 .. 3 .. 5 .. .. .. .. .. .. 14 .. .. .. 19 11 2 .. .. .. 1 .. 9 .. 17 .. .. .. 7 28 8 20 5 19 33 .. 15 .. ..

10 21 11 1 .. 5 9 10 43 .. 8 2 10 .. 5 .. 0 4 5 3 38 2 2 .. 3 2 7 .. 9 2 .. 14 .. 4 2 4 .. .. 8 .. 4 4 7 3 9 3 .. 35 .. .. 4 7 20 7 6 .. 5

56 33 40 .. .. 33 .. 54 .. .. 12 58 .. .. 63 .. 21 27 .. 56 .. 8 .. .. .. .. .. .. 36 .. .. .. .. 71 56 .. .. .. .. .. 77 .. 29 .. .. .. 8 2 30 26 31 33 15 .. 43 .. ..

63 51 54 34 .. 40 32 66 6 .. 30 69 31 .. 57 .. 58 34 10 70 36 14 24 .. 68 49 25 .. 46 15 .. 31 .. 56 45 27 .. .. 13 .. 79 38 36 9 53 67 .. 21 .. .. 29 47 20 71 51 .. 21

13 0 2 .. .. 1 .. 6 .. .. 4 .. 2 .. 3 .. 7 .. .. 0 .. 3 .. .. .. .. .. .. 1 .. .. .. .. 1 0 .. .. .. 4 .. 3 .. 14 .. .. .. 0 .. 1 1 0 8 .. .. 7 .. ..

2011 World Development Indicators

ECONOMY

Central government expenses

8 7 12 14 .. 1 9 6 31 .. 2 2 1 .. 17 .. 15 3 10 4 2 6 .. .. 6 4 14 .. 0 17 .. 10 .. 6 1 13 .. .. 14 .. 4 7 5 28 .. 5 .. 18 .. .. 9 7 2 7 1 .. 16

243

4.13 Romania Russian Federation Rwandaa Saudi Arabia Senegala Serbiaa Sierra Leonea Singaporea Slovak Republic Sloveniaa Somalia South Africa Spain Sri Lankaa Sudana Swazilanda Sweden Switzerlanda Syrian Arab Republic a Tajikistana Tanzania Thailand Timor-Leste Togo Trinidad and Tobagoa Tunisiaa Turkeya Turkmenistan Ugandaa Ukrainea United Arab Emiratesa United Kingdom United States Uruguaya Uzbekistan Venezuela, RBa Vietnam West Bank and Gaza Yemen, Rep.a Zambiaa Zimbabwea World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

Central government expenses Goods and services

Compensation of employees

Interest payments

Subsidies and other transfers

Other expense

% of expense 1995 2009

% of expense 1995 2009

% of expense 1995 2009

% of expense 1995 2009

% of expense 1995 2009

.. .. 52 .. .. .. .. 38 .. 19 .. .. 5 23 44 .. .. 24 .. 47 .. .. .. .. 20 7 .. .. .. .. 48 14 .. 13 .. 6 .. .. 8 32 16 .. m .. .. .. .. .. .. .. .. .. .. .. 10 5

13 12 .. .. .. 13 24 36 7 13 .. 13 4 14 .. .. .. 6 .. .. .. 31 .. 24 14 7 10 .. 31 12 .. 18 15 12 .. .. .. 12 .. 32 .. 12 m .. 12 15 12 15 27 13 13 9 17 .. 9 7

.. .. 36 .. .. .. .. 39 .. 21 .. .. 14 20 38 .. .. 6 .. 8 .. .. .. .. 36 37 .. .. .. .. 33 15 .. 17 .. 22 .. .. 67 35 34 .. m .. .. .. .. .. .. .. .. .. .. .. 15 14

19 16 .. .. .. 26 28 27 12 20 .. 13 8 28 .. .. .. 6 .. .. .. 36 .. 40 21 36 23 .. 14 13 .. 14 12 25 .. .. .. 67 .. 30 .. 21 m .. 25 31 20 27 33 17 27 36 14 .. 14 15

Note: Components may not sum to 100 percent because of rounding or missing data. a. Data were reported on a cash basis and have been adjusted to the accrual framework.

244

2011 World Development Indicators

.. .. 12 .. .. .. .. 8 .. 3 .. .. 11 22 8 .. .. 4 .. 12 .. .. .. .. 20 13 .. .. .. .. .. 9 .. 6 .. 27 .. .. 16 16 31 .. m .. .. .. .. .. .. .. .. .. 27 .. 9 10

2 1 .. .. .. 2 7 0 4 3 .. 7 5 25 .. .. .. 4 .. .. .. 5 .. 5 6 7 20 .. 9 3 .. 4 7 9 .. .. .. 1 .. 7 .. 5m .. 7 7 7 6 5 2 9 7 21 .. 5 5

.. .. 5 .. .. .. .. 15 .. 55 .. .. 42 24 10 .. .. 66 .. 33 .. .. .. .. 24 36 .. .. .. .. .. 57 .. 64 .. 61 .. .. 8 19 19 .. m .. .. .. .. .. .. .. .. .. 24 .. 56 55

60 68 .. .. .. 58 23 0 68 62 .. 63 80 23 .. .. .. 83 .. .. .. 28 .. 18 38 38 44 .. 45 70 .. 53 62 47 .. .. .. 18 .. 24 .. 46 m .. 45 36 47 37 28 58 35 36 28 .. 62 62

.. .. .. .. .. .. .. .. .. 3 .. .. 2 10 .. .. .. 0 .. .. .. .. .. .. 1 7 .. .. .. .. .. 8 .. 0 .. 2 .. .. 0 0 .. .. m .. .. .. .. .. .. .. .. .. .. .. 4 5

8 10 .. .. .. 1 18 .. 14 3 .. 4 5 10 .. .. .. 3 .. .. .. 3 .. 13 21 13 5 .. 1 2 .. 12 6 7 .. .. .. 1 .. 7 .. 6m .. 7 8 6 7 2 6 13 9 10 .. 5 4

About the data

4.13

ECONOMY

Central government expenses Definitions

The term expense has replaced expenditure in the

to households are shown as subsidies and other

• Goods and services are all government payments

table since the 2005 edition of World Development

transfers, and other expenses. The economic clas-

in exchange for goods and services used for the

Indicators in accordance with use in the International

sification can be problematic. For example, subsidies

production of market and nonmarket goods and ser-

Monetary Fund’s (IMF) Government Finance Statis-

to public corporations or banks may be disguised

vices. Own-account capital formation is excluded.

tics Manual 2001. Government expenses include all

as capital financing or hidden in special contractual

• Compensation of employees is all payments in

nonrepayable payments, whether current or capital,

pricing for goods and services. For further discussion

cash, as well as in kind (such as food and hous-

requited or unrequited. The concept of total central

of government finance statistics, see About the data

ing), to employees in return for services rendered,

government expense as presented in the IMF’s Gov-

for tables 4.12 and 4.14.

and government contributions to social insurance

ernment Finance Statistics Yearbook is comparable to

schemes such as social security and pensions that

the concept used in the 1993 United Nations System

provide benefits to employees. • Interest payments

of National Accounts.

are payments made to nonresidents, to residents,

Expenses can be measured either by function

and to other general government units for the use of

(health, defense, education) or by economic type

borrowed money. (Repayment of principal is shown

(interest payments, wages and salaries, purchases

as a financing item, and commission charges are

of goods and services). Functional data are often

shown as purchases of services.) • Subsidies and

incomplete, and coverage varies by country because

other transfers include all unrequited, nonrepayable

functional responsibilities stretch across levels of

transfers on current account to private and public

government for which no data are available. Defense

enterprises; grants to foreign governments, inter-

expenses, usually the central government’s respon-

national organizations, and other government units;

sibility, are shown in table 5.7. For more information

and social security, social assistance benefits, and

on education expenses, see table 2.11; for more on

employer social benefits in cash and in kind. • Other

health expenses, see table 2.16.

expense is spending on dividends, rent, and other

The classification of expenses by economic type in

miscellaneous expenses, including provision for con-

the table shows whether the government produces

sumption of fixed capital.

goods and services and distributes them, purchases the goods and services from a third party and distributes them, or transfers cash to households to make the purchases directly. When the government produces and provides goods and services, the cost is reflected in compensation of employees, use of goods and services, and consumption of fixed capital. Purchases from a third party and cash transfers Interest payments are a large part of government expenses for some developing economies

4.13a

Central government interest payments as a share of total expense, 2009 (percent) 50 40 30 20

Data sources 10

Data on central government expenses are from the iu

s

p.

rit

Re

au

a

IMF’s Government Finance Statistics database. Each country’s accounts are reported using the

M

ab Ar

t, yp

bi

an a Gh Eg

az il Br

om

Co l

s

ke y Tu r

s Se yc

he lle

a

ne

di In

pp i ili Ph

sh

a an k

la de

iL

ng Ba

& s itt

.K St

Sr

an

Ne vis

st

no n

Pa ki

Le ba

Ja

m ai

ca

0

Interest payments accounted for more than 14 percent of total expenses in 2009 for 15 countries.

system of common definitions and classifications in the IMF’s Government Finance Statistics Manual 2001. See these sources for complete and authoritative explanations of concepts, definitions, and

Source: International Monetary Fund, Government Finance Statistics data files.

data sources.

2011 World Development Indicators

245

4.14

Central government revenues Taxes on income, proﬁts, and capital gains

Taxes on goods and services

Taxes on International trade

Other taxes

Social contributions

Grants and other revenue

% of revenue

% of revenue 1995 2009

% of revenue 1995 2009

% of revenue 1995 2009

% of revenue 1995 2009

% of revenue 1995 2009

1995

Afghanistana Albaniaa Algeria Angola Argentina Armeniaa Australia Austria Azerbaijana Bangladesha Belarusa Belgium Benina Bolivia Bosnia and Herzegovina Botswanaa Brazila Bulgariaa Burkina Faso Burundia Cambodia Cameroona Canadaa Central African Republica Chad Chile Chinaa Hong Kong SAR, China Colombia Congo, Dem. Rep.a Congo, Rep.a Costa Rica Côte d’Ivoire Croatiaa Cuba Czech Republica Denmark Dominican Republic Ecuadora Egypt, Arab Rep.a El Salvador Eritrea Estonia Ethiopiaa Finland France Gabon Gambia, Thea Georgiaa Germany Ghanaa Greece Guatemalaa Guineaa Guinea-Bissau Haiti Honduras

246

2009

.. 8 .. .. .. .. .. 21 .. .. 16 36 .. .. .. 21 14 17 .. 14 .. 17 48 .. .. .. 9 .. .. 21 6 .. .. 11 .. 15 37 .. 50 17 .. .. 18 19 16 17 .. .. 7 16 15 17 19 8 .. .. ..

2011 World Development Indicators

4 .. 60 .. .. 20 65 23 33 19 6 34 17 10 5 .. 30 16 14 .. 11 .. 55 .. .. 28 26 44 26 .. .. 17 15 10 .. 15 45 22 .. 28 27 .. 8 .. 20 22 .. .. 32 16 23 21 29 .. .. .. 20

.. 39 .. .. .. .. .. 22 .. .. 33 23 .. .. .. 4 24 28 .. 30 .. 25 18 .. .. .. 61 .. .. 12 21 .. .. 42 .. 32 40 .. 26 13 .. .. 35 13 31 25 .. .. 48 20 31 32 46 4 .. .. ..

3 .. 28 .. .. 41 23 23 23 29 29 24 39 43 43 .. 33 45 37 .. 36 .. 15 .. .. 46 55 9 32 .. .. 32 20 43 .. 27 36 54 .. 22 39 .. 39 .. 32 23 .. .. 51 24 29 29 56 .. .. .. 39

.. 14 .. .. .. .. .. 0 .. .. 6 .. .. .. .. 15 2 8 .. 20 .. 28 3 .. .. .. 7 .. .. 21 18 .. .. 9 .. 4 .. .. 11 10 .. .. 0 27 0 0 .. .. 10 .. 24 0 23 62 .. .. ..

5 .. 4 .. .. 3 2 0 4 24 16 .. 18 3 0 .. 2 1 12 .. 16 .. 1 .. .. 1 5 0 5 .. .. 4 33 2 .. 0 .. 10 .. 5 5 .. .. .. .. 0 .. .. 1 .. 16 0 7 .. .. .. 3

.. 1 .. .. .. .. .. 5 .. .. 11 2 .. .. .. 0 4 3 .. 1 .. 3 .. .. .. .. 0 .. .. 5 1 .. .. 1 .. 1 8 .. 1 10 .. .. 0 3 1 3 .. .. .. 0 .. 3 3 2 .. .. ..

0 .. 1 .. .. 8 0 5 1 3 3 0 6 9 2 .. 2 0 2 .. 0 .. .. .. .. 2 3 13 5 .. .. 3 8 2 .. 1 5 5 .. 2 0 .. .. .. 2 4 .. .. 1 .. .. 3 2 .. .. .. 1

.. 15 .. .. .. .. .. 43 .. .. 31 36 .. .. .. .. 31 21 .. 5 .. 2 21 .. .. .. .. .. .. 1 .. .. .. 33 .. 40 4 .. .. 10 .. .. 34 1 34 47 .. .. 13 58 .. 31 2 1 .. .. ..

0 .. .. .. .. 14 .. 42 .. .. 33 37 2 7 39 .. 26 23 .. .. .. .. 24 .. .. 7 .. 0 6 .. .. 34 6 35 .. 45 3 2 .. .. 12 .. 36 .. 31 45 .. .. 17 55 .. 36 3 .. .. .. 13

.. 22 .. .. .. .. .. 9 .. .. 3 3 .. .. .. 59 26 23 .. 30 .. 25 10 .. .. .. 22 .. .. 41 54 .. .. 4 .. 8 11 .. 12 41 .. .. .. 36 17 8 .. .. 22 6 9 16 6 23 .. .. ..

88 .. 6 .. .. 14 10 7 39 24 13 3 18 28 11 .. 6 16 36 .. 37 .. 8 .. .. 16 12 34 25 .. .. 10 18 9 .. 12 .. 8 .. 43 17 .. .. .. 15 6 .. .. 15 4 32 12 4 .. .. .. 23

Taxes on income, proﬁts, and capital gains

Taxes on goods and services

Taxes on International trade

Other taxes

Social contributions

Grants and other revenue

% of revenue

% of revenue 1995 2009

% of revenue 1995 2009

% of revenue 1995 2009

% of revenue 1995 2009

% of revenue 1995 2009

1995

Hungary Indiaa Indonesiaa Iran, Islamic Rep.a Iraq Ireland Israel Italy Jamaica Japan Jordana Kazakhstana Kenyaa Korea, Dem. Rep. Korea, Rep.a Kosovo Kuwaita Kyrgyz Republica Lao PDR Latviaa Lebanon Lesothoa Liberiaa Libya Lithuania Macedonia, FYRa Madagascar Malawi Malaysiaa Mali Mauritania Mauritius Mexicoa Moldovaa Mongoliaa Moroccoa Mozambique Myanmar a Namibiaa Nepala Netherlands New Zealand Nicaraguaa Niger Nigeriaa Norway Omana Pakistana Panamaa Papua New Guineaa Paraguaya Perua Philippinesa Poland Portugal Puerto Rico Qatara

4.14

16 23 52 12 .. 37 .. 32 .. .. 10 11 35 .. 31 .. 1 26 .. 7 .. 15 .. .. .. .. .. .. 38 .. .. .. 27 6 31 .. .. 20 27 10 26 .. 9 .. .. .. 21 18 20 40 15 15 33 .. 23 .. ..

2009

23 47 37 19 .. 33 26 32 25 .. 17 24 40 .. 28 .. 1 12 21 8 15 17 28 .. 10 13 12 .. 46 19 .. 23 .. 1 21 28 .. .. 28 14 26 57 29 12 1 28 .. 25 .. .. 16 30 39 14 23 .. 40

28 28 32 5 .. .. .. 21 .. .. 23 28 40 .. 32 .. 0 56 .. 41 .. 12 .. .. .. .. .. .. 27 .. .. .. 54 38 18 .. .. 26 32 33 24 .. 52 .. .. .. 1 27 17 8 36 46 26 .. 33 .. ..

32 23 31 3 .. .. 31 20 37 .. 38 20 41 .. 26 .. .. 42 46 35 44 12 15 .. 36 40 15 .. 16 29 .. 46 .. 46 30 31 .. .. 19 35 27 26 49 18 2 24 .. 32 .. .. 43 39 29 37 31 .. ..

10 24 5 9 .. 0 .. .. .. .. 22 3 14 .. 7 .. 2 5 .. 3 .. 49 .. .. .. .. .. .. 12 .. .. .. 4 5 9 .. .. 12 28 26 .. .. 7 .. .. .. 3 24 11 27 18 10 29 .. 0 .. ..

0 13 2 6 .. 0 1 .. 7 .. 6 6 10 .. 4 .. 1 9 9 0 6 57 39 .. .. 5 31 .. 2 10 .. 2 .. 4 6 6 .. .. 44 16 .. 3 4 26 .. 0 .. 8 .. .. 7 2 20 0 0 .. 2

1 0 1 1 .. 2 .. 5 .. .. 9 5 1 .. 10 .. 0 1 .. 0 .. 1 .. .. .. .. .. .. 6 .. .. .. 2 1 0 .. .. .. 2 4 2 .. 0 .. .. .. 2 7 3 2 4 8 4 .. 2 .. ..

1 0 4 1 .. 2 5 7 10 .. 3 0 1 .. 9 .. 0 .. 1 0 10 3 1 .. 0 0 6 .. 3 10 .. 7 .. 0 0 5 .. .. 1 5 2 0 0 3 .. 1 .. 0 .. .. 1 6 .. 1 2 .. ..

35 0 .. 6 .. 17 .. 35 .. .. .. 48 0 .. 8 .. .. .. .. 35 .. .. .. .. .. .. .. .. .. .. .. .. 14 38 15 .. .. .. .. .. 40 .. .. .. .. .. .. .. 16 0 6 10 .. .. 30 .. ..

32 0 .. 19 .. 22 17 36 3 .. 0 .. .. .. 16 .. .. .. .. 31 1 .. .. .. 42 29 4 .. .. .. .. 4 .. 33 17 12 .. .. 0 .. 35 0 .. .. .. 21 .. .. .. .. 7 10 .. 37 33 .. ..

9 25 10 66 .. .. .. 6 .. .. 36 6 10 .. 12 .. 97 11 .. 13 .. 24 .. .. .. .. .. .. 17 .. .. .. 16 2 27 .. .. 42 11 27 8 .. 31 .. .. .. 74 24 34 23 22 11 8 .. .. .. ..

2011 World Development Indicators

ECONOMY

Central government revenues

12 18 26 52 .. .. 19 5 18 .. 36 51 8 .. 17 .. 98 37 22 26 23 11 18 .. 13 13 32 .. 33 31 .. 17 .. 16 26 17 .. .. 7 29 10 15 18 41 97 26 .. 35 .. .. 26 13 13 10 .. .. 58

247

4.14

Central government revenues Taxes on income, proﬁts, and capital gains

Taxes on goods and services

Taxes on International trade

Other taxes

Social contributions

Grants and other revenue

% of revenue

% of revenue 1995 2009

% of revenue 1995 2009

% of revenue 1995 2009

% of revenue 1995 2009

% of revenue 1995 2009

1995

Romania Russian Federation Rwandaa Saudi Arabia Senegala Serbiaa Sierra Leonea Singaporea Slovak Republic Sloveniaa Somalia South Africa Spain Sri Lankaa Sudana Swazilanda Sweden Switzerland a Syrian Arab Republic a Tajikistana Tanzania Thailand Timor-Leste Togo Trinidad and Tobagoa Tunisiaa Turkeya Turkmenistan Ugandaa Ukrainea United Arab Emiratesa United Kingdom United States Uruguaya Uzbekistan Venezuela, RBa Vietnam West Bank and Gaza Yemen, Rep.a Zambiaa Zimbabwea World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

2009

.. .. 11 .. 17 .. 15 26 .. 13 .. .. 28 12 17 .. 13 11 23 6 .. .. .. .. 50 16 .. .. 10 .. .. 37 .. 10 .. 38 .. .. 17 27 36 .. m .. .. 17 .. .. 32 .. .. 16 15 .. 21 22

22 1 .. .. .. 9 17 36 9 13 .. 53 24 18 .. .. 11 24 .. .. .. 38 .. 17 63 27 26 .. 22 10 .. 36 47 18 .. .. .. 2 .. 33 .. 23 m .. 25 26 23 21 37 10 27 27 19 .. 24 23

.. .. 25 .. 19 .. 34 20 .. 33 .. .. 21 49 41 .. 33 21 37 63 .. .. .. .. 26 20 .. .. 45 .. 15 32 .. 32 .. 33 .. .. 10 22 22 .. m .. .. 27 .. .. 26 .. .. 16 31 .. 28 24

35 16 .. .. .. 43 25 26 33 33 .. 32 13 45 .. .. 37 26 .. .. .. 38 .. 34 13 31 51 .. 47 34 .. 28 3 41 .. .. .. 21 .. 36 .. 32 m .. 36 33 36 36 31 42 39 31 29 .. 27 27

.. .. 23 .. 36 .. 39 1 .. 9 .. .. 0 17 27 .. .. 1 13 12 .. .. .. .. 6 28 .. .. 7 .. .. .. .. 4 .. 9 .. .. 18 36 17 .. m .. .. 16 .. .. 10 .. .. 16 24 .. .. 0

0 18 .. .. .. 5 14 0 0 0 .. 3 .. 14 .. .. .. 6 .. .. .. 5 .. 18 4 6 1 .. 10 2 .. .. 1 3 .. .. .. 11 .. 8 .. 5m .. 5 6 4 7 6 4 4 6 13 .. 0 0

Note: Components may not sum to 100 percent because of missing data or adjustment to tax revenue. a. Data were reported on a cash basis and have been adjusted to the accrual framework.

248

2011 World Development Indicators

.. .. 3 .. 2 .. 0 15 .. 0 .. .. 0 4 1 .. 4 2 8 0 .. .. .. .. 1 4 .. .. 2 .. .. 6 .. 10 .. 0 .. .. 3 0 3 .. m .. .. 2 .. .. 2 .. .. 6 4 .. 2 2

0 0 .. .. .. 0 .. 14 0 0 .. 2 0 8 .. .. 13 3 .. .. .. 1 .. 3 8 4 5 .. 0 0 .. 7 1 2 .. .. .. 0 .. 0 .. 2m .. 2 1 2 2 1 0 2 3 0 .. 2 2

.. .. 2 .. .. .. .. .. .. 42 .. .. 40 1 .. .. 32 49 0 13 .. .. .. .. 2 15 .. .. .. .. 1 20 .. 31 .. 4 .. .. .. 0 2 .. m .. .. .. .. .. .. .. .. .. .. .. 34 36

33 17 .. .. .. 35 .. .. 43 41 .. 2 58 1 .. .. 25 36 .. .. .. 5 .. .. 4 19 .. .. .. 37 .. 23 43 30 .. .. .. 0 .. .. .. .. m .. .. .. 22 .. .. 29 10 6 0 .. 36 37

.. .. 36 .. 26 .. 12 38 .. 3 .. .. .. 18 14 .. .. 17 19 5 .. .. .. .. 15 17 .. .. 37 .. 84 5 .. 8 .. 19 .. .. 51 15 19 .. m .. .. 23 .. .. 23 .. .. 38 25 .. 10 7

10 48 .. .. .. 7 44 24 15 12 .. 8 4 14 .. .. .. 5 .. .. .. 14 .. 28 9 12 16 .. 22 17 .. 6 6 6 .. .. .. 66 .. 23 .. 17 m .. 17 17 16 18 26 16 17 23 29 .. 12 8

About the data

4.14

ECONOMY

Central government revenues Definitions

The International Monetary Fund (IMF) classifi es

Direct taxes tend to be progressive, whereas indirect

• Taxes on income, proﬁts, and capital gains are

government revenues as taxes, grants, and property

taxes are proportional.

levied on the actual or presumptive net income

income. Taxes are classified by the base on which

Social security taxes do not reflect compulsory pay-

of individuals, on the profi ts of corporations and

the tax is levied, grants by the source, and property

ments made by employers to provident funds or other

enterprises, and on capital gains, whether real-

income by type (for example, interest, dividends,

agencies with a like purpose. Similarly, expenditures

ized or not, on land, securities, and other assets.

or rent). The most important source of revenue is

from such funds are not refl ected in government

taxes. Grants are unrequited, nonrepayable, non-

expenses (see table 4.13). For further discussion of

compulsory receipts from other government units

taxes and tax policies, see About the data for table

and foreign governments or from international orga-

5.6. For further discussion of government revenues

nizations. Transactions are generally recorded on an

and expenditures, see About the data for tables 4.12

accrual basis.

and 4.13.

Intra-governmental payments are eliminated in consolidation. • Taxes on goods and services include general sales and turnover or value added taxes, selective excises on goods, selective taxes on services, taxes on the use of goods or property, taxes on extraction and production of minerals, and profits of fiscal monopolies. • Taxes on international

The IMF’s Government Finance Statistics Manual

trade include import duties, export duties, profits

2001 describes taxes as compulsory, unrequited

of export or import monopolies, exchange profi ts,

payments made to governments by individuals, busi-

and exchange taxes. • Other taxes include employer

nesses, or institutions. Taxes are classified in six

payroll or labor taxes, taxes on property, and taxes

major groups by the base on which the tax is levied:

not allocable to other categories, such as penalties

income, profits, and capital gains; payroll and work-

for late payment or nonpayment of taxes. • Social

force; property; goods and services; international

contributions include social security contributions by

trade and transactions; and other. However, the dis-

employees, employers, and self-employed individu-

tinctions are not always clear. Taxes levied on the

als, and other contributions whose source cannot be determined. They also include actual or imputed

income and profits of individuals and corporations

contributions to social insurance schemes operated

are classified as direct taxes, and taxes and duties

by governments. • Grants and other revenue include

levied on goods and services are classified as indi-

grants from other foreign governments, international

rect taxes. This distinction may be a useful simplifica-

organizations, and other government units; interest;

tion, but it has no particular analytical significance

dividends; rent; requited, nonrepayable receipts

except with respect to the capacity to fix tax rates.

for public purposes (such as fines, administrative fees, and entrepreneurial income from government

4.14a

Rich economies rely more on direct taxes

ownership of property); and voluntary, unrequited, nonrepayable receipts other than grants.

Taxes on income and capital gains as a share of central government revenue, 2009 (percent) 70

60

50

40

30

Data sources 20

Data on central government revenues are from the 10

IMF’s Government Finance Statistics database. Each country’s accounts are reported using the

0 100

10,000

1,000

100,000

GNI per capita ($, log scale) Low income

Middle income

system of common definitions and classifications in the IMF’s Government Finance Statistics Manual 2001. The IMF receives additional information

High income

from the Organisation for Economic Co-operation

High-income economies tend to tax income and property, whereas low-income economies tend to rely

and Development on the tax revenues of some of

on indirect taxes on international trade and goods and services. But there are exceptions in all groups.

its members. See the IMF sources for complete and authoritative explanations of concepts, defini-

Note: Data are for the most recent year for 2005–09. Source: International Monetary Fund, Government Finance Statistics data files, and World Development Indicators data files.

tions, and data sources.

2011 World Development Indicators

249

4.15

Monetary indicators Broad money

annual % growth 2000 2009

Afghanistan Albania Algeria Angolaa Argentinaa Armenia Australiaa Austria b Azerbaijan Bangladesh Belarus Belgiumb Benina Bolivia Bosnia and Herzegovinaa Botswana Brazil Bulgaria Burkina Fasoa Burundi Cambodia Cameroona Canada Central African Republic a Chad a Chile Chinaa Hong Kong SAR, Chinaa Colombia Congo, Dem. Rep.a Congo, Rep.a Costa Rica Côte d’Ivoire a Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopiaa Finlandb Franceb Gabona Gambia, Thea Georgia Germany b Ghana Greeceb Guatemala Guineaa Guinea-Bissaua Haiti Honduras

250

.. 12.0 14.1 303.7 1.5 38.6 3.7 .. 73.4 19.3 219.3 .. 26.0 1.6 11.3 1.4 19.7 30.8 6.2 15.5 26.9 19.1 6.6 2.4 19.4 9.1 12.3 9.3 3.6 40.0 58.5 24.0 –1.9 29.1 .. 16.0 –12.1 16.8 47.0 11.6 1.6 17.3 25.7 13.1 .. .. 18.3 34.8 39.2 .. 54.2 .. 21.4 12.9 60.8 20.3 15.4

33.0 6.8 1.6 62.6 17.0 16.4 0.5 .. –0.3 20.3 25.9 .. 8.0 11.8 –0.1 –1.3 15.8 4.2 22.3 14.5 35.6 6.3 15.1 13.3 1.1 1.3 28.4 5.2 8.1 50.4 5.0 8.0 17.2 –0.6 .. 0.2 7.0 13.4 10.1 9.5 2.1 15.7 –0.1 23.4 .. .. 2.1 19.4 8.2 .. 39.2 .. 11.3 .. 6.9 10.3 0.6

2011 World Development Indicators

Claims on domestic economy

Claims on central government

Annual growth % of broad money 2000 2009

Annual growth % of broad money 2000 2009

.. 0.9 8.4 35.8 –2.9 0.3 13.3 .. –23.9 10.7 59.9 .. 8.5 –1.3 10.3 10.3 8.3 6.5 8.3 15.0 5.4 7.4 3.6 2.9 0.4 4.1 9.5 1.7 8.9 3.8 –23.0 14.1 2.9 21.3 .. –11.0 26.1 13.2 –10.8 4.1 2.6 3.7 .. 3.0 .. .. 6.2 4.2 18.7 .. 7.5 .. 4.2 2.3 5.5 12.3 7.9

8.0 5.4 7.7 33.3 5.1 15.0 8.7 .. 13.2 13.3 64.6 .. 6.7 6.0 –3.8 5.3 6.7 4.2 1.0 8.3 6.3 4.5 23.3 2.8 5.7 –0.6 22.7 3.6 2.7 19.2 5.2 4.9 6.0 –0.6 .. 0.7 –4.4 5.3 5.5 0.5 –4.1 0.2 –9.0 17.7 .. .. –2.6 5.4 –18.1 .. 30.4 .. –2.4 .. 3.9 6.2 7.1

.. 4.8 –11.6 –413.7 –0.8 –5.7 –1.8 .. 15.4 5.6 22.2 .. 0.9 3.1 –0.4 –56.2 13.5 8.5 5.3 –22.6 –6.9 –12.3 2.4 6.8 15.1 4.0 0.0 0.4 6.0 –34.0 –11.7 –0.2 –7.6 2.0 .. 2.6 3.0 2.8 –28.1 7.7 2.3 25.7 –3.2 19.8 .. .. –42.2 2.7 19.8 .. 32.9 .. 10.2 7.9 16.2 13.8 –2.6

–9.5 2.4 0.2 48.1 18.9 –11.8 –2.7 .. 4.3 1.3 –40.9 .. 7.5 –3.0 –0.1 18.7 1.2 2.5 2.7 13.0 5.7 0.9 4.7 –0.3 72.5 0.6 0.6 8.8 7.2 –14.5 12.0 2.8 7.4 0.2 .. 3.9 6.3 8.0 8.8 10.5 –1.3 11.9 –3.6 2.5 .. .. 4.0 5.2 11.0 .. 22.1 .. 6.8 .. –13.3 –12.4 4.8

Interest rate

% Lending

Deposit

Real

2000

2009

2000

2009

2000

2009

.. 8.3 7.5 39.6 8.3 18.1 4.2 2.2 12.9 8.6 37.6 3.6 3.5 11.0 14.7 9.4 17.2 3.1 3.5 .. 6.8 5.0 3.5 5.0 5.0 9.2 2.3 4.8 12.1 .. 5.0 13.4 3.5 3.7 .. 3.4 3.2 17.7 8.8 9.5 9.3 .. 3.8 6.0 1.6 2.6 5.0 12.5 10.2 3.4 28.6 6.1 10.2 7.5 3.5 12.1 15.9

.. 6.8 1.8 7.6 11.6 8.7 2.8 .. 12.2 8.2 10.7 .. 3.5 3.4 3.6 7.5 9.3 6.2 3.5 .. 1.7 3.3 0.1 3.3 3.3 2.0 2.3 0.0 6.1 15.9 3.3 7.0 3.5 3.2 .. 1.3 .. 7.8 4.8 6.5 .. .. 4.8 4.7 .. 1.9 3.3 15.5 10.3 .. 17.1 .. 5.6 .. 3.5 1.1 10.8

.. 22.1 10.0 103.2 11.1 31.6 9.3 5.6 19.7 15.5 67.7 8.0 .. 34.6 30.5 15.5 56.8 11.3 .. 15.8 .. 22.0 7.3 22.0 22.0 14.8 5.9 9.5 18.8 .. 22.0 24.9 .. 12.1 .. 7.2 8.1 26.8 17.1 13.2 14.0 .. 7.4 10.9 5.6 6.7 22.0 24.0 32.8 9.6 .. 12.3 20.9 19.4 .. 19.1 26.8

15.0 12.7 8.0 15.7 15.7 18.8 6.0 .. 20.0 14.6 11.7 9.2 .. 12.4 7.9 13.8 44.7 11.3 .. 14.1 .. 15.0 2.4 15.0 15.0 7.3 5.3 5.0 13.0 65.4 15.0 19.7 .. 11.6 .. 6.0 .. 18.1 12.1 12.0 .. .. 9.4 8.0 .. .. 15.0 27.0 25.5 .. .. .. 13.8 .. .. 17.3 19.4

.. 17.0 –11.7 –60.8 9.9 33.4 6.5 5.2 6.4 13.4 –41.2 5.9 .. 27.9 1.3 15.4 47.7 4.4 .. 2.3 .. 18.6 3.0 18.3 15.9 9.8 3.7 13.6 –10.3 .. –17.0 16.7 .. 7.1 .. 5.6 4.9 18.6 26.0 7.9 10.5 .. 2.4 3.8 2.9 5.2 –4.8 19.6 26.8 10.4 .. 8.6 13.2 7.4 .. 7.3 –3.1

36.1 10.1 19.2 22.8 5.2 17.1 1.0 .. 44.2 7.6 7.5 7.1 .. 15.1 7.9 20.6 36.8 7.0 .. 0.4 .. 12.7 4.6 12.2 9.4 2.9 6.0 4.8 7.7 27.0 14.4 9.9 .. 8.0 .. 3.2 .. 14.7 6.3 1.0 .. .. 10.0 –17.2 .. .. 9.3 24.1 28.1 .. .. .. 11.2 .. .. 13.3 14.4

Broad money

annual % growth 2000 2009

Hungary Indiaa Indonesia Iran, Islamic Rep.a Iraq Irelandb Israela Italy b Jamaica Japan Jordana Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep.a Kosovo Kuwait Kyrgyz Republica Lao PDRa Latvia Lebanona Lesotho Liberiaa Libyaa Lithuania Macedonia, FYR Madagascara Malawia Malaysia Malia Mauritaniaa Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar a Namibia Nepal Netherlandsb New Zealanda Nicaragua Niger a Nigeria Norwaya Oman Pakistan Panama Papua New Guinea Paraguay Perua Philippines Poland Portugalb Puerto Rico Qatar

12.6 15.2 16.6 22.4 .. .. 8.0 .. –7.0 1.3 7.6 45.0 4.9 .. 25.4 –12.2 6.3 11.7 46.0 27.0 9.8 1.4 18.3 3.1 16.5 22.2 17.2 45.5 10.0 12.2 16.1 9.2 –4.5 41.7 17.6 8.4 38.3 42.5 13.2 18.8 .. 1.5 9.4 12.4 48.1 8.7 6.0 12.1 9.3 5.0 2.8 –0.4 8.1 11.6 .. .. 10.7

3.3 18.0 13.0 27.7 26.7 .. 6.1 .. 5.4 2.1 24.3 19.5 16.5 .. 12.2 11.2 13.4 33.2 18.3 –2.7 19.6 17.7 43.4 17.4 0.6 5.5 11.3 24.6 7.7 14.6 .. 8.1 11.5 3.2 26.9 5.8 32.6 30.6 5.9 29.4 .. –0.6 14.3 18.7 14.4 .. 4.7 14.8 10.3 21.9 22.2 2.6 10.0 8.1 .. .. 16.9

Claims on domestic economy

Claims on central government

Annual growth % of broad money 2000 2009

Annual growth % of broad money 2000 2009

14.5 9.9 7.2 15.8 .. .. 10.7 .. 9.1 –5.4 3.2 32.2 4.7 .. 21.9 12.1 8.5 3.5 22.4 31.2 2.9 6.6 –10.0 0.2 14.4 2.7 7.9 16.5 5.5 –1.5 41.1 5.8 10.1 24.4 29.6 3.6 11.9 13.9 19.4 –4.6 .. 8.0 7.0 14.8 5.8 18.0 1.1 2.0 –8.4 1.2 1.7 –2.7 2.2 .. .. .. –1.7

–4.2 7.8 6.9 10.2 2.0 .. –0.5 .. 2.6 –2.9 0.8 14.1 11.5 .. 3.9 7.6 7.1 29.2 19.4 –25.9 4.8 7.2 17.1 2.0 –12.9 3.1 3.7 19.3 5.5 7.0 .. 0.8 8.1 –4.0 1.2 9.0 32.7 5.2 11.3 26.4 .. 1.3 –7.4 12.1 17.5 .. 7.3 8.0 2.5 8.4 14.8 0.8 5.4 7.9 .. .. 2.9

–2.0 4.7 17.2 –7.9 .. .. –4.8 .. –2.3 2.6 –1.2 –3.2 –2.1 .. –1.4 –37.7 –7.4 7.8 –17.6 7.8 10.5 14.9 197.0 –10.4 0.5 –15.9 0.1 7.7 2.1 –5.0 –64.3 –4.7 3.5 –5.7 –7.1 3.6 6.9 25.0 –4.0 2.6 .. –0.9 10.0 –14.1 –43.0 –4.8 9.5 2.6 0.2 –4.6 4.7 2.3 1.5 –5.8 .. .. –23.1

0.1 9.4 2.5 2.0 33.6 .. 1.1 .. 9.4 4.4 2.5 –4.7 8.2 .. 2.2 1.8 –1.0 –8.8 –3.4 –9.6 4.5 –0.5 47.7 1.8 –4.1 1.3 8.9 21.2 3.3 –13.3 .. 1.1 4.1 4.0 –6.4 –1.1 0.2 29.9 –4.1 –1.6 .. 2.7 7.0 28.9 12.7 .. 1.4 7.4 –0.6 10.1 –3.5 0.3 2.5 1.7 .. .. 26.7

4.15

ECONOMY

Monetary indicators Interest rate

% Lending

Deposit

Real

2000

2009

2000

2009

2000

2009

9.5 .. 12.5 11.7 .. 0.1 8.6 1.8 11.6 0.1 7.0 .. 8.1 .. 7.9 .. 5.9 18.4 12.0 4.4 11.2 4.9 6.2 3.0 3.9 11.2 15.0 33.3 3.4 3.5 9.4 9.6 8.3 24.9 16.8 5.2 9.7 9.8 7.4 6.0 2.9 6.4 10.8 3.5 11.7 6.7 7.6 .. 7.1 8.5 15.7 9.8 8.3 14.2 2.4 .. 0.0

5.8 .. 9.3 13.1 7.8 .. 1.1 .. 7.0 0.4 4.9 .. 6.0 .. 3.5 4.0 2.8 3.9 4.7 8.0 7.3 4.9 4.1 2.5 4.8 7.0 11.5 3.5 2.1 3.5 8.0 8.4 2.0 14.9 13.3 3.8 9.5 12.0 6.2 2.5 2.6 4.0 6.0 3.5 13.3 2.3 4.1 8.7 3.5 2.3 1.5 2.8 2.7 2.2 .. .. 4.2

12.6 12.3 18.5 .. .. 4.8 12.9 7.0 23.3 2.1 11.8 .. 22.3 .. 8.5 .. 8.9 51.9 32.0 11.9 18.2 17.1 20.5 7.0 12.1 18.9 26.5 53.1 7.7 .. 25.6 20.8 16.9 33.8 37.0 13.3 19.0 15.3 15.3 9.5 4.8 9.3 18.1 .. 21.3 8.9 10.1 .. 10.5 17.5 26.8 30.0 10.9 20.0 5.2 .. ..

11.0 12.2 14.5 12.0 15.6 .. 3.7 4.8 16.4 1.7 9.2 .. 14.8 .. 5.6 14.1 6.2 23.0 24.0 16.2 9.6 13.0 14.2 6.0 8.4 10.1 45.0 25.3 5.1 .. 23.5 19.3 7.1 20.5 21.7 .. 15.7 17.0 11.1 8.0 2.0 10.4 14.0 .. 18.4 4.3 7.4 14.5 8.2 10.1 28.3 21.0 8.6 5.5 .. .. 7.0

0.9 8.5 –1.7 .. .. –1.1 11.1 5.0 11.5 3.9 12.2 .. 15.3 .. 3.4 .. –9.7 19.5 5.5 7.4 20.7 14.4 22.1 –9.6 11.1 9.9 18.0 17.3 –1.1 .. 23.9 18.3 4.3 5.1 8.6 14.0 6.3 12.5 –9.0 4.8 0.6 5.9 8.8 .. –12.2 –5.8 –8.3 .. 11.9 3.9 13.1 25.4 4.3 12.0 1.8 .. ..

6.1 4.3 5.6 11.3 61.5 .. –1.4 2.6 9.3 2.7 1.1 .. 7.6 .. 2.2 18.1 2.5 20.5 14.4 17.1 3.5 9.2 6.3 57.8 10.8 7.1 33.8 15.6 12.6 .. 15.1 17.5 2.7 18.1 21.3 .. 12.0 .. 4.4 –3.6 2.3 8.6 –2.0 .. 19.1 8.7 –16.0 –4.6 4.0 14.2 28.4 17.5 5.9 3.9 .. .. 31.0

2011 World Development Indicators

251

4.15

Monetary indicators Broad money

annual % growth 2000 2009

Romania Russian Federation Rwandaa Saudi Arabiaa Senegala Serbia Sierra Leonea Singaporea Slovak Republicb Sloveniab Somalia South Africa Spainb Sri Lankaa Sudan Swaziland Sweden Switzerlanda Syrian Arab Republic Tajikistana Tanzania Thailand Timor-Leste Togoa Trinidad and Tobagoa Tunisiaa Turkey Turkmenistana Uganda Ukraine United Arab Emiratesa United Kingdoma United States Uruguay Uzbekistan Venezuela, RBa Vietnama West Bank and Gaza Yemen, Rep.a Zambia Zimbabwe a

40.8 57.9 15.6 4.5 10.7 160.8 12.1 –2.0 .. .. .. 7.2 .. 12.9 36.9 –6.6 1.9 –16.9 19.0 63.3 14.8 4.9 41.1 15.2 11.7 14.1 40.7 83.3 18.1 44.5 15.3 11.1 8.1 9.5 .. 33.7 35.4 .. 25.3 73.8 45.7

9.0 16.4 .. 10.8 11.4 21.3 27.5 11.3 .. .. .. 1.8 .. 18.7 23.7 26.8 2.5 7.6 8.6 –3.6 17.7 6.8 39.3 16.0 30.6 12.5 12.7 .. 17.5 –5.5 9.8 0.0 –0.6 –2.6 .. 26.1 26.2 .. 12.8 7.7 111.3

Claims on domestic economy

Claims on central government

Annual growth % of broad money 2000 2009

Annual growth % of broad money 2000 2009

20.0 33.2 10.3 3.3 19.1 –71.0 1.6 5.1 .. .. .. –11.8 .. 9.1 16.9 16.9 8.5 –1.2 –4.1 8.2 12.2 6.2 45.7 0.5 8.8 23.7 16.2 10.8 8.2 30.9 8.7 17.4 5.0 45.1 .. 14.3 29.6 .. 3.6 –11.4 27.2

1.9 2.1 .. 0.0 2.6 18.1 14.2 1.6 .. .. .. 0.1 .. –4.6 13.6 12.5 3.8 5.1 8.6 145.1 5.8 3.6 0.6 9.7 –3.1 9.7 9.4 .. 10.1 –3.4 1.4 –2.6 –1.3 –10.3 .. 18.6 35.0 .. –1.2 –3.4 56.4

–1.1 –18.1 –11.4 –3.5 –3.9 22.5 54.6 –1.6 .. .. .. 0.2 .. 12.5 33.9 1.7 2.4 2.1 –6.1 36.6 0.7 0.5 –36.8 –0.5 –13.2 5.6 26.8 –53.4 29.4 –1.7 –9.6 –2.4 0.5 –1.8 .. –6.4 –2.4 .. –45.6 162.0 29.5

10.7 14.0 .. 8.9 4.3 4.9 4.0 8.9 .. .. .. 5.5 .. 4.4 13.0 17.4 1.6 0.6 1.4 –9.8 6.2 0.9 12.1 6.3 25.3 1.4 12.4 .. 0.4 9.4 13.3 7.9 4.5 3.0 .. –1.9 7.0 .. 26.2 16.2 –28.7

Interest rate

% Lending

Deposit

Real

2000

2009

2000

2009

2000

2009

33.1 6.5 10.1 .. 3.5 78.7 9.2 1.7 8.5 10.0 .. 9.2 3.0 9.2 .. 6.5 2.2 3.0 4.0 1.3 7.4 3.3 0.8 3.5 8.2 .. 47.2 .. 9.8 13.7 6.2 4.5 .. 18.3 .. 16.3 3.7 .. 14.0 20.2 50.2

12.0 8.6 6.7 .. 3.5 11.8 9.7 0.3 3.7 1.4 .. 8.5 .. 10.6 .. 5.4 .. 0.1 6.4 5.8 8.0 1.0 0.8 3.5 3.4 .. 17.6 .. 9.8 13.8 .. .. .. 4.4 .. 16.4 12.7 .. 10.7 7.1 121.5

53.9 24.4 17.0 .. .. 6.3 26.3 5.8 14.9 15.8 .. 14.5 5.2 16.2 .. 14.0 5.8 4.3 9.0 25.6 21.6 7.8 16.7 .. 16.5 .. .. .. 22.9 41.5 9.7 6.0 9.2 46.1 .. 25.2 10.6 .. 19.5 38.8 68.2

17.3 15.3 16.5 .. .. 11.8 24.5 5.4 5.8 5.9 .. 11.7 .. 15.7 .. 11.4 .. 2.8 10.0 22.9 15.0 6.0 11.2 .. 11.9 .. .. .. 21.0 20.9 .. 0.6 3.3 15.3 .. 19.9 10.1 .. 18.0 22.1 579.0

6.7 –9.6 20.6 .. .. –40.1 19.0 2.0 5.0 9.9 .. 5.2 1.7 8.3 .. 13.8 4.3 3.1 –0.6 2.4 13.0 6.4 11.4 .. 3.2 .. .. .. 10.6 15.0 –9.9 4.7 6.9 41.1 .. –3.3 6.9 .. –4.9 6.7 67.8

10.1 12.5 3.3 .. .. 1.6 12.0 7.4 2.8 4.0 .. 4.1 .. 9.5 .. 5.6 .. 2.5 19.0 8.5 7.1 3.9 1.1 .. 32.8 .. .. .. 3.8 6.6 .. –0.7 2.3 8.9 .. 10.6 3.8 .. 23.1 8.3 ..

a. For these countries data reported under Claims on domestic economy include claims on private sector only. b. As members of the European Monetary Union, these countries share a single currency, the euro.

252

2011 World Development Indicators

About the data

4.15

ECONOMY

Monetary indicators Definitions

Money and the financial accounts that record the

reporting period. The valuation of financial deriva-

• Broad money (IFS line 35L..ZK) is the sum of

supply of money lie at the heart of a country’s

tives and the net liabilities of the banking system

currency outside banks; demand deposits other

financial system. There are several commonly used

can also be difficult. The quality of commercial bank

than those of the central government; the time,

defi nitions of the money supply. The narrowest,

reporting also may be adversely affected by delays in

savings, and foreign currency deposits of resident

M1, encompasses currency held by the public and

reports from bank branches, especially in countries

sectors other than the central government; bank

demand deposits with banks. M2 includes M1 plus

where branch accounts are not computerized. Thus

and traveler’s checks; and other securities such

time and savings deposits with banks that require

the data in the balance sheets of commercial banks

as certifi cates of deposit and commercial paper.

prior notice for withdrawal. M3 includes M2 as well

may be based on preliminary estimates subject to

Change in broad money is measured as the differ-

as various money market instruments, such as cer-

constant revision. This problem is likely to be even

ence in end-of-year totals relative to the preceding

tificates of deposit issued by banks, bank deposits

more serious for nonbank financial intermediaries.

year. For countries reporting under the old presen-

denominated in foreign currency, and deposits with

Many interest rates coexist in an economy, reflect-

tation of monetary statistics and for all countries

fi nancial institutions other than banks. However

ing competitive conditions, the terms governing

prior to 2001, data are based on money plus quasi

defined, money is a liability of the banking system,

loans and deposits, and differences in the position

money. • Claims on domestic economy (IFS line

distinguished from other bank liabilities by the spe-

and status of creditors and debtors. In some econo-

32S..ZK) include gross credit from the fi nancial

cial role it plays as a medium of exchange, a unit of

mies interest rates are set by regulation or adminis-

system to households, nonprofi t institutions serv-

account, and a store of value.

trative fiat. In economies with imperfect markets, or

ing households, nonfinancial corporations, state

The banking system’s assets include its net for-

where reported nominal rates are not indicative of

and local governments, and social security funds.

eign assets and net domestic credit. Net domestic

effective rates, it may be difficult to obtain data on

For countries where claims on domestic economy

credit includes credit extended to the private sector

interest rates that reflect actual market transactions.

are not available, data are claims on private sec-

and general government and credit extended to the

Deposit and lending rates are collected by the Inter-

tor (IFS line 32D..ZK or 32D..ZF) • Claims on cen-

nonfinancial public sector in the form of investments

national Monetary Fund (IMF) as representative inter-

tral government (IFS line 32AN..ZK) include loans

in short- and long-term government securities and

est rates offered by banks to resident customers.

to central government institutions net of deposits.

loans to state enterprises; liabilities to the public

The terms and conditions attached to these rates

• Deposit interest rate is the rate paid by commer-

and private sectors in the form of deposits with the

differ by country, however, limiting their comparabil-

cial or similar banks for demand, time, or savings

banking system are netted out. Net domestic credit

ity. Real interest rates are calculated by adjusting

deposits. • Lending interest rate is the rate charged

also includes credit to banking and nonbank financial

nominal rates by an estimate of the inflation rate in

by banks on loans to prime customers. • Real inter-

institutions.

the economy. A negative real interest rate indicates

est rate is the lending interest rate adjusted for infla-

Domestic credit is the main vehicle through which

a loss in the purchasing power of the principal. The

tion as measured by the GDP deflator.

changes in the money supply are regulated, with cen-

real interest rates in the table are calculated as (i –

tral bank lending to the government often playing the

P) / (1 + P), where i is the nominal lending interest

most important role. The central bank can regulate

rate and P is the inflation rate (as measured by the

lending to the private sector in several ways—for

GDP deflator).

example, by adjusting the cost of the refinancing

In 2009 the IMF began publishing a new presenta-

facilities it provides to banks, by changing market

tion of monetary statistics for countries that report

interest rates through open market operations, or by

data in accordance with the IMF’s Monetary and

controlling the availability of credit through changes

Financial Statistics Manual 2000. The presentation

in the reserve requirements imposed on banks and

for countries that report data in accordance with the

Data on monetary and financial statistics are

ceilings on the credit provided by banks to the pri-

IMF’s International Financial Statistics (IFS) remains

published by the IMF in its monthly International

vate sector.

the same.

Financial Statistics and annual International Finan-

Data sources

Monetary accounts are derived from the balance

cial Statistics Yearbook. The IMF collects data on

sheets of financial institutions—the central bank,

the financial systems of its member countries. The

commercial banks, and nonbank financial interme-

World Bank receives data from the IMF in elec-

diaries. Although these balance sheets are usually reliable, they are subject to errors of classification,

tronic files that may contain more recent revisions Data sources than the published sources. The discussion of

valuation, and timing and to differences in account-

monetary indicators draws from an IMF publication

ing practices. For example, whether interest income

by Marcello Caiola, A Manual for Country Econo-

is recorded on an accrual or a cash basis can make

mists (1995). Also see the IMF’s Monetary and

a substantial difference, as can the treatment of non-

Financial Statistics Manual (2000) for guidelines

performing assets. Valuation errors typically arise

for the presentation of monetary and financial sta-

for foreign exchange transactions, particularly in

tistics. Data on real interest rates are derived from

countries with flexible exchange rates or in countries

World Bank data on the GDP deflator.

that have undergone currency devaluation during the

2011 World Development Indicators

253

4.16 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austriac Azerbaijan Bangladesh Belarus Belgiumc Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, SAR China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finlandc Francec Gabon Gambia, The Georgia Germanyc Ghana Greecec Guatemala Guinea Guinea-Bissau Haiti Honduras

254

Exchange rates and prices Ofﬁcial exchange rate

Purchasing power parity (PPP) conversion factor

local currency units to $ 2009 2010a

local currency units to international $ 1995 2009

50.23 94.98 72.65 79.33 3.71 363.28 1.28 0.72 0.80 69.04 2,789.49 0.72 472.19 7.02 1.41 7.16 2.00 1.41 472.19 1,230.18 4,139.33 472.19 1.14 472.19 472.19 560.86 6.83 7.75 2,166.79 809.79 472.19 573.29 472.19 5.28 .. 19.06 5.36 36.03 .. 5.54 8.75 15.38 11.26 11.78 0.72 0.72 472.19 26.64 1.67 0.72 1.41 0.72 8.16 .. 472.19 41.20 18.90

45.21 .. 104.95 24.4 74.25 15.3 92.35 0.0 3.96 1.0 360.50 116.6 1.01 1.3 0.76 0.9 0.80 0.2 70.63 19.2 3,010.98 3.4 0.76 0.9 496.24 187.4 7.02 1.7 1.48 0.6 6.58 1.4 1.70 0.7 1.48 0.0 496.24 189.5 1,230.91 126.6 4,096.00 1,142.3 496.24 241.1 1.01 1.2 496.24 271.9 496.24 163.1 474.78 264.1 6.65 3.4 7.77 7.9 1,925.90 417.8 907.62 0.0 496.24 149.2 512.34 103.0 496.24 261.8 5.59 3.1 .. .. 19.03 11.1 5.64 8.5 37.41 7.3 .. 0.4 5.74 1.2 8.75 0.4 15.38 1.9 11.82 4.8 .. 2.1 0.76 1.0 0.76 1.0 496.24 187.9 28.12 3.9 1.76 0.4 0.76 1.0 1.49 0.1 0.76 0.6 7.98 2.9 .. 747.4 496.24 58.6 39.90 5.8 18.90 3.0

2011 World Development Indicators

18.1 41.5 35.8 55.7 2.0 194.5 1.5 0.8 0.4 26.8 1,085.6 0.9 233.3 2.8 0.7 3.2 1.6 0.7 205.5 500.6 1,526.8 243.3 1.2 282.8 221.6 377.1 3.8 5.4 1,233.7 414.3 289.8 329.5 306.9 3.8 .. 13.5 8.0 19.7 0.5 2.2 0.5 9.8 8.1 4.3 0.9 0.9 245.7 8.1 0.9 0.8 1.0 0.7 4.6 2,066.8 229.0 23.1 9.4

Ratio of PPP conversion factor to market exchange rate

2009

0.4 0.4 0.5 0.7 0.5 0.5 1.1 1.2 0.5 0.4 0.4 1.2 0.5 0.4 0.5 0.5 0.8 0.5 0.4 0.4 0.4 0.5 1.1 0.6 0.5 0.7 0.6 0.7 0.6 0.5 0.6 0.6 0.7 0.7 .. 0.7 1.5 0.6 0.5 0.4 0.5 0.6 0.7 0.4 1.3 1.2 0.5 0.3 0.5 1.1 0.7 1.0 0.6 0.4 0.5 0.6 0.5

Real effective exchange rate

GDP implicit deﬂator

Consumer price index

Wholesale price index

Index average annual average annual average annual 2000 = 100 % growth % growth % growth 2009 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09

.. .. 102.1 .. .. 124.4 100.8 101.5 .. .. .. 104.3 .. 127.6 .. .. .. 126.0 .. 109.4 .. 108.0 96.8 115.8 .. 100.3 119.8 .. 113.1 597.2 .. 108.4 105.7 108.6 .. 120.4 105.6 96.2 98.8 .. .. .. .. .. 103.8 101.8 105.3 104.4 124.3 102.3 91.9 106.9 .. .. .. .. ..

.. 37.7 18.5 739.4 5.2 212.5 1.4 1.6 203.0 4.1 355.1 1.8 8.7 8.6 4.1 9.7 211.8 102.1 3.7 13.4 4.4 6.3 1.5 4.5 7.1 7.9 7.9 4.5 22.6 964.9 9.0 15.9 9.2 90.0 6.4 12.8 1.6 9.8 4.4 8.7 6.2 7.9 53.7 6.5 1.9 1.3 7.0 4.2 356.7 1.7 26.7 9.2 10.4 5.5 32.5 18.1 19.9

9.0 3.5 8.6 41.1 12.9b 4.5 4.1 1.7 9.9 5.2 23.1 2.1 3.4 6.9 3.9 9.0 8.3 6.0 2.5 10.4 5.0 2.1 2.6 2.7 5.6 6.3 4.3 –1.3 6.1 27.2 7.4 10.2 3.5 3.9 3.3 2.2 2.3 13.7 9.1 8.3 3.6 18.6 5.3 10.8 1.1 2.1 5.0 9.8 7.0 1.1 27.2 3.1 5.4 16.1 11.8 15.3 6.4

.. 27.8 17.3 711.0 8.9 70.5 2.1 2.2 179.7 5.5 271.3 1.9 8.7 8.7 .. 10.4 199.5 117.5 5.5 16.1 6.3 6.5 1.7 5.3 6.9 .. 8.6 5.9 20.2 930.2 9.3 15.6 7.2 86.3 .. 7.8 2.1 8.7 37.1 8.8 8.5 .. 21.6 5.5 1.5 1.6 4.6 4.0 24.7 2.1 28.4 9.0 10.1 .. 34.0 21.9 18.8

9.5 2.8 3.0 41.1 10.0 4.0 3.0 2.0 8.3 6.8 18.7 2.1 3.2 5.3 .. 8.9 6.9 6.4 3.1 9.2 6.0 2.5 2.1 3.2 2.7 .. 2.3 0.3 5.8 26.9 3.4 11.2 3.0 2.9 .. 2.5 2.0 14.6 6.6 8.0 3.9 .. 4.4 12.3 1.5 1.8 2.1 7.6 7.0 1.7 16.2 3.2 7.3 .. 2.4 16.5 7.9

.. .. .. .. 0.1 .. 1.1 0.3 .. .. 267.8 1.2 .. .. .. .. 204.9 85.7 .. .. .. .. 2.7 6.0 .. 7.0 .. 0.6 16.4 .. .. 14.1 .. 69.8 .. 8.2 1.1 .. .. 6.1 .. .. 8.1 .. 0.9 .. .. .. .. 0.4 .. 3.6 .. .. .. .. ..

.. 4.5 4.0 .. 15.7 1.3 3.6 2.4 .. .. 22.5 2.9 .. .. .. .. 10.0 6.2 .. .. .. .. 1.4 4.4 .. 6.5 .. –0.2 4.9 .. .. 13.0 .. 3.0 .. 2.3 2.4 .. 7.9 9.6 4.7 .. 3.4 .. 2.1 1.8 .. .. 6.7 2.5 .. 4.3 .. .. .. .. ..

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland c Israel Italyc Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands c New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugalc Puerto Rico Qatar

Ofﬁcial exchange rate

Purchasing power parity (PPP) conversion factor

local currency units to $ 2009 2010a

local currency units to international $ 1995 2009

202.34 48.41 10,389.94 9,864.30 1,170.00 0.72 3.93 0.72 87.89 93.57 0.71 147.50 77.35 .. 1,276.93 0.72 0.29 42.90 8,516.05 0.51 1,507.50 8.47 68.29 1.25 2.48 44.10 1,956.21 141.17 3.52 472.19 262.37 31.96 13.51 11.11 1,437.80 8.06 27.52 5.52 8.47 77.55 0.72 1.60 20.34 472.19 148.90 6.29 0.38 81.71 1.00 2.76 4,965.39 3.01 47.68 3.12 0.72 .. 3.64

209.67 61.7 45.16 10.8 8,948.00 1,031.3 10,364.64 567.2 1,170.00 252.5 0.76 0.8 3.60 2.8 0.76 0.8 85.67 14.6 83.43 175.0 0.71 0.4 147.41 17.5 80.57 15.8 .. .. 1,146.23 709.6 0.76 .. 0.28 0.2 47.00 3.5 8,245.42 327.6 0.53 0.2 1,507.50 774.7 6.84 2.1 71.85 0.6 1.23 .. 2.61 1.2 46.55 18.0 2,117.83 287.5 150.80 4.2 3.13 1.4 496.24 226.7 .. 62.4 30.54 10.5 12.40 2.9 12.15 1.2 1,256.47 158.6 8.43 4.9 35.64 4.0 5.42 .. 6.84 2.2 72.38 15.4 0.76 0.9 1.29 1.5 21.84 3.5 496.24 203.1 148.57 15.5 5.98 9.2 0.38 0.2 85.77 10.1 1.00 0.5 2.64 0.7 4,667.57 948.9 2.82 1.2 43.95 14.1 3.02 1.2 0.76 0.7 .. .. 3.64 ..

128.2 17.2 5,813.6 3,875.0 689.4 0.9 3.7 0.8 52.0 114.7 0.5 93.0 36.3 .. 804.7 .. 0.3 16.2 3,548.2 0.4 942.9 4.5 38.2 0.7 1.6 17.8 852.8 55.1 1.8 275.4 125.0 16.8 7.7 5.9 643.7 5.0 13.0 .. 5.6 28.4 0.9 1.5 8.2 241.0 75.6 8.9 0.3 28.8 0.6 1.4 2,462.5 1.6 23.6 1.9 0.6 .. 2.8

Ratio of PPP conversion factor to market exchange rate

2009

0.6 0.4 0.6 0.4 0.6 1.3 1.0 1.1 0.6 1.2 0.8 0.6 0.5 .. 0.6 .. 0.9 0.4 0.4 0.7 0.6 0.5 0.6 0.6 0.6 0.4 0.4 0.4 0.5 0.6 0.5 0.5 0.6 0.5 0.5 0.6 0.5 .. 0.7 0.4 1.2 1.0 0.4 0.5 0.5 1.4 0.9 0.4 0.6 0.5 0.5 0.5 0.5 0.6 0.9 .. 0.8

Real effective exchange rate

GDP implicit deﬂator

Consumer price index

ECONOMY

4.16

Exchange rates and prices

Wholesale price index

Index average annual average annual average annual 2000 = 100 % growth % growth % growth 2009 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09

103.8 .. .. 142.1 .. 107.4 110.2 103.2 .. 101.4 .. .. .. .. .. .. .. .. .. .. .. 93.2 .. .. .. 104.2 .. 107.5 103.3 .. .. .. .. 135.3 .. 102.4 .. .. .. .. 102.7 86.4 107.8 .. 109.4 97.8 .. 98.6 .. 116.1 135.7 .. 121.3 98.5 102.1 .. ..

19.6 8.1 15.8 27.7 .. 3.6 11.0 3.8 24.8 0.0 3.2 204.7 16.6 .. 5.9 .. 1.5 110.6 27.2 48.0 19.0 9.7 51.8 .. 75.0 79.3 19.1 33.6 4.1 7.0 8.7 6.3 19.0 119.6 57.8 4.0 34.1 25.3 11.1 8.0 2.1 1.7 42.4 6.0 29.5 2.7 0.1 11.1 3.6 7.6 11.5 26.7 8.4 24.7 5.2 3.0 ..

4.9 5.6 11.1 16.4 11.6 2.1 1.3 2.6 11.2 –1.1 6.1 14.9 6.0 .. 2.2 0.8 9.8 8.3 8.9 8.8 2.6 8.1 10.3 17.9 4.1 3.8 11.2 17.0 4.0 4.5 10.8 6.0 7.8 11.0 14.6 2.0 8.0 .. 7.1 6.6 2.1 3.1 7.7 3.1 15.3 4.6 9.8 8.5 2.4 6.5 10.2 3.5 5.1 2.7 2.6 .. 10.6

20.3 9.1 13.7 26.0 .. 2.3 9.7 3.7 23.5 0.8 3.5 67.8 15.6 .. 5.1 .. 2.0 23.3 28.3 29.2 .. 5.9 .. 5.6 32.6 10.6 18.7 33.8 3.6 5.2 6.1 6.9 19.5 21.4 35.7 3.9 31.8 25.9 .. 8.7 2.4 1.8 .. 6.1 32.5 2.2 .. 9.7 1.1 9.3 13.1 27.3 7.7 25.3 4.5 .. 2.8

5.5 5.3 9.1 15.4 .. 3.2 1.8 2.3 11.7 –0.1 4.4 8.6 11.3 .. 3.1 1.5 3.4 6.9 8.3 6.5 .. 7.8 .. 0.4 3.1 2.4 10.7 12.2 2.4 2.5 7.3 6.3 4.5 10.8 8.7 2.0 10.9 22.4 5.9 6.2 1.9 2.7 8.8 2.8 12.5 1.8 2.9 8.0 2.5 5.9 8.4 2.4 5.5 2.5 2.7 .. 7.2

16.8 7.4 15.4 28.4 .. 1.6 8.1 2.9 .. –1.0 .. 16.3 .. .. 3.7 .. 1.4 35.6 .. 12.0 .. .. .. .. 24.8 8.5 .. .. 3.4 .. .. .. 18.4 .. .. 2.9 .. .. .. .. 1.3 1.5 .. .. .. 1.6 .. 10.4 1.0 .. .. 23.7 6.3 19.8 .. .. ..

2011 World Development Indicators

3.5 5.1 11.2 10.8 .. –0.1 4.5 2.7 .. 0.7 9.1 13.3 .. .. 2.5 .. 2.5 10.2 .. 7.3 .. .. .. .. 4.8 2.5 .. .. 4.8 .. .. .. 6.1 .. .. .. .. .. .. .. 2.7 3.3 .. .. .. 7.9 .. 8.9 3.8 .. 10.3 2.8 7.0 2.7 2.6 .. ..

255

4.16 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic c Sloveniac Somalia South Africa Spainc Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe

Exchange rates and prices Ofﬁcial exchange rate

Purchasing power parity (PPP) conversion factor

local currency units to $ 2009 2010a

local currency units to international $ 1995 2009

3.05 3.24 0.1 31.74 30.85 1.7 568.28 594.45 126.3 3.75 3.75 1.8 472.19 496.24 251.9 67.58 80.39 2.9 3,385.65 .. 379.5 1.45 1.31 1.3 0.72 0.76 0.4 0.72 0.76 0.4 .. .. .. 8.47 6.84 2.3 0.72 0.76 0.7 114.94 111.11 18.2 2.30 .. 0.3 8.47 6.84 2.2 7.65 6.85 9.4 1.09 0.97 2.0 11.23 11.23 12.8 4.14 4.40 0.0 1,320.31 1,462.88 159.4 34.29 30.12 15.1 .. .. .. 472.19 496.24 238.5 6.32 6.37 2.8 1.35 1.45 0.5 1.55 1.52 0.0 .. .. 0.0 2,030.31 .. 500.3 7.79 7.96 0.3 3.67 3.67 1.7 0.64 0.64 0.6 1.00 1.00 1.0 22.57 19.99 5.5 .. .. 11.2 2.15 2.59 0.1 17,065.08 18,932.00 3,168.8 .. .. .. 202.85 214.40 22.1 5,046.11 4,735.74 404.0 .. .. ..

1.6 14.6 261.0 2.4 265.2 33.4 1,399.7 1.1 0.5 0.6 .. 4.8 0.7 49.8 1.4 4.2 8.9 1.5 24.4 1.5 487.3 16.7 0.6 239.5 3.9 0.6 0.9 1.5 767.5 3.2 3.2 0.6 1.0 16.1 602.5 2.0 6,434.3 .. 91.8 3,492.7 ..

Ratio of PPP conversion factor to market exchange rate

2009

0.5 0.5 0.5 0.6 0.6 0.5 0.4 0.7 0.7 0.9 .. 0.6 1.0 0.4 0.6 0.5 1.2 1.4 0.5 0.4 0.4 0.5 0.6 0.5 0.6 0.5 0.6 0.5 0.4 0.4 0.9 1.0 1.0 0.7 0.4 0.9 0.4 .. 0.5 0.7 ..

Real effective exchange rate

GDP implicit deﬂator

Consumer price index

Wholesale price index

Index average annual average annual average annual 2000 = 100 % growth % growth % growth 2009 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09

102.2 115.3 .. 103.8 .. .. 104.5 107.9 137.2 .. .. 87.8 106.2 .. .. .. 89.5 101.6 .. .. .. .. .. 104.8 123.7 94.2 .. .. 103.2 96.8 .. 80.8 95.2 120.9 .. 191.2 .. .. .. 119.5 ..

98.0 161.5 14.3 1.6 6.0 .. 31.9 1.3 11.1 29.3 .. 9.9 3.9 9.1 65.5 10.5 2.2 1.1 7.9 235.0 23.0 4.2 .. 7.0 5.4 4.4 81.7 408.2 11.6 271.0 2.2 2.8 2.0 32.6 245.8 45.3 15.2 5.7 22.4 52.1 –3.9

15.9 15.8 10.5 7.6 2.8 16.5 9.5 1.2 3.4 4.0 .. 7.2 3.7 10.7 10.0 7.9 1.7 1.2 8.0 20.9 7.3 3.2 4.5 1.4 6.5 3.2 15.3 13.0 5.6 16.4 10.2 2.6 2.6 8.4 24.7 25.0 8.3 3.4 13.0 16.4 4.1

100.5 99.1 16.2 1.0 5.4 50.2 .. 1.7 8.4 12.0 .. 8.7 3.8 9.9 72.0 9.5 1.9 1.6 6.4 .. 20.9 4.9 .. 8.5 5.7 4.4 79.9 .. 8.3 155.7 .. 2.9 2.7 33.9 .. 49.0 4.1 .. 26.3 57.0 29.0

11.5 12.5 8.9 2.2 2.2 15.4 .. 1.5 4.8 4.2 .. 5.7 3.1 11.1 8.6 7.3 1.5 1.0 6.2 12.7 6.5 2.9 5.1 2.8 6.5 3.3 16.9 .. 6.7 10.9 .. 2.9 2.7 9.1 .. 21.2 7.8 .. 11.4 15.9 497.7

93.8 99.8 .. 1.3 .. .. .. –1.0 9.5 9.1 .. 7.7 2.4 8.1 .. .. 2.5 –0.4 4.7 .. .. 3.8 .. .. 2.8 3.6 75.2 .. .. 161.6 .. 2.4 1.2 27.2 .. 44.1 .. .. .. 101.4 25.9

15.3 15.7 .. 2.5 .. .. .. 2.8 4.7 3.9 .. 6.7 3.2 12.4 .. .. 2.9 1.1 3.2 .. .. 5.5 .. .. 3.8 4.5 16.9 .. .. 14.6 .. 1.8 4.2 13.6 .. 26.0 .. .. .. .. ..

Note: The differences in the growth rates of the GDP deflator and the consumer and wholesale price indexes are due mainly to differences in data availability for each of the indexes during the period. a. Average for December or latest monthly data available. b. Private analysts estimate that consumer price index inflation was considerably higher for 2007–09 and that GDP volume growth has been significantly lower than official reports indicate since the last quarter of 2008. c. As members of the euro area, these countries share a single currency, the euro.

256

2011 World Development Indicators

About the data

4.16

ECONOMY

Exchange rates and prices Definitions

In a market-based economy, household, producer,

cost indicator of relative normalized unit labor costs

• Ofﬁcial exchange rate is the exchange rate deter-

and government choices about resource allocation

in manufacturing. For selected other countries the

mined by national authorities or the rate determined

are influenced by relative prices, including the real

nominal effective exchange rate index is based on

in the legally sanctioned exchange market. It is cal-

exchange rate, real wages, real interest rates, and

manufactured goods and primary products trade with

culated as an annual average based on monthly aver-

other prices in the economy. Relative prices also

partner or competitor countries. For these countries

ages (local currency units relative to the U.S. dollar).

largely reflect these agents’ choices. Thus relative

the real effective exchange rate index is the nomi-

• Purchasing power parity (PPP) conversion factor

prices convey vital information about the interaction

nal index adjusted for relative changes in consumer

is the number of units of a country’s currency required

of economic agents in an economy and with the rest

prices; an increase represents an appreciation of

to buy the same amount of goods and services in the

of the world.

the local currency. Because of conceptual and data

domestic market that a U.S. dollar would buy in the

limitations, changes in real effective exchange rates

United States. • Ratio of PPP conversion factor to

should be interpreted with caution.

market exchange rate is the result obtained by divid-

The exchange rate is the price of one currency in terms of another. Offi cial exchange rates and exchange rate arrangements are established by

Inflation is measured by the rate of increase in a

ing the PPP conversion factor by the market exchange

governments. Other exchange rates recognized by

price index, but actual price change can be nega-

rate. • Real effective exchange rate is the nominal

governments include market rates, which are deter-

tive. The index used depends on the prices being

effective exchange rate (a measure of the value of a

mined largely by legal market forces, and for coun-

examined. The GDP deflator reflects price changes

currency against a weighted average of several for-

tries with multiple exchange arrangements, principal

for total GDP. The most general measure of the over-

eign currencies) divided by a price deflator or index

rates, secondary rates, and tertiary rates.

all price level, it accounts for changes in government

of costs. • GDP implicit deﬂator measures the aver-

Official or market exchange rates are often used

consumption, capital formation (including inventory

age annual rate of price change in the economy as a

to convert economic statistics in local currencies to

appreciation), international trade, and the main com-

whole for the periods shown. • Consumer price index

a common currency in order to make comparisons

ponent, household final consumption expenditure.

reflects changes in the cost to the average consumer

across countries. Since market rates reflect at best

The GDP deflator is usually derived implicitly as the

of acquiring a basket of goods and services that may

the relative prices of tradable goods, the volume of

ratio of current to constant price GDP—or a Paasche

be fixed or may change at specified intervals, such

goods and services that a U.S. dollar buys in the

index. It is defective as a general measure of inflation

as yearly. The Laspeyres formula is generally used.

United States may not correspond to what a U.S.

for policy use because of long lags in deriving esti-

• Wholesale price index refers to a mix of agricul-

dollar converted to another country’s currency at

mates and because it is often an annual measure.

tural and industrial goods at various stages of pro-

the official exchange rate would buy in that country,

Consumer price indexes are produced more fre-

particularly when nontradable goods and services

quently and so are more current. They are also con-

account for a significant share of a country’s output.

structed explicitly, based on surveys of the cost of

An alternative exchange rate—the purchasing power

a defined basket of consumer goods and services.

parity (PPP) conversion factor—is preferred because

Nevertheless, consumer price indexes should be

it reflects differences in price levels for both tradable

interpreted with caution. The definition of a house-

and nontradable goods and services and therefore

hold, the basket of goods, and the geographic (urban

provides a more meaningful comparison of real out-

or rural) and income group coverage of consumer

put. See table 1.1 for further discussion.

price surveys can vary widely by country. In addi-

The ratio of the PPP conversion factor to the official

tion, weights are derived from household expendi-

exchange rate—the national price level or compara-

ture surveys, which, for budgetary reasons, tend to

tive price level—measures differences in the price

be conducted infrequently in developing countries,

level at the gross domestic product (GDP) level. The

impairing comparability over time. Although useful for

price level index tends to be lower in poorer coun-

measuring consumer price inflation within a country,

tries and to rise with income. The real effective

consumer price indexes are of less value in compar-

exchange rate is a nominal effective exchange rate

ing countries.

duction and distribution, including import duties. The Laspeyres formula is generally used.

index adjusted for relative movements in national

Wholesale price indexes are based on the prices

price or cost indicators of the home country, selected

at the first commercial transaction of commodities

countries, and the euro area. A nominal effective

that are important in a country’s output or consump-

exchange rate index is the ratio (expressed on the

tion. Prices are farm-gate for agricultural commodi-

base 2000 = 100) of an index of a currency’s period-

ties and ex-factory for industrial goods. Preference

Data on offi cial and real effective exchange rates

average exchange rate to a weighted geometric aver-

is given to indexes with the broadest coverage of

and consumer and wholesale price indexes are

age of exchange rates for currencies of selected

the economy. The least squares method is used to

from the International Monetary Fund’s Interna-

countries and the euro area. For most high-income

calculate growth rates of the GDP implicit deflator,

tional Financial Statistics. PPP conversion fac-

countries weights are derived from industrial coun-

consumer price index, and wholesale price index.

tors and GDP deflators are from the World Bank’s

try trade in manufactured goods. Data are compiled

Data sources

data files.

from the nominal effective exchange rate index and a

2011 World Development Indicators

257

4.17

Balance of payments current account Goods and services

Net income

Net current transfers

Current account balance

Total reservesa

$ millions 1995 2009

$ millions 1995 2009

$ millions 1995 2009

$ millions 1995 2009

$ millions Exports 1995

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China† Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras †Data for Taiwan, China

258

Imports 2009

.. .. 304 3,458 .. .. 3,836 41,451 24,987 66,563 300 1,338 69,710 234,298 89,906 189,999 785 22,847 4,431 17,011 5,269 24,843 334,175b 190,686 b 614 1,630 1,234 5,433 .. 5,480 2,421 4,179 52,641 180,723 6,776 23,270 272 744 129 116 969 5,927 2,040 5,313 219,501 383,759 179 .. 190 .. 19,358 62,242 147,240 1,333,346 .. 408,142 12,294 38,222 .. .. 1,374 6,127 4,451 12,566 4,337 11,478 6,972 22,626 .. .. 28,202 132,920 65,655 147,276 5,731 10,465 5,196 15,574 13,260 44,609 2,040 4,696 135 .. 2,573 13,539 768 3,433 47,973 90,571 362,717 617,335 2,945 .. 175 278 575 3,207 600,347 1,376,861 1,582 7,809 15,523 59,150 2,823 9,220 700 1,122 30 172 192 933 1,635 6,028 128,369 235,091

2011 World Development Indicators

1995

2009

.. .. .. 836 6,495 44 .. .. .. 3,519 41,829 –767 26,066 48,951 –4,636 726 3,688 40 74,841 242,311 –14,036 92,055 175,559 –1,597 1,290 9,872 –6 7,589 23,165 68 5,752 30,360 –51 178,798 b 328,387b ..b 895 2,400 –8 1,574 5,159 –207 .. 9,464 .. 2,050 5,131 –32 63,293 174,679 –11,105 6,502 27,196 –432 483 2,858 –29 259 520 –13 1,375 6,898 –57 1,608 6,540 –412 200,991 407,655 –22,721 244 .. –23 411 .. –7 18,301 49,335 –2,714 135,282 1,113,234 –11,774 .. 393,077 .. 16,012 38,404 –1,596 .. .. .. 1,346 6,386 –695 4,717 12,286 –226 3,806 8,803 –787 9,152 24,900 –53 .. .. .. 30,044 122,069 –104 57,860 134,738 –4,549 6,137 14,160 –769 5,708 16,876 –930 17,140 53,842 –405 3,623 7,966 –67 498 .. 8 2,860 12,435 3 1,446 9,046 –19 37,705 83,807 –4,440 333,746 663,242 –8,964 1,723 .. –665 230 343 –5 1,413 5,266 127 586,662 1,212,133 –2,814 2,120 10,789 –129 24,711 84,204 –1,684 3,728 12,726 –159 1,011 1,391 –85 89 284 –21 802 2,813 –31 1,852 8,641 –226 124,171 202,629 4,188

.. .. .. .. –145 477 1,307 –12 .. .. .. .. –6,823 156 –370 –295 –9,013 597 34 –5,118 166 168 814 –218 –39,399 –109 –374 –19,277 –1,148 –1,702 –2,296 –5,448 –3,519 111 722 –401 –1,376 2,265 10,875 –824 –1,114 76 242 –458 6,641b 7,822b –8,907b ..b –11 121 245 –167 –674 244 1,213 –303 535 .. 2,275 .. –452 –39 878 300 –33,684 3,621 3,338 –18,136 –2,116 132 1,291 –26 –4 255 409 15 –17 153 257 10 –468 277 574 –186 –303 69 393 90 –12,591 –117 –1,892 –4,328 .. 63 .. –25 .. 191 .. –38 –10,306 307 1,616 –1,350 43,282 1,435 33,748 1,618 5,530 .. –3,177 .. –9,432 799 4,614 –4,516 .. .. .. .. –1,885 42 –38 –625 –1,176 134 359 –358 –890 –237 –115 –492 –2,491 802 1,450 –1,431 .. .. .. .. –12,194 572 –805 –1,374 3,933 –1,391 –5,248 1,855 –1,769 992 3,305 –183 –1,463 442 2,497 –1,000 –2,076 4,031 7,960 –254 –664 1,389 3,561 –262 .. 324 .. –31 –529 126 318 –158 –37 736 3,459 39 2,394 –597 –2,344 5,231 31,844 –9,167 –37,796 10,840 .. –42 .. 515 –8 52 135 –8 –118 197 967 –514 47,352 –38,768 –46,610 –27,897 –296 523 2,078 –144 –12,516 8,008 1,657 –2,864 –1,111 491 4,626 –572 –168 179 34 –216 –15 46 98 –35 13 553 1,635 –87 –487 243 2,652 –201 12,512 –2,912 .. 5,474

.. .. .. –1,875 265 2,369 .. 4,164 155,112 –7,572 213 13,664 8,632 15,979 48,007 –1,369 111 2,004 –47,786 14,952 41,742 10,995 23,369 17,904 10,178 121 5,364 3,345 2,376 10,342 –6,389 377 5,640 3,522b 24,120 b 23,862b –536 198 1,230 813 1,005 8,575 –1,175 80 3,245 –526 4,695 8,704 –24,302 51,477 238,539 –4,751 1,635 18,522 –1,709 347 1,296 –164 216 323 –866 192 3,286 –1,137 15 3,676 –38,380 16,369 54,356 .. 238 211 .. 147 617 4,217 14,860 25,292 297,142 80,288 2,452,899 17,418 55,424 255,841 –5,001 8,452 24,987 .. 157 1,615 –2,181 64 3,806 –537 1,060 4,068 1,670 529 3,267 –3,314 1,896 14,895 .. .. .. –2,147 14,613 41,608 11,222 11,652 76,618 –2,159 373 2,905 –268 1,788 3,792 –3,349 17,122 34,897 –373 940 3,122 .. 40 58 893 583 3,981 –2,191 815 1,781 6,814 10,657 11,429 –51,857 58,510 131,786 .. 153 1,993 63 106 224 –1,210 199 2,110 165,471 121,816 179,040 –1,198 804 .. –35,913 16,119 5,486 8 783 5,205 –403 87 .. –29 20 169 –232 199 790 –449 270 2,492 42,911 95,559 363,010

Goods and services

4.17

ECONOMY

Balance of payments current account Net income

Net current transfers

Current account balance

Total reservesa

$ millions 1995 2009

$ millions 1995 2009

$ millions 1995 2009

$ millions 1995 2009

$ millions Exports

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

Imports

1995

2009

1995

19,765 38,013 52,923 18,953 .. 49,439 27,478 295,618 3,394 493,991 3,479 5,975 3,526 .. 147,761 .. 14,215 448 408 2,088 .. 199 .. 7,513 3,191 1,302 749 470 83,369 529 504 2,349 89,321 884 508 9,044 411 1,307 1,734 1,029 241,517 17,883 662 321 12,342 56,058 6,078 10,214 7,610 2,992 4,802 6,622 26,795 35,716 32,260 .. ..

100,098 258,822 133,255 .. 65,695 199,942 67,877 509,797 4,038 673,615 10,915 48,258 7,414 .. 432,097 .. 61,692 2,560 1,444 11,231 21,600 789 454 37,440 20,309 3,548 .. .. 186,424 2,551 .. 4,181 245,206 2,000 2,300 26,381 2,464 .. 4,057 1,493 518,122 33,210 2,857 1,043 61,545 160,687 29,443 22,220 16,652 4,579 7,253 30,538 47,611 171,071 67,268 .. ..

19,916 48,225 54,461 15,113 .. 42,169 35,287 250,319 3,729 419,556 4,903 6,102 5,922 .. 155,104 .. 12,615 726 748 2,193 .. 1,046 .. 5,755 3,902 1,773 987 660 86,851 991 510 2,454 82,168 1,006 521 11,243 1,055 2,020 2,100 1,624 216,558 17,248 1,150 457 12,841 46,848 5,035 14,185 7,768 1,905 5,200 9,597 33,317 33,825 39,545 .. ..

2009

93,412 –1,701 –7,890 328,036 –3,734 –6,514 112,233 –5,874 –15,140 .. –478 .. 37,731 .. 2,106 166,569 –7,325 –38,752 63,129 –2,654 –4,558 520,563 –15,644 –38,480 6,356 –371 –668 650,364 44,285 131,339 16,300 –279 612 38,877 –146 –12,729 11,314 –219 –58 .. .. .. 393,172 –1,303 4,554 .. .. .. 30,679 4,881 7,726 3,680 –35 –190 1,581 –6 –47 11,486 19 1,655 30,215 .. –767 1,792 314 424 1,704 .. –128 27,065 133 578 20,605 –13 318 5,665 –30 –128 .. –167 .. .. –44 .. 144,873 –4,144 –4,170 3,760 –41 –313 .. –48 .. 5,106 –19 27 257,976 –12,689 –14,925 3,989 –18 303 2,632 –25 –195 37,307 –1,318 –1,495 4,305 –140 –95 .. –110 .. 5,128 139 –70 5,086 9 158 459,194 7,247 –12,001 31,953 –3,955 –5,148 4,482 –372 –235 1,951 –47 26 47,843 –2,878 –10,020 104,496 –1,919 –1,660 21,607 –374 –2,810 35,008 –1,939 –3,619 15,446 –466 –1,460 4,802 –488 –625 7,374 110 –312 25,777 –2,482 –7,371 54,950 3,662 –69 170,631 –1,995 –16,575 83,259 21 –10,952 .. .. .. .. .. ..

203 8,382 981 –4 .. 1,776 5,673 –4,579 607 –7,676 1,444 59 1,037 .. –19 .. –1,465 79 110 71 .. 210 .. –220 109 213 129 157 –1,017 219 76 101 3,960 56 77 2,330 339 562 403 230 –6,434 255 138 31 799 –2,059 –1,469 2,562 153 75 195 832 880 958 7,132 .. ..

505 49,102 4,861 .. –2,936 –1,109 7,402 –16,952 1,860 –12,397 3,523 –900 2,297 .. –811 .. –10,133 1,208 193 883 1,827 547 1,101 –1,572 1,625 1,599 .. .. –5,580 455 .. 224 21,468 1,221 186 7,451 764 .. 1,261 3,426 –10,345 267 1,018 230 17,977 –4,408 –5,313 12,824 210 176 519 2,856 15,960 6,537 2,992 .. ..

–1,650 –5,563 –6,431 3,358 .. 1,721 –4,790 25,076 –99 111,044 –259 –213 –1,578 .. –8,665 .. 5,016 –235 –237 –16 .. –323 .. 1,672 –614 –288 –276 –78 –8,644 –284 22 –22 –1,576 –85 39 –1,186 –445 –261 176 –356 25,773 –3,065 –722 –152 –2,578 5,233 –801 –3,349 –471 674 –92 –4,625 –1,980 854 –132 .. ..

–699 –26,626 10,743 .. 27,133 –6,488 7,592 –66,199 –1,126 142,194 –1,251 –4,248 –1,661 .. 42,668 .. 28,605 –102 9 2,284 –7,555 –32 –277 9,381 1,646 –646 .. .. 31,801 –1,066 .. –675 –6,228 –465 –342 –4,971 –1,171 .. 120 –10 36,581 –3,624 –841 –651 21,659 50,122 –287 –3,583 –44 –672 86 247 8,552 –9,598 –23,952 .. ..

12,017 22,865 14,908 .. 8,347 8,770 8,123 60,690 681 192,620 2,279 1,660 384 .. 32,804 .. 4,543 134 99 602 8,100 457 28 7,415 829 275 109 115 24,699 323 90 887 17,046 257 158 3,874 195 651 221 646 47,162 4,410 142 95 1,709 22,976 1,943 2,528 781 267 1,106 8,653 7,781 14,957 22,063 .. 848

2011 World Development Indicators

44,181 284,683 66,119 .. 46,461 2,151 60,611 131,497 2,076 1,048,991 12,135 23,183 3,850 .. 270,437 830 23,028 1,584 1,010 6,902 39,132 .. 372 103,754 6,657 2,288 1,135 163 96,704 1,604 238 2,316 99,889 1,480 1,327 23,568 2,181 .. 2,051 .. 39,284 15,594 1,573 656 45,510 48,859 12,204 13,606 3,028 2,629 3,862 33,225 44,206 79,522 15,829 .. 18,804

259

4.17

Balance of payments current account Goods and services

Net income

Net current transfers

Current account balance

Total reservesa

$ millions 1995 2009

$ millions 1995 2009

$ millions 1995 2009

$ millions 1995 2009

$ millions Exports 1995

Imports 2009

Romania 9,404 50,491 Russian Federation 92,987 344,934 Rwanda 75 534 Saudi Arabia 53,450 201,964 Senegal 1,506 3,500 Serbia .. 11,858 Sierra Leone 128 323 Singapore 159,488 364,332 Slovak Republic 10,969 61,792 Slovenia 10,377 28,542 Somalia .. .. South Africa 34,402 78,563 Spain 133,910 346,893 Sri Lanka 4,617 8,977 Sudan 681 8,226 Swaziland 1,020 1,860 Sweden 95,525 194,516 Switzerland 123,320 280,162 Syrian Arab Republic 5,757 19,374 Tajikistan .. 1,218 Tanzania 1,265 5,219 Thailand 70,292 180,653 Timor-Leste .. .. Togo 465 1,136 Trinidad and Tobago 2,799 19,622 Tunisia 7,979 19,917 Turkey 36,581 142,865 Turkmenistan 1,774 .. Uganda 664 3,954 Ukraine 17,090 54,253 United Arab Emirates .. .. United Kingdom 322,114 595,914 United States 794,397 1,570,797 Uruguay 3,507 8,557 Uzbekistan .. .. Venezuela, RB 20,753 59,600 Vietnam 9,498 62,752 West Bank and Gaza 764 1,168 Yemen, Rep. 2,160 7,092 Zambia 1,222 4,560 Zimbabwe 2,344 .. World 6,395,661 t 15,641,184 t 104,191 Low income 29,028 Middle income 1,087,422 4,483,392 Lower middle income 492,428 2,563,013 Upper middle income 594,996 1,906,819 Low & middle income 1,115,105 4,583,161 East Asia & Pacific 397,583 1,969,911 Europe & Central Asia 193,610 795,858 Latin America & Carib. 273,265 796,196 Middle East & N. Africa .. .. South Asia 58,893 310,779 Sub-Saharan Africa 89,266 296,829 High income 5,304,481 11,224,885 Euro area 2,100,300 4,450,297

1995

2009

11,306 60,470 82,809 253,233 374 1,479 44,874 160,639 1,821 7,020 .. 18,486 260 628 144,904 325,605 10,658 61,806 10,749 27,980 .. .. 33,375 80,816 135,000 374,259 5,982 11,708 1,238 11,212 1,274 2,344 81,142 165,275 108,916 243,800 5,541 19,309 .. 3,062 2,139 7,543 82,246 155,777 .. .. 671 1,666 2,110 9,948 8,811 21,091 40,113 151,453 1,796 .. 1,490 5,210 18,280 56,206 .. .. 327,000 650,834 890,784 1,945,705 3,568 7,794 .. .. 16,905 48,064 12,334 72,446 2,789 4,962 2,471 10,001 1,338 4,119 2,515 .. 6,248,111 t 15,144,783 t 46,738 149,627 1,137,135 4,125,043 532,363 2,390,741 604,453 1,722,447 1,182,581 4,271,461 413,806 1,684,481 205,686 759,347 288,584 781,728 106,423 334,137 78,652 407,949 99,774 327,513 5,072,079 11,020,075 1,977,018 4,275,187

–241 –3,372 7 2,800 –124 .. –30 541 –14 201 .. –2,875 –5,402 –137 –3 81 –6,473 10,708 –560 .. –110 –2,114 .. –34 –390 –716 –3,204 17 –96 –434 .. 3,393 20,899 –227 .. –1,943 –384 607 –561 –249 –294 .. .. .. .. .. .. .. .. .. .. .. .. .. ..

a. International reserves including gold valued at London gold price. b. Includes Luxembourg.

260

2011 World Development Indicators

–2,968 –39,474 –37 8,613 –48 –710 –36 –3,061 –1,837 –1,081 .. –6,389 –42,120 –488 –2,402 –123 7,303 14,922 –1,149 –71 –175 –7,499 .. –15 –1,202 –2,011 –8,121 .. –329 –2,440 .. 40,655 121,418 –689 .. –2,652 –3,028 911 –1,171 –1,363 .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

369 5,649 –1,774 157 –2,862 6,963 350 604 57 –16,694 –27,172 –5,318 195 1,685 –244 .. 4,925 .. 43 148 –118 –894 –3,037 14,230 93 –959 390 95 –202 –75 .. .. .. –645 –2,684 –2,493 4,525 –10,889 –1,967 732 3,005 –770 60 1,480 –500 144 192 –30 –2,970 –5,083 4,940 –4,409 –12,312 20,703 607 1,150 263 .. 1,735 .. 395 683 –590 487 4,484 –13,582 .. .. .. 118 324 –122 –4 47 294 774 1,951 –774 4,398 2,299 –2,338 5 .. 0 639 1,133 –281 472 2,661 –1,152 .. .. .. –11,943 –22,786 –13,436 –38,073 –124,944 –113,561 76 140 –213 .. .. .. 109 –323 2,014 1,200 6,448 –2,020 435 3,418 –984 1,056 1,515 184 182 516 –182 40 .. –425 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

–7,298 2,624 49,365 18,024 –379 99 22,765 10,399 –1,884 272 –2,412 .. –193 35 32,628 68,816 –2,810 3,863 –720 1,821 .. .. –11,327 4,464 –80,375 40,531 –215 2,112 –3,908 163 –414 298 31,460 25,870 38,972 68,620 66 448 –180 39 –1,816 270 21,861 36,939 .. .. –222 130 8,519 379 –1,234 1,689 –14,410 13,891 .. 1,168 –451 459 –1,732 1,069 .. 7,778 –37,050 49,144 –378,435 175,996 215 1,813 .. .. 8,561 10,715 –6,274 1,324 535 .. –2,565 638 –406 223 .. 888 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

44,383 439,342 743 420,984 2,123 15,228 405 187,803 1,804 1,078 .. 39,603 28,051 5,354 1,094 959 47,255 134,566 18,300 .. 3,470 138,419 250 703 9,245 11,294 74,933 .. 2,994 26,501 36,104 66,550 404,099 8,038 .. 34,318 16,447 .. 6,990 1,892 .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

About the data

4.17

ECONOMY

Balance of payments current account Definitions

The balance of payments records an economy’s

system, external debt records, information provided

• Exports and imports of goods and services are all

transactions with the rest of the world. Balance of

by enterprises, surveys to estimate service transac-

transactions between residents of an economy and

payments accounts are divided into two groups:

tions, and foreign exchange records. Differences in

the rest of the world involving a change in ownership

the current account, which records transactions in

collection methods—such as in timing, definitions

of general merchandise, goods sent for processing

goods, services, income, and current transfers, and

of residence and ownership, and the exchange rate

and repairs, nonmonetary gold, and services. • Net

the capital and financial account, which records capi-

used to value transactions—contribute to net errors

income is receipts and payments of employee com-

tal transfers, acquisition or disposal of nonproduced,

and omissions. In addition, smuggling and other ille-

pensation for nonresident workers, and investment

nonfinancial assets, and transactions in financial

gal or quasi-legal transactions may be unrecorded or

income (receipts and payments on direct investment,

assets and liabilities. The table presents data from

misrecorded. For further discussion of issues relat-

portfolio investment, and other investments and

the current account plus gross international reserves.

ing to the recording of data on trade in goods and

receipts on reserve assets). Income derived from

services, see About the data for tables 4.4–4.7.

the use of intangible assets is recorded under busi-

The balance of payments is a double-entry accounting system that shows all flows of goods and

The concepts and definitions underlying the data in

ness services. • Net current transfers are recorded

services into and out of an economy; all transfers

the table are based on the fifth edition of the Inter-

in the balance of payments whenever an economy

that are the counterpart of real resources or financial

national Monetary Fund’s (IMF) Balance of Payments

provides or receives goods, services, income, or

claims provided to or by the rest of the world without

Manual (1993). That edition redefined as capital

financial items without a quid pro quo. All transfers

a quid pro quo, such as donations and grants; and

transfers some transactions previously included in the

not considered to be capital are current. • Current

all changes in residents’ claims on and liabilities to

current account, such as debt forgiveness, migrants’

account balance is the sum of net exports of goods

nonresidents that arise from economic transactions.

capital transfers, and foreign aid to acquire capital

and services, net income, and net current transfers.

All transactions are recorded twice—once as a credit

goods. Thus the current account balance now reflects

• Total reserves are holdings of monetary gold, spe-

and once as a debit. In principle the net balance

more accurately net current transfer receipts in addi-

cial drawing rights, reserves of IMF members held by

should be zero, but in practice the accounts often do

tion to transactions in goods, services (previously

the IMF, and holdings of foreign exchange under the

not balance, requiring inclusion of a balancing item,

nonfactor services), and income (previously factor

control of monetary authorities. The gold component

net errors and omissions.

income). Many countries maintain their data collection

of these reserves is valued at year-end (December

Discrepancies may arise in the balance of pay-

systems according to the fourth edition of the Balance

31) London prices ($386.75 an ounce in 1995 and

ments because there is no single source for balance

of Payments Manual (1977). Where necessary, the IMF

$1,087.50 an ounce in 2009).

of payments data and therefore no way to ensure

converts such reported data to conform to the fifth

that the data are fully consistent. Sources include

edition (see Primary data documentation). Values are

customs data, monetary accounts of the banking

in U.S. dollars converted at market exchange rates.

4.17a

Top 15 economies with the largest reserves in 2009 Total reserves ($ billions)

Share of world total (%) 2009

Annual change (%) 2008–09

Months of imports 2009

2008

2009

China

1,966

2,453

26.1

24.8

25.0

Japan

1,031

1,049

11.2

1.8

18.1

426

439

4.7

3.0

16.1 29.4

Russian Federation Saudi Arabia

451

421

4.5

–6.7

United States

294

404

4.3

37.4

2.0

Taiwan, China

304

363

3.9

19.6

20.7

India

257

285

3.0

10.6

9.8

Korea, Rep.

202

270

2.9

34.2

8.0

Hong Kong SAR, China

183

256

2.7

40.2

6.3

Data sources Data on the balance of payments are published in the IMF’s Balance of Payments Statistics Yearbook and International Financial Statistics. The World Data sources Bank exchanges data with the IMF through elec-

Brazil

194

239

2.5

23.1

13.2

Singapore

174

188

2.0

7.8

5.9

Germany

139

179

1.9

29.1

1.5

Algeria

148

155

1.7

4.7

..

Thailand

111

138

1.5

24.7

9.8

the IMF’s Balance of Payments Manual, fifth edition

74

135

1.4

81.6

5.0

(1993), Balance of Payments Textbook (1996), and

Switzerland

Source: International Monetary Fund, International Financial Statistics data files.

tronic files that in most cases are more timely and cover a longer period than the published sources. More information about the design and compilation of the balance of payments can be found in

Balance of Payments Compilation Guide (1995).

2011 World Development Indicators

261

STATES AND MARKETS

Introduction

N

ew firm creation recently declined sharply in most countries, according to the 2010 World Bank Group Entrepreneurial Snapshots. The economic and financial crisis that began in 2008 increased unemployment in many countries, and the fight against poverty could be hampered as spending for human and productive capital is strained. Governments around the world face fiscal deficits and pressure to improve public spending and accelerate business reforms. Partnership between the private sector, which employs people and makes investments, and a capable public sector, which creates a stable regulatory environment, is a key ingredient to successful development. This section includes a range of indicators showing how effective and accountable government, together with a vibrant private sector, produces employment opportunities and services that empower poor people. Its 13 tables cover cross-cutting themes: private sector development, public sector policies, infrastructure, information, communications, telecommunications, and science and technology. New data show that business reforms are making it easier to do business and create new firms and that more-inclusive financial systems are removing barriers to economic growth and development.

Businesses are created faster in a good business environment The World Bank Group Entrepreneurship Snapshots (www.enterprisesurveys.org), which cover 112 countries, show that new businesses are created faster in countries with good governance, low corporate taxes, minimal red tape, and a strong legal and regulatory environment. Countries with well developed financial markets also have higher new firm creation than countries with less developed financial markets. The downside is that countries with well developed financial markets also had steeper declines in new firm creation during the recent financial and economic crisis, probably due to the credit crunch. High-income countries created more new limited liability firms—more than 4 per 1,000 working-age people, compared with only about 0.3 in low-income countries. Data on business entry and density are in table 5.1. The Doing Business database (www.doing business.org) shows that between June 2009 and May 2010, 117 countries adopted 216 business

regulation reforms, making it easier to start and operate businesses, strengthening property rights, and improving commercial dispute resolution and bankruptcy procedures. Using data from the Enterprise Snapshots and Doing Business to analyze whether some reforms are more important than others, Klapper and Love (2010a) find that small reforms that reduce costs, time, or number of procedures to register a business by less than 40 percent do not have a significant impact on new firm registration. This suggests that “token” reforms do not boost private sector activity and that countries with weak business environments require larger reforms to increase new firm registration. They find that two reforms occurring simultaneously tend to have more impact than two reforms occuring sequentially over a longer period.

5

Forty countries made it easier to pay taxes between 2009 and 2010 The World Bank’s Doing Business project collects information for 183 countries on tax payments, time spent paying taxes, and the total tax rate borne by a standard firm. In cooperation with PricewaterhouseCoopers, the project collects information on business tax systems around the world, allowing governments to benchmark their tax system with others to identify good practices, and researchers to analyze the impact of higher corporate tax rates on business start-ups and investments. Over June 2009–May 2010, 40 countries made tax compliance easier, reducing costs for firms and encouraging job creation. Higher tax compliance costs are associated with larger informal sectors and more corruption, ultimately limiting employment, investment, and growth. Keeping rules simple

2011 World Development Indicators

263

and clear improves compliance and reduces tax evasion. And better compliance keeps the system working and supports government programs and services. In the past six years more than 60 percent of the countries covered by the Doing Business project made paying taxes easier or lowered the tax burden for local enterprises. Countries that make paying taxes easy for domestic firms usually offer electronic systems for tax filing and payment, have one tax per tax base, and use a filing system based on self-assessment. In high-income countries the average business spends about 180 hours a year preparing, filing, and paying taxes; in Latin America and the Caribbean, more than 400 hours a year (figure 5a). Previous editions of World Development Indicators included data on the highest marginal corporate tax rate (the statutory rate of corporate income tax). It is not a comprehensive indicator of the amount of tax a company pays, however, because it is only one of the many taxes businesses pay. Generous tax allowances in some countries signifi cantly reduce the corporate income tax paid, while The average business in Latin America and the Caribbean spends about 400 hours a year in preparing, ﬁling, and paying business taxes, 2009

5a

Time to prepare, file, and pay taxes (hours a year) 500 400 300 200 100 0 East Asia & Pacific

Europe & Central Asia

Latin America Middle East & & Caribbean North Africa

South Asia

Sub-Saharan Africa

High income

Source: Doing Business 2011.

Firms in East Asia and the Paciﬁc have the lowest business tax rate, 2010

5b

Total tax rate (% of commercial profits) 80 60

disallowances in others can increase the effective rate. In this year’s edition table 5.6 on tax policies includes the total business tax rate as a percent of commercial profi t, with details on corporate taxes, labor taxes paid by the employer, social contributions, and other taxes. The total tax rate is a comprehensive measure of the cost of all the taxes a business bears. It differs from the statutory tax rate, which merely provides the factor to be applied to the tax base. In computing the total tax rate, tax payable is divided by commercial profit. The total tax rate is lowest in East Asia and Pacifi c and is highest in Sub-Saharan Africa (figure 5b). Note that these tax rates are “de jure” tax rates based on case studies of a “standardized business” as defined by the Doing Business project.

Benchmarking the quality of the business environment— Doing Business and Enterprise Surveys are complementary The World Bank’s Enterprise Surveys are based on firm-level surveys of a representative sample of the nonagricultural private sector in a country. The surveys cover a broad range of business environment topics including corruption, infrastructure, crime, competition, performance measures, and access to finance. Data from Enterprise Surveys are presented in table 5.2. The Doing Business project uses indicator sets and rankings to measure business regulations and quantify the ease of doing business across countries. The indicators cover common transactions such as starting a business or registering property based on standardized case studies. Data are collected through surveys of local experts on business transactions and reflect the country’s laws and regulations. Data on Doing Business indicators are in tables 5.3 and 5.6. Box 5c compares the data sources, coverage, and information collected by Enterprise Surveys and the Doing Business project.

40

About half the world’s households do not have deposit accounts in formal financial institutions

20 0 East Asia & Pacific

Europe & Central Asia

Latin America Middle East & & Caribbean North Africa

Source: Doing Business 2011.

264

2011 World Development Indicators

South Asia

Sub-Saharan Africa

High income

Financial exclusion is a barrier to economic development. Evidence from household surveys indicates that access to basic financial

STATES AND MARKETS

services such as savings, payments, and credit can make an important difference in poor people’s lives. For firms, lack of access to finance is often the main obstacle to growth. In an increasingly digitized and globalized world many countries are promoting access to financial services—from establishing a credit facility for indigenous farmers in rural areas to introducing broad consumer protection legislation. Although financial inclusion mandates, from consumer protection to rural finance promotion, are on the agenda of many financial regulators, insufficient authority and resources to provide broad financial access limit implementation capacity in many developing countries. Nevertheless, more than 70 percent of financial regulators in developing countries have programs to protect consumers, and almost 60 percent promote financial literacy. Five new financial indicators from Financial Access 2010 (www.cgap.org/financialindicators) are included in table 5.5 this year: commercial bank deposits, commercial bank loans, commercial bank branches, automated teller machines (ATMs), and point-of-sale terminals. Although many nonbank institutions (cooperatives, specialized state financial institutions, and microfinance institutions) provide financial services, the most complete information available to central banks and financial regulators is on commercial banks, which account for 85 percent of deposits and 96 percent of accounts. Although financial inclusion, measured as people with commercial bank accounts, is high in some developing country regions such as East Asia and Pacific, it remains low in Sub-Saharan Africa (figure 5d). Access to deposit and credit services varies by region. Access is greater in countries with higher incomes, better infrastructure, and a well functioning legal environment. People without access to bank accounts and credit from regulated institutions have to rely on informal nonregulated financial services, often more costly and less reliable. Low- and middleincome countries lag behind high-income countries in the number of bank branches, ATMs, and point-of-sale terminals, but the number of ATMs exceeds the number of bank branches in low-income countries. And new technology, including the expansion of electronic payments through mobile and Internet banking, offer hope for bringing financial services to the unbanked.

Two approaches to collecting business environment data: Doing Business and Enterprise Surveys

5c

Topic

Enterprise Surveys

Global coverage

125 countries

Doing Business

Data source

Collects firm-level data; face-toface interview with owner or top manager. Businesses surveyed include manufacturing, retail, construction, transport, communications, and other services

Collects information through surveys administered by local experts (lawyers, accountants, and architects). The information is confirmed through the underlying laws and regulations

Number of observations

150–360 observations in smaller countries; 1,200–1,800 interviews in larger countries

Underlying laws and regulations in addition to an average of 39 surveys per country

183 countries

Geographical Main cities or regions of coverage within economic activity a country

Main (most populous) business city and subnational studies in other cities

Information gathered

Time and cost to complete common business transactions based on standardized case studies; underlying laws and regulations

Objective data on the business environment as experienced by firms, performance measures, firm characteristics, and perceptions regarding obstacles to growth

Business characteristics; approxiStandardized business; 10 mately 20 Investment climate topics business regulation topics Examples of data

Hard data: number of days to obtain a construction permit. Soft data: opinion on whether access to land is an obstacle faced by the establishment

Hard data: laws and regulations, number of procedures, and costs to build a warehouse. Soft data: experts’ estimates on the number of days required for each procedure

Inference from the data

Stratified random sampling design of the surveys, which ensures that data are representative of the universe of formal firms (with five or more employees)

Standardized case studies that relate to a common business situation, which makes comparisons and benchmarks valid across countries

Measures what happens to existing firms—their actual experiences with investment climate issues such as payment of taxes. Also surveys obstacles to business growth

Expectations of a standardized firm following official legal requirements and costs. For instance, “paying taxes” measures the number of payments, time to file, and tax rates

Measures what happens in practice in the normal course of business; for instance, whether a firm pays a bribe when obtaining an import license and the actual time it takes to obtain the license

Assumes that firms comply with all formal regulations and minimize information gathering time and that all regulations are enforced. Measures what would happen if the firm complied with all regulatory requirements in a lawful manner.

Can be used to identify potential areas of reform in the business environment as well as assess the impact of reforms on businesses.

Can be used to identify areas for reform based on bottlenecks or weaknesses in specific areas of private sector regulation and learn from practices in other countries.

Source: Summary of www.enterprisesurveys.org/Methodology/Compare.aspx.

People living in developing countries of East Asia and Paciﬁc have more commercial bank accounts than those in other developing country regions, 2009

5d

Deposit accounts in commercial banks (median per 1,000 adults) 2,000 1,500 1,000

Developing country median

500 0 East Asia & Pacific

Europe & Central Asia

Latin America Middle East & & Caribbean North Africa

South Asia

Sub-Saharan Africa

High income

Source: Financial Access 2010, CGAP and World Bank.

2011 World Development Indicators

265

Tables

5.1

Private sector in the economy Investment commitments in infrastructure projects with private participationa

$ millions Telecommunications

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

266

Energy

2000–05

2006–09

2000–05

466.1 569.2 3,422.5 278.7 5,836.8 317.1 .. .. 355.6 1,294.3 735.4 .. 116.9 520.5 0.0 104.0 41,053.8 2,179.1 41.9 53.6 136.1 394.4 .. 0.0 11.0 3,561.6 8,548.0 .. 1,570.9 473.4 61.8 .. 134.9 1,205.7 60.0 .. .. 393.0 357.8 3,471.9 1,110.6 40.0 .. .. .. .. 26.6 6.6 173.8 .. 156.5 .. 560.1 50.6 21.9 18.0 135.0

1,040.4 670.0 1,925.0 1,129.0 5,033.6 488.8 .. .. 1,283.5 3,729.8 2,219.2 .. 399.7 284.7 1,086.6 183.9 31,121.4 1,866.5 680.6 0.0 436.9 701.4 .. 20.8 246.4 4,167.6 0.0 .. 5,294.7 880.0 330.7 .. 885.4 3,035.0 0.0 .. .. 220.1 1,764.7 8,864.0 901.9 0.0 .. .. .. .. 278.8 35.0 612.2 .. 2,916.0 .. 1,511.4 242.2 96.4 306.0 930.5

1.6 790.6 962.0 45.0 3,826.9 74.0 .. .. 375.2 501.5 .. .. 590.0 884.4 .. .. 26,171.6 3,253.5 .. .. 82.1 91.8 .. .. 0.0 1,590.5 10,970.9 .. 351.6 .. .. 80.0 0.0 7.1 116.0 .. .. 1,306.6 302.0 678.0 85.0 .. .. .. .. .. 0.0 .. 40.0 .. 590.0 .. 110.0 .. .. 5.5 358.8

2011 World Development Indicators

2006–09

Transport

Domestic credit to private sector

Water and sanitation

Businesses registered

% of GDP

New

Entry density 2009

2000–05

2006–09

2000–05

2006–09

2009

2009

.. .. 664.0 308.0 2,320.0 120.9 9.4 .. 3,479.0 203.6 127.0 63.0 .. .. .. .. .. .. 243.5 0.0 1,875.0 .. .. .. .. .. 137.3 16.6 800.0 .. .. .. 46,690.5 3,398.4 2,246.7 2.1 .. .. .. .. 695.8 125.3 440.0 0.0 .. .. .. .. .. .. 2,397.7 4,821.2 7,170.5 15,350.1 .. .. 944.6 1,005.4 .. .. .. .. 190.0 465.2 0.0 176.4 85.0 451.0 60.0 0.0 .. .. .. .. 0.0 898.9 129.0 685.0 469.0 821.5 0.0 .. .. .. .. .. 4.0 .. .. .. .. .. 0.0 177.4 0.0 .. 634.2 .. .. .. 100.0 10.0 .. .. 263.8 .. .. .. .. .. .. .. .. 120.0

.. .. 269.0 53.0 1,402.6 715.0 .. .. .. 0.0 4.0 .. .. .. .. .. 22,086.9 536.2 .. .. 40.1 .. .. .. .. 1,311.1 15,795.0 .. 2,344.4 .. 735.0 373.0 .. 492.0 .. .. .. 879.9 766.0 1,370.0 .. .. .. .. .. .. 3.9 .. 573.0 .. .. .. .. 159.0 .. .. ..

.. 8.0 510.0 .. 791.6 0.0 .. .. 0.0 .. .. .. .. .. .. .. 1,234.4 152.0 .. .. .. .. .. .. .. 1,495.2 3,505.2 .. 314.3 .. 0.0 .. .. 298.7 600.0 .. .. .. 510.0 .. .. .. .. .. .. .. .. .. .. .. 0.0 .. .. .. .. .. 207.9

.. 0.0 1,572.0 .. .. 0.0 .. .. .. .. .. .. .. .. .. .. 1,365.4 .. .. .. .. 0.0 .. .. .. 3.1 3,992.2 .. 305.0 .. .. .. 0.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 435.0 .. .. .. 6.7 .. .. 0.0 ..

9.1 37.0 16.2 21.2 13.5 23.1 127.8 126.9 19.6 41.5 37.3 97.9 22.2 37.0 57.3 25.5 54.0 75.6 17.5 21.7 24.5 11.3 128.6 7.0 5.2 97.5 127.3 158.0 29.9 7.5 4.8 49.4 17.1 66.3 .. 55.3 231.6 21.3 25.3 36.2 41.3 16.6 110.2 17.8 94.4 110.3 10.1 18.9 31.2 112.3 15.9 91.7 25.4 .. 5.6 14.5 52.6

.. 2,045 10,544 .. 11,924 2,698 89,960 3,228 5,314 .. 5,508 29,548 .. 2,504 1,896 .. 315,645 35,545 610 .. 2,003 .. 174,000 .. .. 23,541 .. 101,023 31,132 .. .. 26,765 .. 7,800 .. 21,717 16,519 12,881 .. 6,291 4,400 .. 7,199 1,327 11,820 128,906 3,490 .. 7,226 64,840 9,606 8,426 5,133 .. .. .. ..

.. 0.84 0.44 .. 0.46 1.28 6.38 0.58 0.93 .. 0.80 4.28 .. 0.43 0.58 .. 2.38 7.20 0.08 .. 0.22 .. 7.56 .. .. 2.12 .. 19.19 1.07 .. .. 8.78 .. 2.57 .. 3.00 4.57 2.13 .. 0.13 1.19 .. 8.10 0.03 3.37 3.08 4.27 .. 2.32 1.19 0.72 1.18 0.68 .. .. .. ..

Investment commitments in infrastructure projects with private participationa

$ millions Telecommunications

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

Energy

Transport

Domestic credit to private sector

Water and sanitation

% of GDP

5.1

STATES AND MARKETS

Private sector in the economy

Businesses registered

New

Entry density

2000–05

2006–09

2000–05

2006–09

2000–05

2006–09

2000–05

2006–09

2009

2009

2009

5,172.8 20,030.5 6,557.2 695.0 984.0 .. .. .. 700.3 .. 1,589.0 1,153.7 1,434.0 .. .. .. .. 11.5 87.7 700.0 138.1 88.4 70.3 .. 993.0 706.6 12.6 36.3 3,777.0 82.6 92.1 413.0 18,758.0 46.1 22.1 6,139.5 123.0 .. 35.0 109.3 .. .. 218.5 85.5 6,949.7 .. .. 6,594.9 211.4 .. 199.0 2,241.4 4,616.4 16,800.1 .. .. ..

1,523.3 33,682.4 9,748.1 1,506.0 4,521.0 .. .. .. 301.6 .. 648.6 3,170.2 2,973.8 400.0 .. .. .. 115.9 135.0 468.1 0.0 30.6 73.8 .. 490.2 489.6 304.8 197.7 1,700.0 583.0 133.1 102.1 12,622.6 392.3 0.0 2,549.6 156.2 .. 8.5 26.0 .. .. 380.1 251.7 11,348.1 .. .. 8,706.5 1,224.0 150.0 591.4 2,485.0 4,177.0 7,750.0 .. .. ..

851.6 8,369.2 1,860.5 650.0 .. .. .. .. 201.0 .. .. 300.0 .. .. .. .. .. .. 1,250.0 158.1 .. 0.0 .. .. 514.3 .. 0.0 0.0 6,637.6 365.9 .. 0.0 6,749.3 227.2 .. 1,049.0 1,205.8 .. 1.0 15.1 .. .. 126.3 .. 1,920.0 .. .. 375.4 449.3 .. .. 2,498.9 3,428.4 2,620.5 .. .. ..

1,707.0 50,754.4 3,779.3 .. 590.0 .. .. .. 78.0 .. 989.0 0.0 332.7 .. .. .. .. .. 1,425.0 184.0 .. .. .. .. 417.6 655.0 .. .. 384.5 .. .. .. 1,483.0 68.0 .. .. .. 556.1 .. .. .. .. 95.0 .. 280.0 .. .. 4,058.2 576.7 .. .. 1,142.9 9,463.3 2,475.4 .. .. ..

3,297.5 4,172.2 159.2 .. .. .. .. .. 565.0 .. 0.0 231.0 .. .. .. .. .. .. 0.0 .. 153.0 .. .. .. .. .. 61.0 .. 4,263.0 55.4 .. .. 2,970.4 0.0 .. 200.0 334.6 .. .. .. .. .. 104.0 .. 2,355.4 .. .. 112.8 51.4 .. .. 522.5 943.5 1,672.0 .. .. ..

1,588.0 23,012.8 1,731.5 .. .. .. .. .. .. .. 1,380.0 31.0 404.0 .. .. .. .. .. .. 135.0 .. .. .. .. .. 295.0 17.5 .. 1,379.0 .. .. .. 11,434.1 60.0 .. 200.0 0.0 .. .. .. .. .. .. .. 644.1 .. .. 923.7 0.0 .. .. 3,157.6 678.9 3,642.3 .. .. ..

0.0 112.9 44.8 .. .. .. .. .. .. .. 169.0 .. .. .. .. .. .. 0.0 .. .. 0.0 .. .. .. .. .. .. .. 6,502.2 .. .. .. 523.7 .. .. .. .. .. 0.0 .. .. .. .. 3.4 .. .. .. .. .. .. .. 152.0 0.0 64.3 .. .. ..

0.0 241.7 20.2 .. .. .. .. .. .. .. 951.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 0.0 .. .. 0.0 303.8 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 530.5 0.8 .. .. ..

71.3 46.8 27.6 36.7 6.4 230.3 84.5 110.8 28.5 171.0 71.7 50.3 31.5 .. 107.6 36.1 63.3 15.1 9.5 107.8 73.9 13.5 16.1 10.9 70.9 44.3 11.5 14.2 117.1 17.4 .. 85.1 23.3 36.2 43.9 64.4 25.1 .. 46.8 59.4 215.3 147.0 34.4 12.2 37.6 .. 49.0 23.5 85.7 32.1 29.1 24.1 30.3 52.9 187.8 .. 51.5

42,951 84,800 28,998 .. .. 13,188 19,758 68,508 2,003 105,698 2,737 27,978 17,896 .. 60,039 141 .. 4,412 .. 7,175 .. .. .. .. 5,399 8,074 724 619 41,638 .. .. 6,626 44,084 4,180 .. 26,166 .. .. .. .. 35,100 47,897 .. 24 65,089 13,805 3,165 2,759 548 .. .. 51,151 11,435 14,434 27,759 .. ..

6.26 0.12 0.18 .. .. 4.67 4.46 1.78 1.16 1.28 0.74 2.59 0.85 .. 1.72 0.12 .. 1.26 .. 4.62 .. .. .. .. 2.18 5.63 0.07 0.08 2.55 .. .. 7.33 0.61 1.32 .. 1.28 .. .. .. .. 3.10 17.08 .. 0.00 0.79 4.49 1.67 0.03 0.26 .. .. 2.65 0.19 0.52 3.92 .. ..

2011 World Development Indicators

267

5.1

Private sector in the economy Investment commitments in infrastructure projects with private participationa

$ millions Telecommunications 2000–05

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

2006–09

Energy 2000–05

3,906.9 4,188.9 1,240.8 22,049.4 24,525.8 1,726.0 72.3 351.0 1.6 .. .. .. 593.1 1,333.0 93.3 563.5 3,297.4 .. 48.8 111.2 .. .. .. .. .. .. .. .. .. .. 13.4 0.0 .. 10,519.5 7,714.0 1,251.3 .. .. .. 766.1 1,444.5 270.8 747.7 1,748.3 .. 27.7 48.3 .. .. .. .. .. .. .. 583.0 307.7 .. 8.5 125.0 16.0 515.3 1,484.5 348.0 5,602.7 3,106.0 4,693.3 0.0 0.0 .. 0.0 44.0 657.7 .. .. .. 751.0 2,805.0 30.0 12,788.6 12,068.7 6,754.8 20.0 158.1 .. 387.6 1,463.0 113.9 3,162.9 4,508.8 160.0 .. .. .. .. .. .. .. .. .. 114.2 158.5 330.0 285.6 942.1 .. 3,337.0 2,619.8 39.5 430.0 1,593.7 2,360.6 279.8 47.0 150.0 376.8 392.2 .. 208.3 624.0 3.0 72.0 343.0 .. .. s .. s .. s 20,932.3 .. 6,362.3 227,575.0 248,323.5 107,077.9 84,109.2 27,585.0 38,840.9 143,465.8 134,452.2 49,324.0 233,937.3 269,255.8 87,324.8 29,862.2 4,662.0 31,290.4 50,274.6 62,911.8 5,316.0 81,401.1 72,021.9 45,682.0 13,435.4 23,566.1 .. 29,314.5 48,647.1 9,533.6 24,654.4 40,481.6 .. .. .. .. .. .. ..

2006–09

Transport 2000–05

2006–09

Domestic credit to private sector

Water and sanitation 2000–05

6,288.7 .. 116.8 116.0 27,214.2 109.4 191.0 904.7 .. .. .. .. .. .. .. .. .. 55.4 398.0 0.0 .. .. .. 0.0 1.2 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 9.9 504.7 3,483.0 31.3 .. .. .. .. .. .. .. .. .. .. 30.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 82.0 .. .. .. .. .. 28.4 27.7 134.0 8.5 2,341.0 939.0 .. 522.7 .. .. .. .. 190.0 .. .. .. .. .. .. .. .. .. 840.0 .. 8,862.7 3,118.6 4,138.5 .. .. .. .. .. 1,000.6 .. 404.0 0.0 54.0 .. 130.0 100.0 .. .. .. .. .. .. .. .. .. .. .. .. .. 251.1 .. 368.0 .. .. 25.0 0.0 .. 34.0 .. 15.0 297.0 20.0 965.0 266.0 .. .. .. .. 15.8 .. 220.0 .. .. 15.6 .. 0.0 .. .. .. .. .. s .. s .. s .. s .. .. .. .. 191,687.3 50,686.8 105,160.9 16,175.1 82,564.1 26,511.7 51,724.0 3,704.9 109,123.2 5,696.9 41,208.8 407.0 196,264.6 5,403.2 86,781.7 .. 26,112.4 21,800.1 20,589.5 10,840.9 47,981.4 .. .. .. 57,940.1 16,150.3 43,755.5 2,516.1 .. .. .. .. 55,257.1 4,285.0 23,936.5 112.9 .. .. .. .. .. .. .. .. .. .. .. ..

2006–09

41.0 1,241.7 .. .. 0.0 .. .. .. .. .. .. 0.0 .. .. 120.7 .. .. .. .. .. .. 18.8 .. .. .. .. .. .. .. 102.0 .. .. .. .. .. .. .. .. .. .. .. .. s .. 6,654.9 5,271.6 .. .. 4,561.7 .. .. .. 241.7 .. .. ..

% of GDP

New

2009

2009

47.1 56,698 45.3 261,633 .. 3,028 52.1 .. 24.7 1,636 42.2 9,715 9.3 .. 103.2 26,416 44.7 15,825 94.0 5,836 .. .. 147.1 24,700 211.5 79,757 24.8 4,223 12.3 .. 25.0 .. 139.3 24,228 174.8 25,250 20.3 .. 29.0 2,171 15.3 .. 116.3 27,520 18.6 .. 21.9 125 31.5 .. 68.4 9,079 36.5 44,472 .. .. 13.1 11,152 73.3 19,300 93.0 .. 213.5 330,100 202.9 .. 20.6 4,664 .. 14,428 21.7 .. 112.7 .. .. .. 7.4 .. 12.0 5,509 .. .. 138.2 w 26.4 72.8 92.9 47.8 72.0 117.1 45.0 40.8 34.5 43.5 65.1 165.1 133.0

a. Data refer to total for the period shown. Includes infrastructure projects with private sector participation that reached financial closure in 1990–2009.

268

2011 World Development Indicators

Businesses registered

Entry density 2009

3.66 2.61 0.51 .. 0.22 1.94 .. 7.40 4.04 4.16 .. 0.77 2.92 0.29 .. .. 4.09 4.88 .. 0.48 .. 0.59 .. 0.04 .. 1.23 0.87 .. 0.72 0.60 .. 8.05 .. 2.08 0.78 .. .. .. .. 0.88 ..

About the data

5.1

STATES AND MARKETS

Private sector in the economy Definitions

Private sector development and investment—tapping

involving local and small-scale operators—may be

• Investment commitments in infrastructure

private sector initiative and investment for socially

omitted because they are not publicly reported.

projects with private participation refers to infra-

useful purposes—are critical for poverty reduction.

The database is a joint product of the World Bank’s

structure projects in telecommunications, energy

In parallel with public sector efforts, private invest-

Finance, Economics, and Urban Development

(electricity and natural gas transmission and dis-

ment, especially in competitive markets, has tre-

Department and the Public-Private Infrastructure

tribution), transport, and water and sanitation that

mendous potential to contribute to growth. Private

Advisory Facility. Geographic and income aggregates

have reached financial closure and directly or indi-

markets are the engine of productivity growth, creat-

are calculated by the World Bank’s Development

rectly serve the public. Incinerators, movable assets,

ing productive jobs and higher incomes. And with gov-

Data Group. For more information, see http://ppi.

standalone solid waste projects, and small projects

ernment playing a complementary role of regulation,

worldbank.org/.

such as windmills are excluded. Included are opera-

funding, and service provision, private initiative and

Credit is an important link in money transmission;

tion and management contracts, concessions (oper-

investment can help provide the basic services and

it finances production, consumption, and capital for-

ation and management contracts with major capital

conditions that empower poor people—by improving

mation, which in turn affect economic activity. The

expenditure), greenfield projects (new facilities built

health, education, and infrastructure.

data on domestic credit to the private sector are

and operated by a private entity or a public-private

Investment in infrastructure projects with private

taken from the banking survey of the International

joint venture), and divestitures. Investment commit-

participation has made important contributions to

Monetary Fund’s (IMF) International Financial Statistics

ments are the sum of investments in physical assets

easing fiscal constraints, improving the efficiency

or, when unavailable, from its monetary survey. The

and payments to the government. Investments in

of infrastructure services, and extending delivery

monetary survey includes monetary authorities (the

physical assets are resources the project company

to poor people. Developing countries have been in

central bank), deposit money banks, and other bank-

commits to invest during the contract period in new

the forefront, pioneering better approaches to infra-

ing institutions, such as finance companies, develop-

facilities or in expansion and modernization of exist-

structure services and reaping the benefits of greater

ment banks, and savings and loan institutions. Credit

ing facilities. Payments to the government are the

competition and customer focus.

to the private sector may sometimes include credit

resources the project company spends on acquir-

to state-owned or partially state-owned enterprises.

ing government assets such as state-owned enter-

The data on investment in infrastructure projects with private participation refer to all investment (pub-

Entrepreneurship is essential to the dynamism of

prises, rights to provide services in a specific area, or

lic and private) in projects in which a private com-

the modern market economy, and a greater entry rate

use of specific radio spectrums. • Domestic credit

pany assumes operating risk during the operating

of new businesses can foster competition and eco-

to private sector is financial resources provided

period or development and operating risk during the

nomic growth. The table includes data on business

to the private sector—such as through loans, pur-

contract period. Investment refers to commitments

registrations from the 2008 World Bank Group Entre-

chases of nonequity securities, and trade credits and

not disbursements. Foreign state-owned companies

preneurship Survey, which includes entrepreneurial

other accounts receivable—that establish a claim for

are considered private entities for the purposes of

activity in more than 100 countries for 2000–08.

repayment. For some countries these claims include

this measure.

Survey data are used to analyze firm creation, its

credit to public enterprises. • New businesses regis-

Investments are classified into two types: invest-

relationship to economic growth and poverty reduc-

tered are the number of limited liability corporations

ments in physical assets—the resources a com-

tion, and the impact of regulatory and institutional

registered in the calendar year. • Entry density is the

pany commits to invest in expanding and modern-

reforms. The 2008 survey improves on earlier sur-

number of newly registered limited liability corpora-

izing facilities—and payments to the government to

veys’ methodology and country coverage for better

tions per 1,000 people ages 15–64.

acquire state-owned enterprises or rights to provide

cross-country comparability. Data on total and newly

services in a specific area or to use part of the radio

registered businesses were collected directly from

spectrum.

national registrars of companies. For cross-country

The data are from the World Bank’s Private Par-

comparability, only limited liability corporations

ticipation in Infrastructure (PPI) Project database,

that operate in the formal sector are included. For

which tracks infrastructure projects with private par-

additional information on sources, methodology,

ticipation in developing countries. It provides infor-

calculation of entrepreneurship rates, and data limi-

mation on more than 4,600 infrastructure projects

tations see http://econ.worldbank.org/research/

in 137 developing economies from 1984 to 2009.

entrepreneurship.

Data sources Data on investment commitments in infra-

The database contains more than 30 fields per proj-

structure projects with private participation are

ect record, including country, financial closure year,

from the World Bank’s PPI Project database

infrastructure services provided, type of private par-

(http://ppi.worldbank.org). Data on domestic

ticipation, investment, technology, capacity, project

credit are from the IMF’s International Financial

location, contract duration, private sponsors, bidding

Statistics. Data on business registration are from

process, and development bank support. Data on the

the World Bank’s Entrepreneurship Survey and

projects are compiled from publicly available infor-

database (http://econ.worldbank.org/research/

mation. The database aims to be as comprehensive

entrepreneurship).

as possible, but some projects—particularly those

2011 World Development Indicators

269

5.2

Business environment: Enterprise Surveys Survey year

Regulations and tax

Time dealing with officials

Average number of times % of management meeting with tax officials time

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

270

2008 2007 2007 2006 2006 2009

2009 2007 2008 2009 2006 2009 2006 2009 2009 2009 2006 2007 2009

2009 2006 2003 2006 2010 2009 2005 2009 2007 2009 2005 2006 2008 2006 2009 2009 2006

2009 2006 2008 2005 2007 2005 2006 2006 2006 2006

6.8 18.7 25.1 7.1 13.8 10.3 .. .. 3.0 3.2 13.6 .. 20.7 13.5 11.2 5.0 18.7 10.6 22.2 5.7 5.6 7.0 .. .. 20.8 9.0 18.3 .. 14.3 29.4 6.0 9.6 1.8 10.9 .. 10.4 .. 8.8 17.3 8.8 9.2 0.5 5.5 3.8 .. .. 2.8 7.3 2.1 1.2 4.0 1.8 9.2 2.7 2.9 .. 4.6

2011 World Development Indicators

1.2 3.9 2.3 3.3 2.2 2.1 .. .. 2.1 1.3 1.1 .. 1.2 1.7 1.0 0.9 1.2 2.2 1.5 1.8 1.0 4.4 .. .. 3.4 3.0 14.4 .. 0.6 8.0 2.7 0.5 3.6 0.7 .. 1.5 .. 0.5 0.6 3.4 2.7 0.2 0.4 1.1 .. .. 15.2 2.5 0.6 1.3 4.1 1.7 2.1 2.8 3.4 .. 1.5

Permits Corruption and licenses

Crime

Informality

Firms formally registered when operations started % of firms

Time required to obtain operating license

Informal payments to public officials

Losses due to theft, robbery, vandalism, and arson

days

% of firms

% of sales

13.8 21.2 19.3 24.1 78.3 20.0 .. .. 15.8 6.0 38.2 .. 64.3 26.0 21.4 13.7 83.5 20.8 35.8 27.3 .. 30.0 .. .. 24.3 67.7 11.6 .. 28.2 40.0 .. .. 14.5 26.5 .. 19.9 .. .. 19.9 90.6 35.4 .. 8.3 11.4 .. .. 12.1 8.4 11.8 .. 6.4 .. 75.4 13.0 30.4 .. 31.6

41.5 57.7 64.7 46.8 18.7 11.6 .. .. 32.0 85.1 13.5 .. 54.5 32.4 8.1 27.6 9.7 8.5 8.5 56.5 61.2 50.8 .. .. 41.8 8.2 72.6 .. 8.2 65.7 49.2 33.8 30.6 14.5 .. 8.7 .. 26.3 21.5 15.2 34.3 0.0 1.6 12.4 .. .. 26.1 52.4 4.1 .. 38.8 21.6 15.7 84.8 62.7 .. 16.7

1.5 0.5 0.9 0.4 1.5 0.6 .. .. 0.3 0.1 0.4 .. 1.9 0.9 0.2 1.3 1.7 0.5 0.3 1.1 0.0 1.7 .. .. 2.5 0.6 0.1 .. 0.7 1.8 3.3 0.4 3.4 0.2 .. 0.4 .. 0.7 0.9 3.0 2.6 0.0 0.9 1.4 .. .. 0.4 2.7 0.7 0.5 0.9 0.0 1.5 2.0 1.1 .. 2.2

88.0 89.4 98.3 .. 93.8 96.2 .. .. 85.1 .. 98.5 .. 87.9 90.5 98.6 .. 95.8 98.5 77.7 .. 87.5 82.1 .. .. 77.1 97.8 .. .. 85.6 61.9 84.3 .. 56.4 98.1 .. 98.0 .. .. 91.1 14.3 79.5 100.0 97.4 .. .. .. 63.7 .. 99.6 .. 66.4 .. 91.3 .. .. .. 89.4

Gender

Finance

Firms with Firms using banks to female finance participation in ownership investment % of firms

% of firms

2.8 10.8 15.0 23.4 30.3 31.8 .. .. 10.8 16.1 52.9 .. 43.9 41.1 32.8 40.9 59.3 33.9 19.2 34.8 .. 15.7 .. .. 40.1 27.8 .. .. 43.0 38.9 31.8 65.3 61.9 33.5 .. 25.0 .. .. 32.7 34.0 39.6 4.2 36.3 30.9 .. .. 33.1 21.3 40.8 20.3 44.0 24.4 28.4 25.4 19.9 .. 39.9

1.4 12.4 8.9 2.1 6.9 31.9 .. .. 19.0 24.7 35.8 .. 4.2 22.2 59.7 11.3 48.4 34.7 25.6 12.3 11.3 31.4 .. .. 4.2 29.1 28.8 .. 30.6 6.7 7.7 14.9 13.9 60.0 .. 33.4 .. 12.5 24.0 5.6 17.3 11.9 41.5 11.0 .. .. 6.3 7.6 38.2 45.0 16.0 25.9 12.8 0.9 0.7 .. 8.5

Infrastructure Innovation

Trade

Average Intertime to nationally recognized clear direct exports quality Value lost due to electrical certification through customs ownership outages % of sales

6.5 13.7 4.0 3.7 1.6 1.8 .. .. 1.8 10.6 0.8 .. 7.5 4.4 1.9 1.4 3.0 1.6 5.8 10.7 2.4 4.9 .. .. 3.3 1.8 1.3 .. 2.3 22.7 16.4 1.9 5.0 0.8 .. 0.6 .. 15.2 2.7 3.4 2.9 0.2 0.5 0.9 .. .. 1.7 11.8 1.4 .. 6.0 .. 4.5 14.0 5.3 .. 3.8

% of firms

8.5 24.6 5.0 5.1 26.9 26.9 .. .. 18.2 7.8 13.9 .. 7.3 13.8 30.1 12.7 25.7 19.9 14.4 7.1 2.8 20.4 .. .. 43.3 22.0 35.9 .. 5.9 8.5 19.6 10.5 4.3 16.5 .. 43.5 .. 9.6 18.2 21.1 11.0 15.1 21.2 4.2 .. .. 18.6 22.2 16.0 .. 6.8 11.7 8.0 5.2 8.4 .. 16.5

days

14.6 1.9 14.1 16.5 5.5 3.3 .. .. 1.9 8.4 2.6 .. 9.6 15.3 1.4 1.4 15.9 4.2 7.4 .. 1.5 15.1 .. .. 11.9 5.8 6.6 .. 7.0 18.0 .. 3.5 16.6 1.3 .. 5.7 .. 11.4 7.0 6.2 2.5 9.6 1.8 4.3 .. .. 3.8 5.0 3.8 4.7 7.8 5.5 4.5 4.3 5.6 .. 6.0

Workforce

Firms offering formal training a % of firms

14.6 19.9 17.3 19.4 52.2 30.4 .. .. 10.5 16.2 44.4 .. 32.4 53.9 66.5 37.7 52.9 30.7 24.8 22.1 48.4 25.5 .. .. 43.4 46.9 84.8 .. 39.5 24.1 37.5 46.4 19.1 28.0 .. 70.7 .. 53.3 61.6 21.7 49.6 26.1 69.3 38.2 .. .. 30.9 25.6 14.5 35.4 33.0 20.0 28.1 21.1 12.4 .. 33.3

Survey year

Regulations and tax

Time dealing with officials

Average number of times % of management meeting with tax officials time

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

2009 2006 2009

2005

2005 2006 2009 2007 2005 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2007 2007 2006 2009 2006 2009 2009 2007 2007 2006 2009

2006 2009 2007 2003 2007 2006 2006 2006 2009 2009 2005

13.5 6.7 1.9 .. .. 2.3 .. .. 6.3 .. 6.7 4.7 5.1 .. 0.1 9.8 .. 4.9 1.6 9.7 8.9 5.6 7.5 .. 9.3 14.5 17.1 3.5 7.8 2.4 5.8 9.4 20.5 7.0 12.1 11.4 3.3 .. 2.9 6.5 .. .. 9.3 21.1 6.1 .. .. 2.2 10.3 .. 7.9 13.5 9.1 12.8 1.1 .. ..

0.8 2.6 0.2 .. .. 1.3 .. .. 1.8 .. 1.7 2.6 6.7 .. 2.2 4.5 .. 2.1 4.4 1.5 2.2 1.8 6.5 .. 0.8 3.0 0.9 2.7 2.6 1.6 1.8 0.5 0.6 1.9 2.0 0.9 1.9 .. 0.3 1.3 .. .. 1.3 1.6 3.0 .. 4.4 1.6 1.4 .. 0.7 1.4 1.6 0.6 1.6 .. ..

Permits Corruption and licenses

Crime

Informality

Firms formally registered when operations started % of firms

Time required to obtain operating license

Informal payments to public officials

Losses due to theft, robbery, vandalism, and arson

days

% of firms

% of sales

35.6 .. 21.1 .. .. .. .. .. .. .. 6.4 30.8 23.4 .. .. 18.8 .. 18.0 13.6 11.5 81.0 16.4 16.0 .. 65.5 33.8 41.3 15.0 22.4 41.0 10.7 19.1 11.2 13.9 43.5 3.4 35.2 .. 9.6 14.5 .. .. 19.7 39.7 12.1 .. 11.8 16.4 41.2 .. 37.8 81.1 10.6 14.6 .. .. ..

4.0 47.5 14.6 .. .. 8.3 .. .. 17.7 .. 18.1 23.3 79.2 .. 14.1 2.2 .. 37.5 39.8 11.3 23.0 14.0 55.2 .. 8.5 11.5 19.2 10.8 .. 28.9 82.1 1.6 22.6 25.4 30.4 13.4 14.8 .. 11.4 15.2 .. .. 17.2 35.2 40.9 .. 33.2 27.2 25.4 .. 84.8 11.3 18.6 5.0 14.5 .. ..

0.1 0.1 0.3 .. .. 0.3 .. .. 1.1 .. 0.1 1.0 3.9 .. 0.0 0.3 .. 0.3 0.3 0.3 0.0 2.9 2.8 .. 0.4 0.7 1.2 5.7 1.0 0.6 0.6 1.4 0.7 0.4 0.6 0.0 1.8 .. 1.3 0.9 .. .. 0.9 0.9 4.1 .. .. 0.5 0.5 .. 0.9 0.4 1.1 0.5 0.2 .. ..

100.0 .. 29.1 .. .. .. .. .. .. .. .. 97.4 .. .. .. 89.2 .. 95.9 93.5 98.5 97.6 86.8 73.8 .. 97.1 99.2 97.5 78.6 53.0 85.4 .. 84.2 94.1 97.9 90.1 86.0 85.9 .. .. 94.0 .. .. 85.4 90.5 .. .. .. .. 98.0 .. 94.0 99.2 97.5 99.3 .. .. ..

Gender

Finance

Firms with Firms using banks to female finance participation in ownership investment % of firms

% of firms

42.4 9.1 42.8 .. .. 41.6 .. .. 32.2 .. 13.1 34.4 37.1 .. 19.1 10.9 .. 60.4 39.4 46.3 33.5 18.4 53.0 .. 38.7 36.4 50.0 23.9 13.1 18.4 17.3 16.9 24.8 53.1 52.0 13.1 24.4 .. 33.4 27.4 .. .. 41.4 17.6 20.0 .. .. 6.7 37.1 .. 44.8 32.8 69.4 47.9 50.8 .. ..

48.7 46.7 11.7 .. .. 37.4 .. .. 37.0 .. 8.6 31.0 22.9 .. 39.9 25.3 .. 17.9 0.0 37.3 23.8 32.7 10.1 .. 47.4 47.0 12.2 20.6 48.6 7.0 3.2 37.5 2.6 30.8 26.5 12.3 10.5 .. 8.1 17.5 .. .. 13.0 9.3 2.7 .. 31.0 9.7 19.2 .. 8.2 30.9 22.0 40.7 24.4 .. ..

Infrastructure Innovation

Trade

Average Intertime to nationally recognized clear direct exports quality Value lost due to electrical certification through customs ownership outages % of sales

0.9 6.6 2.4 .. .. 1.5 .. .. 11.8 .. 1.7 3.7 6.4 .. .. 17.1 .. 10.5 4.3 1.1 9.4 6.7 2.9 .. 0.7 5.9 7.7 17.0 3.0 1.8 1.6 2.2 2.4 2.0 0.8 1.3 2.4 .. 0.7 27.0 .. .. 8.7 1.9 8.9 .. 4.2 9.9 2.4 .. 2.5 3.2 3.4 1.9 .. .. ..

% of firms

39.4 22.5 2.9 .. .. 17.2 .. .. 16.4 .. 15.5 10.8 9.8 .. 17.6 7.9 .. 16.2 7.2 18.2 17.9 24.7 2.4 .. 15.6 21.5 8.7 17.9 54.1 8.6 5.9 11.1 20.3 9.1 16.7 17.3 18.7 .. 17.6 3.1 .. .. 18.7 4.6 8.5 .. 10.8 9.6 14.7 .. 7.1 14.6 15.7 17.3 12.7 .. ..

days

4.3 15.1 2.4 .. .. 2.6 .. .. 4.3 .. 3.8 8.5 5.6 .. 7.2 1.7 .. 15.8 7.5 1.9 7.6 5.4 .. .. 2.4 2.5 14.2 4.9 2.7 4.8 3.9 10.3 5.2 2.4 18.6 1.8 10.1 .. 1.4 5.6 .. .. 5.0 2.6 7.5 .. 3.4 4.8 5.7 .. 5.5 5.4 8.1 6.0 7.2 .. ..

2011 World Development Indicators

STATES AND MARKETS

5.2

Business environment: Enterprise Surveys

Workforce

Firms offering formal training a % of firms

14.8 15.9 4.7 .. .. 73.2 .. .. 53.5 .. 23.9 40.9 40.7 .. 39.5 24.6 .. 29.7 11.1 43.4 52.4 42.5 17.0 .. 46.0 19.0 27.0 48.4 50.1 22.5 25.5 25.6 24.6 33.1 61.2 24.7 22.1 .. 44.5 8.8 .. .. 28.9 32.1 25.7 .. 20.9 6.7 43.9 .. 46.9 57.7 31.1 60.9 31.9 .. ..

271

5.2

Business environment: Enterprise Surveys Survey year

Regulations and tax

Time dealing with officials

Average number of times % of management meeting with tax officials time

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe

2009 2009 2006 2007 2009 2009 2009 2009 2007 2005 2004 2006

2009 2008 2006 2006 2009 2009

2008 2006 2008

2006 2008 2006 2009 2006 2010 2007

9.2 19.9 5.9 .. 2.9 12.2 7.4 .. 6.7 7.3 .. 6.0 0.8 3.5 .. 4.4 .. .. 12.2 11.7 4.0 0.4 4.1 2.7 .. .. 27.1 .. 5.2 11.3 .. .. .. 7.0 11.1 33.6 4.9 5.7 11.8 4.6 ..

2.3 1.6 3.3 .. 1.3 1.4 1.9 .. 0.9 0.3 .. 0.8 1.5 4.9 .. 1.4 .. .. 2.3 1.4 2.7 1.0 0.9 1.2 .. .. 1.3 .. 2.4 2.1 .. .. .. 0.7 0.7 2.9 1.1 1.7 7.3 1.9 ..

Permits Corruption and licenses

Crime

Informality

Firms formally registered when operations started % of firms

Time required to obtain operating license

Informal payments to public officials

Losses due to theft, robbery, vandalism, and arson

days

% of firms

% of sales

9.8 29.4 20.0 .. 18.1 18.0 18.8 .. 9.1 5.4 .. 15.1 4.4 16.3 .. 40.6 .. .. 83.8 40.5 49.5 .. 19.4 16.7 .. .. 17.7 .. 51.7 22.9 .. .. .. 7.3 56.2 .. 52.1 13.3 68.2 14.3 ..

0.3 0.8 1.3 .. 0.5 0.6 0.8 .. 0.7 0.4 .. 1.0 0.2 0.5 .. 1.3 .. .. 0.8 0.3 1.2 0.1 1.5 2.4 .. .. 0.4 .. 1.0 0.6 .. .. .. 0.7 0.7 1.4 0.3 1.2 0.6 1.0 ..

23.7 57.4 6.5 .. 21.4 28.0 12.6 .. 32.1 56.1 .. 36.2 .. 49.5 .. 24.0 .. .. 169.2 22.6 15.9 32.1 16.6 56.4 .. .. 36.0 .. 9.3 31.0 .. .. .. 133.8 9.1 41.6 15.9 21.3 6.5 48.3 ..

98.7 94.7 .. .. 78.9 95.0 89.2 .. 100.0 99.9 .. 91.0 .. .. .. .. .. .. .. 92.7 .. .. 91.8 75.8 .. .. 94.1 .. .. 95.8 .. .. .. 97.8 100.0 97.3 87.5 .. 81.7 96.2 ..

Gender

Firms with Firms using banks to female finance participation in ownership investment % of firms

% of firms

47.9 33.1 41.0 .. 26.3 28.8 7.9 .. 29.6 42.2 .. 22.6 34.1 .. .. 28.6 .. .. 14.4 34.4 30.9 .. 42.9 31.8 .. .. 40.7 .. 34.7 47.1 .. .. .. 41.6 39.8 .. 59.2 18.0 6.4 37.2 ..

37.3 30.6 15.9 .. 19.8 42.8 6.9 .. 33.5 52.2 .. 34.8 32.6 26.2 .. 7.7 .. .. 20.7 21.4 6.8 74.4 1.6 16.9 .. .. 51.9 .. 7.7 32.1 .. .. .. 6.9 8.2 35.7 21.5 4.2 4.2 10.2 ..

Note: Enterprise surveys are updated several times a year; see www.enterprisesurveys.org for the most recent updates. a. For survey data collected in 2006 and 2007, data refer to the manufacturing module only.

272

2011 World Development Indicators

Finance

Infrastructure Innovation

Trade

Average Intertime to nationally recognized clear direct exports quality Value lost due to electrical certification through customs ownership outages % of sales

2.2 1.2 8.7 .. 5.0 1.3 6.6 .. 0.3 0.5 .. 1.6 3.0 .. .. 2.5 .. .. 8.6 15.1 9.6 1.5 7.6 10.5 .. .. 2.8 .. 10.2 4.4 .. .. .. 0.9 5.4 4.4 3.7 4.6 13.2 3.7 ..

Workforce

Firms offering formal training a

% of firms

days

% of firms

26.1 11.7 10.8 .. 6.1 21.8 13.8 .. 28.6 28.0 .. 26.4 21.3 .. .. 22.1 .. .. 7.4 16.7 14.7 39.0 2.2 6.6 .. .. 30.0 .. 15.5 13.0 .. .. .. 6.8 1.3 12.5 16.7 18.2 4.4 17.2 ..

2.0 4.6 6.7 .. 7.4 1.6 .. .. 2.4 2.2 .. 4.5 4.9 7.6 .. 2.1 .. .. 5.1 20.4 5.7 1.3 .. 6.7 .. .. 5.2 .. 3.2 3.4 .. .. .. 2.5 5.1 14.1 4.5 6.0 6.2 2.3 ..

24.9 52.2 27.6 .. 16.3 36.5 18.6 .. 33.1 47.5 .. 36.8 51.3 32.6 .. 51.0 .. .. 38.3 21.1 36.5 75.3 49.7 31.0 .. .. 28.8 .. 35.0 24.8 .. .. .. 24.6 9.6 42.3 43.6 26.5 12.9 26.0 ..

About the data

5.2

STATES AND MARKETS

Business environment: Enterprise Surveys Definitions

The World Bank Group’s Enterprise Survey gath-

The reliability and availability of infrastructure ben-

• Survey year is the year in which the underlying data

ers firm-level data on the business environment

efit households and support development. Firms with

were collected. • Time dealing with ofﬁcials is the

to assess constraints to private sector growth and

access to modern and efficient infrastructure—tele-

average percentage of senior management’s time

enterprise performance. Standardized surveys are

communications, electricity, and transport—can be

that is spent in a typical week dealing with require-

conducted all over the world, and data are available

more productive. Firm-level innovation and use of

ments imposed by government regulations. • Aver-

on more than 120,000 firms in 125 countries. The

modern technology may help firms compete.

age number of times meeting with tax ofﬁcials is

survey covers 11 dimensions of the business envi-

Delays in clearing customs can be costly, deterring

the average number of visits or required meetings

ronment, including regulation, corruption, crime,

firms from engaging in trade or making them uncom-

with tax officials. • Time required to obtain operat-

informality, finance, infrastructure, trade. For some

petitive globally. Ill-considered labor regulations dis-

ing license is the average wait to obtain an operating

countries, firm-level panel data are available, making

courage firms from creating jobs, and while employed

license from the day applied for to the day granted.

it possible to track changes in the business environ-

workers may benefit, unemployed, low-skilled, and

• Informal payments to public ofﬁ cials are the

ment over time.

informally employed workers will not. A trained labor

percentage of firms that answered positively to the

force enables firms to thrive, compete, innovate, and

question “Was a gift or informal payment expected

adopt new technology.

or requested during a meeting with tax officials?”

Firms evaluating investment options, governments interested in improving business conditions, and economists seeking to explain economic perfor-

The data in the table are from Enterprise Surveys

• Losses due to theft, robbery, vandalism, and

mance have all grappled with defining and measur-

implemented by the World Bank’s Financial and Pri-

arson are the estimated losses from those causes

ing the business environment. The firm-level data

vate Sector Development Enterprise Analysis Unit. All

that occurred on establishments’ premises as a

from Enterprise Surveys provide a useful tool for

economies in East Asia and Pacific, Europe and Cen-

percentage of annual sales. • Firms formally regis-

benchmarking economies across a large number of

tral Asia, Latin America and the Caribbean, Middle

tered when operations started are the percentage

indicators measured at the firm level.

East and North Africa, and Sub-Saharan Africa (for

of firms formally registered when they started opera-

Most countries can improve regulation and taxa-

2009) and Afghanistan, Bangladesh, and India draw

tions in the country. Firms not formally registered (the

tion without compromising broader social interests.

a sample of registered nonagricultural businesses,

residual) are in the informal sector of the economy.

Excessive regulation may harm business perfor-

excluding those in the financial and public sectors.

• Firms with female participation in ownership are

mance and growth. For example, time spent with

Samples for other economies are drawn only from the

the percentage of firms with a woman among the own-

tax officials is a burden firms may face in paying

manufacturing sector and are footnoted in the table.

ers. • Firms using banks to ﬁnance investment are

taxes. The business environment suffers when gov-

Typical Enterprise Survey sample sizes range from

the percentage of firms that invested in fixed assets

ernments increase uncertainty and risks or impose

150 to 1,800, depending on the size of the economy.

during the last fiscal year that used banks to finance

unnecessary costs and unsound regulation and taxa-

In each country samples are selected by stratified

fixed assets. • Value lost due to electrical outages

tion. Time to obtain licenses and permits and the

random sampling, unless otherwise noted. Stratified

is losses that resulted from power outages as a per-

associated red tape constrain firm operations.

random sampling allows indicators to be computed

centage of annual sales. • Internationally recognized

In some countries doing business requires informal

by sector, firm size, and region and increases the

quality certiﬁcation ownership is the percentage of

payments to “get things done” in customs, taxes,

precision of economywide indicators compared with

firms that have an internationally recognized quality

licenses, regulations, services, and the like. Such

alternative simple random sampling. Stratification

certification, such as International Organization for

corruption harms the business environment by dis-

by sector of activity divides the economy into manu-

Standardization 9000, 9001, 9002, or 14000 or

torting policymaking, undermining government cred-

facturing and retail and other services sectors. For

Hazard Analysis and Critical Control Points. • Aver-

ibility, and diverting public resources. Crime, theft,

medium-size and large economies the manufacturing

age time to clear direct exports through customs

and disorder also impose costs on businesses and

sector is further stratified by industry. Firm size is

is the average number of days to clear direct exports

society.

stratified into small (5–19 employees), medium-size

through customs. • Firms offering formal training

In many developing countries informal businesses

(20–99 employees), and large (more than 99 employ-

are the percentage of firms offering formal training

operate without formal registration. These firms have

ees). Geographic stratification divides the national

programs for their permanent, full-time employees.

less access to financial and public services and can

economy into the main centers of economic activity.

engage in fewer types of contracts and investments, constraining growth. Equal opportunities for men and women contribute to development. Female participation in firm ownership is a measure of women’s integration as decision makers. Financial markets connect firms to lenders and investors, allowing firms to grow their businesses:

Data sources

creditworthy firms can obtain credit from financial

Data on the business environment are from the

intermediaries at competitive prices. But too often

World Bank Group’s Enterprise Surveys website

market imperfections and government-induced distor-

(www.enterprisesurveys.org).

tions limit access to credit and thus restrain growth.

2011 World Development Indicators

273

5.3

Business environment: Doing Business indicators Starting a business

Number of procedures June 2010

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

274

4 5 14 8 14 6 2 8 6 7 5 3 7 15 12 10 15 4 4 11 9 6 1 8 13 8 14 3 9 10 10 12 10 6 .. 9 4 8 13 6 8 13 5 5 3 5 9 8 3 9 7 15 12 13 17 13 13

Time required days June 2010

7 5 24 68 26 15 2 28 8 19 5 4 31 50 55 61 120 18 14 32 85 19 5 22 75 22 38 6 14 84 160 60 40 7 .. 20 6 19 56 7 17 84 7 9 14 7 58 27 3 15 12 19 37 41 216 105 14

2011 World Development Indicators

Registering property

Cost % of per capita income June 2010

26.7 16.8 12.9 163.0 14.2 3.1 0.7 5.2 3.1 33.3 1.6 5.4 152.6 100.8 17.7 2.2 7.3 1.6 49.8 129.3 128.3 51.2 0.4 228.4 226.9 6.8 4.5 2.0 14.7 735.1 111.4 10.5 133.0 8.6 .. 9.3 0.0 19.2 32.6 6.3 45.0 69.2 1.9 14.1 1.1 0.9 21.9 199.6 5.0 4.8 20.3 20.7 49.1 146.6 183.3 212.0 47.2

Number of procedures June 2010

9 6 11 7 6 3 5 3 4 8 3 8 4 7 7 5 14 8 4 5 7 5 6 5 6 6 4 5 7 6 6 6 6 5 .. 4 3 7 9 7 5 11 3 10 3 8 7 5 1 5 5 11 4 6 9 5 7

Time required days June 2010

250 42 47 184 52 7 5 21 11 245 15 79 120 92 33 16 42 15 59 94 56 93 17 75 44 31 29 36 20 54 55 21 62 104 .. 43 42 60 16 72 31 78 18 41 14 59 39 66 2 40 34 22 23 104 211 405 23

Dealing with construction permits

Number of procedures to build a warehouse June 2010

13 24 22 12 28 20 16 14 31 14 16 14 15 17 16 24 18 24 15 25 23 14 14 21 14 18 37 7 10 14 17 23 21 13 .. 36 6 17 19 25 34 .. 14 12 18 13 16 17 10 12 18 15 22 32 15 11 17

Time required to build a warehouse days June 2010

340 331 240 328 338 137 221 194 207 231 151 169 320 249 255 167 411 139 122 212 709 213 75 239 164 155 336 67 50 128 169 191 592 315 .. 150 69 214 155 218 155 .. 134 128 66 137 210 146 98 100 220 169 178 255 167 1,179 106

Enforcing contracts

Number of procedures June 2010

47 39 46 46 36 49 28 25 39 41 28 26 42 40 37 29 45 39 37 44 44 43 36 43 41 36 34 24 34 43 44 40 33 38 .. 27 35 34 39 41 30 39 36 37 32 29 38 32 36 30 36 39 31 50 40 35 45

Time required days June 2010

1,642 390 630 1,011 590 285 395 397 237 1,442 225 505 825 591 595 625 616 564 446 832 401 800 570 660 743 480 406 280 1,346 625 560 852 770 561 .. 611 410 460 588 1,010 786 405 425 620 375 331 1,070 434 285 394 487 819 1,459 276 1,140 508 900

Protecting Closing a investors business

Disclosure index 0–10 (least to most disclosure) June 2010

Time to resolve insolvency years June 2010

1 8 6 5 6 5 8 3 7 6 5 8 6 1 3 7 6 10 6 4 5 6 8 6 6 8 10 10 8 3 6 2 6 1 .. 2 7 5 1 8 5 4 8 4 6 10 6 2 8 5 7 1 3 6 6 2 0

.. .. 2.5 6.2 2.8 1.9 1.0 1.1 2.7 4.0 5.8 0.9 4.0 1.8 3.3 1.7 4.0 3.3 4.0 .. .. 3.2 0.8 4.8 .. 4.5 1.7 1.1 3.0 5.2 3.3 3.5 2.2 3.1 .. 3.2 1.1 3.5 5.3 4.2 4.0 .. 3.0 3.0 0.9 1.9 5.0 3.0 3.3 1.2 1.9 2.0 3.0 3.8 .. 5.7 3.8

Starting a business

Number of procedures June 2010

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

4 12 9 6 11 4 5 6 6 8 8 6 11 .. 8 10 13 2 7 5 5 7 5 .. 6 3 2 10 9 6 9 5 6 8 7 6 9 .. 10 7 6 1 6 9 8 5 5 10 6 6 7 6 15 6 6 7 8

Time required days June 2010

4 29 47 8 77 13 34 6 8 23 13 19 33 .. 14 58 35 10 100 16 9 40 20 .. 22 3 7 39 17 8 19 6 9 10 13 12 13 .. 66 31 8 1 39 17 31 7 12 21 9 51 35 27 38 32 6 7 12

Registering property

Cost % of per capita income June 2010

8.2 56.5 22.3 4.0 107.8 0.4 4.3 18.5 5.2 7.5 44.6 1.0 38.3 .. 14.7 28.7 1.3 3.7 11.3 1.5 75.0 26.0 54.6 .. 2.8 2.5 12.9 108.4 17.5 79.7 33.6 3.8 12.3 10.9 3.2 15.8 13.9 .. 18.5 46.6 5.7 0.4 117.9 118.6 78.9 1.8 3.3 10.7 10.3 17.7 55.1 13.6 29.7 17.5 6.5 0.7 9.7

Number of procedures June 2010

4 5 6 9 5 5 7 8 6 6 7 4 8 .. 7 8 8 4 9 6 8 6 10 .. 3 5 7 6 5 5 4 4 5 5 5 8 8 .. 9 3 5 2 8 4 13 1 2 6 8 4 6 4 8 6 1 8 10

Time required days June 2010

17 44 22 36 51 38 144 27 37 14 21 40 64 .. 11 33 55 5 135 42 25 101 50 .. 3 58 74 49 56 29 49 26 74 5 11 47 42 .. 23 5 7 2 124 35 82 3 16 50 32 72 46 7 33 152 1 194 16

Dealing with construction permits

Enforcing contracts

Number of procedures to build a warehouse June 2010

Time required to build a warehouse days June 2010

Number of procedures June 2010

31 37 14 17 14 11 20 14 10 15 19 34 11 .. 13 21 25 13 24 24 21 15 24 .. 17 21 16 21 25 15 25 18 11 30 21 19 17 .. 12 15 18 7 17 17 18 14 15 12 20 24 13 19 26 32 19 22 19

189 195 160 322 215 192 235 257 156 187 87 219 120 .. 34 320 104 143 172 186 218 601 77 .. 162 146 178 268 261 168 201 107 105 292 215 163 381 .. 139 424 230 65 219 265 350 252 186 223 116 217 179 188 169 311 272 209 76

35 46 40 39 51 20 35 41 35 30 38 38 40 .. 35 53 50 39 42 27 37 41 41 .. 30 37 38 42 30 36 46 36 38 31 32 40 30 .. 33 39 26 30 35 39 40 33 51 47 31 42 38 41 37 38 31 39 43

Time required days June 2010

395 1,420 570 505 520 515 890 1,210 655 360 689 390 465 .. 230 420 566 260 443 309 721 785 1,280 .. 275 370 871 312 585 620 370 645 415 365 314 615 730 .. 270 735 514 216 540 545 457 280 598 976 686 591 591 428 842 830 547 620 570

5.3

STATES AND MARKETS

Business environment: Doing Business indicators

Protecting Closing a investors business

Disclosure index 0–10 (least to most disclosure) June 2010

Time to resolve insolvency years June 2010

2 7 10 5 4 10 7 7 4 7 5 8 3 .. 7 3 7 8 2 5 9 2 4 .. 5 9 5 4 10 6 5 6 8 7 5 7 5 .. 5 6 4 10 4 6 5 7 8 6 1 5 6 8 2 7 6 7 5

2.0 7.0 5.5 4.5 .. 0.4 4.0 1.8 1.1 0.6 4.3 1.5 4.5 .. 1.5 2.0 4.2 4.0 .. 3.0 4.0 2.6 3.0 .. 1.5 2.9 .. 2.6 2.3 3.6 8.0 1.7 1.8 2.8 4.0 1.8 5.0 .. 1.5 5.0 1.1 1.3 2.2 5.0 2.0 0.9 4.0 2.8 2.5 3.0 3.9 3.1 5.7 3.0 2.0 3.8 2.8

2011 World Development Indicators

275

5.3

Business environment: Doing Business indicators Starting a business

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

Number of procedures June 2010

Time required days June 2010

Cost % of per capita income June 2010

6 9 2 4 4 7 6 3 6 2 .. 6 10 4 10 12 3 6 7 8 12 7 10 7 9 10 6 .. 18 10 8 6 6 11 7 17 9 11 6 6 9 8u 8 8 8 8 8 8 6 9 8 7 9 6 6

10 30 3 5 8 13 12 3 16 6 .. 22 47 35 36 56 15 20 13 27 29 32 83 75 43 11 6 .. 25 27 15 13 6 65 15 141 44 49 12 18 90 34 u 41 39 35 43 39 40 18 60 23 25 43 18 14

2.6 3.6 8.8 7.0 63.1 7.9 110.7 0.7 1.9 0.0 .. 6.0 15.1 5.4 33.6 33.0 0.6 2.1 38.1 36.9 30.9 5.6 18.4 178.1 0.8 5.0 17.2 .. 94.4 6.1 6.4 0.7 1.4 42.1 11.9 30.2 12.1 93.7 82.1 27.9 182.8 40.7 u 107.9 31.7 44.4 16.1 52.6 31.5 8.9 39.6 54.6 24.5 95.2 7.3 6.7

Note: Regional aggregates are for developing countries only.

276

Registering property

2011 World Development Indicators

Dealing with construction permits

Enforcing contracts

Number of procedures June 2010

Time required days June 2010

Number of procedures to build a warehouse June 2010

Time required to build a warehouse days June 2010

Number of procedures June 2010

Time required days June 2010

8 6 4 2 6 6 7 3 3 6 .. 6 4 8 6 9 1 4 4 6 9 2 .. 5 8 4 6 .. 13 10 1 2 4 8 12 8 4 7 6 5 5 6u 7 6 6 6 6 5 6 7 7 6 7 5 5

48 43 55 2 122 91 86 5 17 113 .. 24 18 83 9 44 7 16 19 37 73 2 .. 295 162 39 6 .. 77 117 2 8 12 66 78 47 57 47 19 40 31 58 u 94 54 65 41 65 99 36 62 39 100 69 38 35

17 53 14 12 16 20 25 11 13 14 .. 17 11 22 19 14 8 14 26 30 22 11 22 15 20 20 25 .. 18 22 17 11 19 30 28 11 13 21 15 17 17 18 u 18 19 18 19 19 19 23 16 20 18 18 17 14

228 540 195 89 210 279 252 25 287 199 .. 174 233 214 271 116 116 154 128 228 328 156 208 277 261 97 188 .. 171 374 64 95 40 234 274 395 194 199 107 254 1,012 207 u 275 201 197 206 221 181 235 220 181 241 240 169 227

31 37 24 43 44 36 40 21 31 32 .. 30 39 40 53 40 30 31 55 34 38 36 51 41 42 39 35 .. 38 30 49 28 32 41 42 29 34 44 36 35 38 38 u 39 39 40 38 39 37 38 39 42 44 39 35 31

512 281 230 635 780 635 515 150 565 1,290 .. 600 515 1,318 810 972 508 417 872 430 462 479 1,285 588 1,340 565 420 .. 490 345 537 399 300 720 195 510 295 540 520 471 410 605 u 613 638 679 588 631 564 382 698 701 1,053 641 532 602

Protecting Closing a investors business

Disclosure index 0–10 (least to most disclosure) June 2010

9 6 7 9 6 7 6 10 3 3 .. 8 5 4 0 2 8 0 7 8 3 10 3 6 4 5 9 .. 2 5 4 10 7 3 4 3 6 6 6 3 8 5u 5 5 5 6 5 5 7 4 6 4 5 6 5

Time to resolve insolvency years June 2010

3.3 3.8 .. 1.5 3.0 2.7 2.6 0.8 4.0 2.0 .. 2.0 1.0 1.7 .. 2.0 2.0 3.0 4.1 1.7 3.0 2.7 .. 3.0 .. 1.3 3.3 .. 2.2 2.9 5.1 1.0 1.5 2.1 4.0 4.0 5.0 .. 3.0 2.7 3.3 2.9 u 3.7 3.1 3.3 2.9 3.3 3.1 2.9 3.2 3.5 4.5 3.4 2.1 1.6

About the data

5.3

STATES AND MARKETS

Business environment: Doing Business indicators Definitions

The economic health of a country is measured not

The Doing Business project encompasses two

• Number of procedures for starting a business is the

only in macroeconomic terms but also by other

types of data: data from readings of laws and regu-

number of procedures required to start a business,

factors that shape daily economic activity such as

lations and data on time and motion indicators that

including interactions to obtain necessary permits and

laws, regulations, and institutional arrangements.

measure efficiency in achieving a regulatory goal.

licenses and to complete all inscriptions, verifications,

The Doing Business indicators measure business

Within the time and motion indicators cost estimates

and notifications to start operations for businesses

regulation, gauge regulatory outcomes, and measure

are recorded from official fee schedules where appli-

with specific characteristics of ownership, size, and

the extent of legal protection of property, the flex-

cable. The data from surveys are subjected to numer-

type of production. • Time required for starting a

ibility of employment regulation, and the tax burden

ous tests for robustness, which lead to revision or

business is the number of calendar days to complete

on businesses.

expansion of the information collected.

the procedures for legally operating a business using

The table presents a subset of Doing Business

The Doing Business methodology has limitations

the fastest procedure, independent of cost. • Cost

indicators covering 6 of the 10 sets of indicators:

that should be considered when interpreting the

for starting a business is normalized as a percentage

starting a business, registering property, dealing with

data. First, the data collected refer to businesses

of gross national income (GNI) per capita. It includes

construction permits, enforcing contracts, protecting

in the economy’s largest city and may not represent

all official fees and fees for legal or professional ser-

investors, and closing a business. Table 5.5 includes

regulations in other locations of the economy. To

vices if such services are required by law. • Number of

Doing Business measures of getting credit, and table

address this limitation, subnational indicators are

procedures for registering property is the number of

5.6 presents data on paying taxes.

being collected for selected economies. These sub-

procedures required for a business to legally transfer

The fundamental premise of the Doing Business

national studies point to significant differences in

property. • Time required for registering property is

project is that economic activity requires good rules

the speed of reform and the ease of doing business

the number of calendar days for a business to legally

and regulations that are efficient, accessible to all

across cities in the same economy. Second, the data

transfer property. • Number of procedures for deal-

who need to use them, and simple to implement.

often focus on a specific business form—generally

ing with licenses to build a warehouse is the number

Thus some Doing Business indicators give a higher

a limited liability company of a specified size—and

of interactions of a company’s employees or manag-

score for more regulation, such as stricter disclosure

may not represent regulation for other types of busi-

ers with external parties, including government staff,

requirements in related-party transactions, and oth-

nesses such as sole proprietorships. Third, transac-

public inspectors, notaries, land registry and cadastre

ers give a higher score for simplified regulations,

tions described in a standardized business case refer

staff, and technical experts apart from architects and

such as a one-stop shop for completing business

to a specific set of issues and may not represent the

engineers. • Time required for dealing with construc-

startup formalities.

full set of issues a business encounters. Fourth, the

tion permits to build a warehouse is the number of

In constructing the indicators, it is assumed that

time measures involve an element of judgment by the

calendar days to complete the required procedures

entrepreneurs know about all regulations and comply

expert respondents. When sources indicate different

for building a warehouse using the fastest procedure,

with them; in practice, entrepreneurs may not be

estimates, the Doing Business time indicators repre-

independent of cost. • Number of procedures for

aware of all required procedures or may avoid legally

sent the median values of several responses given

enforcing contracts is the number of independent

required procedures altogether. But where regula-

under the assumptions of the standardized case.

actions, mandated by law or court regulation, that

tion is particularly onerous, levels of informality are

Fifth, the methodology assumes that a business has

demand interaction between the parties to a con-

higher, which comes at a cost: firms in the informal

full information on what is required and does not

tract or between them and the judge or court officer.

sector usually grow more slowly, have less access

waste time when completing procedures.

• Time required for enforcing contracts is the number

to credit, and employ fewer workers—and those

of calendar days from the time of the filing of a law-

workers remain outside the protections of labor law.

suit in court to the final determination and payment.

The indicators in the table can help policymakers

• Extent of disclosure index measures the degree

understand the business environment in a country

to which investors are protected through disclosure

and—along with information from other sources such

of ownership and financial information. Higher values

as the World Bank’s Enterprise Surveys—provide

indicate more disclosure. • Time to resolve insolvency

insights into potential areas of reform.

is the number of years from time of filing for insolvency

Doing Business data are collected with a standardized survey that uses a simple business case

in court until resolution of distressed assets and payment of creditors.

to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys in 183 countries are administered through more than 8,200 local experts, including

Data sources

lawyers, business consultants, accountants, freight

Data on the business environment are from

forwarders, government officials, and other profes-

the World Bank’s Doing Business project

sionals who routinely administer or advise on legal

(www.doingbusiness.org).

and regulatory requirements.

2011 World Development Indicators

277

5.4

Stock markets Market capitalization

$ millions 2000

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

278

.. .. .. .. 166,068 2 372,794 29,935 3 1,186 .. 182,481 .. 1,742 .. 978 226,152 617 .. .. .. .. 841,385 .. .. 60,401 580,991 623,398 9,560 .. .. 2,924 1,185 2,742 .. 11,002 107,666 .. 704 28,741 2,041 .. 1,846 .. 293,635 1,446,634 .. .. 24 1,270,243 502 110,839 172 .. .. .. 458

% of GDP 2010

2000

.. .. .. .. 63,910 28 1,454,547 67,683 .. 47,000 .. 269,342 .. 3,388 .. 4,076 1,545,566 7,276 .. .. .. .. 2,160,229 .. .. 341,584 4,762,837 2,711,334 208,502 .. .. 1,445 7,099 24,912 .. 43,056 231,746 .. 5,263 82,495 4,227 .. 2,260 .. 118,160 1,926,488 .. .. 1,060 1,429,707 3,531 72,639 .. .. .. .. ..

.. .. .. .. 58.4 0.1 89.4 15.7 0.1 2.5 .. 78.5 .. 20.7 .. 17.4 35.1 4.8 .. .. .. .. 116.1 .. .. 80.3 48.5 368.6 9.5 .. .. 18.3 11.4 12.8 .. 19.4 67.3 .. 4.4 28.8 15.5 .. 32.5 .. 241.2 108.9 .. .. 0.8 66.8 10.1 88.3 0.9 .. .. .. 8.8

2011 World Development Indicators

2009

.. .. .. .. 15.9 1.6 136.1 14.1 .. 7.9 .. 55.5 .. 16.1 .. 33.8 73.2 14.6 .. .. .. .. 125.8 .. .. 128.0 100.4 1,088.3 57.0 .. .. 5.0 26.4 40.7 .. 27.7 60.4 .. 7.4 47.7 21.0 .. 13.9 .. 38.2 74.4 .. .. 6.8 39.0 9.6 16.6 .. .. .. .. ..

Market liquidity

Turnover ratio

Value of shares traded % of GDP

Value of shares traded % of market capitalization

Listed domestic companies

number

S&P/Global Equity Indices

% change

2000

2009

2000

2010

2000

2010

2009

2010

.. .. .. .. 2.1 0.0 54.3 4.9 .. 1.6 .. 16.4 .. 0.8 .. 0.8 15.7 0.4 .. .. .. .. 87.6 .. .. 8.1 60.2 223.4 0.4 .. .. 0.7 0.3 0.9 .. 11.6 57.2 .. 0.1 11.1 0.2 .. 5.7 .. 169.8 81.6 .. .. 0.1 56.3 0.2 75.7 0.1 .. .. .. ..

.. .. .. .. 0.9 0.0 82.4 6.7 .. 16.3 .. 27.1 .. 0.1 .. 0.9 40.7 0.8 .. .. .. .. 92.8 .. .. 23.0 179.6 707.4 5.5 .. .. 0.1 0.6 2.3 .. 10.8 47.9 .. 2.4 28.0 .. .. 2.0 .. 38.3 51.6 .. .. 0.0 38.7 0.2 15.7 .. .. .. .. ..

.. .. .. .. 4.8 11.9 56.5 29.8 .. 74.8 .. 20.7 .. 5.7 .. 4.7 44.6 8.7 .. .. .. .. 77.3 .. .. 9.5 158.3 61.3 3.8 .. .. 4.0 2.5 7.1 .. 57.7 86.0 .. 2.0 36.1 1.2 .. 18.0 .. 64.3 74.1 .. .. 11.3 79.1 1.4 60.4 6.4 .. .. .. ..

.. .. .. .. 4.6 0.2 90.1 79.4 .. 54.4 .. 42.0 .. 0.4 .. 3.5 66.4 2.8 .. .. .. .. 71.1 .. .. 19.7 164.4 63.9 13.4 .. .. 2.8 2.0 4.1 .. 29.4 69.1 .. 3.8 43.0 .. .. 13.1 .. 97.4 42.5 .. .. 0.3 103.0 3.4 67.7 .. .. .. .. ..

.. .. .. .. 127 105 1,330 97 2 221 .. 174 .. 26 .. 16 459 503 .. .. .. .. 1,418 .. .. 258 1,086 779 126 .. .. 21 41 64 .. 131 225 .. 30 1,076 40 .. 23 .. 154 808 .. .. 269 1,022 22 329 7 .. .. .. 94

.. .. .. .. 101 2 1,913 72 .. 302 .. 161 .. 38 .. 21 373 390 .. .. .. .. 3,805 .. .. 227 2,063 1,396 84 .. .. 9 38 221 .. 16 196 .. 40 211 61 .. 15 .. 123 901 .. .. 143 571 35 287 .. .. .. .. ..

.. .. .. .. 97.8 a .. 72.4 57.0 .. 38.6a .. 54.5 .. .. .. 24.3 a 125.1 17.2a .. .. .. .. 57.5 .. .. 84.0 66.3 67.1 75.7a .. .. .. –10.7a 31.1 a .. 23.0 40.6 .. –13.1 a 35.6 .. .. 32.9 a .. 17.5 25.6 b .. .. .. 25.8 c –42.7a 22.1 .. .. .. .. ..

.. .. .. .. 55.3a .. 12.5 10.9 .. 37.6a .. 0.5 .. .. .. –6.8a 6.5 –15.2a .. .. .. .. 22.0 .. .. 47.2 6.9 21.3 44.1a .. .. .. 19.3a –0.4 a .. 0.2 25.1 .. 9.7a 11.5 .. .. 56.0 a .. 10.7 –9.9 b .. .. .. 7.4 c 94.1a –43.8 .. .. .. .. ..

Market capitalization

$ millions 2000

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

12,021 148,064 26,834 7,350 .. 81,882 64,081 768,364 3,582 3,157,222 4,943 1,342 1,283 .. 171,587 .. 20,772 4 .. 563 1,583 .. .. .. 1,588 7 .. .. 116,935 .. .. 1,331 125,204 38 37 10,899 .. .. 311 790 640,456 18,866 .. .. 4,237 65,034 3,463 6,581 2,794 1,520 224 10,562 25,957 31,279 60,681 .. 5,152

% of GDP 2010

27,708 1,615,860 360,388 86,616 .. 33,722 218,055 318,140 6,626 4,099,591 30,864 60,742 14,461 .. 1,089,217 .. 119,621 79 .. 1,252 12,586 .. .. .. 5,661 2,647 .. 1,363 410,534 .. .. 6,506 454,345 .. 1,093 69,153 .. .. 1,176 4,843 661,204 36,295 .. .. 50,883 250,922 20,267 38,169 10,917 9,742 42 99,831 157,321 190,235 81,996 .. 123,592

2000

25.1 32.2 16.3 7.3 .. 84.8 51.4 70.0 39.8 67.6 58.4 7.3 10.1 .. 32.2 .. 55.1 0.3 .. 7.2 9.2 .. .. .. 13.9 0.2 .. .. 124.7 .. .. 29.0 21.5 3.2 3.4 29.4 .. .. 8.0 14.4 166.3 36.7 .. .. 9.2 38.6 17.4 8.9 24.0 49.3 3.5 19.8 34.2 18.3 51.9 .. 29.0

Market liquidity

Turnover ratio

Listed domestic companies

Value of shares traded % of GDP

Value of shares traded % of market capitalization

number

5.4

STATES AND MARKETS

Stock markets

S&P/Global Equity Indices

% change

2009

2000

2009

2000

2010

2000

2010

2009

2010

21.9 85.6 33.0 19.1 .. 13.2 93.2 15.0 51.4 66.6 127.0 50.0 36.6 .. 100.5 .. 72.4 1.6 .. 7.0 37.3 .. .. .. 12.0 10.0 .. 29.3 132.6 .. .. 55.2 38.9 .. 10.2 68.8 .. .. 9.1 43.8 68.5 52.9 .. .. 19.3 59.5 37.5 20.5 32.6 116.1 0.3 53.5 49.7 31.5 42.4 .. 89.4

25.4 110.8 8.7 1.1 .. 14.9 18.8 70.9 0.8 57.7 4.9 0.5 0.4 .. 200.2 .. 11.2 1.7 .. 2.9 0.7 .. .. .. 1.8 3.3 .. .. 62.4 .. .. 1.6 7.8 1.9 0.7 3.0 .. .. 0.6 0.6 175.9 21.0 .. .. 0.6 35.7 2.8 44.6 1.3 0.0 0.1 2.9 10.8 8.5 46.5 .. 1.3

20.1 79.1 21.3 5.2 .. 8.1 45.2 21.8 1.0 82.7 54.4 3.5 1.7 .. 190.0 .. 82.9 1.5 .. 0.1 3.0 .. .. .. 0.8 0.7 .. 0.4 37.8 .. .. 3.8 8.8 0.2 0.4 32.2 .. .. 0.2 1.8 76.3 29.4 .. .. 2.6 64.9 12.6 14.5 0.2 0.2 0.1 2.4 10.7 13.0 19.7 .. 25.9

85.8 306.5 31.5 7.4 .. 19.2 36.6 104.0 2.5 69.9 7.7 4.9 3.5 .. 376.6 .. 21.3 580.6 .. 47.8 6.7 .. .. .. 14.8 1,612.9 .. .. 44.6 .. .. 5.1 32.5 80.2 23.2 8.9 .. .. 4.4 5.4 101.4 45.9 .. .. 7.3 93.4 14.2 486.8 4.7 0.1 3.5 12.7 24.1 48.1 85.5 .. 4.5

94.5 75.6 48.1 22.9 .. 52.9 66.7 169.7 3.3 114.5 30.1 3.9 8.6 .. 168.9 .. 38.8 11.9 .. 1.8 14.7 .. .. .. 5.8 2.0 .. 1.5 27.1 .. .. 6.4 27.3 .. 6.4 16.3 .. .. 1.8 1.9 98.4 20.8 .. .. 12.5 90.8 18.2 36.2 2.0 .. .. 4.7 22.6 47.6 34.6 .. 17.3

60 5,937 290 304 .. 76 654 291 46 2,561 163 23 57 .. 1,308 .. 77 80 .. 64 12 .. .. .. 54 1 .. .. 795 .. .. 40 179 34 410 53 .. .. 13 110 234 142 .. .. 195 191 131 762 29 7 56 230 228 225 109 .. 22

48 4,987 420 341 .. 50 596 291 39 3,553 277 60 53 .. 1,781 .. 215 11 .. 33 10 .. .. .. 39 34 .. 14 957 .. .. 86 130 .. 336 73 .. .. 7 190 113 102 .. .. 215 195 120 644 34 10 50 199 251 569 47 .. 43

73.0 94.1 130.1 .. .. 44.7 56.8 23.1 –15.8 a 16.4 d –13.9 a 1.5a 0.6a .. 67.2 .. –10.4 a .. .. 2.2a 43.4 a .. .. .. 36.7a .. .. .. 46.7 .. .. 44.2a 55.8 .. .. –1.7 .. .. 22.6a .. 41.7 40.4 .. .. –35.4 a 91.4 22.0 a 56.7a 15.4 a .. .. 79.3 71.5 41.9 35.0 .. 5.1 a

–10.8 18.7 37.9 .. .. –7.7 7.4 –17.4 22.4 a 9.6d –8.6a –1.0a 33.8a .. 25.3 .. 29.1a .. .. 39.4 a –8.7a .. .. .. 44.0a .. .. .. 35.1 .. .. 8.2a 26.6 .. .. 13.1 .. .. 24.2a .. 1.2 5.2 .. .. 20.3a 13.7 12.2a 15.3a 12.8a .. .. 51.3 56.7 11.3 –16.6 .. 27.7a

2011 World Development Indicators

279

5.4

Stock markets Market capitalization

$ millions 2000

% of GDP 2010

Romania 1,069 32,385 Russian Federation 38,922 1,004,525 Rwanda .. .. Saudi Arabia 67,171 353,414 Senegal .. .. Serbia 734 9,690 Sierra Leone .. .. Singapore 152,827 370,091 Slovak Republic 1,217 4,150 Slovenia 2,547 9,428 Somalia .. .. South Africa 204,952 1,012,538 Spain 504,219 1,171,615 Sri Lanka 1,074 19,924 Sudan .. .. Swaziland 73 .. Sweden 328,339 581,174 Switzerland 792,316 1,229,357 Syrian Arab Republic .. .. Tajikistan .. .. Tanzania 233 1,264 Thailand 29,489 277,732 Timor-Leste .. .. Togo .. .. Trinidad and Tobago 4,330 12,158 Tunisia 2,828 10,682 Turkey 69,659 306,662 Turkmenistan .. .. Uganda 35 .. Ukraine 1,881 39,457 United Arab Emirates 5,727 104,669 United Kingdom 2,576,992 3,107,038 United States 15,104,037 17,138,978 Uruguay 161 157 Uzbekistan 32 .. Venezuela, RB 8,128 3,991 Vietnam .. 20,385 West Bank and Gaza 765 2,450 Yemen, Rep. .. .. Zambia 236 2,817 Zimbabwe 2,432 11,476 World 32,187,124 s 56,172,634 s 86,835 Low income .. Middle income 1,941,548 13,277,006 Lower middle income 879,123 7,570,880 Upper middle income 1,062,425 5,706,126 Low & middle income 1,948,214 13,363,841 East Asia & Pacific 780,487 6,001,435 Europe & Central Asia 115,145 1,473,816 Latin America & Carib. 620,023 2,750,758 Middle East & N. Africa 57,110 294,845 South Asia 157,695 1,725,795 Sub-Saharan Africa 217,754 1,117,191 High income 30,238,910 42,808,793 Euro area 5,435,393 6,276,893

2000

2.9 15.0 .. 35.6 .. 4.9 .. 164.8 4.2 12.8 .. 154.2 86.8 6.6 .. 4.9 132.8 317.0 .. .. 2.3 24.0 .. .. 53.1 14.5 26.1 .. 0.6 6.0 8.1 174.4 152.6 0.7 0.2 6.9 .. 18.6 .. 7.3 36.8 101.7 w .. 36.5 36.2 36.8 36.1 47.1 17.5 31.7 19.9 26.1 89.8 115.2 86.8

Market liquidity

Turnover ratio

Value of shares traded % of GDP

Value of shares traded % of market capitalization

2009

2000

18.8 69.9 .. 84.8 .. 26.8 .. 170.5 5.3 24.3 .. 247.0 88.8 19.4 .. 6.9 106.5 217.7 .. .. 5.4 52.4 .. .. 52.6 23.1 36.7 .. .. 14.8 47.6 128.6 106.8 0.4 .. 2.7 21.8 .. .. 17.4 161.4 85.2 w 37.7 73.2 82.2 62.1 72.6 91.0 50.8 52.9 38.0 73.3 154.1 89.9 49.3

0.6 7.8 .. 9.2 .. 0.1 .. 98.7 3.1 2.3 .. 58.3 169.8 0.9 .. 0.0 157.7 243.7 .. .. 0.4 19.0 .. .. 1.7 3.2 67.2 .. 0.0 0.9 0.2 124.2 321.9 0.0 0.1 0.6 .. 4.6 .. 0.2 4.2 151.4 w .. 34.5 54.6 17.5 34.0 49.8 30.1 8.4 5.1 90.2 32.3 175.5 80.2

2009

1.2 55.4 .. 89.7 .. 1.3 .. 138.4 0.2 2.1 .. 120.0 109.5 2.1 .. .. 96.1 161.7 .. .. 0.1 51.2 .. .. 1.1 3.2 39.6 .. .. 0.5 28.5 156.5 331.0 0.0 0.0 0.0 6.8 .. .. 0.8 16.1 142.5 w 7.9 82.7 124.3 31.5 81.9 149.0 38.3 20.9 16.2 67.0 48.1 165.3 45.6

2000

24.3 36.6 .. 27.1 .. .. .. 52.1 78.7 19.7 .. 33.2 210.7 10.8 .. 0.3 111.2 82.0 .. .. 19.4 52.9 .. .. 3.1 22.6 196.5 .. 1.7 19.2 1.8 66.6 200.8 0.9 25.7 8.8 .. 23.4 .. 3.1 11.3 140.2 w 18.3 93.8 162.2 44.2 93.5 116.2 131.0 27.1 21.4 308.8 31.7 143.0 90.1

2010

5.4 85.7 .. 60.5 .. 2.2 .. 82.9 3.9 2.6 .. 39.6 76.0 23.6 .. .. 86.8 75.6 .. .. .. 104.8 .. .. 1.2 17.2 158.4 .. .. 7.5 25.6 101.9 189.1 .. .. 0.8 141.4 18.7 .. .. .. 122.0 w 32.5 101.1 132.4 55.7 100.8 146.0 91.2 46.1 27.7 73.5 37.1 128.5 75.0

Listed domestic companies

number 2000

2010

S&P/Global Equity Indices

% change 2009

5,555 1,383 26.1 a 249 345 106.6 .. .. .. 75 146 28.5 e .. .. .. 6 7 .. .. .. .. 418 461 76.7 493 90 –23.1 a 38 71 16.1 a .. .. .. 616 360 53.7 1,019 3,310 29.0 239 241 118.0 a .. .. .. 6 5 .. 292 331 66.0 252 246 24.5 .. .. .. .. .. .. 4 11 .. 381 541 72.8 .. .. .. .. .. .. 27 37 –10.2 a 44 54 40.6 a 315 337 99.6 .. .. .. 2 8 .. 139 183 31.1a 54 101 24.6 a 1,904 2,056 35.2 f 7,524 4,279 23.5g 16 6 .. 5 .. .. 85 55 .. .. 164 46.9 a 24 41 .. .. .. .. 9 19 16.7a 69 76 –83.8 47,751 s 47,071 s .. 719 21,522 16,778 11,444 11,088 10,078 5,690 22,094 17,497 3,190 4,758 7,199 2,963 1,672 1,457 1,676 1,007 7,269 6,364 1,088 948 25,657 29,574 5,051 6,278

2010

–6.6a 21.7 .. 9.0e .. .. .. 18.4 5.4a –20.3a .. 32.1 –24.5 84.6a .. .. 32.6 11.0 .. .. .. 52.1 .. .. 0.8a 11.7a 21.4 .. .. 53.8 a –6.8a 5.2f 12.8g .. .. .. 0.5a .. .. 17.4 a ..

a. Refers to the S&P Frontier BMI index. b. Refers to the CAC 40 index. c. Refers to the DAX index. d. Refers to the Nikkei 225 index. e. Refers to Saudi Arabia country index. f. Refers to the FTSE 100. g. Refers to the S&P 500 index.

280

2011 World Development Indicators

About the data

5.4

STATES AND MARKETS

Stock markets Definitions

The development of an economy’s financial markets

countries. Market capitalization shows the overall

• Market capitalization (also known as market

is closely related to its overall development. Well

size of the stock market in U.S. dollars and as a

value) is the share price times the number of shares

functioning financial systems provide good and eas-

percentage of GDP. The number of listed domestic

outstanding. • Market liquidity is the total value

ily accessible information. That lowers transaction

companies is another measure of market size. Mar-

of shares traded during the period divided by gross

costs, which in turn improves resource allocation and

ket size is positively correlated with the ability to

domestic product (GDP). This indicator complements

boosts economic growth. Both banking systems and

mobilize capital and diversify risk.

the market capitalization ratio by showing whether

stock markets enhance growth, the main factor in

Market liquidity, the ability to easily buy and sell

market size is matched by trading. • Turnover ratio

poverty reduction. At low levels of economic develop-

securities, is measured by dividing the total value

is the total value of shares traded during the period

ment commercial banks tend to dominate the finan-

of shares traded by GDP. The turnover ratio—the

divided by the average market capitalization for the

cial system, while at higher levels domestic stock

value of shares traded as a percentage of market

period. Average market capitalization is calculated as

markets tend to become more active and efficient

capitalization—is also a measure of liquidity as well

the average of the end-of-period values for the cur-

relative to domestic banks.

as of transaction costs. (High turnover indicates low

rent period and the previous period. • Listed domes-

Open economies with sound macroeconomic poli-

transaction costs.) The turnover ratio complements

tic companies are the domestically incorporated

cies, good legal systems, and shareholder protection

the ratio of value traded to GDP, because the turn-

companies listed on the country’s stock exchanges

attract capital and therefore have larger financial mar-

over ratio is related to the size of the market and the

at the end of the year. This indicator does not include

kets. Recent research on stock market development

value traded ratio to the size of the economy. A small,

investment companies, mutual funds, or other col-

shows that modern communications technology and

liquid market will have a high turnover ratio but a low

lective investment vehicles. • S&P/Global Equity

increased financial integration have resulted in more

value of shares traded ratio. Liquidity is an impor-

Indices measure the U.S. dollar price change in the

cross-border capital flows, a stronger presence of

tant attribute of stock markets because, in theory,

stock markets.

financial firms around the world, and the migration of

liquid markets improve the allocation of capital and

stock exchange activities to international exchanges.

enhance prospects for long-term economic growth.

Many firms in emerging markets now cross-list on inter-

A more comprehensive measure of liquidity would

national exchanges, which provides them with lower

include trading costs and the time and uncertainty

cost capital and more liquidity-traded shares. However,

in finding a counterpart in settling trades.

this also means that exchanges in emerging markets

Standard & Poor’s Index Services, the source for

may not have enough financial activity to sustain them,

all the data in the table, provides regular updates on

putting pressure on them to rethink their operations.

21 emerging stock markets and 36 frontier markets.

The indicators in the table are from Standard &

Standard & Poor’s maintains a series of indexes for

Poor’s Emerging Markets Data Base. They include

investors interested in investing in stock markets in

measures of size (market capitalization, number of

developing countries. The S&P/IFCI index, Standard

listed domestic companies) and liquidity (value of

& Poor’s leading emerging markets index, is designed

shares traded as a percentage of gross domestic

to be sufficiently investable to support index tracking

product, value of shares traded as a percentage of

portfolios in emerging market stocks that are legally

market capitalization). The comparability of such indi-

and practically open to foreign portfolio investment.

cators across countries may be limited by concep-

The S&P/Frontier BMI measures the performance of

tual and statistical weaknesses, such as inaccurate

36 smaller and less liquid markets. The individual

reporting and differences in accounting standards.

country indexes include all publicly listed equities

The percentage change in stock market prices in U.S.

representing an aggregate of at least 80 percent or

dollars for developing economies is from Standard

more of market capitalization in each market. These

& Poor’s Global Equity Indices (S&P IFCI) and Stan-

indexes are widely used benchmarks for international

dard & Poor’s Frontier Broad Market Index (BMI). The

portfolio management. See www.standardandpoors.

percentage change for France, Germany, Japan, the

com for further information on the indexes. Data sources

United Kingdom, and the United States is from local

Because markets included in Standard & Poor’s

stock market prices. The indicator is an important

emerging markets category vary widely in level of

Data on stock markets are from Standard & Poor’s

measure of overall performance. Regulatory and

development, it is best to look at the entire category

Global Stock Markets Factbook 2010, which draws

institutional factors that can affect investor confi -

to identify the most significant market trends. And it

on the Emerging Markets Data Base, supple-

dence, such as entry and exit restrictions, the exis-

is useful to remember that stock market trends may

mented by other data from Standard & Poor’s.

tence of a securities and exchange commission, and

be distorted by currency conversions, especially when

The firm collects data through an annual survey

the quality of laws to protect investors, may influence

a currency has registered a significant devaluation.

of the world’s stock exchanges, supplemented by

the functioning of stock markets but are not included in the table. Stock market size can be measured in various

About the data is based on Demirgüç-Kunt and

information provided by its network of correspon-

Levine (1996), Beck and Levine (2001), and Claes-

dents and by Reuters. Data on GDP are from the

sens, Klingebiel, and Schmukler (2002).

World Bank’s national accounts data files.

ways, and each may produce a different ranking of

2011 World Development Indicators

281

5.5

Financial access, stability, and efficiency Getting credit Strength of legal rights index 0–10 (weak to strong)

Financial access and outreach

Deposit Loan accounts accounts Depth of at at Commercial Automated Pointcredit commercial commercial bank teller of-sale banks banks branches machines terminals information per per per per per index 100,000 100,000 100,000 1,000 1,000 0–6 adults adults adults adults adults (low to high)

June 2010 June 2010

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

282

6 9 3 4 4 6 9 7 6 7 3 7 3 1 5 7 3 8 3 2 8 3 6 3 3 4 6 10 5 3 3 5 3 6 .. 6 9 3 3 3 5 2 7 4 7 7 3 5 7 7 8 3 8 3 3 3 6

0 4 2 3 6 5 5 6 5 2 5 4 1 6 5 4 5 6 1 1 0 2 6 2 1 5 4 5 5 0 2 5 1 4 .. 5 4 6 5 6 6 0 5 2 5 4 2 0 6 6 3 5 6 0 1 2 6

2011 World Development Indicators

2009

2009

2009

.. 451 683 .. 875 572 .. 2,442 702 319 .. 3,725 .. 274 380 481 .. 1,987 .. 21 76 .. .. .. .. 746 .. .. 1,151 6 .. .. .. .. .. 1,680 .. .. 494 .. 737 .. 2,752 82 .. .. .. 269 661 .. 270 3,219 1,050 .. .. 330 744

4 102 .. .. 503 192 .. .. .. 42 .. .. .. 72 344 80 390 456 .. 1 25 .. .. .. .. 629 .. .. .. .. .. .. .. .. .. .. .. 310 .. .. .. .. 1,022 1 .. .. .. 44 349 .. .. 1,297 374 .. .. 11 ..

1.1 21.4 5.3 5.5 13.3 15.7 31.8 .. 8.6 5.2 44.9 50.0 .. 6.3 25.0 6.9 12.2 88.1 .. 1.7 3.7 .. 23.7 .. .. 15.0 .. 24.4 13.7 0.3 .. .. .. 33.2 .. 22.4 46.7 10.0 1.6 .. 8.2 .. 22.2 1.2 18.5 23.0 .. 5.5 18.6 16.3 4.4 38.8 33.1 .. .. .. 1.5

2009

0.18 26.87 4.13 7.82 33.04 22.22 159.30 118.37 23.05 .. 29.71 85.96 .. 15.11 27.14 29.26 110.19 78.22 .. 0.04 .. .. 202.78 .. .. 55.56 .. .. 26.31 .. .. 53.35 .. 88.62 .. 38.40 70.42 27.21 26.01 .. 22.86 .. 89.09 .. 38.74 102.55 .. 1.48 28.77 79.74 4.16 76.06 22.18 .. .. 0.58 21.89

Ratio of Bank bank noncapital to asset performing loans to total ratio gross loans

Domestic credit provided by banking sector

Interest rate spread

Risk premium on lending Prime lending rate minus treasury bill rate percentage points 2009

%

%

% of GDP

Lending rate minus deposit rate percentage points

2009

2009

2009

2009

2009

.. 123 8 25 .. 94 3,939 4,890 112 .. 165 1,086 .. 33 502 .. 1,471 683 .. 0 36 .. 2,202 .. .. 450 .. .. .. .. .. 0 .. 2,121 .. 651 2,023 .. .. .. 250 .. 1,417 .. 66 2,153 .. 5 169 799 4 3,827 486 .. .. .. ..

.. 8.7 .. .. 13.3 21.0 5.0 7.0 .. 6.5 16.6 4.5 .. 8.7 15.2 .. 9.5 10.8 .. .. .. .. 5.7 .. .. 7.4 5.6 12.7 13.6 .. .. 13.9 .. 13.9 .. 6.1 5.7 9.1 7.7 6.4 13.2 .. 8.5 .. 6.4 4.5 16.2 .. 18.3 4.8 17.0 6.1 10.5 .. .. .. ..

1.5 68.5 -8.9 29.2 28.0 19.9 143.6 141.1 23.1 60.4 34.6 119.3 19.1 49.5 58.3 -1.0 97.5 69.4 15.2 36.5 19.0 6.9 178.1 17.2 8.3 98.8 145.2 166.8 37.2 7.6 -15.9 54.3 22.8 76.9 .. 62.4 223.0 40.6 18.9 75.4 44.5 112.1 106.2 37.1 98.7 128.4 7.5 38.7 33.2 131.8 27.9 112.7 37.7 .. 4.9 25.8 54.1

.. 5.9 6.3 8.1 4.1 10.1 3.2 .. 7.8 6.4 1.0 .. .. 8.9 4.3 6.3 35.4 5.2 .. .. .. 10.8 2.3 10.8 10.8 5.2 3.1 5.0 6.9 49.5 10.8 12.8 .. 8.4 .. 4.7 .. 10.3 7.1 5.5 .. .. 4.6 3.3 .. .. 10.8 11.5 15.2 .. .. .. 8.3 .. .. 16.2 8.6

.. 10.5 .. .. 3.0 4.8 1.2 2.3 .. 11.2 4.2 2.7 .. 3.5 5.9 .. 4.2 6.4 .. .. .. .. 1.3 .. .. 3.0 1.6 1.1 4.1 .. .. 2.0 .. 7.8 .. 4.6 0.3 4.0 2.9 13.4 3.6 .. 5.2 .. 0.7 3.6 9.8 .. 6.3 3.3 16.2 7.7 2.7 .. .. .. ..

.. 6.4 7.3 .. .. 9.3 2.9 .. 16.7 .. .. 5.6 .. 9.5 .. .. 34.9 6.2 .. .. .. .. 2.0 .. .. .. .. 4.9 .. .. .. .. .. .. .. 4.7 .. .. .. 2.1 .. .. .. 7.3 .. .. .. .. 19.5 .. .. .. .. .. .. .. ..

Getting credit Strength of legal rights index 0–10 (weak to strong)

Financial access and outreach

Deposit Loan accounts accounts Depth of at at Commercial Automated Pointcredit commercial commercial bank teller of-sale banks banks branches machines terminals information per per per per per index 100,000 100,000 100,000 1,000 1,000 0–6 adults adults adults adults adults (low to high)

June 2010 June 2010

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

7 8 3 4 3 8 9 3 8 7 4 4 10 .. 7 8 4 10 4 9 3 6 4 .. 5 7 2 7 10 3 3 5 5 8 6 3 2 .. 8 6 6 10 3 3 8 7 4 6 6 5 3 7 3 9 3 7 3

5 4 4 4 0 5 5 5 0 6 2 5 4 .. 6 4 4 3 0 5 5 0 1 .. 6 4 0 0 6 1 1 3 6 0 3 5 4 .. 5 2 5 5 5 1 0 4 2 4 6 3 6 6 3 4 5 5 2

2009

2009

2009

1,571 680 484 .. .. .. 2,254 763 1,172 .. 814 .. 296 .. .. .. .. 115 .. 1,219 1,310 199 .. .. 2,142 1,302 34 124 2,227 .. 37 2,110 1,014 .. 1,935 277 112 .. 466 165 1,772 .. 198 .. .. .. .. 226 757 .. 80 716 517 1,527 .. 1,026 ..

.. 124 181 .. .. .. 1,055 597 215 .. 160 .. 70 .. .. .. .. 25 .. 687 .. 18 .. .. 381 962 21 17 973 .. .. 417 .. .. 272 .. 20 .. 356 38 .. .. 185 .. .. .. .. 47 435 .. 89 367 .. .. .. .. ..

17.1 9.3 6.7 28.8 .. 34.1 19.8 53.0 7.2 12.5 16.2 21.6 4.0 .. 12.6 .. 15.1 6.3 1.7 12.0 29.1 1.9 .. .. 28.8 22.1 1.0 1.8 11.6 .. 3.8 19.4 14.0 9.7 56.7 11.6 2.9 .. 7.3 3.2 26.1 31.7 6.8 .. .. 35.0 22.1 7.5 18.9 2.8 6.2 7.5 10.5 32.6 55.9 16.6 ..

2009

54.24 3.55 13.44 23.97 .. .. 47.38 93.93 21.89 .. .. 52.83 6.67 .. .. .. 50.05 .. 3.06 .. 38.55 7.13 .. .. 51.69 45.98 0.96 1.48 43.25 .. 0.74 37.71 40.15 .. 18.18 16.65 4.32 .. 27.31 1.13 63.78 72.34 .. .. .. 59.73 .. 3.39 36.94 .. .. 17.67 13.33 42.16 189.60 43.33 ..

Ratio of Bank bank noncapital to asset performing loans to total ratio gross loans

Domestic credit provided by banking sector

5.5 Interest rate spread

Risk premium on lending Prime lending rate minus treasury bill rate percentage points 2009

%

%

% of GDP

Lending rate minus deposit rate percentage points

2009

2009

2009

2009

2009

585 .. 120 1,353 .. .. .. 2,386 674 .. .. 173 .. .. .. .. 904 .. .. .. 1,293 .. .. .. 1,413 1,297 2 2 941 .. .. 647 .. .. 448 46 34 .. 217 .. 2,286 3,916 .. .. .. 2,827 .. 47 427 .. .. 40 .. 253 2,548 1,398 ..

8.5 6.4 10.3 .. .. 5.6 6.0 8.0 .. 4.7 11.0 -9.3 12.7 .. 10.9 .. 12.1 .. .. 7.4 7.0 7.9 .. .. 7.9 11.4 .. .. 9.0 .. .. .. 9.7 16.0 .. 7.6 7.7 .. 7.9 .. 4.3 .. .. .. 18.4 6.0 13.5 10.1 11.7 .. 8.7 9.9 11.1 9.0 6.5 .. ..

79.9 69.4 36.9 37.2 -16.3 219.8 78.1 141.6 59.8 320.5 99.3 54.6 44.8 .. 112.4 14.3 65.1 14.0 10.5 93.2 165.0 -15.5 149.5 -65.9 69.3 44.0 11.6 32.0 137.4 10.7 .. 109.7 44.1 41.6 32.2 100.5 22.8 .. 43.5 69.6 224.4 154.2 67.5 12.2 35.9 .. 41.9 48.4 81.6 39.1 25.5 18.1 49.4 61.5 196.1 .. 75.7

5.2 .. 5.2 -1.1 7.8 .. 2.6 .. 9.5 1.3 4.3 .. 8.8 .. 2.2 10.1 3.3 19.2 19.3 8.2 2.3 8.2 10.1 3.5 3.6 3.0 33.5 21.8 3.0 .. 15.5 10.8 5.1 5.6 8.4 .. 6.2 5.0 4.9 5.5 -0.6 6.3 8.0 .. 5.1 2.0 3.3 5.9 4.8 7.8 26.8 18.2 5.8 .. .. .. 2.8

6.7 2.3 3.3 .. .. 9.0 1.5 7.0 .. 1.7 6.7 21.2 7.9 .. 1.2 4.4 9.7 .. .. 16.4 6.0 4.0 .. .. 19.3 8.9 .. .. 3.7 .. .. .. 3.1 16.3 .. 5.5 1.8 .. 2.7 .. .. .. .. .. 6.6 1.5 3.5 12.2 1.4 .. 1.6 2.7 4.1 7.6 3.2 .. ..

STATES AND MARKETS

Financial access, stability, and efficiency

2011 World Development Indicators

2.6 .. .. .. 1.8 .. 2.3 3.8 -3.5 1.6 .. .. 7.4 .. .. .. 5.2 12.5 11.5 5.8 4.7 5.2 .. .. -0.1 .. 37.4 15.1 3.0 .. 13.1 .. 1.6 9.2 15.0 .. 5.1 .. 2.9 1.7 .. 7.6 .. .. 14.6 .. .. 2.0 .. 3.0 .. .. 5.3 .. .. .. ..

283

5.5

Financial access, stability, and efficiency Getting credit Strength of legal rights index 0–10 (weak to strong)

Financial access and outreach

Deposit Loan accounts accounts Depth of at at Commercial Automated Pointcredit commercial commercial bank teller of-sale banks banks branches machines terminals information per per per per per index 100,000 100,000 100,000 1,000 1,000 0–6 adults adults adults adults adults (low to high)

June 2010 June 2010

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

284

8 3 8 5 3 8 6 10 9 5 .. 9 6 4 5 6 5 8 1 3 8 4 1 3 8 3 4 .. 7 9 4 9 8 5 2 2 8 0 2 9 6 5.5 u 4.9 5.1 4.6 5.7 5.0 5.8 6.3 5.2 2.5 5.4 4.6 6.7 6.3

5 5 4 6 1 5 0 4 4 2 .. 6 5 5 0 5 4 5 2 0 0 5 0 1 4 5 5 .. 4 3 5 6 6 6 3 0 5 3 2 5 0 3.0 u 1.3 3.1 2.6 3.6 2.6 1.9 4.1 3.4 3.1 2.1 1.6 4.3 4.1

2011 World Development Indicators

2009

2009

2009

.. .. 202 .. .. .. .. 2,305 .. 1,394 .. 788 741 1,652 .. 270 .. .. 157 .. .. 1,498 .. .. .. 672 1,851 .. 154 3,755 .. .. 1,761 507 .. 518 .. .. 106 293 139

431 .. 2 .. .. .. .. 899 .. .. .. 297 310 487 .. 98 .. .. 23 .. .. 276 .. .. .. 176 315 .. 21 .. .. .. .. 439 .. 484 .. .. 6 19 ..

27.6 2.9 3.1 .. .. 44.9 .. 11.0 25.7 15.7 .. 8.0 40.5 9.1 .. 2.9 22.8 .. 2.2 3.9 1.8 10.9 .. .. .. 13.6 17.3 .. 1.9 3.3 .. .. 35.4 13.9 .. 18.5 3.3 .. 1.8 3.5 2.8

2009

50.63 65.60 0.38 .. .. 41.31 .. 50.64 47.76 99.47 .. 54.85 157.10 10.46 .. 15.96 36.94 93.70 0.95 2.97 2.63 65.48 .. .. .. 14.26 40.99 .. 2.24 70.09 .. 127.07 169.23 30.57 .. 27.99 .. .. 2.44 4.54 ..

2009

460 275 1 .. .. 959 .. 1,887 611 1,925 .. .. 3,523 .. .. 52 .. 2,004 .. 2 11 .. .. .. .. 172 3,046 .. 3 293 .. 2,177 2,156 275 .. .. .. .. 17 11 ..

Ratio of Bank bank noncapital to asset performing loans to total ratio gross loans

Domestic credit provided by banking sector

Interest rate spread

Risk premium on lending Prime lending rate minus treasury bill rate percentage points 2009

%

%

% of GDP

Lending rate minus deposit rate percentage points

2009

2009

2009

2009

15.3 9.7 13.1 3.3 18.7 15.5 16.5 2.3 5.3 2.3 .. 5.9 5.1 .. .. 8.1 2.0 0.4 .. .. .. 5.3 .. .. .. 13.2 5.6 .. 4.2 40.2 4.8 3.5 5.4 1.0 .. 3.0 .. .. .. .. .. 4.2 m .. 4.8 5.1 4.2 5.3 .. 9.3 3.0 .. 10.5 .. 3.4 3.6

52.7 33.8 .. 0.6 26.6 44.8 10.7 91.2 53.8 94.5 .. 183.5 228.4 39.6 20.0 9.1 143.8 191.0 45.1 27.5 18.1 136.9 -18.4 30.2 26.5 75.2 63.0 .. 11.2 88.5 114.5 228.9 230.5 27.9 .. 20.5 123.0 .. 19.3 18.5 .. 169.0 w 35.1 89.4 110.3 63.3 88.4 134.2 47.1 67.1 40.9 65.6 78.5 201.8 152.0

5.3 6.7 9.8 .. .. 0.0 14.8 5.1 4.3 4.5 .. 3.2 .. 5.1 .. 6.0 .. 2.7 3.7 17.1 7.1 4.9 10.3 .. 8.5 .. .. .. 11.2 7.1 .. .. .. 10.9 .. 3.5 3.1 .. 7.3 15.0 457.5 6.2 m 11.5 6.3 7.3 5.5 6.8 7.1 5.7 7.7 4.3 5.9 8.5 .. ..

7.6 15.7 13.0 11.9 9.3 21.0 18.9 10.5 9.6 8.3 .. 6.7 6.8 .. .. 16.9 5.0 5.5 .. .. .. 9.8 .. .. .. .. 13.3 .. 13.4 13.1 16.0 5.4 11.0 8.9 .. 9.4 .. .. .. .. .. 9.4 m .. 10.1 10.0 9.7 .. .. 13.3 9.6 .. 6.4 .. 6.8 6.5

6.4 .. 8.9 .. .. 1.4 9.0 5.0 .. 4.8 .. 3.9 .. 2.7 .. 3.4 .. 2.8 .. .. 7.9 4.7 .. .. 9.2 .. .. .. 13.9 .. .. 0.1 3.1 3.4 .. .. 2.0 .. 4.5 6.7 330.2

5.5

STATES AND MARKETS

Financial access, stability, and efficiency About the data Access to finance can expand opportunities for all with

all nonfinancial and financial assets. Data are from

consumer loans, business loans, trade loans, student

higher levels of access and use of banking services

internally consistent financial statements.

loans, emergency loans, agricultural loans, and the

associated with lower financing obstacles for people

The ratio of bank nonperforming loans to total gross

like. • Commercial banks branches are retail locations

and businesses. A stable financial system that pro-

loans, a measure of bank health and efficiency, helps

offering a wide array of face-to-face and automated

motes efficient savings and investment is also crucial

identify problems with asset quality in the loan portfo-

financial services. • Automated teller machines are

for a thriving democracy and market economy.

lio. A high ratio may signal deterioration of the credit

computerized telecommunications devices that pro-

There are several aspects of access to financial ser-

portfolio. International guidelines recommend that

vide clients of a financial institution with access to

vices: availability, cost, and quality of services. The

loans be classified as nonperforming when payments

financial transactions in a public place. • Point-of-sale

development and growth of credit markets depend on

of principal and interest are 90 days or more past

terminals are the equipment used to manage the sell-

access to timely, reliable, and accurate data on bor-

due or when future payments are not expected to be

ing process by a salesperson-accessible interface in

rowers’ credit experiences. Access to credit can be

received in full. Domestic credit provided by the bank-

the location where a transaction takes place. • Bank

improved by making it easy to create and enforce col-

ing sector as a share of GDP is a measure of bank-

capital to asset ratio is the ratio of bank capital and

lateral agreements and increasing information about

ing sector depth and financial sector development in

reserves to total assets. Capital and reserves include

potential borrowers’ creditworthiness. Lenders look at

terms of size. In a few countries governments may hold

funds contributed by owners, retained earnings, gen-

a borrower’s credit history and collateral. Where credit

international reserves as deposits in the banking sys-

eral and special reserves, provisions, and valuation

registries and effective collateral laws are absent—

tem rather than in the central bank. Since the claims

adjustments. • Ratio of bank nonperforming loans to

as in many developing countries—banks make fewer

on the central government are a net item (claims on

total gross loans is the value of nonperforming loans

loans. Indicators that cover getting credit include the

the central government minus central government

divided by the total value of the loan portfolio (including

strength of legal rights index and the depth of credit

deposits), this net figure may be negative, resulting

nonperforming loans before the deduction of loan loss

information index.

in a negative figure of domestic credit provided by the

provisions). The amount recorded as nonperforming

banking sector.

should be the gross value of the loan as recorded

The “unbanked” have to resort to informal services to manage their money—saving under the

The interest rate spread—the margin between

on the balance sheet, not just the amount overdue.

mattress, borrowing from family and friends, or

the cost of mobilizing liabilities and the earnings on

• Domestic credit provided by banking sector is all

money lenders—that are usually less reliable and

assets—is a measure of financial sector efficiency in

credit to various sectors on a gross basis, except to

more costly than formal banking institutions. The

intermediation. A narrow interest rate spread means

the central government, which is net. The banking

table presents data on fi nancial access cover-

low transaction costs, which reduces the cost of funds

sector includes monetary authorities, deposit money

ing deposits and loans, and outreach indicators

for investment, crucial to economic growth.

banks, and other banking institutions for which data

The risk premium on lending is the spread between

are available. • Interest rate spread is the interest rate

the lending rate to the private sector and the “risk-

charged by banks on loans to prime customers minus

Data on financial access cover 142 coun-

free” government rate. Spreads are expressed as

the interest rate paid by commercial or similar banks

tries and present indicators on savings, credit,

annual averages. A small spread indicates that the

for demand, time, or savings deposits. • Risk premium

and payment services in banks and regulated

market considers its best corporate customers to be

on lending is the interest rate charged by banks on

nonbank fi nancial institutions. Data were col-

low risk. A negative rate indicates that the market

loans to prime private sector customers minus the

lected for commercial banks and regulated

considers its best corporate clients to be lower risk

“risk-free” treasury bill interest rate at which short-

nonbank financial institutions such as cooperatives,

than the government.

term government securities are issued or traded in

such as the number of branches, automatic teller machines, and point-of-sale terminals.

credit unions, specialized state financial institutions,

Definitions

the market.

and microfinance institutions. The size and mobility of international capital flows

• Strength of legal rights index measures the degree

make it increasingly important to monitor the strength

to which collateral and bankruptcy laws protect the

of financial systems. Robust financial systems can

rights of borrowers and lenders and thus facilitate

increase economic activity and welfare, but instability

lending. Higher values indicate that the laws are bet-

in the financial system can disrupt financial activity and

ter designed to expand access to credit. • Depth of

impose widespread costs on the economy. The ratio

credit information index measures rules affecting

Data sources

of bank capital to assets, a measure of bank solvency

the scope, accessibility, and quality of information

Data on getting credit are from the World Bank’s

and resiliency, shows the extent to which banks can

available through public or private credit registries.

Doing Business project (www.doingbusiness.org).

deal with unexpected losses. Capital includes tier 1

Higher values indicate the availability of more credit

Data on financial access and outreach are from

capital (paid-up shares and common stock), a com-

information. • Deposit accounts are accounts at com-

the Consultative Group to Assist the Poor and the

mon feature in all countries’ banking systems, and

mercial banks that allow money to be deposited and

World Bank Group’s Financial Access 2010. Data

total regulatory capital, which includes several types of

withdrawn by the account holder. The major types of

on bank capital and nonperforming loans are from

subordinated debt instruments that need not be repaid

deposits are checking accounts, savings accounts,

the IMF’s Global Financial Stability Report. Data

if the funds are required to maintain minimum capital

and time deposits. • Loan accounts at commer-

on credit and interest rates are from the IMF’s

levels (tier 2 and tier 3 capital). Total assets include

cial banks include loans from banks to individuals,

International Financial Statistics.

businesses, and others, including home mortgages,

2011 World Development Indicators

285

5.6

Tax policies Tax revenue collected by central government

% of GDP

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

286

Taxes payable by businesses

Number of payments

Time to prepare, file, and pay taxes hours

Profi t tax % of commercial profi ts

Labor tax and contributions % of commercial profi ts

Other taxes % of commercial profi ts

Total tax rate % of commercial profi ts

June 2010

June 2010

June 2010

June 2010

June 2010

2000

2009

June 2010

.. 16.1 .. .. 9.8a .. 23.0a 19.9a .. 7.6 16.6 27.4a 15.5a 13.2a .. .. 14.0 17.9 10.5a 13.6 8.2a 11.2 15.3a .. .. 16.7a 6.8 9.1a 11.0a 3.5 5.9 .. .. 22.4 .. 15.4 30.8a .. .. 13.4 10.7a .. 15.8 a 8.1 24.7a 23.2a .. .. 7.7 11.9a 17.2 23.3a 10.1 11.1 .. .. ..

7.3 .. 34.3a .. .. 16.4 22.1a 18.7a 16.7 8.6 19.4 24.0a 16.1a 17.0a 19.6a .. 15.6 20.9 12.9a .. 9.6a .. 11.8 a .. .. 15.3a 10.3 13.0a 11.9a .. .. 13.9a 16.4 a 19.1 .. 13.5 34.5a 14.9a .. 15.7 12.5a .. 17.6a .. 21.3a 19.6a .. .. 23.2 12.0a 12.5 19.1a 10.4 .. .. .. 14.4a

8 44 34 31 9 50 11 22 18 21 82 11 55 42 51 19 10 17 46 32 39 44 8 54 54 9 7 3 20 32 61 42 64 17 .. 12 9 9 8 29 53 18 7 19 8 7 26 50 18 16 33 10 24 56 46 42 47

2011 World Development Indicators

275 360 451 282 453 581 109 170 306 302 798 156 270 1,080 422 152 2,600 616 270 211 173 654 131 504 732 316 398 80 208 336 606 272 270 196 .. 557 135 324 654 433 320 216 81 198 243 132 488 376 387 215 224 224 344 416 208 160 224

0.0 8.5 6.6 24.6 2.8 16.6 25.9 15.7 13.8 25.7 22.0 4.8 14.8 0.0 5.3 15.9 21.4 4.6 16.1 19.4 18.9 29.9 9.8 176.8 31.3 18.0 6.0 18.7 17.7 58.9 0.0 18.9 8.8 11.4 .. 7.4 21.9 20.5 18.4 13.2 17.0 8.8 8.0 26.8 15.9 8.2 18.4 41.4 13.3 23.0 18.1 13.9 25.9 19.4 14.9 23.3 26.7

0.0 27.3 29.7 9.0 29.4 23.0 20.7 34.6 24.8 0.0 39.3 50.4 27.3 15.5 12.6 0.0 40.9 20.4 22.6 7.8 0.1 18.3 12.6 8.1 28.4 3.8 49.6 5.3 33.9 7.9 32.9 29.5 20.1 19.4 .. 38.4 3.6 18.3 13.7 25.8 17.2 0.0 39.2 0.0 27.7 51.7 22.7 12.9 0.0 22.0 14.1 31.7 14.3 24.5 24.8 12.4 10.7

36.4 4.9 35.7 19.5 76.0 1.1 1.3 5.1 2.2 9.2 19.2 1.8 23.9 64.6 5.0 3.6 6.6 3.9 6.2 126.2 3.5 0.9 6.9 18.9 5.7 3.2 7.9 0.1 27.1 272.8 32.6 6.6 15.5 1.6 .. 3.0 3.7 1.8 3.2 3.6 0.8 75.8 2.4 4.3 1.0 5.9 2.3 238.0 2.0 3.3 0.5 1.6 0.7 10.8 6.1 4.3 10.9

36.4 40.6 72.0 53.2 108.2 40.7 47.9 55.5 40.9 35.0 80.4 57.0 66.0 80.0 23.0 19.5 69.0 29.0 44.9 153.4 22.5 49.1 29.2 203.8 65.4 25.0 63.5 24.1 78.7 339.7 65.5 55.0 44.4 32.5 .. 48.8 29.2 40.7 35.3 42.6 35.0 84.5 49.6 31.1 44.6 65.8 43.5 292.3 15.3 48.2 32.7 47.2 40.9 54.6 45.9 40.1 48.3

Tax revenue collected by central government

% of GDP

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

5.6

STATES AND MARKETS

Tax policies Taxes payable by businesses

Number of payments

Time to prepare, file, and pay taxes hours

Profi t tax % of commercial profi ts

Labor tax and contributions % of commercial profi ts

Other taxes % of commercial profi ts

Total tax rate % of commercial profi ts

2000

2009

June 2010

June 2010

June 2010

June 2010

June 2010

June 2010

21.9a

23.5a

9.0 11.6 6.3 .. 26.0 a 28.7a 23.2a .. .. 19.0 10.2 16.8 .. 15.4 .. 1.3 11.7 .. 14.2 11.9 a 37.4 .. .. 14.6a .. 11.3a .. 13.7 13.2a .. .. 11.7 14.7 14.5 19.9a .. 3.0 27.5 8.7 22.3a 29.2a 13.8 .. .. 27.4a 7.2 10.1 10.2 19.0 10.9 12.2 13.7 16.0a 20.6a .. ..

9.8 11.4 9.3 .. 20.8a 23.0a 23.0a 21.9a 9.2a 16.2 8.1 19.6 .. 15.5 21.1 0.9 15.4 12.5 12.6 17.3a 60.0 0.3 .. 13.8 a 19.7 13.0a .. 15.7 14.7a .. 19.2a .. 17.8 18.0 23.8a .. .. 27.3 12.2 22.7a 30.8a 17.8 11.5a 0.3 25.4a .. 9.3 .. .. 13.0 13.4 12.8 16.4a 19.7a .. 19.8

14 56 51 20 13 9 33 15 72 14 26 9 41 .. 14 33 15 48 34 7 19 21 32 .. 11 40 23 19 12 59 38 7 6 48 43 28 37 .. 37 34 9 8 64 41 35 4 14 47 62 33 35 9 47 29 8 16 3

277 258 266 344 312 76 235 285 414 355 101 271 393 .. 250 163 118 202 362 293 180 324 158 .. 175 119 201 157 145 270 696 161 404 228 192 358 230 .. 375 338 134 192 222 270 938 87 62 560 482 194 311 380 195 325 298 218 36

16.7 24.0 26.6 17.8 14.9 11.9 23.8 22.8 28.6 27.9 15.2 16.3 33.1 .. 15.3 10.2 4.7 8.9 25.2 6.5 6.1 16.4 0.0 .. 0.0 6.3 15.8 23.3 16.7 12.9 44.2 11.8 23.1 0.0 9.5 18.1 27.7 .. 4.0 16.2 20.9 30.4 24.8 20.1 21.8 24.4 9.7 14.3 17.0 22.0 9.6 26.0 21.3 17.7 14.9 26.3 0.0

34.4 18.2 10.6 25.9 13.5 11.6 5.3 43.4 13.0 14.7 12.4 11.5 6.8 .. 12.9 5.6 10.7 21.5 5.6 27.2 24.1 0.0 5.4 .. 35.1 0.6 20.3 1.1 15.6 32.6 17.6 5.0 26.1 30.2 12.4 22.2 4.5 .. 1.0 11.3 17.9 3.0 19.2 19.6 9.7 15.9 11.8 15.0 22.6 11.7 18.6 11.0 10.3 22.1 26.8 14.4 11.3

2.2 21.1 0.1 0.4 0.0 3.0 2.6 2.4 8.5 6.0 3.6 1.9 9.9 .. 1.6 0.6 0.0 26.7 2.9 4.8 0.0 3.2 38.3 .. 3.6 3.8 1.6 0.7 1.4 6.7 6.6 7.3 1.3 0.7 1.0 1.4 2.1 .. 4.6 10.7 1.7 0.9 19.2 6.8 0.7 1.3 0.1 2.3 10.5 8.6 6.7 3.2 14.2 2.5 1.6 27.0 0.0

53.3 63.3 37.3 44.1 28.4 26.5 31.7 68.6 50.1 48.6 31.2 29.6 49.7 .. 29.8 16.5 15.5 57.2 33.7 38.5 30.2 19.6 43.7 .. 38.7 10.6 37.7 25.1 33.7 52.2 68.4 24.1 50.5 30.9 23.0 41.7 34.3 .. 9.6 38.2 40.5 34.3 63.2 46.5 32.2 41.6 21.6 31.6 50.1 42.3 35.0 40.2 45.8 42.3 43.3 67.7 11.3

2011 World Development Indicators

287

5.6

Tax policies Tax revenue collected by central government

% of GDP

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

2000

2009

11.7a 13.6 a .. .. 16.1 .. 10.2 15.4 .. 20.6 .. 24.0 a 16.2a 14.5 6.4 24.9 23.6a 11.1 .. 7.7 .. .. .. .. 22.1 21.3 .. .. 10.4 14.1 1.7 28.4a 12.5a 14.7 .. 13.3 .. .. 9.4 18.6 .. 15.5 w 10.4 10.9 8.2 .. 10.9 7.7 .. 13.0 12.0 9.3 .. 16.4 19.1

17.9a 12.9a .. .. .. 21.0 10.8 13.8 12.4 a 18.3 .. 25.4 a 8.5a 13.3 .. .. 21.5a 10.9 .. .. .. 15.1a .. 17.0 a 31.6 21.9 18.9a .. 12.0 16.4 .. 26.0a 8.2a 18.8 .. .. .. .. .. 17.1 .. 14.2 w 11.6 14.1 11.3 15.8 14.0 11.1 15.0 .. 17.5 9.7 17.9 14.2 17.1

Taxes payable by businesses

Number of payments

Time to prepare, file, and pay taxes hours

Profi t tax % of commercial profi ts

Labor tax and contributions % of commercial profi ts

Other taxes % of commercial profi ts

Total tax rate % of commercial profi ts

June 2010

June 2010

June 2010

June 2010

June 2010

June 2010

222 320 148 79 666 279 357 84 257 260 .. 200 197 256 180 104 122 63 336 224 172 264 276 270 210 144 223 .. 161 657 12 110 187 336 205 864 941 154 248 132 242 282 u 271 337 326 351 319 233 340 408 263 283 311 179 190

10.4 9.0 21.2 2.1 14.8 11.6 0.0 7.4 7.0 14.8 .. 24.4 20.9 27.4 13.8 28.1 16.4 8.9 23.2 17.7 19.9 28.9 0.0 8.8 21.6 15.0 17.0 .. 23.3 10.4 0.0 23.1 27.6 23.6 1.6 10.0 12.5 16.2 35.1 1.7 24.0 17.9 u 24.8 17.1 16.3 18.0 19.2 18.4 10.0 21.4 16.6 17.8 23.3 14.3 13.9

32.3 31.8 5.7 12.4 24.1 20.2 11.3 14.9 39.6 18.2 .. 2.5 35.0 16.9 19.2 4.0 36.6 17.5 19.3 28.5 18.0 5.7 0.0 28.3 5.8 25.2 23.1 .. 11.3 43.3 14.1 10.8 10.0 15.6 27.1 18.0 20.3 0.0 11.3 10.4 6.2 16.3 u 12.6 15.8 14.3 17.5 14.9 10.3 22.7 15.3 18.9 7.8 13.2 20.1 29.2

2.2 5.7 4.4 0.0 7.0 2.2 224.3 3.1 2.1 2.4 .. 3.7 0.7 20.3 3.1 4.7 1.6 3.6 0.5 39.9 7.3 2.8 0.2 13.7 5.8 22.5 4.4 .. 1.1 1.8 0.0 3.3 9.2 2.9 66.9 24.6 0.3 0.6 1.4 4.0 10.1 13.7 u 39.2 8.6 9.6 7.5 17.0 7.8 9.6 11.2 6.1 14.2 31.7 4.2 2.4

44.9 46.5 31.3 14.5 46.0 34.0 235.6 25.4 48.7 35.4 .. 30.5 56.5 64.7 36.1 36.8 54.6 30.1 42.9 86.0 45.2 37.4 0.2 50.8 33.1 62.8 44.5 .. 35.7 55.5 14.1 37.3 46.8 42.0 95.6 52.6 33.1 16.8 47.8 16.1 40.3 47.8 u 76.5 41.5 40.2 43.0 51.1 36.5 42.2 47.9 41.6 39.9 68.2 38.6 45.5

113 11 26 14 59 66 29 5 31 22 .. 9 8 62 42 33 2 19 20 54 48 23 6 53 40 8 15 .. 32 135 14 8 11 53 44 70 32 27 44 37 49 30 u 38 34 36 32 35 27 47 34 25 31 37 15 15

Note: Regional aggregates for Taxes payable by businesses are for developing countries only. a. Data were reported on a cash basis and have been adjusted to the accrual framework of the International Monetary Fund’s Government Finance Statistics Manual 2001.

288

2011 World Development Indicators

About the data

5.6

STATES AND MARKETS

Tax policies Deﬁnitions

Taxes are the main source of revenue for most

To make the data comparable across countries,

• Tax revenue collected by central government

governments. The sources of tax revenue and their

several assumptions are made about businesses.

is compulsory transfers to the central government

relative contributions are determined by government

The main assumptions are that they are limited liabil-

for public purposes. Certain compulsory transfers

policy choices about where and how to impose taxes

ity companies, they operate in the country’s most

such as fines, penalties, and most social security

and by changes in the structure of the economy. Tax

populous city, they are domestically owned, they per-

contributions are excluded. Refunds and corrections

policy may refl ect concerns about distributional

form general industrial or commercial activities, and

of erroneously collected tax revenue are treated as

effects, economic efficiency (including corrections

they have certain levels of start-up capital, employ-

negative revenue. The analytic framework of the

for externalities), and the practical problems of

ees, and turnover. For details about the assump-

International Monetary Fund’s (IMF) Government

administering a tax system. There is no ideal level

tions, see the World Bank’s Doing Business 2011.

Finance Statistics Manual 2001 (GFSM 2001) is

of taxation. But taxes influence incentives and thus

The Doing Business methodology on business

based on accrual accounting and balance sheets.

the behavior of economic actors and the economy’s

taxes is consistent with the Total Tax Contribution

For countries still reporting government finance data

competitiveness.

framework developed by PricewaterhouseCoopers,

on a cash basis, the IMF adjusts reported data to the

The level of taxation is typically measured by tax

which measures the taxes that are borne by compa-

GFSM 2001 accrual framework. These countries are

revenue as a share of gross domestic product (GDP).

nies and affect their income statements. However,

footnoted in the table. • Number of tax payments

Comparing levels of taxation across countries pro-

PricewaterhouseCoopers bases its calculation on

by businesses is the total number of taxes paid by

vides a quick overview of the fiscal obligations and

data from the largest companies in the economy,

businesses during one year. When electronic filing is

incentives facing the private sector. The table shows

while Doing Business focuses on a standardized

available, the tax is counted as paid once a year even

only central government data, which may significantly

medium-sized company.

if payments are more frequent. • Time to prepare,

understate the total tax burden, particularly in coun-

ﬁle, and pay taxes is the time, in hours per year, it

tries where provincial and municipal governments are

takes to prepare, file, and pay (or withhold) three

large or have considerable tax authority.

major types of taxes: the corporate income tax, the

Low ratios of tax revenue to GDP may reflect weak

value-added or sales tax, and labor taxes, includ-

administration and large-scale tax avoidance or eva-

ing payroll taxes and social security contributions.

sion. Low ratios may also reflect a sizable parallel

• Proﬁt tax is the amount of taxes on profits paid

economy with unrecorded and undisclosed incomes.

by the business. • Labor tax and contributions is

Tax revenue ratios tend to rise with income, with

the amount of taxes and mandatory contributions on

higher income countries relying on taxes to finance

labor paid by the business. • Other taxes includes

a much broader range of social services and social

the amounts paid for property taxes, turnover taxes,

security than lower income countries are able to.

and other small taxes such as municipal fees and

The total tax rate payable by businesses provides

vehicle and fuel taxes. • Total tax rate measures

a comprehensive measure of the cost of all the taxes

the amount of taxes and mandatory contributions

a business bears. It differs from the statutory tax

payable by the business in the second year of opera-

rate, which is the factor applied to the tax base. In

tion, expressed as a share of commercial profi ts.

computing business tax rates, actual tax payable is

Doing Business 2011 reports the total tax rate for

divided by commercial profit. The indicators cover-

fiscal 2009. Taxes withheld (such as sales or value

ing taxes payable by businesses measure all taxes

added tax or personal income tax) but not paid by

and contributions that are government mandated

the company are excluded. For further details on the

(at any level—federal, state, or local), apply to stan-

method used for assessing the total tax payable, see

dardized businesses, and have an impact in their

the World Bank’s Doing Business 2011.

income statements. The taxes covered go beyond the definition of a tax for government national accounts (compulsory, unrequited payments to general government) and also measure any imposts that affect business accounts. The main differences are in labor contributions and value-added taxes. The indicators account for government-mandated contributions paid

Data sources

by the employer to a requited private pension fund

Data on central government tax revenue are from

or workers insurance fund but exclude value-added

print and electronic editions of the IMF’s Govern-

taxes because they do not affect the accounting prof-

ment Finance Statistics Yearbook. Data on taxes

its of the business—that is, they are not reflected in

payable by businesses are from Doing Business

the income statement.

2011 (www.doingbusiness.org).

2011 World Development Indicators

289

5.7

Military expenditures and arms transfers Military expenditures

% of central government expenditure

% of GDP

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

290

Armed forces personnel

thousands

2000

2009

2000

2009

2000

2009

.. 1.2 3.4 6.4 1.1 3.6 1.9 1.0 2.3 1.4 1.3 1.4 0.6 1.9 3.6 3.3 1.8 2.7 1.2 6.0 2.2 1.3 1.1 1.0 1.9 3.7 1.8a .. 2.8 1.0 1.4 .. .. 3.1 .. 2.0 1.5 0.7 1.7 3.2 0.9 36.4 1.4 7.6 1.3 2.5 1.8 0.8 0.6 1.5 1.0 4.3 0.8 1.5 4.4 .. 0.5

1.8 2.1 3.8 4.2 0.8 4.0 1.9 0.9 3.5 1.1 1.8 1.1 1.0 1.6 1.5 3.1 1.6 2.3 1.3 3.8 1.2 1.5 1.4 1.8 6.4 3.1 2.0a .. 4.1 1.1 1.2 .. 1.6 1.8 3.2 1.5 1.4 0.6 3.3 2.1 0.6 .. 2.3 1.3 1.5 2.4 1.1 0.7 5.6 1.4 0.4 4.0 0.4 .. .. .. 0.8

.. 5.4 .. .. 5.5 .. 7.8 2.5 .. 14.9 5.3 3.2 4.7 7.6 .. .. 8.1 8.6 9.8 30.3 16.8 12.4 6.0 .. .. 17.7 19.8a .. 15.6 11.4 5.9 .. .. 7.8 .. 6.1 4.3 .. .. 12.3 4.3 .. 4.7 29.7 3.7 5.7 .. .. 5.3 4.7 3.3 9.8 7.5 11.8 .. .. ..

4.6 .. 15.0 .. .. 17.1 7.3 2.3 22.9 10.0 5.5 2.5 6.8 7.9 3.8 .. 6.4 7.2 10.4 .. 13.9 .. 7.5 .. .. 13.6 16.1a .. 20.9 .. .. .. 8.8 5.0 .. 4.1 3.3 3.8 .. 7.1 3.0 .. 6.2 .. 3.8 5.1 .. .. 18.1 4.3 2.4 7.9 3.5 .. .. .. 3.2

400 68 305 118 102 42 52 41 87 137 91 39 7 70 76 10 673 114 11 46 360 22 69 5 35 117 3,910 .. 247 93 15 15 15 101 85 63 22 40 58 679 29 200 8 353 35 389 7 1 33 221 8 163 53 19 9 5 14

256 15 334 117 104 56 57 26 82 221 183 39 7 83 11 11 713 65 11 51 191 23 66 3 35 104 2,945 .. 442 159 12 10 19 22 76 27 19 40 59 866 33 202 5 138 25 342 7 1 32 251 16 143 34 19 6 0 20

2011 World Development Indicators

Arms transfers

% of labor force 2000

5.4 5.2 2.7 1.9 0.6 2.9 0.5 1.0 2.5 0.2 1.9 0.9 0.3 2.0 4.1 1.3 0.8 3.2 0.2 1.4 6.1 0.4 0.4 0.3 1.1 1.9 0.5 .. 1.6 0.5 1.2 1.0 0.2 5.1 1.8 1.2 0.8 1.1 1.2 3.1 1.3 14.5 1.2 1.2 1.3 1.5 1.2 0.1 1.4 0.5 0.1 3.3 1.3 0.5 1.7 0.1 0.6

Trend indicator values 1990 $ millions Exports Imports

2009

2000

2009

2.7 1.0 2.3 1.4 0.5 3.4 0.5 0.6 2.0 0.3 3.7 0.8 0.2 1.8 0.5 1.1 0.7 1.8 0.2 1.1 2.4 0.3 0.3 0.2 0.8 1.4 0.4 .. 2.3 0.6 0.8 0.5 0.2 1.1 1.5 0.5 0.6 0.9 1.0 3.2 1.3 9.4 0.8 0.3 0.9 1.2 0.9 0.1 1.4 0.6 0.1 2.8 0.6 0.4 1.0 0.0 0.7

.. .. .. 2 2 .. 43 21 .. .. 295 24 .. .. 4 .. 26 2 .. .. 1 .. 110 .. .. 1 272 .. .. .. .. .. .. 2 .. 78 20 .. .. .. .. 0 .. .. 9 1,055 .. .. 54 1,603 .. 2 .. .. .. .. ..

.. .. .. .. .. .. 51 33 .. .. 292 217 .. .. .. .. 49 7 .. .. .. .. 177 .. .. 133 870 .. .. .. .. 0 .. .. .. 19 12 .. .. .. .. .. .. .. 40 1,851 .. .. .. 2,473 .. .. .. .. .. .. ..

2000

2009

33 .. 418 200 209 2 364 25 3 205 41 39 6 19 25 52 124 7 .. 1 .. 1 550 .. 15 179 2,015 .. 62 74 0 .. 33 70 .. 16 64 13 12 788 16 17 27 124 516 106 .. .. 6 135 1 710 1 19 .. .. ..

344 25 942 11 11 1 757 330 49 12 .. 84 2 5 .. 10 210 153 1 .. 4 1 80 .. 23 231 595 .. 250 .. 0 .. .. 3 .. 5 47 6 46 217 4 4 56 .. 70 149 21 .. 81 137 13 1,269 0 0 .. 1 0

Military expenditures

% of central government expenditure

% of GDP

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

Armed forces personnel

thousands

2000

2009

2000

2009

2000

2009

1.7 3.1 1.0 3.8 .. 0.7 7.8 2.0 0.5 1.0 6.2 0.8 1.3 .. 2.6 .. 7.1 2.9 0.8 0.9 5.4 4.1 .. 3.2 1.7 1.9 1.2 0.7 1.6 2.4 3.5 0.2 0.6 0.4 2.2 2.3 1.3 2.3 2.4 1.0 1.6 1.2 0.8 1.1 0.8 1.7 10.6 4.0 1.0 0.9 1.1 2.0 1.1 1.8 1.9 .. 4.7

1.3 2.7 0.9 2.7 6.3 0.6 6.9 1.7 0.6 1.0 5.5 1.2 1.9 .. 2.9 .. 3.2 3.6 0.4 2.6 4.1 2.8 0.8 1.2 1.7 2.1 1.1 1.2 2.0 2.0 3.8 0.2 0.5 0.5 1.4 3.3 0.9 .. 3.3 1.6 1.5 1.1 0.7 .. 0.9 1.5 8.7 3.0 .. 0.5 0.9 1.2 0.8 2.0 2.0 .. 2.2

4.1 19.5 5.8 22.5 .. 2.6 17.6 5.2 .. .. 23.1 5.7 7.8 .. 15.6 .. 24.9 18.0 .. 3.2 17.7 7.8 .. .. 6.5 .. 11.5 .. 9.9 20.7 .. .. 3.7 1.4 9.5 12.0 .. .. 8.3 .. 4.0 3.5 4.7 .. .. 5.3 40.4 23.4 4.6 2.9 6.4 10.9 6.2 5.4 5.1 .. ..

2.9 16.6 5.6 12.2 .. 1.5 17.0 3.9 1.6 .. 19.3 6.9 8.7 .. 13.2 .. 7.5 21.4 3.6 7.5 14.0 3.1 .. .. 4.4 5.8 9.3 .. 8.9 13.4 .. .. .. 1.2 5.8 12.0 .. .. 10.7 .. 3.4 3.1 3.2 .. 10.8 4.1 .. 18.0 .. .. 5.2 6.7 4.6 5.7 4.6 .. 13.7

58 2,372 492 753 479 12 181 503 3 249 149 99 27 1,244 688 .. 20 14 129 9 77 2 15 77 17 24 29 6 116 15 21 2 208 13 16 241 6 429 9 90 57 9 16 11 107 27 48 900 12 4 35 193 149 239 91 .. 12

42 2,626 582 563 659 10 185 327 3 260 111 81 29 1,379 660 .. 23 20 129 6 79 2 2 76 25 8 22 5 134 12 21 2 332 8 17 246 11 513 15 158 43 10 12 11 162 26 47 921 12 3 25 192 166 121 91 .. 12

Arms transfers

% of labor force 2000

1.4 0.6 0.5 3.4 8.0 0.7 7.2 2.2 0.3 0.4 10.4 1.3 0.2 11.2 3.0 .. 1.8 0.7 5.2 0.8 6.5 0.2 1.3 4.2 1.0 2.8 0.4 0.1 1.2 0.5 2.0 0.3 0.5 0.7 1.4 2.4 0.1 1.7 1.5 0.9 0.7 0.5 0.9 0.3 0.3 1.1 5.4 2.2 0.9 0.2 1.5 1.7 0.5 1.4 1.7 .. 3.6

5.7

STATES AND MARKETS

Military expenditures and arms transfers

2009

1.0 0.6 0.5 1.9 8.6 0.5 6.0 1.3 0.2 0.4 5.7 0.9 0.2 11.2 2.7 .. 1.5 0.8 4.2 0.5 5.4 0.2 0.1 3.2 1.6 0.9 0.2 0.1 1.1 0.3 1.5 0.4 0.7 0.5 1.2 2.1 0.1 1.9 1.9 1.2 0.5 0.4 0.5 0.2 0.3 1.0 4.3 1.6 0.8 0.1 0.8 1.4 0.4 0.7 1.6 .. 1.2

Trend indicator values 1990 $ millions Exports Imports 2000

34 16 16 0 .. .. 354 189 .. .. .. 19 .. 13 8 .. 99 .. .. .. 45 .. .. 11 3 .. .. 1 8 .. .. .. .. 6 .. .. .. .. .. .. 280 1 .. .. .. 3 .. 3 .. .. .. 10 .. 45 .. .. 9

2009

6 22 .. 5 .. 4 760 588 .. .. 44 .. .. .. 163 .. .. 16 .. .. .. .. .. 12 .. .. .. .. .. .. .. .. .. 11 .. .. .. .. .. .. 608 .. .. .. .. 17 .. .. .. .. .. .. 4 93 40 .. ..

2000

2009

14 911 171 415 .. 0 357 37 5 431 130 147 9 18 1,262 .. 238 .. 7 3 4 6 8 145 5 11 .. .. 30 7 31 .. 227 .. .. 123 0 3 18 11 141 45 .. .. 38 263 120 158 0 .. 6 24 9 159 2 .. 11

2 2,116 452 91 365 1 158 112 2 391 195 49 35 5 1,172 .. 17 .. 7 0 47 .. .. 11 26 .. .. .. 1,494 7 .. .. 57 .. 12 49 .. 3 10 .. 243 48 .. 0 73 576 93 1,146 .. .. .. 33 4 94 431 .. 285

2011 World Development Indicators

291

5.7

Military expenditures and arms transfers Military expenditures

% of GDP

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

Armed forces personnel

% of central government expenditure

thousands

2000

2009

2000

2009

2000

2.5 3.7 3.5 10.6 1.3 5.5 3.7 4.7 1.7 1.1 .. 1.6 1.2 5.0 4.7 1.6 2.0 1.1 5.3 1.2 1.3 1.4 .. .. .. 1.7 3.7 2.9 2.5 3.6 9.4 2.4 3.0 1.3 1.2 1.5 .. .. 5.0 1.8 5.2 2.3 w 2.2 2.1 2.2 2.0 2.1 1.7 3.4 1.4 3.5 3.1 2.0 2.3 1.8

1.4 4.3 1.4 11.0 1.6 2.2 2.3 4.3 1.5 1.8 .. 1.4 1.3 3.5 .. 2.1 1.3 0.8 4.2 .. 1.0 1.8 11.8 2.0 .. 1.4 2.8 .. 2.2 2.9 5.6 2.7 4.7 1.6 .. 1.3 2.2 .. 4.4 1.7 2.8 2.6 w 1.5 2.2 2.1 2.2 2.1 1.9 3.3 1.5 3.5 2.6 1.7 2.8 1.7

8.9 19.3 .. .. 10.4 .. 12.8 28.7 .. 2.9 .. 5.6 3.9 21.9 53.0 7.3 .. 4.2 .. 13.4 .. .. .. .. .. 6.2 .. .. 16.0 13.5 .. 6.6 15.6 5.0 .. 7.1 .. .. 23.9 10.3 .. 10.2 w .. 15.0 18.0 .. 15.0 18.7 .. 7.2 12.7 19.9 .. 10.1 4.8

4.4 14.0 .. .. .. 5.9 11.2 27.9 4.0 4.1 .. 4.4 4.1 18.5 .. .. .. 4.7 .. .. .. 9.1 .. 13.0 .. 4.6 10.1 .. 15.9 7.0 .. 5.8 17.8 5.3 .. .. .. .. .. 5.7 .. 10.0 w .. 12.2 14.3 9.8 12.2 14.6 12.0 .. 12.3 16.5 .. 9.9 4.2

283 1,427 76 217 15 136 4 169 41 14 50 72 242 204 120 3 88 28 425 7 35 417 .. 8 8 47 828 15 51 420 66 213 1,455 25 79 79 524 .. 136 23 62 29,353 s 4,040 18,924 12,446 6,478 22,965 7,794 3,871 2,084 3,379 4,114 1,724 6,388 1,869

2009

152 1,495 35 249 19 29 11 148 17 12 2 77 222 223 127 .. 22 26 403 16 28 420 1 9 4 48 613 22 47 215 51 178 1,564 25 87 115 495 56 138 17 51 27,924 s 3,845 18,350 12,108 6,242 22,195 6,978 3,227 2,439 3,591 4,404 1,554 5,729 1,569

Arms transfers

% of labor force 2000

2009

2.4 2.0 2.0 3.4 0.4 .. 0.2 8.2 1.6 1.4 1.7 0.5 1.3 2.6 1.1 0.8 2.0 0.7 8.6 0.4 0.2 1.2 .. 0.4 1.3 1.5 3.6 0.8 0.5 1.8 3.5 0.7 1.0 1.6 0.9 0.8 1.4 .. 3.2 0.6 1.2 1.1 w 1.3 1.0 0.8 1.6 1.0 0.8 2.1 0.9 3.8 0.8 0.7 1.2 1.3

1.6 2.0 0.7 2.9 0.3 .. 0.5 5.5 0.6 1.2 0.1 0.4 1.0 2.7 0.9 .. 0.4 0.6 5.8 0.6 0.1 1.1 0.3 0.3 0.6 1.2 2.4 0.9 0.3 0.9 1.8 0.6 1.0 1.5 0.7 0.9 1.1 5.9 2.2 0.3 1.0 0.9 w 1.0 0.8 0.7 1.4 0.8 0.6 1.7 0.9 3.1 0.7 0.5 1.0 1.0

Trend indicator values 1990 $ millions Exports Imports 2000

2009

3 3 3,985 4,469 .. .. .. .. .. .. .. .. .. .. 10 124 92 8 .. .. .. .. 18 154 46 925 .. .. .. .. .. .. 306 353 176 270 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 15 36 .. .. .. .. 288 214 .. 3 1,484 1,024 7,220 6,795 1 .. .. 90 .. 17 .. .. .. .. .. .. .. .. 3 .. .. s .. s .. .. .. .. 983 1,251 .. .. .. .. 389 870 4,667 4,830 .. .. .. .. 19 22 .. .. 13,136 16,637 3,319 6,779

2000

23 .. 14 80 .. .. 13 622 2 1 1 16 332 274 107 1 210 14 19 .. .. 90 .. .. 10 11 1,170 .. 6 .. 243 829 301 4 6 108 5 .. 158 27 2 18,088 s 572 8,353 5,109 3,244 8,925 2,339 .. 970 2,056 1,548 647 9,163 2,075

2009

56 1 6 626 3 .. .. 1,729 1 6 .. 139 430 64 39 .. 46 31 175 7 0 34 .. .. 6 8 675 47 1 .. 604 288 831 37 .. 172 44 14 45 3 .. 22,223 s 329 10,467 5,682 4,785 10,889 2,644 1,162 1,058 2,065 3,606 354 11,334 3,322

Note: For some countries data are partial or uncertain or based on rough estimates. See SIPRI (2010). a. Estimates differ from statistics of the government of China, which has published the following estimates: military expenditure as 1.2 percent of GDP in 2000 and 1.4 percent in 2008 and 7.6 percent of national government expenditure in 2000 and 6.7 percent in 2008 (see National Bureau of Statistics of China, www.stats.gov.cn).

292

2011 World Development Indicators

About the data

5.7

STATES AND MARKETS

Military expenditures and arms transfers Definitions

Although national defense is an important function of

always strictly comparable across countries. How-

• Military expenditures are SIPRI data derived from

government and security from external threats that

ever, SIPRI puts a high priority on ensuring that the data

the NATO definition, which includes all current and

contributes to economic development, high levels of

series for each country is comparable over time. More

capital expenditures on the armed forces, including

military expenditures for defense or civil conflicts bur-

information on SIPRI’s military expenditure project can

peacekeeping forces; defense ministries and other gov-

den the economy and may impede growth. Data on

be found at www.sipri.org/contents/milap/.

ernment agencies engaged in defense projects; para-

military expenditures as a share of gross domestic

Data on armed forces refer to military personnel on

military forces, if judged to be trained and equipped

product (GDP) are a rough indicator of the portion of

active duty, including paramilitary forces. Because

for military operations; and military space activities.

national resources used for military activities and of

data exclude personnel not on active duty, they

Such expenditures include military and civil person-

the burden on the national economy. As an “input”

underestimate the share of the labor force working

nel, including retirement pensions and social services

measure military expenditures are not directly related

for the defense establishment. Governments rarely

for military personnel; operation and maintenance;

to the “output” of military activities, capabilities, or

report the size of their armed forces, so such data

procurement; military research and development;

security. Comparisons of military spending between

typically come from intelligence sources.

and military aid (in the military expenditures of the

countries should take into account the many fac-

SIPRI’s Arms Transfers Programme collects data

donor country). Excluded are civil defense and current

tors that influence perceptions of vulnerability and

on arms transfers from open sources. Since publicly

expenditures for previous military activities, such as

risk, including historical and cultural traditions, the

available information is inadequate for tracking all

for veterans benefits, demobilization, and weapons

length of borders that need defending, the quality of

weapons and other military equipment, SIPRI covers

conversion and destruction. This definition cannot be

relations with neighbors, and the role of the armed

only what it terms major conventional weapons. Data

applied for all countries, however, since that would

forces in the body politic.

cover the supply of weapons through sales, aid, gifts,

require more detailed information than is available

Data on military spending reported by governments

and manufacturing licenses; therefore the term arms

about military budgets and off-budget military expen-

are not compiled using standard definitions. They

transfers rather than arms trade is used. SIPRI data

ditures (for example, whether military budgets cover

are often incomplete and unreliable. Even in coun-

also cover weapons supplied to or from rebel forces

civil defense, reserves and auxiliary forces, police and

tries where the parliament vigilantly reviews bud-

in an armed conflict as well as arms deliveries for

paramilitary forces, and military pensions). • Armed

gets and spending, military expenditures and arms

which neither the supplier nor the recipient can be

forces personnel are active duty military personnel,

transfers rarely receive close scrutiny or full, public

identified with acceptable certainty; these data are

including paramilitary forces if the training, organiza-

disclosure (see Ball 1984 and Happe and Wakeman-

available in SIPRI’s database.

tion, equipment, and control suggest they may be used

Linn 1994). Therefore, the Stockholm International

SIPRI’s estimates of arms transfers are designed

to support or replace regular military forces. Reserve

Peace Research Institute (SIPRI) has adopted a defi -

as a trend-measuring device in which similar weap-

forces, which are not fully staffed or operational in

nition of military expenditure derived from the North

ons have similar values, reflecting both the quantity

peace time, are not included. The data also exclude

Atlantic Treaty Organization (NATO) definition (see

and quality of weapons transferred. SIPRI cautions

civilians in the defense establishment and so are not

Definitions). The data on military expenditures as a

that the estimated values do not reflect financial

consistent with the data on military expenditures on

share of GDP and as a share of central government

value (payments for weapons transferred) because

personnel. • Arms transfers cover the supply of military

expenditure are estimated by SIPRI. Central govern-

reliable data on the value of the transfer are not avail-

weapons through sales, aid, gifts, and manufacturing

ment expenditures are from the International Mon-

able, and even when values are known, the transfer

licenses. Weapons must be transferred voluntarily by

etary Fund (IMF). Therefore the data in the table may

usually includes more than the actual conventional

the supplier, have a military purpose, and be destined

differ from comparable data published by national

weapons, such as spares, support systems, and

for the armed forces, paramilitary forces, or intelligence

governments.

training, and details of the financial arrangements

agencies of another country. The trends shown in the

(such as credit and loan conditions and discounts)

table are based on actual deliveries only. Data cover

are usually not known.

major conventional weapons such as aircraft, armored

SIPRI’s primary source of military expenditure data is official data provided by national governments. These data are derived from national budget docu-

Given these measurement issues, SIPRI’s method

vehicles, artillery, radar systems and other sensors,

ments, defense white papers, and other public docu-

of estimating the transfer of military resources

missiles, and ships designed for military use, as well as

ments from official government agencies, including

includes an evaluation of the technical parameters

some major components such as turrets for armored

governments’ responses to questionnaires sent by

of the weapons. Weapons for which a price is not

vehicles and engines. Excluded are transfers of other

SIPRI, the United Nations, or the Organization for

known are compared with the same weapons for

military equipment such as most small arms and light

Security and Co-operation in Europe. Secondary

which actual acquisition prices are available (core

weapons, trucks, small artillery, ammunition, support

sources include international statistics, such as

weapons) or for the closest match. These weapons

equipment, technology transfers, and other services.

those of NATO and the IMF’s Government Finance

are assigned a value in an index that reflects their

Statistics Yearbook. Other secondary sources include

military resource value in relation to the core weap-

country reports of the Economist Intelligence Unit,

ons. These matches are based on such characteris-

Data on military expenditures are from SIPRI’s Year-

country reports by IMF staff, and specialist journals

tics as size, performance, and type of electronics,

book 2010: Armaments, Disarmament, and Interna-

and newspapers.

and adjustments are made for secondhand weap-

tional Security. Data on armed forces personnel are

In the many cases where SIPRI cannot make inde-

ons. More information on SIPRI’s Arms Transfers

from the International Institute for Strategic Stud-

pendent estimates, it uses the national data pro-

Programme is available at www.sipri.org/research/

ies’ The Military Balance 2011. Data on arms trans-

vided. Because of the differences in definitions and

armaments/transfers.

fers are from SIPRI’s Arms Transfers Programme

the difficulty in verifying the accuracy and complete-

Data sources

(www.sipri.org/research/armaments/transfers).

ness of data, data on military expenditures are not

2011 World Development Indicators

293

5.8

Fragile situations International Development Association Resource Allocation Index

Peacebuilding and peacekeeping

Operation namea

1–6 (low to high)

December 2010

2009

Afghanistan Angola Bosnia and Herzegovina Burundi Central African Republic Chad Comoros Congo, Dem. Rep. Congo, Rep. Côte d’Ivoire Eritrea Georgia Guinea Guinea-Bissau Haiti Iraq Kiribati Kosovo Liberia Myanmar Nepal São Tomé and Príncipe Sierra Leone Solomon Islands Somalia Sudan Tajikistan Timor-Leste Togo West Bank and Gaza Western Saharaj Yemen, Rep. Zimbabwe Fragile situations Low income

2.8 2.8 3.7 3.1 2.6 2.5 2.5 2.7 2.8 2.8 2.2 4.4 2.8 2.6 2.9 .. 3.1 3.4 2.8 .. 3.3 2.9 3.2 2.8 .. 2.5 3.2 2.9 2.8 .. .. 3.2 1.9

UNAMA BINUB MINURCATe MINURCAT MONUC UNOCI MINUSTAH UNAMI UNMIK UNMIL UNMIN RAMSI UNMIS g UNMIT MINURSO

Battlerelated deaths

Troops, police, and military observers number December 2010

16 .. .. 4 3 .. .. 19,105 .. 9,071 .. .. .. .. 11,984 235 .. 16 9,392 .. 72 .. .. 580 .. 10,416 .. 1,517 .. .. 242 .. ..

number

Intentional homicides per 100,000 people Law enforcement Public and criminal health justice sources sources

Military expenditures

% of GDP

2000–08 b

2004

2004–08c

2009

26,589 3,534 0 4,937 350 4,328 0 75,118 116 1,265 57 648 1,174 0 244 124,002 0 0 2,487 2,833 11,520 0 212 0 3,983 12,363 0 0 0 0 .. 0 0 275,761 s 146,844

3.4 38.6 1.9 37.4 29.8 19.2 11.9 35.0 19.9 50.8 16.1 3.7 16.9 17.6 21.8 7.3 6.6 .. 17.4 15.6 13.6 5.3 37.2 1.5 3.2 27.2 1.9h 12.5 14.3 .. .. 2.5 34.3 21.1 w 17.6

.. 5.0 1.9 .. .. .. .. .. .. 0.4 .. 7.6 0.4 .. .. .. .. .. .. .. 2.2 .. 2.6 .. .. .. 2.3 .. .. 3.9 .. 4.0 8.7 .. ..

1.8 4.2 1.5 3.8 1.8 6.4 .. 1.1 1.2 1.6 .. 5.6 .. .. 0.0 6.3 .. .. 0.8 .. 1.6 .. 2.3 .. .. .. .. 11.8 2.0 .. .. 4.4 2.8 3.2 w 1.4

Business environment

Survey year

2008 2006 2009 2006 2009 2010 2009 2009 2009 2008 2006 2006 2009 2009 2009 2009 2008 2009 2009 2006 2010

Losses due to theft, robbery, vandalism, and arson

Firms formally registered when operations started

% of sales

% of firms

1.5 0.4 0.2 1.1 .. 2.5 .. 1.8 3.3 3.4 0.0 0.7 2.0 1.1 .. .. .. 0.3 2.8 .. 0.9 .. 0.8 .. .. .. 0.3 1.5 2.4 1.2 .. 0.6 ..

88.0 .. 98.6 .. .. 77.1 .. 61.9 84.3 56.4 100.0 99.6 .. .. .. .. .. 89.2 73.8 .. 94.0 .. 89.2 .. .. .. 92.7 91.8 75.8 .. .. 81.7 ..

Note: The countries with fragile situations in the table are primarily International Development Association–eligible countries and nonmember or inactive countries and territories with a 3.2 or lower harmonized average of the World Bank's Country Policy and Institutional Assessment rating and the corresponding rating by a regional development bank, or that have had a UN or regional peacebuilding and political mission (for example, by the African Union, European Union, or Organization of American States) or peacekeeping mission (for example, by the African Union, European Union, North Atlantic Treaty Organization, or Organization of American States) during the last three years. This definition is pursuant to an agreement between the World Bank and other multilateral development banks at the start of the International Development Association 15 round in 2007. The list of countries and territories with fragile situations is an interim one, and the World Bank will continue to improve and refine its understanding of fragility. a. UNAMA is United Nations Assistance Mission in Afghanistan, BINUB is Bureau Intégré des Nations Unies au Burundi (United Nations Integrated Office in Burundi), MINURCAT is United Nations Mission in the Central African Republic and Chad, MONUC is United Nations Organization Mission in DR Congo, UNOCI is United Nations Operation in Côte d'Ivoire, MINUSTAH is United Nations Stabilization Mission in Haiti, UNAMI is United Nations Assistance Mission for Iraq, UNMIK is Interim Administration Mission in Kosovo, UNMIL is United Nations Mission in Liberia, UNMIN is United Nations Mission in Nepal, RAMSI is Regional Assistance Mission to Solomon Islands, UNMIS is United Nations Missions in Sudan, UNMIT is United Nations Integrated Mission in Timor-Leste, and MINURSO is United Nations Mission for the Referendum in Western Sahara. b. Total over the period. c. Data are for the most recent year available. d. Average over the period. e. Includes peacekeepers in Chad. The mission ended in 2010. f. The Internal Displacement Monitoring Centre's (IDMC) high estimate; the low estimate is 50,000. g. Does not include 22,444 troops, police, and military observers from the African Union–UN Hybrid Operation in Darfur. h. Data are for 2005. i. Includes Palestinian refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East, who are not included in data from the UN High Commissioner for Refugees. j. The designation Western Sahara is used instead of Former Spanish Sahara (the designation used on the maps on the front and back cover flaps) because it is the designation used by the UN operation established there by Security Council resolution 690/1991. Neither designation expresses any World Bank view on the status of the territory so-identified. k. IDMC's high estimate; the low estimate is 570,000.

294

2011 World Development Indicators

Children in employment

Refugees

Internally displaced persons

Access to an improved water source

Access to improved sanitation facilities

Maternal mortality ratio

Under-ﬁve Depth of mortality hunger rate

per 100,000 live births

Survey year

Afghanistan Angola Bosnia and Herzegovina Burundi Central African Republic Chad Comoros Congo, Dem. Rep. Congo, Rep. Côte d’Ivoire Eritrea Georgia Guinea Guinea-Bissau Haiti Iraq Kiribati Kosovo Liberia Myanmar Nepal São Tomé and Príncipe Sierra Leone Solomon Islands Somalia Sudan Tajikistan Timor-Leste Togo West Bank and Gaza Western Saharaj Yemen, Rep. Zimbabwe Fragile situations Low income

2001 2006 2005 2000 2004 2000 2005 2006 2006 1994 2006 2005 2006 2007 1999 2007 2006 2000 2005 2006 2006 1999

% of children ages 7–14

.. 30.1 10.6 11.7 67.0 60.4 .. 39.8 30.1 45.7 .. 31.8 48.3 50.5 33.4 14.7 .. .. 37.4 .. 47.2 .. 14.9 .. 43.5 19.1 8.9 .. 38.7 .. .. 18.3 14.3

By country of origin

By country of asylum

number

2009

2009

2009

2,887,123 141,021 70,018 94,239 159,554 55,014 268 455,852 20,544 23,153 209,168 15,020 10,920 1,109 24,116 1,785,212 33 .. 71,599 406,669 5,108 33 15,417 66 678,309 368,195 562 7 18,378 95,201 .. 1,934 22,449 7,636,291 s 5,427,548

37 14,734 7,132 24,967 27,047 338,495 .. 185,809 111,411 24,604 4,751 870 15,325 7,898 3 35,218 .. .. 6,952 .. 108,461 .. 9,051 .. 1,815 186,292 2,679 1 8,531 1,885,188 i .. 170,854 3,995 3,182,120 s 1,893,823

% of % of population population

297,000 20,000 114,000 100,000 162,000 168,000 .. 1,900,000 7,800 621,000 10,000 230,000 .. .. .. 2,764,000 .. 19,700 .. 470,000 70,000f .. .. .. 1,500,000 4,900,000 .. 400 1,500 .. 160,000 175,000 1,000,000k 14,047,900 s ..

2008

48 50 99 72 67 50 95 46 71 80 61 98 71 61 63 79 61 .. 68 71 88 89 49 69 30 57 70 69 60 91 .. 62 82 64 w 64

2008

National estimates

Modeled estimates

per 1,000

2004–09 c

2008

2009

37 57 95 46 34 9 36 23 30 23 14 95 19 21 17 73 31 .. 17 81 31 26 13 29 23 34 94 50 12 89 .. 52 44 43 w 35

.. .. 3 615 543 1,099 .. 549 781 543 .. 14 980 405 630 84 .. .. 994 316 281 148 857 .. 1,044 1,107 38 .. .. .. .. .. 555 .. ..

1,400 610 9 970 850 1,200 340 670 580 470 280 48 680 1,000 300 75 .. .. 990 240 380 .. 970 100 1,200 750 64 370 350 .. .. 210 790 640 w 580

STATES AND MARKETS

5.8

Fragile situations

Primary gross enrollment ratio

kilocalories per person % of relevant age group per day

199 161 14 166 171 209 104 199 128 119 55 29 142 193 87 44 46 .. 112 71 48 78 192 36 180 108 61 56 98 30 .. 66 90 132 w 118

2005–07d

.. 320 140 380 300 310 300 410 230 230 350 150 260 250 430 .. 180 .. 340 230 220 160 340 180 .. 240 240 260 280 190 .. 270 300 290 w 285

2009

104 128 109 147 89 90 119 90 120 74 48 108 90 120 .. 103 116 .. 91 116 .. 131 158 107 33 74 102 113 115 79 .. 85 .. 94 w 104

About the data The table focuses on countries with fragile situations

According to the Geneva Declaration on Armed Vio-

have to build their own institutions tailored to their

and highlights the links among weak institutions,

lence and Development, more than 740,000 people

own needs. Peacekeeping operations in post-conflict

poor development outcomes, fragility, and risk of

die each year because of the violence associated with

situations have been effective in reducing the risks

conflict. These countries and territories often have

armed conflict and large- and small-scale criminality.

of reversion to conflict.

weak institutions that are ill-equipped to handle eco-

Recovery and rebuilding can take years, and the chal-

The countries with fragile situations in the table

nomic shocks, natural disasters, and illegal trade

lenges are numerous: infrastructure to be rebuilt,

are primarily International Development Association–

or to resist conflict, which increasingly spills across

persistently high crime, widespread health problems,

eligible countries and nonmember or inactive coun-

borders. Organized violence, including violent crime,

education systems in disrepair, and landmines to be

tries or territories of the World Bank with a 3.2 or

interrupts economic and social development through

cleared. Most countries emerging from conflict lack

lower harmonized average of the World Bank’s Country

lost human and social capital, disrupted services,

the capacity to rebuild the economy. Thus, capacity

Policy and Institutional Assessment rating and the cor-

displaced populations and reduced confidence for

building is one of the first tasks for restoring growth

responding rating by a regional development bank or

future investment. As a result, countries with fragile

and is linked to building peace and creating the con-

that have had a UN or regional peacebuilding mission

situations achieve lower development outcomes and

ditions that lead to sustained poverty reduction. The

(for example, by the African Union, European Union,

make slower progress toward the Millennium Develop-

World Bank and other international development agen-

or Organization of American States) or peacekeeping

ment Goals.

cies can help, but countries with fragile situations

mission (for example, by the African Union, European

2011 World Development Indicators

295

5.8

Fragile situations

About the data (continued) • Troops, police, and military observers in peace-

Union, North Atlantic Treaty Organization (NATO), or

fewer types of contracts and investments, constrain-

Organization of American States) during the last three

ing growth. The table presents data on the loss of

building and peacekeeping refer to people active in

years. Peacebuilding and peacekeeping involve many

sales due to theft, robbery, vandalism, and arson and

peacebuilding and peacekeeping as part of an official

elements—military, police, and civilian—working

on the percentage of firms operating informally. For

operation. Peacekeepers deploy to war-torn regions

together to lay the foundations for sustainable peace.

further information on enterprise surveys, see About

where no one else is willing or able to go to prevent

The list of countries and territories with fragile situa-

the data for table 5.2.

conflict from returning or escalating. • Battle-related

As the table shows, the human toll of armed vio-

deaths are deaths of members of warring parties in

lence across various contexts is severe. Additionally,

battle-related confl icts. Typically, battle-related

An armed conflict is a contested incompatibility

in countries with fragile situations weak institutional

deaths occur in warfare involving the armed forces

that concerns a government or territory where the

capacity often results in poor performance and fail-

of the warring parties (battlefield fighting, guerrilla

use of armed force between two parties (one of them

ure to meet expectations of effective service deliv-

activities, and all kinds of bombardments of military

the government) results in at least 25 battle-related

ery. Failure to deliver water, health, and education

units, cities, and villages). The targets are usually

deaths in a calendar year. There were 35 active

services can weaken struggling governments. The

the military and its installations or state institutions

armed conflicts in 26 locations in 2009. Separate

table includes several indicators related to living con-

and state representatives, but there is often sub-

measures are presented for intentional homicides—

ditions in fragile situations: children in employment,

stantial collateral damage of civilians killed in cross-

unlawful deaths purposefully inflicted on a person

refugees, internally displaced persons, access to

fire, indiscriminate bombings, and other military

by another person—which exclude deaths arising

water and sanitation, maternal and under-five mortal-

activities. All deaths—civilian as well as military—

from armed conflict. One measure draws from inter-

ity, depth of hunger, and primary school enrollment.

incurred in such situations are counted as bat-

national public health data sources, while the other

For more detailed information on these indicators,

tlerelated deaths. • Intentional homicides are esti-

draws from estimates by the United Nations Office on

see About the data for table 2.6 (children in employ-

mates of unlawful homicides purposely inflicted as

Drugs and Crime, which obtains data from national

ment), table 6.18 (refugees), table 2.18 (access to

a result of domestic disputes, interpersonal violence,

and international law enforcement and criminal jus-

improved water and sanitation), table 2.19 (maternal

violent conflicts over land resources, intergang vio-

tice sources. Data from these two sources measure

mortality), table 2.22 (under-five mortality), and table

lence over turf or control, and predatory violence and

different phenomena and are therefore unlikely to

2.12 (primary school enrollment).

killing by armed groups. Intentional homicide does

tions is an interim one, and the World Bank will continue to improve and refine its understanding of fragility.

provide identical numbers. Data on military expenditures reported by govern-

not include all intentional killing; the difference is Definitions

ments are not compiled using standard definitions

usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas

and are often incomplete and unreliable. Even in

• International Development Association Resource

killing in armed conflict is usually committed by fairly

countries where the parliament vigilantly reviews

Allocation Index is from the Country Policy and Insti-

cohesive groups of up to several hundred members

budgets and spending, military expenditures and

tutional Assessment rating, which is the average

and is thus usually excluded. Data are from interna-

arms transfers rarely receive close scrutiny or full

score of four clusters of indicators designed to mea-

tional public health organizations such as the World

public disclosure. Data are from the Stockholm

sure macroeconomic, governance, social, and struc-

Health Organization (WHO) and the Pan American

International Peace Research Institute (SIPRI), which

tural dimensions of development: economic manage-

Health Organization and from the United Nations

uses NATO’s pre-2004 definition of military expen-

ment, structural policies, policies for social inclusion

Survey of Crime Trends and Operations of Criminal

diture (see Definitions). Therefore, the data in the

and equity, and public sector management and insti-

Justice Systems (CTS), which draws from national

table may differ from comparable data published by

tutions (see table 5.9). Countries are rated on a

and international law enforcement and criminal jus-

national governments. For a more detailed discus-

scale of 1 (low) to 6 (high). • Peacebuilding and

tice sources. • Military expenditures are SIPRI data

sion of military expenditures, see About the data for

peacekeeping refer to operations that engage in

derived from NATO's pre-2004 definition, which

table 5.7.

peacebuilding (reducing the risk of lapsing or relaps-

includes all current and capital expenditures on the

Along with public sector efforts, private sector

ing into conflict by strengthening national capacities

armed forces, including peacekeeping forces;

development and investment, especially in competi-

for conflict management and laying the foundation

defense ministries and other government agencies

tive markets, has tremendous potential to contribute

for sustainable peace and development) or peace-

engaged in defense projects; paramilitary forces, if

to growth and poverty reduction. The World Bank’s

keeping (providing essential security to preserve the

judged to be trained and equipped for military opera-

Enterprise Surveys review the business environment,

peace where fighting has been halted and to assist

tions; and military space activities. Such expendi-

assessing constraints to private sector growth and

in implementing agreements achieved by the peace-

tures include military and civil personnel, including

enterprise performance. In some countries doing

makers). UN peacekeeping operations are authorized

retirement pensions and social services for military

business requires informal payments to “get things

by the UN Secretary-General and planned, managed,

personnel; operation and maintenance; procure-

done” in customs, taxes, licenses, regulations, ser-

directed, and supported by the United Nations

ment; military research and development; and mili-

vices, and the like. Crime, theft, and disorder also

Department of Peacekeeping Operations and the

tary aid (in the military expenditures of the donor

impose costs on businesses and society. And in

Department of Field Support. The UN Charter gives

country). Excluded are civil defense and current

many developing countries informal businesses oper-

the Security Council primary responsibility for main-

expenditures for previous military activities, such as

ate without licenses. These firms have less access

taining international peace and security, including

for veterans benefits, demobilization, and weapons

to financial and public services and can engage in

the establishment of a UN peacekeeping operation.

conversion and destruction. This definition cannot

296

2011 World Development Indicators

5.8

STATES AND MARKETS

Fragile situations be applied to all countries, however, since the neces-

disposal facilities that can effectively prevent

on intentional homicides are from the UN Office

sary detailed information is missing in some cases

human, animal, and insect contact with excreta.

on Drugs and Crime’s International Homicide Sta-

for military budgets and off-budget military expendi-

Improved facilities range from protected pit latrines

tistics database. Data on military expenditures are

tures (for example, whether military budgets cover

to flush toilets. • Maternal mortality ratio is the

from SIPRI’s Yearbook 2010: Armaments, Disar-

civil defense, reserves and auxiliary forces, police

number of women who die from pregnancy-related

mament, and International Security and database

and paramilitary forces, and military pensions).

causes during pregnancy and childbirth per 100,000

(www.sipri.org/databases/milex). Data on the

• Survey year is the year in which the underlying

live births. National estimates are based on national

business environment are from the World Bank’s

data were collected. • Losses due to theft, robbery,

surveys, vital registration records, and surveillance

Enterprise Surveys (www.enterprisesurveys.

vandalism, and arson are the estimated losses from

data or are derived from community and hospital

org). Data on children in employment are esti-

those causes that occurred on business establish-

records. Modeled estimates are based on an exer-

mates produced by the Understanding Children’s

ment premises calculated as a percentage of annual

cise by the WHO, United Nations Children’s Fund

Work project based on household survey data

sales. • Firms formally registered when operations

(UNICEF), United Nations Population Fund (UNFPA),

sets made available by the International Labour

started are the percentage of firms formally regis-

and the World Bank. See About the data for table

Organization’s International Programme on the

tered when they started operations in the country.

2.19 for further details. • Under-ﬁve mortality rate

Elimination of Child Labour under its Statistical

• Children in employment are children involved in

is the probability per 1,000 that a newborn baby will

Monitoring Programme on Child Labour, UNICEF

any economic activity for at least one hour in the

die before reaching age 5, if subject to current age-

under its Multiple Indicator Cluster Survey pro-

reference week of the survey. • Refugees are people

specific mortality rates. • Depth of hunger, or the

gram, the World Bank under its Living Standards

who are recognized as refugees under the 1951 Con-

intensity of food deprivation, indicates how much

Measurement Study program, and national sta-

vention Relating to the Status of Refugees or its

people who are food-deprived fall short of minimum

tistical offices (see table 2.6). Data on refugees

1967 Protocol, the 1969 Organization of African

food needs in terms of dietary energy. It is measured

are from the UNHCR’s Statistical Yearbook 2009,

Unity Convention Governing the Specific Aspects of

by comparing the average amount of dietary energy

complemented by statistics on Palestinian refu-

Refugee Problems in Africa, people recognized as

that undernourished people get from the foods they

gees under the mandate of the United Nations

refugees in accordance with the UN Refugee Agency

eat with the minimum amount of dietary energy they

Relief and Works Agency for Palestine Refugees

(UNHCR) statute, people granted refugee-like human-

need to maintain body weight and undertake light

in the Near East as published on its website (www.

itarian status, and people provided temporary protec-

activity. Depth of hunger is low when it is less than

unrwa.org). Data on internally displaced persons

tion. Asylum seekers—people who have applied for

200 kilocalories per person per day and high when

are from the Internal Displacement Monitoring

asylum or refugee status and who have not yet

it is above 300. • Primary gross enrollment ratio is

Centre. Data on access to water and sanitation

received a decision, or who are registered as asylum

the ratio of total enrollment, regardless of age, to the

are from the WHO and UNICEF’s Progress on Sani-

seekers—are excluded. Palestinian refugees are

population of the age group that offi cially corre-

tation and Drinking Water (2010). National esti-

people (and their descendants) whose residence was

sponds to the primary level of education. Primary

mates of maternal mortality are from UNICEF’s The

Palestine between June 1946 and May 1948 and

education provides children with basic reading, writ-

State of the World’s Children 2009 and Childinfo

who lost their homes and means of livelihood as a

ing, and mathematics skills along with an elementary

and Demographic and Health Surveys by Macro

result of the 1948 Arab-Israeli conflict. • Country of

understanding of such subjects as history, geogra-

International. Modeled estimates for maternal

origin refers to the nationality or country of citizen-

phy, natural science, social science, art, and music.

mortality are from WHO, UNICEF, UNFPA, and

ship of a claimant. • Country of asylum is the country

the World Bank’s Trends in Maternal Mortality in

where an asylum claim was filed and granted. • Inter-

1990–2008 (2010). Data on under-fi ve mortal-

nally displaced persons are people or groups of

Data sources

people who have been forced or obliged to flee or to

ity estimates by the Inter-agency Group for Child Mortality Estimation (which comprises UNICEF,

leave their homes or places of habitual residence, in

Data on the International Development Asso-

WHO, the World Bank, United Nations Population

particular as a result of armed conflict, or to avoid

ciation Resource Allocation Index are from

Division, and other universities and research insti-

the effects of armed conflict, situations of general-

the World Bank Group’s International Develop-

tutes) and are based mainly on household surveys,

ized violence, violations of human rights, or natural

ment Association database (www.worldbank.

censuses, and vital registration data, supple-

or human-made disasters and who have not crossed

org/ida). Data on peacebuilding and peace-

mented by the World Bank’s Human Development

an international border. • Access to an improved

keeping operations are from the UN Depart-

Network estimates based on vital registration and

water source refers to people with reasonable

ment of Peacekeeping Operations. Data on

sample registration data (see table 2.22). Data on

access to water from an improved source, such as

battle-related deaths are primarily from the

depth of hunger are from the Food and Agriculture

piped water into a dwelling, public tap, tubewell, pro-

Peace Research Institute Oslo/Uppsala Conflict

Organization’sFood Security Statistics (www.fao.

tected dug well, and rainwater collection. Reasonable

Data Program (UCDP) Armed Conflict Dataset (v.4-

org/economic/ess/food-security-statistics/en/).

access is the availability of at least 20 liters a person

2010) 1946-2009 (www.pcr.uu.se/research/ucdp/

Data on primary gross enrollment are from the

a day from a source within 1 kilometer of the dwell-

datasets), supplemented with data from the UCDP

United Nations Educational, Scientific, and Cul-

ing. • Access to improved sanitation facilities refers

Battle-Related Deaths Dataset (v.5-2010). Data

tural Organization’s Institute for Statistics.

to people with at least adequate access to excreta

2011 World Development Indicators

297

5.9

Public policies and institutions International Development Association Resource Allocation Index 1–6 (low to high)

Afghanistan Angola Armenia Azerbaijan Bangladesh Benin Bhutan Bolivia Bosnia and Herzegovina Burkina Faso Burundi Cambodia Cameroon Cape Verde Central African Republic Chad Comoros Congo, Dem. Rep. Congo, Rep. Côte d’Ivoire Djibouti Dominica Eritrea Ethiopia Gambia, The Georgia Ghana Grenada Guinea Guinea-Bissau Guyana Haiti Honduras India Kenya Kiribati Kosovo Kyrgyz Republic

Economic management

Structural policies

1–6 (low to high)

1–6 (low to high)

Macroeconomic management

Fiscal policy

Debt policy

Average

2009

2009

2009

2009

2009

2.8 2.8 4.2 3.8 3.5 3.5 3.9 3.8 3.7 3.8 3.1 3.3 3.2 4.2 2.6 2.5 2.5 2.7 2.8 2.8 3.2 3.8 2.2 3.4 3.3 4.4 3.8 3.7 2.8 2.6 3.4 2.9 3.5 3.8 3.7 3.1 3.4 3.7

3.5 3.0 5.0 4.0 4.0 4.0 4.5 4.0 4.0 4.5 3.5 4.5 4.0 4.5 3.5 2.5 3.0 3.5 3.5 3.5 3.5 4.0 2.0 3.5 4.0 4.5 3.5 3.5 2.5 2.5 3.5 4.0 3.0 4.5 4.5 2.5 3.5 4.5

3.0 3.0 5.0 4.5 4.0 3.5 4.5 4.0 3.5 4.5 3.5 3.5 4.0 4.5 3.0 2.5 2.0 3.5 3.0 2.5 3.0 4.5 2.0 4.0 3.5 4.5 3.5 2.5 2.5 2.5 3.0 3.5 3.5 3.5 4.0 3.0 3.0 4.0

3.5 3.0 5.0 5.0 4.0 3.5 4.5 4.5 4.0 4.0 3.0 3.5 3.0 4.5 2.5 2.5 2.0 2.5 2.5 2.5 2.5 3.0 1.5 3.5 3.0 5.0 4.0 3.0 2.0 1.5 4.0 2.5 4.0 4.0 4.0 5.0 3.5 4.0

3.3 3.0 5.0 4.5 4.0 3.7 4.5 4.2 3.8 4.3 3.3 3.8 3.7 4.5 3.0 2.5 2.3 3.2 3.0 2.8 3.0 3.8 1.8 3.7 3.5 4.7 3.7 3.0 2.3 2.2 3.5 3.3 3.5 4.0 4.2 3.5 3.3 4.2

Trade

Financial sector

Business regulatory environment

Average

2009

2009

2009

2009

3.0 4.0 4.5 4.0 3.5 4.0 3.0 5.0 4.0 4.0 4.0 4.0 3.5 4.0 3.5 3.0 3.0 3.5 3.5 4.0 4.0 4.0 1.5 3.0 3.5 6.0 4.0 4.5 4.0 4.0 4.0 4.0 4.5 3.5 4.0 3.0 5.0 5.0

2.5 2.5 4.0 3.5 3.5 3.5 3.0 4.0 4.0 3.0 2.5 2.5 3.0 4.0 2.5 3.0 2.5 2.0 3.0 3.0 3.5 3.5 1.0 3.0 3.0 3.5 4.0 4.0 3.0 3.0 3.5 3.0 3.0 4.0 4.0 3.0 3.5 3.0

2.5 2.0 4.0 4.0 3.5 3.5 3.5 2.5 4.0 3.5 2.5 3.5 3.0 3.5 2.0 2.5 2.5 2.0 2.5 3.0 3.5 4.5 2.0 3.5 3.5 5.5 4.0 4.0 3.0 2.5 3.0 2.5 3.5 3.5 4.0 3.0 3.5 3.5

2.7 2.8 4.2 3.8 3.5 3.7 3.2 3.8 4.0 3.5 3.0 3.3 3.2 3.8 2.7 2.8 2.7 2.5 3.0 3.3 3.7 4.0 1.5 3.2 3.3 5.0 4.0 4.2 3.3 3.2 3.5 3.2 3.7 3.7 4.0 3.0 4.0 3.8

About the data The International Development Association (IDA) is the

assessments have been carried out annually since

terms. The IRAI is a key element in the country per-

part of the World Bank Group that helps the poorest

the mid-1970s by World Bank staff. Over time the cri-

formance rating.

countries reduce poverty by providing concessional loans

teria have been revised from a largely macroeconomic

The CPIA exercise is intended to capture the quality

and grants for programs aimed at boosting economic

focus to include governance aspects and a broader

of a country’s policies and institutional arrangements,

growth and improving living conditions. IDA funding helps

coverage of social and structural dimensions. Country

focusing on key elements that are within the country’s

these countries deal with the complex challenges they

performance is assessed against a set of 16 criteria

control, rather than on outcomes (such as economic

face in meeting the Millennium Development Goals.

grouped into four clusters: economic management,

growth rates) that are influenced by events beyond

The World Bank’s IDA Resource Allocation Index

structural policies, policies for social inclusion and

the country’s control. More specifically, the CPIA

(IRAI), presented in the table, is based on the results

equity, and public sector management and institu-

measures the extent to which a country’s policy and

of the annual Country Policy and Institutional Assess-

tions. IDA resources are allocated to a country on per

institutional framework supports sustainable growth

ment (CPIA) exercise, which covers the IDA-eligible

capita terms based on its IDA country performance

and poverty reduction and, consequently, the effective

countries. The table does not include Myanmar and

rating and, to a limited extent, based on its per capita

use of development assistance.

Somalia because they were not rated in the 2009

gross national income. This ensures that good per-

All criteria within each cluster receive equal weight,

exercise even though they are IDA eligible. Country

formers receive a higher IDA allocation in per capita

and each cluster has a 25 percent weight in the overall

298

2011 World Development Indicators

International Development Association Resource Allocation Index 1–6 (low to high)

Lao PDR Lesotho Liberia Madagascar Malawi Maldives Mali Mauritania Moldova Mongolia Mozambique Nepal Nicaragua Niger Nigeria Pakistan Papua New Guinea Rwanda Samoa São Tomé and Príncipe Senegal Sierra Leone Solomon Islands Sri Lanka St. Lucia St. Vincent & Grenadines Sudan Tajikistan Tanzania Timor-Leste Togo Tonga Uganda Uzbekistan Vanuatu Vietnam Yemen, Rep. Zambia Zimbabwe

Economic management

Structural policies

1–6 (low to high)

1–6 (low to high)

Macroeconomic management

Fiscal policy

Debt policy

Average

2009

2009

2009

2009

2009

3.2 3.5 2.8 3.5 3.4 3.4 3.7 3.2 3.7 3.4 3.7 3.3 3.7 3.3 3.5 3.2 3.3 3.8 4.1 2.9 3.7 3.2 2.8 3.5 3.8 3.8 2.5 3.2 3.8 2.9 2.8 3.5 3.9 3.3 3.4 3.8 3.2 3.4 1.9

4.0 4.0 3.5 4.0 3.0 2.5 4.5 3.5 3.5 3.5 4.5 3.5 4.0 4.0 4.0 3.0 4.0 4.0 4.0 3.0 4.0 4.0 3.5 3.0 4.0 4.0 3.5 3.5 4.5 3.0 3.0 3.0 4.5 4.0 4.0 4.5 3.5 4.0 2.0

4.0 4.0 3.5 3.0 3.5 2.0 4.0 2.5 3.5 3.0 4.5 3.5 4.0 3.5 4.5 3.0 3.5 4.0 4.0 3.0 4.0 3.5 2.5 3.0 3.5 3.5 3.0 3.5 4.5 3.5 3.0 3.0 4.5 4.0 3.5 4.5 2.5 3.0 2.0

3.0 4.0 2.5 4.0 3.0 3.0 4.5 3.5 4.0 3.0 4.5 3.0 4.5 4.0 4.5 3.5 4.5 3.5 5.0 2.5 4.0 3.5 3.0 3.5 3.5 3.5 1.5 3.5 4.0 3.5 2.5 3.0 4.5 4.0 4.5 4.0 3.5 3.5 1.0

3.7 4.0 3.2 3.7 3.2 2.5 4.3 3.2 3.7 3.2 4.5 3.3 4.2 3.8 4.3 3.2 4.0 3.8 4.3 2.8 4.0 3.7 3.0 3.2 3.7 3.7 2.7 3.5 4.3 3.3 2.8 3.0 4.5 4.0 4.0 4.3 3.2 3.5 1.7

STATES AND MARKETS

5.9

Public policies and institutions

Trade

Financial sector

Business regulatory environment

Average

2009

2009

2009

2009

3.5 3.5 3.0 4.0 4.0 4.0 4.0 4.0 4.5 4.5 4.5 3.5 4.5 4.0 3.5 3.5 4.5 4.0 5.0 4.0 4.0 3.5 3.0 3.5 4.0 4.0 2.5 4.0 4.0 4.5 4.0 5.0 4.0 2.5 3.5 3.5 4.5 4.0 3.0

2.0 3.5 2.5 3.0 3.0 3.0 3.0 2.5 3.5 2.0 3.5 3.0 3.0 3.0 3.5 3.5 3.0 3.5 4.0 2.5 3.5 3.0 3.0 3.5 3.5 3.5 2.5 2.5 4.0 2.5 2.5 3.5 3.5 3.0 3.0 3.0 2.0 3.5 1.5

3.0 3.0 3.0 3.5 3.5 4.0 3.5 3.5 3.5 3.5 3.0 3.0 3.5 3.0 3.5 4.0 3.0 4.0 3.5 2.5 4.0 3.0 2.5 4.0 4.5 4.5 3.0 3.0 3.5 1.5 3.0 3.0 4.0 3.0 3.5 3.5 3.5 3.0 2.0

2.8 3.3 2.8 3.5 3.5 3.7 3.5 3.3 3.8 3.3 3.7 3.2 3.7 3.3 3.5 3.7 3.5 3.8 4.2 3.0 3.8 3.2 2.8 3.7 4.0 4.0 2.7 3.2 3.8 2.8 3.2 3.8 3.8 2.8 3.3 3.3 3.3 3.5 2.2

score, which is obtained by averaging the average

criteria are designed in a developmentally neutral man-

two key phases. In the benchmarking phase a small

scores of the four clusters. For each of the 16 criteria

ner. Accordingly, higher scores can be attained by a

representative sample of countries drawn from all

countries are rated on a scale of 1 (low) to 6 (high).

country that, given its stage of development, has a

regions is rated. Country teams prepare proposals

The scores depend on the level of performance in

policy and institutional framework that more strongly

that are reviewed first at the regional level and then

a given year assessed against the criteria, rather

fosters growth and poverty reduction.

in a Bankwide review process. A similar process is

than on changes in performance compared with the

The country teams that prepare the ratings are very

followed to assess the performance of the remaining

previous year. All 16 CPIA criteria contain a detailed

familiar with the country, and their assessments are

countries, using the benchmark countries’ scores as

description of each rating level. In assessing country

based on country diagnostic studies prepared by the

guideposts. The final ratings are determined following

performance, World Bank staff evaluate the country’s

World Bank or other development organizations and

a Bankwide review. The overall numerical IRAI score

performance on each of the criteria and assign a rat-

on their own professional judgment. An early con-

and the separate criteria scores were first publicly

ing. The ratings reflect a variety of indicators, observa-

sultation is conducted with country authorities to

disclosed in June 2006.

tions, and judgments based on country knowledge and

make sure that the assessments are informed by

on relevant publicly available indicators. In interpreting

up-to-date information. To ensure that scores are

the assessment scores, it should be noted that the

consistent across countries, the process involves

See IDA’s website at www.worldbank.org/ida for more information.

2011 World Development Indicators

299

5.9 Afghanistan Angola Armenia Azerbaijan Bangladesh Benin Bhutan Bolivia Bosnia and Herzegovina Burkina Faso Burundi Cambodia Cameroon Cape Verde Central African Republic Chad Comoros Congo, Dem. Rep. Congo, Rep. Côte d'Ivoire Djibouti Dominica Eritrea Ethiopia Gambia, The Georgia Ghana Grenada Guinea Guinea-Bissau Guyana Haiti Honduras India Kenya Kiribati Kosovo Kyrgyz Republic

Public policies and institutions Policies for social inclusion and equity

Public sector management and institutions

1–6 (low to high)

1–6 (low to high)

Policies and institutions for Social protection environmental and labor sustainability Average

Quality of budgetary and Property financial rights and rule-based management governance

Transparency, accountability, and corruption Quality Efficiency in the public of public of revenue sector Average mobilization administration

Gender equality

Equity of public resource use

Building human resources

2009

2009

2009

2009

2009

2009

2009

2009

2009

2009

2009

2009

2.0 3.5 4.5 4.0 4.0 3.5 4.0 4.0 4.5 3.5 4.0 4.0 3.0 4.5 2.5 2.5 3.0 2.5 3.0 2.5 3.0 3.5 3.5 3.0 3.5 4.5 4.0 4.5 3.5 2.5 4.0 3.0 4.0 3.5 3.0 2.5 3.5 4.5

3.0 2.5 4.5 4.0 3.5 3.0 4.0 4.0 3.5 4.0 3.5 3.0 3.0 4.5 2.5 2.5 2.5 3.0 2.5 2.0 3.0 3.5 2.5 4.5 3.5 4.5 4.0 3.5 3.0 3.0 3.5 3.0 4.0 4.0 3.5 3.5 3.5 3.5

3.0 2.5 4.0 4.0 4.0 3.5 4.0 4.0 3.5 3.5 3.0 3.5 3.5 4.5 2.5 2.5 3.0 3.0 3.0 2.5 3.5 4.0 3.5 4.0 3.5 4.5 4.5 4.0 3.0 2.0 4.0 2.5 3.5 4.0 4.0 2.5 2.5 3.5

2.5 3.0 4.5 4.0 3.5 3.0 3.5 3.5 3.5 3.5 3.0 3.0 3.0 4.5 2.0 2.5 2.5 3.0 2.5 2.5 3.0 3.5 2.5 3.5 2.5 4.5 3.5 3.5 3.0 2.5 3.0 2.5 3.5 3.5 3.5 3.0 3.5 3.5

2.5 3.0 3.0 3.0 3.0 3.5 4.5 3.5 3.5 3.5 3.0 3.0 3.0 3.5 3.0 2.0 2.0 2.5 2.5 2.5 3.5 3.5 2.0 3.0 3.5 3.0 3.5 4.0 2.5 2.5 3.0 2.5 3.5 3.5 3.5 3.0 3.0 3.0

2.6 2.9 4.1 3.8 3.6 3.3 4.0 3.8 3.7 3.6 3.3 3.3 3.1 4.3 2.5 2.4 2.6 2.8 2.7 2.4 3.2 3.6 2.8 3.6 3.3 4.2 3.9 3.9 3.0 2.5 3.5 2.7 3.7 3.7 3.5 2.9 3.2 3.6

1.5 2.0 3.5 3.0 3.0 3.0 3.5 2.5 3.0 3.5 2.5 2.5 2.5 4.0 2.0 2.0 2.5 2.0 2.5 2.0 2.5 4.0 2.5 3.0 3.0 3.5 3.5 3.5 2.0 2.5 3.0 2.0 3.0 3.5 2.5 3.5 3.0 2.5

3.5 2.5 4.5 4.0 3.0 3.5 3.5 3.5 3.5 4.5 3.0 3.5 3.0 4.0 2.5 2.0 2.0 2.5 2.5 2.5 3.0 3.5 2.5 3.5 3.0 4.0 3.5 4.0 3.0 2.5 3.5 3.0 4.0 4.0 3.5 3.0 4.0 3.5

3.0 2.5 3.5 3.5 3.0 3.5 4.0 4.0 4.0 3.5 3.0 3.0 3.5 3.5 2.5 2.5 2.5 2.5 3.0 4.0 3.5 4.0 3.5 3.5 3.5 4.5 4.5 3.5 3.0 3.0 3.5 2.5 4.0 4.0 4.0 3.0 3.5 3.5

2.0 2.5 4.0 3.0 3.0 3.0 4.0 3.0 3.0 3.5 2.5 2.5 3.0 4.0 2.5 2.5 2.5 2.0 2.5 2.0 2.5 3.5 3.0 3.5 3.0 4.0 3.5 3.5 3.0 2.5 2.5 2.5 2.5 3.5 3.5 3.0 2.5 3.0

2.0 2.5 3.0 2.5 3.0 3.5 4.5 3.5 3.0 3.5 2.0 2.0 2.5 4.5 2.5 2.0 2.5 2.0 2.5 2.5 2.5 4.0 2.0 2.5 2.0 3.0 4.0 4.0 2.0 2.5 3.0 2.5 3.0 3.5 3.0 3.0 3.0 2.5

2.4 2.4 3.7 3.2 3.0 3.3 3.9 3.3 3.3 3.7 2.6 2.7 2.9 4.0 2.4 2.2 2.4 2.2 2.6 2.6 2.8 3.8 2.7 3.2 2.9 3.8 3.8 3.7 2.6 2.6 3.1 2.5 3.3 3.7 3.3 3.1 3.2 3.0

Definitions • International Development Association Resource

long-term debt sustainability. • Structural policies

protection under law. • Equity of public resource use

Allocation Index is obtained by calculating the aver-

cluster: Trade assesses how the policy framework

assesses the extent to which the pattern of public

age score for each cluster and then by averaging

fosters trade in goods. • Financial sector assesses

expenditures and revenue collection affects the poor

those scores. For each of 16 criteria countries are

the structure of the financial sector and the poli-

and is consistent with national poverty reduction

rated on a scale of 1 (low) to 6 (high) • Economic

cies and regulations that affect it. • Business regu-

priorities. • Building human resources assesses

management cluster: Macro economic manage-

latory environment assesses the extent to which

the national policies and public and private sec-

ment assesses the monetary, exchange rate, and

the legal, regulatory, and policy environments help

tor service delivery that affect the access to and

aggregate demand policy framework. • Fiscal policy

or hinder private businesses in investing, creating

quality of health and education services, including

assesses the short- and medium-term sustainability

jobs, and becoming more productive. • Policies for

prevention and treatment of HIV/AIDS, tuberculosis,

of fiscal policy (taking into account monetary and

social inclusion and equity cluster: Gender equal-

and malaria. • Social protection and labor assess

exchange rate policy and the sustainability of the

ity assesses the extent to which the country has

government policies in social protection and labor

public debt) and its impact on growth. • Debt policy

installed institutions and programs to enforce laws

market regulations that reduce the risk of becoming

assesses whether the debt management strategy is

and policies that promote equal access for men

poor, assist those who are poor to better manage

conducive to minimizing budgetary risks and ensuring

and women in education, health, the economy, and

further risks, and ensure a minimal level of welfare

300

2011 World Development Indicators

Lao PDR Lesotho Liberia Madagascar Malawi Maldives Mali Mauritania Moldova Mongolia Mozambique Nepal Nicaragua Niger Nigeria Pakistan Papua New Guinea Rwanda Samoa São Tomé and Príncipe Senegal Sierra Leone Solomon Islands Sri Lanka St. Lucia St. Vincent & Grenadines Sudan Tajikistan Tanzania Timor-Leste Togo Tonga Uganda Uzbekistan Vanuatu Vietnam Yemen, Rep. Zambia Zimbabwe

5.9

Policies for social inclusion and equity

Public sector management and institutions

1–6 (low to high)

1–6 (low to high)

Policies and institutions for Social protection environmental and labor sustainability Average

Quality of budgetary and Property financial rights and rule-based management governance

STATES AND MARKETS

Public policies and institutions

Transparency, accountability, and corruption Quality Efficiency in the public of public of revenue sector Average mobilization administration

Gender equality

Equity of public resource use

Building human resources

2009

2009

2009

2009

2009

2009

2009

2009

2009

2009

2009

2009

3.5 4.0 2.5 3.5 3.5 4.0 3.5 4.0 5.0 3.5 3.5 4.0 3.5 2.5 3.0 2.0 2.5 3.5 3.5 3.0 3.5 3.0 3.0 4.0 3.5 4.0 2.0 4.0 3.5 3.5 3.0 3.0 3.5 4.0 3.5 4.5 2.0 3.5 2.5

4.0 3.0 3.0 4.0 3.5 4.0 3.5 3.5 3.5 3.5 3.5 4.0 3.5 3.5 3.5 3.5 3.5 4.5 4.5 3.0 3.5 3.0 2.5 3.5 4.0 3.5 2.5 3.5 4.0 3.0 2.0 4.0 4.0 3.5 3.5 4.5 3.5 3.5 1.5

3.0 3.5 2.5 3.5 3.5 3.5 3.5 3.5 4.0 4.0 3.5 4.0 3.5 3.5 3.0 3.0 2.5 4.5 4.0 3.0 3.5 3.5 3.0 4.5 4.0 4.0 2.5 3.0 4.0 2.5 3.0 4.0 4.0 4.0 2.5 4.0 3.0 4.0 1.0

2.5 3.0 2.5 3.5 3.5 3.5 3.5 3.0 3.5 3.5 3.0 3.0 3.5 3.0 3.5 3.0 3.0 3.5 3.5 2.5 3.0 3.5 2.5 3.5 3.5 3.5 2.5 3.5 3.5 2.5 3.0 3.0 3.5 3.5 2.0 3.5 3.5 3.0 1.0

4.0 3.0 2.0 3.5 3.5 4.0 3.0 3.0 3.5 3.0 3.0 3.5 3.5 3.0 3.0 3.0 2.0 3.5 4.0 2.5 3.5 2.5 2.0 3.5 3.5 3.5 2.0 3.0 3.5 2.5 2.5 3.0 4.0 3.5 3.0 3.5 3.5 3.5 2.0

3.4 3.3 2.5 3.6 3.5 3.8 3.4 3.4 3.9 3.5 3.3 3.7 3.5 3.1 3.2 2.9 2.7 3.9 3.9 2.8 3.4 3.1 2.6 3.8 3.7 3.7 2.3 3.4 3.7 2.8 2.7 3.4 3.8 3.7 2.9 4.0 3.1 3.5 1.6

3.0 3.5 2.5 3.5 3.5 4.0 3.5 3.0 3.5 3.0 3.0 2.5 3.0 3.0 2.5 2.5 2.0 3.0 4.0 2.5 3.5 2.5 3.0 3.5 4.0 4.0 2.0 2.5 3.5 2.0 2.5 3.5 3.5 2.5 3.5 3.5 2.5 3.0 1.5

3.5 3.0 2.5 3.0 3.0 3.0 3.5 3.0 4.0 4.0 4.0 3.0 4.0 3.5 3.0 3.5 3.0 4.0 3.5 3.0 3.0 3.5 2.5 4.0 3.5 3.5 2.0 3.0 3.5 3.0 2.5 3.5 4.0 3.5 3.5 4.0 3.5 3.5 2.0

3.0 4.0 3.5 4.0 4.0 4.0 3.5 3.5 3.5 3.5 4.0 3.5 4.0 3.5 3.0 3.0 3.5 3.5 4.5 3.5 4.0 2.5 2.5 3.5 4.5 4.0 3.0 3.0 4.0 3.0 3.0 4.0 3.5 3.5 3.5 4.0 3.0 3.5 3.5

3.0 3.0 2.5 3.5 3.5 3.5 3.0 3.0 3.0 3.5 3.0 3.0 3.0 3.0 3.0 3.5 2.5 3.5 4.0 3.0 3.5 3.0 2.0 3.0 3.5 3.5 2.5 3.0 3.5 2.5 2.0 3.5 3.0 3.0 3.0 3.5 3.0 3.0 1.5

2.0 3.5 3.0 2.5 3.0 3.0 3.5 2.5 3.0 3.0 3.0 3.0 3.0 2.5 3.0 2.5 3.0 3.5 4.0 3.5 3.0 3.0 3.0 3.0 4.5 4.0 1.5 2.0 3.0 3.0 2.0 3.5 2.5 1.5 3.0 3.0 3.0 3.0 1.5

2.9 3.4 2.8 3.3 3.4 3.5 3.4 3.0 3.4 3.4 3.4 3.0 3.4 3.1 2.9 3.0 2.8 3.5 4.0 3.1 3.4 2.9 2.6 3.4 4.0 3.8 2.2 2.7 3.5 2.7 2.4 3.6 3.3 2.8 3.3 3.6 3.0 3.2 2.0

to all people. • Policies and institutions for envi-

and timely and accurate accounting and fiscal report-

extent to which public employees within the executive

ronmental sustainability assess the extent to which

ing, including timely and audited public accounts.

are required to account for administrative decisions,

environmental policies foster the protection and sus-

• Efﬁciency of revenue mobilization assesses the

use of resources, and results obtained. The three

tainable use of natural resources and the manage-

overall pat tern of revenue mobilization—not only

main dimensions assessed are the accountability

ment of pollution. • Public sector management and

the de facto tax structure, but also revenue from

of the executive to oversight institutions and of pub-

institutions cluster: Property rights and rule-based

all sources as actually collected. • Quality of public

lic employees for their performance, access of civil

governance assess the extent to which private eco-

administration assesses the extent to which civilian

society to information on public affairs, and state

nomic activity is facilitated by an effective legal sys-

central government staff is structured to design and

capture by narrow vested interests.

tem and rule-based governance structure in which

implement government policy and deliver services

property and contract rights are reliably respected

effectively. • Transparency, accountability, and cor-

and enforced. • Quality of budgetary and ﬁnancial

ruption in the public sector assess the extent to

management assesses the extent to which there is

which the executive can be held accountable for its

a comprehensive and credible budget linked to policy

use of funds and for the results of its actions by the

priorities, effective financial management systems,

electorate, the legislature, and the judiciary and the

Data sources Data on public policies and institutions are from the World Bank Group’s CPIA database available at www.worldbank.org/ida.

2011 World Development Indicators

301

5.10

Transport services Roads

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

302

Total road network km

Paved roads %

Passengers carried million passengerkm

2000–08 a

2000–08a

2000–08a

42,150 18,000 111,261 51,429 231,374 7,704 818,356 110,778 52,942 239,226 94,797 153,595 19,000 62,479 21,846 25,798 1,751,868 40,231 92,495 12,322 38,257 51,346 1,409,000 24,307 40,000 79,814 3,730,164 2,040 164,183 153,497 17,000 38,049 81,996 29,248 .. 130,573 73,257 12,600 43,670 104,918 10,029 4,010 58,034 44,359 78,860 951,200 9,170 3,742 20,329 644,288 57,614 116,711 14,095 44,348 3,455 4,160 13,600

29.3 39.0 73.5 10.4 30.0 90.5 .. 100.0 50.6 9.5 88.6 78.2 9.5 7.0 52.3 32.6 5.5 98.4 4.2 10.4 6.3 8.4 39.9 .. 0.8 20.2 53.5 100.0 .. 1.8 7.1 25.3 7.9 86.9 49.0 100.0 100.0 49.4 14.8 86.9 19.8 21.8 28.8 13.7 65.5 100.0 10.2 19.3 94.1 100.0 14.9 91.8 34.5 9.8 27.9 24.3 20.4

2011 World Development Indicators

.. 197 .. 166,045 .. 2,742 302,369 69,000 14,041 .. 8,184 132,404 .. .. .. .. .. 13,688 .. .. 201 .. 493,814 .. .. .. 1,247,611 .. 157 .. .. 27 .. 4,093 6,551 88,468 70,173 .. 11,819 12,793 .. .. 3,190 219,113 71,800 769,000 .. 16 5,269 949,306 .. .. .. .. .. .. ..

Railways

Goods hauled million ton-km 2000–08a

.. 2,200 .. 4,709 .. 179 189,847 26,411 9,947 .. 22,767 46,891 .. .. 300 .. .. 11,843 .. .. .. .. 129,600 .. .. .. 3,286,819 .. 39,726 .. .. 1 .. 11,042 2,222 50,877 10,717 .. 1,193 .. .. .. 7,641 2,456 28,500 313,000 .. .. 586 472,700 .. 18,360 .. .. .. .. ..

Passengers carried million Rail lines total route- passengerkm km 2000–09a

.. 423 4,723 .. 25,023 845 9,674 5,784 2,079 2,835 5,510 3,578 758 2,866 1,016 888 29,817 4,150 622 .. 650 977 58,345 .. .. 5,352 65,491 .. 1,672 3,641 795 .. 639 2,723 5,076 9,539 2,131 .. .. 5,195 .. .. 929 .. 5,919 33,778 810 .. 1,566 33,706 953 1,552 .. .. .. .. ..

2000–09a

.. 32 1,141 .. 6,979 27 1,546 10,210 1,025 5,609 7,401 10,493 .. 313 61 94 .. 2,144 .. .. 45 377 2,901 .. .. 840 787,890 .. .. 35 211 .. 10 1,835 1,285 6,462 7,312 .. .. 40,837 .. .. 274 .. 3,876 87,667 95 .. 626 76,772 85 1,413 .. .. .. .. ..

Ports

Air

Goods hauled million ton-km

Port container traffic thousand TEU

Registered carrier departures worldwide thousands

Passengers carried thousands

Air freight million ton-km

2000–09a

2009

2009

2009

2009

.. 231 4,371 275 5,695 653 50,027 8,521 840 1,409 333 4,859 .. 1,537 80 234 67,946 798 79 .. 184 466 52,584 .. .. 8,097 229,062 23,973 12,115 .. .. 933 .. 1,679 780 5,048 6,773 .. 2,897 6,216 1,997 .. 396 2,914 7,423 58,318 525 .. 294 103,397 .. 8,795 .. .. .. .. ..

.. 0 4 64 112 6 2,769 342 7 0 1 1,427 .. 7 0 0 1,782 2 0 .. 1 23 1,347 .. .. 1,179 11,976 13,293 2,420 .. .. 9 .. 2 27 22 14 .. 3 180 15 .. 1 424 484 6,625 62 .. 2 10,188 .. 31 .. .. .. .. ..

.. 46 1,184 .. 12,025 354 62,083 20,202 7,592 870 42,742 6,542 36 1,060 988 674 267,700 3,152 .. .. 92 978 258,280 .. .. 4,032 2,523,917 .. 11,884 182 234 .. 675 2,641 1,351 11,249 2,030 .. .. 3,840 .. .. 5,780 .. 8,872 26,482 2,485 .. 5,417 93,946 181 538 .. .. .. .. ..

.. .. .. .. 1,555 .. 6,197 .. .. 1,182 .. 9,701 .. .. .. .. 6,246 .. .. .. .. .. 4,175 .. .. 2,814 105,977 21,040 2,042 .. .. 876 .. .. .. .. .. 1,263 1,001 6,250 .. .. .. .. 1,064 4,491 .. .. .. 12,765 .. 935 906 .. .. .. ..

.. 5 53 3 75 8 403 139 10 16 6 250 .. 19 1 6 752 11 1 .. 3 10 1,198 .. .. 97 2,140 150 196 .. .. 33 .. 25 11 78 86 .. 46 56 19 .. 9 44 105 772 5 .. 5 1,081 .. 113 .. .. .. .. ..

Roads

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

Total road network km

Paved roads %

Passengers carried million passengerkm

2000–08 a

2000–08a

2000–08a

197,534 4,236,429 437,759 174,301 45,550 96,424 18,096 487,700 22,210 1,200,858 7,816 93,612 63,265 25,554 104,237 .. 5,749 34,000 34,994 69,684 6,970 5,940 10,600 83,200 81,030 13,922 49,827 15,451 98,722 18,912 11,066 2,028 366,096 12,778 49,250 58,256 30,331 27,000 66,467 17,782 136,135 93,911 20,333 18,948 193,200 93,247 53,430 260,420 13,727 19,600 29,500 102,887 200,037 383,313 82,900 25,645 7,790

37.7 49.3 59.1 73.3 84.3 100.0 100.0 100.0 73.3 79.6 100.0 89.9 14.1 2.8 78.5 .. 85.0 91.1 13.5 100.0 .. 18.3 6.2 57.2 28.6 56.5 11.6 45.0 82.8 19.0 26.8 98.0 35.3 85.8 3.5 67.8 20.8 11.9 12.8 55.9 90.0 65.9 12.0 20.7 15.0 80.5 43.5 65.4 38.1 3.5 50.8 13.9 9.9 68.2 86.0 95.0 90.0

20,449 .. .. .. .. .. .. 97,560 .. 947,562 .. 106,878 .. .. 97,854 .. .. 6,468 2,113 17,966 .. .. .. .. 42,739 1,239 .. .. .. .. .. .. 463,865 1,640 1,215 .. .. .. 47 .. .. .. 123 .. .. 63,362 .. 263,788 .. .. .. .. .. 26,791 .. .. ..

Railways

Goods hauled million ton-km 2000–08a

35,743 .. .. .. .. 15,900 .. 192,700 .. 327,632 .. 63,481 22 .. 12,545 .. .. 903 287 12,344 .. .. .. .. 20,419 3,978 .. .. .. .. .. .. 227,290 1,577 782 794 .. .. 591 .. 77,100 .. .. .. .. 17,564 .. 129,249 .. .. .. .. .. 174,223 46,406 10 ..

Passengers carried million Rail lines total route- passengerkm km 2000–09a

7,793 63,273 3,370 7,555 2,025 1,919 1,005 16,959 .. 20,036 294 14,205 1,917 .. 3,378 .. .. 417 .. 1,885 .. .. .. .. 1,767 699 854 797 1,665 733 728 .. 26,704 1,157 1,814 2,110 3,116 .. .. .. 2,886 .. .. .. 3,528 4,114 .. 7,791 .. .. .. 2,020 479 19,764 2,842 .. ..

2000–09a

5,708 838,032 14,344 15,312 54 1,683 1,968 45,590 .. 253,555 .. 14,860 226 .. 31,298 .. .. 106 .. 75 .. .. .. .. 357 154 10 44 1,527 196 47 .. 449 423 1,009 4,190 114 4,163 .. .. 15,400 .. .. .. 174 2,877 .. 24,731 .. .. .. 78 83 16,454 3,766 .. ..

Ports

5.10

STATES AND MARKETS

Transport services

Air

Goods hauled million ton-km

Port container traffic thousand TEU

Registered carrier departures worldwide thousands

Passengers carried thousands

Air freight million ton-km

2000–09a

2009

2009

2009

2009

447 551,448 4,390 20,540 121 79 1,055 13,569 .. 22,100 353 197,302 1,399 .. 9,273 .. .. 745 .. 18,693 .. .. .. .. 11,888 497 12 33 1,384 189 7,566 .. 71,136 1,017 7,852 4,111 695 885 .. .. 4,331 4,078 .. .. 77 2,092 .. 6,187 .. .. .. 900 1 29,940 872 .. ..

.. 7,889 6,394 2,206 .. 817 2,033 9,532 1,690 16,286 .. .. .. .. 16,054 .. .. .. .. .. 995 .. .. .. .. .. .. .. 15,843 .. .. .. 2,869 .. .. 1,222 .. .. .. .. 10,066 2,955 .. .. .. .. 3,768 2,058 4,597 .. .. 1,335 4,116 859 1,042 1,674 ..

46 602 330 134 .. 528 48 383 17 642 32 19 34 2 256 .. 18 5 10 27 14 .. .. 10 12 1 10 4 182 .. 1 11 222 5 5 62 11 28 5 7 292 217 .. .. 17 110 26 51 66 21 10 66 87 83 124 .. 77

2,953 54,446 27,421 13,053 .. 77,747 4,605 33,195 1,380 86,897 2,324 1,193 2,949 101 34,169 .. 2,597 309 303 1,302 1,308 .. .. 1,147 617 87 500 157 23,766 .. 142 1,093 15,728 402 257 4,931 490 1,527 455 484 29,109 12,104 .. .. 1,365 8,786 2,361 5,303 6,348 847 428 5,843 10,481 4,279 9,904 .. 10,211

2011 World Development Indicators

10 1,235 277 96 .. 121 985 400 10 10,486 163 15 272 2 15,163 .. 281 2 2 18 94 .. .. 0 7 0 14 1 2,853 .. 0 153 714 1 3 63 6 3 0 6 4,520 799 .. .. 8 14 39 304 0 19 0 257 227 55 314 .. 2,276

303

5.10

Transport services Roads

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

Total road network km

Paved roads %

Passengers carried million passengerkm

2000–08 a

2000–08a

2000–08a

198,817 963,000 14,008 221,372 14,805 40,130 11,300 3,325 43,848 38,872 22,100 362,099 667,064 97,286 11,900 3,594 574,741 71,355 64,983 27,767 87,524 180,053 .. 11,652 8,320 19,371 426,951 24,000 70,746 169,502 4,080 419,634 6,506,221 77,732 81,600 96,155 160,089 5,147 71,300 66,781 97,267

30.2 20,194 80.1 78,000 19.0 .. 21.5 .. 29.3 .. 47.7 4,719 8.0 .. 100.0 5,964 87.0 32,214 100.0 815 11.8 .. 17.3 .. 99.0 397,117 81.0 21,067 36.3 .. 30.0 .. 23.6 108,100 100.0 93,675 91.0 589 .. 150 7.4 .. 98.5 .. .. .. 21.0 .. 51.1 .. 75.2 .. .. 206,098 81.2 .. 23.0 .. 97.8 60,671 100.0 .. 100.0 736,000 67.4 7,980,611 .. 2,032 87.3 56,674 33.6 .. 47.6 49,372 100.0 .. 8.7 .. 22.0 .. 19.0 .. 49.0 m .. m .. 12.0 35.4 .. 36.3 .. 36.8 .. 24.3 .. 11.4 .. .. 27,816 22.0 .. 81.0 .. 51.8 .. 12.1 .. 93.4 .. 100.0 69,000

Railways

Goods hauled million ton-km 2000–08a

56,377 206,000 .. .. .. 1,112 .. .. 22,114 16,261 .. 434 132,868 .. .. .. 42,400 16,226 .. 14,572 .. .. .. .. .. 16,611 181,935 .. .. 36,866 .. 173,077 1,889,923 .. 21,038 .. 24,647 .. .. .. .. ..m .. .. .. .. .. .. 21,038 .. .. .. .. 29,505 45,032

Passengers carried million Rail lines total route- passengerkm km 2000–09a

2000–09a

Ports

Air

Goods hauled million ton-km

Port container traffic thousand TEU

Registered carrier departures worldwide thousands

Passengers carried thousands

Air freight million ton-km

2000–09a

2009

2009

2009

2009

3,268 34,403 .. 17,508 573 927 22 18,427 3,441 953 .. 12,504 49,289 2,418 607 .. 5,824 14,701 1,343 765 684 19,619 .. .. 1,014 2,279 31,339 1,706 64 3,428 31,762 102,465 679,423d 564 1,850 5,121 11,074 .. 1,050 62 261 2,270,901 s 13,439 664,804 398,922 265,882 678,243 326,294 83,523 138,460 38,022 64,196 27,749 1,592,658 399,964

4 2,306 .. 1,838 0 2 8 7,391 0 3 .. 676 1,080 279 42 .. 16 1,058 11 6 1 2,133 .. .. 70 14 856 9 27 63 8,960 6,615 61,684 d 4 76 2 312 .. 26 .. 7 202,136 s 783 31,329 17,548 13,781 32,112 17,878 3,365 6,576 653 1,825 1,815 170,024 33,950

10,776 5,975 8,902 85,194 153,500 1,865,305 .. .. .. 1,020 337 1,748 906 129 384 4,058 683 3,013 .. .. .. .. .. .. 3,623 2,247 6,465 1,228 840 2,668 .. .. .. 22,051 13,865 113,342 15,043 22,959 7,348 1,463 4,767 135 4,508 34 766 300 .. .. 9,946 7,038 11,500 3,544 17,417 12,460 1,801 1,120 2,370 616 45 1,282 475b 728 b 2,600 b 4,429 8,037 3,161 .. .. .. .. .. .. .. .. .. 1,991 1,493 2,073 8,686 5,374 9,681 3,095 1,685 11,547 259 .. 218 21,678 48,327 196,188 .. .. .. 16,173 51,467 12,512 226,205 9,476 2,431,181c 2,993 15 284 4,230 2,832 24,238 336 .. 81 2,347 4,129 3,807 .. .. .. .. .. .. 1,273 183 .. 2,583 .. 1,580 .. s 2,264 m 5,321 m .. .. .. .. 1,343 4,072 .. 1,917 4,049 .. 1,083 5,812 .. .. 3,910 .. 4,248 3,483 171,322 1,025 7,592 .. .. .. .. 1,493 2,222 .. 24,731 3,529 .. .. .. .. 7,038 8,872 130,021 10,210 6,542

1,381 58 2,178 475 .. .. 4,431 157 .. 0 .. 17 .. 0 25,866 84 .. 32 .. 25 .. .. 3,726 151 10,193 548 3,464 17 .. 7 .. .. 1,251 62 .. 168 685 19 .. 10 .. 21 5,898 124 .. .. .. .. .. 14 .. 24 4,522 272 .. 15 .. 0 1,112 59 14,425 171 5,987 1,004 34,300 9,182d .. 9 .. 23 1,168 124 4,751 84 .. .. .. 15 .. 4 .. 6 443,740 s 26,379 s .. 228 206,537 7,169 150,612 3,954 55,926 3,215 207,719 7,398 142,980 3,093 11,018 1,018 28,362 1,794 .. 419 14,593 700 .. 373 236,021 18,981 62,931 4,488

a. Data are for the latest year available in the period shown. b. Includes Tazara railway. c. Refers to class 1 railways only. d. Covers only carriers designated by the U.S. Department of Transportation as major and national air carriers.

304

2011 World Development Indicators

About the data

5.10

STATES AND MARKETS

Transport services Definitions

Transport infrastructure—highways, railways, ports

But when traffic is merely transshipment, much of

• Total road network covers motorways, highways,

and waterways, and airports and air traffic control

the economic benefit goes to the terminal operator

main or national roads, secondary or regional roads,

systems—and the services that flow from it are cru-

and ancillary services for ships and containers rather

and all other roads in a country. • Paved roads are

cial to the activities of households, producers, and

than to the country more broadly. In transshipment

roads surfaced with crushed stone (macadam) and

governments. Because performance indicators vary

centers empty containers may account for as much

hydrocarbon binder or bituminized agents, with con-

widely by transport mode and focus (whether physical

as 40 percent of traffic.

crete, or with cobblestones. • Passengers carried

infrastructure or the services flowing from that infra-

The air transport data represent the total (interna-

by road are the number of passengers transported

structure), highly specialized and carefully specified

tional and domestic) scheduled traffic carried by the

by road times kilometers traveled. • Goods hauled

indicators are required. The table provides selected

air carriers registered in a country. Countries submit

by road are the volume of goods transported by road

indicators of the size, extent, and productivity of

air transport data to ICAO on the basis of standard

vehicles, measured in millions of metric tons times

roads, railways, and air transport systems and of the

instructions and definitions issued by ICAO. In many

kilometers traveled. • Rail lines are the length of rail-

volume of traffic in these modes as well as in ports.

cases, however, the data include estimates by ICAO

way route available for train service, irrespective of

Data for transport sectors are not always inter-

for nonreporting carriers. Where possible, these esti-

the number of parallel tracks. • Passengers carried

nationally comparable. Unlike for demographic sta-

mates are based on previous submissions supple-

by railway are the number of passengers transported

tistics, national income accounts, and international

mented by information published by the air carriers,

by rail times kilometers traveled. • Goods hauled

trade data, the collection of infrastructure data has

such as flight schedules.

by railway are the volume of goods transported by

not been “internationalized.” But data on roads are

The data cover the air traffic carried on scheduled

railway, measured in metric tons times kilometers

collected by the International Road Federation (IRF)

services, but changes in air transport regulations

traveled. • Port container trafﬁc measures the flow

and data on air transport by the International Civil

in Europe have made it more diffi cult to classify

of containers from land to sea transport modes and

Aviation Organization (ICAO).

traffic as scheduled or nonscheduled. Thus recent

vice versa in twenty-foot-equivalent units (TEUs), a

National road associations are the primary source

increases shown for some European countries may

standard-size container. Data cover coastal shipping

of IRF data. In countries where a national road asso-

be due to changes in the classification of air traffic

as well as international journeys. Transshipment traf-

ciation is lacking or does not respond, other agencies

rather than actual growth. For countries with few air

fic is counted as two lifts at the intermediate port

are contacted, such as road directorates, ministries

carriers or only one, the addition or discontinuation

(once to off-load and again as an outbound lift) and

of transport or public works, or central statistical

of a home-based air carrier may cause significant

includes empty units. • Registered carrier depar-

offices. As a result, definitions and data collection

changes in air traffic.

tures worldwide are domestic takeoffs and takeoffs

methods and quality differ, and the compiled data

abroad of air carriers registered in the country. • Pas-

are of uneven quality. Moreover, the quality of trans-

sengers carried by air include both domestic and

port service (reliability, transit time, and condition of

international passengers of air carriers registered

goods delivered) is rarely measured, though it may be

in the country. • Air freight is the volume of freight,

as important as quantity in assessing an economy’s

express, and diplomatic bags carried on each flight

transport system.

stage (operation of an aircraft from takeoff to its next

Unlike the road sector, where numerous qualified motor vehicle operators can operate anywhere on

landing), measured in metric tons times kilometers traveled.

the road network, railways are a restricted transport system with vehicles confined to a fixed guideway. Considering the cost and service characteristics, railways generally are best suited to carry—and can effectively compete for—bulk commodities and con-

Data sources

tainerized freight for distances of 500–5,000 kilo-

Data on roads are from the IRF’s World Road

meters, and passengers for distances of 50–1,000

Statistics, supplemented by World Bank staff

kilometers. Below these limits road transport

estimates. Data on railways are from a database

tends to be more competitive, while above these

maintained by the World Bank’s Transport, Water,

limits air transport for passengers and freight and

and Information and Communication Technologies

sea transport for freight tend to be more competi-

Department, Transport Division, based on data

tive. The railways indicators in the table focus on

from the International Union of Railways. Data on

scale and output measures: total route-kilometers,

port container traffic are from Containerisation

passenger-kilometers, and goods (freight) hauled in

International’s Containerisation International Year-

ton-kilometers.

book. Data on air transport are from the ICAO’s

Measures of port container traffi c, much of it commodities of medium to high value added, give

Civil Aviation Statistics of the World and ICAO staff estimates.

some indication of economic growth in a country.

2011 World Development Indicators

305

5.11

Power and communications Telephonesa

Electric power Access and use

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

306

.. 1,372 957 189 2,789 1,578 11,217 8,218 2,317 208 3,427 8,523 76 561 2,467 1,503 2,232 4,594 .. .. 113 263 17,061 .. .. 3,319 2,455 5,866 974 95 150 1,866 186 3,878 1,327 6,464 6,460 1,377 1,137 1,425 953 .. 6,348 42 16,350 7,931 1,158 .. 1,678 7,149 268 5,723 543 .. .. 23 708

Affordability and efficiency

Telecommunications revenue % of GDP

Mobile cellular and fi xed-line subscribers per employee

Total

2008

2009

2009

2008

2008

2008

2009

2009

2008

2008

.. 50 18 15 13 15 7 5 13 5 11 5 .. 13 17 52 17 10 .. .. 13 10 8 .. .. 9 6 13 19 11 77 10 24 14 16 6 6 11 20 11 2 .. 11 9 4 6 18 .. 13 5 22 8 14 .. .. 53 21

0 12 7 2 24 20 41 39 16 1 41 39 1 8 27 7 21 29 1 0 0 2 54 0 0 21 24 60 16 0 1 33 1 42 10 20 37 10 15 12 18 1 37 1 27 57 2 3 15 59 1 53 10 0 0 1 11

40 132 94 44 129 85 111 141 88 31 100 115 56 72 86 96 90 140 21 10 38 38 68 4 24 97 56 174 92 15 59 43 63 136 4 136 134 86 100 67 123 3 203 5 144 95 93 84 67 128 63 118 123 56 35 36 103

1 127 15 .. 42 .. .. .. .. 6 .. .. 12 80 109 115 .. 27 11 .. .. 4 .. .. .. 35 9 1,435 142 .. .. 120 .. 229 .. 136 210 .. 3 27 578 17 .. 2 .. 242 .. .. 44 .. 6 .. .. .. .. .. 39

7 263 34 .. .. .. .. .. 77 .. .. .. 309 .. .. .. .. 105 .. .. .. .. .. .. .. 43 .. 1,435 .. 6 .. 132 .. 302 .. 197 357 .. .. 44 510 29 .. 5 .. 301 .. .. 268 .. 61 .. 206 .. .. .. 224

75 99 82 40 94 88 99 99 99 90 99 100 80 46 99 99 91 100 61 80 87 58 98 19 24 100 97 100 83 50 53 69 59 100 77 100 .. .. 84 95 95 80 100 10 100 99 79 85 98 99 73 100 76 80 65 .. 90

.. 6.0 4.2 16.6 3.9 4.1 26.0 27.3 2.5 1.6 1.0 33.6 10.0 23.5 8.8 18.0 13.4 13.8 11.5 .. 7.8 14.1 18.3 10.1 .. 23.6 2.3 7.1 5.7 .. .. 4.1 21.7 19.2 13.2 29.3 24.5 12.3 1.3 3.0 11.5 .. 13.2 0.9 18.5 29.3 .. 2.4 3.5 32.7 3.8 25.4 7.8 3.0 .. .. ..

.. 13.4 6.3 11.0 13.7 5.8 34.9 6.8 4.4 1.3 3.4 20.8 14.8 7.3 9.4 8.1 34.6 17.6 14.4 .. 5.0 14.0 17.7 12.9 .. 10.2 3.7 0.8 9.5 .. .. 2.3 11.5 18.4 22.7 17.7 6.5 8.5 9.4 4.1 7.1 .. 12.3 2.4 13.4 35.2 .. 6.3 7.6 9.5 4.3 23.6 7.3 3.1 .. .. ..

0.0 6.0 2.5 .. 3.1 4.5 3.4 1.7 2.4 .. 2.1 2.8 1.0 6.8 5.5 2.9 4.5 5.1 4.0 3.1 .. 3.1 2.5 .. .. .. 2.5 b 3.6 3.7 7.4 .. 1.8 5.5 4.6 .. 3.8 2.4 .. 4.1 3.7 4.8 3.0 4.5 1.3 2.3 2.0 2.0 .. 6.9 2.5 .. 3.7 .. .. .. .. 7.2

58 871 285 .. 1,929 .. 346 843 484 .. .. 732 1,652 376 567 1,018 358 565 .. 492 1,712 1,050 .. 293 .. 592 1,310 980 .. 3,628 .. 497 3,274 892 .. 812 543 .. 513 855 2,275 117 742 233 708 695 .. 466 355 787 1,780 813 .. .. .. .. 391

Transmission and Consumption distribution losses per capita % of output kWh 2008

Quality Population covered by mobile cellular network %

2011 World Development Indicators

per 100 people Mobile cellular Fixed subscriptions lines

Inter national voice traffic minutes per person Fixed lines

$ per month Mobile cellular Residential prepaid fi xed-line tariff tariff

Telephonesa

Electric power Access and use

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

3,989 566 591 2,423 1,164 6,301 7,054 5,661 2,552 8,071 2,087 4,689 155 820 8,853 .. 16,747 1,449 .. 3,087 2,267 .. .. 3,909 3,557 3,723 .. .. 3,490 .. .. .. 2,020 1,287 1,473 736 461 97 1,797 89 7,226 9,492 457 .. 126 24,867 4,894 436 1,646 .. 1,002 1,032 588 3,732 4,822 .. 15,682

Quality

Affordability and efficiency Mobile cellular and fi xed-line subscribers per employee

Total

Population covered by mobile cellular network %

2008

2009

2009

2008

2008

2008

2009

2009

2008

2008

10 23 10 18 7 8 2 7 12 5 14 9 15 16 4 .. 12 31 .. 15 16 .. .. 14 8 23 .. .. 3 .. .. .. 17 53 11 11 9 27 18 19 4 7 24 .. 9 7 13 21 14 .. 5 8 13 8 9 .. 7

31 3 15 35 4 47 44 35 11 35 8 24 2 5 40 .. 20 9 2 29 18 2 0 17 22 22 1 1 16 1 2 30 18 32 7 11 0 2 7 3 44 43 4 0 1 39 11 2 16 1 6 10 4 25 38 22 20

118 45 69 72 63 109 121 150 110 90 101 94 49 0 98 .. 107 84 51 99 36 32 21 78 149 95 31 16 111 29 66 85 78 77 84 79 26 1 56 26 128 109 56 17 47 111 140 61 164 13 88 85 81 117 143 68 175

120 .. .. .. 0 .. 413 .. 39 .. 67 47 3 .. 33 .. .. .. .. .. .. .. .. 65 57 159 1 .. .. 2 4 100 174 155 5 21 .. .. .. .. .. 310 39 .. 1 .. 30 .. 61 .. 35 .. .. .. .. .. ..

159 .. .. .. .. .. .. .. 224 .. 258 52 6 .. 64 .. .. .. .. .. 190 .. .. .. 132 256 8 .. .. 13 57 215 .. 457 .. 87 .. 3 .. .. .. .. .. .. 26 .. 431 .. 118 .. .. 113 .. 32 .. .. ..

99 61 90 95 72 99 100 100 95 100 99 94 83 0 94 .. 100 24 .. 99 100 55 .. 71 100 100 23 93 92 22 62 99 100 98 82b 98 44 10 95 60 b 98 97 .. 45 83 .. 96 90 83 .. .. 95 99 99 99 .. 100

24.0 3.1 5.6 0.2 .. 43.8 17.0 28.2 9.6 22.8 9.4 1.9 10.1 .. 5.2 .. 8.6 1.3 3.8 11.2 10.3 12.8 .. .. 14.3 13.4 12.2 3.3 4.8 9.4 11.9 5.6 17.3 2.9 0.7 23.5 13.1 0.9 13.0 3.0 27.8 33.1 4.7 12.9 5.7 29.4 12.8 2.9 12.0 4.0 6.6 14.3 15.9 17.4 27.5 .. 9.1

15.4 1.4 2.8 3.6 .. 20.9 13.8 18.4 5.6 44.3 5.7 8.8 7.5 .. 12.2 .. 7.8 2.9 3.5 7.3 15.8 12.9 .. .. 8.6 13.4 10.5 10.8 4.9 10.0 9.9 4.5 8.6 8.2 3.6 22.2 8.0 12.8 12.8 1.2 29.7 27.9 14.0 15.3 10.4 8.7 6.2 1.0 5.0 17.8 5.3 8.9 6.2 9.6 9.2 .. 8.6

3.8 1.9 .. .. .. 2.5 4.0 2.9 1.4 3.1 6.3 2.9 6.4 .. 4.7 .. .. 4.8 .. 4.0 .. .. 8.2 .. 2.8 6.3 3.9 3.6 .. 4.3 6.9 3.6 2.7 10.1 6.7b 5.1 1.2 .. .. 1.0 0.7 2.9 .. .. 3.4 1.2 2.5 2.7 3.2 .. 4.8 3.1 .. 3.9 4.5 .. 1.7

1,127 .. .. 913 1,098 .. .. 1,657 .. 12 1,132b 253 2,354 .. 657 .. .. 311 748 697 .. .. .. 1,717 402 1,065 2,427 .. .. 2,059 2,842 .. 838 294 341b .. .. 90 .. 565 .. 605 .. .. .. .. 967 50 380 .. 799 624 .. 396 1,534 .. 597

Transmission and Consumption distribution losses per capita % of output kWh 2008

5.11

STATES AND MARKETS

Power and communications

per 100 people Mobile cellular Fixed subscriptions lines

Inter national voice traffic minutes per person Fixed lines

$ per month Mobile cellular Residential prepaid fi xed-line tariff tariff

Telecommunications revenue % of GDP

2011 World Development Indicators

307

5.11

Power and communications Telephonesa

Electric power Access and use

Transmission and Consumption distribution losses per capita % of output kWh 2008

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

2,488 6,435 .. 7,527 158 4,284 .. 8,185 5,268 6,920 .. 4,759 6,315 409 96 .. 14,869 8,307 1,521 2,072 84 2,079 .. 99 5,789 1,298 2,308 2,273 .. 3,534 16,891 6,061 13,654 2,393 1,646 3,074 799 .. 220 602 1,022 2,875 w 231 1,670 1,318 3,001 1,505 1,972 4,052 1,907 1,494 503 531 9,518 6,970

2008

11 11 .. 9 20 16 .. 5 3 5 .. 9 5 11 12 .. 7 6 24 18 19 6 .. .. 2 12 14 14 .. 12 12 7 6 20 9 28 10 .. 23 23 6 8w 15 11 10 13 11 6 12 16 15 22 11 6 5

per 100 people Mobile cellular Fixed subscriptions lines 2009

25 32 0 16 2 42 1 37 19 51 1 9 44 17 1 4 55 60 18 4 0 10 .. 3 24 12 22 9 1 28 34 54 50 29 7 24 35 9 5 1 3 18 w 1 15 13 22 13 20 25 18 16 3 1 45 48

2009

118 162 24 177 55 135 20 133 101 103 7 94 111 69 36 55 123 120 46 70 40 123 .. 33 147 93 84 29 29 120 232 130 97 114 59 99 101 30 16 34 24 69 w 27 67 58 101 61 62 119 90 67 45 37 111 123

Quality

Affordability and efficiency

Fixed lines

Total

Population covered by mobile cellular network %

2008

2008

2008

2009

2009

124 .. 8 .. 101 203b .. .. 228 220 .. .. .. .. 13 41 .. .. .. .. 1 .. .. 28 443 .. 60 .. .. .. .. .. 216 125 .. 79 .. .. .. .. 19 .. w .. .. .. .. .. .. .. .. .. .. .. .. ..

98 95 92 98 85 94b 70 100 100 100 .. 100 99 95 66 91 98 100 96 .. 65 38 .. 85 100 100b 100 14 100 100 100 100 100 100 93 90 70 95 68 50 75 80 w 53 80 77 94 76 93 91 92 93 61 56 99 99

19.3 5.4 8.1 9.2 24.0 3.9 .. 7.7 22.7 19.6 .. 21.6 28.5 4.7 3.9 4.9 26.2 31.5 1.3 0.9 12.2 8.3 .. 12.8 19.5 2.8 13.8 .. 9.9 2.8 4.1 24.1 12.8 12.5 1.1 9.0 2.1 .. 0.7 24.6 .. 10.1 m 8.8 5.7 4.7 10.0 6.6 4.0 3.9 10.6 3.0 3.0 11.5 22.8 27.6

10.6 5.8 6.6 7.4 8.3 5.2 .. 3.9 24.9 15.8 .. 12.6 31.6 0.9 3.4 12.8 14.8 33.7 7.6 2.9 10.2 2.4 .. 12.4 6.5 7.2 23.9 .. 7.9 4.3 4.1 16.5 15.3 12.7 1.1 28.6 3.2 .. 4.8 12.7 .. 8.7 m 8.0 7.6 7.1 8.8 7.9 3.7 7.6 8.8 6.3 1.2 10.4 14.8 19.6

Inter national voice traffic minutes per person

41 .. 11 .. 27 136b .. 1,531 123 96 .. .. .. 34 6 .. .. .. 78 .. 0 .. .. 6 .. 79 39 .. 7 0 .. .. .. .. .. .. .. .. .. .. 22 .. w .. .. .. .. .. 9 .. .. 27 .. .. .. ..

$ per month Mobile cellular Residential prepaid fi xed-line tariff tariff

Telecommunications revenue % of GDP

Mobile cellular and fi xed-line subscribers per employee

2008

2008

3.4 2.6 3.0 2.7 9.8 4.9 b .. 2.6 3.3 3.3 .. 7.3 4.1 .. 3.2 4.5 2.7 3.3 3.0 .. .. 4.0 7.9 7.4 2.6 4.3 2.3 .. .. 5.7 3.1 4.3 2.8 3.2 2.5 3.5 .. .. .. 2.6 .. 3.1 w .. 3.2 3.0 3.3 3.2 2.6 2.8 3.8 .. 2.0 .. 3.0 2.6

564 .. 1,952 1,618 1,859 883b .. .. 665 644 .. .. 855 919 2,168 1,118 894 601 409 .. .. 1,957 .. 1,059 .. 1,004 2,145 .. .. .. 924 .. 416 692 739 1,500 .. 880 .. .. 711 755 m .. 665 .. 576 624 .. 462 586 880 565 .. 765 765

a. Data are from the International Telecommunication Union’s (ITU) World Telecommunication Report database. Please cite ITU for third-party use of these data. b. Data are for 2009.

308

2011 World Development Indicators

About the data

5.11

STATES AND MARKETS

Power and communications Definitions

• Electric power consumption per capita measures

The quality of an economy’s infrastructure, includ-

Access to telephone services rose on an unprece-

ing power and communications, is an important ele-

dented scale over the past 15 years. This growth was

the production of power plants and combined heat

ment in investment decisions for both domestic and

driven primarily by wireless technologies and liberal-

and power plants less transmission, distribution,

foreign investors. Government effort alone is not

ization of telecommunications markets, which have

and transformation losses and own use by heat and

enough to meet the need for investments in modern

enabled faster and less costly network rollout. In

power plants divided by midyear population. • Elec-

infrastructure; public-private partnerships, especially

2002 the number of mobile phones in the world sur-

tric power transmission and distribution losses are

those involving local providers and financiers, are

passed the number of fixed telephones. The Interna-

losses in transmission between sources of supply

critical for lowering costs and delivering value for

tional Telecommunication Union (ITU) estimates that

and points of distribution and in distribution to con-

money. In telecommunications, competition in the

there were 5 billion mobile subscriptions globally in

sumers, including pilferage. • Fixed telephone lines

marketplace, along with sound regulation, is lower-

2010. No technology has ever spread faster around

are telephone lines connecting a subscriber to the

ing costs, improving quality, and easing access to

the world. Mobile communications have a particu-

telephone exchange equipment. • Mobile cellular

services around the globe.

larly important impact in rural areas. The mobility,

telephone subscriptions are subscriptions to a pub-

An economy’s production and consumption of elec-

ease of use, flexible deployment, and relatively low

lic mobile telephone service using cellular technol-

tricity are basic indicators of its size and level of

and declining rollout costs of wireless technologies

ogy, which provide access to the public switched

development. Although a few countries export elec-

enable them to reach rural populations with low lev-

telephone network. Post-paid and prepaid subscrip-

tric power, most production is for domestic consump-

els of income and literacy. The next billion mobile

tions are included. • International voice trafﬁc is

tion. Expanding the supply of electricity to meet the

subscribers will consist mainly of the rural poor.

the sum of international incoming and outgoing tele-

growing demand of increasingly urbanized and indus-

Access is the key to delivering telecommunications

phone traffic (in minutes) divided by total population.

trialized economies without incurring unacceptable

services to people. If the service is not affordable to

• Population covered by mobile cellular network is

social, economic, and environmental costs is one

most people, then goals of universal usage will not

the percentage of people that live in areas served by

of the great challenges facing developing countries.

be met. Two indicators of telecommunications afford-

a mobile cellular signal regardless of whether they

Data on electric power production and consump-

ability are presented in the table: fixed-line telephone

use it. • Residential ﬁxed-line tariff is the monthly

tion are collected from national energy agencies by

service tariff and prepaid mobile cellular service tar-

subscription charge plus the cost of 30 three-minute

the International Energy Agency (IEA) and adjusted

iff. Telecommunications efficiency is measured by

local calls (15 peak and 15 off-peak). • Mobile cel-

by the IEA to meet international definitions (for data

total telecommunications revenue divided by GDP

lular prepaid tariff is based on the Organisation for

on electricity production, see table 3.10). Electricity

and by mobile cellular and fixed-line telephone sub-

Economic Co-operation and Development’s low-user

consumption is equivalent to production less power

scribers per employee.

definition, which includes the cost of monthly mobile

plants’ own use and transmission, distribution, and

Operators have traditionally been the main source

use for 25 outgoing calls per month spread over the

transformation losses less exports plus imports. It

of telecommunications data, so information on sub-

same mobile network, other mobile networks, and

includes consumption by auxiliary stations, losses

scribers has been widely available for most coun-

mobile to fixed-line calls and during peak, off-peak,

in transformers that are considered integral parts

tries. This gives a general idea of access, but a

and weekend times as well as 30 text messages

of those stations, and electricity produced by pump-

more precise measure is the penetration rate—the

per month. • Telecommunications revenue is the

ing installations. Where data are available, it covers

share of households with access to telecommunica-

revenue from the provision of telecommunications

electricity generated by primary sources of energy—

tions. During the past few years more information

services such as fixed-line, mobile, and data divided

coal, oil, gas, nuclear, hydro, geothermal, wind, tide

on information and communication technology use

by GDP. • Mobile cellular and ﬁxed-line subscribers

and wave, and combustible renewables. Neither pro-

has become available from household and business

per employee are telephone subscribers (fixed-line

duction nor consumption data capture the reliability

surveys. Also important are data on actual use of

plus mobile) divided by the total number of telecom-

of supplies, including breakdowns, load factors, and

telecommunications equipment. Ideally, statistics

munications employees.

frequency of outages.

on telecommunications (and other information and

Over the past decade new financing and technol-

communications technologies) should be compiled

ogy, along with privatization and liberalization, have

for all three measures: subscription and possession,

spurred dramatic growth in telecommunications

access, and use. The quality of data varies among

in many countries. With the rapid development of

reporting countries as a result of differences in regu-

mobile telephony and the global expansion of the

lations covering data provision and availability.

Data sources Data on electricity consumption and losses are

Internet, information and communication technolo-

from the IEA’s Energy Statistics and Balances of

gies are increasingly recognized as essential tools of

Non-OECD Countries 2010, the IEA’s Energy Sta-

development, contributing to global integration and

tistics of OECD Countries 2010, and the United

enhancing public sector effectiveness, efficiency,

Nations Statistics Division’s Energy Statistics

and transparency. The table presents telecommuni-

Yearbook. Data on telecommunications are from

cations indicators covering access and use, quality,

the ITU’s World Telecommunication Development

and affordability and efficiency.

Report database and TeleGeography.

2011 World Development Indicators

309

5.12

The information age Daily Households newspapers with televisiona

Personal computers and the Internet Access and use

per 100 people

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

310

Quality Affordability International Fixed Internet Fixed broadband bandwidtha broadband Internet a Internet subscribers bits per second per access tariffa per 100 capita people $ per month

per 1,000 people

%

Personal computersa

Internet usersa

2000–05b

2008

2008

2009

2009

0.4 4.6 .. 0.6 .. .. .. .. 8.0 2.3 .. .. 0.7 .. 6.4 6.2 .. 11.0 0.6 0.9 0.4 .. 94.3 .. .. .. 5.7 69.3 11.2 .. .. .. .. .. 5.6 .. 54.9 .. 13.0 3.9 .. 1.0 25.5 0.7 .. 65.2 3.4 3.5 5.5 65.6 1.1 9.4 .. .. .. 5.1 2.5

3.4 41.2 13.5 3.3 30.4 6.8 72.0 73.5 42.0 0.4 45.9 75.2 2.2 11.2 37.7 6.2 39.2 44.8 1.1 0.8 0.5 3.8 77.7 0.5 1.7 34.0 28.8 61.4 45.5 0.6 6.7 34.5 4.6 50.4 14.3 63.7 85.9 26.8 15.1 20.0 14.4 4.9 72.3 0.5 83.9 71.3 6.7 7.6 30.5 79.5 5.4 44.1 16.3 0.9 2.3 10.0 9.8

0.00 2.85 2.34 0.11 8.80 0.19 24.69 22.45 1.14 0.03 11.30 29.05 0.02 2.86 7.76 0.77 7.51 12.91 0.04 0.00 0.20 0.00 29.55 0.00 0.00 9.81 7.78 29.42 4.64 0.00 0.00 6.01 0.05 15.45 0.02 19.26 37.46 3.93 1.77 1.30 2.42 0.00 25.25 0.00 29.33 30.98 0.20 0.02 3.52 30.53 0.11 16.99 0.78 0.00 0.00 0.00 0.00

.. 24 .. 2 36 8 155 311 16 .. 81 165 0 .. .. 41 36 79 .. .. .. .. 175 .. .. 51 74 222 23 .. .. 65 .. .. 65 183 353 39 99 .. 38 .. 191 5 431 164 .. .. 4 267 .. .. .. .. .. .. ..

.. .. .. 36 .. 97 .. 97 99 30 95 99 25 69 97 .. 97 98 18 .. .. 32 99 .. .. 100 .. 99 88 14 .. 96 .. 97 88 .. 98 77 83 97 83 .. 98 .. 93 97 .. .. .. 95 43 100 69 .. .. 25 68

2011 World Development Indicators

2009

550 1,902 .. 17 2,320 .. 5,457 20,323 1,399 4 2,277 24,945 35 225 1,195 220 2,108 37,657 15 2 19 23 16,193 .. 1 4,076 651 560,989 2,940 1 0 4,333 40 15,892 27 7,075 34,506 1,387 484 1,172 243 6 12,680 3 17,221 29,356 141 38 752 25,654 97 4,537 186 0 1 16 241

2009

.. 22 15 157 31 31 26 36 49 50 7 29 118 35 19 62 28 15 91 .. 89 89 25 1,329 .. 48 18 13 35 .. .. 6 44 21 1,630 43 29 26 40 8 20 .. 28 487 39 36 .. 307 42 43 44 24 34 503 .. .. ..

Information and communications technology trade Application Secure Internet servers per million people December 2010

1 9 1 3 26 17 1,761 857 5 0 9 490 0 8 16 9 41 73 0 0 2 1 1,237 0 .. 53 2 455 14 0 1 108 1 168 0 318 1,866 15 15 2 13 .. 434 0 1,246 306 8 3 13 874 2 124 10 0 1 1 8

Goods Imports Exports % of total % of total goods goods imports exports

Services Exports % of total service exports

2009

2009

2009

.. 1.1 0.0 .. 0.4 1.5 1.4 5.5 0.0 0.6 0.7 2.8 .. 0.0 0.6 0.4 1.8 3.6 0.0 1.9 0.1 .. 4.4 .. .. 0.2 29.5 44.6 0.3 .. .. 18.7 0.4 5.1 .. 15.6 4.8 3.6 0.2 1.8 2.9 .. 5.8 0.7 12.6 5.6 .. 0.4 0.4 6.8 0.1 3.0 0.7 0.0 .. .. 0.2

0.4 5.4 4.9 .. 11.2 5.0 11.4 7.0 8.5 5.7 2.4 4.3 .. 4.6 3.7 5.5 11.4 6.4 2.0 10.9 4.0 .. 9.6 .. .. 6.8 24.0 43.6 9.9 .. .. 17.9 4.5 6.3 .. 16.7 8.9 5.4 7.5 4.4 5.5 .. 6.5 9.5 11.3 7.8 .. 4.0 7.8 9.3 7.3 5.9 6.3 5.8 .. .. 6.6

.. 5.7 .. 5.4 12.2 16.1 4.9 6.5 4.7 11.5 9.0 9.8 0.7 12.6 9.2 3.3 2.0 5.6 11.6 0.0 6.5 6.6 11.2 .. .. 2.8 6.0 1.7 7.4 .. .. 21.9 0.0 3.6 .. 8.9 .. 4.1 4.9 4.7 16.9 .. 8.6 5.3 25.4 4.3 .. 17.8 2.6 8.4 0.0 2.2 14.1 21.6 0.2 2.5 26.8

Daily Households newspapers with televisiona

Personal computers and the Internet Access and use

per 100 people

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

per 1,000 people

%

2000–05b

217 71 .. .. .. 182 .. 137 .. 551 .. .. .. .. .. .. .. 1 3 154 54 .. .. .. 108 89 .. .. 109 .. .. 77 93 .. 20 12 3 .. 28 .. 307 182 .. 0 .. 516 .. 50 65 9 .. .. 79 114 .. .. ..

Quality Affordability International Fixed Internet Fixed broadband bandwidtha broadband Internet a Internet subscribers bits per second per access tariffa per 100 capita people $ per month

Personal computersa

Internet usersa

2008

2008

2009

2009

2009

99 55 69 .. .. 98 90 94 .. 99 97 .. .. .. .. .. .. 99 .. 99 .. .. 9 .. 98 99 .. 9 97 22 .. 96 93 .. 88 .. .. .. 37 33 98 99 67 10 39 95 88 58 83 .. 85 73 71 98 99 .. ..

25.6 3.3 2.0 10.6 .. 58.2 .. .. .. .. 7.6 .. .. .. 57.6 .. .. .. .. 32.7 10.2 .. .. .. 24.2 36.8 .. .. 23.1 0.8 4.5 17.6 14.4 11.4 24.6 5.7 .. 0.9 23.9 .. 91.2 52.6 .. .. .. 62.9 16.9 .. 6.3 .. .. .. 7.2 16.9 18.2 .. 15.7

61.6 5.3 8.7 38.3 1.0 68.4 49.7 48.5 58.6 77.7 29.3 33.4 10.0 0.0 80.9 .. 39.4 41.2 4.7 66.7 23.7 3.7 0.5 5.5 58.8 51.8 1.6 4.7 57.6 1.9 2.3 22.7 26.5 35.9 13.1 32.2 2.7 0.2 5.9 2.1 90.0 83.4 3.5 0.8 28.4 91.8 43.5 12.0 27.8 1.9 15.8 27.7 6.5 58.8 48.6 25.2 28.3

18.76 0.67 0.74 0.55 0.00 21.94 24.86 19.59 4.16 24.86 3.42 8.61 0.02 0.00 33.54 .. 1.61 0.10 0.13 11.48 5.26 0.02 .. 0.16 18.98 10.59 0.02 0.02 6.09 0.07 0.27 7.25 9.24 5.19 0.91 1.49 0.05 0.03 0.02 0.26 35.70 22.73 0.82 0.01 0.05 37.19 1.44 0.37 5.82 0.00 2.22 2.79 1.87 13.54 17.54 10.78 9.22

5,987 32 110 151 3 15,261 2,003 12,989 741 5,770 1,811 1,342 477 0 6,065 .. 871 112 142 3,537 223 5 .. 50 14,300 17 12 5 5,097 51 76 364 312 6,660 2,920 1,600 56 20 27 5 78,156 4,544 144 11 5 26,904 1,365 43 15,964 2 662 2,646 113 2,748 4,790 1,764 2,044

2009

30 5 21 30 .. 36 7 29 22 37 30 17 40 .. 25 .. 19 48 194 25 23 50 .. .. 15 14 102 493 19 55 58 17 16 13 8 17 80 28 47 22 36 29 34 266 105 51 31 15 17 142 22 36 22 14 29 .. 55

5.12

STATES AND MARKETS

The information age

Information and communications technology trade Application Secure Internet servers per million people December 2010

166 2 2 1 0 1,005 399 154 39 650 20 5 3 0 1,167 .. 133 1 1 173 28 0 1 1 176 24 0 0 42 1 2 87 22 13 11 3 1 0 14 2 2,276 1,489 8 0 1 1,653 27 1 127 3 7 14 7 211 174 84 99

Goods Imports Exports % of total % of total goods goods imports exports

Services Exports % of total service exports

2009

2009

2009

24.6 3.8 5.7 .. .. 11.5 19.2 3.0 0.8 14.7 3.1 0.1 1.3 .. 22.6 .. 0.4 0.3 .. 6.1 3.0 .. .. .. 2.9 0.5 1.6 0.3 38.1 0.2 .. 0.6 22.9 7.5 0.1 4.6 0.4 .. 0.6 0.2 12.6 1.8 0.4 0.7 0.0 2.4 1.5 0.3 0.0 .. 0.2 0.1 54.2 7.5 4.6 .. 0.0

18.8 8.8 9.7 .. .. 14.0 11.0 6.7 3.9 12.0 5.4 4.3 6.2 .. 14.6 .. 7.2 2.6 .. 6.2 3.5 .. .. .. 4.3 5.5 3.9 5.3 32.0 3.6 1.6 4.2 20.9 5.1 5.1 6.0 3.9 .. 4.9 5.6 13.5 9.4 4.4 3.6 7.2 8.8 3.2 3.7 7.3 .. 21.6 8.3 34.5 8.9 6.6 .. 8.2

8.8 53.1 8.4 .. 0.6 37.1 36.1 2.4 7.1 1.2 .. 3.0 14.5 .. 1.5 .. 60.9 1.2 8.5 5.9 2.9 .. .. 2.6 3.8 14.3 .. .. 7.0 23.2 .. 3.7 1.3 20.2 3.0 7.5 5.8 .. 2.4 .. 11.3 4.8 7.2 11.6 1.6 8.6 .. 12.0 4.8 1.2 1.4 3.5 16.2 5.3 4.5 .. ..

2011 World Development Indicators

311

5.12

The information age Daily Households newspapers with televisiona

Personal computers and the Internet Access and use

per 100 people

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

per 1,000 people

%

Personal computersa

2000–05b

2008

2008

70 92 .. .. 9 .. .. 361 126 173 .. 30 144 26 .. 24 481 420 .. .. 2 .. .. 2 149 23 .. 9 .. 131 .. 290 193 .. .. 93 .. 10 4 5 .. 105 w .. 68 71 .. 59 74 .. 64 .. 68 .. 255 201

97 .. 3 .. 46 .. 10 .. 99 99 .. 69 100 76 .. 35 94 92 .. .. 9 92 .. .. .. .. 98 .. 7 97 94 99 .. 91 .. 95 .. 95 .. 24 31 .. m .. .. .. 93 .. .. .. 85 .. 55 .. 98 98

19.2 13.3 0.3 69.3 .. 25.8 .. 74.3 58.1 42.5 .. .. 39.3 .. 10.7 3.7 88.1 96.2 9.0 .. .. .. .. .. 13.2 9.7 6.1 .. 1.7 4.5 33.1 80.2 80.5 .. 3.1 .. 9.6 .. 2.8 .. 7.6 15.3 w .. 5.5 4.5 .. 5.1 5.6 9.8 .. 5.7 3.3 .. 65.4 56.0

Internet usersa 2009

36.2 42.1 4.5 38.6 7.4 56.1 0.3 73.3 75.0 63.6 1.2 9.0 61.2 8.7 9.9 7.6 90.3 70.9 18.7 10.1 1.5 25.8 .. 5.4 36.2 33.5 35.3 1.6 9.8 33.3 82.2 83.2 78.1 55.5 16.9 31.2 27.5 8.8 1.8 6.3 11.4 27.1 w 2.7 20.9 17.2 34.6 18.1 24.1 36.4 31.5 21.5 5.5 8.8 72.3 67.3

Quality Affordability International Fixed Internet Fixed broadband bandwidtha broadband Internet a Internet subscribers bits per second per access tariffa per 100 capita people $ per month 2009

13.05 9.09 0.08 5.66 0.47 8.07 0.00 22.52 14.36 22.79 0.00 0.98 21.05 0.84 0.11 0.13 40.85 33.91 0.16 0.05 0.02 1.47 .. 0.04 7.84 3.57 8.54 0.05 0.02 4.15 15.01 29.68 27.78 7.33 0.32 6.56 3.04 5.76 0.00 0.06 0.14 7.30 w 0.04 4.07 3.37 6.69 3.53 5.81 7.66 6.62 1.25 0.55 0.13 25.78 25.90

2009

18,271 573 35 1,731 372 12,660 .. 22,783 7,567 6,720 .. 70 11,008 190 322 35 49,828 29,413 261 37 2 818 .. 23 7,916 2,699 4,323 48 36 206 13,233 39,664 11,279 903 46 628 581 313 28 8 17 3,526 w 7 348 151 1,120 299 742 1,087 1,408 323 31 31 19,521 32,455

Information and communications technology trade Application Secure Internet servers per million people

Goods Imports Exports % of total % of total goods goods imports exports

2009

December 2010

2009

2009

7 13 88 27 40 14 .. 17 29 22 .. 27 29 4 23 858 35 33 31 364 64 19 .. 186 13 12 18 .. 194 7 41 24 20 18 199 31 15 .. 220 51 .. 30 m 90 22 30 19 31 21 17 30 23 15 88 29 29

40 20 1 18 1 20 0 523 128 301 0 63 233 4 0 10 1,266 1,876 0 0 0 13 1 2 72 14 95 0 1 13 243 1,396 1,443 45 0 7 3 4 0 1 1 156 w 1 9 3 32 8 3 33 27 2 2 5 906 545

8.4 0.6 1.4 0.3 0.4 2.2 .. 35.4 17.5 3.8 .. 2.0 3.0 1.0 0.0 0.1 10.0 3.3 0.2 .. 0.6 19.8 .. 0.1 0.2 6.0 2.3 .. 4.9 1.3 2.0 8.6 13.0 0.1 .. 0.1 3.8 .. 0.1 0.1 0.6 13.0 w 0.6 16.3 21.3 12.2 16.2 28.9 1.5 11.6 .. 3.0 1.0 12.2 6.6

9.3 8.4 12.3 4.6 4.5 5.4 .. 28.2 14.7 5.6 .. 9.8 8.4 3.6 4.7 3.6 11.5 6.6 1.4 .. 6.9 18.1 .. 4.2 4.0 7.5 5.9 .. 9.3 2.6 5.3 10.5 15.1 7.0 .. 9.3 7.1 .. 2.5 3.7 4.8 13.9 w .. 16.6 18.4 15.1 16.4 24.4 6.6 15.2 .. 7.4 7.8 13.3 8.6

Services Exports % of total service exports 2009

18.9 6.3 0.1 .. 15.6 8.0 2.2 2.9 8.0 7.2 .. 3.9 6.6 17.2 1.2 11.3 14.8 .. 4.4 19.6 2.7 .. .. 18.6 .. 4.9 1.9 .. 6.1 5.6 .. 7.9 4.6 9.5 .. 7.4 .. 5.4 8.5 8.0 .. 9.1 w 6.5 13.3 19.9 5.4 13.1 6.8 6.1 5.5 .. 49.9 4.5 8.1 9.8

a. Data are from the International Telecommunicaton Union’s (ITU) World Telecommunication Development Report database. Please cite the ITU for third party use of these data. b. Data are for the most recent year available.

312

2011 World Development Indicators

About the data

5.12

STATES AND MARKETS

The information age Definitions

The digital and information revolution has changed

counts reported by Internet service providers by a

• Daily newspapers are newspapers issued at least

the way the world learns, communicates, does busi-

multiplier. This method may undercount actual

four times a week that report mainly on events in the

ness, and treats illnesses. New information and

users, particularly in developing countries, where

24-hour period before going to press. The indicator is

communications technologies (ICT) offer vast oppor-

many commercial subscribers rent out computers

average circulation (or copies printed) per 1,000 peo-

tunities for progress in all walks of life in all coun-

connected to the Internet or prepaid cards are used

ple. • Households with television are the percentage

tries—opportunities for economic growth, improved

to access the Internet.

of households with a television set. • Personal com-

health, better service delivery, learning through dis-

Broadband refers to technologies that provide

puters are self-contained computers designed for use

tance education, and social and cultural advances.

Internet speeds of at least 256 kilobits a second

by a single individual, including laptops and notebooks

Comparable statistics on access, use, quality,

of upstream and downstream capacity and includes

and excluding terminals connected to mainframe and

and affordability of ICT are needed to formulate

digital subscriber lines, cable modems, satellite

minicomputers intended primarily for shared use and

growth-enabling policies for the sector and to moni-

broadband Internet, fiber-to-home Internet access,

devices such as smart phones and personal digital

tor and evaluate the sector’s impact on develop-

ethernet local access networks, and wireless area

assistants. • Internet users are people with access

ment. Although basic access data are available for

networks. Bandwidth refers to the range of frequen-

to the worldwide network. • Fixed broadband Internet

many countries, in most developing countries little

cies available for signals. The higher the bandwidth,

subscribers are the number of broadband subscrib-

is known about who uses ICT; what they are used for

the more information that can be transmitted at

ers with a digital subscriber line, cable modem, or

(school, work, business, research, government); and

one time. Reporting countries may have different

other high-speed technology. • International Internet

how they affect people and businesses. The global

definitions of broadband, so data are not strictly

bandwidth is the contracted capacity of international

Partnership on Measuring ICT for Development is

comparable.

connections between countries for transmitting

helping to set standards, harmonize information and

The number of secure Internet servers, from the

Internet traffic. • Fixed broadband Internet access

communications technology statistics, and build sta-

Netcraft Secure Server Survey, indicates how many

tariff is the lowest sampled cost per 100 kilobits a

tistical capacity in developing countries. For more

companies conduct encrypted transactions over the

second per month and are calculated from low- and

information see www.itu.int/ITU-D/ict/partnership/.

Internet. The survey examines the use of encrypted

high-speed monthly service charges. Monthly charges

Data on daily newspapers in circulation are from

transactions through extensive automated explora-

do not include installation fees or modem rentals.

United Nations Educational, Scientific, and Cultural

tion, tallying the number of Web sites using a secure

• Secure Internet servers are servers using encryp-

Organization (UNESCO) Institute for Statistics surveys

socket layer (SSL). The country of origin of more than

tion technology in Internet transactions. • Information

on circulation, online newspapers, journalists, com-

a third of the 1.5 million distinct valid third-party

and communication technology goods exports and

munity newspapers, and news agencies.

certificates is unknown. Some countries, such as the

imports include telecommunications, audio and video,

Estimates of households with television are derived

Republic of Korea, use application layers to estab-

computer and related equipment; electronic compo-

from household surveys. Some countries report only

lish the encryption channel, which is SSL equivalent;

nents; and other information and communication

the number of households with a color television set,

these data are reported in the table.

technology goods. Software is excluded. • Informa-

and so the true number may be higher than reported.

Information and communication technology goods

tion and communication technology service exports

Estimates of personal computers are from an

exports and imports are defi ned by the Working

include computer and communications services (tele-

annual International Telecommunication Union (ITU)

Party on Indicators for the Information Society and

communications and postal and courier services) and

questionnaire sent to member states, supplemented

are reported in the Organisation for Economic Co-

information services (computer data and news-related

by other sources. Many governments lack the capac-

operation and Development’s Guide to Measuring the

service transactions).

ity to survey all places where personal computers

Information Society (2005). Information and commu-

are used (homes, schools, businesses, government

nication technology service exports data are based

offices, libraries, Internet cafes) so most estimates

on the International Monetary Fund’s (IMF) Balance of

are derived from the number of personal computers

Payments Statistics Yearbook classification.

Data sources Data on newspapers are compiled by the UNESCO

sold each year. Annual shipment data can also be

Institute for Statistics. Data on televisions, per-

multiplied by an estimated average useful lifespan

sonal computers, Internet users, Internet broad-

before replacement to approximate the number of

band users and cost, and Internet bandwidth are

personal computers. There is no precise method

from the ITU’s World Telecommunication Develop-

for determining replacement rates, but in general

ment Report database and TeleGeography. Data

personal computers are replaced every three to five

on secure Internet servers are from Netcraft (www.

years.

netcraft.com/) and official government sources.

Data on Internet users and related indicators

Data on information and communication tech-

(broadband and bandwidth) are based on nation-

nology goods trade are from the United Nations

ally reported data to the ITU. Some countries derive

Statistics Division’s Commodity Trade (Comtrade)

these data from surveys, but since survey questions

database. Data on information and communication

and definitions differ, the estimates may not be

technology service exports are from the IMF’s Bal-

strictly comparable. Countries without surveys gen-

ance of Payments Statistics database.

erally derive their estimates by multiplying subscriber

2011 World Development Indicators

313

5.13

Science and technology Researchers Technicians Scientiﬁc Expenditures in R&D in R&D and for R&D technical journal articles

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

314

per million people

per million people

2000–08d

2000–08d

.. .. 170 .. 980 .. 4,224 4,123 .. .. .. 3,435 .. 120 197 .. 694 1,499 .. .. 17 .. 4,260 .. .. 833 1,071 2,650 126 .. 34 122 66 1,514 .. 2,886 5,670 .. 69 617 49 .. 2,966 21 7,707 3,496 .. .. .. 3,532 .. 1,873 29 .. .. .. ..

.. .. 35 .. 196 .. .. 1,960 .. .. .. 1,407 .. .. 71 .. .. 476 .. .. 13 .. 1,690 .. .. 302 .. 459 .. .. 37 .. .. 605 .. 1,466 2,166 .. 20 378 .. .. 617 12 .. 1,880 .. .. .. 1,301 .. 764 37 .. .. .. ..

2011 World Development Indicators

High-technology exports

Royalty and license fees

% of GDP

$ millions

% of manufactured exports

2007

2000–08 d

2009

2009

2009

2009

4 12 481 3 3,362 175 17,831 4,825 97 235 412 7,071 43 51 54 62 11,885 801 43 3 26 154 27,800 4 3 1,740 56,806 .. 489 7 21 100 37 1,102 244 3,689 5,236 8 66 1,934 5 8 502 149 4,989 30,740 16 17 129 44,408 109 4,980 22 4 10 4 6

.. .. 0.07 .. 0.51 0.21 2.06 2.66 0.17 .. 0.96 1.92 .. 0.28 0.03 0.50 1.10 0.49 0.11 .. 0.05 .. 1.84 .. .. 0.68 1.44 0.81 0.16 0.48 .. 0.32 .. 0.90 0.49 1.47 2.72 .. 0.15 0.23 0.09 .. 1.29 0.17 3.46 2.02 .. .. 0.18 2.54 .. 0.57 0.06 .. .. .. 0.04

.. 10 4 .. 1,548 7 3,550 12,097 6 97 315 29,676 0 15 76 24 8,316 714 0 2 4 3 25,080 .. .. 266 348,295 1,849 466 .. .. 1,682 187 756 248 15,200 10,743 177 51 95 136 .. 656 7 8,599 83,827 71 0 21 142,449 6 1,212 141 0 .. .. 7

.. 1 1 .. 9 4 13 11 1 1 3 10 0 5 3 1 14 8 1 12 0 3 18 .. .. 4 31 31 5 .. .. 41 12 11 35 16 18 5 4 1 5 .. 10 4 18 23 32 1 3 16 1 11 5 0 .. .. 1

.. 6 .. 0 106 0 703 752 2 0 9 2,376 0 3 12 1 434 9 0 0 0 0 3,221 .. .. 59 429 380 48 .. .. 1 0 32 .. 96 .. 0 0 0 0 .. 24 2 1,738 9,397 .. 0 7 13,785 0 48 13 0 .. 3 0

.. 14 .. 0 1,331 0 3,026 1,280 19 11 73 2,144 3 19 6 12 2,512 117 0 0 8 0 7,716 .. .. 461 11,065 1,610 258 .. .. 65 0 213 .. 726 .. 53 47 285 26 .. 46 3 1,282 5,274 .. 0 9 14,104 0 654 86 0 0 0 18

Patent applications ﬁleda,b

$ millions

Trademark applications ﬁleda,c

Receipts

Payments

Residents

Nonresidents

Total

2009

2009

2009

.. .. 84 .. .. 116 2,821 2,263 222 29 1,510 669 .. .. 59 .. 4,023 242 .. .. .. .. 5,067 .. .. 531 229,096 149 121 .. .. .. .. 250 69 789 1,518 .. .. 490 .. .. 76 12 1,806 14,295 .. .. 250 47,859 .. 698 7 .. .. .. ..

.. .. 765 .. .. 11 23,525 292 5 270 220 148 .. .. 12 .. 17,802 24 .. .. .. .. 32,410 .. .. 3,421 85,477 11,708 1,860 .. .. .. .. 68 189 92 131 .. 794 1,452 .. .. 20 25 127 1,809 .. .. 218 11,724 .. 22 322 .. .. .. ..

.. 3,072 2,144 .. 73,717 4,398 8,611 11,699 3,221 8,232 5,403 25,566 e .. 6,081 3,786 712 119,841 7,904 .. .. 2,866 .. 40,956 .. .. 33,026 808,546 24,754 23,952 .. .. 11,754 .. 5,990 1,450 11,047 8,329 5,208 12,605 2,828 .. .. 3,230 719 5,564 84,213 .. 327 4,382 74,676 677 2,458 11,003 .. 6 .. 7,403

Researchers Technicians Scientiﬁc Expenditures in R&D in R&D and for R&D technical journal articles

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

per million people

per million people

2000–08d

2000–08d

1,733 137 205 706 .. 3,090 .. 1,616 .. 5,573 .. .. .. .. 4,627 .. 166 .. 16 1,935 .. 10 .. .. 2,547 521 50 .. 372 42 .. .. 353 726 .. 647 16 18 .. 59 3,089 4,365 .. 8 .. 5,468 .. 152 144 .. 71 .. 81 1,623 3,799 .. ..

512 94 .. .. .. 684 .. .. .. 589 .. .. .. .. 720 .. 33 .. .. 543 .. 11 .. .. 553 75 15 .. 44 13 .. .. 186 117 .. 48 35 137 .. 137 1,764 894 .. 10 .. .. .. 64 106 .. .. .. 10 191 403 .. ..

2007

2,452 18,194 198 4,366 73 2,487 6,623 26,544 49 52,896 344 106 262 10 18,467 .. 242 16 12 147 238 3 0 30 456 58 48 63 808 19 3 18 4,223 70 21 378 24 13 14 72 14,210 3,173 11 22 427 4,079 129 741 78 21 12 153 195 7,136 3,424 .. 48

High-technology exports

Royalty and license fees

% of GDP

$ millions

% of manufactured exports

2000–08 d

2009

2009

2009

2009

0.96 0.80 0.05 0.67 .. 1.42 4.86 1.18 0.06 3.44 0.34 0.22 .. .. 3.21 .. 0.09 0.23 0.04 0.61 .. 0.06 .. .. 0.80 0.21 0.14 .. 0.64 .. .. 0.37 0.37 0.55 0.23 0.64 0.53 0.16 .. .. 1.63 1.21 0.05 .. .. 1.62 .. 0.67 0.21 .. 0.09 0.15 0.12 0.61 1.51 .. ..

17,444 10,143 5,940 375 0 24,738 10,268 25,988 4 99,210 49 1,802 78 .. 103,400 .. 6 11 .. 363 138 .. .. .. 931 42 10 3 51,560 3 .. 13 37,354 10 7 646 24 .. 21 2 58,450 504 7 2 46 4,694 7 227 0 .. 38 87 21,531 7,172 1,288 .. 0

26 9 13 6 0 25 23 8 1 20 1 30 5 .. 32 .. 0 5 .. 8 7 .. .. .. 10 3 2 3 47 3 .. 1 22 5 8 7 10 .. 1 0 24 10 6 8 3 20 0 2 0 .. 11 3 66 5 4 .. 0

862 193 38 .. 1,312 1,697 761 1,115 9 21,698 0 0 19 .. 3,185 .. 0 4 0 7 0 18 .. 0 0 6 .. .. 266 0 .. 0 656 4 0 2 0 .. 0 .. 5,473 159 0 0 0 637 .. 6 0 .. 295 2 2 103 148 .. ..

1,369 1,860 1,530 .. 396 34,873 897 1,899 45 16,835 0 64 21 .. 7,049 .. 0 12 0 26 1 .. .. 0 29 20 .. .. 1,133 2 .. 5 0 11 1 49 4 .. 6 .. 4,073 529 0 0 208 553 .. 90 25 .. 2 147 421 1,542 507 .. ..

5.13

Patent applications ﬁleda,b

$ millions

Trademark applications ﬁleda,c

Receipts

Payments

Residents

Nonresidents

Total

2009

2009

2009

757 5,314 282 5,970 .. 908 1,387 8,814 21 295,315 59 11 38 7,956 127,316 .. .. 135 .. 114 .. .. .. .. 91 34 1 .. 818 .. .. 2 822 134 103 177 18 .. .. .. 2,575 1,555 .. .. .. 1,140 .. 170 .. 1 .. 37 216 2,899 381 .. ..

30 23,626 4,324 557 .. 53 5,387 903 132 53,281 507 162 33 55 36,207 .. .. 3 .. 37 .. .. .. .. 16 406 43 .. 4,485 .. .. 22 13,459 5 110 834 22 .. .. .. 279 4,803 .. .. .. 4,280 .. 1,375 371 45 .. 657 3,095 241 24 .. ..

2011 World Development Indicators

STATES AND MARKETS

Science and technology

6,671 130,172 52,649 3,013 .. 4,091 10,742 40,702 1,708 110,622 9,145 3,500 1,430 1,351 134,211 .. .. 2,580 .. 3,566 .. 634 489 .. 4,465 3,788 1,605 804 24,070 .. .. 24 75,250 5,046 1,399 3,774 870 .. 858 1,132 .. 16,190 5,975 .. .. 13,607 2,103 14,872 8,553 612 .. 24,825 14,912 17,877 2,681 .. ..

315

5.13

Science and technology Researchers Technicians Scientiﬁc Expenditures in R&D in R&D and for R&D technical journal articles

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

per million people

per million people

2000–08d

2000–08d

2007

216 493 .. .. .. 299 .. 529 392 1,696 .. 123 1,143 65 .. .. 1,871 2,317 .. .. .. 160 .. 17 .. 43 102 .. .. 325 .. 893 .. .. .. .. .. .. .. .. .. .. w .. .. .. .. .. .. 351 .. .. 87 .. .. 1,376

1,252 13,953 12 589 68 1,057 3 3,792 971 1,280 0 2,805 20,981 125 36 4 9,914 9,190 80 22 123 1,728 .. 12 67 757 8,638 2 164 1,847 214 47,121 209,695 215 166 497 283 .. 18 36 80 758,132 s 1,690 144,072 85,227 58,845 145,762 60,164 29,335 23,240 8,700 19,375 4,946 612,370 167,647

908 3,191 .. .. 276 1,196 .. 6,088 2,331 3,490 .. 393 2,944 93 .. .. 5,239 3,436 .. .. .. 311 .. 34 .. 1,588 680 .. .. 1,458 .. 4,269 4,663 346 .. 187 115 .. .. .. .. 1,281 w .. 596 479 1,112 579 1,071 2,064 487 .. 129 .. 3,945 2,977

High-technology exports

% of GDP

$ millions

% of manufactured exports

2000–08 d

2009

2009

0.59 3,230 1.03 4,576 .. 11 0.05 40 0.09 104 0.35 .. .. .. 2.52 97,207 0.47 3,171 1.66 1,264 .. .. 0.93 1,418 1.34 10,841 0.17 44 0.29 11 .. 0 3.75 17,059 2.90 38,556 .. 83 0.06 .. .. 24 0.25 28,655 .. .. .. 0 0.06 3 1.02 663 0.72 1,463 .. .. 0.39 5 0.85 1,519 .. 29 1.88 57,178 2.82 141,519 0.64 73 .. .. .. 66 0.19 1,685 .. .. .. 0 0.03 6 .. 7 2.07 w 1,858,138 s .. .. 0.98 576,048 1.19 414,058 0.79 117,380 0.98 540,234 1.44 .. 0.88 16,275 0.68 50,434 .. 1,571 0.79 .. .. 3,260 2.29 1,116,596 1.68 392,305

Royalty and license fees

Patent applications ﬁleda,b

$ millions

Trademark applications ﬁleda,c

Receipts

Payments

Residents

Nonresidents

Total

2009

2009

2009

2009

2009

1,054 25,598 .. 128 .. 319 .. 750 176 373 .. .. 3,596 201 3 .. 2,549 1,684 124 11 .. 802 .. .. .. .. 2,555 .. 6 2,434 .. 15,985 224,912 33 238 .. .. .. 11 .. .. 994,324 s .. 179,049 134,475 36,842 185,505 237,052 33,042 5,287 .. 5,580 .. 764,583 84,182

37 12,966 .. 642 .. 40 .. 7,986 63 12 .. 10,753 207 264 13 .. 306 394 133 1 .. 5,939 .. .. 551 .. 177 .. 1 2,380 .. 6,480 231,194 706 174 .. .. .. 24 .. .. 634,131 s .. 198,050 131,207 55,416 198,493 85,532 16,049 41,517 .. 25,831 .. 406,316 15,710

12,977 49,189 238 .. .. 7,237 750 15,332 5,534 4,073 .. 26,621 46,711 5,916 743 680 12,706 28,945 2,432 2,496 556 36,087 .. .. .. .. 71,466 2,337 .. 8,568 .. 33,542 266,845 11,501 4,541 .. 4,187 .. 4,518 795 .. 2,884,372 s .. 1,559,267 955,629 603,638 1,575,589 890,552 214,396 313,022 14,191 151,906 .. 1,104,532 313,484

10 193 339 9 494 4,107 31 0 1 0 .. .. 14 0 9 .. 63 144 .. 1 1 49 1,340 11,686 5 92 155 7 36 290 .. .. .. 6 48 1,658 5 1,041 3,449 1 0 0 34 0 0 0 0 116 17 4,709 1,832 25 .. .. 2 0 30 .. 1 0 4 0 0 26 145 2,250 .. .. .. 0 0 5 0 .. .. 6 25 14 2 .. 648 .. .. .. 1 3 3 3 112 644 3 .. .. 23 12,928 9,498 23 89,791 25,230 5 0 17 .. .. .. 4 0 352 5 .. .. .. 0 1 0 33 –5 2 0 0 1 .. .. 20 w 181,636 s 188,861 s 3 34 67 20 3,767 32,422 25 1,336 18,747 14 2,431 13,675 20 3,800 32,489 32 881 16,411 9 923 6,257 13 1,629 5,477 2 60 343 8 208 1,962 6 99 2,039 19 177,835 156,372 16 38,296 70,574

a. Original information was provided by the World Intellectual Property Organization (WIPO). The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data. b. Excludes applications filed under the auspices of the African Intellectual Property Organization (448 by nonresidents), European Patent Office (134,580 by nonresidents), and the Eurasian Patent Organization (2,801 by nonresidents). c. Excludes applications filed under the auspices of the Office for Harmonization in the Internal Market (88,086). d. Data are for the most recent year available. e. Includes Luxembourg and the Netherlands.

316

2011 World Development Indicators

About the data

5.13

STATES AND MARKETS

Science and technology Definitions

The United Nations Educational, Scientifi c, and

for filing patent applications. An applicant files an

• Researchers in R&D are professionals engaged

Cultural Organization (UNESCO) Institute for Statis-

international application for which the 142 eligible

in conceiving of or creating new knowledge, prod-

tics collects data on researchers, technicians, and

countries in 2009 are automatically designated.

ucts, processes, methods, and systems and in

expenditure on R&D through surveys and from other

The application is searched and published, and,

managing the projects concerned. Postgraduate

international sources. R&D covers basic research,

optionally, a supplementary international search or

doctoral students (ISCED97 level 6) engaged in R&D

applied research, and experimental development.

preliminary examination can be conducted. In the

are considered researchers. • Technicians in R&D

Data on researchers and technicians are calculated

national or regional phase the applicant requests

and equivalent staff are people whose main tasks

as full-time equivalents.

national processing of the application and initiates

require technical knowledge and experience in engi-

Scientific and technical article counts are from jour-

the national search and granting procedure in the

neering, physical and life sciences (technicians),

nals classified by the Institute for Scientific Informa-

countries where protection is sought. International

and social sciences and humanities (equivalent

tion’s Science Citation Index (SCI) and Social Sci-

applications under the treaty provide for a national

staff). They engage in R&D by performing scientific

ences Citation Index (SSCI). Counts are based on

patent grant only—there is no international patent.

and technical tasks involving the application of

fractional assignments; articles with authors from

The national filing represents the applicant’s seeking

concepts and operational methods, normally under

different countries are allocated proportionately to

of patent protection for a given territory, whereas

researcher supervision. • Scientiﬁc and technical

each country (see Definitions for fields covered).

international filings, while representing a legal right,

journal articles are published articles in physics,

The SCI and SSCI databases cover the core set of

do not accurately reflect where patent protection is

biology, chemistry, mathematics, clinical medicine,

scientific journals but may exclude some of local

sought. Resident filings are those from residents of

biomedical research, engineering and technology,

importance and may reflect some bias toward Eng-

the country concerned. Nonresident filings are from

and earth and space sciences. • Expenditures for

lish-language journals.

applicants abroad. For regional offices such as the

R&D are current and capital expenditures on creative

R&D expenditures include all expenditures for R&D

European Patent Office, applications from residents

work undertaken to increase the stock of knowledge,

performed within a country, including capital costs

of any member state of the regional patent conven-

including on humanity, culture, and society, and the

and current costs (wages and associated costs of

tion are considered nonresident filings. Some offices

use of knowledge to devise new applications. • High-

researchers, technicians, and supporting staff and

(notably the U.S. Patent and Trademark Office) use

technology exports are products with high R&D inten-

other current costs, including noncapital purchases

the residence of the inventor rather than the appli-

sity, such as in aerospace, computers, pharmaceuti-

of materials, supplies, and R&D equipment such as

cant to classify filings. For further information on

cals, scientific instruments, and electrical machinery.

utilities, reference materials, subscriptions to librar-

the PCT, see the PCT Yearly Review at http://www.

• Royalty and license fees are payments and receipts

ies and scientific societies, and lab materials).

wipo.int/export/sites/www/ipstats/en/statistics/

between residents and nonresidents for authorized

pct/pdf/901e_2009.pdf.

use of intangible, nonproduced, nonfinancial assets

The method for determining high-technology exports was developed by the Organisation for Eco-

A trademark is a distinctive sign identifying goods

and proprietary rights (such as patents, copyrights,

nomic Co-operation and Development in collabora-

or services as produced or provided by a specific

trademarks, and industrial processes) and for

tion with Eurostat. It takes a “product approach” (as

person or enterprise. A trademark protects the owner

the use, through licensing, of produced originals

distinguished from a “sectoral approach”) based on

of the mark by ensuring the exclusive right to use it

of prototypes (such as films and manuscripts).

R&D intensity (expenditure divided by total sales)

to identify goods or services or to authorize another

• Patent applications ﬁled are patent applications

for groups of products from Germany, Italy, Japan,

to use it. Period of protection varies, but a trade-

filed at a national or regional office; an international

the Netherlands, Sweden, and the United States.

mark can be renewed indefinitely for an additional

patent application (or PCT filing) is in the interna-

Because industrial sectors specializing in a few high-

fee. Detailed components of trademark filings, avail-

tional phase of the PCT. • Trademark applications

technology products may also produce low-technol-

able on the World Development Indicators CD-ROM

ﬁled are applications to register a trademark with a

ogy products, the product approach is more appro-

and WDI Online, include applications filed by direct

national or regional IP office.

priate for international trade. The method takes only

residents (domestic applicants filing directly at a

R&D intensity into account, but other characteristics

given national or regional intellectual property [IP]

Data sources

of high technology are also important, such as know-

office); direct nonresident (applicants from abroad

Data on R&D are provided by the UNESCO Institute

how, scientific personnel, and technology embodied

filing directly at a given national or regional IP office);

for Statistics. Data on scientific and technical journal

in patents. Considering these characteristics would

aggregate direct (applicants not identified as direct

articles are from the U.S. National Science Board’s

yield a different list (see Hatzichronoglou 1997).

resident or direct nonresident by the national or

Science and Engineering Indicators 2010. Data on

A patent is an exclusive right granted for a specified

regional office); and Madrid (designations received

high-technology exports are from the United Nations

period (generally 20 years) for a new way of doing

by the national or regional IP office based on inter-

Statistics Division’s Commodity Trade (Comtrade)

something or a new technical solution to a problem—

national applications filed via the World Intellectual

database. Data on royalty and license fees are

an invention. The invention must be of practical use

Property Organization (WIPO)–administered Madrid

from the International Monetary Fund’s Balance of

and display a characteristic unknown in the existing

System). Data are based on information supplied to

Payments Statistics Yearbook. Data on patents and

body of knowledge in its field.

WIPO by IP offices in annual surveys supplemented

trademarks are from the World Intellectual Property

by data in national IP office reports. Data may be

Organization’s World Intellectual Property Indicators

missing for some offices or periods.

(2010) and www.wipo.int/econ_stat.

Most countries have systems to protect patentable inventions. The international Patent Cooperation Treaty (PCT) provides a two-phase system

2011 World Development Indicators

317 Text figures, tables, and boxes

GLOBAL LINKS

Introduction

T

he past three years show dramatically how events in one part of the world can affect people in the rest of the world, though sometimes with a lag. The financial crisis that struck high-income economies in 2008 reached low- and middle-income economies in 2009. World exports of goods and services fell 20 percent, from $19.6 trillion in 2008 to $15.6 trillion in 2009, more in high-income economies and somewhat less in low- and middle-income economies. Developing economies’ share of world exports increased by 1 percentage point over 2008, continuing a rising trend from 19 percent in 2000 to 27 percent in 2009. Imports of goods and services by high-income economies fell 22 percent, from $14.0 trillion in 2008 to $10.9 trillion in 2009; imports by low and middle income economies fell 19 percent. The financial crisis also reduced the external financing available to developing economies from private sources, which dropped to $521 billion in 2009 from the record high of $932 billion in 2007. Net inflows of foreign direct investment dropped to $359 billion in 2009 from a high of $597 billion in 2008. In contrast, net inflows of portfolio equity investments rose to $108 billion following net outflows of $53 billion in 2008. Bond issuances, which dropped from $88 billion in 2007 to $24 billion in 2008, recovered in 2009 to reach $51 billion. But commercial and traderelated lending, which declined from $195 billion in 2007 to $172 billion in 2008, dried up in 2009, dropping to $1.7 billion. Total debt flows from private creditors fell 70 percent in 2009, to $59 billion. But net flows from official creditors reached $171 billion in 2009, a 50 percent increase over 2008, driven by such multilateral institutions as the International Monetary Fund (IMF) and the World Bank. Global food prices soared again in 2010 and 2011, with some commodities exceeding their record high in 2008. The World Bank food price index (table 6.6) averaged 311 in February 2011, exceeding the June 2008 record of 293. Food price inflation has accelerated in several low- and middleincome economies, where consumers often spend more than half their income on food. During the 12 months ending in August 2010, food prices rose 13.2 percent a year in Indonesia, 10.4 percent a year in India, and 9.6 percent a year in Bangladesh. The financial crisis has also demonstrated the need for more data and more frequently updated data to monitor global transactions. The World Development Indicators database contains more

than 400 indicators for monitoring exchanges between economies on an annual basis, and the topics covered have expanded each year. Many others are not included in the database because of their structure or limited country coverage, but they are necessary for understanding global links. Most high-income economies and some low- and middleincome economies now produce economic statistics on a quarterly or monthly basis. This introduction highlights some of these data.

6

Data sources for bilateral trade flows World Development Indicators publishes data on merchandise trade values by commodity groups (tables 4.4 and 4.5), values of trade in services (tables 4.6 and 4.7), intra- and extra-regional trade (table 6.5), merchandise trade indices (table 6.2), tariff rates (table 6.8), and indicators for measuring trade facilitation (table 6.9). Demand is rising for more detailed data, such as trade flows by partner economies and by commodities and sectors. Table 6a summarizes the main sources of data on bilateral trade flows. Some of these databases are accessible through the World Integrated Trade Solutions platform (http://wits.worldbank.org).

Barriers to trade in services Trade in services makes up 22 percent of world trade, up from 20 percent in 2000. In developing economies the nominal value of trade in services grew 16 percent a year over 2000–09, doubling the rate of growth over 1990–2000 and surpassing that of high-income economies, which grew at 11 percent a year over 2000–09. Despite this growing

2011 World Development Indicators

319

6a

Source of data for bilateral trade ﬂows

Compiling organization

International Monetary Fund

Name of publication and database

Country coverage

Direction of Trade Statistics database

Most developing and developed economies

Data coverage

Periodicity

Merchandise trade data, no breakdowns of sectors and partners. Available through subscription

Quarterly and annual

Links

http://www2. imfstatistics.org/DOT/ This is a link to a 5-day trial

United Nations Conference on Trade and Development

UNCTADstat Merchandise Trade Matrix

Most developing and developed economies

Merchandise trade by partner economies and by product groups

Annual

http://unctadstat. unctad.org/

United Nations Statistics Division

Commodity Trade Statistics (Comtrade)

Most developing and developed economies

Merchandise trade by partner economies and by commodity classifications

Annual

http://wits.worldbank. org/wits/

Organisation for Economic Co-operation and Development (OECD)

Monthly Statistics of International Trade

OECD member economies

Total merchandise trade by partners

Monthly

http://stats.oecd.org/

International Trade by Commodity Statistics

OECD member economies plus EU

Merchandise trade by partners and by products

Annual

Trade in services

OECD member economies plus EU and a few more economies

Trade in services by partners and by service category

Annual

External Trade database

27 EU members

Merchandise trade by partners and by products

Monthly, quarterly, and annual

Eurostat

importance, little is known about policies affecting services trade, a major impediment to the analysis of trade policy and trade flows. To address this gap, the World Bank has built the Services Policy Restrictiveness Database, with information on 102 countries for five major service sectors disaggregated by subsectors and relevant modes of supply in each subsector. So far, the information focuses mainly on discriminatory policy measures affecting foreign service providers. The full database will be released in the second quarter of 2011 at http://econ.worldbank.org/programs/trade/ services. Restrictiveness is assessed by the newly created Services Trade Restrictiveness Index score. The index reveals patterns of restrictiveness by major service sector and across 6b

Trade in professional services faces the highest barriers Services trade restrictiveness index, 0 (fully open) to 100 (entirely closed)

High-income economies

Low- and middle-income economies

50 40 30 20 10 0 Financial services

Professional services

Retail services

Telecommunications services

Transportation services

Note: Aggregate values are the simple average of individual country scores. Data are for 102 countries. Source: World Bank Services Policy Restrictiveness Database.

320

2011 World Development Indicators

Extract databases are available under “International Trade and balance of Payments” theme Full databases are subscription based http://epp.eurostat. ec.europa.eu/portal/ page/portal/external_ trade/data/database

low- and middle-income and high-income economies (figure 6b). In both high-income and lowand middle-income economies, professional services (including the movement of individuals) face the highest trade barriers, followed by transportation services. High-income economies exhibit more open financial, telecommunications, and retail distribution sectors than do low- and middle-income economies (Borchert, Gootiiz, and Mattoo forthcoming).

Foreign direct investment Countries are increasingly compiling more data on foreign direct investment (FDI) transactions and stocks. Despite recent improvements, however, deficiencies in coverage remain. For example, if recording of FDI transactions were complete and comparable, the total outflows of FDI from investing economies would equal the total inflows recorded by the recipient economies. But in 2009 the divergence between outflows and inflows of FDI at the global level was about $82 billion (7 percent of global outflows; figure 6c). The discrepancies arise from differences in reporting practices. For example, some countries include reinvested earnings in their outflow statistics while others do not include them in their inflow statistics. Furthermore, corporate accounting practices and valuation methods may differ by reporters. Discrepancies exist among FDI statistics published by various international agencies,

GLOBAL LINKS

even when the agencies adopt common methodological standards. Such discrepancies may reflect differences in comparability and timing of FDI data reported by different countries, discrepancies in sector coverage, and lags in reporting revisions. Recognizing these issues, the IMF is leading a worldwide statistical data collection effort to improve the quality of FDI data (the Coordinated Direct Investment Survey; http://cdis.imf.org). Preliminary results were released in December 2010. Data on FDI are published in table 6.12. These data cover FDI net inflows received by the reporting economy from foreign residents, and FDI net outflows by the reporting economy residents. Breakdowns of FDI transactions and investment positions by sector and partner, increasingly sought by users, are not published in World Development Indicators but are available from other sources. Table 6d summarizes the availability of FDI statistics for some of the main data compilers.

Bilateral remittance flows World Development Indicators publishes data on total workers’ remittances and compensation of employees received and sent by the reporting economies (table 6.18). Data coverage and quality have been improving, but inconsistencies and lack of reporting remain. For example, if all economies reported completely and consistently, the sum of remittances flows recorded by receiving economies would equal the sum of remittance flows recorded by sending economies. But as of 2009 there was a discrepancy of $127 billion (30 percent of total inflows; figure 6e). Large amounts of remittance flows are sent through private and informal channels that are not officially recorded. No comprehensive dataset is available on the bilateral flow of remittances. Bilateral remittance flows estimated through approximation and allocation methods using the proportions of migrant stocks in destination and sending countries or the incomes of destination and sending countries are available at www.worldbank.org/prospects/migrationandremittances (Ratha and Shaw 2007). The data shed light on patterns of remittance flows, but the estimates are sensitive to the assumptions and allocation method chosen.

Bilateral migration stocks Because migration data come mostly from destination countries, the quality of global migration

Discrepancies persist in measures of FDI net ﬂows

6c

Global FDI net flows ($ trillions) 3.0

Inflows

Outflows

2.5 2.0 1.5 1.0 0.5 0.0 2004

2005

2006

2007

2008

2009

Source: World Development Indicators data files.

data depends on how well the destination countries survey migrants within their borders. Systematic recording of migrants is difficult, especially for countries with weak statistical capacity and for those affected by civil disorder and natural disasters. Moreover, ensuring the comparability of migration data is a long-standing challenge, in part because destination countries classify migrants using various criteria. Many countries compile migration data based on immigrants’ nationality, while others collect data based on the immigrants’ place of birth. World Development Indicators publishes aggregate data on international migrant stocks and net migration estimated by the United Nations Population Division based on population censuses supplemented by border statistics, administrative records, surveys, and refugee registrations (tables 6.1 and 6.18). Efforts to produce complete data on bilateral migration have been rare. A 2008 database on immigrants in OECD countries contains data on bilateral migrant stock for OECD members (http://stats.oecd.org/). The dataset includes sociodemographic information such as age, gender, education, and occupation. A series of studies have published data on OECD immigrants by educational attainment (Docquier and Marfouk 2006), gender and educational attainment (Docquier, Lowell, and Marfouk 2009), and age of entry and educational attainment (Beine, Docquier, and Marfouk 2006). Global bilateral databases have been constructed for the 2000 census round (Parsons and others 2007) and for bilateral migration and remittance flows (Ratha and Shaw 2007). The United Nations Population Division in cooperation with the World Bank, the United Nations Statistics Division, and the Universities of Nottingham and Sussex created the Global 2011 World Development Indicators

321

6d

Source of data on FDI

Compiling organization

Name of publication and database

Country coverage

Data coverage

Periodicity

Links

Balance of Payments Statistics Yearbook and database

Most developing and developed economies

Aggregate FDI flows and stock by reporting economy. By-partner, bysector breakdowns are not available. Available through subscription

Quarterly and annual

http://www2. imfstatistics.org/BOP

World Investment Report and Foreign Direct Investment database

Most developing and developed economies

Aggregate FDI flows and stock by reporting economy

Annual

http://unctadstat. unctad.org

Transnational Corporations Statistics database

Transnational Corporations Worldwide

Detailed data on transactions of transnational corporations and mergers and acquisitions, by partner and by sector; available through data extract service

Annual

www.unctad.org/ Templates/Page.asp ?intItemID=3159& lang=1

Organisation for Economic Cooperation and Development (OECD)

International Direct Investment database

32 OECD member economies

FDI stock (annual) and flows (annual and quarterly) by partner economies and by sectors. Full dataset is available to subscribers

Quarterly and annual

http://stats.oecd.org/

Eurostat

European Union Foreign Direct Investment Yearbook and database

27 EU members

Aggregate and bilateral FDI flows and stock, by partner and by sector

Annual

http://epp.eurostat. ec.europa.eu/ portal/page/portal/ balance_of_payments/ data/database

Association of Southeast Asian Nations

Foreign Direct Investment Statistics

10 ASEAN member economies

Bilateral FDI inflows and outflows

Annual

www.aseansec. org/18144.htm

Centre d’Etudes Prospectives et d’Informations Internationales

Foreign Direct Investment database

96 countries of the GTAP 6.2 database for stocks and 70 countries for flows

Harmonized bilateral flows and stocks of FDI for 26 sectors. Data are gap filled using gravity-based regressions and raw data from IMF, UNCTAD, OECD, and Eurostat.

Annual for 2004 only

www.cepii.fr/ anglaisgraph/ bdd/fdi.htm

Financial Times

FDI database

All countries with greenfield FDI projects;

Greenfield FDI projects since 2003; subscription based. Methodology differs significantly from balance of payments and international investment position standards. The data are based on press reports.

Daily

www.fdimarkets.com

Mergers and acquisitions activity worldwide covering an array of transactions

Information for mergers and acquisitions activity, including information on target and acquiror, deal value, and financials.

Monthly

www.dealogic.com

International Monetary Fund (IMF)

United Nations Conference on Trade and Development (UNCTAD)

FDI Intelligence

Dealogic

M&A Analytics

Migration database (www.unmigration.org) in 2008. It contains all publicly available data from more than 230 destination countries and territories over the last five decades on international migrants, classified by age, gender, place of birth, and country of citizenship. However, it still does not include all raw data points needed for a global migration matrix. These raw data were assembled to construct a global bilateral migration matrix using empirical methods to fill holes in the data (Özden and others forthcoming). The resulting database covers 226 origin and 226 destination countries

322

2011 World Development Indicators

This is a link to 5-day trial

Extract databases are available under Globalisation theme

(forthcoming at www.data.worldbank.org/data -catalog). Construction of such a matrix entails formidable challenges, including selecting the most relevant sources, allocating migrants who “originated” in aggregate geographic regions and migrants of unknown origins to specific countries, and accounting for varying survey dates and definitions. Of all cell-level values in the final matrix, about 12–14 percent are from raw census data, 40–60 percent are based primarily on raw data scaled to United Nations Population Division estimates of migrant stocks or augmented by the disaggregation of aggregate categories,

GLOBAL LINKS

and the remaining 26–48 percent are estimated through interpolation and extrapolation. This new dataset reveals that the total stock of migrants increased from 92 million in 1960 to 165 million in 2000. The number of migrants from high-income economies remained stable, while the number from low- and middle-income economies rose from 14 million in 1960 to 60 million in 2000 (figure 6f). The increase was driven largely by an increase in migrants residing in the United States (up 24 million) and Western Europe (up 22 million).

At least 30 percent of remittance inﬂows go unrecorded by the sending economies

6e

Global remittance flows ($ billions)

Inflows

Outflows

500 400 300 200 100 0 2004

2005

2006

2007

2008

2009

Note: Incudes workers’ remittances and compensation of employees. Source: World Development Indicators data files.

Public sector debt World Development Indicators publishes data on public and publicly guaranteed external debt (tables 6.10, 6.11, and 6.13). But these data present only a portion of total public sector debt, much of which is held by domestic creditors. Domestic debt data are important for economic policymaking because of the implications for local financial markets. To fill the gap, the World Bank and the IMF launched an online Quarterly Public Sector Debt database in 2009 (http://data.worldbank.org/data-catalog). The database provides data on clearly defined tiers of debt for central, state, and local government in developing or emerging market economies as well as on extrabudgetary agencies and funds. It also includes debt data by instruments, valuation methods, maturity types, and creditors. The level and composition of public sector debt are affected by many external and domestic economic factors. The recent global financial crisis limited the private sector’s ability to borrow. The public sector, usually more creditworthy, increased external borrowing to stimulate sluggish domestic economies. Most external financing for developing economies in 2009 was provided by official multilateral institutions such as the IMF and the World Bank. After the Asian financial crises in the late 1990s many governments switched from external to domestic borrowing to reduce their exposure to exchange rate fluctuations, dramatically increasing the size of domestic debt in emerging market economies. Today, domestic debt represents about 78 percent of the total general government debt in developing economies with data. Comparison with earlier period is not possible due to lack of data. Emerging market economies have also issued local currency–denominated debt to correct currency and maturity mismatches. In September 2010 the estimated local currency

debt among developing economies averaged 67 percent of total government debt (excluding Brazil and China, with upwards of 96 percent). Financing needed to support fiscal deficits led to a significant increase in the ratio of sovereign debt to GDP. Among developing economies, central government debt for 2009 averaged 46 percent of GDP, up from 42 percent in 2009. Brazil, which undertook aggressive countercyclical spending and tax cuts to stimulate the economy, had the highest share of gross debt in gross domestic product (about 70 percent; figure 6g). Migrants originating from low- and middle-income economies and residing in high-income economies rose ﬁvefold over 1960–2000

6f

International migrant stock by origin and destination, millions 80 60

Low- and middle-income to low- and middle-income Low- and middle-income to high-income

40

High-income to high-income

20

High-income to low- and middle-income 0 1960

1970

1980

1990

2000

Source: Özden and others forthcoming.

The ratio of central government debt to GDP has increased for most economies, 2007–10

6g

Central government debt (percent of GDP) 80 Brazil Pakistan

60 40

Mauritius

Kenya

Turkey Mexico

20

Honduras Lithuania

0 Q3 2007

Q3 2008

Q3 2009

Q3 2010a

a. Derived using 2009 GDP because 2010 GDP was not available. Source: World Bank Quarterly Public Sector Debt database

2011 World Development Indicators

323

Tables

6.1

Integration with the global economy Trade

International ﬁnance

Financing through international capital markets % of GDP Gross Merchandise Services inflows

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

324

Movement of people

Communication

% of GDP

Emigration of people with tertiary Workers’ International International education to remittances Internet Foreign direct voice OECD countries International and bandwidth a investment traffic a migrant stock % of population age compensation bits per 25 and older with % of total Net Net of employees Net migration second minutes tertiary education population thousands inflows outflows received per capita per person

2009

2009

2009

2009

2009

2009

2005–10

2010

2000

2008

31.3 46.9 60.1 75.6 30.7 45.9 34.6 73.8 64.2 41.3 101.6 153.2 45.7 53.4 74.5 69.2 18.0 81.7 36.0 35.2 105.3 32.7 48.4 20.9 69.5 58.8 44.3 323.7 28.1 63.4 88.7 69.0 64.2 50.3 30.9 114.9 56.9 37.9 50.5 36.1 52.4 29.6 100.4 33.5 51.9 39.4 66.0 43.5 51.3 62.0 52.1 24.2 50.2 58.7 41.2 40.5 90.7

.. 38.6 .. 26.2 7.4 16.6 9.0 24.1 11.9 6.0 11.3 33.0 12.8 8.8 11.9 15.9 4.7 24.5 8.7 17.1 26.8 15.2 10.3 .. .. 11.1 5.8 62.1 4.8 .. 46.1 17.5 14.8 25.0 .. 20.7 34.3 14.6 6.7 18.8 9.9 .. 36.4 14.4 22.4 10.2 .. 25.5 21.3 14.6 18.0 17.5 10.7 9.8 15.2 17.9 14.1

0.0 0.0 0.0 2.2 0.2 0.0 .. .. 0.1 0.2 0.5 .. 0.0 0.0 0.0 0.0 3.9 0.0 0.0 0.0 0.0 0.6 .. 0.0 0.0 3.2 1.0 .. 3.4 0.0 0.0 0.0 0.0 .. 0.0 .. .. 0.0 0.0 1.4 0.0 0.0 .. 0.0 .. .. 0.4 0.0 0.0 .. 4.7 .. 0.0 0.0 0.0 0.0 0.2

1.3 8.1 2.0 2.9 1.3 8.9 2.4 2.3 1.1 0.8 3.8 –8.2 1.4 2.4 1.4 2.1 1.6 9.4 2.1 0.0 5.4 1.5 1.5 2.1 6.8 7.8 1.6 24.9 3.1 9.0 21.7 4.6 1.6 4.7 .. 1.4 0.9 4.4 0.6 3.6 2.0 0.0 9.2 0.8 0.0 2.3 0.3 5.4 6.1 1.2 6.4 0.7 1.6 1.2 1.7 0.6 3.5

.. 0.3 .. 0.0 0.2 0.6 3.7 1.4 0.8 0.0 0.2 –16.7 –0.1 0.0 –0.1 0.0 –0.6 –0.3 0.6 0.0 0.2 1.8 3.0 .. .. 4.9 0.9 30.4 1.3 .. .. 0.0 0.0 2.1 .. 0.7 2.1 0.0 0.0 0.3 –0.6 .. 8.2 0.0 1.6 5.6 .. 0.0 0.0 1.8 0.0 0.6 0.1 0.0 –0.1 0.0 0.0

.. 11.0 1.5b 0.1 0.2 8.8 0.4b 0.9 3.0 11.8 0.7 2.2 3.6b 6.2 12.2 0.7 0.3 3.2 1.2b 2.1 3.4 0.7 .. .. .. 0.0 1.0b 0.2 1.8 .. 0.1b 1.8 0.8 2.3 .. 0.6 0.3 7.4 4.4 3.8 16.5 .. 1.7 0.9 0.4 0.6 0.1b 10.9 6.6 0.3 0.4 0.6 10.8 1.6 5.6 21.2 17.6

1,000 –75 –140 80 30 –75 500 160 –50 –570 0 200 50 –100 –10 15 –229 –50 –65 323 –5 –19 1,050 5 –75 30 –1,731c 113 –120 –100 –50 30 –145 10 –194 226 30 –140 –350 –340 –280 55 0 –300 55 500 5 15 –250 550 –51 150 –200 –300 –12 –140 –100

0.3 2.8 0.7 0.3 3.6 10.5 21.1 15.6 3.0 0.7 11.3 9.0 2.5 1.5 0.7 5.8 0.4 1.4 6.4 0.7 2.2 1.0 21.1 1.8 3.4 1.9 0.1c 38.9 0.2 0.7 3.8 10.5 11.2 15.8 0.1 4.3 8.7 4.2 2.9 0.3 0.7 0.3 13.6 0.6 4.2 10.6 18.9 16.6 4.0 13.2 7.6 10.0 0.4 3.8 1.2 0.4 0.3

22.6 17.5 9.5 3.7 2.8 8.9 2.7 13.5 1.8 4.4 3.2 5.5 8.7 5.8 20.3 5.1 2.0 9.6 2.6 9.3 21.5 17.3 4.7 7.3 9.1 6.0 3.8 29.6 10.4 14.9 28.2 7.1 6.2 24.6 28.8 8.5 7.8 22.4 9.5 4.7 31.7 35.2 9.9 9.8 7.2 3.5 14.6 67.8 2.8 5.8 44.7 12.2 23.9 4.7 27.7 83.4 24.8

7 263 34 .. .. .. .. .. 77 .. .. .. 309 .. .. .. .. 105 .. .. .. .. .. .. .. 43 .. 1,435 .. 6 .. 132 .. 302 .. 197 357 .. .. 44 510 29 .. 5 .. 301 .. .. 268 .. 61 .. 206 .. .. .. 224

2011 World Development Indicators

2009

550 1,902 .. 17 2,320 .. 5,457 20,323 1,399 4 2,277 24,945 35 225 1,195 220 2,108 37,657 15 2 19 23 16,193 .. 1 4,076 651 560,989 2,940 1 0 4,333 40 15,892 27 7,075 34,506 1,387 484 1,172 243 6 12,680 3 17,221 29,356 141 38 752 25,654 97 4,537 186 0 1 16 241

Trade

International ﬁnance

Financing through international capital markets % of GDP Gross Merchandise Services inflows

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

2009

2009

125.6 29.9 39.1 38.8 116.2 77.9 49.8 38.7 52.9 22.3 81.5 62.1 49.8 .. 82.5 .. 75.9 97.8 37.0 66.6 60.1 171.0 80.1 73.4 93.2 83.9 51.1 55.4 145.7 52.7 92.6 66.0 53.9 84.5 96.0 51.2 60.4 .. 93.6 41.5 119.2 39.8 79.3 44.6 52.9 49.8 99.0 30.5 35.4 95.4 71.0 37.3 52.3 65.4 48.6 .. 64.6

27.3 12.5 7.7 .. 11.5 86.9 20.0 10.4 37.5 5.5 33.3 12.4 16.1 .. 16.1 .. 18.0 37.7 8.6 23.4 90.4 12.5 162.0 8.7 18.4 18.3 .. .. 29.1 17.0 .. 44.8 4.5 25.6 23.1 21.1 17.1 .. 12.2 11.5 22.6 12.5 16.7 13.7 11.4 19.8 15.9 6.4 31.2 26.9 13.9 6.5 11.6 12.4 15.9 .. ..

2009

.. 1.6 2.3 0.0 0.0 .. .. .. 9.0 .. 0.0 2.1 0.2 .. .. 0.0 .. 0.0 0.0 0.0 2.7 0.0 0.0 0.0 6.4 2.6 0.0 0.0 5.8 0.0 0.0 0.0 3.1 0.0 0.1 0.0 0.6 .. 0.0 0.0 .. .. 0.0 0.0 0.7 .. .. 0.2 8.8 58.3 0.0 2.6 4.5 3.8 .. .. ..

Movement of people

6.1

GLOBAL LINKS

Integration with the global economy

Communication

% of GDP

Emigration of people with tertiary Workers’ International International education to remittances Internet Foreign direct voice OECD countries International and bandwidth a investment traffic a migrant stock % of population age compensation bits per 25 and older with % of total Net Net of employees Net migration second minutes tertiary education population thousands inflows outflows received per capita per person 2009

2009

2009

2.2 2.5 0.9 0.9 1.6 11.1 2.0 1.4 4.5 0.2 9.5 11.8 0.5 .. 0.2 7.5 0.0 4.1 5.4 0.4 13.9 4.0 24.9 2.7 0.6 2.7 6.3 1.3 0.7 1.2 –1.3 3.0 1.7 2.4 14.8 2.2 9.0 .. 5.3 0.3 4.2 –1.0 7.1 13.7 3.3 3.0 4.8 1.5 7.2 5.4 1.4 3.7 1.2 3.2 1.2 .. ..

2.1 1.1 0.5 .. 0.0 10.6 0.6 2.1 0.5 1.5 0.3 2.7 0.2 .. 1.3 .. 6.1 0.0 0.0 –0.2 3.3 0.0 0.0 1.9 0.5 0.1 .. .. 4.2 0.0 .. 0.4 0.9 0.1 1.3 0.5 0.0 .. 0.0 .. 3.5 –0.5 0.0 0.5 0.1 7.1 0.9 0.0 0.0 0.1 0.1 0.3 0.2 1.2 0.5 .. ..

1.7 3.6 1.3 0.3b 0.1 0.3 0.6 0.1 15.8 0.0 14.3 0.1 5.7b .. 0.3 .. .. 21.7b 0.6 2.3 21.9 26.2 6.2b 0.0 b 3.1 4.1 0.1b 0.0 b 0.6 4.5b 0.1b 2.5b 2.5 22.4 4.8 6.9 1.1 .. 0.1 23.8 0.5 0.5 12.5 1.7 5.5b 0.2 0.1 5.4 0.7 0.2 4.3 1.8 12.3 1.9 1.5 .. ..

2005–10

75 –1,000 –730 –500 –577 200 85 1,650 –100 150 250 –100 –189 0 –30 .. 120 –75 –75 –10 –13 –36 248 20 –100 –10 –5 –20 130 –202 10 0 –2,430 –172 –10 –425 –20 –500 –1 –100 100 50 –200 –28 –300 135 20 –1,416 11 0 –40 –625 –900 –120 200 –21 562

2010

2000

2008

2009

3.7 0.5 0.1 2.9 0.3 20.2 38.8 7.4 1.1 1.7 48.8 19.1 2.0 0.2 1.1 .. 73.3 4.2 0.3 14.9 17.8 0.3 2.3 10.4 3.9 6.3 0.2 1.8 8.4 1.2 2.9 3.3 0.7 11.4 0.4 0.2 1.9 0.2 6.3 3.2 10.5 22.0 0.7 1.3 0.7 9.9 28.4 2.4 3.4 0.4 2.5 0.1 0.5 2.2 8.6 8.1 86.5

12.8 4.3 2.9 14.3 10.9 33.7 7.8 9.7 84.7 1.2 7.4 1.2 38.5 .. 7.5 .. 7.1 0.9 37.2 8.5 43.9 4.1 44.3 4.3 8.4 29.4 7.7 20.9 10.5 14.8 8.6 56.0 15.5 4.1 7.4 18.6 22.6 3.9 3.4 4.0 9.6 21.8 30.2 5.5 10.5 6.2 0.4 12.7 16.7 27.8 3.8 5.8 13.6 14.3 19.0 .. 2.1

159 .. .. .. .. .. .. .. 224 .. 258 52 6 .. 64 .. .. .. .. .. 190 .. .. .. 132 256 8 .. .. 13 57 215 .. 457 .. 87 .. 3 .. .. .. .. .. .. 26 .. 431 .. 118 .. .. 113 .. 32 .. .. ..

5,987 32 110 151 3 15,261 2,003 12,989 741 5,770 1,811 1,342 477 0 6,065 .. 871 112 142 3,537 223 5 .. 50 14,300 17 12 5 5,097 51 76 364 312 6,660 2,920 1,600 56 20 27 5 78,156 4,544 144 11 5 26,904 1,365 43 15,964 2 662 2,646 113 2,748 4,790 1,764 2,044

2011 World Development Indicators

325

6.1

Integration with the global economy Trade

International ﬁnance

Financing through international capital markets % of GDP Gross Merchandise Services inflows 2009

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

58.9 40.2 27.2 76.6 53.8 55.7 38.7 282.9 127.0 109.0 .. 47.6 34.7 41.8 32.1 103.3 61.8 66.8 51.2 71.9 44.2 108.5 .. 80.6 75.8 84.8 39.5 66.9 42.3 75.0 136.8 38.4 18.8 39.0 61.5 30.1 141.0 .. 53.5 63.3 91.9 42.8 w 48.9 44.7 46.7 42.3 44.8 51.5 48.3 33.6 53.5 31.0 52.7 42.0 57.1

2009

12.4 8.4 16.5 22.1 20.6 16.2 8.7 95.1 16.3 21.6 .. 9.4 14.4 10.5 5.6 25.4 25.6 23.0 13.3 9.5 16.7 25.7 .. 22.2 4.9 21.4 8.2 .. 14.9 22.3 .. 18.6 6.1 10.5 .. 3.6 14.1 .. 12.8 7.4 .. 11.2 w 13.3 8.9 9.2 8.5 9.0 7.9 10.5 5.9 .. 11.5 13.4 12.5 16.9

2009

0.1 2.4 0.0 .. 2.8 0.0 0.0 .. .. .. .. 2.7 .. 1.3 0.0 0.0 .. .. 0.1 0.0 0.0 0.3 0.0 19.9 .. 0.1 1.7 0.0 0.0 0.9 .. .. .. 1.6 0.0 1.5 1.5 .. 0.0 0.5 0.0 .. w 0.6 1.8 1.2 2.5 1.8 1.4 1.8 2.9 0.3 1.3 1.4 .. ..

Movement of people

Communication

% of GDP

Emigration of people with tertiary Workers’ International International education to remittances Internet Foreign direct voice OECD countries International and bandwidth a investment traffic a migrant stock % of population age compensation bits per 25 and older with % of total Net Net of employees Net migration second minutes tertiary education population thousands inflows outflows received per capita per person 2009

2009

2009

3.9 3.0 2.3 2.8 1.6 4.5 3.8 9.2 0.0 –1.2 .. 1.9 0.4 1.0 4.9 2.2 2.8 5.6 2.7 0.3 1.9 1.9 .. 1.8 3.3 4.0 1.4 6.8 3.8 4.2 .. 3.4 1.0 4.0 2.3 –1.0 8.4 .. 0.5 5.5 1.1 1.8 w 2.7 2.2 2.0 2.4 2.2 1.6 3.3 1.9 2.6 2.3 3.1 2.0 3.0

0.1 3.6 0.0 0.6 1.0 0.1 0.0 3.3 0.5 0.3 .. 0.5 0.5 0.0 0.0 0.2 7.9 6.8 0.0 0.0 0.0 1.6 .. –0.5 2.7 0.2 0.3 .. 0.0 0.1 .. 2.0 1.9 0.0 .. 0.6 0.8 .. 0.0 0.0 .. 2.1 w 0.0 0.9 0.8 1.1 0.9 1.0 2.0 0.3 .. 0.9 0.2 2.8 3.8

3.1 0.4 1.8 0.1 10.6 12.6b,d 2.4 .. 1.9 0.6 .. 0.3 0.7 8.0 5.5b 3.1 0.2 0.5 2.6 b 35.1 0.1 0.6 .. 10.7b 0.5b 5.0 0.2 .. 4.7 4.5 .. 0.3 0.0 0.3 .. 0.0 7.4 b .. 4.4 0.3 .. 0.8 w 6.6 1.8 2.4 1.1 1.9 1.4 1.4 1.4 3.2 4.5 2.5 0.3 0.5

2005–10

–200 250 15 150 –100 0 60 500 20 22 –250 700 1,750 –300 135 –6 150 100 800 –200 –300 300 10 –5 –20 –20 –44 –25 –135 –80 343 948 5,052 –50 –400 40 –200 –10 –135 –85 –700 .. e s –2,737 –13,203 –9,231 –3,972 –15,941 –3,781 –1,671 –5,214 –1,089 –2,376 –1,810 15,894 5,607

2010

0.6 8.7 4.5 28.0 1.6 7.2 1.8 38.3 2.4 7.9 0.2 3.7 13.8 1.7 1.7 3.4 13.9 22.6 10.2 4.0 1.5 1.7 1.2 2.7 2.6 0.3 1.9 4.0 1.9 11.5 70.0 10.4 13.8 2.4 4.2 3.5 0.1 46.3 2.1 1.8 2.9 3.1 w 1.5 1.4 0.9 3.3 1.4 0.3 6.8 1.1 3.6 0.8 2.1 12.0 11.0

2000

2008

11.3 1.4 31.7 0.9 17.2 .. 49.2 14.5 14.3 11.0 34.5 7.4 4.2 28.2 6.8 5.4 4.5 9.6 6.2 0.6 12.1 2.2 16.5 16.5 78.9 12.6 5.8 0.4 36.0 4.3 0.7 17.1 0.5 9.0 0.8 3.8 27.0 12.0 6.0 16.4 13.1 5.4 w 13.1 6.8 6.6 7.0 7.1 7.0 3.4 10.6 10.5 5.3 12.6 4.1 7.1

124 .. 8 .. 101 203 .. .. 228 220 .. .. .. .. 13 41 .. .. .. .. 1 .. .. 28 443 .. 60 .. .. .. .. .. 216 125 .. 79 .. .. .. .. 19 .. .. .. .. .. .. .. .. .. .. .. .. .. ..

2009

18,271 573 35 1,731 372 12,660 .. 22,783 7,567 6,720 .. 70 11,008 190 322 35 49,828 29,413 261 37 2 818 .. 23 7,916 2,699 4,323 48 36 206 13,233 39,664 11,279 903 46 628 581 313 28 8 17 3,526 w 7 348 151 1,120 299 742 1,087 1,408 323 31 31 19,521 32,455

a. Data are from the International Telecommunication Union’s (ITU) World Telecommunication Development Report database. Please cite the ITU for third-party use of these data. b. World Bank estimate. c. Includes Taiwan, China. d. Includes Montenegro. e. World totals computed by the United Nations sum to zero, but because the aggregates shown here refer to World Bank definitions, regional and income group totals do not equal zero.

326

2011 World Development Indicators

About the data

6.1

GLOBAL LINKS

Integration with the global economy Definitions

Globalization—the integration of the world econ-

statistics agencies (see About the data for table

• Trade in merchandise is the sum of merchandise

omy— has been a persistent theme of the past 25

6.12). FDI data are recorded on a directional basis,

exports and imports. • Trade in services is the sum

years. Growth of cross-border economic activity has

as an inward flow to the economy of the direct invest-

of services exports and imports. • Financing through

changed countries’ economic structure and political

ment enterprise, and as an outward flow from the

international capital markets is the sum of the abso-

and social organization. Not all effects of globaliza-

economy of the direct investor. Net flows refer to

lute values of new bond issuance, syndicated bank

tion can be measured directly. But the scope and

new investments during the reporting period netted

lending, and new equity placements. • Foreign direct

pace of change can be monitored along four key

against disinvestments.

investment net inﬂows and outﬂows are net inflows

The data on workers’ remittances and compensa-

and outflows of FDI (equity capital, reinvestment of

tion of employees are the sum of three items defined

earnings, and other short- and long-term capital).

Trade data are based on gross flows that capture

in the IMF’s Balance of Payments Manual, 5th edi-

• Workers’ remittances and compensation of employ-

the two-way flow of goods and services. In conven-

tion: workers’ remittances, compensation of employ-

ees received are current transfers by migrant work-

tional balance of payments accounting, exports

ees, and migrants’ transfers. The distinction among

ers and wages and salaries of nonresident workers.

are recorded as a credit and imports as a debit.

these three items is not always consistent in the

• Net migration is the number of immigrants minus

The data on merchandise trade are from the World

data reported by countries to the IMF. In some cases

the number of emigrants, including citizens and nonciti-

Trade Organization (WTO), which obtains data from

countries compile data on the basis of the citizenship

zens, for the five-year period. • International migrant

national statistical offi ces and the International

of migrant workers rather than their residency status.

stock is the number of people born in a country other

Monetary Fund’s (IMF) International Financial Sta-

Some countries also report remittances entirely as

than that in which they live, including refugees. • Emi-

tistics, supplemented by the Comtrade database and

worker’s remittances or compensation of employees.

gration of people with tertiary education to OECD

publications or databases of regional organizations,

Following the fifth edition of the Balance of Payments

countries is adults ages 25 and older, residing in an

specialized agencies, economic groups, and private

Manual in 1993, migrants’ transfers are considered

OECD country other than that in which they were born,

sources. Because of differences in timing and defi -

a capital transaction, but previous editions regarded

with at least one year of tertiary education. • Interna-

nitions, trade flow estimates from customs reports

them as current transfers. For these reasons the

tional voice trafﬁc is the sum of international incoming

and balance of payments may differ. See tables 4.4

figures presented in the table take all three items

and outgoing telephone traffic (in minutes) divided by

and 4.5 for data on the main trade components of

into account. See About the data for table 6.18 for

total population. • International Internet bandwidth

merchandise trade and tables 4.6 and 4.7 for the

more information.

is the contracted capacity of international connections

dimensions: trade in goods and services, financial flows, movement of people, and communication.

same data on services trade.

Migration has increased in importance, accounting

between countries for transmitting Internet traffic.

Financing through international capital markets

for a substantial part of global integration. Data on

includes gross bond issuance, bank lending, and new

net migration are estimated by the United Nations

equity placement as reported by Dealogic, a com-

Population Division, based on data on immigrant

pany specializing in the investment banking industry.

stock and on fertility and mortality assumptions, tak-

Data on merchandise trade are from the WTO’s

In financial accounting inward investment is a credit

ing into account the migration history of a country or

Annual Report. Data on trade in services are from the

and outward investment a debit. Gross flow is a bet-

area, the migration policy of a country, and the influx

International Monetary Fund’s (IMF) Balance of Pay-

ter measure of integration than net flow because

of refugees in recent periods. The estimates of the

ments database. Data on international capital market

gross flow shows the total value of financial trans-

international migrant stock are derived from data on

financing are based on data from Dealogic. Data on

actions over a period, while net flow is the sum of

people who reside in one country but were born in

FDI are based on balance of payments data from the

credits and debits and represents a balance in which

another, mainly from population censuses (see About

IMF, supplemented by staff estimates using data from

many transactions are canceled out. Components of

the data and Definitions for table 6.18).

the United Nations Conference on Trade and Develop-

Data sources

One negative effect of migration is “brain drain”—

ment and official national sources. Data on workers’

emigration of highly educated people. The table

remittances are World Bank staff estimates based

Foreign direct investment (FDI) includes equity

shows data on emigration of people with tertiary

on IMF balance of payments data. Data on net migra-

investment, reinvested earnings, and short- and

education, drawn from Docquier, Lowell, and Mar-

tion are from the United Nations Population Division’s

long-term loans between parent firms and foreign

fouk (2009), who analyzed skilled migration using

World Population Prospects: The 2008 Revision. Data

affiliates. Distinguished from other kinds of interna-

data from censuses and registers of Organisation

on international migrant stock are from the United

tional investment, FDI establishes a lasting interest

for Economic Development and Co-operation (OECD)

Nations Population Division’s Trends in Total Migrant

in or effective management control over an enter-

countries and provide data disaggregated by gender

Stock: The 2008 Revision. Data on emigration of

prise in another country. FDI may be understated

for 1990 and 2000.

people with tertiary education are from Docquier, Low-

financing through international capital markets are reported in U.S. dollars by market sources.

in developing countries because some fail to report

Well developed communications infrastructure

ell, and Marfouk’s “A Gendered Assessment of Highly

reinvested earnings and because the definition of

attracts investments and allows investors to capi-

Skilled Emigration” (2009). Data on international voice

long-term loans differs by country. However, data

talize on benefits of the digital age. See About the

traffic and international Internet bandwidth are from

quality and coverage are improving as a result of

data for tables 5.11 and 5.12 for more information.

the International Telecommunication Union’s World

continuous efforts by international and national

Telecomunication Development Report database.

2011 World Development Indicators

327

6.2

Growth of merchandise trade Export volume

Import volume

Export value

Import value

average annual % growth

average annual % growth

average annual % growth

average annual % growth

1990–2000

Afghanistana Albania Algeria Angola Argentina Armenia Australiaa Austriaa Azerbaijan Bangladesh Belarusb Belgiuma Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canadaa Central African Republic Chada Chile China† Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark a Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finlanda Francea Gabon Gambia, The Georgia Germanya Ghana Greecea Guatemala Guinea Guinea-Bissaua Haiti Honduras †Data for Taiwan, China

328

2000–09

.. .. 2.8 6.2 8.4 .. 7.3 6.2 .. 12.9 .. 6.0 1.0 2.8 .. 4.8 5.1 .. 13.2 8.6 .. 0.3 9.1 20.0 –0.9 11.1 13.8 8.4 4.5 –1.8 6.6 14.0 5.0 .. .. .. 5.4 3.9 6.3 –0.2 2.9 –28.3 .. 10.5 .. 8.3 5.2 –11.6 .. 6.5 7.7 8.9 8.5 5.0 12.2 12.6 2.5 3.1

2011 World Development Indicators

15.2 .. –0.1 12.9 5.9 .. 7.6 4.9 .. 11.0 6.0 2.9 5.9 9.9 .. 2.9 7.6 .. 11.7 –4.2 12.8 –1.8 –0.7 –3.7 31.7 5.1 21.9 7.1 6.6 8.3 1.2 7.6 0.9 .. 1.5 .. 2.7 –0.7 8.2 10.2 2.4 –8.7 .. 7.8 .. 4.9 –1.2 –3.0 .. 5.6 4.8 .. 8.8 –7.4 3.6 5.9 3.5 7.6

1990–2000

.. .. –0.8 7.1 17.7 .. 9.2 5.6 .. 5.9 .. 5.7 8.2 9.1 .. 4.0 16.7 .. 3.6 4.0 .. 5.0 9.0 4.3 2.0 10.7 12.8 8.9 8.5 4.6 4.9 14.9 –0.3 .. .. .. 5.8 11.6 5.9 1.8 7.6 –3.2 .. 7.3 .. 6.6 2.5 0.1 .. 4.9 8.6 9.3 10.0 –1.4 –16.0 13.3 12.7 4.8

2000–09

3.9 .. 12.7 20.1 11.3 .. 7.5 4.2 .. 4.6 10.1 3.5 6.8 7.8 .. 5.1 7.2 .. 7.3 10.4 9.4 3.7 3.3 5.7 6.5 11.3 15.4 6.9 11.4 14.6 17.7 7.6 6.6 .. 8.2 .. 3.7 2.7 12.3 8.8 4.2 –5.2 .. 17.5 .. 6.4 7.2 2.2 .. 5.1 10.0 .. 5.8 3.4 7.2 2.3 4.3 2.3

1990–2000

–0.2 .. 2.0 6.2 10.1 .. 5.7 .. .. 15.8 .. 4.8 3.3 4.3 .. 4.8 5.9 .. 12.9 –4.3 26.9 –3.6 9.4 3.5 –3.5 9.4 14.5 8.3 7.3 –7.2 7.5 17.0 6.1 .. –1.7 .. 4.1 4.2 6.8 0.7 9.0 –31.0 .. 10.7 .. 4.9 0.8 –12.3 .. 3.7 9.0 8.2 10.1 0.6 18.6 12.2 5.3 7.2

Net barter terms of trade index

2000 = 100

2000–09

1990–2000

2000–09

1995

2009

24.0 .. 16.3 30.7 12.2 .. 20.0 .. .. 12.5 18.4 10.9 14.0 21.9 .. 7.4 16.2 .. 17.1 6.2 15.2 11.2 5.6 –0.5 49.6 18.3 23.7 7.9 14.9 18.8 16.9 7.5 12.4 .. 11.9 .. 9.8 2.3 17.3 24.6 4.7 –5.1 .. 17.8 .. 10.5 13.8 2.1 .. 11.6 16.6 .. 13.1 6.6 9.1 8.6 5.8 8.0

20.6 .. –1.3 7.8 17.0 .. 8.7 .. .. 10.4 .. 5.3 9.7 9.7 .. 4.2 12.6 .. 3.6 –6.9 25.2 2.1 8.9 0.2 0.5 10.3 13.0 8.8 9.7 –0.5 8.7 13.9 3.0 .. 2.5 .. 4.9 12.0 7.8 4.7 10.9 –0.2 .. 7.3 .. 3.7 2.2 0.2 .. 2.9 8.3 8.2 10.4 –2.6 –15.7 14.4 12.8 8.5

9.4 .. 18.8 24.6 14.4 .. 12.3 .. .. 13.0 19.4 11.4 16.1 13.3 .. 11.5 14.3 .. 15.6 15.9 15.1 13.1 7.3 12.7 12.2 15.3 21.6 8.2 15.8 21.5 24.4 9.9 15.7 .. 13.7 .. 10.7 6.4 17.8 17.2 7.3 1.7 .. 25.1 .. 12.2 12.4 9.8 .. 10.9 16.5 .. 11.5 10.5 17.5 9.8 9.3 7.8

.. .. 57.9 80.8 91.6 .. 99.4 .. .. 111.8 .. 104.3 106.6 89.4 .. 89.3 110.4 .. 131.0 163.6 .. 90.4 103.2 193.0 92.6 135.6 101.9 99.1 86.8 79.8 52.0 104.6 122.0 .. .. .. 102.1 98.2 80.6 116.3 121.1 101.7 .. 151.0 110.6 106.4 125.4 100.0 .. 107.5 106.7 89.6 117.9 89.6 102.7 113.2 96.3 89.9

107.6 .. 161.0 170.8 126.0 .. 163.0 .. .. 64.5 121.0 103.1 83.1 136.9 .. 79.1 107.8 .. 78.6 137.9 85.0 121.6 114.8 78.5 136.0 166.7 79.7 97.6 114.4 112.0 147.5 87.2 140.4 .. 111.1 .. 102.9 96.8 109.7 128.1 99.1 73.3 .. 121.1 83.1 99.8 155.3 85.5 .. 105.9 178.4 90.8 91.4 143.3 66.0 70.6 81.9 69.2

Export volume

Import volume

Export value

Import value

average annual % growth

average annual % growth

average annual % growth

average annual % growth

1990–2000

Hungarya India Indonesia Iran, Islamic Rep. Iraq a Irelanda Israela Italya Jamaica Japan a Jordan Kazakhstana Kenya Korea, Dem. Rep.a Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latviaa Lebanon Lesotho Liberiaa Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlandsa New Zealanda Nicaragua Niger Nigeria Norwaya Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Polanda Portugala Puerto Rico Qatar

10.1 6.9 9.1 .. .. 15.2 9.7 4.8 2.2 2.6 4.7 .. 3.9 .. 15.8 .. .. .. .. 7.2 .. 13.3 .. .. .. .. 4.1 2.7 13.6 10.3 1.9 2.7 15.5 .. .. 7.5 15.2 15.5 2.4 .. 8.0 4.7 10.4 3.1 3.3 6.6 4.0 2.5 6.0 –7.7 –0.2 9.4 16.0 9.8 0.3 .. ..

2000–09

10.7 12.3 8.7 2.4 1.0 1.9 3.7 0.3 0.3 2.4 4.2 .. 5.1 4.3 12.4 .. 4.6 .. 9.9 .. 12.9 14.7 –6.3 4.5 .. .. 2.8 5.7 5.8 2.0 10.4 3.2 2.7 .. 4.5 0.1 12.5 6.7 7.3 –1.5 4.6 3.0 9.1 –2.6 3.2 0.2 –1.3 7.0 1.5 –3.5 14.5 8.1 2.6 11.8 –2.0 .. 4.8

1990–2000

11.6 9.0 2.9 .. .. 11.3 8.9 4.2 .. 5.3 3.8 .. 7.4 .. 10.0 .. .. .. .. .. .. 3.1 .. 0.0 .. .. 4.5 –2.4 10.6 6.4 4.2 3.4 13.2 .. .. 7.2 1.0 13.8 7.7 .. 8.4 6.0 9.3 –2.1 2.5 7.8 .. 2.4 7.8 .. 5.4 10.6 11.3 19.0 0.5 .. ..

2000–09

8.0 18.4 5.7 10.8 6.8 0.9 1.8 0.6 1.2 1.7 6.8 .. 8.6 –3.1 7.2 .. 9.8 .. 7.7 .. 4.0 7.7 5.4 16.6 .. .. 10.6 8.0 5.2 8.7 11.9 6.6 3.5 .. 12.0 8.6 8.7 –1.0 11.0 2.9 4.5 5.8 5.7 10.0 14.6 5.7 11.8 8.0 9.7 7.0 15.5 9.7 0.6 9.4 –1.4 .. 25.6

1990–2000

10.1 5.3 8.1 1.2 118.9 13.8 10.0 4.6 2.2 2.1 6.6 .. 6.3 –8.5 10.1 .. 16.5 .. 15.4 11.8 4.6 12.4 –14.5 –2.6 .. .. 8.5 0.9 12.2 6.3 –1.9 2.2 16.1 .. 0.7 7.2 10.2 14.4 0.9 11.0 5.7 4.3 10.3 0.0 2.9 5.7 5.7 4.3 9.4 3.7 1.7 8.9 18.8 9.5 –3.0 .. 10.1

6.2

GLOBAL LINKS

Growth of merchandise trade

Net barter terms of trade index

2000 = 100

2000–09

1990–2000

2000–09

1995

2009

16.2 20.3 10.9 18.0 17.2 5.4 8.7 9.3 6.0 3.2 16.3 .. 12.6 10.9 12.7 .. 20.5 .. 18.3 .. 21.7 15.0 –0.4 21.2 .. .. 6.0 10.9 9.6 15.4 24.0 3.1 7.0 .. 21.3 10.3 22.4 17.2 14.9 4.0 11.5 9.6 12.2 15.9 19.7 12.8 14.7 10.0 3.4 13.8 19.0 21.6 3.0 21.8 4.0 .. 21.4

11.8 7.9 2.7 –4.8 70.3 10.9 8.2 3.2 6.9 5.2 5.1 .. 6.0 1.0 7.1 .. 5.5 .. 12.7 .. 8.7 2.0 2.6 –1.4 .. .. 6.4 –0.6 9.5 4.7 –1.6 3.3 14.2 .. 0.5 5.5 1.1 22.6 3.9 9.3 5.5 5.9 11.6 0.8 3.1 4.4 6.1 3.1 8.7 –0.8 6.7 12.7 12.5 17.0 –2.5 .. 7.4

14.3 25.3 15.6 18.4 13.7 5.4 7.1 10.2 9.2 8.0 17.2 .. 17.7 6.3 13.0 .. 14.4 .. 14.5 .. 11.9 12.4 9.9 23.9 .. .. 17.6 15.4 8.7 17.1 18.7 9.5 6.8 .. 20.6 16.3 16.3 6.5 15.5 12.4 11.1 10.5 11.0 18.6 21.7 12.5 18.1 18.3 13.6 15.8 19.4 17.3 5.6 18.4 4.5 .. 30.9

104.3 108.0 90.4 .. .. 103.9 92.1 96.6 .. 114.9 115.6 .. 103.9 .. 138.5 .. .. .. .. .. .. 100.0 .. .. .. .. 79.6 105.7 108.6 109.6 102.2 88.5 92.5 .. .. 89.1 151.1 214.3 82.6 .. 97.6 99.0 128.9 121.4 55.6 60.3 .. 119.2 100.0 .. 118.3 123.4 80.2 102.4 104.7 .. ..

95.6 99.4 63.2 132.4 140.8 96.6 102.7 103.3 77.1 74.4 120.4 .. 94.7 83.9 68.6 .. 156.1 .. 103.9 .. 109.1 78.3 111.4 140.4 .. .. 75.5 94.2 99.7 165.4 150.9 81.3 104.0 .. 170.2 137.4 98.2 117.1 113.5 80.7 102.5 111.0 83.9 185.2 145.3 128.6 150.1 63.4 92.4 164.1 104.9 129.1 72.0 107.1 107.6 .. 173.1

2011 World Development Indicators

329

6.2

Growth of merchandise trade Export volume

Import volume

Export value

Import value

average annual % growth

average annual % growth

average annual % growth

average annual % growth

1990–2000

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone a Singapore Slovak Republic Slovenia Somaliaa South Africa Spaina Sri Lanka Sudan Swaziland Swedena Switzerlanda Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom a United Statesa Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe

2000–09

.. .. –8.0 2.9 10.6 .. .. 11.7 .. .. .. 4.5 11.4 7.4 12.6 4.0 8.9 3.7 2.2 .. 6.0 9.6 .. 9.1 .. 5.7 10.7 .. 17.8 .. .. 6.3 6.6 6.1 .. 5.2 .. .. .. 6.1 8.8

.. .. 3.6 0.5 0.8 .. 28.7 10.9 .. .. 0.4 0.5 3.1 3.1 8.3 2.5 3.5 3.7 0.4 .. 6.4 7.4 .. 3.2 2.8 7.7 11.5 .. 15.7 .. 7.8 1.1 4.0 8.1 .. –1.9 11.8 .. –4.7 8.9 –5.1

1990–2000

.. .. 0.8 .. 4.9 .. .. 8.3 .. .. .. 7.6 9.3 8.0 8.4 3.1 6.4 4.2 .. .. –2.0 2.6 .. 6.0 .. 4.3 11.1 .. 22.4 .. .. 6.5 9.1 10.5 .. 4.8 .. .. 4.4 2.9 8.0

2011 World Development Indicators

2000 = 100

2000–09

1990–2000

2000–09

1990–2000

2000–09

1995

2009

.. .. 15.2 11.4 7.1 .. 3.1 8.0 .. .. 5.4 6.6 4.7 1.7 17.9 4.3 4.4 2.5 12.5 .. 11.8 7.8 .. –1.7 2.8 5.0 9.8 .. 8.8 .. 15.9 3.2 2.9 5.9 .. 12.3 12.8 .. 9.5 15.1 –2.1

.. .. –4.0 3.1 4.0 .. .. 9.9 .. .. 2.3 2.5 8.6 11.3 14.0 5.9 7.4 4.4 0.9 .. 6.4 10.5 .. 6.6 6.8 6.0 9.1 .. 15.6 .. 6.6 6.2 7.2 5.2 .. 5.4 22.7 .. 20.6 –4.6 3.4

.. .. 17.8 17.6 9.4 .. 35.2 12.6 .. .. 8.3 12.8 10.6 6.3 24.7 8.9 9.7 5.7 14.1 .. 17.0 12.6 .. 9.9 17.9 13.4 19.3 .. 25.9 .. 20.9 6.2 6.6 14.3 .. 13.9 19.7 .. 9.9 25.7 2.8

.. .. –1.7 0.8 3.6 .. .. 7.8 .. .. 4.5 5.9 6.2 8.9 9.8 5.0 5.4 3.6 3.6 .. 0.1 5.0 .. 5.5 12.1 5.2 10.3 .. 21.0 .. 10.7 6.5 9.5 10.1 .. 5.2 22.7 .. 0.6 1.3 1.9

.. .. 22.7 17.0 16.5 .. 14.3 12.0 .. .. 13.0 15.7 11.8 9.1 23.5 10.2 12.1 4.4 20.5 .. 20.5 13.0 .. 14.8 12.1 11.7 18.4 .. 16.0 .. 21.6 7.9 6.5 13.3 .. 15.7 21.2 .. 18.4 21.8 7.4

.. .. 110.1 .. 156.3 .. .. 104.4 .. .. .. 106.0 104.3 99.0 100.0 100.0 110.2 96.4 .. .. 98.0 116.0 .. 99.1 .. 95.8 105.7 .. 197.2 .. .. 100.1 103.3 116.2 .. 63.4 .. .. .. 189.7 96.8

.. .. 155.3 175.6 99.2 .. 64.6 82.6 .. .. 101.3 135.0 107.2 78.5 152.5 112.8 89.6 106.6 148.3 .. 121.1 97.1 .. 28.6 131.0 94.3 95.0 .. 120.4 .. 134.7 104.0 99.0 98.5 .. 187.1 97.4 .. 126.6 155.9 90.9

a. Data are from the International Monetary Fund’s International Financial Statistics database. b. Data are from national sources.

330

Net barter terms of trade index

About the data

6.2

GLOBAL LINKS

Growth of merchandise trade Definitions

Data on international trade in goods are available

from national and international sources such as the

• Export and import volumes are indexes of the

from each country’s balance of payments and

IMF’s International Financial Statistics database,

quantity of goods traded. They are derived from

customs records. While the balance of payments

the United Nations Economic Commission for Latin

UNCTAD’s volume index series and are the ratio of

focuses on the financial transactions that accom-

America and the Caribbean, the U.S. Bureau of Labor

the export or import value indexes to the correspond-

pany trade, customs data record the direction of

Statistics, Japan Customs and Bank of Japan, and

ing unit value indexes. Unit value indexes are based

trade and the physical quantities and value of goods

UNCTAD’s Commodity Price Statistics. The IMF also

on data reported by countries that demonstrate

entering or leaving the customs area. Customs data

compiles data on trade prices and volumes in its

consistency under UNCTAD quality controls, supple-

may differ from data recorded in the balance of pay-

International Financial Statistics (IFS) database.

mented by UNCTAD’s estimates using the previous

ments because of differences in valuation and time

Unless otherwise noted, the growth rates and

year’s trade values at the Standard International

of recording. The 1993 United Nations System of

terms of trade in the table were calculated from

Trade Classifi cation three-digit level as weights.

National Accounts and the fifth edition of the Inter-

index numbers compiled by UNCTAD. The growth

To improve data coverage, especially for the latest

national Monetary Fund’s (IMF) Balance of Payments

rates and terms of trade for selected economies

periods, UNCTAD constructs a set of average prices

Manual (1993) attempted to reconcile definitions and

were calculated from index numbers compiled in

indexes at the three-digit product classification of the

reporting standards for international trade statistics,

the IMF’s International Financial Statistics. In some

Standard International Trade Classification revision 3

but differences in sources, timing, and national prac-

cases price and volume indexes from different

using UNCTAD’s Commodity Price Statistics, interna-

tices limit comparability. Real growth rates derived

sources vary significantly as a result of differences

tional and national sources, and UNCTAD secretariat

from trade volume indexes and terms of trade based

in estimation procedures. Because the IMF does not

estimates and calculates unit value indexes at the

on unit price indexes may therefore differ from those

publish trade value indexes, for selected economies

country level using the current year’s trade values as

derived from national accounts aggregates.

the trade value indexes were derived from the vol-

weights. For economies for which UNCTAD does not

ume and price indexes. All indexes are rescaled to

publish data, the export and import volume indexes

a 2000 base year.

(lines 72 and 73) in the IMF’s International Financial

Trade in goods, or merchandise trade, includes all goods that add to or subtract from an economy’s material resources. Trade data are collected on the

The terms of trade measures the relative prices of

Statistics are used to calculate the average annual

basis of a country’s customs area, which in most

a country’s exports and imports. There are several

growth rates. • Export and import values are the cur-

cases is the same as its geographic area. Goods

ways to calculate it. The most common is the net

rent value of exports (free on board, f.o.b.) or imports

provided as part of foreign aid are included, but

barter (or commodity) terms of trade index, or the

(cost, insurance, and freight, c.i.f.), converted to U.S.

goods destined for extraterritorial agencies (such

ratio of the export price index to the import price

dollars and expressed as a percentage of the aver-

as embassies) are not.

index. When a country’s net barter terms of trade

age for the base period (2000). UNCTAD’s export or

index increases, its exports become more valuable

import value indexes are reported for most econo-

or its imports cheaper.

mies. For selected economies for which UNCTAD

Collecting and tabulating trade statistics are difficult. Some developing countries lack the capacity to report timely data, especially landlocked coun-

does not publish data, the value indexes are derived

tries and countries whose territorial boundaries are

from export or import volume indexes (lines 72 and

porous. Their trade has to be estimated from the data

73) and corresponding unit value indexes of exports

reported by their partners. (For further discussion of

or imports (lines 74 and 75) in the IMF’s International

the use of partner country reports, see About the

Financial Statistics. • Net barter terms of trade index

data for table 6.3.) Countries that belong to common

is calculated as the percentage ratio of the export

customs unions may need to collect data through

unit value indexes to the import unit value indexes,

direct inquiry of companies. Economic or political

measured relative to the base year 2000.

concerns may lead some national authorities to suppress or misrepresent data on certain trade flows, such as oil, military equipment, or the exports of a dominant producer. In other cases reported trade data may be distorted by deliberate under- or overinvoicing to affect capital transfers or avoid taxes. And in some regions smuggling and black market trading result in unreported trade flows. By international agreement customs data are reported to the United Nations Statistics Division,

Data sources

which maintains the Commodity Trade (Comtrade)

Data on trade indexes are from UNCTAD’s annual

and Monthly Bulletin of Statistics databases. The

Handbook of Statistics for most economies and

United Nations Conference on Trade and Develop-

from the IMF’s International Financial Statistics for

ment (UNCTAD) compiles international trade sta-

selected economies.

tistics, including price, value, and volume indexes,

2011 World Development Indicators

331

6.3

Direction and growth of merchandise trade

Direction of trade High-income importers

% of world trade, 2009

Source of exports High-income economies European Union Japan United States Other high-income economies Low- and middle-income economies East Asia & Pacific China Europe & Central Asia Russian Federation Latin America & Caribbean Brazil Middle East & N. Africa Algeria South Asia India Sub-Saharan Africa South Africa World

European Union

Japan

United States

Other highincome

Total

28.5 21.9 0.6 1.8 4.2 6.5 2.3 1.8 1.9 0.9 0.7 0.3 0.8 0.2 0.4 0.3 0.4 0.1 35.0

2.3 0.4 .. 0.4 1.5 1.6 1.3 0.8 0.1 0.1 0.1 0.0 0.1 0.0 0.0 0.0 0.1 0.0 4.0

6.5 2.3 0.8 .. 3.4 5.4 2.3 1.8 0.1 0.1 2.2 0.1 0.2 0.1 0.2 0.1 0.3 0.0 11.8

13.1 4.6 1.5 3.1 3.8 6.2 4.2 2.9 0.6 0.3 0.5 0.2 0.2 0.0 0.5 0.4 0.2 0.1 19.3

50.4 29.2 2.8 5.3 13.0 19.7 10.2 7.3 2.7 1.3 3.5 0.6 1.3 0.3 1.1 0.9 1.0 0.3 70.1

Low- and middle-income importers

% of world trade, 2009

Source of exports High-income economies European Union Japan United States Other high-income economies Low- and middle-income economies East Asia & Pacific China Europe & Central Asia Russian Federation Latin America & Caribbean Brazil Middle East & N. Africa Algeria South Asia India Sub-Saharan Africa South Africa World

332

East Asia & Paciﬁc

Europe & Central Asia

Latin America & Caribbean

Middle East & N. Africa

South Asia

Sub-Saharan Africa

Total

8.2 1.2 1.4 0.8 4.8 2.7 1.7 0.6 0.2 0.2 0.4 0.2 0.2 0.0 0.2 0.2 0.1 0.1 11.7

2.6 2.0 0.0 0.1 0.4 1.6 0.5 0.4 1.0 0.3 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.0 4.3

3.3 0.7 0.2 1.8 0.5 1.7 0.5 0.4 0.0 0.0 1.0 0.3 0.0 0.0 0.0 0.0 0.1 0.0 5.2

1.4 0.9 0.0 0.1 0.4 0.9 0.3 0.2 0.3 0.1 0.1 0.0 0.2 0.0 0.1 0.1 0.0 0.0 2.3

1.5 0.4 0.1 0.2 0.9 1.1 0.5 0.3 0.1 0.0 0.1 0.0 0.2 0.0 0.1 0.1 0.1 0.0 2.5

1.0 0.6 0.1 0.1 0.3 0.8 0.3 0.3 0.0 0.0 0.1 0.0 0.0 0.0 0.1 0.1 0.2 0.1 1.8

18.2 5.7 1.8 3.2 7.5 9.2 3.9 2.4 1.6 0.6 1.8 0.6 0.7 0.1 0.5 0.4 0.7 0.2 27.5

2011 World Development Indicators

6.3

GLOBAL LINKS

Direction and growth of merchandise trade Nominal growth of trade High-income importers

average annual % growth, 1999–2009

Source of exports High-income economies European Union Japan United States Other high-income economies Low- and middle-income economies East Asia & Pacific China Europe & Central Asia Russian Federation Latin America & Caribbean Brazil Middle East & N. Africa Algeria South Asia India Sub-Saharan Africa South Africaa World

European Union

Japan

United States

Other highincome

Total

9.1 9.3 2.8 5.4 11.3 16.7 18.9 26.4 19.8 20.3 12.5 12.6 13.3 13.8 14.5 16.5 11.7 10.4 10.2

6.6 4.5 .. 0.1 9.9 11.3 10.7 13.3 17.1 16.9 11.5 10.5 14.0 22.9 6.7 8.1 19.5 22.0 8.3

3.8 5.8 –0.6 .. 3.9 11.0 15.2 21.8 7.8 4.6 7.1 7.3 19.9 26.4 8.6 10.8 18.2 14.8 6.4

9.1 10.8 6.9 5.5 11.8 17.4 17.3 22.7 18.5 17.5 15.7 19.8 17.1 25.1 20.5 22.9 14.3 14.3 11.1

8.1 9.1 3.4 5.0 8.6 14.6 16.1 21.9 18.6 18.3 9.0 12.0 14.9 17.5 14.6 17.2 14.6 13.3 9.6

Low- and middle-income importers

average annual % growth, 1999–2009

Source of exports High-income economies European Union Japan United States Other high-income economies Low- and middle-income economies East Asia & Pacific China Europe & Central Asia Russian Federation Latin America & Caribbean Brazil Middle East & N. Africa Algeria South Asia India Sub-Saharan Africa South Africaa World

East Asia & Paciﬁc

Europe & Central Asia

Latin America & Caribbean

Middle East & N. Africa

South Asia

Sub-Saharan Africa

Total

15.4 15.8 12.6 12.0 17.0 22.6 21.4 27.4 18.7 19.0 30.9 32.2 25.0 42.4 26.1 27.9 20.5 28.0 17.3

19.4 19.0 27.1 13.5 22.7 22.9 37.9 40.9 20.1 19.3 20.3 21.9 17.8 12.5 15.9 14.2 24.7 18.7 20.8

8.0 8.8 8.9 6.5 12.6 17.6 26.6 31.3 20.4 21.0 14.5 16.6 15.2 8.4 21.8 24.2 21.8 12.0 10.7

13.6 11.8 11.9 12.1 19.2 23.2 25.5 30.5 22.9 21.5 17.4 20.8 24.9 21.6 23.7 26.8 13.8 20.4 16.4

19.6 15.9 12.1 20.3 22.5 25.2 27.2 35.3 23.2 19.8 25.1 20.3 34.1 60.7 19.9 20.1 15.8 20.7 21.2

13.0 12.0 10.8 13.1 15.6 21.7 26.9 31.7 22.1 14.6 24.1 26.8 25.0 10.1 24.6 25.8 15.7 12.9 16.0

14.2 14.7 12.4 8.8 17.5 22.1 25.2 32.2 20.7 19.6 17.9 20.6 24.7 17.2 22.7 24.2 21.0 17.0 16.3

a. Data for 1999 are based on imports from South Africa reported by other economies because data on exports for South Africa were not available.

2011 World Development Indicators

333

6.3

Direction and growth of merchandise trade

About the data

Definitions

The table provides estimates of the flow of trade in

Most countries report their trade data in national

• Merchandise trade includes all trade in goods;

goods between groups of economies. The data are

currencies, which are converted into U.S. dollars

trade in services is excluded. • High-income econo-

from the International Monetary Fund’s (IMF) Direc-

using the IMF’s published period average exchange

mies are those classified as such by the World Bank

tion of Trade database. All high-income economies

rate (series rf or rh, monthly averages of the mar-

(see front cover flap). • European Union is defined

and major developing economies report trade on

ket or official rates) for the reporting country or, if

as all high-income EU members: Austria, Belgium,

a timely basis, covering about 85 percent of trade

unavailable, monthly average rates in New York.

Cyprus, Czech Republic, Denmark, Estonia, Finland,

for recent years. Trade by less timely reporters and

Because imports are reported at cost, insurance,

France, Germany, Greece, Hungary, Ireland, Italy, Lux-

by countries that do not report is estimated using

and freight (c.i.f.) valuations, and exports at free on

embourg, Malta, the Netherlands, Portugal, Slovak

reports of trading partner countries. Because the

board (f.o.b.) valuations, the IMF adjusts country

Republic, Slovenia, Spain, Sweden, and the United

largest exporting and importing countries are reli-

reports of import values by dividing them by 1.10

Kingdom. • Other high-income economies include

able reporters, a large portion of the missing trade

to estimate equivalent export values. The accuracy

all high-income economies (both Organisation for

flows can be estimated from partner reports. Part-

of this approximation depends on the set of part-

Economic Co-operation and Development members

ner country data may introduce discrepancies due to

ners and the items traded. Other factors affecting

and others) except the high-income European Union,

smuggling, confidentiality, different exchange rates,

the accuracy of trade data include lags in reporting,

Japan, and the United States. • Low- and middle-

overreporting of transit trade, inclusion or exclusion

recording differences across countries, and whether

income regional groupings are based on World

of freight rates, and different points of valuation and

the country reports trade according to the general or

Bank classifications (see back cover flap for regional

times of recording.

special system of trade. (For further discussion of

groupings) and may differ from those used by other

the measurement of exports and imports, see About

organizations.

In addition, estimates of trade within the European Union (EU) have been significantly affected by

the data for tables 4.4 and 4.5.)

changes in reporting methods following the creation

The regional trade flows in the table are calculated

of a customs union. The current system for collect-

from current price values. The growth rates are in

ing data on trade between EU members—Intrastat,

nominal terms; that is, they include the effects of

introduced in 1993—has less exhaustive coverage

changes in both volumes and prices.

than the previous customs–based system and has resulted in some problems of asymmetry (estimated imports are about 5 percent less than exports). Despite these issues, only a small portion of world trade is estimated to be omitted from the IMF’s Direction of Trade Statistics Yearbook and Direction of Trade database.

More than half of the world’s merchandise trade takes place between high-income economies. But low- and middle-income economies’ participation in the global trade has increased in the past 15 years

1996

Low- and middle-income to low- and middle-income 4.5%

Unspecified 3.0%

6.3a

2009

Low- and middle-income to low- and middle-income 9.2%

Unspecified 2.5%

Low- and middle-income to high-income 14.1%

High-income to low- and middleincome 16.0%

High-income to high-income 62.4%

Low- and middle-income to high-income 19.7%

High-income to high-income 50.4%

High-income to low- and middleincome 18.2%

Data sources Data on the direction and growth of merchandise trade were calculated using the IMF’s Direction of Trade database. Regional and income group

Trade among low- and middle-income economies accounted for about 9.2 percent of the world’s merchandise trade in 2009, compared with 4.5 percent in 1996. The share of trade from low- and middleincome economies to high-income economies increased 9.8 percentage points between 1996 and 2009. Source: World Bank staff calculations based on data from the International Monetary Fund’s Direction of Trade database.

334

2011 World Development Indicators

classifications are according to the World Bank classification of economies as of July 1, 2010, and are as shown on the cover flaps of this report.

6.4

GLOBAL LINKS

High-income economy trade with low- and middle-income economies Exports to low-income economies High-income economies

Total ($ billions) % of total exports Food Cereals Agricultural raw materials Ores and nonferrous metals Fuels Crude petroleum Petroleum products Manufactured goods Chemical products Iron and steel Machinery and transport equipment Furniture Textiles Footwear Other Miscellaneous goods

European Union

Japan

United States

1999

2009

1999

2009

1999

2009

1999

2009

32.0

86.9

15.7

40.2

3.5

6.1

3.4

12.0

12.5 4.0 2.5 1.0 4.9 0.1 4.4 77.1 12.3 2.6 44.2 0.4 5.9 0.2 11.6 2.0

10.5 4.1 2.1 1.4 11.5 0.4 10.7 67.3 11.0 2.9 42.0 0.3 2.2 0.1 9.0 7.2

14.2 3.2 1.8 0.9 3.1 0.1 2.7 78.4 15.2 2.3 43.6 0.6 2.5 0.2 14.0 1.5

9.8 3.0 1.5 1.3 15.6 0.0 15.3 68.0 12.0 2.2 40.4 0.4 1.7 0.2 11.1 3.8

0.4 0.2 1.2 0.6 0.3 0.0 0.3 96.4 3.4 6.9 74.2 0.1 3.0 0.0 8.8 1.2

0.3 0.2 2.3 0.7 0.3 0.0 0.1 94.4 3.1 8.1 74.5 0.1 1.2 0.0 7.4 2.1

25.2 17.4 4.8 0.6 1.8 0.0 1.2 62.0 10.6 0.8 37.9 0.3 5.2 0.2 6.8 5.5

17.2 12.6 4.6 1.5 5.9 0.0 5.1 58.7 7.3 1.2 41.9 0.2 1.0 0.2 6.9 12.2

40.2

100.4

20.1

47.7

2.1

2.9

11.8

34.5

23.0 0.7 5.5 5.1 23.3 21.2 1.7 41.5 0.6 0.5 1.9 0.2 30.1 0.4 7.9 1.7

15.1 0.7 2.4 5.1 39.5 34.1 1.6 33.2 0.8 0.1 1.6 0.2 27.4 0.7 2.4 4.8

31.9 0.3 6.8 5.6 13.3 12.5 0.5 41.2 0.9 0.4 2.4 0.2 25.5 0.5 11.3 1.2

22.0 0.3 3.6 5.0 29.6 23.5 0.2 38.9 1.2 0.1 2.9 0.1 31.0 0.9 2.7 0.9

37.1 0.0 9.9 17.1 8.9 7.6 0.1 24.0 0.3 2.3 1.6 0.1 15.0 1.7 2.9 3.0

23.7 0.0 2.7 23.3 23.0 5.3 5.0 26.4 0.6 0.8 0.8 0.1 14.3 7.6 2.1 1.0

7.5 0.1 1.1 2.1 41.4 36.3 4.6 47.6 0.1 0.4 0.3 0.2 42.3 0.0 4.4 0.4

4.3 0.1 0.6 0.6 64.3 61.2 2.8 29.5 0.2 0.0 0.1 0.3 28.0 0.1 1.0 0.7

3.0 9.1 2.6 0.7 1.3 2.0 1.2 6.3 2.1 1.1 0.2 0.1 0.0 3.6 6.4 0.7 0.0

1.3 2.6 5.3 0.1 0.0 0.5 0.0 1.1 1.3 0.2 0.0 0.0 0.0 1.6 4.4 1.1 0.0

5.2 3.8 2.7 0.4 0.1 0.4 0.3 0.9 5.9 0.3 0.4 0.2 0.6 10.7 12.5 1.0 0.4

Imports from low-income economies Total ($ billions) % of total imports Food Cereals Agricultural raw materials Ores and nonferrous metals Fuels Crude petroleum Petroleum products Manufactured goods Chemical products Iron and steel Machinery and transport equipment Furniture Textiles Footwear Other Miscellaneous goods

Simple applied tariff rates on imports from low-income economies (%)a Average Food Cereals Agricultural raw materials Ores and nonferrous metals Fuels Crude petroleum Petroleum products Manufactured goods Chemical products Iron and steel Machinery and transport equipment Furniture Textiles Footwear Other Miscellaneous goods

4.3 6.8 16.9 3.3 1.2 3.1 1.3 4.5 4.1 2.7 4.2 1.7 3.2 7.5 7.2 2.1 0.7

2.7 3.0 5.8 1.6 1.1 1.2 0.5 1.6 2.8 2.1 2.4 1.3 2.2 4.5 4.4 1.6 0.8

1.3 3.1 24.0 0.1 0.2 0.2 0.0 0.4 1.1 1.2 0.8 0.4 0.1 2.5 2.9 0.6 0.2

0.8 0.7 0.1 0.1 0.2 0.0 0.0 0.0 0.9 0.3 0.2 0.2 0.1 2.4 1.7 0.2 0.2

2011 World Development Indicators

3.5 2.3 0.4 0.2 0.1 0.2 0.0 0.4 4.0 0.1 0.0 0.1 0.9 7.3 8.7 0.7 0.0

335

6.4

High-income economy trade with low- and middle-income economies

Exports to middle-income economies High-income economies

Total ($ billions) % of total exports Food Cereals Agricultural raw materials Ores and nonferrous metals Fuels Crude petroleum Petroleum products Manufactured goods Chemical products Iron and steel Machinery and transport equipment Furniture Textiles Footwear Other Miscellaneous goods

European Union

Japan

United States

1999

2009

1999

2009

1999

2009

1999

2009

646.4

1845.4

224.9

700.5

89.0

222.8

184.4

346.7

6.7 1.7 1.8 2.0 3.1 0.5 1.9 83.8 11.7 2.5 48.8 0.5 6.2 0.1 14.1 2.6

6.5 1.4 1.9 4.5 6.2 0.5 4.4 75.2 14.0 3.4 41.8 0.4 2.6 0.2 13.3 5.7

8.1 1.5 1.3 1.5 1.7 0.2 1.3 85.7 13.7 2.4 46.3 0.9 5.4 0.3 16.7 1.6

6.1 1.1 1.5 2.7 3.2 0.1 2.7 82.5 14.6 3.3 45.4 0.7 3.3 0.4 14.8 4.0

0.4 0.1 1.0 1.9 0.5 0.0 0.4 93.3 8.2 5.9 63.9 0.1 3.6 0.0 11.6 2.9

0.4 0.0 1.1 3.8 1.6 0.0 1.4 88.2 9.9 6.8 58.0 0.3 1.7 0.0 11.6 4.8

8.2 3.0 2.1 1.5 2.1 0.0 1.5 81.6 10.5 1.0 50.0 0.7 5.7 0.1 13.7 4.4

12.8 2.8 3.6 3.9 7.1 0.0 5.4 63.4 14.4 1.5 33.2 0.3 1.9 0.0 12.1 9.3

1,010.3

2,816.6

285.1

998.3

106.5

243.4

364.4

796.0

10.0 0.4 2.3 4.8 13.3 9.0 1.9 67.8 2.9 1.9 29.2 1.7 13.9 2.6 15.7 1.8

7.6 0.5 1.1 3.7 19.8 12.7 3.6 64.0 3.6 1.7 31.8 1.8 9.5 1.7 14.2 3.8

14.2 0.3 3.4 6.3 18.8 13.0 2.5 56.4 3.7 2.1 18.6 1.4 15.3 2.1 13.3 0.9

9.4 0.4 1.3 3.3 25.6 16.2 3.8 56.9 3.7 1.8 24.6 1.6 11.0 2.0 12.3 3.4

15.6 0.4 4.3 8.8 13.8 6.6 1.1 56.2 2.7 1.0 21.6 1.5 14.9 1.7 12.7 1.3

9.2 0.3 2.0 8.7 16.6 7.0 1.8 62.0 4.1 1.0 27.8 1.7 12.0 1.4 14.0 1.6

6.5 0.2 1.2 2.7 11.3 8.9 2.1 75.5 2.0 1.6 36.9 2.4 12.6 3.2 16.8 2.8

5.9 0.3 0.7 1.8 19.1 15.8 2.9 69.6 2.9 1.0 36.2 2.7 9.4 2.1 15.3 2.8

1.1 2.9 0.7 0.4 0.5 0.1 0.0 0.1 1.0 0.6 0.1 0.2 0.0 3.3 3.4 0.3 0.5

2.9 13.5 10.0 0.9 0.1 1.3 1.2 4.2 1.6 0.6 0.1 0.0 0.0 4.2 19.7 0.4 0.0

2.2 6.9 10.5 0.5 0.0 0.2 0.0 0.6 1.8 0.3 0.2 0.0 0.1 4.9 16.9 0.7 0.0

3.4 3.6 2.3 0.5 0.3 0.6 0.5 1.7 3.6 1.2 2.0 0.4 0.3 10.3 13.3 0.9 0.5

2.5 2.9 1.1 0.4 0.4 1.3 0.0 3.0 2.5 1.1 0.3 0.5 0.4 6.8 8.0 0.8 0.3

Imports from middle-income economies Total ($ billions) % of total imports Food Cereals Agricultural raw materials Ores and nonferrous metals Fuels Crude petroleum Petroleum products Manufactured goods Chemical products Iron and steel Machinery and transport equipment Furniture Textiles Footwear Other Miscellaneous goods

Simple applied tariff rates on imports from middle-income economies (%)a Average Food Cereals Agricultural raw materials Ores and nonferrous metals Fuels Crude petroleum Petroleum products Manufactured goods Chemical products Iron and steel Machinery and transport equipment Furniture Textiles Footwear Other Miscellaneous goods

5.6 10.3 15.2 2.5 1.9 2.8 1.5 5.5 5.2 3.6 3.6 3.0 5.0 9.7 11.6 3.8 1.7

a. Includes ad valorem equivalents of specific rates.

336

2011 World Development Indicators

3.2 4.3 6.7 1.9 1.3 1.5 0.4 2.1 3.1 2.0 1.6 1.9 3.2 6.0 6.4 2.3 0.9

3.9 9.7 22.1 1.0 1.4 0.9 0.0 2.9 3.4 3.3 2.3 1.6 0.7 7.6 8.6 2.3 1.2

About the data

6.4

GLOBAL LINKS

High-income economy trade with low- and middle-income economies Definitions

Developing economies are becoming increasingly

trade between developing economies has grown

The product groups in the table are defined in accor-

important in the global trading system. Since the

substantially over the past decade, a result of their

dance with SITC revision 2: food (0, 1, 22, and 4) and

early 1990s trade between high-income economies

increasing share of world output and liberalization of

cereals (04); agricultural raw materials (2 excluding

and low- and middle-income economies has grown

trade, among other influences.

22, 27, and 28); ores and nonferrous metals (27, 28,

faster than trade among high-income economies.

Yet trade barriers remain high. The table includes

and 68); fuels (3), crude petroleum (crude petroleum

The increased trade benefi ts consumers and pro-

information about tariff rates by selected product

oils and oils obtained from bituminous minerals;

ducers. But as was apparent at the World Trade Orga-

groups. Applied tariff rates are the tariffs in effect

333), and petroleum products (noncrude petroleum

nization’s (WTO) Ministerial Conferences in Doha,

for partners in preferential trade agreements such

and preparations; 334); manufactured goods (5–8

Qatar, in October 2001; Cancun, Mexico, in Septem-

as the North American Free Trade Agreement. When

excluding 68), chemical products (5), iron and steel

ber 2003; and Hong Kong SAR, China, in December

these rates are unavailable, most favored nation

(67), machinery and transport equipment (7), furni-

2005, achieving a more pro-development outcome

rates are used. The difference between most favored

ture (82), textiles (65 and 84), footwear (85), and

from trade remains a challenge. Doing so will require

nation and applied rates can be substantial. Simple

other manufactured goods (6 and 8 excluding 65,

strengthening international consultation. After the

averages of applied rates are shown because they

67, 68, 82, 84, and 85); and miscellaneous goods

Doha meetings negotiations were launched on ser-

are generally a better indicator of tariff protection

(9). • Exports are all merchandise exports by high-

vices, agriculture, manufactures, WTO rules, the

than weighted average rates are.

income economies to low-income and middle-income

environment, dispute settlement, intellectual prop-

The data on trade flows are from the United Nations

economies as recorded in the United Nations Sta-

erty rights protection, and disciplines on regional

Statistics Division’s Commodity Trade (Comtrade)

tistics Division’s Comtrade database. Exports are

integration. At the most recent negotiations in Hong

database. Partner country reports by high-income

recorded free on board (f.o.b.). • Imports are all

Kong SAR, China, trade ministers agreed to eliminate

economies were used for both exports and imports.

merchandise imports by high-income economies

subsidies of agricultural exports by 2013; to abolish

Because of differences in sources of data, timing,

from low–income and middle-income economies as

cotton export subsidies and grant unlimited export

and treatment of missing data, the numbers in the

recorded in the United Nations Statistics Division’s

access to selected cotton-growing countries in Sub-

table may not be fully comparable with those used

Commodity Trade (Comtrade) database. Imports

Saharan Africa; to cut more domestic farm supports

to calculate the direction of trade statistics in tables

include insurance and freight charges (c.i.f.). • High-,

in the European Union, Japan, and the United States;

6.3 and 6.5 or the aggregate flows in tables 4.4, 4.5,

middle-, and low-income economies are those

and to offer more aid to developing countries to help

and 6.2. Tariff data are from United Nations Confer-

classified as such by the World Bank as of July 1,

them compete in global trade.

ence on Trade and Development (UNCTAD)’s Trade

2010 (see front cover flap). • European Union is

Trade flows between high-income and low- and

Analysis and Information System (TRAINS) database.

defined as all high-income EU members: Austria, Bel-

middle-income economies reflect the changing mix of

Tariff line data were matched to Standard Interna-

gium, Cyprus, Czech Republic, Denmark, Estonia,

exports to and imports from developing economies.

tional Trade Classification (SITC) revision 2 codes to

Finland, France, Germany, Greece, Hungary, Ireland,

While food and primary commodities have continued

define commodity groups. For further discussion of

Italy, Luxembourg, Malta, the Netherlands, Portugal,

to fall as a share of high-income economies’ imports,

merchandise trade statistics, see About the data for

Slovak Republic, Slovenia, Spain, Sweden, and the

manufactures as a share of goods imports from both

tables 4.4, 4.5, 6.2, 6.3, and 6.5, and for informa-

United Kingdom.

low- and middle-income economies have grown. And

tion about tariff barriers, see table 6.8.

Low-income economies have a small market share in the global market of various commodities

6.4a

Exports from upper middle-income economies Exports from lower middle-income economies Exports from low-income economies

Share of world exports (percent) 70 60 50 40 30 20 10

Data sources

0 1990 2009

1990 2009

1990 2009

1990 2009

1990 2009

1990 2009

Agricultural products

Manufactured goods

Textiles

Fuels

Clothing

Footwear

Data on trade values are from United Nations Statistics Division’s Comtrade database. Data

Low-income economies specialize in labor-intensive sectors, but their share in the global market of labor intensive products is very small. Lower middle-income economies provided most of the textiles, clothing, and footwear traded globally in 2009. High-income economies accounted for the majority of trade in agricultural products and manufactured goods.

on tariffs are from UNCTAD’s TRAINS database

Source: World Bank staff estimates, based on data from United Nations Statistics Division’s Comtrade database.

at http://wits.worldbank.org.

and are calculated by World Bank staff using the World Integrated Trade Solution system, available

2011 World Development Indicators

337

6.5

Direction of trade of developing economies Exports

Imports

% of total merchandise exports To developing economies Within region 1999 2009

East Asia & Paciﬁc Cambodia China Fiji Indonesia Korea, Dem. Rep. Lao PDR Malaysia Mongolia Myanmar Papua New Guinea Philippines Thailand Vietnam Europe & Central Asia Albania Armenia Azerbaijan Belarus Bosnia and Herzegovina Bulgaria Georgia Kazakhstan Kyrgyz Republic Lithuania Macedonia, FYR Moldova Romania Russian Federation Serbia Tajikistan Turkey Turkmenistan Ukraine Uzbekistan Latin America & Carib. Argentina Bolivia Brazil Chile Colombia Costa Rica Cuba Dominican Republic Ecuador El Salvador Guatemala Haiti Honduras Jamaica Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela, RB

338

8.1 w 7.4 4.2 8.9 11.1 7.5 .. 9.9 .. 20.1 8.0 8.7 13.2 20.7 22.5 w 3.3 24.8 31.0 65.6 5.6 25.8 58.3 29.0 42.7 19.5 29.0 66.4 11.7 20.0 .. 46.1 8.7 52.1 38.5 51.4 14.4 w 45.1 38.4 23.0 20.3 24.1 12.4 8.8 2.8 21.6 60.9 22.2 2.5 7.7 2.9 3.2 31.9 23.6 62.0 16.2 53.0 15.2

11.9 w 3.6 6.6 19.6 22.4 50.8 .. 23.8 .. 57.6 10.1 16.2 27.0 20.2 19.9 w 5.1 35.7 12.4 46.5 5.9 27.4 66.3 26.1 80.0 24.5 31.5 62.3 16.0 14.5 32.3 36.7 13.3 45.2 42.9 69.3 18.8 w 42.3 64.4 22.4 16.3 29.3 26.2 24.9 17.4 42.3 44.0 37.7 9.7 29.0 5.0 6.3 47.7 45.9 69.6 15.5 44.1 12.0

2011 World Development Indicators

Outside region 1999 2009

7.2 w 0.3 8.6 0.1 8.1 42.0 0.6 6.6 14.5 13.4 0.1 1.8 6.0 6.2 11.3 w 0.1 10.5 5.3 11.3 .. 5.7 5.8 14.6 .. 1.3 1.6 1.7 8.1 11.5 .. .. 11.6 .. 21.8 .. 4.0 w 15.3 0.6 10.9 5.3 1.1 0.7 36.0 0.5 5.5 1.5 1.0 0.8 0.1 7.1 0.3 0.1 0.8 0.6 8.6 9.2 ..

15.5 w 1.4 17.7 1.5 14.1 35.7 0.2 10.9 4.1 22.1 1.3 2.5 11.9 6.7 14.2 w 6.9 7.0 12.6 10.6 .. 6.9 4.4 18.1 .. 3.6 1.8 1.9 6.6 12.2 1.9 .. 24.1 .. 26.0 .. 13.9 w 23.6 3.7 28.2 28.7 6.7 12.1 31.6 2.6 6.4 1.2 2.2 2.1 3.0 3.9 1.9 0.6 10.7 14.4 17.9 22.2 10.5

% of total merchandise imports From developing economies

To high-income economies 1999 2009

Within region 1999 2009

Outside region 1999 2009

From high-income economies 1999 2009

83.9 w 60.5 87.2 80.6 80.8 50.5 32.1 83.5 28.4 53.2 63.3 88.2 79.2 72.0 64.0 w 96.6 56.6 62.3 22.8 91.7 66.5 35.6 49.5 47.8 79.1 68.8 32.0 79.7 67.2 .. 50.7 74.9 25.2 39.6 40.2 77.6 w 39.5 59.4 64.3 67.9 73.4 26.9 55.3 96.5 72.2 37.1 74.2 96.9 82.6 89.5 96.1 62.4 73.6 30.3 75.1 36.8 63.2

11.0 w 38.0 6.8 8.0 14.6 34.8 81.8 12.7 19.1 49.2 11.6 12.4 14.7 17.8 27.9 w 12.3 32.4 46.3 66.5 4.0 28.4 55.7 46.9 46.6 25.3 31.2 59.6 12.9 29.7 .. 78.9 11.2 51.9 60.0 33.9 14.3 w 30.4 41.8 19.0 28.1 25.9 20.1 17.0 17.5 32.7 40.0 29.3 15.2 15.1 11.7 2.3 48.6 24.4 54.9 30.4 47.8 18.2

8.9 w 1.8 8.0 2.2 7.9 25.4 1.0 3.1 37.1 1.7 1.7 4.2 7.3 4.9 12.9 w 1.1 13.6 7.6 3.4 .. 7.1 3.4 6.6 10.1 4.0 4.3 1.6 5.4 13.8 .. .. 12.2 .. 5.3 .. 3.5 w 9.4 2.3 9.9 8.6 4.4 3.3 17.5 1.7 4.4 2.1 3.9 3.9 2.8 3.7 3.4 0.4 1.2 3.2 3.3 8.9 0.2

80.5 w 60.0 82.8 89.1 76.8 39.8 16.0 82.8 53.8 49.0 85.8 82.2 76.0 77.0 61.1 w 86.5 50.4 45.7 30.1 95.8 64.0 40.9 46.4 41.7 69.1 64.5 38.9 80.3 56.1 .. 18.6 72.9 35.0 34.5 63.4 78.0 w 58.4 55.7 71.0 49.9 68.7 41.1 65.5 80.7 61.9 56.3 65.7 80.5 72.2 81.4 93.7 45.4 60.7 41.7 66.2 42.8 69.4

73.7 w 96.5 77.9 51.2 64.0 13.7 18.0 65.5 34.3 14.3 50.4 79.7 60.7 69.1 55.7 w 87.7 56.3 77.3 42.9 92.4 64.7 29.4 43.2 9.5 72.8 55.2 34.9 77.9 55.9 57.4 15.1 59.1 36.3 29.8 13.1 66.0 w 32.6 31.5 48.0 51.4 63.1 61.3 43.5 71.1 50.7 58.3 56.7 87.9 68.0 89.9 94.5 50.9 43.0 14.1 81.2 33.5 56.0

15.9 w 54.6 9.1 18.7 26.4 43.2 83.5 28.1 27.0 66.7 24.3 26.2 27.0 37.2 26.1 w 16.8 43.1 46.2 65.5 9.9 33.1 53.8 41.7 22.6 33.9 33.3 51.5 15.5 12.8 20.6 62.2 21.0 42.8 48.1 41.9 19.2 w 40.1 67.2 17.1 29.5 25.9 22.5 43.5 25.1 39.9 41.4 34.6 34.8 44.4 24.5 4.3 53.5 9.2 48.5 33.5 52.6 38.4

18.0 w 2.7 17.1 3.4 10.0 50.3 1.7 6.6 41.1 4.8 1.2 5.1 7.1 7.8 14.7 w 8.3 19.2 14.7 6.3 .. 7.8 9.1 27.7 71.5 4.4 11.2 11.0 8.7 23.4 4.7 .. 23.1 .. 12.0 .. 12.8 w 17.5 4.6 26.8 14.9 15.8 9.6 23.2 7.6 11.7 6.2 8.9 11.4 7.8 7.3 18.5 12.1 15.7 32.7 23.2 20.6 10.9

64.1 w 44.0 65.4 76.0 63.4 6.5 13.5 64.7 32.0 28.4 73.3 68.7 64.3 53.5 54.6 w 72.9 37.7 39.5 26.8 89.2 59.1 37.8 30.7 6.0 62.4 55.6 37.6 77.1 62.2 64.6 15.8 55.6 35.4 40.3 36.9 62.1 w 38.1 28.0 56.1 45.0 55.0 63.5 33.4 63.6 47.3 61.4 55.0 53.7 47.8 66.2 80.7 35.5 68.5 18.4 48.1 28.3 47.3

Exports

Imports

% of total merchandise exports To developing economies Within region 1999 2009

Middle East & N. Africa Algeria Egypt, Arab Rep. Iran, Islamic Rep. Iraq Jordan Lebanon Libya Morocco Syrian Arab Republic Tunisia Yemen, Rep. South Asia Afghanistan Bangladesh India Nepal Pakistan Sri Lanka Sub-Saharan Africa Angola Benin Burkina Faso Burundi Cameroon Central African Republic Chad Comoros Congo, Dem. Rep. Congo, Rep. Cote d’Ivoire Ethiopia Gabon Gambia, The Ghana Guinea Guinea-Bissau Kenya Liberia Madagascar Malawi Mali Mauritania Mauritius Mozambique Niger Nigeria Rwanda Senegal Sierra Leone Somalia South Africa Sudan Tanzania Togo Uganda Zambia Zimbabwe

3.2 w 1.8 7.2 .. 4.1 20.8 17.6 4.1 2.4 8.5 5.7 0.9 4.3 w 46.6 1.9 4.2 29.6 4.5 3.1 13.3 w 1.0 5.9 10.2 2.1 6.9 1.4 5.5 .. 1.1 1.6 24.8 1.7 0.9 18.1 7.8 4.7 1.6 30.7 2.4 5.8 19.4 5.7 11.3 6.7 45.1 39.1 10.6 4.8 25.5 .. 0.7 16.3 10.4 16.7 24.0 3.1 35.6 29.1

8.0 w 3.0 21.1 2.1 2.4 31.2 40.5 3.7 3.3 52.5 11.8 2.7 5.4 w 48.3 2.6 4.5 64.6 12.4 5.7 13.7 w 3.9 30.8 15.5 8.8 12.8 9.2 0.4 .. 21.0 1.3 27.6 5.1 3.2 5.9 10.3 2.6 26.8 34.3 18.3 4.8 19.7 9.2 14.1 14.2 16.5 26.9 10.9 56.5 44.3 9.3 4.2 18.7 1.6 18.1 58.8 46.8 22.8 49.1

Outside region 1999 2009

13.0 w 15.1 11.9 13.7 5.9 35.4 12.2 8.5 10.4 11.9 5.9 62.0 14.7 w 17.6 4.4 17.5 .. 12.0 10.4 13.8 w 8.9 68.7 31.6 .. 8.4 14.6 .. .. 0.6 8.1 16.5 18.4 10.1 4.7 12.1 1.1 .. 16.2 9.3 8.5 9.0 32.3 9.0 0.9 13.2 0.3 27.6 14.7 18.8 .. 30.9 8.5 24.2 24.1 30.5 5.0 12.2 14.5

25.8 w 15.6 22.8 42.1 29.0 22.2 10.5 14.1 22.3 5.7 7.5 73.5 25.1 w 19.3 6.8 27.4 .. 23.8 16.0 27.9 w 47.1 54.7 37.9 12.5 18.1 32.8 .. .. 46.9 33.2 7.7 24.9 24.9 61.7 24.6 24.1 .. 14.8 8.9 11.0 32.6 50.9 46.9 3.5 8.3 0.5 24.8 19.8 11.7 11.0 21.4 21.8 77.0 28.4 32.5 7.3 16.0 20.6

6.5

GLOBAL LINKS

Direction of trade of developing economies

% of total merchandise imports From developing economies

To high-income economies 1999 2009

Within region 1999 2009

Outside region 1999 2009

From high-income economies 1999 2009

78.2 w 83.1 65.6 73.4 90.0 40.8 69.4 87.4 80.7 76.2 84.1 35.8 78.8 w 35.8 78.7 78.2 60.2 81.2 82.4 66.4 w 90.1 25.1 55.8 72.3 84.1 84.0 81.3 93.4 98.0 88.0 58.7 70.5 83.3 77.2 74.0 90.0 16.8 51.9 88.3 74.6 71.2 60.1 78.6 92.4 40.6 60.6 61.2 43.3 49.1 66.9 68.4 60.1 65.2 57.3 41.1 92.0 32.0 54.8

3.5 w 1.5 1.1 .. 12.8 15.3 6.0 11.4 1.7 4.8 4.4 4.0 3.8 w 24.8 13.5 0.9 14.0 2.3 10.1 12.0 w 11.5 24.3 39.5 23.8 20.0 18.0 31.6 .. 50.6 12.9 16.8 2.3 5.7 8.4 23.9 11.1 15.4 9.5 4.6 8.7 67.5 23.7 6.3 13.9 29.9 31.0 3.8 27.8 12.3 11.4 12.9 3.7 4.0 18.8 21.4 49.4 55.8 46.0

12.3 w 17.2 21.7 22.3 37.1 17.1 18.5 9.9 9.0 26.8 7.8 20.7 10.7 w 35.2 16.4 29.6 .. 23.3 14.3 12.7 w 12.7 16.9 5.1 8.1 10.7 7.9 .. .. 5.6 9.0 14.3 19.1 2.6 24.2 15.0 16.3 .. 15.2 1.8 22.1 6.3 5.8 17.2 24.1 7.1 21.1 24.6 6.2 19.6 11.9 61.1 14.4 37.1 23.4 9.7 10.3 4.5 6.6

72.7 w 81.4 69.3 65.6 50.1 65.4 73.9 78.6 79.0 45.9 85.9 72.8 66.8 w 40.0 52.5 69.5 45.1 72.8 61.4 70.3 w 75.8 58.5 51.1 57.8 68.1 57.9 62.4 51.4 41.5 64.3 63.0 70.5 90.9 67.4 60.4 72.5 44.1 74.6 93.5 59.5 25.1 38.6 68.3 62.0 20.2 46.2 71.3 47.3 66.5 72.0 15.0 81.7 58.8 57.7 65.2 39.7 35.3 39.1

61.1 w 81.4 52.9 39.8 68.6 44.6 48.3 82.1 73.1 41.8 77.9 22.8 67.4 w 32.4 76.8 65.3 29.6 61.9 115.8 57.9 w 49.0 14.5 43.1 66.7 68.4 58.0 96.0 66.5 31.9 65.3 84.4 55.1 56.0 32.4 53.2 51.8 2.4 42.9 72.9 76.2 47.3 28.3 37.8 82.4 63.2 72.8 63.1 23.0 37.9 75.7 74.4 60.3 21.3 44.6 8.1 43.2 63.8 30.4

7.4 w 3.2 2.9 0.6 22.9 10.7 14.2 11.9 6.2 17.4 8.6 3.9 3.6 w 30.1 14.3 0.6 52.9 4.2 19.9 11.8 w 5.1 7.2 37.2 24.2 18.6 14.9 19.1 .. 48.1 4.7 26.8 2.7 9.7 16.3 24.5 5.8 18.7 12.2 0.9 8.4 56.4 27.2 5.4 11.4 36.6 17.6 4.7 42.0 15.9 24.5 8.5 7.0 6.5 16.2 16.0 25.2 60.2 73.3

22.1 w 32.9 34.7 38.8 42.5 28.2 26.1 27.9 18.3 36.2 16.0 43.9 15.5 w 24.5 36.1 39.7 .. 31.8 35.5 22.7 w 32.7 58.4 13.8 18.9 26.5 14.3 .. .. 15.2 30.0 26.3 35.1 16.8 53.4 33.0 17.3 .. 33.6 19.0 46.7 16.1 10.9 35.6 46.0 18.8 31.7 27.2 12.4 32.8 32.9 65.0 35.3 48.7 39.5 31.6 23.1 9.7 8.7

59.9 w 64.5 60.9 59.5 34.7 60.9 58.6 60.1 75.8 46.5 74.8 51.3 58.1 w 45.5 43.5 59.4 15.8 63.1 61.2 52.3 w 62.7 34.5 44.5 46.9 55.1 43.8 55.9 46.4 36.7 63.9 53.3 33.8 72.3 30.4 41.8 33.6 36.6 53.4 80.1 37.2 27.7 32.9 49.8 42.6 32.1 51.0 52.0 44.7 74.0 38.2 14.1 58.0 41.3 40.7 50.5 51.8 30.2 14.0

Note: Bilateral trade data are not available for Timor-Leste, Kosovo, West Bank and Gaza, Botswana, Eritrea, Lesotho, Namibia, and Swaziland. Components may not sum to 100 percent because of trade with unspecified partners or with economies not covered by World Bank classification.

2011 World Development Indicators

339

6.5

Direction of trade of developing economies

About the data Developing economies are an increasingly important

those in Sub-Saharan Africa—are not well recorded,

affinity. The direction of trade is also influenced by

part of the global trading system. Their share of world

and the value of trade among developing economies

preferential trade agreements that a country has

trade rose from 15 percent in 1990 to 30 percent

may be understated. The table does not include some

made with other economies. Though formal agree-

in 2009. And trade between high-income economies

developing economies because data on their bilateral

ments on trade liberalization do not automatically

and low- and middle-income economies has grown

trade flows are not available. Data on the direction

increase trade, they nevertheless affect the direction

faster than trade between high-income economies.

of trade between selected high-income economies

of trade between the participating economies. Table

This increased trade benefits both producers and

are presented and discussed in tables 6.3 and 6.4.

6.7 illustrates the size of existing regional trade blocs

consumers in developing and high-income economies.

At the regional level most exports from developing

The table shows trade in goods between develop-

economies are to high-income economies, but the

Although global integration has increased, develop-

ing economies in the same region and other regions

share of intraregional trade is increasing. Geographic

ing economies still face trade barriers when accessing

and between developing economies and high-income

patterns of trade vary widely by country and commod-

other markets (see table 6.8).

economies. Data on exports and imports are from

ity. Larger shares of exports from oil- and resource-

the International Monetary Fund’s (IMF) Direction of

rich economies are to high-income economies.

that have formal preferential trade agreements.

Definitions

Trade database and should be broadly consistent with

The relative importance of intraregional trade is

• Exports to developing economies within region

data from other sources, such as the United Nations

higher for both landlocked countries and small coun-

are the sum of merchandise exports from the report-

Statistics Division’s Commodity Trade (Comtrade)

tries with close trade links to the largest regional

ing economy to other developing economies in the

database. All high-income economies and major devel-

economy. For most developing economies—especially

same World Bank region as a percentage of total

oping economies report trade to the IMF on a timely

smaller ones—there is a “geographic bias” favoring

merchandise exports by the economy. • Exports to

basis, covering about 85 percent of trade for recent

intraregional trade. Despite the broad trend toward

developing economies outside region are the sum

years. Trade by less timely reporters and by countries

globalization and the reduction of trade barriers,

of merchandise exports from the reporting econ-

that do not report is estimated using reports of trading

the relative share of intraregional trade increased

omy to other developing economies in other World

partner countries. Therefore, data on trade between

for most economies between 1999 and 2009. This

Bank regions as a percentage of total merchandise

developing and high-income economies shown in the

is due partly to trade-related advantages, such as

exports by the economy. • Exports to high-income

table should be generally complete. But trade flows

proximity, lower transport costs, increased knowledge

economies are the sum of merchandise exports from

between many developing economies—particularly

from repeated interaction, and cultural and historical

the reporting economy to high-income economies as a percentage of total merchandise exports by the

6.5a

Developing economies are trading more with other developing economies

economy. • Imports from developing economies within region are the sum of merchandise imports by

Low-income economies

the reporting economy from other developing econo-

Share of merchandise exports (percent) 100

mies in the same World Bank region as a percentage of total merchandise imports by the economy.

Exports to high-income economies

75

• Imports from developing economies outside region are the sum of merchandise imports by the

50

reporting economy from other developing economies Exports to developing economies within region

25

Exports to developing economies outside region

0 1990

1995

2000

2005

2009

in other World Bank regions as a percentage of total merchandise imports by the economy. • Imports from high-income economies are the sum of merchandise imports by the reporting economy from

Middle-income economies

high-income economies as a percentage of total merchandise imports by the economy.

100 Exports to high-income economies

75

Data sources

50

Data on merchandise trade flows are published in

25

Exports to developing economies within region

the IMF’s Direction of Trade Statistics Yearbook and Exports to developing economies outside region

0 1990

1995

2000

2005

2009

Direction of Trade Statistics Quarterly; the data in the table were calculated using the IMF’s Direction

Share of merchandise exports to high-income economies have been declining for both low- and middle-income

of Trade database. Regional and income group

economies. On the other hand, their exports to other developing economies have increased, especially

classifications are according to the World Bank

exports to developing economies within the same region.

classification of economies as of July 1, 2010,

Source: World Bank staff calculations based on data from International Monetary Fund’s Direction of Trade database.

and are as shown on the cover flaps of this report.

340

2011 World Development Indicators

1970

World Bank commodity price index (2000= 100) Energy 19 Nonenergy commodities 183 Agriculture 188 Beverages 230 Food 201 Fats and oils 237 Grains 204 Other food 151 Raw materials 136 Timber 97 Other raw materials 179 Fertilizers 82 Metals and minerals 185 Base metals 200 .. Steel products a Commodity prices (2000 prices) Energy Coal, Australian ($/mt) .. Natural gas, Europe ($/mmBtu) .. Natural gas, U.S. ($/mmBtu) 0.57 Natural gas, liquefied, Japan ($mmBtu) .. Petroleum, avg., spot ($/bbl) 4 Beverages (cents/kg) Cocoa 233 Coffee, Arabica 397 Coffee, robusta 316 Tea, avg., 3 auctions 289 Tea, Colombo auctions 217 Tea, Kolkata auctions 343 Tea, Mombasa auctions 307 Food Fats and oils ($/mt) Coconut oil 1,376 779 Copraa Groundnut oil 1,312 Palm oil 901 .. Palmkernell oila Soybeans 405 Soybean meal 355 Soybean oil 992 Grains ($/mt) Barley .. Maize 202 Rice, Thailand, 5% 438 .. Rice, Thailand, 25% a .. Rice, Thailand, A1a 179 Sorghuma 218 Wheat, Canadaa Wheat, U.S., hard red winter 190 197 Wheat, U.S., soft red wintera

1980

1990

1995

153 177 195 273 199 196 199 205 143 92 198 177 141 145 134

79 115 113 117 116 105 121 124 105 88 124 98 122 124 131

49 5.21 1.91 7.02 45

6.6

GLOBAL LINKS

Primary commodity prices

2000

2004

2005

2006

2007

2008

2009

2010

53 117 122 136 117 126 124 101 125 105 146 110 106 112 118

100 100 100 100 100 100 100 100 100 100 100 100 100 100 100

123 121 118 109 123 134 115 117 109 90 129 125 126 127 153

171 135 121 125 121 120 115 129 119 100 140 148 162 152 170

197 172 134 130 131 123 134 140 143 113 177 151 251 253 162

209 192 154 145 158 178 161 127 149 117 185 205 268 272 155

274 218 184 168 198 222 225 142 157 120 196 453 261 230 231

179 178 165 184 171 181 179 152 141 116 168 245 197 174 190

225 224 192 210 186 203 179 170 197 119 282 232 288 247 190

39 2.48 1.65 3.54 22

33 2.26 1.43 2.86 14

26 3.86 4.31 4.71 28

48 3.88 5.35 4.66 34

43 5.74 8.09 5.44 48

44 7.57 6.01 6.32 57

56 7.30 5.96 6.56 61

102 10.72 7.09 10.04 78

60 7.27 3.30 7.46 52

82 6.87 3.64 9.00 66

321 427 400 205 137 253 224

123 192 115 200 182 273 144

119 277 230 124 118 145 108

91 192 91 188 179 181 203

141 161 72 153 162 156 141

140 230 101 150 167 147 134

142 225 133 168 171 157 175

167 232 163 174 215 164 142

206 247 186 194 223 180 177

241 265 137 227 262 210 210

260 358 144 239 273 233 212

831 558 1,059 719 .. 365 324 737

327 224 937 282 .. 240 195 435

556 364 823 521 .. 215 164 519

450 305 714 310 444 212 189 338

600 409 1,054 428 588 278 219 559

560 376 963 383 569 249 195 495

542 360 867 427 519 240 187 535

784 518 1,154 666 758 328 263 752

979 653 1,705 759 904 418 340 1,007

606 401 988 570 585 365 340 709

932 622 1,164 747 982 373 314 833

96 154 506 .. .. 159 235 213 208

78 106 263 254 152 101 152 132 125

86 103 266 247 218 99 172 147 139

77 89 202 173 143 88 147 114 99

90 102 216 205 186 100 169 142 131

86 90 260 241 198 87 179 138 123

104 109 272 248 196 110 194 172 142

147 140 279 262 232 139 256 218 204

160 178 520 425 386 166 364 261 217

107 138 463 382 273 126 251 187 155

131 154 405 366 318 137 259 185 190

2011 World Development Indicators

341

6.6

Primary commodity prices

1970

Commodity prices (continued) (2000 prices) Food (continued) Other food Bananas, U.S. ($/mt) Beef (cents/kg) Chicken meat (cents/kg) Fishmeal ($/mt)a Oranges ($/mt) Shrimp. Mexico (cents/kg) Sugar, EU domestic (cents/kg) Sugar, U.S. domestic (cents/kg) Sugar, world (cents/kg) Agricultural raw materials Cotton A index (cents/kg) Logs, Cameroon ($/cu. m)a Logs, Malaysia ($/cu. m) Rubber, Singapore (cents/kg) Rubber, TSR 20 (cents/kg)a Plywood (cents/sheet)a Sawnwood, Malaysia ($/cu. m) Tobacco ($/mt)a Woodpulp ($/mt)a Fertilizers ($/mt) Diammonium phosphate Phosphate rock Potassium chloride Triple superphosphate Urea Metals and minerals Aluminum ($/mt) Copper ($/mt) Gold ($/toz)a Iron ore (cents/dmtu) Iron ore, spot, cfr China ($/dmtu) Lead (cents/kg) Nickel ($/mt) Silver (cents/toz)a Tin (cents/kg) Zinc (cents/kg) MUV G-5 index

573 452 .. 682 582 .. 39 57 29

1980

1990

1995

467 340 85 621 482 1,420 60 82 78

526 249 96 401 516 1,039 57 50 27

369 158 92 411 441 1,253 57 42 24

2000

424 193 119 413 363 1,515 56 43 18

2004

476 228 138 589 780 928 61 41 14

2005

547 238 135 664 794 939 60 43 20

2006

2007

605 228 124 1,040 741 915 58 44 29

577 222 134 1,005 817 862 58 39 19

2008

675 251 136 906 886 855 56 37 23

2010

707 220 143 1,027 759 789 44 46 33

720 278 143 1,399 857 1,033 37 66 39

219 149 149 141 .. 357 608 3,727 615

252 310 241 176 .. 338 489 2,806 661

177 334 172 84 .. 345 518 3,297 792

177 282 212 131 .. 485 614 2,194 708

130 275 190 67 63 448 595 2,976 664

124 301 179 116 110 422 528 2,488 582

110 304 184 135 126 462 599 2,533 577

113 285 214 186 174 532 670 2,653 624

119 325 229 193 184 547 688 2,830 655

126 421 234 207 202 516 711 2,871 656

115 352 240 160 150 471 673 3,541 513

189 355 231 303 280 472 703 3,570 719

187 38 109 147 63

274 58 143 222 237

167 39 95 128 116

180 29 98 124 155

154 44 123 138 101

201 37 113 169 159

224 38 144 183 199

233 40 156 180 199

369 61 171 289 264

774 276 456 703 394

270 102 526 215 208

415 102 275 317 239

1,926 4,904 125 34 .. 105 9,860 614 1,273 102

1,795 2,690 750 35 .. 112 8,037 2,544 2,068 94

1,593 2,586 373 32 .. 79 8,614 475 591 147

1,499 2,437 319 24 .. 52 6,830 431 516 86

1,549 1,813 279 29 .. 45 8,638 500 544 113

1,558 2,602 372 34 .. 80 12,551 607 773 95

1,724 3,340 404 59 .. 89 13,387 666 670 125

2,297 6,007 540 69 .. 115 21,675 1,034 785 293

2,252 6,076 595 72 108 220 31,778 1,145 1,241 277

2,058 5,564 697 112 125 167 16,888 1,200 1,481 150

1,390 4,300 812 84 69 144 12,237 1,227 1,133 138

1,802 6,248 1,016 134 126 178 18,084 1,675 1,692 179

29

81

103

120

100

110

110

112

117

125

120

121

Note: bbl = barrel, cu. m = cubic meter, dmtu = dry metric ton unit, kg = kilogram, mmBtu = million British thermal unit, mt = metric ton, toz = troy ounce. a. Series not included in the nonenergy index.

342

2009

2011 World Development Indicators

About the data

6.6

GLOBAL LINKS

Primary commodity prices Definitions

Primary commodities—raw or partially processed

commodity price index contains 41 price series for

• Energy price index is the composite price index for

materials that will be transformed into fi nished

34 nonenergy commodities.

coal, petroleum, and natural gas, weighted by exports

goods—are often developing countries’ most impor-

Separate indexes are compiled for energy and steel

of each commodity from low- and middle-income

tant exports, and commodity revenues can affect liv-

products, which are not included in the nonenergy

countries. • Nonenergy commodity price index cov-

ing standards. Price data are collected from various

commodity price index.

ers the 34 nonenergy primary commodities that

sources, including international commodity study

The MUV index is a composite index of prices

make up the agriculture, fertilizer, and metals and

groups, government agencies, industry trade jour-

for manufactured exports from the five major (G-5)

minerals indexes. • Agriculture includes beverages,

nals, and Bloomberg and Datastream. Prices are

industrial economies (France, Germany, Japan, the

food, and agricultural raw materials. • Beverages

compiled in U.S. dollars or converted to U.S. dollars

United Kingdom, and the United States) to low- and

include cocoa, coffee, and tea. • Food includes

when quoted in local currencies.

middle-income economies, valued in U.S. dollars.

fats and oils, grains, and other food items. Fats

The table is based on frequently updated price

The index covers products in groups 5–8 of SITC

and oils include coconut oil, groundnut oil, palm oil,

reports. Prices are those received by exporters when

revision 1. For the MUV G-5 index, unit value indexes

soybeans, soybean oil, and soybean meal. Grains

available, or the prices paid by importers or trade

in local currency for each country are converted to

include barley, maize, rice, and wheat. Other food

unit values. Annual price series are generally simple

U.S. dollars using market exchange rates and are

items include bananas, beef, chicken meat, oranges,

averages based on higher frequency data. The con-

combined using weights determined by each coun-

shrimp, and sugar. • Agricultural raw materials

stant price series in the table are deflated by the

try’s export share in the base year (1995). The export

include timber and other raw materials. Timber

manufactures unit value (MUV) index for the Group

shares were 8.2 percent for France, 17.4 percent

includes tropical hard logs and sawnwood. Other

of Five (G-5) countries (see below).

for Germany, 35.6 percent for Japan, 6.6 percent

raw materials include cotton, natural rubber, and

for the United Kingdom, and 32.2 percent for the

tobacco. • Fertilizers include phosphate, phosphate

United States.

rock, potassium, and nitrogenous products. • Met-

Commodity price indexes are calculated as Laspeyres index numbers; the fixed weights are the 2002–04 average export values for low- and middle-

als and minerals include base metals and iron ore.

income economies (based on 2001 gross national

• Base metals include aluminum, copper, lead,

income) rebased to 2000. Data for exports are from

nickel, tin, and zinc. • Steel products price index

the United Nations Statistics Division’s Commod-

is the composite price index for eight steel prod-

ity Trade Statistics (Comtrade) database Standard

ucts based on quotations free on board (f.o.b.)

International Trade Classification (SITC) revision 3,

Japan excluding shipments to the United States

the Food and Agriculture Organization’s FAOSTAT

for all years and to China prior to 2001, weighted

database, the International Energy Agency data-

by product shares of apparent combined consump-

base, BP’s Statistical Review of World Energy, the

tion (volume of deliveries) for Germany, Japan, and

World Bureau of Metal Statistics, and World Bank

the United States. • Commodity prices—for defi -

staff estimates.

nitions and sources, see “Commodity price data”

Each index in the table represents a fixed basket of

(also known as the “Pink Sheet”) at the World Bank

primary commodity exports over time. The nonenergy

Prospects for Development website (www.worldbank. org/prospects, click on Products). • MUV G-5 index is the manufactures unit value index for G-5 country

6.6a

Primary commodity prices soared again in 2010

exports to low- and middle-income economies.

World Bank commodity price index, current prices (2000 = 100) 500 Energy 400 Raw materials 300 Food 200

100 2005

Data sources Data on commodity prices and the MUV G-5 2006

2007

2008

2009

2010

2011

index are compiled by the World Bank’s Develop-

The food commodity price index started rising again in the beginning of 2009, and by the end of February

ment Prospects Group. Monthly updates of com-

2011 exceeded the record high in June 2008. The price index for raw materials reached new highs, and

modity prices are available at www.worldbank.

the energy price index also rose throughout 2009 and 2010.

org/prospects and http://data.worldbank.org/

Source: World Bank commodity price data.

data-catalog.

2011 World Development Indicators

343

6.7

Regional trade blocs

Merchandise exports within bloc

Year of creation High-income and lowand middle-income economies 1989 APECb EEA 1994 EFTA 1960 European Union 1957 NAFTA 1994 SPARTECA 1981 Trans-Pacific SEP 2006 East Asia and Paciﬁc and South Asia APTA 1975 ASEAN 1967 MSG 1993 PICTA 2001 SAARC 1985 Europe, Central Asia, and Middle East Agadir Agreement 2004 CEFTA 1992 CEZ 2003 CIS 1991 EAEC 1997 ECO 1985 GCC 1981 PAFTA (GAFTA) 1997 UMA 1989 Latin America and the Caribbean Andean Community 1969 CACM 1961 CARICOM 1973 LAIA (ALADI) 1980 MERCOSUR 1991 OECS 1981 Sub-Saharan Africa CEMAC 1994 CEPGL 1976 COMESA 1994 EAC 1996 ECCAS 1983 ECOWAS 1975 Indian Ocean Commission 1984 SADC 1992 UEMOA 1994

Year of entry into Type force of the of most most recent recent agreement agreementa

$ millions 1990

1995

2000

2005

2007

2008

2009

1994 2002 1958 1994 1981 2006

None EIA EIA EIA, CU FTA PTA EIA, FTA

1976 1992 1994 2003 2006

PTA FTA PTA FTA FTA

2,429 27,365 5 4 945

21,728 79,544 18 4 2,081

37,895 98,060 22 8 2,894

127,340 165,458 51 22 8,619

193,951 216,727 78 34 12,747

233,617 251,285 89 38 13,177

204,745 198,915 78 34 11,095

1994 2004 1994 2000 2003 2003c 1998 1994 c

NNA FTA FTA FTA CU PTA CU FTA NNA

156 .. .. .. .. 1,243 6,906 13,204 958

226 619 10,154 31,277 10,919 4,746 6,832 12,948 1,109

294 1,187 13,283 28,422 13,936 4,518 8,029 16,188 1,041

635 2,847 23,469 58,113 24,818 12,579 15,408 41,659 1,885

1,046 6,160 43,003 98,050 45,714 22,064 24,372 61,100 2,695

1,913 7,543 47,731 123,052 51,186 26,739 31,514 82,267 4,570

2,075 5,083 19,094 60,389 21,872 18,412 21,849 61,881 3,422

1988 1961 1997 1981 2005 1981c

CU CU EIA PTA EIA NNA

544 667 456 13,350 4,127 29

1,788 1,594 877 35,986 14,199 39

2,046 2,655 1,078 44,253 17,829 38

4,572 4,311 2,235 71,711 21,128 68

5,926 5,637 3,112 110,006 32,421 104

7,029 6,475 3,808 143,283 46,657 118

5,785 5,287 2,716 98,510 32,689 104

1999

CU NNA FTA CU NNA PTA NNA FTA CU

139 7 1,146 335 160 1,532 63 1,655 621

120 8 1,367 628 157 1,875 113 3,615 560

96 10 1,443 689 182 2,715 106 4,427 741

201 20 2,695 1,075 255 5,497 162 7,799 1,390

305 29 4,021 1,385 385 6,717 214 12,051 1,735

355 73 6,676 1,797 449 9,355 217 16,011 2,281

300 64 6,114 1,572 378 7,312 183 11,697 1,927

1994 2000 2004 c 1993 2005c 2000 2000

901,560 1,688,708 2,261,791 3,318,699 4,192,784 4,606,339 3,738,989 1,079,711 1,463,232 1,714,018 3,037,759 4,025,418 4,446,686 3,392,597 782 925 831 1,252 2,196 2,910 2,006 1,032,397 1,404,255 1,641,609 2,905,551 3,846,547 4,233,112 3,237,024 226,273 394,472 676,141 824,359 951,258 1,013,245 768,820 5,299 9,135 8,579 15,201 18,617 20,263 17,079 1,110 2,614 1,438 2,345 3,290 4,262 3,548

Note: Regional bloc memberships are as follows: Agadir Agreement, the Arab Republic of Egypt, Jordan, Morocco, and Tunisia; Andean Community, Bolivia, Colombia, Ecuador, and Peru; Arab Maghreb Union (UMA), Algeria, Libyan Arab Republic, Mauritania, Morocco, and Tunisia; Asia Paciﬁc Economic Cooperation (APEC), Australia, Brunei Darussalam, Canada, Chile, China, Hong Kong SAR, China, Indonesia, Japan, the Republic of Korea, Malaysia, Mexico, New Zealand, Papua New Guinea, Peru, the Philippines, the Russian Federation, Singapore, Taiwan (China), Thailand, the United States, and Vietnam; Asia-Paciﬁc Trade Agreement (APTA; formerly Bangkok Agreement), Bangladesh, China, India, the Republic of Korea, the Lao People’s Democratic Republic, and Sri Lanka; Association of South East Asian Nations (ASEAN), Brunei Darussalam, Cambodia, Indonesia, the Lao People’s Democratic Republic, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam; Caribbean Community and Common Market (CARICOM), Antigua and Barbuda, the Bahamas, Barbados, Belize, Dominica, Grenada, Guyana, Haiti, Jamaica, Montserrat, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Suriname, and Trinidad and Tobago; Central American Common Market (CACM), Costa Rica, El Salvador, Guatemala, Honduras, and Nicaragua; Central European Free Trade Area (CEFTA), Albania, Bosnia and Herzegovina, Croatia, Kosovo, Macedonia, Moldova, Montenegro, and Serbia; Common Economic Zone (CEZ), Belarus, Kazakhstan, and the Russian Federation; Common Market for Eastern and Southern Africa (COMESA), Burundi, Comoros, the Democratic Republic of Congo, Djibouti, the Arab Republic of Egypt, Eritrea, Ethiopia, Kenya, Libyan Arab Republic, Madagascar, Malawi, Mauritius, Rwanda, Seychelles, Sudan, Swaziland, Uganda, Zambia, and Zimbabwe; Commonwealth of Independent States (CIS), Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyz Republic, Moldova, the Russian Federation, Tajikistan, Turkmenistan, Ukraine, and Uzbekistan; East African Community (EAC), Burundi, Kenya, Rwanda, Tanzania, and Uganda; Economic and Monetary Community of Central Africa (CEMAC; formerly Central African Customs and Economic Union [UDEAC]), Cameroon, the Central African Republic, Chad, the Republic of Congo, Equatorial Guinea, and Gabon; Economic Community of Central African States (ECCAS), Angola, Burundi, Cameroon, the Central African Republic, Chad, the Democratic Republic of Congo, the Republic of Congo, Equatorial Guinea, Gabon, and São Tomé and Príncipe; Economic Community of the Great Lakes Countries (CEPGL), Burundi, the Democratic Republic of Congo, and Rwanda; Economic Community of West African States (ECOWAS), Benin, Burkina Faso, Cape Verde, Côte d’Ivoire, the Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone, and Togo; Economic Cooperation Organization (ECO), Afghanistan, Azerbaijan, the Islamic Republic of Iran, Kazakhstan, the Kyrgyz Republic, Pakistan, Tajikistan, Turkey, Turkmenistan, and

344

2011 World Development Indicators

6.7

GLOBAL LINKS

Regional trade blocs Merchandise exports within bloc

Year of creation High-income and lowand middle-income economies 1989 APECb EEA 1994 EFTA 1960 European Union 1957 NAFTA 1994 SPARTECA 1981 Trans-Pacific SEP 2006 East Asia and Paciﬁc and South Asia APTA 1975 ASEAN 1967 MSG 1993 PICTA 2001 SAARC 1985 Europe, Central Asia, and Middle East Agadir Agreement 2004 CEFTA 1992 CEZ 2003 CIS 1991 EAEC 1997 ECO 1985 GCC 1981 PAFTA (GAFTA) 1997 UMA 1989 Latin America and the Caribbean Andean Community 1969 CACM 1961 CARICOM 1973 LAIA (ALADI) 1980 MERCOSUR 1991 OECS 1981 Sub-Saharan Africa CEMAC 1994 CEPGL 1976 COMESA 1994 EAC 1996 ECCAS 1983 ECOWAS 1975 Indian Ocean Commission 1984 SADC 1992 UEMOA 1994

Year of entry into Type force of the of most most recent recent agreement agreementa

% of total bloc exports 1990

1995

2000

2005

2007

2008

2009

1994 2002 1958 1994 1981 2006

None EIA EIA EIA, CU FTA PTA EIA, FTA

68.3 68.8 0.8 67.3 41.4 10.5 1.5

71.7 67.9 0.7 66.5 46.2 12.9 1.7

73.0 69.0 0.6 67.7 55.7 10.7 0.8

70.8 73.0 0.5 71.6 55.7 11.4 0.8

67.3 73.3 0.7 71.9 51.3 10.5 0.8

65.2 72.8 0.8 71.4 49.5 8.9 1.0

66.3 71.9 0.7 70.4 48.0 9.1 1.0

1976 1992 1994 2003 2006

PTA FTA PTA FTA FTA

1.6 18.9 0.3 0.3 3.5

6.8 24.4 0.4 0.1 4.5

8.0 23.0 0.6 0.3 4.6

11.0 25.3 0.8 0.4 6.6

11.0 25.2 0.8 0.4 6.6

11.4 25.5 0.8 0.4 5.9

11.6 24.5 0.8 0.4 5.4

1994 2004 1994 2000 2003 2003 c 1998 1994 c

NNA FTA FTA FTA CU PTA CU FTA NNA

1.3 .. .. .. .. 3.2 8.0 10.2 2.9

1.4 9.0 11.6 28.4 12.3 7.9 6.8 9.8 3.8

1.4 14.5 11.0 19.8 11.5 5.6 4.9 7.2 2.2

1.8 16.3 8.4 17.7 8.9 6.9 4.4 9.2 1.9

2.0 21.2 10.4 20.1 10.9 8.0 5.0 9.4 2.0

2.7 22.4 8.8 18.0 9.3 6.8 4.5 8.9 2.5

3.8 20.2 5.6 14.8 6.3 7.2 5.1 10.6 3.1

1988 1961 1997 1981 2005 1981c

CU CU EIA PTA EIA NNA

4.0 15.3 8.0 11.6 8.9 8.1

8.6 21.8 12.0 17.3 20.3 12.6

7.7 19.6 14.4 13.2 20.0 10.0

9.0 23.2 12.1 13.6 12.9 11.5

7.8 23.5 13.1 15.3 14.7 12.1

7.5 24.8 12.9 16.5 14.7 12.0

7.5 22.3 13.7 15.5 15.2 13.0

1999

CU NNA FTA CU NNA PTA NNA FTA CU

2.3 0.5 4.7 17.7 1.4 8.0 3.9 6.6 13.0

2.1 0.5 6.1 19.5 1.5 9.0 5.9 10.2 10.3

1.0 0.8 4.6 22.6 1.0 7.6 4.4 9.5 13.1

0.9 1.2 4.6 18.0 0.6 9.3 4.9 9.3 13.4

1.1 1.4 4.5 17.8 0.6 7.8 5.8 10.2 14.9

0.8 1.9 5.3 19.2 0.4 8.5 5.7 10.3 15.9

1.2 2.2 7.2 18.9 0.6 9.9 5.8 11.0 13.2

1994 2000 2004 c 1993 2005c 2000 2000

Uzbekistan; Eurasian Economic Community (EAEC), Belarus, Kazakhstan, Kyrgyz Republic, the Russian Federation, Tajikistan, and Uzbekistan; European Economic Area (EEA), European Union plus Iceland, Liechtenstein, and Norway; European Free Trade Association (EFTA), Iceland, Liechtenstein, Norway, and Switzerland; European Union (EU; formerly European Economic Community and European Community), Austria, Belgium, Bulgaria, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Poland, Portugal, Romania, Slovak Republic, Slovenia, Spain, Sweden, and the United Kingdom; Gulf Cooperation Council (GCC), Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates; Indian Ocean Commission, Comoros, Madagascar, Mauritius, Réunion, and Seychelles; Latin American Integration Association (LAIA; formerly Latin American Free Trade Area), Argentina, Bolivia, Brazil, Chile, Colombia, Cuba, Ecuador, Mexico, Paraguay, Peru, Uruguay, and Bolivarian Republic of Venezuela; Melanesian Spearhead Group (MSG), Fiji, Papua New Guinea, Solomon Islands, and Vanuatu; North American Free Trade Agreement (NAFTA), Canada, Mexico, and the United States; Organization of Eastern Caribbean States (OECS), Anguilla, Antigua and Barbuda, British Virgin Islands, Dominica, Grenada, Montserrat, St. Kitts and Nevis, St. Lucia, and St. Vincent and the Grenadines; Paciﬁc Island Countries Trade Agreement (PICTA), Cook Islands, Kiribati, Nauru, Niue, Papua New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu, and Vanuatu; Pan-Arab Free Trade Area (PAFTA; also known as Greater Arab Trade Area [GAFTA]), Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Qatar, Saudi Arabia, Sudan, Syrian Arab Republic, Tunisia, the United Arab Emirates, and Yemen; South Asian Association for Regional Cooperation (SAARC), Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka; South Paciﬁc Regional Trade and Economic Cooperation Agreement (SPARTECA), Australia, Cook Islands, Fiji, Kiribati, Marshall Islands, Federated States of Micronesia, Nauru, New Zealand, Niue, Papua New Guinea, Solomon Islands, Tonga, Tuvalu, Vanuatu, and Western Samoa; Southern African Development Community (SADC), Angola, Botswana, the Democratic Republic of Congo, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Swaziland, Tanzania, Zambia, and Zimbabwe; Southern Common Market (MERCOSUR), Argentina, Brazil, Paraguay, Uruguay, and Bolivarian Republic of Venezuela; Trans-Paciﬁc Strategic Economic Partnership (Trans-Paciﬁc SEP), Brunei Darussalam, Chile, New Zealand, and Singapore; West African Economic and Monetary Union (WAEMU or UEMOA), Benin, Burkina Faso, Côte d’Ivoire, Guinea-Bissau, Mali, Niger, Senegal, and Togo.

2011 World Development Indicators

345

6.7

Regional trade blocs

Merchandise exports by bloc

Year of creation High-income and lowand middle-income economies 1989 APECb EEA 1994 EFTA 1960 European Union 1957 NAFTA 1994 SPARTECA 1981 Trans-Pacific SEP 2006 East Asia and Paciﬁc and South Asia APTA 1975 ASEAN 1967 MSG 1993 PICTA 2001 SAARC 1985 Europe, Central Asia, and Middle East Agadir Agreement 2004 CEFTA 1992 CEZ 2003 CIS 1991 EAEC 1997 ECO 1985 GCC 1981 PAFTA (GAFTA) 1997 UMA 1989 Latin America and the Caribbean Andean Community 1969 CACM 1961 CARICOM 1973 LAIA (ALADI) 1980 MERCOSUR 1991 OECS 1981 Sub-Saharan Africa CEMAC 1994 CEPGL 1976 COMESA 1994 EAC 1996 ECCAS 1983 ECOWAS 1975 Indian Ocean Commission 1984 SADC 1992 UEMOA 1994

Year of entry into Type force of the of most most recent recent agreement agreementa

% of world exports 1990

1995

2000

2005

2007

2008

2009

39.0 46.4 2.9 45.3 16.2 1.5 2.2

46.4 42.4 2.4 41.5 16.8 1.4 3.0

48.5 38.9 2.2 38.0 19.0 1.3 2.7

45.1 40.1 2.3 39.1 14.3 1.3 2.9

44.8 39.5 2.3 38.5 13.4 1.3 2.9

44.1 38.1 2.3 37.0 12.8 1.4 2.8

45.7 38.3 2.4 37.3 13.0 1.5 2.9

1994 2002 1958 1994 1981 2006

None EIA EIA EIA, CU FTA PTA EIA, FTA

1976 1992 1994 2003 2006

PTA FTA PTA FTA FTA

4.5 4.3 0.1 0.0 0.8

6.3 6.4 0.1 0.1 0.9

7.5 6.7 0.1 0.0 1.0

11.2 6.3 0.1 0.1 1.3

12.7 6.2 0.1 0.1 1.4

12.8 6.1 0.1 0.1 1.4

14.3 6.6 0.1 0.1 1.7

1994 2004 1994 2000 2003 2003c 1998 1994 c

NNA FTA FTA FTA CU PTA CU FTA NNA

0.3 .. .. .. .. 1.1 2.6 3.8 1.0

0.3 0.1 1.7 2.2 1.7 1.2 2.0 2.6 0.6

0.3 0.1 1.9 2.2 1.9 1.3 2.6 3.5 0.8

0.3 0.2 2.7 3.2 2.7 1.8 3.3 4.4 0.9

0.4 0.2 3.0 3.5 3.0 2.0 3.5 4.7 1.0

0.4 0.2 3.4 4.3 3.4 2.5 4.3 5.8 1.1

0.4 0.2 2.8 3.3 2.8 2.1 3.5 4.7 0.9

1988 1961 1997 1981 2005 1981c

CU CU EIA PTA EIA NNA

0.4 0.1 0.2 3.4 1.4 0.0

0.4 0.1 0.1 4.1 1.4 0.0

0.4 0.2 0.1 5.3 1.4 0.0

0.5 0.2 0.2 5.1 1.6 0.0

0.5 0.2 0.2 5.2 1.6 0.0

0.6 0.2 0.2 5.4 2.0 0.0

0.6 0.2 0.2 5.2 1.7 0.0

1999

CU NNA FTA CU NNA PTA NNA FTA CU

0.2 0.0 0.7 0.1 0.3 0.6 0.0 0.7 0.1

0.1 0.0 0.4 0.1 0.2 0.4 0.0 0.7 0.1

0.1 0.0 0.5 0.0 0.3 0.6 0.0 0.7 0.1

0.2 0.0 0.6 0.1 0.4 0.6 0.0 0.8 0.1

0.2 0.0 0.6 0.1 0.5 0.6 0.0 0.9 0.1

0.3 0.0 0.8 0.1 0.7 0.7 0.0 1.0 0.1

0.2 0.0 0.7 0.1 0.5 0.6 0.0 0.9 0.1

1994 2000 2004 c 1993 2005c 2000 2000

a. CU is customs union; EIA is economic integration agreement; FTA is free trade agreement; PTA is preferential trade agreement; and NNA is not notified agreement, which refers to preferential trade arrangements established among member countries that are not notified to the World Trade Organization (these agreements may be functionally equivalent to any of the other agreements). b. No preferential trade agreement. c. Years of the most recent agreement are collected from the official website of the trade bloc.

346

2011 World Development Indicators

6.7

GLOBAL LINKS

Regional trade blocs About the data Trade blocs are groups of countries that have estab-

preferential arrangements, it is included because of

one trade bloc, so shares of world exports exceed

lished preferential arrangements governing trade

the volume of trade between its members.

100 percent. Exports include all commodity trade,

between members. Although in some cases the pref-

The data on country exports are from the Interna-

which may include items not specified in trade bloc

erences—such as lower tariff duties or exemptions

tional Monetary Fund’s (IMF) Direction of Trade data-

agreements. Differences from previously published

from quantitative restrictions—may be no greater than

base and should be broadly consistent with those

estimates may be due to changes in membership or

those available to other trading partners, such arrange-

from sources such as the United Nations Statistics

revisions in underlying data.

ments are intended to encourage exports by bloc mem-

Division’s Commodity Trade (Comtrade) database. All

bers to one another—sometimes called intratrade.

high-income economies and major developing econo-

Definitions

Most countries are members of a regional trade

mies report trade to the IMF on a timely basis, cover-

• Merchandise exports within bloc are the sum of

bloc, and more than a third of the world’s trade takes

ing about 85 percent of trade for recent years. Trade

merchandise exports by members of a trade bloc to

place within such arrangements. While trade blocs

by less timely reporters and by countries that do not

other members of the bloc. They are shown both in

vary in structure, they all have the same objective:

report is estimated using reports of trading partner

U.S. dollars and as a percentage of total merchan-

to reduce trade barriers between member countries.

countries. Therefore, data on trade between develop-

dise exports by the bloc. • Merchandise exports by

But effective integration requires more than reduc-

ing and high-income economies shown in the table

bloc as a share of world exports are the bloc’s total

ing tariffs and quotas. Economic gains from compe-

should be generally complete. But trade flows between

merchandise exports (within the bloc and to the rest

tition and scale may not be achieved unless other

many developing countries—particularly those in Sub-

of the world) as a share of total merchandise exports

barriers that divide markets and impede the free flow

Saharan Africa—are not well recorded, and the value of

by all economies in the world. • Type of most recent

of goods, services, and investments are lifted. For

trade among developing countries may be understated.

agreement includes customs union, under which

example, many regional trade blocs retain contingent

Membership in the trade blocs shown is based

members substantially eliminate all tariff and nontariff

protections on intrabloc trade, including antidumping,

on the most recent information available (see Data

barriers among themselves and establish a common

countervailing duties, and “emergency protection” to

sources). Other types of preferential trade agreements

external tariff for nonmembers; economic integration

address balance of payments problems or protect an

may have entered into force earlier than those shown

agreement, which liberalizes trade in services among

industry from import surges. Other barriers include

in the table and may still be effective. Unless other-

members and covers a substantial number of sec-

differing product standards, discrimination in public

wise indicated in the footnotes, information on the type

tors, affects a sufficient volume of trade, includes

procurement, and cumbersome border formalities.

of agreement and date of enforcement are based on

substantial modes of supply, and is nondiscriminatory

Membership in a regional trade bloc may reduce

the World Trade Organization’s (WTO) list of regional

(in the sense that similarly situated service suppliers

the frictional costs of trade, increase the credibility

trade agreements. Information on trade agreements

are treated the same); free trade agreement, under

of reform initiatives, and strengthen security among

not notified to the WTO was collected from the Global

which members substantially eliminate all tariff and

partners. But making it work effectively is challenging.

Preferential Trade Agreements database (box 6.7a)

nontariff barriers but set tariffs on imports from non-

All economic sectors may be affected, and some may

and from official websites of the trade blocs.

members; preferential trade agreement, which is an

expand while others contract, so it is important to

Although bloc exports have been calculated back

agreement notified to the WTO that is not a free trade

weigh the potential costs and benefits of membership.

to 1990 on the basis of current membership, several

agreement, a customs union, or an economic integra-

The table shows the value of merchandise intra-

blocs came into existence after that and membership

tion agreement; and not notified agreement, which is

trade (service exports are excluded) for important

may have changed over time. For this reason, and

a preferential trade arrangement established among

regional trade blocs and the size of intratrade rela-

because systems of preferences also change over

member countries that is not notified to the World

tive to each bloc’s exports of goods and the share

time, intratrade in earlier years may not have been

Trade Organization (the agreement may be functionally

of the bloc’s exports in world exports. Although the

affected by the same preferences as in recent years.

equivalent to any of the other agreements).

Asia Pacific Economic Cooperation (APEC) has no

In addition, some countries belong to more than

Global Preferential Trade Agreements Database

6.7a

Data sources

The Global Preferential Trade Agreement Database (GPTAD) provides information on preferential trade

Data on merchandise trade flows are published in

agreements around the world, including those not notified to the World Trade Organization (WTO). It is

the IMF’s Direction of Trade Statistics Yearbook and

designed to help trade policymakers, scholars, and business operators better understand and navigate the

Direction of Trade Statistics Quarterly; the data in

world of preferential trade agreements. The GPTAD is updated regularly and currently comprises more than

the table were calculated using the IMF’s Direction

330 preferential trade agreements in their original language, which have been indexed by WTO criteria and

of Trade database. Data on trade bloc membership

can be downloaded as PDFs. Users can search by provision or keyword, compare provisions across multiple

are from the World Bank Policy Research Report

agreements, and sort agreements by membership, date of signature, in-force status, and other key criteria.

Trade Blocs (2000), UNCTAD’s Trade and Develop-

The database was developed jointly by the World Bank and the Center for International Business at the

ment Report 2007, WTO’s Regional Trade Agree-

Tuck School of Business at Dartmouth College. It is supported by the Multidonor Trust Fund for Trade and

ments Information System, and the World Bank

Development with financing from the governments of Finland, Norway, Sweden, and the United Kingdom.

and the Center for International Business at the

The GPTAD is integrated with the World Integrated Trade Solution database and is part of the World Bank’s

Tuck School of Business at Dartmouth College’s

Open Data initiative (http://wits.worldbank.org/gptad/).

Global Preferential Trade Agreements Database.

2011 World Development Indicators

347

6.8

Tariff barriers All products

Primary products

Manufactured products

%

Afghanistan Albania Algeria Angola Antigua and Barbuda Argentina Armenia Australia Azerbaijan Bahamas, The Bahrain Bangladesh Barbados Belarus Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Brunei Darussalam Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Central African Republic Chad Chile China† Hong Kong SAR, China Macao SAR, China Colombia Comoros Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Djibouti Dominica Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Equatorial Guinea Eritrea Ethiopia European Union Fiji French Polynesia Gabon Gambia, The Georgia Ghana †Data for Taiwan, China

348

Most recent year

Binding coverage

2008 2009 2009 2009 2009 2010 2008 2010 2009 2006 2009 2008 2007 2009 2009 2010 2009 2007 2010 2009 2010 2010 2010 2010 2010 2008 2009 2010 2010 2007 2009 2010 2009 2010 2010 2010 2008 2009 2007 2009 2010 2010 2010 2009 2007 2008 2010 2009 2010 2007 2006 2009 2010 2009 2009 2009 2009 2009 2009 2010

.. 100.0 .. 100.0 97.9 100.0 100.0 97.0 .. .. 73.6 15.9 97.8 .. 97.9 39.5 .. .. 100.0 .. 96.1 100.0 95.3 39.4 22.3 100.0 13.7 99.7 100.0 62.5 13.9 100.0 100.0 45.8 28.2 100.0 .. 100.0 16.5 100.0 33.8 100.0 31.7 100.0 94.7 100.0 100.0 99.3 100.0 .. .. .. 100.0 51.4 .. 100.0 13.7 100.0 14.4 100.0

2011 World Development Indicators

Simple mean bound rate

.. 7.1 .. 59.2 58.7 31.9 8.5 10.0 .. .. 34.8 169.9 78.1 .. 58.4 28.7 .. .. 40.0 .. 19.0 31.4 24.1 42.5 67.8 19.1 79.9 5.2 15.8 36.0 79.9 25.1 10.0 0.0 0.0 43.1 .. 96.2 27.4 43.2 11.2 6.0 21.4 41.2 58.7 34.9 21.7 37.3 36.9 .. .. .. 4.2 40.1 .. 21.4 101.8 7.2 92.5 6.0

Simple mean tariff

Weighted mean tariff

6.2 5.7 14.2 7.4 13.8 11.4 3.7 2.9 8.3 28.5 4.3 13.9 15.1 8.0 11.2 13.3 18.1 18.2 9.6 3.7 8.8 13.4 3.8 12.4 9.8 12.4 18.4 3.3 14.7 17.5 17.6 4.9 8.2 0.0 0.0 11.2 7.8 12.9 18.6 4.8 13.1 2.4 10.5 20.6 11.9 9.0 9.3 12.6 5.1 18.3 9.6 18.1 1.8 11.9 6.8 18.7 18.7 0.5 13.0 5.3

6.4 5.1 8.6 7.4 14.6 6.2 2.3 1.9 3.9 23.9 3.6 13.0 14.8 2.3 5.9 15.4 27.8 17.8 5.4 2.0 5.2 7.6 4.1 8.8 5.5 9.9 15.0 1.0 11.6 13.6 14.7 4.0 4.2 0.0 0.0 8.9 7.8 11.0 14.7 2.4 7.3 1.2 8.7 15.2 7.9 4.9 6.0 8.0 5.5 15.6 5.4 9.7 1.4 10.1 4.2 14.5 14.8 0.4 8.6 2.5

Share of tariff Share of tariff lines lines with international with specific rates peaks

4.4 0.0 53.2 23.4 49.4 24.3 0.0 0.0 46.5 77.4 0.2 38.0 44.9 27.2 30.1 50.2 66.7 50.7 11.9 5.7 20.2 26.4 20.8 44.5 29.8 19.7 52.5 7.2 44.3 47.4 47.4 0.0 13.4 0.0 0.0 19.8 42.8 42.5 52.6 0.7 47.9 4.1 11.6 69.4 43.3 28.8 20.2 18.3 1.9 52.3 22.4 55.4 1.1 20.9 28.1 53.1 91.2 0.0 40.5 6.0

0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

%

%

Simple mean tariff

Weighted mean tariff

Simple mean tariff

Weighted mean tariff

7.0 6.8 14.5 11.6 17.2 7.5 5.6 1.3 9.5 24.4 6.7 16.3 26.3 6.8 17.2 15.5 10.0 43.5 8.4 1.6 6.1 8.1 0.2 11.4 15.4 13.8 20.5 2.1 16.2 18.9 22.5 4.4 8.1 0.0 0.0 10.9 4.2 14.2 21.9 6.3 15.1 4.5 11.1 15.9 19.2 11.6 9.0 37.5 8.4 21.5 9.2 19.2 2.4 13.7 4.1 21.2 16.9 4.0 16.6 8.4

6.7 5.4 7.8 13.9 14.8 1.6 2.2 0.4 3.8 15.1 6.9 8.8 21.9 0.6 4.0 12.4 16.1 44.9 5.8 1.3 0.5 1.5 0.1 8.1 9.4 11.8 12.9 0.3 12.2 13.8 17.2 2.7 1.7 0.0 0.0 8.8 3.8 10.8 18.6 3.3 5.4 1.9 6.2 8.7 5.7 4.5 4.3 6.2 7.4 21.4 3.5 5.6 0.6 7.7 2.7 15.1 12.8 1.0 8.9 2.0

6.1 5.5 14.0 6.7 13.0 11.8 3.5 3.1 8.0 29.4 4.0 13.5 13.4 8.2 10.1 12.9 19.5 15.6 9.6 3.9 9.0 13.9 4.4 12.5 9.1 12.1 18.1 3.5 14.3 17.3 16.7 4.9 8.1 0.0 0.0 11.2 8.7 12.6 18.1 4.6 12.8 2.1 10.4 21.4 10.5 8.6 9.3 9.3 4.7 17.7 9.5 17.9 1.6 11.6 7.3 18.3 19.1 0.1 12.4 4.7

6.3 4.9 8.8 5.9 14.5 7.0 2.4 2.5 3.9 29.7 3.1 14.0 12.2 4.3 9.3 17.0 28.8 16.0 5.2 2.5 6.6 9.6 5.0 9.2 4.5 9.6 16.0 1.3 10.9 13.3 13.8 4.8 5.5 0.0 0.0 8.8 10.3 11.1 14.1 2.0 9.3 0.9 9.8 18.6 9.3 5.2 6.7 9.1 4.2 14.3 7.1 12.8 1.9 12.8 5.2 14.3 16.9 0.0 8.5 2.7

All products

Primary products

6.8

GLOBAL LINKS

Tariff barriers

Manufactured products

% Most recent year

Grenada Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Iceland India Indonesia Iran, Islamic Rep. Iraq Israel Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Lebanon Lesotho Liberia Libya Macedonia, FYR Madagascar Malawi Malaysia Maldives Mali Mauritania Mauritius Mayotte Mexico Moldova Mongolia Montenegro Morocco Mozambique Myanmar Namibia Nepal New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Palau Panama Papua New Guinea Paraguay Peru Philippines Puerto Rico

2008 2009 2009 2010 2008 2009 2009 2009 2009 2009 2008 2009 2007 2010 2009 2008 2010 2010 2009 2009 2008 2007 2010 2006 2009 2008 2009 2009 2009 2010 2007 2009 2009 2010 2008 2009 2009 2009 2009 2008 2010 2009 2010 2009 2010 2010 2009 2009 2009 2006 2009 2008 2010 2010 2010

Binding coverage

100.0 100.0 38.6 97.6 100.0 89.8 100.0 95.0 74.5 96.6 .. .. 75.2 100.0 99.7 100.0 .. 15.2 .. 95.1 .. 99.9 99.9 .. .. 100.0 .. .. 100.0 30.5 32.0 83.9 97.0 40.5 39.4 17.7 .. 100.0 99.9 100.0 .. 100.0 14.0 17.6 96.1 99.4 100.0 100.0 96.6 19.5 100.0 100.0 98.6 .. 99.9 100.0 100.0 100.0 67.2 ..

Simple mean bound rate

56.8 42.3 20.3 48.6 56.8 17.6 32.5 13.5 50.2 37.5 .. .. 22.0 49.7 3.0 16.3 .. 95.3 .. 16.1 .. 100.0 7.5 .. .. 78.9 .. .. 6.9 27.3 75.9 14.6 37.2 28.9 19.6 98.3 .. 35.1 6.7 17.5 .. 41.3 97.4 83.8 19.4 26.2 10.0 41.7 44.9 119.4 3.0 13.9 60.0 .. 23.5 31.5 33.5 30.1 25.8 ..

Simple mean tariff

Weighted mean tariff

10.6 4.4 13.5 13.3 10.7 3.0 6.4 1.9 10.2 5.2 24.8 .. 5.5 9.2 2.6 9.7 4.3 12.1 .. 10.3 .. 4.1 3.6 9.3 5.6 9.5 .. 0.0 4.3 12.1 13.0 5.3 21.7 12.8 12.6 2.0 5.3 7.8 4.2 4.9 2.2 9.1 7.7 4.0 6.3 12.8 2.5 4.4 13.0 10.9 0.4 3.6 14.8 2.6 7.6 4.8 8.1 4.8 5.3 ..

8.8 2.7 11.9 9.9 6.8 5.1 6.5 0.9 7.9 3.1 19.6 .. 3.2 9.0 1.6 5.2 2.7 9.2 .. 8.7 .. 4.2 8.4 13.2 4.8 10.5 .. 0.0 3.2 8.3 7.0 3.1 20.6 8.4 10.1 1.0 1.8 6.1 3.0 5.1 3.2 7.1 4.5 3.2 1.8 14.3 1.6 2.6 9.1 10.6 0.3 3.2 9.5 2.2 7.6 2.6 3.7 2.5 4.8 ..

Share of tariff Share of tariff lines lines with international with specific rates peaks

43.3 18.1 56.1 51.8 41.3 5.1 0.5 5.7 6.6 11.4 56.5 .. 1.1 36.1 8.6 29.5 8.8 36.6 .. 7.0 .. 0.0 0.9 20.4 11.6 21.6 .. 0.0 14.5 41.1 47.5 16.3 88.1 47.9 49.0 10.4 2.6 6.4 7.7 0.1 5.4 23.6 25.4 4.1 16.7 50.4 0.0 17.1 48.9 34.9 0.5 0.2 45.3 0.5 2.8 24.4 18.3 10.0 5.4 ..

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 11.5 0.0 .. 0.0 .. 0.0 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.7 0.0 0.0 0.0 ..

%

%

Simple mean tariff

Weighted mean tariff

Simple mean tariff

Weighted mean tariff

14.1 4.9 15.6 14.6 17.7 5.8 9.9 2.4 20.0 5.6 21.7 .. 5.5 16.1 5.1 14.2 7.3 16.0 .. 26.3 .. 3.2 4.4 16.0 8.2 9.2 .. 0.0 7.6 13.9 14.8 2.4 17.5 12.8 11.1 1.2 3.8 10.7 6.6 5.2 6.2 18.0 8.2 5.1 4.1 15.6 1.4 5.9 14.0 11.8 1.8 4.4 14.2 0.5 11.5 15.2 5.8 3.8 6.8 ..

9.9 2.1 13.9 10.0 5.9 4.1 8.1 1.1 7.3 2.0 12.5 .. 2.2 8.6 1.6 3.9 1.3 12.6 .. 12.7 .. 3.1 1.3 14.2 5.0 1.6 .. 0.0 6.0 4.2 8.6 2.1 18.4 7.9 9.2 0.3 1.3 11.5 3.6 5.4 5.2 8.9 4.4 2.7 2.1 11.0 0.4 3.0 10.7 9.1 1.0 3.3 6.4 0.6 8.4 3.3 0.8 1.3 5.1 ..

10.0 4.3 13.2 12.9 9.7 2.5 5.9 1.8 8.7 5.2 24.8 .. 5.4 8.3 2.1 8.9 4.0 11.7 .. 7.3 .. 4.2 3.5 8.4 5.2 9.5 .. 0.0 3.9 11.9 12.7 5.8 22.8 12.8 12.8 2.1 5.5 7.4 3.8 4.9 1.6 8.2 7.5 3.9 6.7 12.5 2.6 4.2 12.8 10.7 0.3 3.5 14.7 3.1 7.1 3.4 8.2 4.9 5.0 ..

8.4 3.1 10.2 9.7 7.3 5.9 5.4 0.8 8.0 3.5 21.1 .. 3.6 9.3 1.6 5.9 3.1 6.6 .. 5.0 .. 4.4 9.4 12.6 5.0 10.9 .. 0.0 2.4 10.4 6.5 3.6 22.6 8.7 11.0 1.6 2.1 4.6 2.7 4.9 2.4 5.7 4.3 3.4 1.6 16.5 2.1 2.2 7.6 10.8 0.2 3.2 12.1 3.2 7.2 2.2 4.8 3.0 4.6 ..

2011 World Development Indicators

349

6.8

Tariff barriers All products

Primary products

Manufactured products

% Most recent year

Qatar Russian Federation Rwanda Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Solomon Islands Somalia South Africa Sri Lanka St. Kitts and Nevis St. Lucia St. Vincent & Grenadines Sudan Suriname Swaziland Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United States Uruguay Uzbekistan Vanuatu Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & North Africa

South Asia Sub-Saharan Africa High income OECD Non-OECD

2009 2009 2010 2009 2010 2005a 2007 2004 2010 2008 2010 2009 2009 2007 2007 2009 2010 2010 2010 2010 2006 2010 2009 2010 2009 2008 2008 2009 2002 2010 2010 2009 2010 2010 2009 2009 2010 2008 2009 2009 2007b

Binding coverage

Simple mean bound rate

100.0 .. 100.0 100.0 100.0 .. .. 100.0 69.6 100.0 .. 96.1 38.1 97.9 99.6 99.7 .. 27.6 96.1 99.8 .. .. 13.8 74.7 .. 14.3 100.0 100.0 58.3 50.3 .. 16.1 100.0 100.0 100.0 100.0 .. .. 100.0 100.0 .. .. 17.1 22.2 77.8 w 42.2 86.6 84.7 88.3 73.9 67.2 100.0 90.0 99.9 81.5 61.7 87.9 99.0 73.1

16.0 .. 89.3 10.8 30.0 .. .. 47.4 7.0 78.7 .. 19.4 30.1 75.9 61.9 62.5 .. 18.1 19.4 0.0 .. .. 120.0 26.1 .. 80.0 17.6 55.8 58.0 29.2 .. 73.5 5.8 14.8 3.7 31.6 .. .. 36.5 11.5 .. .. 106.9 91.4 27.3 w 57.7 30.3 31.8 29.0 35.5 25.8 5.8 32.5 30.4 41.6 41.8 7.9 10.7 9.1

a. Includes Montenegro. b. Rates are most favored nation rates.

350

2011 World Development Indicators

Simple mean tariff

4.2 8.1 9.9 4.0 13.4 8.1 6.5 .. 0.0 9.9 .. 7.6 10.1 14.3 9.6 11.3 13.4 11.6 10.9 0.0 6.7 4.9 12.9 10.8 .. 12.8 10.8 8.7 21.9 2.4 5.4 12.1 4.5 4.3 2.9 9.6 11.8 16.8 13.1 8.0 .. 5.5 10.8 16.7 6.2 w 12.1 8.9 8.4 9.2 9.5 5.3 4.5 9.2 6.7 13.0 11.1 2.7 3.6 1.8

Weighted mean tariff

3.8 5.9 6.0 3.9 8.9 6.0 28.3 .. 0.0 17.3 .. 4.4 6.4 13.7 9.0 8.4 7.9 11.9 10.2 0.0 6.1 3.8 8.2 4.9 .. 14.2 7.3 10.0 16.0 2.3 2.9 8.2 2.8 3.7 1.8 3.6 6.9 15.0 10.6 5.2 .. 4.2 3.8 17.3 2.5 w 10.0 6.3 5.8 6.4 6.4 4.8 2.8 6.6 6.1 8.2 7.5 1.8 2.2 0.6

Share of tariff Share of tariff lines lines with international with specific rates peaks

0.2 24.6 31.4 0.0 50.5 17.8 12.8 .. 0.0 2.6 .. 17.9 42.7 43.1 39.9 44.4 25.4 36.2 26.2 0.0 27.6 0.1 39.9 19.3 .. 47.3 64.7 43.6 57.8 4.6 14.8 37.5 1.1 0.2 3.4 29.3 20.1 65.0 21.9 19.8 .. 1.4 51.2 38.8 10.8 w 40.6 16.0 15.4 16.3 18.5 5.4 1.1 15.7 27.6 37.4 33.6 3.5 4.0 3.2

0.0 0.0 0.0 0.0 0.0 0.0 0.0 .. 0.0 0.8 .. 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.7 0.0 0.0 .. 0.0 0.0 0.4 0.0 0.0 2.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 w 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

%

%

Simple mean tariff

Weighted mean tariff

Simple mean tariff

Weighted mean tariff

5.0 7.7 11.5 3.3 14.1 10.9 14.0 .. 0.0 14.8 .. 5.4 15.3 16.5 12.7 15.1 15.9 18.3 9.7 0.0 6.5 5.4 17.5 14.0 .. 14.4 12.1 16.6 26.8 13.8 14.7 15.7 5.9 4.5 2.6 5.6 12.6 19.5 12.2 10.7 .. 7.1 9.2 17.4 6.6 w 14.4 8.6 9.7 7.9 9.8 6.8 5.9 8.6 6.5 17.1 12.1 4.2 5.3 2.5

4.0 4.4 6.4 2.8 7.7 4.5 50.5 .. 0.0 23.3 .. 1.9 8.4 13.5 4.9 7.8 7.7 15.0 1.3 0.0 6.1 2.1 8.7 2.7 .. 12.4 5.5 3.1 12.0 4.3 12.6 8.8 2.5 2.7 1.2 1.1 3.9 16.9 10.0 4.1 .. 3.8 3.1 20.4 2.4 w 9.4 5.4 4.8 5.6 5.7 5.1 2.5 6.2 6.1 7.3 5.9 1.9 2.3 0.7

4.1 8.2 9.7 4.1 13.2 7.8 4.8 .. 0.0 9.2 .. 7.8 9.4 13.7 9.1 10.5 13.0 10.4 11.1 0.0 6.5 4.9 12.4 10.2 .. 12.6 10.5 7.6 21.2 1.2 3.8 11.6 4.3 4.2 3.0 9.9 11.7 16.1 13.1 7.4 .. 5.2 10.9 16.1 6.1 w 11.8 8.9 8.2 9.3 9.4 5.0 4.3 9.2 6.5 12.3 10.9 2.5 3.3 1.6

3.8 6.2 5.9 4.2 10.2 6.8 6.4 .. 0.0 8.8 .. 5.6 5.2 13.7 12.2 8.6 7.9 10.4 15.9 0.0 5.7 5.3 8.0 5.9 .. 14.9 9.0 17.2 17.9 1.4 1.1 7.9 3.0 4.2 2.0 5.2 7.3 14.2 10.7 5.7 .. 4.6 4.1 14.7 2.5 w 10.2 6.5 6.2 6.6 6.6 4.6 3.0 6.7 5.7 8.4 8.1 1.8 2.1 0.6

About the data

6.8

GLOBAL LINKS

Tariff barriers Definitions

Poor people in developing countries work primarily

trade and reduce the weights applied to these tariffs.

• Binding coverage is the percentage of product

in agriculture and labor–intensive manufactures,

Bound rates result from trade negotiations incorpo-

lines with an agreed bound rate. • Simple mean

sectors that confront the greatest trade barriers.

rated into a country’s schedule of concessions and

bound rate is the unweighted average of all the lines

Removing barriers to merchandise trade could

are thus enforceable.

in the tariff schedule in which bound rates have been

Some countries set fairly uniform tariff rates

set. • Simple mean tariff is the unweighted average

across all imports. Others are selective, setting high

of effectively applied rates or most favored nation

In general, tariffs in high-income countries on

tariffs to protect favored domestic industries. The

rates for all products subject to tariffs calculated

imports from developing countries, though low, are

share of tariff lines with international peaks provides

for all traded goods. • Weighted mean tariff is the

twice those collected from other high-income coun-

an indication of how selectively tariffs are applied.

average of effectively applied rates or most favored

tries. But protection is also an issue for developing

The effective rate of protection—the degree to which

nation rates weighted by the product import shares

countries, which maintain high tariffs on agricultural

the value added in an industry is protected—may

corresponding to each partner country. • Share of

commodities, labor-intensive manufactures, and

exceed the nominal rate if the tariff system system-

tariff lines with international peaks is the share

other products and services.

atically differentiates among imports of raw materi-

of lines in the tariff schedule with tariff rates that

als, intermediate products, and finished goods.

exceed 15 percent. • Share of tariff lines with spe-

increase growth in these countries—even more if trade in services.

Countries use a combination of tariff and nontariff measures to regulate imports. The most common

The share of tariff lines with specific rates shows

cific rates is the share of lines in the tariff schedule

form of tariff is an ad valorem duty, based on the

the extent to which countries use tariffs based on

that are set on a per unit basis or that combine ad

value of the import, but tariffs may also be levied

physical quantities or other, non–ad valorem mea-

valorem and per unit rates. • Primary products are

on a specific, or per unit, basis or may combine ad

sures. Some countries such as Switzerland apply

commodities classified in SITC revision 2 sections

valorem and specific rates. Tariffs may be used to

mainly specific duties. To the extent possible, these

0–4 plus division 68 (nonferrous metals). • Manu-

raise fiscal revenues or to protect domestic indus-

specifi c rates have been converted to their ad

factured products are commodities classified in

tries from foreign competition—or both. Nontariff

valorem equivalent rates and have been included in

SITC revision 2 sections 5–8 excluding division 68.

barriers, which limit the quantity of imports of a par-

the calculation of simple and weighted tariffs.

ticular good, include quotas, prohibitions, licensing

Data are classified using the Harmonized System

schemes, export restraint arrangements, and health

at the six- or eight-digit level. Tariff data are from

and quarantine measures. Because of the difficulty

the United Nations Conference on Trade and Devel-

of combining nontariff barriers into an aggregate indi-

opment’s (UNCTAD) Trade Analysis and Information

cator, they are not included in the table.

System (TRAINS) database and the World Trade

Unless specified as most favored nation rates, the

Organization’s (WTO) Integrated Data Base (IDB)

tariff rates used in calculating the indicators in the

and Consolidated Tariff Schedules (CTS) database.

table are effectively applied rates. Effectively applied

Tariff line data were matched to Standard Interna-

rates are those in effect for partners in preferen-

tional Trade Classification (SITC) revision 2 codes to

tial trade arrangements such as the North Ameri-

define commodity groups and import weights. Import

can Free Trade Agreement. The difference between

weights were calculated using the United Nations

most favored nation and applied rates can be sub-

Statistics Division’s Commodity Trade (Comtrade)

stantial. Because more countries now report their

database. The table shows tariff rates for three com-

free trade agreements, suspensions of tariffs, and

modity groups: all products, primary products, and

other special preferences, this year’s World Develop-

manufactured products. Effectively applied rates at

ment Indicators includes effectively applied rates for

the six- and eight-digit product level are averaged for

most countries. All estimates are calculated using

products in each commodity group. When an effec-

the most recent information, which is not necessarily

tively applied rate is not available, the most favored

revised every year. As a result, data for the same year

nation rate is used instead.

may differ from data in last year’s edition.

Data are shown only for the last year for which comData sources

Three measures of average tariffs are shown: sim-

plete data are available and for all economies with

ple bound rates and the simple and the weighted

populations of 1 million or more and for economies

All indicators in the table were calculated by World

tariffs. Bound rates are based on all products in a

with populations of less than 1 million when avail-

Bank staff using the World Integrated Trade Solu-

country’s tariff schedule, while the most favored

able. EU member countries apply a common tariff

tion system, available at http://wits.worldbank.

nation or applied rates are calculated using all traded

schedule that is listed under European Union and

org. Data on tariffs were provided by UNCTAD’s

items. Weighted mean tariffs are weighted by the

are thus not listed separately.

TRAINS database and the WTO’s IDB and CTS

value of the country’s trade with each trading part-

database. Data on global imports are from the

ner. Simple averages are often a better indicator of

United Nations Statistics Division’s Comtrade

tariff protection than weighted averages, which are

database.

biased downward because higher tariffs discourage

2011 World Development Indicators

351

6.9

Trade facilitation Logistics Performance Index

1–5 (worst to best) 2009

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

352

Burden of customs procedures

1–7 (worst to best) 2009–10b

2.24 2.46 2.36 2.25 3.10 2.52 3.84 3.76 2.64 2.74 2.53 3.94 2.79 2.51 2.66 2.32 3.20 2.83 2.23 2.29 2.37 2.55 3.87 .. 2.49 3.09 3.49 3.88 2.77 2.68 2.48 2.91 2.53 2.77 2.07 3.51 3.85 2.82 2.77 2.61 2.67 1.70 3.16 2.41 3.89 3.84 2.41 2.49 2.61 4.11 2.47 2.96 2.63 2.60 2.10 2.59 2.78

2011 World Development Indicators

.. 4.0 3.2 2.8 2.7 2.6 5.0 5.3 3.5 3.4 .. 4.6 4.2 2.7 3.6 4.7 3.3 3.5 4.4 3.0 3.5 3.8 4.9 .. 2.7 5.7 4.5 6.5 4.1 .. .. 4.0 3.8 4.1 .. 4.6 5.6 4.7 3.5 4.5 4.2 .. 5.3 3.6 5.7 4.9 .. 5.4 4.7 5.1 3.8 4.1 4.2 .. .. .. 4.2

Lead time

Documents

days

number

To export 2009

2.0 1.7 4.6 6.0 3.7 .. 2.6 2.0 7.0 1.4 .. 1.7 3.0 15.0 2.0 .. 2.8 2.0 4.0 .. 1.3 3.4 2.8 .. 74.0 3.5 2.8 1.7 7.0 2.0 .. 2.0 1.0 1.0 .. 2.5 1.0 2.2 2.1 1.3 2.0 3.0 4.0 5.0 1.6 3.2 4.3 4.6 .. 3.6 2.9 3.0 2.6 3.5 .. 4.2 2.4

To import 2009

4.0 2.0 7.1 8.0 3.8 .. 2.8 3.7 3.0 1.4 .. 1.6 7.0 28.3 2.0 .. 3.9 3.9 14.0 .. 4.0 8.9 3.7 .. 35.0 3.0 2.6 1.6 7.0 3.0 .. 2.0 1.0 1.0 .. 3.5 1.0 3.5 3.4 3.1 2.0 3.0 4.0 6.0 1.8 4.5 13.0 3.5 .. 2.4 6.8 3.5 3.4 3.9 .. 5.3 3.2

To export June 2010

12 7 8 11 9 3 6 4 9 6 8 4 7 8 5 6 8 5 10 9 10 11 3 9 6 6 7 4 6 8 11 6 10 7 .. 4 4 6 9 6 8 9 3 8 4 2 7 6 4 4 6 5 10 7 6 8 6

To import June 2010

11 9 9 8 7 6 5 5 14 8 8 5 7 7 7 9 7 7 10 10 10 12 4 17 10 7 5 4 8 9 10 7 9 8 .. 7 3 7 7 6 8 13 4 8 5 2 8 8 4 5 7 6 10 9 6 10 10

Liner Quality of Freight Shipping port costs to the Connectivity infrastructure United Index States

0–100 (low to high) 2010

.. 4.3 31.4 10.7 27.6 .. 28.1 .. .. 7.5 .. 84.0 11.5 .. .. .. 31.7 5.5 .. .. 4.5 11.3 42.4 .. .. 22.1 143.6 113.6 26.1 5.2 10.5 12.8 17.5 9.0 6.6 0.4 26.8 22.2 18.7 47.5 9.6 0.0 5.7 .. 8.4 74.9 8.5 5.4 4.0 90.9 17.3 34.3 13.3 6.3 3.5 7.6 9.1

1–7 (worst to best) 2009–10b

1 kilogram DHL nondocument air packagea $ 2011

.. 3.5 3.2 2.1 3.8 2.9 c 4.9 4.8c 4.2c 3.4 .. 6.4 4.0 2.9 c 1.6 3.8c 2.9 3.8 3.9 c 3.0 c 3.9 3.3 5.7 .. 2.6c 5.5 4.3 6.8 3.5 .. .. 2.7 5.0 4.0 .. 4.6 c 6.1 4.3 3.7 4.2 4.1 .. 5.6 4.4c 6.4 5.9 .. 5.1 4.0 6.4 4.5 4.0 4.5 .. .. .. 5.3

143.10 155.85 157.10 157.10 90.75 143.10 98.00 129.45 155.85 98.00 155.85 112.50 157.10 90.75 155.85 157.10 90.75 155.85 157.10 157.10 95.70 157.10 72.20 157.10 157.10 90.75 84.55 90.45 90.75 157.10 157.10 90.75 157.10 155.85 75.05 155.85 129.45 75.05 90.75 143.10 90.75 157.10 155.85 157.10 129.45 112.50 157.10 157.10 155.85 112.50 157.10 129.45 90.75 157.10 157.10 75.05 90.75

Logistics Performance Index

1–5 (worst to best) 2009

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

2.99 3.12 2.76 2.57 2.11 3.89 3.41 3.64 2.53 3.97 2.74 2.83 2.59 .. 3.64 .. 3.28 2.62 2.46 3.25 3.34 2.30 2.38 2.33 3.13 2.77 2.66 2.42 3.44 2.27 2.63 2.72 3.05 2.57 2.25 2.38 2.29 2.33 2.02 2.20 4.07 3.65 2.54 2.54 2.59 3.93 2.84 2.53 3.02 2.41 2.75 2.80 3.14 3.44 3.34 .. 2.95

Burden of customs procedures

1–7 (worst to best) 2009–10b

4.3 4.0 3.9 3.5 .. 5.2 4.3 4.2 3.8 4.6 4.5 3.5 3.3 .. 4.5 .. 4.1 3.0 .. 4.1 3.5 3.8 .. 3.5 4.8 4.3 3.9 3.9 4.8 4.1 4.5 4.6 3.9 3.4 3.3 4.3 3.7 .. 4.2 3.4 5.2 5.8 3.6 .. 3.1 5.2 5.2 3.6 4.4 .. 3.8 4.5 3.0 4.3 4.9 4.7 4.9

Lead time

Documents

days

number

To export 2009

3.5 2.3 2.1 2.6 .. 1.0 2.0 2.6 10.0 1.0 3.2 2.8 3.0 .. 1.6 .. 2.0 2.0 .. 1.3 3.4 .. 4.0 3.2 2.0 .. .. 4.2 2.6 5.0 2.0 3.0 2.1 .. 14.0 2.0 .. 4.6 3.0 1.8 1.8 1.3 3.2 .. 2.5 1.0 .. 2.3 1.4 .. 1.0 2.0 1.8 3.0 2.5 .. 3.8

To import 2009

5.0 5.3 5.4 28.3 .. 1.0 2.0 3.0 10.0 1.0 4.6 11.5 5.9 .. 2.0 .. 3.0 .. .. 1.6 2.2 .. 5.0 10.0 2.3 .. .. 3.7 2.8 4.0 3.0 2.4 2.5 .. 12.0 3.2 .. 8.4 3.0 6.3 1.9 1.6 3.2 .. 4.1 2.0 .. 1.6 1.4 .. 4.0 3.8 5.0 3.6 5.0 .. 2.3

To export June 2010

5 8 5 7 10 4 5 4 6 4 7 10 8 .. 3 8 8 7 9 5 5 6 10 .. 6 6 4 11 7 7 11 5 5 6 8 7 7 .. 11 9 4 7 5 8 10 4 9 9 3 7 8 6 8 5 4 7 5

To import June 2010

7 9 6 8 10 4 4 4 6 5 7 12 7 .. 3 8 10 7 10 6 7 8 9 .. 6 6 9 10 7 10 11 6 4 7 8 10 10 .. 9 10 5 5 5 10 9 4 9 8 4 9 10 8 8 5 5 10 7

6.9

GLOBAL LINKS

Trade facilitation

Liner Quality of Freight Shipping port costs to the Connectivity infrastructure United Index States

0–100 (low to high) 2010

.. 41.4 25.6 30.7 4.2 7.6 33.2 59.6 33.1 67.4 17.8 .. 13.1 .. 82.6 .. 8.3 .. .. 6.0 30.3 .. 5.9 5.4 9.5 .. 7.4 .. 88.1 .. 5.6 16.7 36.3 .. .. 49.4 8.2 3.7 14.4 .. 90.0 18.4 8.7 .. 18.3 7.9 48.5 29.5 41.1 6.4 0.0 21.8 15.2 26.2 38.1 .. 7.7

1–7 (worst to best) 2009–10b

1 kilogram DHL nondocument air packagea $ 2011

4.0 c 3.9 3.6 3.9 .. 4.4 4.6 3.9 5.3 5.2 4.4 3.3 c 3.8 .. 5.5 .. 4.4 1.4 c .. 4.7 4.5 3.1c .. 3.2 4.7 3.7c 3.4 3.6 c 5.6 3.7c 3.6 4.5 3.7 2.9 3.3c 4.4 3.5 .. 5.6 2.9 c 6.6 5.4 2.9 .. 3.0 5.7 5.3 4.0 6.0 .. 3.4 c 3.3 2.8 3.3 4.9 5.4 5.4

155.85 98.00 98.00 143.10 143.10 112.00 143.10 112.50 75.05 120.80 143.10 155.85 157.10 95.70 98.00 .. 143.10 155.85 95.70 155.85 143.10 157.10 157.10 157.10 155.85 155.85 157.10 157.10 98.00 157.10 157.10 157.10 58.80 155.85 95.70 157.10 157.10 95.70 157.10 95.70 112.50 98.00 90.75 157.10 157.10 129.45 143.10 143.10 90.75 95.70 90.75 90.75 98.00 155.85 129.45 .. 143.10

2011 World Development Indicators

353

6.9

Trade facilitation Logistics Performance Index

1–5 (worst to best) 2009

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

Burden of customs procedures

Lead time

Documents

days

number

1–7 (worst to best) 2009–10b

To export 2009

To import 2009

To export June 2010

To import June 2010

3.9 2.9 4.8 4.9 4.7 3.6 .. 6.3 4.4 5.2 .. 4.4 4.6 4.2 .. 3.5 5.8 5.1 2.8 3.6 3.4 4.1 3.6 .. 3.1 4.7 3.8 .. 4.1 3.0 5.8 4.8 4.5 4.0 .. 2.2 3.6 .. .. 4.2 3.6 4.2 u 3.8 3.8 3.7 3.9 3.8 3.8 3.7 3.8 3.8 3.7 3.9 4.9 4.9

2.0 4.0 .. 2.3 1.4 2.0d 2.0 2.2 3.0 1.0 .. 2.3 4.0 1.3 39.0 .. 1.0 2.6 2.5 7.0 3.2 1.6 .. .. .. 1.7 2.2 3.0 5.5 1.7 2.5 3.3 2.8 3.0 1.4 9.4 1.4 .. 3.1 9.2 25.0 3.8 e u 6.8 3.8 4.4 3.1 4.6 3.6 2.9 3.9 2.7 1.9 8.1 2.1 2.2

2.0 2.9 .. 6.3 2.7 3.0 d 32.0 1.8 5.0 2.0 .. 3.3 7.1 2.5 5.0 .. 2.6 2.6 3.2 .. 7.1 2.6 .. .. .. 7.0 3.8 .. 14.0 7.0 2.0 1.9 4.0 3.0 2.0 12.1 1.7 .. 3.6 4.0 18.0 4.6 e u 7.2 5.0 5.1 4.9 5.6 4.9 3.1 5.5 7.2 3.3 7.0 2.7 2.9

5 8 8 5 6 6 7 4 6 6 .. 8 6 8 6 9 3 4 8 10 5 4 6 6 5 4 7 .. 6 6 4 4 4 10 7 8 6 6 6 6 7 7u 8 7 7 7 7 7 7 7 7 9 8 5 4

6 13 8 5 5 6 7 4 8 8 .. 9 7 6 6 10 3 5 9 9 7 3 7 8 6 7 8 .. 8 8 5 4 5 10 9 9 8 6 9 8 9 7u 9 8 8 8 8 7 8 7 8 9 9 5 5

2.84 2.61 2.04 3.22 2.86 2.69d 1.97 4.09 3.24 2.87 1.34 3.46 3.63 2.29 2.21 .. 4.08 3.97 2.74 2.35 2.60 3.29 1.71 2.60 .. 2.84 3.22 2.49 2.82 2.57 3.63 3.95 3.86 2.75 2.79 2.68 2.96 .. 2.58 2.28 2.29 2.87 e u 2.38 2.69 2.62 2.75 2.59 2.73 2.68 2.74 2.60 2.49 2.42 3.54 3.57

Liner Quality of Freight Shipping port costs to the Connectivity infrastructure United Index States

0–100 (low to high) 2010

15.5 20.9 .. 50.4 13.0 3.0 d 5.8 103.8 .. 20.6 4.2 32.5 74.3 40.2 10.1 .. 30.6 2.6 15.2 .. 10.6 43.8 .. 14.2 15.8 6.5 36.1 .. .. 21.1 63.4 87.5 83.8 24.5 .. 18.6 31.4 .. 12.5 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

1–7 (worst to best) 2009–10b

1 kilogram DHL nondocument air packagea $ 2011

3.0 3.7 2.8 5.2 4.7 2.8 .. 6.8 4.0 c 5.3 .. 4.7 5.6 4.9 .. 4.2 6.2 5.2c 3.1 1.9 c 3.0 5.0 2.5 .. 4.3 5.0 4.1 .. 3.5 c 3.6 6.2 5.5 5.5 5.2 .. 2.4 3.6 .. .. 3.6 c 4.4 c 4.3 u 3.5 3.8 3.8 3.9 3.7 3.8 3.3 3.9 4.0 3.8 3.8 5.3 5.3

155.85 155.85 157.10 143.10 157.10 155.85 157.10 90.45 155.85 155.85 157.10 157.10 129.45 98.00 157.10 157.10 129.45 129.45 143.10 155.85 157.10 98.00 95.70 157.10 75.05 157.10 143.10 155.85 157.10 155.85 143.10 112.50 .. 90.75 155.85 90.75 98.00 .. 143.10 157.10 157.10 .. .. .. .. .. .. .. .. .. .. .. .. .. ..

a. Transportation charges only; excludes fuel, assessorial/surcharges, duties and taxes. b. Average of the 2009 and 2010 survey ratings. c. Landlocked country. d. Includes Montenegro. e. Aggregates are computed according to the World Bank classification of economies as of July 1, 2010 and may differ from data published in the original source.

354

2011 World Development Indicators

About the data

6.9

GLOBAL LINKS

Trade facilitation Definitions

Broadly defi ned, trade facilitation encompasses

include the value of time to import or export and the

• Logistics Performance Index refl ects percep-

customs efficiency and other physical and regulatory

risk of delay or loss of shipments. Long lead times

tions of a country’s logistics based on efficiency of

environments where trade takes place, harmoniza-

and burdensome regulatory procedures may lower

customs clearance process, quality of trade- and

tion of standards and conformance to international

competitiveness. Data on lead time are from the LPI

transport-related infrastructure, ease of arranging

regulations, and the logistics of moving goods and

survey. Respondents provided separate values for

competitively priced shipments, quality of logistics

associated documentation through countries and

the best case (10 percent of shipments) and the

services, ability to track and trace consignments, and

ports. Though collection of trade facilitation data

median case (50 percent of shipments). The data

frequency with which shipments reach the consignee

has improved over the last decade, data that allow

are exponentiated averages of the logarithm of sin-

within the scheduled time. The index ranges from 1

meaningful evaluation, especially for developing

gle value responses and of midpoint values of range

to 5, with a higher score representing better perfor-

economies, are lacking. Data on trade facilitation

responses for the median case.

mance. • Burden of customs procedure measures

are drawn from research by private and international

Data on the number of documents needed to export

business executives’ perceptions of their country’s

agencies. Most data are perception-based evalua-

or import are from the World Bank’s Doing Business

efficiency of customs procedures. Values range from

tions by business executives and professionals.

surveys, which compile procedural requirements for

1 to 7, with a higher rating indicating greater effi -

Because of different backgrounds, values, and per-

exporting and importing a standardized cargo of goods

ciency. • Lead time to export is the median time (the

sonalities, those surveyed may evaluate the same

by ocean transport from local freight forwarders, ship-

value for 50 percent of shipments) from shipment

situation quite differently. Caution should thus be

ping lines, customs brokers, port officials, and banks.

used when interpreting perception-based indicators.

To make the data comparable across economies, sev-

Nevertheless, they convey much needed information

eral assumptions about the business and the traded

on trade facilitation.

goods are used (see www.doingbusiness.org).

point to port of loading. • Lead time to import is the median time (the value for 50 percent of shipments) from port of discharge to arrival at the consignee. • Documents to export and documents to import are

The table presents data from Logistics Performance

Access to global shipping and air freight networks

Surveys conducted by the World Bank in partnership

and the quality and accessibility of ports and roads

with academic and international institutions and

affect logistics performance. The table shows two

private companies and individuals engaged in inter-

indicators related to trade and transport service infra-

national logistics. The Logistics Performance Index

structure: the Liner Shipping Connectivity Index and

assesses logistics performance across six aspects

the quality of port infrastructure rating. The Liner Ship-

of the logistics environment (see Definitions), based

ping Connectivity Index captures how well countries

on more than 5,000 country assessments by nearly

are connected to global shipping networks. It is com-

1,000 international freight forwarders. Respondents

puted by the United Nations Conference on Trade and

evaluate eight markets on six core dimensions on

Development (UNCTAD) based on five components of

a scale from 1 (worst) to 5 (best). The markets are

the maritime transport sector: number of ships, their

chosen based on the most important export and

container-carrying capacity, maximum vessel size,

import markets of the respondent’s country, random

number of services, and number of companies that

selection, and, for landlocked countries, neighboring

deploy container ships in a country’s ports. For each

countries that connect them with international mar-

component a country’s value is divided by the maxi-

kets. Scores for the six areas are averaged across all

mum value of each component in 2004, the five com-

the DHL international U.S. inbound worldwide priority

respondents and aggregated to a single score. Details

ponents are averaged for each country, and the aver-

express rate for a 1 kilogram nondocument air pack-

of the survey methodology and index construction

age is divided by the maximum average for 2004 and

age. Fuel, assessorial/surcharges, duties, and taxes

methodology are in Arvis and others (2010).

multiplied by 100. The index generates a value of 100

are excluded.

Data on the burden of customs procedures are

for the country with the highest average index in 2004.

from the World Economic Forum’s Executive Opinion

The quality of port infrastructure measures busi-

all documents required per shipment by government ministries, customs authorities, port and container terminals, health and technical control agencies, and banks to export or import goods. Documents renewed annually and not requiring renewal per shipment are excluded. • Liner Shipping Connectivity Index indicates how well countries are connected to global shipping networks based on the status of their maritime transport sector. The highest value in 2004 is 100. • Quality of port infrastructure measures business executives’ perceptions of their country’s port facilities. Values range from 1 to 7, with a higher rating indicating better development of port infrastructure. • Freight costs to the United States is

Data sources

Survey. The 2010 round included more than 15,000

ness executives’ perception of their country’s port

Data on the Logistics Performance Index and lead

respondents from 139 countries. Sampling follows

facilities. Values range from 1 (port infrastructure

time to export and import are from Arvis and others’

a dual stratification based on company size and the

considered extremely underdeveloped) to 7 (port

Connecting to Compete: Trade Logistics in the Global

sector of activity. Data are collected online or through

infrastructure considered efficient by international

Economy 2010. Data on the burden of customs

in-person interviews. Responses are aggregated using

standards). Respondents in landlocked countries

procedure and quality of port infrastructure ratings

sector-weighted averaging. The data for the latest

were asked: “How accessible are port facilities (1 =

are from the World Economic Forum’s Global Com-

year are combined with the data for the previous year

extremely inaccessible; 7 = extremely accessible.)”

petitiveness Report 2010–2011. Data on number of

to create a two-year moving average. Respondents

The costs of transport services are a crucial deter-

documents to export and import are from the World

evaluated the efficiency of customs procedures in

minant of export competitiveness. The proxy indica-

Bank’s Doing Business project (www.doingbusiness.

their country. The lowest value (1) rates the customs

tor in the table is the shipping rates to the United

org). Data on the Liner Shipping Connectivity Index are

procedure as extremely inefficient, and the highest

States of an international freight moving business.

from UNCTAD’s Review of Maritime Transport 2010.

score (7) as extremely efficient. The direct costs of cross-border trade include

Freight costs to the United States are based on DHL’s “DHL Express Standard Rate Guideline 2011” (2011).

freight, customs, and storage fees. Indirect costs

2011 World Development Indicators

355

6.10

External debt Total external debt

Long-term debt

$ millions

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

356

1995

2009

.. 456 33,053 11,500 98,465 371 .. .. 321 15,726 1,694 .. 1,398 5,272 .. 717 160,469 10,379 1,271 1,162 2,284 10,950 .. 946 843 22,038 118,090 .. 25,044 13,239 5,887 3,766 18,899 .. .. .. .. 4,447 13,877 33,475 2,509 37 .. 10,322 .. .. 4,361 426 1,240 .. 5,495 .. 3,282 3,248 895 821 4,851

2,328 4,719 5,345 16,715 120,183 4,935 .. .. 4,865 23,820 17,158 .. 1,073 5,745 9,583 1,617 276,932 40,582 1,835 518 4,364 2,941 .. 396 1,743 71,646 428,442 .. 52,223 12,183 5,041 8,070 11,701 .. .. .. .. 11,003 12,930 33,257 11,384 1,019 .. 5,025 .. .. 2,130 520 4,231 .. 5,720 .. 13,801 2,926 1,111 1,244 3,675

2011 World Development Indicators

1995

$ millions Public and publicly guaranteed IBRD loans Total and IDA credits 2009 1995 2009

.. 330 31,314 9,543 54,913 298 .. .. 206 14,905 1,301 .. 1,267 4,459 .. 707 98,260 8,808 1,140 1,099 2,110 9,620 .. 854 777 7,178 94,674 .. 13,946 9,636 4,867 3,097 11,902 .. .. .. .. 3,653 11,951 30,687 1,979 37 .. 9,788 .. .. 3,977 385 1,039 .. 4,200 .. 2,328 2,991 794 766 4,247

2,203 2,829 2,871 13,722 72,923 2,376 .. .. 3,403 21,206 4,758 .. 990 2,545 3,569 1,388 87,317 4,772 1,725 420 4,099 2,128 .. 250 1,711 9,282 93,125 .. 35,364 10,788 4,785 3,190 10,979 .. .. .. .. 7,714 6,910 30,622 6,131 1,013 .. 4,812 .. .. 2,022 449 2,596 .. 4,126 .. 4,931 2,827 950 1,078 2,446

.. 109 2,049 81 4,913 96 .. .. 30 5,692 116 .. 498 865 472 108 6,038 444 608 591 65 1,082 .. 414 379 1,383 14,248 .. 2,559 1,413 279 303 2,386 .. .. .. .. 300 1,108 2,356 327 24 .. 1,470 .. .. 110 162 84 .. 2,434 .. 158 847 210 389 828

471 874 10 385 5,305 1,214 .. .. 939 10,746 256 .. 309 316 1,520 5 10,065 1,509 721 147 566 303 .. 9 896 216 22,226 .. 6,571 2,497 298 58 1,823 .. .. .. .. 756 542 3,250 578 477 .. 1,422 .. .. 18 64 1,253 .. 1,581 .. 1,112 1,269 304 39 502

Private nonguaranteed 1995 2009

.. 0 0 0 16,066 0 .. .. 0 0 0 .. 0 239 .. 0 30,830 342 0 0 0 288 .. 0 0 11,429 1,090 .. 5,553 0 0 214 2,660 .. .. .. .. 19 440 313 5 0 .. 0 .. .. 0 0 0 .. 27 .. 142 0 0 0 123

0 983 982 0 27,723 1,461 .. .. 590 0 1,504 .. 0 2,647 4,051 0 149,826 17,232 0 0 0 615 .. 0 0 44,888 94,808 .. 12,749 0 0 2,538 271 .. .. .. .. 843 4,600 74 3,139 0 .. 0 .. .. 0 0 518 .. 0 .. 7,644 0 0 0 880

Short-term debt

Use of IMF credit

$ millions 1995 2009

$ millions 1995 2009

.. 20 62 835 261 1,492 1,958 2,634 21,355 19,537 2 512 .. .. .. .. 14 810 199 1,939 110 8,024 .. .. 47 45 307 554 .. 1,677 10 229 31,238 39,789 512 18,578 56 0 15 7 102 265 991 23 .. .. 57 67 17 4 3,431 17,476 22,325 240,509 .. .. 5,545 4,110 3,118 596 1,002 213 430 2,341 3,910 99 .. .. .. .. .. .. .. .. 616 1,679 1,312 1,419 2,372 2,561 525 2,114 0 6 .. .. 460 45 .. .. .. .. 287 108 15 42 85 330 .. .. 620 1,323 .. .. 811 1,226 164 40 95 151 27 0 382 317

.. 65 1,478 0 6,131 70 .. .. 101 622 283 .. 84 268 48 0 142 717 75 48 72 51 .. 35 49 0 0 .. 0 485 19 24 427 .. .. .. .. 160 173 103 0 0 .. 73 .. .. 97 26 116 .. 648 .. 0 94 6 29 99

106 71 0 359 0 587 .. .. 62 675 2,871 .. 39 0 286 0 0 0 110 91 0 175 .. 78 29 0 0 .. 0 800 43 0 352 .. .. .. .. 767 0 0 0 0 .. 168 .. .. 0 29 786 .. 271 .. 0 59 10 166 32

Total external debt

Long-term debt

$ millions

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

1995

2009

.. 95,174 124,413 21,565 .. .. .. .. 4,581 .. 7,661 3,750 7,309 .. .. .. .. 609 2,155 .. 2,974 684 2,466 .. 769 1,277 4,302 2,238 34,343 2,958 2,396 1,416 165,379 695 531 23,771 7,458 5,771 .. 2,410 .. .. 10,396 1,604 34,092 .. .. 30,169 6,098 2,506 2,574 30,833 39,379 .. .. .. ..

.. 237,692 157,517 13,435 .. .. .. .. 10,959 .. 6,615 109,873 8,005 .. .. 359 .. 2,900 5,539 .. 24,864 705 1,660 .. 31,717 5,589 2,213 1,093 66,390 2,667 2,029 742 192,008 3,457 2,212 23,752 4,168 8,186 .. 3,683 .. .. 4,420 991 7,846 .. .. 53,710 12,418 1,555 4,323 29,593 62,911 .. .. .. ..

1995

.. 81,091 65,323 15,116 .. .. .. .. 3,721 .. 6,624 2,834 5,857 .. .. .. .. 472 2,091 .. 1,559 642 1,153 .. 430 788 3,687 2,078 16,023 2,739 2,127 1,148 93,902 450 472 23,190 5,209 5,378 .. 2,339 .. .. 8,572 1,347 28,140 .. .. 23,727 3,781 1,668 1,453 18,931 28,525 .. .. .. ..

$ millions Public and publicly guaranteed IBRD loans Total and IDA credits 2009 1995 2009

.. 76,531 86,020 7,524 .. .. .. .. 6,664 .. 5,445 2,487 6,543 .. .. 359 .. 2,320 2,923 .. 20,979 681 677 .. 9,059 1,874 1,846 899 21,364 2,592 1,851 661 99,374 783 1,817 19,219 3,354 6,320 .. 3,563 .. .. 2,461 909 4,157 .. .. 41,484 11,282 1,037 2,308 20,791 41,738 .. .. .. ..

.. 27,348 13,259 316 .. .. .. .. 595 .. 806 295 2,412 .. .. .. .. 141 285 .. 113 207 269 .. 62 181 1,121 1,306 1,059 863 347 157 13,823 152 59 3,999 890 777 .. 1,023 .. .. 341 598 3,489 .. .. 6,403 175 407 189 1,729 5,185 .. .. .. ..

.. 34,028 10,111 836 .. .. .. .. 398 .. 1,109 547 3,156 .. .. 359 .. 656 680 .. 318 313 69 .. 23 653 1,105 213 39 698 282 212 10,143 443 392 2,557 1,356 777 .. 1,483 .. .. 418 266 2,852 .. .. 11,844 435 231 296 2,846 2,669 .. .. .. ..

Private nonguaranteed 1995 2009

.. 6,618 33,123 0 .. .. .. .. 128 .. 0 103 445 .. .. .. .. 0 0 .. 50 0 0 .. 29 289 0 0 11,046 0 0 267 18,348 9 0 331 1,769 0 .. 0 .. .. 0 133 301 .. .. 1,593 0 711 338 1,288 4,847 .. .. .. ..

.. 118,211 52,834 0 .. .. .. .. 3,241 .. 0 98,710 0 .. .. 0 .. 332 2,601 .. 670 0 0 .. 16,708 1,816 4 0 21,332 0 0 81 69,299 1,203 141 2,354 0 0 .. 0 .. .. 1,093 7 175 .. .. 3,265 1,136 397 1,263 4,073 17,171 .. .. .. ..

6.10

GLOBAL LINKS

External debt

Short-term debt

Use of IMF credit

$ millions 1995 2009

$ millions 1995 2009

.. 5,049 25,966 6,449 .. .. .. .. 492 .. 785 381 634 .. .. .. .. 13 0 .. 1,365 4 978 .. 49 143 542 44 7,274 72 169 1 37,300 6 12 198 279 393 .. 23 .. .. 1,785 72 5,651 .. .. 3,235 2,207 78 784 9,659 5,279 .. .. .. ..

.. 42,950 18,662 5,911 .. .. .. .. 1,054 .. 1,158 8,676 1,011 .. .. 0 .. 81 0 .. 3,096 0 92 .. 5,949 1,900 262 67 23,695 32 163 0 23,335 1,318 72 2,179 643 1,866 .. 44 .. .. 716 18 3,514 .. .. 1,466 0 121 752 4,730 4,002 .. .. .. ..

.. 2,416 0 0 .. .. .. .. 240 .. 251 432 374 .. .. .. .. 124 64 .. 0 38 336 .. 262 57 73 116 0 147 100 0 15,828 230 47 52 202 0 .. 48 .. .. 39 52 0 .. .. 1,613 111 50 0 955 728 .. .. .. ..

2011 World Development Indicators

.. 0 0 0 .. .. .. .. 0 .. 12 0 451 .. .. 0 .. 167 16 .. 119 24 891 .. 0 0 101 127 0 44 16 0 0 154 182 0 171 0 .. 76 .. .. 150 57 0 .. .. 7,495 0 0 0 0 0 .. .. .. ..

357

6.10

External debt Total external debt

Long-term debt

$ millions 1995

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

1995

Private nonguaranteed 1995 2009

Use of IMF credit

$ millions 1995 2009

$ millions 1995 2009

6,832 117,511 3,957 17,904 844 2,995 534 69,031 1,303 21,032 121,401 381,339 101,582 99,990 1,524 3,211 0 250,725 10,201 30,624 1,029 747 971 725 512 254 0 0 32 6 .. .. .. .. .. .. .. .. .. .. 3,916 3,503 3,266 2,961 1,160 921 44 357 260 18 33,402 6,788 a 8,725 1,252a 2,459 1,773a 19,076 2,139a 4,000 10,785a 1,220 444 1,028 371 234 124 0 0 27 0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 2,678 2,973 1,961 1,987 432 448 0 0 551 810 25,358 42,101 9,837 15,063 0 21 4,935 13,764 9,673 13,274 .. .. .. .. .. .. .. .. .. .. 8,395 17,208 7,175 13,647 1,512 2,487 90 967 535 1,873 17,603 20,139 9,779 12,998 1,279 1,306 496 0 6,368 6,739 249 418 238 391 25 10 0 0 11 27 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 21,897 5,236 16,955 4,480 471 16 0 0 4,942 756 634 2,514 590 1,603 0 373 0 855 43 15 7,365 7,325 6,204 4,637 2,269 2,598 0 1,016 964 1,342 100,039 58,755 16,826 11,185 1,906 133 39,117 19,689 44,095 27,881 .. .. .. .. .. .. .. .. .. .. 1,476 1,640 1,286 1,502 541 586 0 0 85 47 .. .. .. .. .. .. .. .. .. .. 10,818 21,709 9,022 14,837 1,766 1,405 193 2,070 1,310 4,801 73,781 251,372 50,317 84,875 5,069 9,816 7,079 118,814 15,701 39,725 402 576 385 463 1 13 0 38 17 75 3,609 2,490 3,089 2,245 1,792 1,379 0 0 103 235 8,429 93,153 6,581 10,449 491 3,294 84 51,857 223 19,873 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 5,318 12,159 3,833 10,955 513 1,099 127 80 1,336 1,124 1,799 4,109 1,415 3,238 157 368 15 727 212 144 35,744 54,503 28,428 35,184 1,639 0 2,013 3,310 3,063 16,009 25,428 28,674 21,778 23,403 231 6,270 0 0 3,272 5,186 .. .. .. .. .. .. .. .. .. .. 6,251 6,356 5,562 5,861 827 2,187 0 0 689 442 6,958 3,049 5,291 1,210 1,434 407 13 1,020 415 474 4,989 5,015 3,462 3,742 896 985 381 89 685 1,068 .. s .. s .. s .. s .. s .. s .. s .. s .. s .. s 135,593 109,551 110,863 33,428 39,578 2,818 5,946 11,139 12,833 130,267 1,729,983 3,409,521 1,151,625 1,296,127 144,453 185,309 205,673 1,346,264 319,724 720,903 841,940 1,417,085 578,607 597,241 97,821 123,481 94,497 394,555 156,647 402,423 888,043 1,992,436 573,018 698,886 46,632 61,827 111,176 951,710 163,077 318,479 1,860,250 3,545,114 1,261,176 1,406,990 177,881 224,887 208,491 1,352,210 330,863 733,736 455,544 825,602 255,399 293,956 37,604 44,253 89,982 208,994 108,826 322,361 246,178 1,126,252 189,044 269,524 11,522 33,110 10,256 656,239 31,250 165,385 608,666 912,980 371,875 432,115 38,485 41,907 87,303 340,984 122,856 138,637 161,737 141,321 140,298 112,569 12,751 11,847 887 6,150 18,375 22,402 152,282 339,983 129,636 159,965 42,036 61,257 8,301 122,442 9,051 48,495 235,842 198,976 174,924 138,861 35,483 32,512 11,760 17,399 40,504 36,456 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

a. Includes Montenegro.

358

2009

$ millions Public and publicly guaranteed IBRD loans Total and IDA credits 2009 1995 2009

Short-term debt

2011 World Development Indicators

1,038 9,544 9,617 0 26 15 .. .. 347 167 84 a 1,601 165 73 .. .. .. .. .. .. 166 176 913 0 .. .. 595 721 960 403 0 0 .. .. .. .. 0 0 0 41 197 329 0 0 .. .. 105 91 .. .. 293 0 685 7,958 0 0 417 9 1,542 10,974 .. .. .. .. .. .. 21 0 157 0 2,239 0 377 84 .. .. 0 53 1,239 345 461 116 .. s .. s 6,760 5,951 52,961 46,227 12,188 22,866 40,772 23,361 59,721 52,179 1,337 291 15,628 35,103 26,632 1,243 2,177 200 5,293 9,081 8,654 6,261 .. .. .. ..

About the data

6.10

GLOBAL LINKS

External debt Definitions

External indebtedness affects a country’s credit-

Variations in reporting rescheduled debt also affect

• Total external debt is debt owed to nonresident

worthiness and investor perceptions. Data on exter-

cross-country comparability. For example, reschedul-

creditors and repayable in foreign currencies, goods,

nal debt are gathered through the World Bank’s

ing of official Paris Club creditors may be subject to

or services by public and private entities in the coun-

Debtor Reporting System. Indebtedness is calculated

lags between completion of the general rescheduling

try. It is the sum of long-term external debt, short-

using loan-by-loan reports submitted by countries on

agreement and completion of the specific bilateral

term debt, and use of IMF credit. Debt repayable

long-term public and publicly guaranteed borrowing

agreements that define the terms of the rescheduled

in domestic currency is excluded. • Long-term debt

and information on short-term debt collected by the

debt. Other areas of inconsistency include country

is debt that has an original or extended maturity of

countries or from creditors through the reporting sys-

treatment of arrears and of nonresident national

more than one year. It has three components: pub-

tems of the Bank for International Settlements (BIS).

deposits denominated in foreign currency.

lic, publicly guaranteed, and private nonguaranteed

These data are supplemented by information from

Aggregate data on long-term private nonguaran-

debt. • Public and publicly guaranteed debt com-

major multilateral banks and official lending agen-

teed debt are reported annually. DRS countries rec-

prises the long-term external obligations of public

cies in major creditor countries and by estimates by

ognize the importance of monitoring borrowing by

debtors, including the national government and politi-

World Bank and International Monetary Fund (IMF)

their private sector, particularly when it accounts for

cal subdivisions (or an agency of either) and autono-

staff. The table includes data on long-term private

a significant share of total external debt, but many

mous public bodies, and the external obligations of

nonguaranteed debt reported to the World Bank or

find doing so difficult. Detailed data are available

private debtors that are guaranteed for repayment

estimated by its staff.

only from countries with registration requirements

by a public entity. • IBRD loans and IDA credits are

Data coverage, quality, and timeliness vary by coun-

for private nonguaranteed debt, most commonly in

extended by the World Bank. The International Bank

try. Coverage varies for debt instruments and borrow-

connection with exchange controls. Where formal

for Reconstruction and Development (IBRD) lends

ers. The widening spectrum of debt instruments and

registration of private nonguaranteed debt is not

at market rates. The International Development

investors alongside the expansion of private nonguar-

mandatory, compilers must rely on balance of pay-

Association (IDA) provides credits at concessional

anteed borrowing makes comprehensive coverage

ments data and financial surveys. The data on private

rates. • Private nonguaranteed debt consists of the

of external debt more complex. Reporting countries

nonguaranteed debt in the table are as reported or

long-term external obligations of private debtors that

differ in their capacity to monitor debt, especially

estimated for countries where this type of external

are not guaranteed for repayment by a public entity.

private nonguaranteed debt. Even data on public

debt is known to be significant. Estimates are based

• Short-term debt is debt owed to nonresidents hav-

and publicly guaranteed debt are affected by cover-

on national data on quarterly external debt statistics.

ing an original maturity of one year or less and inter-

age and reporting accuracy—because of monitoring

The DRS encourages debtor countries to volun-

est in arrears on long-term debt and on the use of

capacity and sometimes because of unwillingness to

tarily provide information on their short-term external

IMF credit. • Use of IMF credit denotes members’

provide information. A key part often underreported

obligations. By its nature, short-term external debt

drawings on the IMF other than those drawn against

is military debt. Currently, 128 developing countries

is difficult to monitor: loan-by-loan registration is

the country’s reserve tranche position and includes

report to the Debtor Reporting System (DRS). Nonre-

normally impractical, and monitoring systems typi-

purchases and drawings under the Extended Credit

porting countries might have outstanding debt with

cally rely on information requested periodically by

Facility, Standby Credit Facility, Rapid Credit Facility,

the World Bank, other international financial institu-

the central bank from the banking sector. The World

Stand-By Arrangements, Flexible Credit Line, and the

tions, and private creditors.

Bank regards the debtor country as the authorita-

Extended Fund Facility.

Debt data, normally reported in the currency of

tive source of information on its short-term debt.

repayment, are converted into U.S. dollars to pro-

Where such information is not available from the

duce summary tables. Stock fi gures (amount of

debtor country, data from creditor sources may be

debt outstanding) are converted using end-of-period

used as an indication of the magnitude of a coun-

exchange rates, as published in the IMF’s Interna-

try’s short-term external debt. These data are derived

tional Financial Statistics (line ae). Flow figures are

from BIS data on international bank lending based

converted at annual average exchange rates (line

on time remaining to original maturity. The data are

rf). Projected debt service is converted using end-

reported based on residual maturity, but an estimate

of-period exchange rates. Debt repayable in multiple

of short-term external liabilities by original maturity

currencies, goods, or services and debt with a provi-

can be derived by deducting from claims due in one

sion for maintenance of the value of the currency of

year those that, 12 months earlier, had a maturity

repayment are shown at book value.

of between one and two years. However, not all com-

Data on external debt are mainly from reports to

Because flow data are converted at annual aver-

mercial banks report to the BIS in a way that allows

the World Bank through its Debtor Reporting Sys-

age exchange rates and stock data at end-of-period

the full maturity distribution to be determined, and

tem from member countries that have received

exchange rates, year-to-year changes in debt out-

the BIS data include liabilities only to banks within

IBRD loans or IDA credits, with additional infor-

standing and disbursed are sometimes not equal to

the BIS reporting area. The results should thus be

mation from the files of the World Bank, the IMF,

net flows (disbursements less principal repayments);

interpreted with caution.

the African Development Bank and African Devel-

Data sources

similarly, changes in debt outstanding, including

Data related to the operations of the IMF are pro-

opment Fund, the Asian Development Bank and

undisbursed debt, differ from commitments less

vided by the IMF Treasurer’s Department. They are

Asian Development Fund, and the Inter-American

repayments. Discrepancies are particularly notable

converted from special drawing rights into U.S. dol-

Development Bank. Summary tables of the exter-

when exchange rates have moved sharply during

lars using end-of-period exchange rates for stocks

nal debt of developing countries are published

the year. Cancellations and reschedulings of other

and average-over-the-period exchange rates for flows.

annually in the World Bank’s Global Development

liabilities into long-term public debt also contribute

The IMF’s loan instruments have changed over time to

Finance, Global Development Finance CD-ROM,

to the differences.

address the specific circumstances of its members.

and Global Development Finance database.

2011 World Development Indicators

359

6.11 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

360

Ratios for external debt Total external debt

Total debt service

Multilateral debt service

% of GNI 1995 2009

% of exports of goods and services and incomea 1995 2009

% of public and publicly guaranteed debt service 1995 2009

.. 18.5 83.5 311.9 38.9 25.3 .. .. 10.6 40.2 12.2 .. 71.2 81.2 .. 15.1 21.2 81.9 53.6 117.6 67.6 133.4 .. 85.9 58.5 32.1 16.5 .. 27.5 271.4 479.3 32.8 188.7 .. .. .. .. 28.5 72.0 55.8 26.4 6.3 .. 136.8 .. .. 101.6 113.0 48.2 .. 86.9 .. 22.6 90.0 379.4 .. 132.9

.. 40.3 3.8 28.2 40.1 55.3 .. .. 12.1 24.0 35.6 .. 16.1 34.5 54.6 14.1 17.9 90.4 22.9 38.9 45.0 13.6 .. 20.0 28.6 46.7 8.7 .. 23.6 121.4 83.8 28.1 53.0 .. .. .. .. 24.6 23.3 17.6 54.3 .. .. 17.6 .. .. 22.3 75.3 40.0 .. 37.3 .. 38.8 48.3 253.2 .. 25.9

2011 World Development Indicators

.. 2.8 .. 12.0 30.2 3.2 .. .. 1.3 16.1 3.4 .. 7.5 29.5 .. 3.1 38.5 16.5 .. 27.6 0.7 21.0 .. .. .. 24.5 9.9 .. 33.5 .. 13.5 14.2 23.1 .. .. .. .. 7.0 26.6 16.0 13.4 0.1 .. 18.5 .. .. 15.3 15.5 .. .. 24.2 .. 12.5 24.9 52.4 51.0 34.7

0.4 6.9 .. 8.4 17.3 20.9 .. .. 1.7 5.6 5.0 .. .. 14.4 10.5 1.2 23.4 21.3 .. .. 0.8 7.4 .. .. 2.8 22.6 2.9 .. 22.4 .. .. 9.6 9.5 .. .. .. .. 12.1 40.8 6.5 25.2 .. .. 3.1 .. .. 8.1 .. 7.3 .. 2.9 .. 18.4 .. .. 4.6 6.8

.. 11.4 17.7 0.6 21.6 69.8 .. .. 21.8 28.0 55.4 .. 54.6 75.5 .. 76.0 18.5 10.5 76.7 70.6 11.9 61.0 .. 100.0 86.1 76.2 7.6 .. 32.7 .. 21.1 50.6 59.3 .. .. .. .. 39.8 32.0 26.3 55.1 100.0 .. 41.9 .. .. 17.9 49.1 0.4 .. 48.4 .. 47.5 30.5 86.3 92.2 55.9

73.4 43.1 0.4 0.3 44.7 55.6 .. .. 28.4 70.2 3.1 .. 74.5 84.0 72.7 59.6 28.3 55.1 65.0 95.8 75.1 37.3 .. 65.0 85.3 3.7 27.2 .. 36.6 35.5 18.4 25.4 96.0 .. .. .. .. 25.2 12.9 30.2 67.9 56.1 .. 45.8 .. .. 16.9 51.4 47.4 .. 18.7 .. 74.9 62.8 100.0 81.0 44.0

Short-term debt

% of total debt 1995 2009

.. 13.7 0.8 17.0 21.7 0.6 .. .. 4.4 1.3 6.5 .. 3.4 5.8 .. 1.4 19.5 4.9 4.4 1.3 4.5 9.0 .. 6.0 2.0 15.6 18.9 .. 22.1 23.6 17.0 11.4 20.7 .. .. .. .. 13.8 9.5 7.1 20.9 0.0 .. 4.5 .. .. 6.6 3.5 6.9 .. 11.3 .. 24.7 5.0 10.6 3.2 7.9

0.9 17.7 27.9 15.8 16.3 10.4 .. .. 16.6 8.1 46.8 .. 4.2 9.6 17.5 14.2 14.4 45.8 0.0 1.4 6.1 0.8 .. 17.0 0.2 24.4 56.1 .. 7.9 4.9 4.2 29.0 0.8 .. .. .. .. 15.3 11.0 7.7 18.6 0.6 .. 0.9 .. .. 5.1 8.1 7.8 .. 23.1 .. 8.9 1.4 13.6 0.0 8.6

Present value of debt

% of total reserves 1995 2009

.. 23.5 6.3 919.7 133.6 1.9 .. .. 11.6 8.4 29.2 .. 23.7 30.5 .. 0.2 60.7 31.3 16.1 6.9 53.1 6,444.5 .. 24.0 11.6 23.1 27.8 .. 65.6 1,980.9 1,575.1 40.5 739.1 .. .. .. .. 165.3 73.4 13.9 55.9 0.0 .. 56.5 .. .. 187.8 14.0 43.0 .. 77.1 .. 103.6 188.9 469.2 13.4 141.7

.. 35.2 1.0 19.3 40.7 25.5 .. .. 15.1 18.8 142.3 .. 3.6 6.5 51.7 2.6 16.7 100.3 0.0 2.3 8.1 0.6 .. 31.9 0.6 69.1 9.8 .. 16.4 36.9 5.6 57.5 3.0 .. .. .. .. 57.8 37.4 7.3 67.7 .. .. 2.5 .. .. 5.4 18.9 15.7 .. .. .. 23.6 .. 89.5 0.0 ..

% of GNIa 2009

% of exports of goods, services, and incomea 2009

5 31 3 24 41 36 .. .. 10 17 30 .. 12b 16b 45 8 17 85 17b 13b 38 4b .. 12b 22b 43 9 .. 20 24b 20 b 27 46b .. .. .. .. 22 23 16 49 34b .. 12b .. .. 19 30b 28 .. 27b .. 33 44b 203 b 15b 13 b

25 96 5 21 156 148 .. .. 14 90 51 .. 62b 34b 106 16 125 132 154b 143b 60 12b .. 75b 41b 84 25 .. 111 71b 18 b 50 88 b .. .. .. .. 73 59 53 162 811b .. 89 b .. .. 18 81b 80 .. 60 b .. 126 152b 647b 113 b 25b

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

Total external debt

Total debt service

Multilateral debt service

% of GNI 1995 2009

% of exports of goods and services and incomea 1995 2009

% of public and publicly guaranteed debt service 1995 2009

% of total debt 1995 2009

.. 34.4 30.3 29.7 .. .. .. .. 18.8 .. 16.7 3.9 25.3 .. .. .. .. 13.3 6.1 .. .. 6.1 .. .. 1.3 .. 7.7 24.9 7.0 16.1 23.1 8.7 28.1 7.9 10.2 40.4 34.5 18.9 .. 7.9 .. .. 43.1 17.1 14.7 .. .. 30.9 3.4 20.8 5.8 17.3 16.3 .. .. .. ..

.. 24.2 28.4 1.3 .. .. .. .. 40.6 .. 33.5 7.8 32.5 .. .. .. .. 59.0 37.4 .. 13.5 60.3 .. .. 31.8 99.9 74.3 51.4 15.5 45.5 49.6 34.5 19.5 79.1 2.8 30.3 17.4 15.0 .. 54.2 .. .. 30.3 95.5 45.4 .. .. 43.2 52.7 31.7 48.0 49.9 29.2 .. .. .. ..

.. 5.3 20.9 29.9 .. .. .. .. 10.7 .. 10.2 10.2 8.7 .. .. .. .. 2.1 0.0 .. 45.9 0.6 39.6 .. 6.4 11.2 12.6 1.9 21.2 2.4 7.1 0.1 22.6 0.9 2.2 0.8 3.7 6.8 .. 0.9 .. .. 17.2 4.5 16.6 .. .. 10.7 36.2 3.1 30.4 31.3 13.4 .. .. .. ..

.. 27.0 63.4 23.9 .. .. .. .. 82.3 .. 118.8 18.5 83.8 .. .. .. .. 37.5 122.6 .. 24.4 55.8 .. .. 9.8 29.0 143.3 165.8 40.6 122.3 175.3 35.2 60.5 40.3 44.2 75.1 360.6 .. .. 54.7 .. .. 368.6 87.6 131.7 .. .. 49.4 80.9 57.3 31.5 60.3 51.7 .. .. .. ..

.. 18.2 30.2 4.1 .. .. .. .. 77.8 .. 28.3 113.0 26.5 .. .. 6.4 .. 65.8 95.5 .. 70.7 33.2 257.5 .. 85.3 62.2 .. 24.7 35.8 29.6 66.6 8.4 22.3 59.7 55.8 26.4 43.0 .. .. 28.7 .. .. 76.2 18.8 5.1 .. .. 31.3 52.5 19.9 29.5 24.8 39.2 .. .. .. ..

.. 5.9 18.4 .. .. .. .. .. 33.9 .. 4.8 80.2 5.0 .. .. 20.8 .. 14.0 .. .. 18.0 3.0 .. .. 31.0 14.8 2.3 .. 5.2 .. .. 2.7 16.0 14.9 4.8 12.5 1.6 .. .. 10.4 .. .. 17.2 4.5 0.8 .. .. 15.0 5.5 11.7 6.1 11.8 18.5 .. .. .. ..

.. 31.9 25.3 4.2 .. .. .. .. 17.6 .. 50.7 45.7 40.7 .. .. 100.0 .. 78.1 79.7 .. 5.7 81.7 30.3 .. 8.9 63.9 61.9 33.4 1.9 57.5 58.1 35.6 9.3 43.1 29.9 49.8 71.4 8.4 .. 77.6 .. .. 49.8 92.0 61.0 .. .. 50.5 22.5 58.0 52.6 33.2 13.5 .. .. .. ..

Short-term debt

.. 18.1 11.8 44.0 .. .. .. .. 9.6 .. 17.5 7.9 12.6 .. .. 0.0 .. 2.8 0.0 .. 12.5 0.0 5.5 .. 18.8 34.0 11.8 6.1 35.7 1.2 8.0 0.0 12.2 38.1 3.3 9.2 15.4 22.8 .. 1.2 .. .. 16.2 1.9 44.8 .. .. 2.7 0.0 7.8 17.4 16.0 6.4 .. .. .. ..

Present value of debt

% of total reserves 1995 2009

.. 22.1 174.2 .. .. .. .. .. 72.2 .. 34.4 23.0 164.9 .. .. .. .. 9.7 0.0 .. 16.9 0.9 3,481.0 .. 6.0 51.9 497.1 37.8 29.5 22.2 187.9 0.1 218.8 2.3 7.4 5.1 142.8 60.4 .. 3.5 .. .. 1,256.8 75.6 330.7 .. .. 128.0 282.4 29.1 70.8 111.6 67.8 .. .. .. ..

6.11

GLOBAL LINKS

Ratios for external debt

.. 15.1 28.2 .. .. .. .. .. 50.8 .. 9.5 37.4 26.3 .. .. 0.0 .. 5.1 0.0 .. 7.9 .. .. .. 89.4 83.0 23.0 41.0 24.5 2.0 68.4 0.0 23.4 89.0 5.4 9.2 .. .. .. .. .. .. 45.5 2.8 7.7 .. .. 10.8 0.0 4.6 19.5 14.2 9.1 .. .. .. ..

% of GNIa 2009

.. 17 30 4 .. .. .. .. 82 .. 27 96 19 .. .. 4 .. 36b 78 .. 80 19 316b .. 72 59 17b 16b 31 14b 83 b 7 18 55 35 23 18 b .. .. 23 .. .. 36b 13b 4 .. .. 24 54 18 26 23 35 .. .. .. ..

2011 World Development Indicators

% of exports of goods, services, and incomea 2009

.. 71 99 .. .. .. .. .. 178 .. 46 157 72 .. .. 25 .. 62b 233 .. 105 27 347b .. 120 100 59 b 65b 27 51b 153b 11 61 109 57 65 53 b .. .. 154 .. .. 68 b 67b 8 .. .. 157 66 21 48 78 90 .. .. .. ..

361

6.11 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

Ratios for external debt Total external debt

Total debt service

Multilateral debt service

% of GNI 1995 2009

% of exports of goods and services and incomea 1995 2009

% of public and publicly guaranteed debt service 1995 2009

19.4 31.0 79.2 .. 82.9 .. 149.0 .. .. .. .. 17.1 .. 65.3 136.3 14.0 .. .. 188.9 53.6 143.5 60.5 .. 116.7 .. 63.0 44.4 16.1 63.3 17.8 .. .. .. 28.0 13.5 49.0 124.0 .. 169.9 215.1 73.5 .. w 88.4 36.8 40.4 33.9 38.8 35.5 32.7 35.8 59.2 32.2 76.1 .. ..

71.6 31.9 14.9 .. 27.1 79.7 23.4 .. .. .. .. 15.1 .. 41.5 40.5 15.4 .. .. 10.3 51.2 34.0 23.3 .. 57.5 .. 58.2 41.2 3.0 16.2 83.8 .. .. .. 34.5 12.5 16.7 32.3 .. 25.5 26.8 .. .. w 30.9 21.8 15.6 30.3 22.1 13.2 44.7 23.7 15.4 20.7 22.9 .. ..

10.5 6.3 20.5 .. 17.8 .. 63.6 .. .. .. .. 9.5 .. 9.3 10.1 1.5 .. .. 4.5 .. 17.4 11.6 .. 6.2 .. 18.3 30.1 .. 19.8 6.6 .. .. .. 22.1 .. 22.9 .. .. 4.6 .. .. .. w .. 18.0 17.2 18.6 18.0 12.7 10.9 27.3 21.1 29.7 16.2 .. ..

31.4 17.7 4.7 .. .. 37.1 2.2 .. .. .. .. 9.3 .. 15.6 5.8 2.1 .. .. .. 38.4 3.5 6.8 .. .. .. 10.1 41.6 .. 2.0 36.2 .. .. .. 21.0 .. 6.4 1.8 .. .. 3.8 .. .. w 3.9 11.6 6.2 19.5 11.3 4.8 26.9 17.9 .. 6.8 5.9 .. ..

21.3 9.7 99.0 .. 62.2 100.0 c 8.4 .. .. .. .. 0.0 .. 14.0 100.0 64.0 .. .. 55.3 .. 66.7 20.9 .. 75.5 .. 45.2 20.7 1.9 69.7 13.6 .. .. .. 27.3 1.9 11.6 2.9 .. 78.3 50.6 33.6 .. w 40.2 22.5 25.5 20.0 23.0 18.2 16.6 26.2 19.7 27.4 35.0 .. ..

44.1 4.7 70.4 .. 59.1 51.9 62.9 .. .. .. .. 2.5 .. 20.0 22.3 82.8 .. .. 30.2 39.8 69.6 4.8 .. 98.3 .. 41.7 13.4 2.2 66.0 16.9 .. .. .. 22.3 21.6 13.4 18.3 .. 58.7 48.6 0.0 .. w 57.1 20.2 27.9 15.4 21.0 18.4 13.3 23.3 23.3 38.4 25.1 .. ..

Short-term debt

% of total debt 1995 2009

19.1 8.4 3.1 .. 6.6 19.8 c 2.2 .. .. .. 20.6 38.1 .. 6.4 36.2 4.5 .. .. 22.6 6.8 13.1 44.1 .. 5.8 .. 12.1 21.3 4.3 2.8 2.6 .. .. .. 25.1 11.8 8.6 12.9 .. 11.0 6.0 13.7 .. w 8.6 18.5 18.6 18.4 17.8 23.9 12.7 20.2 11.4 5.9 17.2 .. ..

Present value of debt

% of total reserves 1995 2009

17.9 49.7 8.0 56.6 0.8 32.3 .. .. 0.5 95.6 12.0 .. 0.0 77.8 .. .. .. .. .. .. 27.2 .. 31.5 216.7 .. .. 10.9 25.3 33.5 3,898.2 6.5 3.7 .. .. .. .. 14.4 1,102.7 0.6 .. 18.3 356.6 47.5 119.4 .. .. 2.9 65.1 .. .. 22.1 77.6 15.8 113.0 13.0 1.5 9.4 22.4 21.3 20.9 .. .. .. .. .. .. 9.2 73.7 3.5 .. 29.4 28.6 18.1 247.2 .. .. 7.0 107.9 15.6 186.2 21.3 77.2 .. w .. w 9.5 96.0 21.1 72.7 28.4 70.4 16.0 74.8 20.7 73.3 39.0 64.9 14.7 67.6 15.2 88.6 15.9 31.1 14.3 29.5 18.3 193.5 .. .. .. ..

47.4 7.0 0.9 .. 0.8 26.3 0.0 .. .. .. .. 33.5 .. 35.0 615.9 2.8 .. .. 4.1 .. 38.7 20.1 .. 6.7 .. 42.5 53.0 .. 7.9 75.0 .. .. .. 14.0 .. 46.6 31.5 .. 6.3 25.1 .. .. w 16.2 14.9 12.2 21.0 15.0 11.3 24.4 25.0 .. 15.4 21.3 .. ..

% of GNIa 2009

53 26 8b .. 20 b 71 20 b .. .. .. .. 15 .. 35 73 b 13 .. .. 9 39 13 b 22 .. 50 b .. 54 35 3 8b 62 .. .. .. 37 12 19 27 .. 17 10 b .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

% of exports of goods, services, and incomea 2009

166 74 64b .. 73b 223 104b .. .. .. .. 44 .. 136 352b 16 .. .. 24 114 57b 28 .. 136b .. 80 144 4 34b 123 .. .. .. 121 29 66 34 .. 47 24b 335 .. .. .. .. .. .. .. .. .. .. .. .. .. ..

a. The numerator refers to 2009, whereas the denominator is a three-year average of 2007–09 data. b. Data are from debt sustainability analyses for low-income countries. Present value estimates for these countries are for public and publicly guaranteed debt only. c. Includes Montenegro.

362

2011 World Development Indicators

About the data

6.11

GLOBAL LINKS

Ratios for external debt Definitions

A country’s external debt burden, both debt outstand-

value of external debt provides a measure of future

• Total external debt is debt owed to nonresidents

ing and debt service, affects its creditworthiness

debt service obligations.

and comprises public, publicly guaranteed, and pri-

and vulnerability. The table shows total external debt

The present value of external debt is calculated by

vate nonguaranteed long-term debt, short-term debt,

relative to a country’s size—gross national income

discounting the debt service (interest plus amortiza-

and use of IMF credit. It is presented as a share of

(GNI). Total debt service is contrasted with countries’

tion) due on long-term external debt over the life of

GNI. • Total debt service is the sum of principal

ability to obtain foreign exchange through exports of

existing loans. Short-term debt is included at face

repayments and interest actually paid in foreign cur-

goods, services, income, and workers’ remittances.

value. The data on debt are in U.S. dollars converted

rency, goods, or services on long-term debt; inter-

Multilateral debt service (shown as a share of the

at official exchange rates (see About the data for

est paid on short-term debt; and repayments (repur-

country’s total public and publicly guaranteed debt

table 6.10). The discount rate on long-term debt

chases and charges) to the IMF. • Exports of goods,

service) are obligations to international financial

depends on the currency of repayment and is based

services, and income are the total value of exports

institutions, such as the World Bank, the Interna-

on commercial interest reference rates established

of goods and services, receipts of compensation of

tional Monetary Fund (IMF), and regional develop-

by the Organisation for Economic Co-operation and

nonresident workers, and investment income from

ment banks. Multilateral debt service takes priority

Development. Loans from the International Bank

abroad. • Multilateral debt service is the repayment

over private and bilateral debt service, and borrowers

for Reconstruction and Development (IBRD), cred-

of principal and interest to the World Bank, regional

must stay current with multilateral debts to remain

its from the International Development Association

development banks, and other multilateral and inter-

creditworthy. While bilateral and private creditors

(IDA), and obligations to the IMF are discounted using

governmental agencies. • Short-term debt includes

often write off debts, international financial institu-

a special drawing rights reference rate. When the

all debt having an original maturity of one year or less

tion bylaws prohibit granting debt relief or canceling

discount rate is greater than the loan interest rate,

and interest in arrears on long-term debt. • Total

debts directly. However, the recent decrease in multi-

the present value is less than the nominal sum of

reserves comprise holdings of monetary gold, spe-

lateral debt service ratios for some countries reflects

future debt service obligations.

cial drawing rights, reserves of IMF members held

debt relief from special programs, such as the Heav-

Debt ratios are used to assess the sustainability of

by the IMF, and holdings of foreign exchange under

ily Indebted Poor Countries (HIPC) Debt Initiative and

a country’s debt service obligations, but no absolute

the control of monetary authorities. • Present value

the Multilateral Debt Relief Initiative (MDRI) (see

rules determine what values are too high. Empirical

of debt is the sum of short-term external debt plus

table 1.4.) Other countries have accelerated repay-

analysis of developing countries’ experience and

the discounted sum of total debt service payments

ment of debt outstanding. Indebted countries may

debt service performance shows that debt service

due on public, publicly guaranteed, and private non-

also apply to the Paris and London Clubs to renegoti-

difficulties become increasingly likely when the pres-

guaranteed long-term external debt over the life of

ate obligations to public and private creditors.

ent value of debt reaches 200 percent of exports.

existing loans.

Because short-term debt poses an immediate

Still, what constitutes a sustainable debt burden var-

burden and is particularly important for monitoring

ies by country. Countries with fast-growing econo-

vulnerability, it is compared with the total debt and

mies and exports are likely to be able to sustain

foreign exchange reserves that are instrumental in

higher debt levels.

providing coverage for such obligations. The present Data sources Ratio of debt services to exports for middle-income economies have sharply increased in 2009 as export revenues declined

6.11a

Data on external debt are mainly from reports to the World Bank through its Debtor Reporting Sys-

Total debt service (% of exports of goods, services, and income) 30

tem from member countries that have received IBRD loans or IDA credits, with additional information from the files of the World Bank, the IMF,

20

the African Development Bank and African DevelMiddle-income economies

opment Fund, the Asian Development Bank and Asian Development Fund, and the Inter-American Development Bank. Data on GNI, exports of goods

10 Low-income economies

and services, and total reserves are from the World Bank’s national accounts files and the IMF’s Balance of Payments and International Financial

0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Statistics databases. Summary tables of the exter-

Due to global financial crisis, export revenues in 2009 declined by 20 percent for middle-income economies, and

nal debt of developing countries are published

by 8 percent for low-income economies. Reduction in export revenues caused sharp raise in the ratio of debt

annually in the World Bank’s Global Development

service to exports, which has been declining since 2000 thanks to debt reduction efforts and export growth.

Finance, Global Development Finance CD-ROM,

Source: Global Development Finance data files.

and Global Development Finance database.

2011 World Development Indicators

363

6.12

Global private financial flows Equity ﬂows

Debt ﬂows

$ millions Foreign direct investment 1995

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

364

0 70 .. 472 5,609 25 12,026 1,901 330 2 15 10,689 a 13 393 .. 70 4,859 90 10 2 151 7 9,319 6 33 2,957 35,849 .. 968 –22 125 337 211 108 .. 2,568 4,139 414 452 598 38 .. 201 14 1,044 23,736 –315 8 .. 11,985 107 1,053 75 1 0 7 50

2011 World Development Indicators

2009

185 978 2,847 2,205 3,902 777 22,572 8,714 473 674 1,884 –38,860 93 423 235 252 25,949 4,595 171 0 530 340 19,898 42 462 12,702 78,193 52,395 7,207 951 2,083 1,347 381 2,951 .. 2,666 2,905 2,067 316 6,712 431 0 1,751 221 60 59,989 33 39 658 39,153 1,685 2,419 600 50 14 38 500

Portfolio equity 1995

.. 0 .. 0 1,552 .. 2,585 1,262 .. –15 .. 6,505a 0 0 .. 6 2,775 0 .. 0 .. 0 –3,077 .. .. –249 0 .. 165 0 0 0 1 4 .. 1,236 .. .. 13 0 0 .. 10 .. 2,027 6,823 .. .. .. –1,513 0 0 .. .. .. .. 0

$ millions Commercial bank and other lending

Bonds

2009

1995

2009

1995

2009

.. 4 .. 0 –212 1 .. 498 0 –154 1 –3,242 .. 0 .. 18 37,071 8 .. .. 0 0 23,349 .. .. 316 28,161 9,492 67 .. .. 0 –9 23 .. -311 8,152 0 2 393 0 .. –131 0 –273 68,285 .. 0 13 11,806 0 764 0 0 .. 0 0

.. 0 –278 0 3,705 0 .. .. 0 0 0 .. 0 0 .. 0 2,636 –6 0 0 0 0 .. 0 0 489 317 .. 1,008 0 0 –4 0 .. .. .. .. 0 0 0 0 0 .. 0 .. .. 0 0 0 .. 0 .. 44 0 0 0 –13

0 0 0 0 –1,114 0 .. .. 0 0 0 .. 0 –10 0 0 19,111 –372 0 0 0 0 .. 0 0 1,900 –39 .. 6,768 0 0 –225 0 .. .. .. .. –125 –2,987 0 0 0 .. 0 .. .. –44 0 0 .. 0 .. –50 0 0 0 50

.. 0 788 123 754 0 .. .. 0 –20 103 .. 0 41 .. -6 8,283 –93 0 –1 13 –65 .. 0 0 1,773 4,696 .. 1,250 0 –53 –20 14 .. .. .. .. –31 59 –311 –31 0 .. –48 .. .. –75 0 0 .. 38 .. –34 –15 0 0 38

0 451 –607 156 –1,849 42 .. .. 400 –13 –31 .. 0 –156 –40 –1 4,731 304 –3 0 0 –12 .. 0 0 2,572 –12,050 .. –1,018 –61 –1 538 –143 .. .. .. .. –213 –997 –33 175 0 .. 1,019 .. .. 74 0 135 .. 224 .. -574 4 0 0 222

Equity ﬂows

Debt ﬂows

$ millions Foreign direct investment

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

GLOBAL LINKS

6.12

Global private financial flows

Portfolio equity

$ millions Commercial bank and other lending

Bonds

1995

2009

1995

2009

1995

2009

1995

2009

4,804 2,144 4,346 17 2 1,447 1,350 4,842 147 39 13 964 42 .. 1,776 .. 7 96 95 180 .. 275 5 –88 73 9 10 6 4,178 111 7 19 9,526 26 10 92 45 280 153 .. 12,206 3,316 89 7 1,079 2,393 46 723 223 455 103 2,557 1,478 3,659 685 .. ..

2,783 34,577 4,877 3,016 1,070 25,233 3,894 28,976 541 11,834 2,382 13,619 141 .. 1,506 406 145 189 319 94 4,804 63 218 1,711 230 248 543 60 1,387 109 –38 257 14,462 128 624 1,970 881 323 490 38 33,287 –1,259 434 739 5,787 11,271 2,210 2,387 1,773 423 205 4,760 1,948 13,796 2,808 .. ..

–62 1,590 1,493 0 .. 0 991 5,358 0 50,597 0 .. 5 .. 4,219 .. 0 .. 0 0 .. .. .. .. 6 .. .. .. 0 .. 0 22 519 –1 0 20 0 .. 46 0 –743 .. 0 .. 0 636 0 10 0 .. 0 171 0 219 –179 .. ..

954 21,112 787 .. .. 29,184 2,122 20,915 0 12,432 -30 46 3 .. 25,661 0 0 1 0 –8 929 .. 0 0 –2 –14 .. .. –449 .. .. –33 4,169 2 4 –4 0 .. 4 .. 19,256 967 0 .. 522 2,470 326 –37 0 .. 0 47 –1,096 1,579 1,616 .. ..

.. 285 2,248 0 .. .. .. .. 13 .. 0 0 0 .. .. .. .. 0 0 .. 350 0 0 .. 0 0 0 0 2,440 0 0 150 3,758 0 0 0 0 0 .. 0 .. .. 0 0 0 .. .. 0 0 –32 0 0 1,110 .. .. .. ..

.. 1,822 5,112 0 .. .. .. .. 740 .. -2 -2,108 0 .. .. 0 .. 0 0 .. 789 0 0 .. 2,488 244 0 0 143 0 0 0 7,499 –6 0 0 0 0 .. 0 .. .. 0 0 0 .. .. –500 1,323 0 0 2,828 3,527 .. .. .. ..

.. 955 60 –37 .. .. .. .. 15 .. –201 240 –163 .. .. .. .. 0 0 .. 333 12 0 .. 55 0 –4 –23 1,231 0 0 126 1,401 24 –14 158 24 36 .. –5 .. .. –81 –24 –448 .. .. 317 –12 –311 –16 43 –215 .. .. .. ..

.. 8,343 5,872 –1,417 .. .. .. .. –62 .. –3 6,554 24 .. .. 0 .. 29 387 .. –41 –1 –32 .. –1,971 244 0 0 –1,592 1 –1 29 –9,314 –18 46 –61 20 0 .. –1 .. .. –75 –7 –55 .. .. 26 70 25 425 –258 –783 .. .. .. ..

2011 World Development Indicators

365

6.12

Global private financial flows Equity ﬂows

Debt ﬂows

$ millions Foreign direct investment 1995

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

419 2,065 2 –1,875 32 45b 7 11,535 236 150 1 1,248 8,086 56 12 52 14,939 4,158 100 10 120 2,068 .. 26 299 264 885 233 121 267 .. 21,731 57,800 157 –24 985 1,780 123 –218 97 118 340,573 s 1,540 93,318 54,045 39,273 94,858 50,797 5,599 30,212 907 2,931 4,411 245,715 89,322

a. Includes Luxembourg. b. Includes Montenegro.

366

2011 World Development Indicators

2009

6,310 36,751 119 10,499 208 1,921 74 16,809 –31 –579 108 5,354 6,451 404 2,682 66 11,538 27,588 1,434 16 415 4,976 .. 50 709 1,595 8,403 1,355 604 4,816 .. 72,924 134,710 1,262 750 –3,105 7,600 .. 129 699 60 1,163,874 s 10,950 348,451 177,583 170,868 359,401 101,428 86,067 76,629 27,766 38,414 29,096 804,473 371,020

Portfolio equity 1995

0 47 0 0 4 .. 0 –159 –16 .. .. 2,914 4,216 .. 0 1 1,853 5,851 0 .. 0 2,253 .. 0 17 12 195 .. 0 .. .. 8,070 16,523 0 .. 270 .. 0 .. .. .. 127,074 s –10 13,835 5,397 8,438 13,824 3,746 248 5,216 32 1,585 2,998 113,249 23,747

2009

7 3,369 0 .. .. 23 6 2,058 182 31 .. 9,364 9,378 –382 0 –7 1,400 9,241 .. 0 3 1,334 .. .. .. –89 2,827 .. 122 105 .. 78,845 160,534 -12 .. 121 128 .. 0 –13 .. 744,295 s –33 108,577 50,913 57,663 108,544 28,868 6,386 41,570 1,200 20,539 9,981 635,751 296,975

$ millions Commercial bank and other lending

Bonds 1995

2009

1995

0 –810 0 .. 0 0 0 .. .. .. 0 731 .. 0 0 0 .. .. 0 0 0 2,123 .. 0 .. 588 627 0 0 –200 .. .. .. 144 0 –468 0 .. 0 0 –30 .. s –30 20,954 6,470 14,484 20,924 8,206 –389 11,311 660 285 851 .. ..

32 –1,968 0 .. 200 0 0 .. .. .. 0 1,750 .. 400 0 0 .. .. 0 0 0 –341 .. 0 .. –313 1,152 0 0 –1,115 .. .. .. –420 0 4,992 –20 .. 0 0 0 .. s 0 51,121 8,555 42,566 51,121 8,383 –1,653 40,290 473 1,722 1,906 .. ..

413 444 0 .. –25 0 –28 .. .. .. 0 748 .. 103 0 0 .. .. –1 0 18 3,702 .. 0 .. –96 174 20 –9 –19 .. .. .. 39 201 -216 356 .. –2 –37 140 .. s –107 26,661 8,991 17,670 26,554 9,554 1,563 13,240 632 1,350 214 .. ..

2009

7,022 7,328 0 .. 157 104 0 .. .. .. 0 2,291 .. 238 0 0 .. .. 0 –54 84 –1,134 .. 0 .. 30 –12,036 –24 0 –1,605 .. .. .. –19 –118 –322 –1 .. –1 –36 0 .. s 1,601 88 –2,246 2,335 1,689 –9,217 6,921 –6,172 –2,132 8,575 3,715 .. ..

About the data

6.12

GLOBAL LINKS

Global private financial flows Definitions

Private financial flows—equity and debt—account for

International Development Association. The reports

• Foreign direct investment is net inflows of invest-

the bulk of development finance. Equity flows com-

are cross-checked with data from market sources

ment to acquire a lasting interest in or management

prise foreign direct investment (FDI) and portfolio

that include transactions data. Information on private

control over an enterprise operating in an economy

equity. Debt flows are financing raised through bond

nonguaranteed bonds and bank lending is collected

other than that of the investor. It is the sum of equity

issuance, bank lending, and supplier credits. Data

from market sources when data are not reported by

capital, reinvested earnings, other long-term capi-

on equity flows are based on balance of payments

countries to the Debtor Reporting System.

tal, and short-term capital, as shown in the balance

data reported by the International Monetary Fund

Data on equity flows are shown for all countries

of payments. Net inflows refer to new investments

(IMF). FDI data are supplemented by staff estimates

for which data are available. Debt flows are shown

made during the reporting period netted against dis-

using data from the United Nations Conference on

only for 128 developing countries that report to the

investments. • Portfolio equity includes net inflows

Trade and Development and official national sources.

Debtor Reporting System; nonreporting countries

from equity securities other than those recorded

may also receive debt flows.

as direct investment and including shares, stocks,

The internationally accepted definition of FDI (from the fifth edition of the IMF’s Balance of Payments

The volume of global private fi nancial fl ows

depository receipts, and direct purchases of shares

Manual [1993]), includes three components: equity

reported by the World Bank generally differs from

in local stock markets by foreign investors • Bonds

investment, reinvested earnings, and short- and

that reported by other sources because of differ-

are securities issued with a fixed rate of interest for a

long-term loans between parent firms and foreign

ences in sources, classification of economies, and

period of more than one year. They include net flows

affiliates. Distinguished from other kinds of interna-

method used to adjust and disaggregate reported

through cross–border public and publicly guaranteed

tional investment, FDI is made to establish a lasting

information. In addition, particularly for debt financ-

and private nonguaranteed bond issues. • Commer-

interest in or effective management control over an

ing, differences may also reflect how some install-

cial bank and other lending includes net commercial

enterprise in another country. A lasting interest in

ments of the transactions and certain offshore issu-

bank lending (public and publicly guaranteed and pri-

investment enterprise typically involves establish-

ances are treated.

vate nonguaranteed) and other private credits.

ing warehouses, manufacturing facilities, and other permanent or long-term organizations abroad. Direct investments may take the form of greenfield investment, where the investor starts a new venture in a foreign country by constructing new operational facilities; joint venture, where the investor enters into a partnership agreement with a company abroad to establish a new enterprise; or merger and acquisition, where the investor acquires an existing enterprise abroad. The IMF suggests that investments should account for at least 10 percent of voting stock to be counted as FDI. In practice many countries set a higher threshold. Many countries fail to report reinvested earnings, and the definition of long-term loans differs among countries. FDI data do not give a complete picture of international investment in an economy. Balance of payments data on FDI do not include capital raised locally, an important source of investment financing in some developing countries. In addition, FDI data omit nonequity cross-border transactions such as intrafirm flows of goods and services. For a detailed

Data sources

discussion of the data issues, see the World Bank’s

Data on equity and debt flows are compiled from a

World Debt Tables 1993–94 (vol. 1, chap. 3).

variety of public and private sources, including the

Statistics on bonds, bank lending, and supplier

World Bank’s Debtor Reporting System, the IMF’s

credits are produced by aggregating transactions of

International Financial Statistics and Balance of

public and publicly guaranteed debt and private non-

Payments databases, and Dealogic. These data

guaranteed debt. Data on public and publicly guar-

are also published annually in the World Bank’s

anteed debt are reported through the Debtor Report-

Global Development Finance, Global Develop-

ing System by World Bank member economies that

ment Finance CD-ROM, and Global Development

have received loans from the International Bank for

Finance database.

Reconstruction and Development or credits from the

2011 World Development Indicators

367

6.13

Net official financial flows Total

International ﬁnancial institutions

$ millions

$ millions

From From bilateral multilateral sourcesa,b,c sources 2009 2009

Afghanistan 1.0 Albania 26.3 Algeria –84.8 Angola 786.6 Argentina 282.5 Armenia 610.9 Australia Austria Azerbaijan –17.5 Bangladesh –146.1 Belarus 975.7 Belgium Benin 25.3 Bolivia 61.9 Bosnia and Herzegovina 33.8 Botswana –5.1 Brazil 2,998.3 Bulgaria –5.2 Burkina Faso 13.6 Burundi 0.0 Cambodia 116.0 Cameroon –38.9 Canada Central African Republic –3.4 Chad –1.9 Chile –20.8 China –339.4 Hong Kong SAR, China .. Colombia –113.6 Congo, Dem. Rep. –168.9 Congo, Rep. –62.6 Costa Rica 74.8 Côte d’Ivoire –15.4 Croatia .. Cuba .. Czech Republic .. Denmark Dominican Republic 203.2 Ecuador –175.1 Egypt, Arab Rep. –907.4 El Salvador –38.8 Eritrea 41.6 Estonia .. Ethiopia 335.1 Finland France Gabon –99.4 Gambia, The 2.9 Georgia 23.8 Germany Ghana 99.2 Greece .. Guatemala –10.8 Guinea 3.9 Guinea-Bissau 0.0 Haiti 109.1 Honduras 12.7

368

World Banka IDA IBRD 2009 2009

IMF Concessional 2009

Nonconcessional 2009

United Nationsb,c

Regional development banks b ConcesNonOther sional concessional institutions 2009 2009 2009

$ millions UNICEF 2009

UNRWA 2009

UNTA 2009

Others 2009

194.1 130.8 7.2 386.4 1,437.1 758.9

26.7 25.5 0.0 13.5 0.0 128.5

0.0 6.9 –0.5 0.0 235.6 48.6

17.4 –12.1 0.0 0.0 0.0 –23.4

0.0 1.9 0.0 353.3 0.0 465.7

73.9 0.0 0.0 1.6 0.0 119.1

0.0 21.1 0.0 –0.4 914.8 1.3

7.4 83.0 –0.8 0.8 282.3 11.4

39.5 1.0 1.0 8.5 0.8 0.8

0.0 0.0 0.0 0.0 0.0 0.0

1.0 0.4 0.9 0.8 1.0 1.6

28.2 3.1 6.6 8.3 2.6 5.3

304.9 1,004.7 3,040.3

36.1 62.8 0.0

121.6 0.0 213.5

–15.6 –23.4 0.0

–2.9 0.0 2,825.2

15.1 149.8 0.0

93.8 701.9 –2.1

47.1 38.7 0.0

1.0 22.2 0.7

0.0 0.0 0.0

0.6 0.8 0.5

8.1 51.9 2.5

134.2 168.3 483.8 982.5 441.6 259.3 270.6 63.5 96.5 225.1

51.4 32.3 18.0 –0.5 0.0 0.0 89.7 8.6 16.4 46.3

0.0 0.0 –24.7 0.0 –597.9 285.0 0.0 0.0 0.0 –5.4

15.7 0.0 0.0 0.0 0.0 0.0 54.2 13.4 0.0 147.3

0.0 0.0 281.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0

25.5 95.7 0.0 –3.6 0.0 0.0 78.3 4.2 47.6 23.6

0.0 –36.1 129.5 971.7 1,018.9 –13.6 0.0 0.0 0.0 –21.9

22.9 67.9 69.4 8.3 12.1 –12.1 4.1 2.6 4.5 12.1

4.9 1.5 0.8 1.2 1.1 .. 17.7 9.9 7.3 6.8

0.0 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0

0.8 0.6 0.8 0.4 1.5 .. 1.1 0.6 0.8 1.0

13.0 6.4 8.3 5.0 5.9 .. 25.5 24.2 19.9 15.3

30.1 25.3 58.6 1,098.7 .. 1,633.2 264.6 2.8 143.9 –289.9 .. .. ..

2.1 –14.6 –0.7 –329.8 .. –0.7 78.1 0.8 –0.2 –27.3 0.0 .. 0.0

0.0 0.0 14.9 298.5 .. 1,115.6 0.0 0.0 16.7 –73.3 39.8 .. 0.0

20.3 –12.7 0.0 0.0 .. 0.0 131.7 3.7 0.0 282.9 .. .. ..

0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 –125.4 .. .. ..

–1.8 0.4 0.0 0.0 .. –2.6 14.3 –0.4 –9.2 –4.2 .. .. ..

0.0 0.0 40.7 1,069.1 .. 534.9 –43.1 –8.5 3.8 –369.4 .. .. ..

–2.0 9.4 0.0 17.7 .. –23.6 –13.1 –3.6 128.7 –4.3 .. .. ..

4.5 13.4 0.8 10.5 .. 1.3 55.4 1.3 0.8 8.4 0.3 1.0 ..

0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ..

0.5 0.5 0.9 2.2 .. 0.8 1.3 0.2 0.7 1.1 0.6 1.4 ..

6.5 28.9 2.0 30.5 .. 7.5 40.0 9.3 2.6 21.6 2.9 3.4 ..

977.0 61.4 858.9 402.1 23.0 .. 977.1

–0.7 –1.1 –50.5 –0.8 0.8 0.0 549.2

298.6 –80.7 595.4 169.3 0.0 –6.7 0.0

0.0 0.0 0.0 0.0 0.0 .. 165.0

261.2 0.0 0.0 0.0 0.0 .. 0.0

–21.3 –26.4 –5.0 –22.9 3.4 .. 163.0

373.5 125.6 145.0 233.3 0.0 .. –6.7

62.3 38.5 160.8 17.6 0.6 .. 21.6

0.8 0.8 3.5 1.3 2.7 .. 35.9

0.0 0.0 0.0 0.0 0.0 .. 0.0

0.8 0.8 1.4 0.7 1.1 .. 1.1

1.8 3.9 8.3 3.6 14.4 .. 48.0

20.1 46.5 655.4

0.0 2.0 155.2

–2.3 0.0 100.0

0.0 15.8 –27.7

0.0 0.0 340.6

–0.2 7.4 111.4

33.3 0.0 –5.9

–16.0 12.9 –27.6

0.7 1.4 0.8

0.0 0.0 0.0

0.4 0.3 0.8

4.2 6.7 7.8

476.8 .. 554.1 –29.3 9.6 159.2 87.4

239.7 0.0 0.0 –27.2 0.0 –11.0 49.4

0.0 0.0 306.3 0.0 0.0 0.0 0.0

104.3 .. 0.0 –12.8 –1.6 57.4 0.0

0.0 .. 0.0 0.0 2.7 0.0 0.0

99.6 .. –6.9 2.9 –1.2 75.6 32.7

–2.0 .. 255.5 –5.6 0.0 0.0 –19.8

2.2 .. –5.4 –12.3 0.0 11.8 16.2

8.2 .. 0.8 7.6 2.7 2.4 0.7

0.0 .. 0.0 0.0 0.0 0.0 0.0

0.9 .. 0.6 0.5 0.2 0.8 1.1

23.9 .. 3.2 17.6 6.8 22.2 7.1

2011 World Development Indicators

Total

International ﬁnancial institutions

$ millions

$ millions

From From bilateral multilateral sourcesa,b,c sources 2009 2009

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

World Banka IDA IBRD 2009 2009

IMF Concessional 2009

Nonconcessional 2009

GLOBAL LINKS

6.13

Net official financial flows

United Nationsb,c

Regional development banks b ConcesNonOther sional concessional institutions 2009 2009 2009

$ millions UNICEF 2009

UNRWA 2009

UNTA 2009

Others 2009

.. –152.3 –1,099.1 –247.4 ..

–23.1 2,079.7 1,131.2 81.8 ..

0.0 455.3 212.8 0.0 ..

–23.1 671.5 908.6 74.7 ..

.. 0.0 0.0 0.0 ..

.. 0.0 0.0 0.0 ..

.. 0.0 88.6 0.0 ..

.. 857.9 –99.4 0.0 ..

.. 12.0 0.0 0.0 ..

.. 42.0 6.3 1.7 2.0

.. 0.0 0.0 0.0 0.0

.. 0.3 1.1 0.6 0.4

.. 40.7 13.2 4.8 7.8

..

..

..

..

..

..

..

..

..

..

..

..

..

–61.3

0.0

71.1

0.0

0.0

–4.6

81.6

34.7

1.1

0.0

0.3

1.2

–65.1 –13.3 59.8 ..

185.4 .. 548.3 604.2 385.0 ..

–2.6 0.0 82.9 ..

240.0 83.8 0.0 ..

0.0 0.0 191.2 ..

–15.9 0.0 0.0 ..

0.0 –0.2 54.0 ..

0.0 532.6 –5.0 ..

190.1 –16.1 11.1 ..

0.8 1.0 11.8 5.5

133.5 0.0 0.0 0.0

0.9 0.3 1.9 1.4

1.5 2.8 37.1 7.6

0.0 .. 332.0 114.9 .. –95.9 12.8 0.0 .. –2.3 6.4 34.8 12.2 –912.1 84.3 33.3 –24.8 466.6 –22.8 57.2 606.8 193.6 –7.9 .. –10.7

–199.4 .. 17.8 44.0 .. 106.3 3.9 37.6 .. 1,000.6 20.5 92.5 84.8 –89.6 383.8 204.6 107.1 6,463.4 11.9 262.9 1,301.8 484.7 34.7 .. 35.6

0.0 .. –4.1 –9.6 0.0 0.0 5.9 –3.3 .. 0.0 –7.0 30.4 24.2 0.0 159.2 37.9 –0.6 0.0 18.0 51.0 –1.4 197.1 0.0 .. –33.5

–207.7 .. 0.0 0.0 273.2 –49.8 –0.7 0.0 .. –3.1 33.3 0.0 0.0 –46.7 0.0 0.0 101.0 4,213.3 –17.6 0.0 2.7 0.0 0.0 .. 0.0

0.0 .. –0.3 –5.6 .. 0.0 –5.9 17.6 .. 0.0 0.0 0.0 0.0 0.0 3.1 0.0 0.0 0.0 –8.6 –6.5 0.0 153.3 0.0 .. –2.2

0.0 .. 0.0 0.0 .. 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 –6.4 165.5 0.0 0.0 0.0 .. 0.0

0.0 .. 12.2 8.1 .. 0.0 –4.6 –1.0 .. 0.0 0.0 27.1 18.3 0.0 58.8 24.8 –0.2 0.0 0.0 43.3 –1.1 68.8 0.0 .. 14.9

0.0 .. –4.6 0.5 .. 0.0 0.0 0.0 .. –8.0 –4.9 0.0 –2.0 –40.3 0.0 –8.0 13.6 2,247.5 –3.5 0.0 545.2 0.0 0.0 .. 0.0

0.0 .. 4.7 33.9 .. 29.1 0.4 0.0 .. 1,011.7 –5.4 –0.9 7.9 –7.2 132.1 133.0 –10.0 0.0 19.2 1.6 751.1 20.6 –0.8 .. 16.1

1.5 .. 1.4 2.7 .. 0.8 1.4 5.7 0.0 .. 0.9 12.7 9.3 0.7 14.7 2.1 0.0 1.0 0.9 0.8 1.5 16.3 17.0 1.1 7.4

0.0 .. 0.0 0.0 .. 123.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.0 .. 1.3 0.7 .. 1.0 0.6 0.4 0.4 .. 1.0 1.3 1.0 0.6 0.7 0.7 0.6 1.0 1.5 1.2 0.9 0.8 1.1 0.7 1.1

6.8 .. 7.2 13.3 .. 2.2 6.8 18.2 2.5 .. 2.6 21.9 26.1 3.3 15.2 14.1 2.7 0.6 8.4 6.0 2.9 27.8 17.4 5.3 31.8

–11.3 6.3 –72.1

66.7 15.8 475.6

0.0 0.0 –96.1

36.7 5.1 0.0

0.0 0.0 0.0

106.6 19.5 15.4

25.8 0.0 –91.3

20.7 35.2 4.3

1.3 18.2 48.8

0.0 0.0 0.0

1.4 0.7 1.0

6.6 16.0 28.5

.. 887.5 15.2 –20.1 –12.9 –961.9 –425.8 ..

265.8 110.5 386.2 .. .. 4,639.2 292.0 6.5 60.3 1,689.9 1,067.9 ..

0.0 988.8 0.0 10.5 –1.5 0.0 –7.0 0.0

0.0 –163.3 164.3 –9.0 68.1 134.4 –32.7 2,658.3

.. –223.3 0.0 0.0 0.0 0.0 0.0 ..

.. 3,307.1 0.0 0.0 0.0 0.0 0.0 ..

.. 223.6 –6.1 –9.2 –15.7 –3.5 –38.9 ..

.. 419.4 131.6 9.5 –2.1 1,380.8 1,116.4 ..

.. 18.1 2.0 –3.9 7.7 171.0 13.0 ..

0.0 19.8 0.7 1.5 0.8 0.9 3.1 ..

0.0 0.0 0.0 0.0 0.0 0.0 0.0 ..

0.1 1.9 0.5 0.0 0.5 0.8 0.8 ..

0.4 47.1 –1.0 7.1 2.5 5.5 13.2 ..

.. ..

.. ..

.. ..

.. ..

.. ..

.. ..

.. ..

.. ..

.. ..

.. ..

.. ..

.. ..

.. ..

2011 World Development Indicators

369

6.13

Net official financial flows Total

International ﬁnancial institutions

$ millions

$ millions

From From bilateral multilateral sourcesa,b,c sources 2009 2009

Romania –14.4 Russian Federation –296.3 Rwanda 12.0 Saudi Arabia .. Senegal 127.2 Serbia 477.1 Sierra Leone –1.5 Singapore .. Slovak Republic .. Slovenia .. Somalia 0.0 South Africa 0.0 Spain Sri Lanka 341.6 Sudan 551.3 Swaziland 9.0 Sweden Switzerland Syrian Arab Republic –324.9 Tajikistan 88.0 Tanzania 4.8 Thailand –334.6 Timor-Leste .. Togo 22.2 Trinidad and Tobago .. Tunisia 40.3 Turkey 405.0 Turkmenistan –87.2 Uganda 9.8 Ukraine –154.6 United Arab Emirates .. United Kingdom United States Uruguay –21.2 Uzbekistan 100.9 Venezuela, RB 151.3 Vietnam 922.2 West Bank and Gaza .. Yemen, Rep. 66.4 Zambia –5.0 12.9 Zimbabwe World .. s Low income 1,421.6 Middle income 4,206.7 Lower middle income 114.0 Upper middle income 4,092.7 Low & middle income 5,628.3 East Asia & Pacific –1,882.0 Europe & Central Asia 2,478.3 Latin America & Carib. 2,975.6 Middle East & N. Africa –997.0 South Asia 1,006.3 Sub-Saharan Africa 2,047.0 High income .. Euro area ..

World Banka IDA IBRD 2009 2009

IMF Concessional 2009

Nonconcessional 2009

United Nationsb,c

Regional development banks b ConcesNonOther sional concessional institutions 2009 2009 2009

$ millions UNICEF 2009

UNRWA 2009

UNTA 2009

Others 2009

12,394.0 –764.1 115.1 .. 324.2 1,916.3 79.5 .. .. .. 38.9 –25.2

0.0 0.0 10.5 .. 134.5 16.6 15.1 .. 0.0 0.0 0.0 0.0

441.6 –634.9 0.0 .. 0.0 55.7 0.0 .. –43.4 –6.1 0.0 –5.5

0.0 0.0 3.6 .. 99.8 0.0 18.8 .. .. .. 0.0 0.0

9,390.6 0.0 0.0 .. 0.0 1,575.1 0.0 .. .. .. 0.0 0.0

0.0 0.0 21.7 .. 38.5 0.0 16.8 .. .. .. 0.0 0.0

–26.0 –130.5 0.0 .. –12.8 109.1 0.0 .. .. .. 0.0 –32.1

2,587.8 1.3 46.4 .. 43.1 151.2 2.9 .. .. .. 0.0 0.0

.. .. 9.6 .. 6.3 0.6 8.4 .. .. .. 10.0 4.0

.. .. 0.0 .. 0.0 0.0 0.0 .. .. .. 0.0 0.0

.. .. 0.8 .. 1.3 1.0 1.1 .. .. .. 0.0 0.5

.. .. 22.5 .. 13.5 7.0 16.4 .. .. .. 28.9 7.9

827.9 99.2 1.9

90.8 0.0 –0.3

0.0 0.0 –6.6

–11.8 0.0 0.0

552.6 –10.6 0.0

60.4 0.0 –1.4

88.5 –2.7 –5.0

21.9 57.6 9.0

3.4 13.8 0.9

0.0 0.0 0.0

1.2 0.8 0.6

20.9 40.3 4.7

181.8 125.3 1,256.8 –46.6 .. 17.4 .. 443.7 1,984.6 –0.2 508.9 6,992.3 ..

–1.5 4.9 607.6 –3.4 .. –21.8 0.0 –2.1 –5.9 0.0 363.3 0.0 ..

0.0 0.0 0.0 9.3 .. 0.0 –7.7 31.7 1,619.0 –1.3 0.0 274.5 ..

0.0 25.1 306.8 0.0 .. 41.3 .. 0.0 0.0 0.0 0.0 0.0 ..

0.0 0.0 0.0 0.0 .. 0.0 .. 0.0 –706.5 0.0 0.0 6,081.6 ..

0.0 62.7 222.9 –46.2 .. –1.9 .. 0.0 0.0 0.0 73.5 0.0 ..

0.0 1.8 –1.0 –4.4 .. 0.0 .. 149.0 0.0 0.0 –0.9 549.7 ..

108.3 16.2 41.2 –11.3 .. –13.5 .. 260.6 1,067.5 –1.5 –1.0 78.3 ..

0.8 3.4 21.4 0.9 1.1 4.5 0.0 0.9 1.3 0.9 22.1 0.8 ..

60.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ..

1.3 0.8 1.1 1.2 0.5 0.5 0.1 0.8 0.6 0.0 1.1 1.8 ..

12.8 10.4 56.8 7.3 6.0 8.3 0.7 2.8 8.6 1.7 50.8 5.6 ..

704.6 0.0 364.7 0.0 0.0 157.1 27.6 –27.3 0.0 0.0 443.9 0.0 0.0 0.0 0.0 2,218.4 1,158.6 0.0 –38.3 0.0 .. .. .. .. .. 121.1 58.8 0.0 –41.0 –2.8 312.7 32.5 0.0 243.6 0.0 25.3 0.0 0.0 –0.1 0.0 .. s .. s .. s .. s .. s 8,153.0 2,579.9 0.0 1,552.6 1.0 65,736.3 3,871.5 11,287.2 198.1 24,750.4 30,161.2 3,782.9 2,998.2 193.2 11,114.1 35,590.5 88.5 8,289.0 4.9 13,636.3 75,594.8 6,451.3 11,287.1 1,750.7 24,751.5 5,945.1 1,099.7 1,126.6 –41.5 165.5 29,917.9 417.2 2,357.9 –62.6 20,246.6 16,487.2 136.2 6,489.2 126.7 269.6 4,246.7 6.4 894.2 –42.9 –18.7 8,845.7 1,614.0 508.2 –240.2 3,861.3 8,987.7 3,177.8 –88.9 2,011.1 227.1 .. .. .. .. .. .. .. .. .. ..

–2.1 18.7 0.0 392.4 .. 0.0 32.4 0.0 .. 1,469.8 1,257.5 1,293.0 –35.6 2,727.3 482.7 338.9 251.2 10.5 545.5 1,098.5 .. ..

318.0 78.0 143.6 647.3 .. 0.0 –5.4 0.0 .. s 619.9 14,527.3 7,063.5 7,463.8 15,147.1 2,699.7 1,315.9 7,784.3 839.2 2,075.8 432.3 .. ..

20.2 0.8 48.2 3.5 292.0 1.4 26.2 3.7 .. 4.9 68.8 9.2 –15.5 9.0 0.0 6.6 .. s 1,086.2 s 579.9 456.0 8,079.3 268.3 2,251.4 236.9 5,827.9 31.5 8,659.3 1,085.1 70.8 66.8 5,162.0 21.7 1,174.5 28.4 1,584.6 29.2 110.4 137.5 557.0 454.7 .. 1.1 .. ..

0.0 0.0 0.0 0.0 455.3 0.0 0.0 0.0 771.8 s 0.0 771.8 648.8 123.0 771.8 0.0 0.0 0.0 771.8 0.0 0.0 0.0 ..

0.6 2.4 0.4 8.0 0.5 6.4 1.5 27.0 0.1 8.5 0.9 27.2 1.6 14.5 0.5 18.3 645.3 s 2,561.1 s 33.1 860.8 111.8 613.1 39.3 539.9 25.0 136.2 644.3 2,319.3 98.3 176.5 13.7 106.6 67.8 159.3 71.9 100.5 6.4 226.8 156.0 962.1 1.0 6.2 .. ..

a. Aggregates include amounts for economies that do not report to the World Bank’s Debtor Reporting System and may differ from aggregates published in Global Development Finance 2011. b. Aggregates include amounts for economies not specified elsewhere. c. World and income group aggregates include flows not allocated by country or region.

370

2011 World Development Indicators

About the data

6.13

GLOBAL LINKS

Net official financial flows Definitions

The table shows concessional and nonconcessional

and the Rapid Credit Facility. Eligibility is based prin-

• Total net ofﬁcial ﬁnancial ﬂows are disbursements

financial flows from offi cial bilateral sources, the

cipally on a country’s per capita income and eligibility

of public or publicly guaranteed loans and credits,

major international financial institutions, and UN

under IDA. Nonconcessional lending from the IMF

less repayments of principal. • IDA is the Interna-

agencies. The international fi nancial institutions

is provided mainly through Stand-by Arrangements,

tional Development Association, the concessional

fund nonconcessional lending operations primarily

the Flexible Credit Line, and the Extended Fund Facil-

arm of the World Bank Group. • IBRD is the Inter-

by selling low-interest, highly rated bonds backed

ity. The IMF’s loan instruments have changed over

national Bank for Reconstruction and Development,

by prudent lending and financial policies and the

time to address the specific circumstances of its

the founding and largest member of the World Bank

strong financial support of their members. Funds

members.

Group. • IMF is the International Monetary Fund, which

are then on-lent to developing countries at slightly

Regional development banks also maintain conces-

provides concessional lending through its Extended

higher interest rates with 15- to 20-year maturities.

sional windows. Their loans are recorded in the table

Credit Facility, Standby Credit Facility, and Rapid Credit

Lending terms vary with market conditions and insti-

according to each institution’s classification and not

Facility and nonconcessional lending through credit

tutional policies.

according to the DAC definition.

to members, mainly for balance of payments needs.

Concessional flows from international financial

Data for flows from international financial institu-

• Regional development banks are the African Devel-

institutions are credits provided through conces-

tions are available for 128 countries that report to

opment Bank, which serves Africa, including North

sional lending facilities. Subsidies from donors or

the World Bank’s Debtor Reporting System. World

Africa; the Asian Development Bank, which serves

other resources reduce the cost of these loans.

Bank flows for nonreporting countries were collected

South and Central Asia and East Asia and Pacific;

Grants are not included in net flows. The Organisa-

from its operational records. Nonreporting countries

the European Bank for Reconstruction and Develop-

tion for Economic Co-operation and Development’s

may have net flows from other international financial

ment, which serves Europe and Central Asia; and

(OECD) Development Assistance Committee (DAC)

institutions.

the Inter-American Development Bank, which serves

defines concessional flows from bilateral donors as

Official flows from the United Nations are mainly

the Americas. • Concessional financial flows are dis-

flows with a grant element of at least 25 percent, eval-

concessional fl ows classifi ed as offi cial develop-

bursements through concessional lending facilities.

uated assuming a 10 percent nominal discount rate.

ment assistance but may include nonconcessional

• Nonconcessional financial flows are all disburse-

World Bank concessional lending is done by the

flows classified as other official flows in OECD DAC

ments that are not concessional. • Other institutions,

databases.

a residual category, include such institutions as the

International Development Association (IDA) based on gross national income (GNI) per capita and per-

Caribbean Development Fund, Council of Europe,

formance standards assessed by World Bank staff.

European Development Fund, Islamic Development

The cutoff for IDA eligibility, set at the beginning of

Bank, and Nordic Development Fund. • United Nations

the World Bank’s fiscal year, has been $1,165 since

includes the United Nations Children’s Fund (UNICEF),

July 1, 2010, measured in 2009 U.S. dollars using

United Nations Relief and Works Agency for Palestine

the Atlas method (see Users Guide). In exceptional

Refugees in the Near East (UNRWA), United Nations

circumstances IDA extends temporary eligibility to

Regular Programme for Technical Assistance (UNTA),

countries above the cutoff that are undertaking

and other UN agencies, such as the International

major adjustments but are not creditworthy for Inter-

Atomic Energy Agency, International Fund for Agricul-

national Bank for Reconstruction and Development

tural Development, Joint United Nations Programme

(IBRD) lending. Exceptions are also made for small

on HIV/AIDS, United Nations Development Programme,

island economies. The IBRD lends to creditworthy

United Nations Economic Commission for Europe,

countries at a variable base rate of six-month LIBOR

United Nations Population Fund, United Nations Refu-

plus a spread, either variable or fixed, for the life of

gee Agency, World Food Programme, and World Health

the loan. The lending rate is reset every six months

Organization.

and applies to the interest period beginning on that date. Although some outstanding IBRD loans have a

Data sources

low enough interest rate to be classified as conces-

Data on net financial flows from international finan-

sional under the DAC definition, all IBRD loans in the

cial institutions are from the World Bank’s Debtor

table are classified as nonconcessional. Lending by

Reporting System and published in the World

the International Finance Corporation, Multilateral

Bank’s Global Development Finance: External Debt

Investment Guarantee Agency, and the International

of Developing Countries and electronically in Global

Centre for the Settlement of Investment Disputes

Development Finance database. Data on official

is excluded.

flows from UN agencies are from the OECD DAC

The International Monetary Fund (IMF) makes con-

annual Development Co-operation Report and are

cessional funds available through its Extended Credit

available electronically on the OECD DAC Interna-

Facility (which replaced the Poverty Reduction and

tional Development Statistics CD-ROM and at www.

Growth Facility in 2010), the Standby Credit Facility,

oecd.org/dac/stats/idsonline.

2011 World Development Indicators

371

6.14

Financial flows from Development Assistance Committee members

Net disbursements Total net ﬂowsa

$ millions Australia Austria Belgium Canada Denmark Finland France Germany Greece Ireland Italy Japan Korea, Rep Luxembourg Netherlands New Zealand Norway Portugal Spain Sweden Switzerland United Kingdom United States Total

2009

3,188 3,273 3,224 7,340 3,757 3,185 38,418 26,003 850 4,188 5,569 49,405 6,442 428 6,045 387 4,089 1,209 12,809 7,164 9,106 68,936 115,276 380,290

Ofﬁcial development assistancea

Other ofﬁcial ﬂowsa

Total

Bilateral grants

Bilateral loans

Contributions to multilateral institutions

2009

2009

2009

2009

2,224 513 1,594 3,182 1,914 765 5,814 6,747 297 693 871 5,327 366 266 4,914 226 3,125 225 4,098 2,919 1,734 6,994 25,992 80,800

88 –6 –9 –41 –8 26 1,205 350 0 0 4 674 214 0 –116 0 43 52 375 90 16 663 –819 2,802

2,762 1,142 2,610 4,000 2,810 1,290 12,600 12,079 607 1,006 3,297 9,469 816 415 6,426 309 4,086 513 6,584 4,548 2,310 11,491 28,831 120,000

450 635 1,025 859 904 499 5,581 4,983 310 313 2,423 3,467 235 149 1,628 83 918 236 2,111 1,539 559 3,834 3,658 36,398

Private ﬂowsa

Total 2009

426 –44 90 –1,138 233 137 294 187 0 0 –72 8,216 452 0 0 8 4 0 0 68 0 –13 988 9,836

2009

0 2,035 147 3,140 599 1,741 25,524 12,367 241 3,000 2,181 31,187 5,018 0 –923 24 0 692 6,225 2,473 6,438 57,129 69,168 228,407

Net grants by NGOsa

Bilateral Multilateral Foreign portfolio portfolio direct investment investment investment 2009

0 2,551 3 6,604 599 791 16,300 9,726 241 0 129 19,440 5,018 0 540 24 0 –2 6,294 885 5,570 55,947 28,275 158,934

2009

0 46 0 –37 0 950 9,434 58 0 3,000 1,590 10,981 0 0 –2,853 0 0 –63 0 0 0 –2,143 27,223 48,185

2009

0 0 0 0 0 0 0 1,242 0 0 0 1,987 0 0 989 0 0 0 0 0 1,462 0 13,160 18,839

Private export credits 2009

0 –562 144 –3,427 0 0 –210 1,341 0 0 463 –1,220 0 0 401 0 0 757 –70 1,588 –593 3,326 510 2,449

2009

0 140 377 1,338 116 17 0 1,369 2 182 162 533 156 13 542 46 0 4 0 74 357 329 16,288 22,047

Official development assistance Commitmentsb

$ millions 2000 2009

Australia Austria Belgium Canada Denmark Finland France Germany Greece Ireland Italy Japan Korea, Rep Luxembourg Netherlands New Zealand Norway Portugal Spain Sweden Switzerland United Kingdom United States DAC Countries, Total

2,251 1,026 1,558 3,412 2,994 618 8,699 9,825 451 450 3,115 16,257 399 257 6,580 229 2,481 822 2,940 2,287 1,536 6,723 15,431 90,339

2,963 1,252 3,068 4,925 2,938 1,639 14,928 16,924 618 1,083 3,918 16,429 2,206 435 6,490 358 5,902 633 6,724 5,230 2,753 17,757 33,018 152,192

Gross disbursementsb

$ millions 2000 2009

1,939 792 1,558 3,023 3,193 661 9,276 9,973 451 450 3,082 15,485 282 257 6,171 216 2,800 822 2,940 2,861 1,513 6,723 13,293 87,757

2,912 1,188 2,750 4,372 2,960 1,323 15,933 13,693 618 1,083 3,514 14,848 949 435 6,841 333 4,650 565 7,213 5,090 2,286 13,400 29,286 136,242

Note: Components may not sum to totals because of gaps in reporting. a. At current prices and exchange rates. b. At 2008 prices and exchange rates.

372

2011 World Development Indicators

Net disbursements

$ millionsb 2000 2009

1,939 787 1,517 2,981 3,159 649 7,616 8,641 451 450 2,653 12,833 260 257 5,995 216 2,787 535 2,531 2,861 1,509 6,649 12,182 79,456

2,912 1,174 2,670 4,328 2,923 1,323 12,920 12,397 618 1,083 3,334 8,545 910 435 6,676 333 4,650 528 6,800 5,085 2,276 13,162 28,469 123,551

Per capita $b

% of general government disbursementsa

2000

2009

% of GNIa 2000 2009

2000

2009

101 97 148 97 592 125 129 105 41 119 46 101 6 583 376 56 621 52 63 323 210 113 44 89

136 141 250 130 530 248 207 151 55 250 56 67 19 889 405 78 969 51 147 549 296 216 94 131

0.27 0.23 0.36 0.25 1.06 0.31 0.30 0.27 0.20 0.29 0.13 0.28 0.04 0.70 0.84 0.25 0.76 0.26 0.22 0.80 0.34 0.32 0.10 0.22

0.71 0.44 0.72 0.59 1.94 0.63 0.60 0.59 0.39 0.77 0.27 0.74 0.18 1.61 1.84 0.56 1.77 0.56 0.53 1.32 1.01 0.83 0.30 0.56

0.87 0.57 1.02 0.68 1.54 0.97 0.85 0.76 0.36 0.93 0.30 0.45 0.31 1.86 1.57 0.61 2.32 0.45 0.98 2.03 1.39 1.03 0.48 0.69

0.29 0.30 0.55 0.30 0.88 0.54 0.47 0.35 0.19 0.54 0.16 0.18 0.10 1.04 0.82 0.28 1.06 0.23 0.46 1.12 0.45 0.52 0.21 0.31

6.14

GLOBAL LINKS

Financial flows from Development Assistance Committee members About the data

The flows of official and private financial resources

to accommodate changes in respect of Kosovo and

discount rate). • Contributions to multilateral insti-

from the members of the Development Assistance

the Former Yugoslav Republic of Macedonia. In the

tutions are concessional funding received by multi-

Committee (DAC) of the Organisation for Economic

past DAC distinguished aid going to Part I and Part

lateral institutions from DAC members as grants

Co-operation and Development (OECD) to developing

II countries. Part I countries, the recipients of ODA,

or capital subscriptions. • Other ofﬁ cial ﬂ ows

economies are compiled by DAC, based principally on

comprised many of the countries classified by the

are transactions by the official sector whose main

reporting by DAC members using standard question-

World Bank as low- and middle-income economies.

objective is other than development or whose grant

naires issued by the DAC Secretariat.

Part II countries, whose assistance was designated

element is less than 25 percent. • Private ﬂows are

The table shows data reported by DAC member

official aid, included the more advanced countries

flows at market terms financed from private sector

economies and does not include aid provided by the

of Central and Eastern Europe, countries of the for-

resources in donor countries. They include changes

European Union Institutions—a multilateral member

mer Soviet Union, and certain advanced developing

in holdings of private long-term assets by reporting

of DAC.

countries and territories. This distinction has been

country residents. • Foreign direct investment is

dropped with the 2005 aid flows.

investment by residents of DAC member countries

DAC exists to help its members coordinate their development assistance and to encourage the

Flows are transfers of resources, either in cash or

to acquire a lasting management interest (at least

expansion and improve the effectiveness of the

in the form of commodities or services measured on

10 percent of voting stock) in an enterprise operating

aggregate resources flowing to recipient economies.

a cash basis. Short-term capital transactions (with

in the recipient country. The data reflect changes in

In this capacity DAC monitors the flow of all financial

one year or less maturity) are not counted. Repay-

the net worth of subsidiaries in recipient countries

resources, but its main concern is official develop-

ments of the principal (but not interest) of ODA loans

whose parent company is in the DAC source coun-

ment assistance (ODA). Grants or loans to countries

are recorded as negative flows. Proceeds from offi -

try. • Bilateral portfolio investment is bank lending

and territories on the DAC list of aid recipients have

cial equity investments in a developing country are

and the purchase of bonds, shares, and real estate

to meet three criteria to be counted as ODA. They

reported as ODA, while proceeds from their later sale

by residents of DAC member countries in recipi-

are provided by official agencies, including state and

are recorded as negative flows.

ent countries. • Multilateral portfolio investment

local governments, or by their executive agencies.

The table is based on donor country reports and

is transactions of private banks and nonbanks in

They promote economic development and welfare as

does not provide a complete picture of the resources

DAC member countries in the securities issued by

the main objective. And they are provided on conces-

received by developing economies for two reasons.

multilateral institutions. • Private export credits are

sional financial terms (loans must have a grant ele-

First, flows from DAC members are only part of the

loans extended to recipient countries by the private

ment of at least 25 percent, calculated at a discount

aggregate resource flows to these economies. Sec-

sector in DAC member countries to promote trade;

rate of 10 percent). The DAC Statistical Reporting

ond, the data that record contributions to multilateral

they may be supported by an official guarantee. • Net

Directives provide the most detailed explanation of

institutions measure the flow of resources made

grants by nongovernmental organizations (NGOs)

this definition and all ODA-related rules.

available to those institutions by DAC members, not

are private grants by NGOs, net of subsidies from

the flow of resources from those institutions to devel-

the official sector. • Commitments are obligations,

oping economies.

expressed in writing and backed by funds, under-

This definition excludes nonconcessional fl ows from official creditors, which are classified as “other official flows,” and aid for military and anti-terrorism

Aid as a share of gross national income (GNI), aid

taken by an official donor to provide specified assis-

purposes. Transfer payments to private individuals,

per capita, and ODA as a share of the general gov-

tance to a recipient country or multilateral organiza-

such as pensions, reparations, and insurance pay-

ernment disbursements of the donor are calculated

tion. • Gross disbursements are the international

outs, are in general not counted. In addition to finan-

by the OECD. The denominators used in calculating

transfer of financial resources, goods, and services,

cial flows, ODA includes technical cooperation, most

these ratios may differ from corresponding values

valued at the cost to the donor.

expenditures for peacekeeping under UN mandates

elsewhere in this book because of differences in tim-

and assistance to refugees, contributions to multi-

ing or definitions.

lateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.

Definitions • Net disbursements are gross disbursements of

The DAC list of aid recipients shows all countries

grants and loans minus repayments of principal

and territories eligible to receive ODA. These con-

on earlier loans. • Total net ﬂows are ODA flows,

sist of all low- and middle-income countries, except

other official flows, private flows, and net grants by

Data on financial flows are compiled by OECD DAC

members of the Group of Eight or the European Union

nongovernmental organizations. • Ofﬁcial develop-

and published in its annual statistical report, Geo-

(including countries with a firm date for EU acces-

ment assistance refers to flows that meet the DAC

graphical Distribution of Financial Flows to Devel-

sion). The DAC revises the list every three years.

definition of ODA and are made to countries and ter-

oping Countries, and its annual Development

Countries that have exceeded the high-income

ritories on the DAC list of aid recipients. • Bilateral

Co-operation Report. Data are available electroni-

threshold for three consecutive years at the time

grants are transfers of money or in kind for which no

cally on the OECD DAC International Development

of the review are removed. In line with this review

repayment is required. • Bilateral loans are loans

Statistics CD-ROM and at www.oecd.org/dac/

process, the DAC last revised the list in September

extended by governments or official agencies with a

stats/idsonline.

2008. A further update took place in August 2009

grant element of at least 25 percent (at a 10 percent

Data sources

2011 World Development Indicators

373

6.15 6.15a

Allocation of bilateral aid from Development Assistance Committee members

Aid by purpose Net disbursements

$ millionsa 2000 2009

Australia Austria Belgium Canada Denmark Finland France Germany Greece Ireland Italy Japan Korea, Rep. Luxembourg Netherlands New Zealand Norway Portugal Spain Sweden Switzerland United Kingdom United States Total

758 273 477 1,160 1,024 217 2,829 2,687 99 154 377 9,768 131 99 2,243 85 934 179 720 1,242 627 2,710 7,405 36,195

2,312 507 1,585 3,141 1,905 791 7,019 7,097 297 693 875 6,001 581 266 4,798 226 3,168 277 4,473 3,009 1,751 7,657 25,174 83,602

Share of bilateral ODA net disbursements Development projects, programs, and other resource provisions 2000 2009

27.8 28.7 33.6 39.6 65.8 40.8 25.4 16.8 69.6 79.1 10.2 60.4 77.8 84.4 41.1 39.7 57.9 30.4 69.3 60.9 58.6 47.7 14.6 40.6

32.7 23.6 40.9 15.4 71.9 31.1 10.9 24.6 14.8 75.9 49.4 59.7 66.8 74.6 71.3 54.3 56.2 49.6 60.1 64.1 42.3 75.9 70.5 54.6

% Technical cooperationb 2000 2009

55.1 41.8 46.9 43.0 25.3 41.4 50.6 63.8 23.8 0.4 8.1 24.9 15.7 3.2 33.7 48.1 23.0 50.4 17.9 13.6 19.4 25.5 64.4 39.3

49.2 49.7 39.2 64.2 11.0 45.6 42.7 65.0 72.2 3.6 10.9 38.4 25.5 3.6 14.5 28.3 27.9 53.4 23.5 15.9 30.1 8.9 6.0 25.2

Debt-related aid 2000 2009

1.1 20.4 6.6 1.1 1.0 0.0 17.0 6.6 0.0 0.0 57.5 4.2 0.0 0.8 6.8 0.0 1.0 14.6 2.3 3.1 0.9 5.7 1.7 5.3

0.1 11.6 6.6 1.5 1.9 0.0 39.5 1.2 0.0 0.0 19.9 –14.5 0.0 0.0 0.9 0.0 0.5 –10.0 2.2 0.7 9.3 0.6 0.7 3.5

Humanitarian assistance 2000 2009

9.7 2.7 5.4 5.0 0.0 10.5 0.4 4.1 6.4 15.5 18.3 0.9 0.4 10.4 9.1 3.4 11.3 1.9 3.7 14.6 20.2 12.7 9.6 6.1

13.3 7.2 7.4 10.3 6.8 13.1 0.6 5.2 5.1 14.1 13.0 4.4 2.9 14.4 6.3 6.9 8.6 0.4 9.9 12.0 9.1 9.5 17.4 10.3

Administrative costs 2000 2009

6.2 6.4 7.5 11.4 8.0 7.2 6.7 8.7 0.2 5.1 5.9 9.5 6.1 1.2 9.4 8.8 6.9 2.7 6.8 7.7 0.9 8.4 9.7 8.6

4.7 7.9 6.0 8.6 8.5 10.1 6.3 4.1 7.9 6.5 6.8 12.1 4.8 7.3 6.9 10.6 6.8 6.6 4.2 7.3 9.3 5.2 5.4 6.3

a. At current exchange rates and prices. b. Includes aid for promoting development awareness and aid provided to refugees in the donor economy.

About the data Aid can be used in many ways. The sector to which

provide debt relief on liabilities that recipient coun-

human resources from donors or action directed to

aid goes, the form it takes, and the procurement

tries have difficulty servicing. Thus, this type of aid

human resources (such as training or advice). Also

restrictions attached to it are important influences

may not provide a full value of new resource flows

included are aid for promoting development aware-

on aid effectiveness. The data on allocation of offi -

for development, in particular for heavily indebted

ness and aid provided to refugees in the donor econ-

cial development assistance (ODA) in the table are

poor countries. Humanitarian assistance provides

omy. Assistance specifically to facilitate a capital

based principally on reporting by members of the

relief following sudden disasters and supports food

project is not included. • Debt-related aid groups

Organisation for Economic Co-operation and Devel-

programs in emergency situations. This type of aid

all actions relating to debt, including forgiveness,

opment (OECD) Development Assistance Committee

does not generally contribute to financing long-term

swaps, buybacks, rescheduling, and refinancing.

(DAC). For more detailed explanation of ODA, see

development.

• Humanitarian assistance is emergency and dis-

About the data for table 6.14. The form in which an ODA contribution reaches

tress relief (including aid to refugees and assistance Definitions

for disaster preparedness). • Administrative costs

the benefiting sector or the economy is important. A

• Net disbursements are gross disbursements of

are the total current budget outlays of institutions

distinction is made between resource provision and

grants and loans minus repayments of principal on

responsible for the formulation and implementation

technical cooperation. Resource provision involves

earlier loans • Development projects, programs, and

of donor’s aid programs and other administrative

mainly cash or in-kind transfers and financing of

other resource provisions are aid provided as cash

costs incurred by donors in aid delivery.

capital projects, with the deliverables being finan-

transfers, aid in kind, development food aid, and the

cial support and the provision of commodities and

financing of capital projects, intended to increase

supplies. Technical cooperation includes grants to

or improve the recipient’s stock of physical capital

nationals of aid-recipient countries receiving educa-

and to support recipient’s development plans and

Data on aid flows are published by OECD DAC in

tion or training at home or abroad, and payments

other activities with finance and commodity supply.

its annual statistical report, Geographical Distri-

to consultants, advisers, and similar personnel and

• Technical cooperation is the provision of resources

bution of Financial Flows to Developing Countries,

to teachers and administrators serving in recipient

whose main aim is to augment the stock of human

and its annual Development Co-operation Report.

countries. Technical cooperation is spent mostly in

intellectual capital, such as the level of knowledge,

Data are available electronically on the OECD DAC

the donor economy.

skills, and technical know-how in the recipient coun-

International Development Statistics CD-ROM and

Two other types of aid are presented because they

try (including the cost of associated equipment).

at www.oecd.org/dac/stats/idsonline.

serve distinctive purposes. Debt-related aid aims to

Contributions take the form mainly of the supply of

374

2011 World Development Indicators

Data sources

6.15b

6.15

GLOBAL LINKS

Allocation of bilateral aid from Development Assistance Committee members Aid by sector

Share of bilateral ODA commitments (%) Australia Austria Belgium Canada Denmark Finland France Germany Greece Ireland Italy Japan Korea, Rep. Luxembourg Netherlands New Zealand Norway Portugal Spain Sweden Switzerland United Kingdom United States Total

Total sectorallocable aid

Social infrastructure and services

Economic infrastructure, Multiservices, and production sector sector or crossTransport Government Water and comand civil supply and cutting

2009

Total 2009

Education 2009

Health 2009

77.7 67.5 73.2 72.7 68.8 72.9 68.8 87.6 74.5 70.8 64.1 74.5 96.2 68.1 71.1 61.9 64.0 70.3 70.8 54.3 42.9 72.4 73.2 72.8

48.7 45.8 39.5 52.4 42.2 32.6 36.3 49.6 62.8 58.0 35.6 28.9 27.6 47.2 26.8 45.5 40.6 56.9 44.6 33.4 21.6 41.7 53.5 42.7

11.9 23.8 13.1 15.4 5.1 6.7 19.2 19.1 32.4 13.0 10.3 5.3 9.7 12.5 4.2 21.0 8.6 24.1 7.1 3.1 3.1 8.9 4.0 8.8

7.5 5.9 9.2 15.2 7.6 3.3 1.5 3.6 4.6 13.5 9.4 2.0 10.4 12.7 2.7 5.5 6.2 2.9 6.2 3.5 3.1 7.8 3.7 4.6

Population 2009

2.2 0.4 1.6 2.5 2.8 0.5 0.3 1.9 2.6 4.0 0.9 0.4 0.2 4.6 2.1 2.3 2.0 0.1 4.3 1.9 0.2 5.0 19.0 6.7

sanitation 2009

society 2009

Total 2009

1.9 4.3 3.2 2.0 8.5 4.3 8.8 8.7 1.0 2.5 4.9 18.9 4.7 8.4 3.7 1.1 1.3 0.1 12.7 2.5 2.4 1.4 1.6 6.2

22.6 9.1 9.7 16.2 16.3 14.1 1.6 14.7 16.0 16.4 6.5 1.2 1.7 5.1 10.7 14.4 19.9 22.3 9.9 19.8 11.8 14.7 18.6 12.5

11.8 15.2 28.0 12.2 18.0 28.1 16.3 27.8 6.1 9.5 23.9 41.3 64.5 10.7 12.7 14.6 14.3 10.2 20.4 12.3 11.5 20.1 15.1 21.3

munication Agriculture 2009 2009

5.3 1.8 4.7 0.4 3.4 6.2 6.8 2.6 2.4 0.1 3.9 26.7 52.1 0.2 1.0 7.2 0.2 8.3 2.5 1.2 0.8 2.6 4.5 7.4

4.6 2.0 7.7 6.8 5.8 7.8 5.3 3.7 1.3 8.0 16.7 4.9 3.5 5.0 3.3 3.7 6.8 1.5 3.8 2.5 3.5 1.7 5.0 4.7

Untied aida

2009

2009

17.2 6.6 5.7 8.1 8.5 12.2 16.2 10.1 5.6 3.3 4.6 4.2 4.0 10.2 31.6 1.9 9.2 3.2 5.8 8.6 9.7 10.6 4.6 8.8

90.8 55.2 95.5 98.3 96.6 90.3 89.2 97.1 49.8 b 100.0b 56.2 94.7 48.3 100.0 b 80.8 90.1 100.0 27.9 76.6 99.9 99.2 100.0 69.8 84.5

a. Excludes technical cooperation and administrative costs. b. Gross disbursements.

About the data

Definitions

The Development Assistance Committee (DAC)

• Bilateral ofﬁcial development assistance (ODA)

administrative apparatus and planning and activities

records the sector classifi cation of aid using a

commitments are firm obligations, expressed in writ-

promoting good governance and civil society. • Eco-

three-level hierarchy. The top level is grouped by

ing and backed by the necessary funds, undertaken

nomic infrastructure, services, and production sec-

themes, such as social infrastructure and services;

by official bilateral donors to provide specified assis-

tor group assistance for networks, utilities, services

economic infrastructure, services, and production;

tance to a recipient country or a multilateral organi-

that facilitate economic activity, and contributions

and multisector or cross-cutting areas. The second

zation. Bilateral commitments are recorded in the

to all directly productive sectors. • Transport and

level is more specifi c. Education and health and

full amount of expected transfer, irrespective of the

communication refer to road, rail, water, and air

transport and storage are examples. The third level

time required for completing disbursements. • Total

transport; post and telecommunications; and televi-

comprises subsectors such as basic education and

sector-allocable aid is the sum of aid that can be

sion and print media. • Agriculture refers to sector

basic health. Some contributions are reported as

assigned to specific sectors or multisector activi-

policy, development, and inputs; crop and livestock

non-sector-allocable aid.

ties. • Social infrastructure and services refer to

production; and agricultural credit, cooperatives, and

Reporting on the sectoral destination and the

efforts to develop the human resources potential and

research. • Multisector or cross-cutting refers to

form of aid by donors may not be complete. Also,

improve the living conditions of aid recipients. • Edu-

support for projects that straddle several sectors.

measures of aid allocation may differ from the per-

cation refers to general teaching and instruction at

• Untied aid is ODA not subject to restrictions by

spectives of donors and recipients because of dif-

all levels, as well as construction to improve or adapt

donors on procurement sources.

ference in classification, available information, and

educational establishments. Training in a particular

recording time.

field is reported for the sector concerned. • Health

Data sources

The proportion of untied aid is reported because

refers to assistance to hospitals, clinics, other medi-

Data on aid flows are published annually by the

tying arrangements may prevent recipients from

cal and dental services, public health administra-

Organisation for Economic Co-operation and

obtaining the best value for their money. Tying

tion, and medical insurance programs. • Population

Development (OECD) DAC in Geographical Distri-

requires recipients to purchase goods and services

refers to all activities related to family planning and

bution of Financial Flows to Developing Countries

from the donor country or from a specified group of

research into population problems. • Water supply

and Development Co-operation Report. Data are

countries. Such arrangements prevent a recipient

and sanitation refer to assistance for water supply

available electronically on the OECD DAC Interna-

from misappropriating or mismanaging aid receipts,

and use, sanitation, and water resources develop-

tional Development Statistics CD-ROM and at www.

but they may also be motivated by a desire to benefit

ment (including rivers). • Government and civil soci-

oecd.org/dac/stats/idsonline.

donor country suppliers.

ety refer to assistance to strengthen government

2011 World Development Indicators

375

6.16

Aid dependency Net ofﬁcial development assistance (ODA)

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

376

Aid dependency ratios

Net ODA as % of imports of goods, services, and income 2000 2009

Net ODA as % of central government expense 2000 2009

Total $ millions 2000 2009

Per capita $ 2000 2009

Net ODA as % of GNI 2000 2009

Net ODA as % of gross capital formation 2000 2009

136 317 200 302 52 216

6,070 358 319 239 128 528

6 103 7 21 1 70

204 113 9 13 3 171

.. 8.4 0.4 4.1 0.0 11.0

.. 3.0 0.2 0.4 0.0 5.9

.. 34.8 1.5 22.0 0.1 60.6

.. 10.3 0.6 2.1 0.2 19.3

.. 21.0 .. 4.1 0.1 21.2

.. 5.1 .. 0.5 0.2 12.5

.. .. .. .. .. ..

112.5 .. 0.9 .. .. 25.6

139 1,172 .. .. 243 482 737 31 231 .. 180 93 396 377

232 1,227 98 .. 683 726 415 280 338 .. 1,084 549 722 649

17 8 .. .. 37 58 199 18 1 .. 15 14 31 24

26 8 10 .. 76 74 110 143 2 .. 69 66 49 33

2.8 2.4 .. .. 10.9 5.9 12.1 0.6 0.0 .. 6.9 12.9 10.9 4.0

0.6 1.3 0.2 .. 10.3 4.4 2.4 2.5 0.0 .. 13.5 41.2 7.7 2.9

12.8 10.8 .. .. 57.0 31.6 65.1 1.7 0.2 .. 41.1 213.8 60.3 22.4

2.5 5.6 0.5 .. 41.1 24.7 11.0 9.8 0.1 .. .. .. 34.3 ..

5.8 11.7 .. .. 32.7 19.7 17.4 1.0 0.2 .. 26.0 56.5 16.1 12.7

1.7 5.0 0.3 .. .. 12.0 4.3 4.7 0.2 .. .. 102.0 9.7 9.4

.. .. .. .. .. .. .. .. 0.2 .. .. .. .. ..

.. 12.2 0.6 .. 68.2 .. 5.9 .. 0.1 .. 102.5 .. 62.9 ..

75 130 49 1,712 .. 186 177 32 10 351 66 44 ..

237 561 80 1,132 .. 1,060 2,354 283 109 2,366 169 116 ..

20 15 3 1 .. 5 3 11 2 20 15 4 ..

54 50 5 1 .. 23 36 77 24 112 38 10 ..

8.0 9.5 0.1 0.1 .. 0.2 4.5 1.4 0.1 3.6 0.3 0.1 ..

11.9 9.2 0.1 0.0 .. 0.5 23.9 4.1 0.4 10.6 0.3 .. ..

82.4 40.4 0.3 0.4 .. 1.2 119.1 4.4 0.4 31.2 1.6 1.2 ..

111.2 24.2 0.3 0.0 .. 2.0 74.6 12.0 1.9 90.4 1.0 .. ..

.. .. 0.2 0.6 .. 1.0 .. 1.6 0.1 7.9 0.6 .. ..

.. .. 0.1 0.1 .. 2.2 .. .. 0.8 23.9 0.6 .. ..

.. .. 0.3 .. .. .. 15.2 5.0 .. .. 0.8 .. ..

.. .. 0.2 .. .. 2.3 .. .. 1.4 57.6 0.7 .. ..

56 146 1,327 180 176 .. 686

120 209 925 277 145 .. 3,820

6 12 19 30 48 .. 10

12 15 11 45 29 .. 46

0.2 1.0 1.3 1.4 27.7 .. 8.4

0.3 0.4 0.5 1.4 7.8 .. 13.4

1.0 4.6 6.8 8.1 116.6 .. 41.4

1.7 1.1 2.5 10.0 .. .. 59.7

0.5 2.3 5.6 3.0 34.4 .. 41.0

0.7 1.1 1.6 3.1 .. .. 42.0

.. .. .. .. .. .. ..

.. .. 1.6 53.2 .. .. ..

12 50 169 .. 598

78 128 908 .. 1,583

9 38 36 .. 31

53 75 213 .. 66

0.3 12.4 5.3 .. 12.4

0.8 18.5 8.6 .. 6.1

1.1 67.8 20.8 .. 50.0

2.5 67.3 69.7 .. 30.9

0.5 .. 13.6 .. 17.2

.. 35.3 15.6 .. 14.1

.. .. 47.9 .. ..

.. .. 27.3 .. 33.8

263 153 81 208 448

376 215 146 1,120 457

23 18 62 24 72

27 21 90 112 61

1.4 5.0 39.9 .. 6.4

1.0 5.8 17.6 .. 3.3

7.6 24.9 333.0 20.7 22.3

7.7 24.2 .. 63.1 16.3

4.4 15.7 .. 15.1 8.9

2.7 13.6 .. 39.5 5.0

12.5 .. .. .. ..

8.0 .. .. .. 13.3

2011 World Development Indicators

Net ofﬁcial development assistance (ODA)

Total $ millions 2000 2009

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

GLOBAL LINKS

6.16

Aid dependency Aid dependency ratios

Per capita $ 2000 2009

Net ODA as % of GNI 2000 2009

Net ODA as % of gross capital formation 2000 2009

Net ODA as % of imports of goods, services, and income 2000 2009

Net ODA as % of central government expense 2000 2009

.. 1,373 1,651 130 100

.. 2,393 1,049 93 2,791

.. 1 8 2 4

.. 2 5 1 89

.. 0.3 1.1 0.1 ..

.. 0.2 0.2 0.0 4.5

.. 1.2 4.5 0.4 ..

.. 0.5 0.6 .. ..

.. 1.7 2.5 0.7 ..

.. 0.7 0.8 .. ..

.. 1.9 .. 0.2 ..

.. 1.1 1.2 0.1 ..

..

..

..

..

..

..

..

..

..

..

..

..

9 .. 552 189 509 73

150 .. 761 298 1,778 67

3 .. 115 13 16 3

55 .. 128 19 45 3

0.1 .. 6.5 1.1 4.1 ..

1.3 .. 3.0 0.3 6.1 ..

.. .. 29.2 5.6 23.0 ..

5.8 .. 20.5 0.8 29.0 ..

0.2 .. 8.7 1.8 12.9 ..

2.1 .. 4.5 0.6 15.4 ..

.. .. 24.1 7.5 23.9 ..

3.0 .. 10.6 1.5 27.9 ..

1 .. 215 281 .. 199 37 67 .. .. 250 320 446 45 288 221 20 –58 123 217 419 906 106 152 386

788 .. 315 420 .. 641 123 505 39 .. 193 445 772 144 985 287 156 185 245 372 912 2,013 357 326 855

1 .. 44 52 .. 53 19 24 .. .. 124 21 38 2 27 85 17 –1 30 91 15 50 2 84 16

437 .. 59 66 .. 152 60 128 6 .. 95 23 51 5 76 87 122 2 68 139 28 88 7 150 29

.. .. 16.7 16.9 .. 1.1 3.8 17.4 .. .. 7.1 8.4 26.1 0.1 12.0 20.2 0.4 0.0 9.4 20.0 1.2 22.6 .. 3.9 7.0

14.0 .. 7.1 7.2 .. 1.8 6.4 78.3 0.1 .. 2.2 5.2 16.6 0.1 11.0 9.4 1.8 0.0 4.3 9.4 1.0 20.8 .. 3.6 6.7

.. .. 78.3 57.2 .. 5.7 11.1 .. .. .. 31.3 54.9 188.6 0.2 48.4 105.5 1.7 0.0 39.7 68.6 4.4 68.9 .. 22.8 28.9

52.7 .. 31.2 .. .. 6.2 24.8 .. .. .. 8.6 15.9 65.6 0.5 .. 37.7 8.5 0.1 16.7 17.6 2.8 98.2 .. 13.0 23.0

.. .. 28.5 44.0 .. .. 4.4 .. .. .. 10.5 20.2 65.6 0.0 27.5 .. 0.7 0.0 11.2 27.4 3.1 51.4 4.0 8.2 21.1

.. .. 8.1 25.2 .. 1.9 6.7 27.3 0.1 .. 3.2 .. .. 0.1 .. .. 2.8 0.1 5.7 13.1 2.3 44.1 .. 5.9 16.6

.. .. 99.2 .. .. 3.8 .. .. .. .. .. 77.8 .. 0.3 102.4 .. .. –0.1 32.9 85.2 .. .. .. 13.7 ..

.. .. 35.7 62.5 .. 6.3 .. .. .. .. .. .. .. 0.3 74.9 .. 8.4 .. 11.8 30.7 3.6 .. .. .. ..

560 208 174

774 470 1,659

110 19 1

135 31 11

15.0 11.7 0.4

13.1 8.9 1.0

47.2 101.4 ..

53.7 .. ..

23.5 43.0 1.1

16.4 .. 2.8

86.4 .. ..

60.2 .. ..

45 700 15 275 82 397 572 ..

212 2,781 66 414 148 442 310 ..

19 5 5 51 15 15 7 ..

75 16 19 61 23 15 3 ..

0.2 1.0 0.1 8.3 1.1 0.8 0.8 ..

.. 1.7 0.3 5.3 1.1 0.4 0.2 ..

1.9 5.5 0.5 35.7 6.1 3.7 3.6 ..

.. 9.1 1.1 26.3 6.7 1.5 1.3 ..

0.6 4.8 0.1 13.7 2.3 3.4 1.1 ..

0.8 7.1 0.4 7.6 1.8 1.3 0.5 ..

0.9 5.7 0.6 26.2 6.6 4.2 4.3 ..

.. 10.6 .. .. 6.1 2.0 1.0 ..

.. ..

.. ..

.. ..

.. ..

.. ..

.. ..

.. ..

.. ..

.. ..

.. ..

.. ..

.. ..

2011 World Development Indicators

377

6.16

Aid dependency Net ofﬁcial development assistance (ODA)

Total $ millions 2000 2009

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

Aid dependency ratios

Per capita $ 2000 2009

Net ODA as % of GNI 2000 2009

.. .. 321 22 429 1,134a 181 .. .. 61 101 486

.. .. 934 .. 1,018 608 437 .. .. .. 662 1,075

.. .. 40 1 43 151a 43 .. .. 31 14 11

.. .. 93 .. 81 83 77 .. .. .. 72 22

.. .. 18.7 0.0 9.3 18.6a 29.3 .. .. 0.3 .. 0.4

.. .. 18.0 .. 8.0 1.4 23.0 .. .. .. .. 0.4

275 220 13

704 2,289 58

15 6 12

35 54 49

1.7 1.9 0.9

1.7 4.6 2.0

158 124 1,063 697 231 70 –2 222 327 31 853 .. ..

245 409 2,934 –77 217 499 7 474 1,362 40 1,786 668 ..

10 20 31 11 284 13 –1 23 5 7 35 .. ..

12 59 67 –1 191 75 5 45 18 8 55 15 ..

0.9 15.0 10.6 0.6 71.6 5.4 0.0 1.2 0.1 1.2 14.0 .. ..

5 8 3 22 212 14 76 14 8w 18 6 5 6 10 5 11 9 16 3 19 0 ..

15 7 2 43 748 21 98 59 19 w 47 11 10 11 22 5 20 16 41 9 53 0 ..

0.1 1.4 0.1 5.5 13.3 3.0 25.8 2.8 0.2 w 7.0 0.5 0.7 0.2 0.9 0.5 0.6 0.2 1.0 0.7 4.0 0.0 ..

17 51 186 190 76 67 1,681 3,744 637 3,026 263 500 795 1,269 176 737 49,527 s 127,527 s 39,834 12,349 25,127 50,840 18,635 39,070 5,777 10,762 49,234 127,093 8,563 10,278 4,462 8,101 4,847 9,104 4,472 13,589 4,114 14,332 13,067 44,510 294 433 .. ..

Net ODA as % of gross capital formation 2000 2009

.. .. 101.2 0.1 44.7 212.2a 413.2 .. .. 1.1 .. 2.3

Net ODA as % of imports of goods, services, and income 2000 2009

Net ODA as % of central government expense 2000 2009

.. .. 82.3 .. 28.4 5.9 148.8 .. .. .. .. 1.9

.. .. 71.2 0.0 22.3 .. 68.8 .. .. 0.5 .. 1.3

.. .. 61.0 .. .. 3.1 64.8 .. .. .. .. 1.2

.. .. .. .. 71.9 .. 98.8 .. .. 187.8 .. 1.3

.. .. .. .. .. 3.8 101.9 .. .. .. .. 1.1

6.0 9.7 5.1

6.8 16.6 11.4

3.2 8.5 0.9

5.7 16.8 2.1

7.3 .. 3.9

.. .. ..

0.5 8.3 13.7 0.0 .. 17.5 0.0 1.3 0.2 0.2 11.4 0.6 ..

4.7 152.5 62.0 2.5 285.9 29.4 –0.1 4.2 0.6 3.1 70.7 .. ..

2.9 37.9 46.1 –0.1 .. .. .. 4.5 1.5 1.8 46.8 3.4 ..

2.4 .. 47.6 0.9 .. 10.5 0.0 2.1 0.5 .. 54.2 .. ..

.. 13.0 37.2 0.0 .. .. .. 2.0 0.8 .. 32.0 1.1 ..

.. 160.3 .. .. .. .. .. 4.1 .. .. 96.5 .. ..

.. .. .. –0.1 .. 100.6 .. 4.0 0.8 .. 86.9 1.4 ..

0.2 0.6 0.0 4.4 .. 2.0 11.1 14.1 0.2 w 9.2 0.3 0.4 0.2 0.8 0.2 0.3 0.2 1.1 0.8 4.9 0.0 ..

0.5 8.3 0.3 18.2 47.4 14.3 140.8 19.6 0.7 w 35.3 1.8 2.5 0.9 3.5 1.6 3.2 1.2 4.0 2.9 23.0 0.0 ..

0.3 .. 0.3 9.3 19.2 6.2 53.1 .. 0.5 w 22.8 1.5 2.4 0.6 2.8 1.4 1.8 0.9 3.3 3.5 10.9 0.0 ..

0.6 .. 0.1 4.9 .. 4.4 23.1 .. 0.7 w 24.9 1.1 1.5 0.5 2.7 0.6 0.9 1.0 3.9 3.3 12.0 0.0 ..

0.3 .. 0.3 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

0.5 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

0.9 2.3 0.1 10.9 .. .. 44.7 581.9 1.0 w 38.7 1.0 1.1 0.7 2.5 0.4 1.6 1.2 .. 2.5 25.0 0.0 ..

Note: Regional aggregates include data for economies not listed in the table. World and income group totals include aid not allocated by country or region—including administrative costs, research on development issues, and aid to nongovernmental organizations. Thus regional and income group totals do not sum to the world total. a. Includes Montenegro.

378

2011 World Development Indicators

6.16

GLOBAL LINKS

Aid dependency About the data

The table shows data for official development assis-

conclusions. For foreign policy reasons some coun-

The nominal values used here may overstate the

tance (ODA; see About the data for table 6.14) for

tries have traditionally received large amounts of

real value of aid to recipients. Changes in interna-

aid-receiving countries. The data cover loans and

aid. Thus aid dependency ratios may reveal as much

tional prices and exchange rates can reduce the pur-

grants from Development Assistance Committee

about a donor’s interests as about a recipient’s

chasing power of aid. Tying aid, still prevalent though

(DAC) member countries, multilateral organizations,

needs. Ratios are generally much higher in Sub-Saha-

declining in importance, also tends to reduce its pur-

and non-DAC donors. They do not reflect aid given by

ran Africa than in other regions, and they increased

chasing power (see About the data for table 6.15).

recipient countries to other developing countries. As

in the 1980s. High ratios are due only in part to aid

The aggregates refer to World Bank classifications

a result, some countries that are net donors (such as

flows. Many African countries saw severe erosion

of economies and therefore may differ from those

Saudi Arabia) are shown in the table as aid recipients

in their terms of trade in the 1980s, which, along

of the Organisation for Economic Co-operation and

(see table 6.16a).

with weak policies, contributed to falling incomes,

Development (OECD).

The table does not distinguish types of aid (pro-

imports, and investment. Thus the increase in aid

gram, project, or food aid; emergency assistance;

dependency ratios reflects events affecting both the

postconflict peacekeeping assistance; or technical

numerator (aid) and the denominator (GNI).

Definitions • Net ofﬁcial development assistance is flows (net

cooperation), which may have different effects on the

Because the table relies on information from

of repayment of principal) that meet the Development

economy. Expenditures on technical cooperation do

donors, it is not necessarily consistent with infor-

Assistance Committee (DAC) definition of ODA and

not always directly benefit the economy to the extent

mation recorded by recipients in the balance of pay-

are made to countries and territories on the DAC list

that they defray costs incurred outside the country

ments, which often excludes all or some technical

of aid recipients. See About the data for table 6.14.

on salaries and benefi ts of technical experts and

assistance—particularly payments to expatriates

• Net ofﬁcial development assistance per capita is

overhead costs of firms supplying technical services.

made directly by the donor. Similarly, grant com-

net ODA divided by midyear population. • Aid depen-

Ratios of aid to gross national income (GNI), gross

modity aid may not always be recorded in trade

dency ratios are calculated using values in U.S. dol-

capital formation, imports, and government spending

data or in the balance of payments. Moreover, DAC

lars converted at official exchange rates. Imports of

provide measures of recipient country dependency

statistics exclude aid for military and antiterrorism

goods, services, and income refer to international

on aid. But care must be taken in drawing policy

purposes.

transactions involving a change in ownership of general merchandise, goods sent for processing

6.16a

Official development assistance from non-DAC donors, 2005–09

of employee compensation for nonresident workers,

Net disbursements ($ millions) 2005

2006

2007

2008

2009

and investment income. For definitions of GNI, gross capital formation, and central government expense,

OECD members (non-DAC) Czech Republic

135

161

179

249

215

Hungary

117

100

149

103

107

Iceland

27

41

48

48

34

Israela

95

90

111

138

124

Poland

205

297

363

372

375

Slovak Republic

56

55

67

92

75

Slovenia

35

44

54

68

71

601

714

602

780

707

Turkey

and repairs, nonmonetary gold, services, receipts

see Definitions for tables 1.1, 4.8, and 4.10.

Arab countries Kuwait

Data sources

218

158

110

283

221

1,026

2,025

1,551

4,979

3,134

Data on financial flows are compiled by OECD DAC

141

219

429

88

834

and published in its annual statistical report, Geo-

483

513

514

435

411

oping Countries, and in its annual Development

Thailand

..

74

67

178

40

Co- operation Report. Data are available electroni-

Othersb

51

77

134

275

313

cally on the OECD DAC International Development

3,175

4,617

4,333

8,094

6,672

Statistics CD-ROM and at www.oecd.org/dac/

Saudi Arabia United Arab Emirates Other donors Taiwan, China

Total

graphical Distribution of Financial Flows to Devel-

Note: The table does not reflect aid provided by several major emerging non–Organisation for Economic Co-operation and Development (OECD) donors because information on their aid has not been disclosed. a. The statistical data for Israel are supplied by and under the responsibility of the relevant Israeli authorities. The use of such data by the OECD is without prejudice to the status of the Golan Heights, East Jerusalem, and Israeli settlements in the West Bank under the terms of international law. The figures include $49.2 million in 2005, $45.5 million in 2006, $42.9 million in 2007, $43.6 million in 2008, and $35.4 million in 2009 for first-year sustenance expenses for people arriving from developing countries (many of which are experiencing civil war or severe unrest) or people who have left their country for humanitarian or political reasons. b. Includes Cyprus, Estonia, Latvia, Liechstenstein, Lithuania, Malta, and Romania.

stats/idsonline. Data on population, GNI, gross capital formation, imports of goods and services, and central government expense used in computing the ratios are from World Bank and International Monetary Fund databases.

Source: Organisation for Economic Co-operation and Development.

2011 World Development Indicators

379

6.17

Distribution of net aid by Development Assistance Committee members Ten major DAC donors

$ millions Total $ millions 2009

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica CÔ te d'Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

380

United States 2009

EU Institutions 2009

United Kingdom 2009

Germany 2009

France 2009

Japan 2009

Netherlands 2009

Spain 2009

Norway 2009

Canada 2009

Other DAC donors $ millions 2009

5,319.2 314.9 282.9 170.4 100.2 273.8

2,979.9 33.0 8.1 41.5 2.6 78.5

395.4 69.3 82.8 38.9 21.3 38.8

324.4 2.2 3.6 4.4 1.0 1.0

337.3 58.8 13.1 8.4 22.7 31.0

49.8 4.2 94.5 4.2 12.3 5.7

170.5 -2.0 1.9 6.8 9.0 98.7

147.9 8.2 0.0 -3.3 0.2 3.0

98.9 14.3 54.4 20.3 24.1 0.4

115.9 1.0 0.9 17.8 0.1 3.1

232.6 0.1 2.8 0.9 2.1 0.7

466.5 125.7 20.9 30.4 4.8 13.0

136.5 849.5 72.3

40.4 63.8 12.2

12.5 131.9 11.1

1.4 250.1 0.6

42.7 67.3 21.7

27.9 -3.6 4.5

-2.0 14.1 0.6

0.0 70.4 0.0

0.7 6.0 0.8

4.0 14.6 2.6

0.3 52.5 0.0

8.7 182.5 18.3

472.3 562.8 349.0 255.7 328.0 .. 618.3 391.9 516.8 326.9

58.9 101.6 31.1 214.4 8.1 .. 51.1 47.6 68.6 31.4

146.6 77.8 72.6 32.3 18.8 .. 165.4 131.1 43.1 59.2

0.0 0.5 9.6 0.9 13.1 .. 0.2 14.4 32.3 2.3

43.1 45.7 27.6 2.1 196.1 .. 47.5 27.9 37.9 91.0

50.4 10.0 4.7 1.0 47.1 .. 77.4 12.9 29.8 90.6

25.8 31.8 5.0 -2.6 -93.2 .. 49.8 20.4 127.5 8.1

42.0 45.6 21.8 0.0 0.6 .. 66.0 18.3 0.1 0.1

3.5 97.6 36.9 0.1 64.9 .. 10.2 5.7 29.1 4.0

0.0 6.4 15.9 1.8 29.5 .. 0.5 25.1 3.2 0.4

7.0 24.3 4.1 1.3 10.8 .. 23.5 6.1 10.9 7.1

94.9 121.6 120.0 4.4 32.2 .. 126.6 82.5 134.4 32.7

153.3 474.5 70.5 1,199.8 .. 1,044.4 1,332.0 252.3 105.5 1,794.4 160.7 103.5 ..

30.5 169.6 1.8 52.9 .. 652.3 238.7 9.3 -0.6 230.7 3.7 20.0 ..

54.7 119.0 10.8 42.9 .. 45.9 232.8 26.2 6.8 71.9 129.9 16.9 ..

2.4 5.6 0.6 116.0 .. 7.8 225.5 0.0 2.6 0.2 1.9 1.0 ..

6.6 27.9 11.5 340.9 .. 45.2 79.4 25.8 15.0 15.1 12.6 2.5 ..

25.9 41.0 9.6 364.4 .. 22.5 30.3 93.2 4.7 1,200.6 4.0 2.7 ..

6.1 14.0 7.9 142.0 .. -6.7 65.7 0.4 58.3 10.4 -0.7 3.6 ..

2.8 8.4 0.2 5.3 .. 32.5 43.4 0.0 3.8 36.5 0.2 0.1 ..

4.3 13.2 9.6 45.8 .. 148.6 42.7 44.4 9.3 50.8 0.7 37.7 ..

0.6 2.2 13.3 21.7 .. 11.6 28.1 0.1 0.7 1.6 3.6 0.8 ..

3.8 12.1 2.0 11.1 .. 25.3 44.9 7.6 2.1 43.7 0.1 7.7 ..

15.6 61.6 3.2 57.1 .. 59.4 300.7 45.4 2.8 133.1 4.6 10.7 ..

118.3 209.9 784.7 284.6 86.3 .. 2,019.0

14.1 52.1 185.1 82.1 3.6 .. 726.0

66.1 62.6 204.7 24.9 42.9 .. 202.5

0.1 -0.2 35.6 0.0 6.5 .. 342.9

–2.2 24.7 138.8 18.1 1.4 .. 79.8

3.4 1.2 111.6 2.4 0.5 .. 38.3

0.2 -11.8 -18.8 -3.8 8.8 .. 97.8

0.0 1.6 17.8 0.4 3.7 .. 85.9

29.2 48.7 20.6 125.7 1.8 .. 94.0

0.3 1.6 0.7 0.5 9.6 .. 37.8

2.6 3.2 17.0 3.2 0.6 .. 87.2

4.4 26.0 71.7 31.1 7.0 .. 226.9

61.8 37.1 603.6

1.2 5.0 279.1

9.2 15.2 167.7

0.0 3.7 7.3

-3.3 0.3 67.0

54.0 0.3 14.0

0.1 11.4 12.3

0.0 0.7 5.1

0.4 3.0 0.9

0.0 0.1 11.0

1.0 1.2 0.8

–0.8 –3.9 38.5

987.2

150.5

166.9

61.2

49.7

64.8

98.3

24.1

2.5

99.8

115.5

369.3 212.2 110.7 806.8 344.5

83.9 34.9 1.1 319.6 128.8

28.0 41.2 60.1 102.7 39.8

16.1 19.5 0.4 16.9 15.9

2.9 82.1 6.1 49.0 1.4

26.0 18.2 9.4 24.8 41.7

28.4 0.0 0.0 0.2 0.8

113.4 5.0 13.1 144.9 58.4

7.7 0.0 0.0 4.3 1.4

7.1 5.2 1.2 119.7 24.1

55.2 5.4 19.2 16.8 32.2

2011 World Development Indicators

153.9 0.7 0.9 0.1 8.0 0.1

GLOBAL LINKS

6.17

Distribution of net aid by Development Assistance Committee members Ten major DAC donors

$ millions Total $ millions 2009

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

United States 2009

EU Institutions 2009

United Kingdom 2009

Germany 2009

France 2009

Japan 2009

Netherlands 2009

Spain 2009

Norway 2009

Canada 2009

Other DAC donors $ millions 2009

.. 1,567.6 446.0 67.7 2,686.0

.. 48.1 121.3 0.7 2,346.3

.. 98.9 113.1 1.9 57.3

.. 521.1 68.8 0.7 48.6

.. 263.4 -34.8 46.1 38.2

.. –29.0 187.1 14.6 9.3

.. 517.0 -512.8 -17.4 28.1

.. 7.2 81.1 4.5 7.3

.. 25.3 3.4 5.2 2.4

.. 16.1 12.9 0.8 11.6

.. 11.5 20.0 3.3 12.1

.. 87.8 385.9 7.3 124.9

..

..

..

..

..

..

..

..

..

..

..

..

112.3

–2.1

105.9

8.3

–6.9

–0.8

–5.3

–4.3

1.2

0.1

5.9

10.2

571.7 185.5 1,308.3 49.8

394.6 97.3 590.2 13.5

85.4 13.3 84.3 3.4

1.5 7.0 131.2 0.1

39.8 17.5 85.7 2.7

58.9 2.9 44.8 0.3

–57.4 37.1 33.7 0.0

0.6 0.6 25.4 1.2

10.2 -0.4 50.7 2.0

0.8 3.1 15.5 4.8

11.0 0.1 31.7 3.6

26.3 7.0 215.1 18.2

744.6 .. 168.4 285.9 .. 463.3 86.8 400.3 34.4 .. 186.5 297.2 519.3 133.0 676.4 157.9 156.8 164.8 202.4 212.6 987.1 1,492.3 310.8 279.1 548.8

207.4 .. 52.5 7.4 .. 136.9 24.7 96.9 5.7 .. 29.9 76.6 111.4 16.3 111.3 10.2 0.1 129.4 32.2 34.9 31.6 255.6 35.2 90.3 73.5

315.9 .. 28.7 25.9 .. 74.3 16.1 59.5 2.2 .. 53.2 55.6 84.1 0.1 101.7 35.7 93.2 6.1 106.2 5.4 282.4 204.7 76.8 32.6 44.0

11.8 .. 8.9 0.3 .. 5.4 8.2 33.4 1.9 .. 2.0 1.3 111.7 4.2 0.1 0.8 20.8 11.6 3.2 0.7 4.8 54.9 53.1 0.7 103.2

32.6 .. 24.0 27.4 .. 31.6 5.4 28.1 3.6 .. 18.8 17.8 30.2 11.0 46.9 11.6 0.5 40.8 9.0 25.4 81.7 113.8 9.7 36.7 59.6

1.0 .. 0.9 19.1 .. 102.5 -1.5 0.3 19.1 .. 3.0 97.5 0.3 –0.1 74.7 35.0 43.2 13.1 7.0 2.1 238.1 14.7 2.1 50.1 –3.4

0.2 .. 17.8 92.4 .. 3.5 2.6 14.7 0.1 .. 24.2 19.0 35.8 91.8 35.5 9.6 –2.1 –30.7 3.1 74.7 97.9 60.7 48.3 39.8 45.3

0.5 .. 0.1 0.0 .. 0.7 0.0 0.0 0.0 .. 18.3 0.3 0.9 0.1 77.3 0.0 0.0 –0.3 2.1 9.6 1.7 99.3 5.8 1.9 3.1

0.9 .. 1.3 1.7 .. 24.2 9.8 5.8 0.0 .. 1.8 4.1 9.8 0.1 24.3 44.7 0.0 –14.5 0.4 –1.3 190.7 68.8 1.1 12.0 49.6

21.2 .. 3.4 3.2 .. 9.8 1.0 15.4 0.0 .. 7.0 8.4 63.6 0.7 12.6 0.7 0.4 0.0 3.7 1.3 0.0 80.4 18.9 –6.7 45.3

0.0 .. 0.1 1.8 .. 13.9 1.0 2.2 0.1 .. 0.0 2.1 19.5 0.1 83.5 1.3 0.3 4.4 0.0 2.7 8.4 75.2 2.5 0.7 5.5

153.3 .. 30.7 106.8 .. 60.7 19.7 144.1 1.8 .. 28.6 14.5 52.0 8.8 108.5 8.3 0.4 4.8 35.5 57.1 49.9 464.4 57.4 21.0 123.2

519.0 319.8 769.4

89.3 37.1 354.0

46.1 64.4 81.9

7.1 6.2 188.9

28.8 22.0 26.7

1.1 57.4 9.1

17.4 35.1 28.9

31.0 0.1 4.5

142.4 22.2 7.0

17.5 1.6 9.2

13.6 9.8 17.5

124.8 63.9 41.8

8.4 1,428.3 60.8 354.5 152.9 412.5 294.8 ..

5.3 613.0 16.7 2.8 26.5 104.4 89.5 ..

0.0 97.6 2.2 32.4 31.5 73.8 50.4 ..

0.6 217.5 0.1 1.0 0.0 1.1 4.4 ..

0.7 107.5 1.7 2.5 6.2 79.8 40.1 ..

0.7 8.8 0.1 0.1 0.6 9.0 –7.3 ..

0.7 131.4 33.5 –4.2 37.3 –36.8 –8.4 ..

0.3 38.9 0.0 0.0 0.0 0.3 2.2 ..

0.0 13.7 6.3 0.9 38.9 100.2 -31.4 ..

0.0 46.6 0.0 1.7 0.9 –7.3 1.8 ..

0.0 41.9 0.8 0.2 2.2 17.9 17.0 ..

0.1 111.4 -0.5 317.1 8.6 70.1 136.3 ..

.. ..

.. ..

.. ..

.. ..

.. ..

.. ..

.. ..

.. ..

.. ..

.. ..

.. ..

.. ..

2011 World Development Indicators

381

6.17

Distribution of net aid by Development Assistance Committee members Ten major DAC donors

$ millions

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

Total $ millions 2009

United States 2009

.. .. 624.3 .. 648.8 565.4 305.3 .. .. .. 607.5 1,014.6

.. .. 145.9 .. 67.7 46.5 17.0 .. .. .. 194.9 523.7

433.2 2,136.8 33.7

116.0 177.6 1,547.2 –71.2 193.3 408.2 6.0 457.6 1,345.1 17.4 1,141.3 574.0 ..

EU Institutions 2009

United Kingdom 2009

Germany 2009

France 2009

.. .. 104.5 .. 134.5 292.9 108.9 .. .. .. 108.0 153.3

.. .. 89.9 .. 6.5 7.7 80.3 .. .. .. 43.8 67.3

.. .. 44.0 .. 22.2 114.5 15.8 .. .. .. 20.9 86.9

.. .. 3.5 .. 140.9 12.7 0.3 .. .. .. 4.7 –15.6

32.3 954.6 15.6

59.2 225.8 15.1

18.2 292.4 –3.8

–5.6 47.2 –0.2

18.6 40.5 283.7 23.6 29.1 3.8 0.5 –5.3 –6.5 10.8 366.9 103.0 ..

54.8 37.3 138.4 21.3 10.3 46.4 1.6 108.1 787.0 4.0 128.0 177.0 ..

1.1 4.5 216.7 9.9 0.1 10.4 0.4 3.8 2.2 0.3 117.4 2.4 ..

0.0 1.8 2.2 93.8 94.9 35.9 73.5 109.9 7,657.0 s 2,622.5 2,083.3 1,852.0 225.9 7,653.4 389.5 74.4 158.6 247.5 1,438.0 2,708.4 3.6 ..

44.2 1.0 11.8 83.6 9.9 6.1 50.2 11.7 3.4 2,127.8 78.1 51.9 2,275.9 844.3 538.3 276.0 26.2 23.6 852.9 231.9 152.4 700.1 249.7 79.7 96,623.9 s 25,173.7 s 13,021.4 s 7,955.5 3,842.7 27,536.0 39,141.0 10,578.1 6,543.1 28,607.7 8,235.8 3,859.8 9,649.3 2,276.5 2,327.5 96,408.4 25,163.7 12,879.0 7,305.6 823.5 526.8 6,418.5 1,340.6 2,240.2 7,714.7 2,030.9 1,117.6 9,508.6 4,082.5 1,623.7 10,370.5 3,906.6 845.4 30,845.3 7,436.4 4,816.7 215.5 9.9 142.4 .. .. ..

Japan 2009

Netherlands 2009

Spain 2009

Norway 2009

Canada 2009

.. .. 21.3 .. 46.7 3.7 37.4 .. .. .. 22.6 4.7

.. .. 54.2 .. 45.7 2.6 1.5 .. .. .. 14.8 48.9

.. .. 25.0 .. 59.3 4.0 3.4 .. .. .. 52.8 5.3

.. .. 3.6 .. 0.5 19.9 3.1 .. .. .. 33.3 36.1

.. .. 13.7 .. 54.5 4.8 8.9 .. .. .. 25.7 13.0

.. .. 118.8 .. 70.5 56.0 28.8 .. .. .. 85.9 90.9

12.7 10.4 0.2

91.6 111.0 1.2

2.7 97.3 0.0

18.6 26.0 1.2

35.3 92.1 3.2

25.0 105.0 0.9

143.2 174.9 0.3

37.8 26.1 87.1 1.9 5.6 24.0 0.2 30.8 6.7 1.9 60.1 121.6 ..

25.7 4.7 7.9 –11.7 0.1 40.5 1.1 170.0 154.6 0.2 14.6 19.5 ..

–54.5 26.2 120.5 –150.3 11.9 34.1 0.1 14.4 210.8 –1.2 54.1 61.9 ..

0.1 0.3 62.6 3.6 0.0 0.9 0.0 –0.8 –0.3 0.0 45.0 0.0 ..

6.3 6.3 25.1 4.5 10.8 3.8 0.1 124.1 135.3 0.0 5.9 3.8 ..

0.0 3.1 116.4 0.7 8.5 0.1 0.0 0.0 0.2 0.6 67.3 3.1 ..

0.9 2.5 94.0 2.8 2.0 2.5 1.8 2.1 –2.3 0.0 16.9 18.0 ..

25.3 26.0 394.9 22.5 114.8 241.7 0.2 10.3 57.5 0.8 265.3 63.7 ..

–0.3 32.1 8.7 112.5 98.7 82.9 55.5 34.7 7,096.7 s 1,702.3 3,199.0 2,103.0 965.2 7,083.1 604.5 714.3 917.4 703.5 844.6 1,781.1 13.5 ..

1.4 2.9 7.1 142.9 79.2 5.9 7.4 4.6 7,019.4 s 993.6 4,510.2 3,157.9 1,254.3 7,010.9 899.2 273.7 231.7 1,000.9 35.4 3,396.9 8.5 ..

2.4 20.4 2.1 1,191.4 76.7 37.2 36.6 12.4 6,001.2 s 1,553.4 2,729.5 2,329.6 398.9 6,001.0 1,228.5 522.3 142.5 142.1 1,013.7 1,374.3 0.3 ..

0.0 0.0 0.1 45.4 46.2 30.9 64.8 22.3 4,798.0 s 1,067.9 899.5 609.1 255.7 4,797.4 157.0 66.2 262.0 108.9 274.8 1,197.6 0.5 ..

12.2 0.2 1.3 14.1 0.7 0.3 0.0 9.4 12.9 0.0 0.5 1.5 32.7 15.9 35.3 327.9 99.4 100.1 41.2 256.9 3.9 0.7 2.5 26.3 11.8 62.7 13.0 143.4 8.2 28.9 28.3 121.4 4,473.1 s 3,168.2 s 3,141.0 s 15,074.3 s 927.6 836.8 1,152.7 4,881.1 2,240.8 655.9 775.7 4,925.8 1,461.4 484.1 610.8 3,904.0 738.7 150.8 144.1 911.8 4,450.5 3,164.6 3,135.4 15,069.4 100.4 97.1 146.1 2,333.0 209.7 110.5 29.4 837.4 1,501.4 138.1 452.6 761.9 589.7 137.1 125.4 747.3 212.4 280.3 372.5 1,146.7 1,127.2 902.4 1,308.7 4,795.8 22.6 3.6 5.6 4.9 .. .. .. ..

Note: Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region.

382

2011 World Development Indicators

Other DAC donors $ millions 2009

About the data

6.17

GLOBAL LINKS

Distribution of net aid by Development Assistance Committee members Definitions

The table shows net bilateral aid to low- and middle-

on behalf of DAC members are classified as bilateral

• Net aid refers to net bilateral official development

income economies from members of the Develop-

aid (since it is the donor country that effectively con-

assistance that meets the DAC definition of official

ment Assistance Committee (DAC) of the Organisation

trols the use of the funds) and are included in the

development assistance and is made to countries

for Economic Co-operation and Development (OECD).

data reported in this table.

and territories on the DAC list of aid recipients. See

DAC has 24 members, of which 23 are economies

The data include aid to some countries and terri-

About the data for table 6.14. • Other DAC donors

and 1 is a multilateral institution (the European Union

tories not shown in the table and aid to unspecified

are Australia, Austria, Belgium, Denmark, Finland,

Institutions). Previous editions of the table included

economies recorded only at the regional or global

Greece, Ireland, Italy, the Republic of Korea, Lux-

only DAC member economies; this year’s edition

level. Aid to countries and territories not shown in

embourg, New Zealand, Portugal, Sweden, and

includes data for the European Union Institutions.

the table has been assigned to regional totals based

Switzerland.

The table is based on donor country reports of

on the World Bank’s regional classification system.

bilateral programs, which may differ from reports by r

Aid to unspecified economies is included in regional

ecipient countries. Recipients may lack access to

totals and, when possible, income group totals. Aid

information on such aid expenditures as develop-

not allocated by country or region—including admin-

ment-oriented research, stipends and tuition costs

istrative costs, research on development, and aid to

for aid-financed students in donor countries, and

nongovernmental organizations—is included in the

payment of experts hired by donor countries. More-

world total. Thus regional and income group totals

over, a full accounting would include donor country

do not sum to the world total.

contributions to multilateral institutions, the flow

Some of the aid recipients shown in table are also

of resources from multilateral institutions to recipi-

aid donors. Development cooperation activities by

ent countries, and flows from countries that are not

non-DAC members have increased in recent years

members of DAC.

and in some cases surpass those of individual DAC

Data in this table exclude DAC members’ multilat-

members. Some non-DAC donors report their devel-

eral aid (contributions to the regular budgets of the

opment cooperation activities to DAC on a voluntary

multilateral institutions). These are included in data

basis. Many others do not yet report their aid flows

reported in table 6.14. Projects executed by multi-

to DAC. See table 6.16a for a summary of ODA from

lateral institutions or nongovernmental organizations

non-DAC countries.

Beyond the DAC: The role of other providers of development assistance

6.17a

Development assistance flows from non-DAC donor countries ($ millions) Country

Year

Source

8,094

2008

OECD/DAC Statistics

Brazil

437

2007

DAC Development Co-operation Report, estimates by Brazilian officials

China

1,800–3,000

2008

Fiscal Yearbook, Ministry of Finance, China. Upper estimate: Brautigam 2009

India

610

2008/09

Annual Reports, Ministry of Foreign Affairs, India

Russian Federation

200

2008

20 countries reporting to DAC (see table 6.16a)

South Africa

Estimate

109

2008/09

Russian Federation statement at DAC Senior Level Meeting, April 2010 Estimates of Public Expenditures 2009, Foreign Affairs, National Treasury of South Africa

Many countries that are not members of the OECD DAC have provided development assistance for decades. The past 10 years have seen their numbers rise fast, and in some cases their levels of development assistance now surpass those of individual DAC members. DAC estimates total net development assistance flows from non-DAC donors at $12–$14 billion in 2008, or 9–10 percent of global official development assistance (ODA) flows (assuming that the flows were consistent with the definition of ODA). Estimating overall aid volumes from non-DAC donors is challenging. Twenty countries, mostly emerging donors and Arab donors, voluntarily report aid volumes to DAC annually (see table 6.16a). Many others, including most major providers of aid from developing countries to developing countries (such as Brazil, China, India, and South Africa), do not. Estimates of aid volumes of countries that do not report to DAC must be treated with caution. Official figures often omit important cooperation activities, such as contributions to international organizations focused on development, leading to underestimates, and they often include expenditures that would not qualify as ODA, such as security-related or culturally motivated spending, or insufficiently concessional loans, leading to overestimates. Source: Smith, Fordelone, and Zimmermann 2010 and OECD 2010.

Data sources Data on financial flows are compiled by DAC and published in its annual statistical report, Geographical Distribution of Financial Flows to Aid Recipients, and its annual Development Cooperation Report. Data are available electronically on the OECD DAC International Development Statistics CD-ROM and at www.oecd.org/dac/stats/idsonline.

2011 World Development Indicators

383

6.18

Movement of people across borders Net migration

International migrant stock

thousands 1990–95 2005–10

thousands 1995 2010

Afghanistan 3,266 Albania –423 Algeria –50 Angola 143 Argentina 120 Armenia –500 Australia 371 Austria 234 Azerbaijan –116 Bangladesh –500 Belarus 0 Belgium 85 Benin 105 Bolivia –100 Bosnia and Herzegovina –1,025 Botswana 14 Brazil –184 Bulgaria –349 Burkina Faso –128 Burundi –250 Cambodia 150 Cameroon –5 Canada 643 Central African Republic 37 Chad –10 Chile 90 China –829b Hong Kong SAR, China 300 Colombia –250 Congo, Dem. Rep. 1,208 Congo, Rep. –14 Costa Rica 62 Côte d’Ivoire 375 Croatia 153 Cuba –120 Czech Republic 8 Denmark 58 Dominican Republic –129 Ecuador –50 Egypt, Arab Rep. –498 El Salvador –249 Eritrea –359 Estonia –108 Ethiopia 768 Finland 43 France 239 Gabon 20 Gambia, The 45 Georgia –544 Germany 2,649 Ghana 40 Greece 470 Guatemala –360 Guinea 350 Guinea-Bissau 20 Haiti –133 Honduras –120

384

1,000 –75 –140 80 30 –75 500 160 –50 –570 0 200 50 –100 –10 15 –229 –50 –65 323 –5 –19 1,050 5 –75 30 –1,731b 113 –120 –100 –50 30 –145 10 –194 226 30 –140 –350 –340 –280 55 0 –300 55 500 5 15 –250 550 –51 150 –200 –300 –12 –140 –100

2011 World Development Indicators

70 71 299 38 1,588 682 3,854 989 525 1,006 1,185 916 146 70 73 39 731 47 464 295 116 246 5,047 67 78 136 437b 2,431 109 1,919 131 228 1,985 721 25 454 297 322 88 174 28 12 309 795 103 6,085 164 148 250 8,992 1,038 549 46 814 32 22 31

91 89 242 65 1,449 324 4,711 1,310 264 1,085 1,090 975 232 146 28 115 688 107 1,043 61 336 197 7,202 80 388 320 686 b 2,742 110 445 143 489 2,407 700 15 453 484 434 394 245 40 16 182 548 226 6,685 284 290 167 10,758 1,852 1,133 59 395 19 35 24

Refugees

Workers’ remittances and compensation of employees

thousands By country of origin By country of asylum 1995 2009 1995 2009

$ millions Received Paid 1995 2009 1995 2009

2,679.1 5.8 1.5 246.7 0.3 201.4 0.0 0.0 200.5 57.0 0.1 0.0 0.1 .. 769.8 0.0 0.1 4.2 0.1 350.6 61.2 2.0 0.0 0.2 59.7 14.3 124.7c 0.2 1.9 89.7 0.2 0.2 0.2 245.6 24.9 2.0 0.0 0.0 0.2 0.9 23.5 286.7 0.4 101.0 0.0 0.0 0.0 0.2 0.3 0.4 13.6 0.2 42.9 0.4 0.8 13.9 1.2

2,887.1 15.7 8.2 141.0 0.6 18.0 0.0 0.0 16.9 10.4 5.5 0.1 0.4 .. 70.0 0.0 1.0 2.7 1.0 94.2 17.0 14.8 0.1 159.6 55.0 1.3 200.6c 0.0 389.8 455.9 20.5 0.3 23.2 76.5 7.5 1.1 0.0 0.2 1.0 7.0 5.1 209.2 0.2 62.9 0.0 0.1 0.1 2.0 15.0 0.2 14.9 0.1 5.8 10.9 1.1 24.1 1.2

19.6 4.7 192.5 10.9 10.3 219.0 62.1 34.4 233.7 51.1 29.0 31.7 23.8 0.7 40.0 0.3 2.1 1.3 29.8 173.0 0.0 45.8 152.1 33.9 0.1 0.3 288.3 1.5 0.2 1,433.8 19.4 24.2 297.9 198.6 1.8 2.7 64.8 1.0 0.2 5.4 0.2 1.1 .. 393.5 10.2 155.2 0.8 6.6 0.1 1,267.9 83.2 4.4 1.5 672.3 15.4 .. 0.1

0.0 0.1 94.1 14.7 3.2 3.6 22.5 38.9 1.6 228.6 0.6 15.5 7.2 0.7 7.1 3.0 4.2 5.4 0.5 25.0 0.1 100.0 169.4 27.0 338.5 1.5 301.0 0.1 0.2 185.8 111.4 19.1 24.6 1.2 0.5 2.3 20.4 .. 116.6 94.4 0.0 4.8 0.0 121.9 7.4 196.4 8.8 10.1 0.9 593.8 13.7 1.7 0.1 15.3 7.9 0.0 0.0

.. 427 1,120 a 5 64 65 1,651 1,012 3 1,202 29 4,937 100 7 .. 59 3,315 42 78 a .. 12 11 .. 0 1 .. 878a .. 815 .. 4 123 151 544 .. 191 523 839 386 3,226 1,064 a .. 1 27 74 4,640 4 19a 284 4,523 17 3,286 358 1 2a .. 124

.. 1,317 2,059a 82 658 769 4,089a 3,286 1,274 10,523 358 10,437 243a 1,069 2,081 88 4,234 1,558 99a 28 338 148 .. .. .. 4 48,729a 348 4,180 .. 14a 513 185 1,476 .. 1,201 894 3,467 2,502 7,150 3,482 .. 325 262 859 15,551 10a 80 714 10,879 114 2,020 4,019 64 47 1,376 2,520

.. .. .. 210 195 17 700 346 9 1 12 3,252 26 9 .. 200 347 34 50a 5 52 22 .. 27 15 13a 86a .. 150 .. 27 36 457 16 .. 101 209 7 4 223 1a .. 3 0 54 4,935 99 .. 12 11,348 5 300 8 10 3 .. 8

.. 10 .. 716 702 145 3,000 a 3,377 652 8 112 4,136 88 103 61 102 1,003 101 100 1 215 94 .. .. .. 6 4,444 413 92 .. 102 239 756 99 .. 2,562 3,413 29 81 255 21 .. 81 27 454 5,224 186 8 32 15,924 6 1,843 22 45 17a 135 12

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

Net migration

International migrant stock

thousands 1990–95 2005–10

104 –960 –725 –1,164 –154 –1 484 294 –113 474 509 –1,509 222 0 –627 .. –598 –273 –30 –134 230 –84 –523 10 –99 –27 –7 –920 287 –260 –15 –7 –1,364 –121 –173 –450 650 –126 –13 –101 191 143 –114 –3 –96 42 23 –2,611 8 0 –30 –300 –900 –77 0 –4 14

75 –1,000 –730 –500 –577 200 85 1,650 –100 150 250 –100 –189 0 –30 .. 120 –75 –75 –10 –13 –36 248 20 –100 –10 –5 –20 130 –202 10 0 –2,430 –172 –10 –425 –20 –500 –1 –100 100 50 –200 –28 –300 135 20 –1,416 11 0 –40 –625 –900 –120 200 –21 562

6.18

GLOBAL LINKS

Movement of people across borders Refugees

Workers’ remittances and compensation of employees

thousands 1995 2010

thousands By country of origin By country of asylum 1995 2009 1995 2009

$ millions Received Paid 1995 2009 1995 2009

293 7,022 219 3,016 134 264 1,919 1,723 22 1,363 1,608 3,295 528 35 584 .. 1,090 482 23 527 656 6 199 506 272 115 44 325 1,193 174 118 18 458 473 7 55 246 114 118 625 1,387 594 27 171 582 237 582 4,077 73 31 183 51 210 964 528 339 406

2.3 5.0 9.8 112.4 718.7 0.0 0.9 0.1 0.0 0.0 0.5 0.1 9.3 0.0 0.0 .. 0.8 0.0 58.2 0.2 13.5 0.0 744.6 0.6 0.1 12.9 0.1 0.0 0.1 77.2 84.3 0.0 0.4 0.5 0.0 0.3 125.6 152.3 0.0 0.0 0.1 .. 23.9 10.3 1.9 0.0 0.0 5.3 0.2 2.0 0.1 5.9 0.5 19.7 0.0 0.0 0.0

152 6,223 651 1,600a .. 347 701 2,364 653 1,151 1,441 116 298a .. 1,080 .. .. 1 22 41 1,225a 411 .. .. 1 68 14 1 116 112 5 132a 4,368 1 .. 1,970 59 81 16 57 1,359 1,652 75 8 804 a 239 39 1,712 112 16 287 599 5,360 724 3,953 .. ..

368 5,436 123 2,129 83 899 2,940 4,463 30 2,176 2,973 3,079 818 37 535 .. 2,098 223 19 335 758 6 96 682 129 130 38 276 2,358 163 99 43 726 408 10 49 450 89 139 946 1,753 962 40 202 1,128 485 826 4,234 121 25 161 38 435 827 919 324 1,305

1.5 19.5 18.2 72.8 1,785.2 0.0 1.3 0.0 0.9 0.2 2.1 3.7 9.6 0.9 0.6 .. 0.9 2.6 8.4 0.8 16.3 0.0 71.6 2.2 0.5 7.9 0.3 0.1 0.5 2.9 39.1 0.0 6.4 5.9 1.5 2.3 0.1 406.7 0.9 5.1 0.0 0.0 1.5 0.8 15.6 0.0 0.1 35.1 0.1 0.1 0.1 6.3 1.0 2.1 0.0 .. 0.1

11.4 227.5 0.0 2,072.0 116.7 0.4 .. 74.3 0.0 5.4 1,288.9d 15.6 234.7 .. 0.0 .. 3.3 13.4 .. .. 348.0 d 0.1 120.1 4.0 0.0 9.0 0.1 1.0 5.3 17.9 34.4 .. 38.7 .. .. 0.1 0.1 .. 1.7 124.8 80.0 3.8 0.6 27.6 8.1 47.6 .. 1,202.5 0.9 9.6 0.1 0.6 0.8 0.6 0.2 .. ..

6.0 185.3 0.8 1,070.5 35.2 9.6 17.7 55.0 0.0 2.3 2,434.5d 4.3 358.9 .. 0.3 .. 0.2 0.4 .. 0.0 476.1d .. 7.0 9.0 0.8 1.5 .. 5.4 66.1 13.5 26.8 .. 1.2 0.1 0.0 0.8 3.5 .. 7.2 108.5 76.0 3.3 0.1 0.3 9.1 37.8 0.0 1,740.7 16.9 9.7 0.1 1.1 0.1 15.3 0.4 .. 0.0

2,130 49,468 6,793 1,045a 71 576 1,267 2,683 1,912 1,776 3,597 124 1,686a .. 2,522 .. .. 992a 38 591 7,558 414 54 a 14 a 1,169 381 10 a 1a 1,131 405a 2a 211a 21,953 1,211 200 6,270 111 137a 14 2,986 3,691 628 768 89 9,585a 631 39 8,717 175 12 609 2,378 19,766 8,126 3,585 .. ..

146 419 .. .. .. 173 1,407 1,824 74 1,820 107 503 9 .. 635 .. 1,354 41 9 1 .. 75 .. 222 1 1 11 1 1,329 42 14 1 .. 1 .. 20 21 .. 11 9 2,802 427 .. 29 5 603 1,537 4 20 16 .. 34 151 262 527 .. ..

2011 World Development Indicators

1,223 2,893 2,702 .. 31a 1,988 3,283 12,986 314 4,069 502 3,138 61 .. 3,120 .. 9,912 188 22 46 5,749 35 1 1,361 620 26 21 0a 6,529 105 .. 12 .. 104 83 61 63 32a 16 12 14,212 977 .. 22a 66 4,174 5,313 8 229 323 .. 85 58 1,330 1,460 .. ..

385

6.18

Movement of people across borders Net migration

International migrant stock

thousands 1990–95 2005–10

Romania –529 –200 Russian Federation 2,220 250 Rwanda –1,681 15 Saudi Arabia –500 150 Senegal –100 –100 Serbia 451 0 Sierra Leone –450 60 Singapore 250 500 Slovak Republic –3 20 Slovenia 38 22 Somalia –893 –250 South Africa 900 700 Spain 324 1,750 Sri Lanka –256 –300 Sudan –168 135 Swaziland –38 –6 Sweden 151 150 Switzerland 227 100 Syrian Arab Republic –70 800 Tajikistan –296 –200 Tanzania 591 –300 Thailand –39 300 Timor-Leste 0 10 Togo –122 –5 Trinidad and Tobago –24 –20 Tunisia –43 –20 Turkey –70 –44 Turkmenistan 50 –25 Uganda 120 –135 Ukraine 100 –80 United Arab Emirates 340 343 United Kingdom 167 948 United States 6,565 5,052 Uruguay –20 –50 Uzbekistan –340 –400 Venezuela, RB 40 40 Vietnam –840 –200 West Bank and Gaza 1 –10 Yemen, Rep. 650 –135 Zambia –11 –85 Zimbabwe –192 –700 ..f s World ..f s –2,737 Low income 287 Middle income –13,401 –13,203 Lower middle income –9,961 –9,231 Upper middle income –3,441 –3,972 Low & middle income –13,114 –15,941 East Asia & Pacific –3,285 –3,781 Europe & Central Asia –3,386 –1,671 Latin America & Carib. –3,388 –5,214 Middle East & N. Africa –1,044 –1,089 South Asia –1,262 –2,376 Sub-Saharan Africa –749 –1,810 High income 13,097 15,894 Euro area 4,604 5,607

Refugees

Workers’ remittances and compensation of employees

thousands 1995 2010

thousands By country of origin By country of asylum 1995 2009 1995 2009

$ millions Received Paid 1995 2009 1995 2009

135 133 11,707 12,270 337 465 4,611 7,289 291 210 874 525 101 107 992 1,967 114 131 200 164 19 23 1,098 1,863 1,041 6,378 426 340 1,111 753 35 40 906 1,306 1,471 1,763 817 2,206 305 284 1,134 659 549 1,157 10 14 169 185 46 34 38 34 1,212 1,411 260 208 661 647 6,172 5,258 1,716 3,293 4,191 6,452 28,522 42,813 93 80 1,474 1,176 1,019 1,007 39 69 1,201 1,924 378 518 271 233 433 372 165,674g s 213,450g s 13,555 13,368 63,453 67,824 31,848 34,166 31,605 33,657 77,009 81,192 3,048 5,434 29,607 27,346 5,454 6,569 8,985 11,957 13,257 12,175 16,659 17,710 88,665 132,259 23,080 36,135

17.0 4.4 0.2 1.1 207.0 109.5 246.7 4.9 1,819.4 129.1 7.8 54.0 0.3 0.6 13.2 0.6 17.6 16.3 66.8 22.2 86.1e 195.6 650.7e 86.4 379.5 15.4 4.7 9.1 0.0 0.1 0.1 0.0 0.0 0.3 2.3 0.4 12.9 0.0 22.3 0.3 638.7 678.3 0.6 1.8 0.5 0.4 101.4 48.0 0.0 0.0 5.9 4.0 107.6 145.7 0.0 0.3 445.3 368.2 674.1 186.3 0.0 0.0 0.7 0.8 0.0 0.0 199.2 81.4 0.0 0.0 82.9 46.2 1,526.6 d 8.0 17.9 373.5d 59.0 0.6 0.6 2.7 0.1 1.2 829.7 118.7 0.2 0.5 106.6 105.3 .. 0.0 .. 0.0 93.2 18.4 10.9 8.5 0.0 0.2 .. 0.0 0.3 2.3 0.2 0.1 44.9 146.4 12.8 10.4 0.0 0.7 23.3 0.1 24.2 7.6 229.4 127.3 1.7 24.5 5.2 7.3 0.0 0.4 0.4 0.3 0.1 0.2 90.9 269.4 0.2 2.4 623.3 275.5 0.3 0.2 0.1 0.2 0.1 6.7 2.6 0.6 0.5 6.2 1.6 201.3 543.5 339.3 34.4 2.4 1,885.2d 72.8 95.2 1,201.0 d 0.4 1.9 53.5 170.9 0.0 0.2 130.0 56.8 0.0 22.4 0.5 4.0 18,068.7s,d h 15,163.2s,d h 18,068.7d s 15,163.2d s 7,990.4 5,427.5 4,727.2 1,893.8 4,260.8 4,558.6 10,086.9 11,285.2 2,733.4 3,451.1 6,322.0 9,104.7 1,527.3 1,107.4 3,764.8 2,180.5 12,251.1 9,986.1 14,814.0 13,179.1 952.9 996.7 447.0 485.5 1,611.6 655.6 1,221.3 163.8 155.5 462.0 93.9 367.4 948.0 2,014.0 5,683.0 7,809.4 2,958.7 3,192.1 1,625.5 2,263.4 5,624.4 2,665.8 5,743.4 2,089.5 287.1 90.8 3,254.7 1,984.1 13.9 1.0 1,690.4 1,011.4

9 2,502 21 .. 146 1,295 24 .. 26 272 .. 105 3,237 809 346 83 288 1,473 339 .. 1 1,695 .. 15 32 680 3,327 4 .. 6 .. 2,469 2,179 .. .. 2 .. 582 1,081 .. 44 101,254 s 2,189 53,012 31,182 21,830 55,202 8,925 6,482 13,322 13,275 10,005 3,193 46,052 30,827

4,929 5,359 93 217 1,365 5,406a 47 .. 1,671 279 .. 902 9,904 3,363 2,993a 93 652 2,524 1,332a 1,748 23 1,637 .. 307a 99a 1,964 970 .. 750 5,073 .. 7,252 2,947 101 .. 131 6,626a 1,261a 1,160 41 .. 416,158 s 22,706 284,357 206,323 78,033 307,063 85,788 35,433 56,590 33,442 75,061 20,749 109,095 67,529

2 3,938 1 16,594 76 .. 0 .. 3 31 .. 629 868 16 1 4 336 10,114 15 .. 1 .. .. 5 14 36 .. 7 .. 1 .. 2,581 22,181 .. .. 203 .. 19 61 59 7 100,950 s 357 10,230 2,147 8,084 10,587 1,703 4,507 1,138 704 476 2,060 90,363 28,741

310 18,548 71 25,969 144a 91 3 .. 134 191 .. 1,158 12,646 435 2a 11 787 19,562 212a 124 81 .. .. 58 a .. 13 141 .. 463 25 .. 3,400 48,308 6 .. 581 .. 9a 337 66 .. 289,122 s 2,047 57,377 15,095 42,283 59,425 14,459 24,427 3,788 8,536 3,471 4,743 229,697 85,677

a. World Bank estimate. b. Includes Taiwan, China. c. Includes Tibetans, who are listed separately by the UN Refugee Agency (UNHCR). d. Includes Palestinian refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), who are not included in data from the UNHCR. e. Includes Montenegro. f. World totals computed by the United Nations sum to zero, but because the aggregates refer to World Bank definitions, regional and income group totals do not. g. World totals are computed by the World Bank and include only economies covered by World Development Indicators, so data may differ from what is published by the United Nations Population Division. h. Includes refugees without specified country of origin and Palestinian refugees under the mandate of the UNRWA, so regional and income group totals do not sum to the world total.

386

2011 World Development Indicators

About the data

6.18

GLOBAL LINKS

Movement of people across borders Definitions

Movement of people, most often through migration, is

granted refugee or refugee-like status or temporary

• Net migration is the net total of migrants during

a significant part of global integration. Migrants con-

protection. Asylum seekers and internally displaced

the period. It is the total number of immigrants less

tribute to the economies of both their host country

people—who are often confused with refugees—are

the total number of emigrants, including both citi-

and their country of origin. Yet reliable statistics on

not included. Unlike refugees, internally displaced

zens and noncitizens. Data are five-year estimates.

migration are difficult to collect and are often incom-

people remain under the protection of their own gov-

• International migrant stock is the number of people

plete, making international comparisons a challenge.

ernment, even if their reason for fleeing was similar

born in a country other than that in which they live.

to that of refugees.

It includes refugees. • Refugees are people who are

The United Nations Population Division provides data on net migration and migrant stock. Net migra-

Registrations, together with other sources—includ-

recognized as refugees under the 1951 Convention

tion is the total number of immigrants minus the

ing estimates and surveys—are the main sources

Relating to the Status of Refugees or its 1967 Proto-

total number of emigrants. However, data on emi-

of refugee data. But there are difficulties in collect-

col, the 1969 Organization of African Unity Convention

grant stock are not collected because it is difficult

ing accurate statistics. Although refugees are often

Governing the Specific Aspects of Refugee Problems

for countries to gather information on people who are

registered individually, the accuracy of registrations

in Africa, people recognized as refugees in accordance

not within their borders. To derive estimates of net

varies greatly. Many refugees may not be aware of

with the UNHCR statute, people granted refugee-like

migration, the migration history of a country or area,

the need to register or may choose not to do so. And

humanitarian status, and people provided temporary

the migration policy of a country, and the influx of

administrative records tend to overestimate the num-

protection. Asylum seekers—people who have applied

refugees in recent periods are taken into account.

ber of refugees because it is easier to register than to

for asylum or refugee status and who have not yet

The data to calculate these official estimates come

de-register. The UN Refugee Agency (UNHCR) collects

received a decision or who are registered as asylum

from a variety of sources, including border statistics,

and maintains data on refugees, except for Palestin-

seekers—are excluded. Palestinian refugees are

administrative records, surveys, and censuses. When

ian refugees residing in areas under the mandate

people (and their descendants) whose residence was

no official estimates can be made because of insuf-

of the United Nations Relief and Works Agency for

Palestine between June 1946 and May 1948 and who

ficient data, net migration is derived through the bal-

Palestine Refugees in the Near East (UNRWA). The

lost their homes and means of livelihood as a result

ance equation, which is the difference between overall

UNRWA provides services to Palestinian refugees who

of the 1948 Arab-Israeli conflict. • Country of origin

population growth and the natural increase during the

live in certain areas and who register with the agency.

refers to the nationality or country of citizenship of a

1990–2000 intercensal period.

Registration is voluntary, and estimates by the UNRWA

claimant. • Country of asylum is the country where an

The data used to estimate the international migrant

are not an accurate count of the Palestinian refugee

asylum claim was filed and granted. • Workers’ remit-

stock at a particular time are obtained mainly from

population. The table shows estimates of refugees

tances and compensation of employees received and

population censuses. The estimates are derived from

collected by the UNHCR, complemented by estimates

paid comprise current transfers by migrant workers

the data on foreign-born population—people who have

of Palestinian refugees under the UNRWA mandate.

and wages and salaries earned by nonresident work-

residence in one country but were born in another

Thus, the aggregates differ from those published by

ers. Remittances are classified as current private

country. When data on the foreign-born population

the UNHCR.

transfers from migrant workers resident in the host

are not available, data on foreign population— that

Workers’ remittances and compensation of employ-

country for more than a year, irrespective of their

is, people who are citizens of a country other than the

ees are World Bank staff estimates based on data

immigration status, to recipients in their country of

country in which they reside—are used as estimates.

from the International Monetary Fund’s (IMF) Bal-

origin. Migrants’ transfers are defined as the net worth

After the breakup of the Soviet Union in 1991 people

ance of Payments Statistics Yearbook. The IMF data

of migrants who are expected to remain in the host

living in one of the newly independent countries who

are supplemented by World Bank staff estimates for

country for more than one year that is transferred to

were born in another were classified as international

missing data for countries where workers’ remittances

another country at the time of migration. Compensa-

migrants. Estimates of migrant stock in the newly

are important. The data reported here are the sum of

tion of employees is the income of migrants who have

independent states from 1990 on are based on the

three items defined in the fifth edition of the IMF’s

lived in the host country for less than a year.

1989 census of the Soviet Union.

Balance of Payments Manual: workers’ remittances,

For countries with information on the international

compensation of employees, and migrants’ transfers.

migrant stock for at least two points in time, inter-

The distinction among these three items is not

polation or extrapolation was used to estimate the

always consistent in the data reported by countries to

Data on net migration are from the United Nations

international migrant stock on July 1 of the reference

the IMF. In some cases countries compile data on the

Population Division’s World Population Prospects:

years. For countries with only one observation, esti-

basis of the citizenship of migrant workers rather than

The 2008 Revision. Data on migration stock are

mates for the reference years were derived using rates

their residency status. Some countries also report

from the United Nations Population Division’s

of change in the migrant stock in the years preceding

remittances entirely as workers’ remittances or com-

Trends in Total Migrant Stock: The 2008 Revision.

or following the single observation available. A model

pensation of employees. Following the fifth edition of

Data on refugees are from the UNHCR’s Statisti-

was used to estimate migrants for countries that had

the Balance of Payments Manual in 1993, migrants’

cal Yearbook 2009, complemented by statistics

no data.

transfers are considered a capital transaction, but

on Palestinian refugees under the mandate of

The table shows data on refugees because they

previous editions regarded them as current transfers.

the UNRWA as published on its website. Data on

are an important part of migrant stock. Refugee fig-

For these reasons the figures presented in the table

remittances are World Bank staff estimates based

ures shown here refer to people who have crossed an

take all three items into account.

on IMF balance of payments data.

Data sources

international border to find sanctuary and have been

2011 World Development Indicators

387

6.19

Travel and tourism International tourists

Inbound tourism expenditure

thousands Inbound Outbound 1995 2009 1995 2009

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

388

.. .. .. 1,856a,b 12 304a,b 1,912a,c 1,090 520a,c 9 366 3 2,289 4,329 3,815 12 575 .. 5,584a 2,519 3,726 a 21,355e 3,713 17,173e .. 1,409 432 156 267 830 161 95 626 6,815e 5,645 5,560 e 138 190 .. 284 671 249 311e .. 115e 521 1,553 .. 1,991 4,802 2,600 3,466 5,739 3,524 269 g .. 124g 201c 36 34 c .. 2,046 31 185g .. 100g 16,932 15,737 18,206 52b .. 26b 25g .. 19 g 1,540 2,750 1,070 20,034 50,875 4,520 7,137 16,926 .. 2,147a 1,057 1,399a 53 b 50 35b 85g .. 37g 785 1,923 273 188 .. .. 9,335 e .. 1,485e 2,405b 72 742b 6,032e .. 3,381e 4,503e 5,035 2,124 e 3,992b,c 168 1,776 b,c 968a,h 271 440a,h 2,871 11,914 2,683 235 1,091 348 79a,c .. 315a,c 530 1,900 1,764 330 c 120 103b 2,644 3,423 5,147 60,033 76,800 18,686 358 203 125b 45 142 .. 1,500a 228 85a 24,220e 55,800 14,847e 803c .. 286c 10,130 14,915 .. 563 a 1,777a 333 30 b .. 12b .. 30 .. 145 304 .. 271 870 149

2011 World Development Indicators

.. 3,404 1,677 .. 4,975 526 6,285 10,121 2,162 2,254 316 11,123 .. 628 .. .. 4,952 4,993 .. .. 340 .. 27,037 11 .. 2,895 47,656 81,958 2,122 .. .. 579 .. 2,497 206 6,618 6,347 415 814 4,531 1,012 .. 752 .. 5,832 23,347 .. 307 1,980 72,300 .. .. 1,326 .. .. .. 395

$ millions 1995 2009

.. 70 32d 27 2,550 14 11,915 14,529 87 25f 28 4,548 f 85f 92 257 176 1,085 662 .. 2 71 75 9,176 4d 43d 1,186 8,730 f 9,604d,f 887 .. 15 763 103 1,349 f 1,100 d 2,880 f 3,691f 1,571f 315 2,954 152 58 d 452 177 2,383 31,295 94 28f 75 24,052 30 4,182 216 1 3 90 f 85

.. 2,012 330 d 554 4,478 374 27,864 21,239 516 76f 562 11,144 236 306 761 454f 5,635 4,273 82 2 1,312 222 15,555 6 .. 2,270 42,632 20,884d 2,671 .. 54 1,985 113f 9,224 2,106 7,396 6,686f 4,051f 674 11,757 549 26 1,444 1,119 4,141 58,480 .. 64 531 47,505 1,049 14,796 820 f 5 38 315 611

% of exports 1995 2009

.. 23.2 .. 0.7 10.2 4.7 17.1 16.2 11.1 0.6 0.5 2.4 13.8 7.5 22.9 7.3 2.1 9.8 .. 1.9 7.3 3.7 4.2 .. .. 6.1 5.9 3.5 7.2 .. 1.1 17.1 2.4 19.3 .. 10.2 5.6 27.4 6.1 22.3 7.5 43.1 17.6 23.1 5.0 8.6 3.2 16.0 13.1 4.0 1.9 26.9 7.7 0.1 5.5 46.8 5.2

.. 58.2 .. 1.3 6.7 27.9 11.9 11.2 2.3 0.4 2.3 3.3 14.5 5.6 13.9 10.9 3.1 18.4 11.0 1.5 22.1 4.2 4.1 .. .. 3.6 3.2 5.1 7.0 .. 0.9 15.8 1.0 40.8 .. 5.6 3.6 38.7 4.3 26.4 11.7 .. 10.7 32.6 4.6 9.5 .. 23.0 16.6 3.5 13.4 25.0 8.9 0.4 22.2 33.8 10.1

Outbound tourism expenditure

$ millions 1995 2009

.. 19 186d 113 4,013 12 7,260 11,686 165 234f 101 8,115f 48 72 97 153 3,982 312 .. 25f 22 140 12,658 43d 38 d 934 3,688 f 10,497d,f 1,162 .. 69 336 312 422 f .. 1,635f 4,288f 267 331 1,371 99 .. 121 30 2,853 20,699 182 16 171 66,527 74 1,495 167 29 6 35f 99

.. 1,692 470 d 270 5,759 379 21,459 12,771 456 651 702 19,673 102 388 284 231f 12,897 1,955 110 71 162 549 30,232 61 .. 1,956 47,108 15,960 d,f 2,302 .. 168 463 345f 1,034 .. 4,157 9,678f 514f 806 2,941 253 .. 697 139 f 5,205 45,938 .. 9 311 92,738 848 3,401 680 28 46 443 355

% of imports 1995 2009

.. 2.3 .. 3.2 15.4 1.7 9.7 12.7 12.8 3.1 1.8 4.5 5.4 4.6 2.4 7.5 6.3 4.8 .. 9.7 1.6 8.7 6.3 .. .. 5.1 2.7 6.5 7.3 .. 5.1 7.1 8.2 4.6 .. 5.4 7.4 4.4 5.8 8.0 2.7 .. 4.2 2.1 7.6 6.2 10.6 7.0 12.1 11.3 3.5 6.0 4.5 2.9 6.5 4.4 5.3

.. 26.0 .. 0.6 11.8 10.3 10.0 7.3 4.6 2.8 2.3 6.0 4.3 7.5 3.0 4.5 7.4 7.2 3.8 13.7 2.3 8.4 7.4 .. .. 4.0 4.2 4.1 6.0 .. 2.6 3.8 3.9 4.2 .. 3.4 5.5 3.6 4.8 5.5 3.2 .. 5.6 1.5 6.2 6.9 .. 2.6 5.9 7.7 7.9 4.0 5.3 2.0 16.2 15.7 4.1

International tourists

Inbound tourism expenditure

thousands Inbound Outbound 1995 2009 1995 2009

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

.. 9,058 5,109h 2,124h 4,324 6,324 489 2,034 .. 61a 4,818 7,189 2,321h 2,215h 31,052 43,239 1,831b,c 1,147b,c 6,790a,h 3,345a,h 3,789c 1,075h .. 3,118 918 1,392 .. .. 7,818a,c 3,753a,c .. .. 297g 72g 36 2,435 60 1,239 539 1,323 450 1,844 87 320 .. .. .. 34 650 1,341 259e 147e 163b 75b 192 755 7,469 23,646 160b,g 42b,g .. .. 422 871 21,454 c 20,241c 32 7 108 411 8,341c 2,602c .. 2,224 117 243 272 931 363 510 9,921e 6,574 e 1,475 2,422 281 932 35 73 656 1,313 2,880 4,288 1,273g 279g 378 823 345 1,200 42 114 439 h 438 h 479 2,140 1,760c 3,017c 19,215 11,890 12,321c 9,511h 3,551b 3,131b 1,405g 309 g

13,083 3,056 .. 1,000 .. 2,547 2,259 18,173 .. 15,298 1,128 523 .. .. 3,819 .. 878 42 .. 1,812 .. .. .. 484 1,925 .. 39 .. 20,642 .. .. 107 8,450 71 .. 1,317 .. .. .. 100 12,313 920 255 10 .. 590 .. .. 185 51 427 508 1,615 36,387 .. 1,237 ..

16,906 11,067 5,053 .. .. 7,047 4,007 29,060 .. 15,446 2,368 5,243 .. .. 9,494 .. 2,649 1,521 .. 3,268 .. .. .. .. 1,288 .. .. .. .. .. .. 196 13,942 93 .. 2,293 .. .. .. 589 18,408 1,917 858 .. .. 3,395 .. .. 336 .. 280 1,958 3,066 50,243 20,989 1,319 ..

$ millions 1995 2009

2,938 2,582 f 5,229 f 205 18 f 2,698 3,491 30,426 1,199 4,894 973 155 785 .. 6,670 .. 307 5f 52 37 710 29 .. 4 102 19 f 106 22 5,044 26 11f 616 6,847 71 33 1,469 49 169 278f 232 10,611 2,318 f 51 7f 47 2,730 193 582 372 25f 162 521 1,141 6,927 5,646 1,828 d ..

6,740 11,509 6,773 2,196 555 8,187 4,332 41,872 2,070 12,537 3,468 1,184 1,095 .. 12,927 .. 553 506 271f 1,013 7,157 40 f 123f 159 1,183 232 518 48 17,231 286 .. 1,390 12,309 235 253f 7,978 217 59 469 397 17,876 4,396f 346 f 86 791 4,444 1,108 903 2,279 1 247 2,471 2,837 9,853 12,329 3,473d 874d

% of exports 1995 2009

14.9 6.8 9.9 1.1 .. 5.5 12.7 10.3 35.3 1.0 28.0 2.6 22.3 .. 4.5 .. 2.2 1.1 12.8 1.8 .. 14.6 .. 0.1 3.2 2.7 14.2 4.7 6.1 4.9 2.2 26.2 7.7 8.0 6.5 16.2 10.2 12.9 16.0 22.5 4.4 13.0 7.7 2.2 0.4 4.9 2.5 5.7 4.9 0.8 3.4 7.9 4.3 19.4 17.5 .. ..

6.7 4.4 5.1 .. 1.4 4.1 6.4 8.2 51.3 1.9 31.8 2.5 14.8 .. 3.0 .. 0.9 19.8 18.8 9.0 33.1 5.1 27.1 0.4 5.8 6.5 .. .. 9.2 11.2 .. 33.2 5.0 11.7 11.0 30.2 8.8 1.2 11.6 26.6 3.5 13.2 12.1 8.2 1.3 2.8 3.8 4.1 13.7 0.0 3.4 8.1 6.0 5.8 18.3 .. ..

6.19

GLOBAL LINKS

Travel and tourism

Outbound tourism expenditure

$ millions 1995 2009

1,501 996f 2,172 f 247 117f 2,034f 2,626 17,219 173 46,966 719 296 230 .. 6,947 .. 2,514 7f 34 62 .. 17 .. 493 107 27f 79 53 2,722 74 30 184 3,587 73 22 356 68 18f 90 f 167 13,151 1,259 f 56 26 938 4,481 349 f 654 181 58 f 173 428 551 5,865 2,539 1,155d ..

4,117 11,507 9,579 9,482 705 8,887 3,869 34,329 259 34,788 1,202 1,320 234f .. 14,648 .. 8,244 391 91f 906 4,928 22 51 1,683 1,140 150 123f 84 7,196 228 .. 384 8,628 307 242 1,712 249 40 109 511 21,076 2,559 f 224 98 5,308 12,366 f 1,277 1,098 503 48 288 1,379 2,989 7,842 4,604 1,613d 3,751d

% of imports 1995 2009

7.5 2.1 4.0 1.6 .. 4.8 7.4 6.9 4.6 11.2 14.7 4.9 3.9 .. 4.5 .. 19.9 1.0 4.5 2.8 .. 1.6 .. 8.6 2.7 1.7 8.0 8.0 3.1 7.5 5.9 7.5 4.4 7.3 4.2 3.2 6.6 0.9 4.3 10.3 6.1 7.3 4.9 5.7 7.3 9.6 6.3 4.6 2.3 3.0 3.3 4.5 1.7 17.3 6.4 .. ..

2011 World Development Indicators

4.4 3.5 8.5 .. 3.3 5.3 6.1 6.6 4.1 5.3 7.4 3.4 2.1 .. 3.7 .. 26.9 10.6 5.8 7.9 16.3 1.2 3.0 6.2 5.5 2.6 .. .. 5.0 6.1 .. 7.5 3.3 7.7 9.2 4.6 5.8 1.4 2.1 10.0 4.6 8.0 5.0 5.0 11.1 11.8 5.9 3.1 3.3 1.0 3.9 5.3 5.4 4.6 5.5 .. ..

389

6.19

Travel and tourism International tourists

thousands Inbound Outbound 1995 2009 1995 2009

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

5,445a 10,290 a .. 3,325 .. .. 38b 6,070 903e 732e .. 4,488 34,920 403h 29 300i 2,310 e 6,946g 815e .. 285 6,952c .. 53g 260 b 4,120 h 7,083 218 160 3,716 2,315c,i 21,719 43,490 2,022 92 700 1,351a 220g 61g 163 1,416 a 537,385 t 6,379 139,405 58,101 82,221 147,674 43,654 33,946 39,151 13,555 3,819 12,978 384,359 203,060

7,575a 23,676 a 699 10,897 875 645 36 b 7,489 1,298e 1,824 e .. 9,934 52,231 448h 420 908 g 4,678 e 8,294g 6,092e .. 714 14,150 .. 150g 413b 6,901h 25,506 8 817 20,798 .. 28,199 54,884 2,056 1,069 615 3,747a 396 g 434 g 710 1,956 a 894,012 t 18,801 329,738 160,100 171,751 352,280 107,674 106,987 60,093 44,880 7,949 31,497 535,465 280,972

Inbound tourism expenditure

$ millions 1995 2009

5,737 11,723 689 1,669 21,329 36,538 4,312f 12,300 .. .. 4 218f .. 6,032 .. 6,678 d .. .. 168 637 .. .. .. 986 6 73 57f 25f 9,200 f 2,867 6,961 7,611f 218 19,917 630 2,539 .. 2,586 1,128 2,733 .. .. .. .. 2,520 4,424 2,654 8,683 3,648 12,844 27,369 58,586 504 963 367 754 299 f 195 .. 8f .. 1,245 54 40 10,127 11,699 4,390 12,114 11,148 11,147 11,354 16,335 1,746 5,215 1,258 f 5,152 .. .. .. 20 1,192 157 .. 502 f 1,820 4,535 9,257 19,421 .. .. .. .. .. .. 13f 44 261 .. 232 557 1,778 2,623 1,838 3,526 24,556 3,981 10,493 4,957f 21 38 13 .. 683 148 337 78f 4,349 6,552 15,334 191f .. .. 632 7,162d 41,345 58,614 27,577 38,545 51,285 61,419 93,700 147,554 562 826 725 1,408 246 1,150 15 64 d 534 1,651 995 853 .. .. .. 3,050 d .. .. 255f 269 f .. .. 50 f 496f .. .. 29 98 256 593 145 294d 555,382 t 961,575 t 487,033 t 1,022,301 t .. .. 3,253 11,845 129,489 327,671 82,794 270,868 35,678 134,442 40,251 135,230 86,296 .. 42,566 135,451 141,222 363,391 85,892 281,994 33,153 .. 31,197 94,687 47,292 106,450 12,014 58,244 21,841 41,194 21,838 49,773 13,407 25,352 9,771 43,050 5,151 17,100 4,016 14,339 .. .. 6,928 22,170 374,257 564,431 401,084 740,277 141,785 235,326 164,475 310,544

% of exports 1995 2009

7.3 4.6 5.4 .. 11.2 .. 44.4 4.8 5.7 10.9 .. 7.7 20.4 7.9 1.2 5.3 4.6 9.2 21.9 .. 39.7 13.2 .. 2.8 8.3 23.0 13.6 0.7 11.7 1.1 .. 8.6 11.8 20.7 .. 4.8 .. 33.4 2.3 2.4 .. 7.6 w 12.2 7.7 8.5 7.1 7.8 7.8 6.3 7.6 13.0 6.8 7.8 7.6 7.8

Outbound tourism expenditure

$ millions 1995 2009

3.3 749 3.6 11,599 f 40.8 13 3.3 .. 18.2 154 8.3 .. 7.7 51 2.5 4,663 f 4.1 338 9.6 606 .. .. 11.1 2,414 16.9 5,826 8.4 279 3.6 43f 2.2 45 6.2 6,816 5.8 9,478 16.4 498f 1.6 .. 22.8 360 f 10.8 4,791 .. .. 3.9 40 2.8 91 17.7 294 17.2 911f .. 74 17.3 80 f 8.0 210 f .. .. 6.5 30,749 9.4 60,924 16.5 332 .. .. 1.4 1,852 4.9 .. 23.0 162 f 7.0 76f 2.1 83 .. 106d 6.4 w 458,869 t 12.9 2,591 6.2 60,850 5.3 20,926 7.3 39,917 6.3 63,336 4.8 14,770 7.3 16,380 6.0 18,774 20.5 4,844 4.6 2,393 7.5 6,810 6.4 394,726 6.9 155,113

1,769 23,529 115 20,964 d 276 1,076 16 15,808f 2,249 1,533 .. 6,420 21,482 735 868 f 98 13,432 12,552 910 f 6f 806 5,659 .. 68 102 492 4,627 .. 336 3,751 13,288d 61,130 105,202 436 .. 2,234 1,100 544f 277 83 .. 923,915 t 7,641 214,809 110,735 104,850 222,402 75,780 48,211 41,573 19,825 14,787 25,420 703,266 280,349

% of imports 1995 2009

6.6 14.0 3.5 .. 8.5 .. 19.4 3.2 3.2 5.6 .. 7.2 4.3 4.7 3.5 3.5 8.4 8.7 9.0 .. 16.8 5.8 .. 6.0 4.3 3.3 2.3 4.1 5.4 1.1 .. 9.4 6.8 9.3 .. 11.0 .. 5.8 3.1 6.2 .. 7.4 w 5.1 5.4 4.0 6.7 5.4 3.5 8.1 6.5 5.7 3.0 6.8 7.9 7.8

2.9 9.3 7.8 13.1 3.9 5.8 2.5 4.9 3.6 5.5 .. 7.9 5.7 6.3 7.7 4.2 8.1 5.1 4.7 0.2 10.7 3.6 .. 4.1 1.0 2.3 3.1 .. 6.4 6.7 .. 9.4 5.4 5.6 .. 4.6 1.5 11.0 2.8 2.0 .. 5.9 w 5.0 5.1 4.5 5.9 5.1 4.4 6.2 5.3 6.7 3.6 6.4 6.3 6.5

Note: Aggregates are based on World Bank country classifications and differ from those of the World Tourism Organization. Regional and income group totals include countries not shown in the table for which data are available. a. Arrivals of nonresident visitors at national borders. b. Excludes nationals residing abroad. c. Includes nationals residing abroad. d. Data are from national sources. e. Arrivals in all types of accommodation establishments. f. Refers to expenditure of travel-related items only; excludes passenger transport items. g. Arrivals in hotels and similar establishments. h. Arrivals in hotels only. i. Arrivals by air only.

390

2011 World Development Indicators

About the data

6.19

GLOBAL LINKS

Travel and tourism Definitions

Tourism is defined as the activities of people trav-

For some countries number of arrivals is limited to

• International inbound tourists (overnight visitors)

eling to and staying in places outside their usual

arrivals by air and for others to arrivals staying in

are the number of tourists who travel to a country

environment for no more than one year for leisure,

hotels. Some countries include arrivals of nationals

other than that in which they usually reside, and out-

business, and other purposes not related to an activ-

residing abroad while others do not. Caution should

side their usual environment, for a period not exceed-

ity remunerated from within the place visited. The

thus be used in comparing arrivals across countries.

ing 12 months and whose main purpose in visiting

social and economic phenomenon of tourism has

The World Tourism Organization is improving its

is other than an activity remunerated from within the

coverage of tourism expenditure data, using balance

country visited. When data on number of tourists are

Statistical information on tourism is based mainly

of payments data from the International Monetary

not available, the number of visitors, which includes

on data on arrivals and overnight stays along with

Fund (IMF) supplemented by data from individual

tourists, same–day visitors, cruise passengers, and

balance of payments information. These data do not

countries. These data, shown in the table, include

crew members, is shown instead. • International out-

completely capture the economic phenomenon of

travel and passenger transport items as defined in

bound tourists are the number of departures that

tourism or provide the information needed for effec-

the IMF’s (1993) Balance of Payments Manual. When

people make from their country of usual residence

tive public policies and efficient business operations.

the IMF does not report data on passenger transport

to any other country for any purpose other than an

Data are needed on the scale and significance of

items, expenditure data for travel items are shown.

activity remunerated in the country visited. • Inbound

tourism. Information on the role of tourism in national

Tourism expenditure does not include all types of

tourism expenditure is expenditures by international

economies is particularly defi cient. Although the

payments that visitors might make. It excludes pay-

inbound visitors, including payments to national carri-

World Tourism Organization reports progress in har-

ments not for consumption of goods and services,

ers for international transport. These receipts include

monizing definitions and measurement, differences

such as taxes and duties that are not part of the

any other prepayment made for goods or services

in national practices still prevent full comparability.

purchase prices of the products acquired by the visi-

received in the destination country. They may include

The usual environment of an individual is a key

tor; purchase of financial and nonfinancial assets

receipts from same–day visitors, except when these

concept in tourism statistics and is defined as the

including land and real estate; purchase of goods for

are important enough to justify separate classifica-

geographical area within which an individual con-

resale; and donations to charities or other individu-

tion. For some countries they do not include receipts

ducts regular life routines. This concept excludes as

als. The timing of tourism expenditure is also impor-

for passenger transport items. Their share in exports

visitors travelers who commute regularly between

tant because transportation and accommodation are

is calculated as a ratio to exports of goods and ser-

their place of usual residence and place of work or

often booked and paid for before being consumed.

vices (all transactions between residents of a coun-

study or who frequently visit places within their cur-

Payment might also happen after consumption of

try and the rest of the world involving a change of

rent life routine—for instance, homes of friends or

such services, such as when a visitor pays off a

ownership from residents to nonresidents of general

relatives; shopping centers, and religious, health-

credit card or a special loan drawn for travel pur-

merchandise, goods sent for processing and repairs,

care, or other facilities a substantial distance away

poses. Tourism expenditure should be reported for

nonmonetary gold, and services). • Outbound tour-

or in a different administrative area that are regularly

the period when the services are actually consumed

ism expenditure is expenditures of international out-

and frequently visited.

and goods are actually acquired, regardless of when

bound visitors in other countries, including payments

Tourism can be either domestic or international.

payment was made. Finally, the valuation of tour-

to foreign carriers for international transport. These

The table shows data relevant to international tour-

ism expenditure depends on the form of acquisition

expenditures may include those by residents travel-

ism, where the traveler’s country of residence dif-

of the goods and services concerned. In a market

ing abroad as same–day visitors, except when these

fers from the visiting country. International tourism

transaction expenditure should be valued using the

are important enough to justify separate classifica-

consists of inbound and outbound tourism. The data

purchaser price—value paid by the visitor. This price

tion. For some countries they do not include expen-

are from the World Tourism Organization, a United

should include all taxes and voluntary and compul-

ditures for passenger transport items. Their share in

Nations agency. The data on inbound and outbound

sory tips prevalent in the accommodation and food

imports is calculated as a ratio to imports of goods

tourists refer to the number of arrivals and depar-

services sectors. Discounts and rebates of sales

and services (all transactions between residents of a

tures, not to the number of people traveling. Thus a

tax or value added tax to nonresidents should be

country and the rest of the world involving a change of

person who makes several trips to a country during

taken into account, even if refunded at the border.

ownership from nonresidents to residents of general

a given period is counted each time as a new arrival.

However, following these recommendations for tour-

merchandise, goods sent for processing and repairs,

Unless otherwise indicated in the footnotes, the data

ism statistics may not be easy for countries. Tourism

nonmonetary gold, and services).

on inbound tourism show the arrivals of nonresident

expenditures reported in the table may not be fully

tourists (overnight visitors) at national borders.

comparable, so caution should be used when making

When data on international tourists are unavailable

cross-country comparisons.

grown substantially over the past quarter century.

Data sources Data on visitors and tourism expenditure are

or incomplete, the table shows the arrivals of inter-

The aggregates are calculated using the World

from the World Tourism Organization’s Yearbook

national visitors, which include tourists, same-day

Bank’s weighted aggregation methodology (see Sta-

of Tourism Statistics and Compendium of Tourism

visitors, cruise passengers, and crew members.

tistical methods) and differ from the World Tourism

Statistics 2011. Data in the table are updated

Organization’s aggregates.

from electronic files provided by the World Tour-

Sources and collection methods for arrivals differ across countries. In some cases data are from bor-

ism Organization. Data on exports and imports

der statistics (police, immigration, and the like) and

are from the IMF’s Balance of Payments Statistics

supplemented by border surveys. In other cases data

Yearbook and data files.

are from tourism accommodation establishments.

2011 World Development Indicators

391 Text figures, tables, and boxes

PRIMARY DATA DOCUMENTATION As a major user of socioeconomic data, the World Bank recognizes the importance of data documentation to inform users of differences in the methods and conventions used by primary data collectors—usually national statistical agencies, central banks, and customs services—and by international organizations, which compile the statistics that appear in the World Development Indicators database. These differences may give rise to significant discrepancies over time both within countries and across them. Delays in reporting data and the use of old surveys as the base for current estimates may further compromise the quality of data reported here. The tables in this section provide information on sources, methods, and reporting standards of the principal demographic, economic, and environmental indicators in World Development Indicators. Additional documentation is available from the World Bank’s Bulletin Board on Statistical Capacity at http://data.worldbank.org/. The demand for good-quality statistical data is increasing. Timely and reliable statistics are key to the broad development strategy often referred to as “managing for results.” Monitoring and reporting on publicly agreed indicators are central to implementing poverty reduction strategies and lie at the heart of the Millennium Development Goals and the Results Measurement System adopted for the 14th replenishment of the International Development Association. A global action plan to improve national and international statistics was agreed on during the Second Roundtable on Managing for Development Results in February 2004 in Marrakech, Morocco. The plan, now referred to as the Marrakech Action Plan for Statistics, or MAPS, has been widely endorsed and forms the overarching framework for statistical capacity building. The third roundtable conference, held in February 2007 in Hanoi, Vietnam, reaffirmed MAPS as the guiding strategy for improving the capacity of the national and international statistical systems. See www.mfdr.org/RT3 for reports from the conference.

2011 World Development Indicators

393

PRIMARY DATA DOCUMENTATION Currency

National accounts

SNA System of price Reference National Accounts valuation year

Base year

Afghanistan Albania Algeria American Samoa Andorra Angola Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas, The Bahrain Bangladesh Barbados Belarus Belgium Belize Benin

Afghan afghani Albanian lek Algerian dinar U.S. dollar Euro Angolan kwanza East Caribbean dollar Argentine peso Armenian dram Aruban florin Australian dollar Euro New Azeri manat Bahamian dollar Bahraini dinar Bangladeshi taka Barbados dollar Belarusian rubel Euro Belize dollar CFA franc

Bermuda

Bermuda dollar

Bhutan Bhutanese ngultrum Bolivia Bolivian Boliviano Bosnia and Herzegovina Bosnia and Herzegovina convertible mark

2002/03 a

Alternative conversion factor

VAB VAB VAB

Government IMF data finance dissemination standard

Balance of Payments Manual in use

External debt

System of trade

Accounting concept

2005

BPM5 BPM4

Actual Actual Actual

G S S

C C B

G G G

2005

BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5

PPP survey year

G 1997 1990 1993

b

a

1996

b

a

2007

b

VAP VAB VAB VAB

1991–96 1971–84 1990–95

2005 2005

1992–95

2005 2005 2005

1995 b

2000 a

2003

a

b b

2006 1985 1995/96 1974

b

2000

b b

2000 2000 1985

b

1996

a

b

1996

b

b

1993/94 2000 2000

Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Channel Islands Chile China Hong Kong SAR, China Macao SAR, China Colombia Comoros Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Djibouti

CFA franc 1999 Burundi franc 1980 Cambodian riel 2000 CFA franc 2000 Canadian dollar 2000 Cape Verde escudo 1980 Cayman Islands dollar CFA franc 2000 CFA franc 1995 Pound sterling 2003, 2007 Chilean peso 2003 Chinese yuan 2000 Hong Kong dollar 2008 Macao pataca 2002 Colombian peso 2005 Comorian franc 1990 Congolese franc 1987 CFA franc 1978 Costa Rican colon 1991 CFA franc 1996 a Croatian kuna Cuban peso 1990 a Euro Czech koruna 2000 Danish krone 2000 Djibouti franc 1990

b

a

VAB VAB VAB VAB VAP VAB VAB VAB VAB VAB VAP

2005 2005 1990–95

2005 2005

1992

2005

1960–85

2005 2005 2005

VAB b

2000 1990

Botswana pula Brazilian real Brunei dollar Bulgarian lev

2011 World Development Indicators

b

1980

Botswana Brazil Brunei Darussalam Bulgaria

394

1996

Balance of payments and trade

2002

b

b b

b

2007

b b b b

b

b

b

2000 2000 1995

b

b b

VAB VAB VAB VAB VAB VAP VAB VAB VAB VAB VAB VAB VAP VAB VAB VAB VAB VAP VAB VAB VAB VAP VAB VAP VAB VAP VAB VAB VAB VAB VAB VAB

Actual C C

G G S S

Actual Actual

G S G G G S G G G G G G S G G

C C B B B C B C C B B

S S G G G G G S S G G

BPM5 BPM5

Actual Actual Actual

G S S

C C C

G G

S S S S

B C C

G S G S

G G G S G S

B C C B C C

Actual Actual

Actual

Actual Actual

BPM5

1978–89, 1991–92 1992–93

1978–93

1992–94 1999–2001 1993

2005 2005 2005 2005

BPM5 BPM5

Actual Actual

BPM5

Actual

2005 2005 2005 2005 2005 2005

BPM4 BPM5 BPM5 BPM5 BPM5 BPM5

Estimate Actual Actual Actual

2005 2005

BPM4 BPM4

Preliminary Actual

S

B

G G

2005 2005 2005 2005 2005 2005 2005 2005

BPM5 BPM5 BPM5 BPM5 BPM5

Actual Preliminary

S G G G G G

C B C C B

S G S G S

C C C C C

G G S G S

C C C

S S S

2005 2005 2005 2005 2005 2005

BPM4 BPM5 BPM5 BPM5 BPM5

Actual

Actual Preliminary Estimate Preliminary Actual Actual

BPM5 BPM5 BPM5 Actual

S S S S G S G

G G G S G

PRIMARY DATA DOCUMENTATION Latest population census

Afghanistan Albania Algeria American Samoa Andorra Angola Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas, The Bahrain Bangladesh Barbados Belarus Belgium Belize Benin

1979 2001 2008 2010

Latest demographic, education, or health household survey

Source of most recent income and expenditure data

MICS, 2003 DHS, 2008/09 MICS, 2006

IHS, 2008 LSMS, 2008 IHS, 1995

Yes

Latest trade data

Latest water withdrawal data

2000 2000 2000

2008 2009 2008 2006 1995 2009 2005 2009 2009 2008 2005

2009 2008 2008 2009 2006 1991 2007 2009 2009 2009 2009 2009 2009 2009 2007 2007 2009 2009 2009 2008 2006

2000 1984–88

2009 2009 2009

2009 2009 2010

2000 2000

1993 1996

2009 2009 2006 2009

2009 2010 2006 2009

2000 2000

2006 2005 2009 2007 2007 2009

2009 2009 2008 2006 2009 2009

2000 2000 2000 2000 2000

1985

2006 2008

2005 1995

2000 2000

1997 1997

2009 2009 2008 2007 2009 2009 2009 2009 2009 2009 2009 2008 2008 2009 2009 2007

2009 2009 2009 2009 2009 2007 1986 2005 2009 2009 2009 2006 2009 2009 2009 2009

2000 2000

1998 2001

2009 2009 2009

Yes Yes

c

1970 2001 2010 2001 2010 2006 2001 2009 2010 2010 2001 2010 2009 2001 2010 2002

Vital Latest Latest registration agricultural industrial complete census data

MICS, 2001; MIS, 2006/07

IHS, 2000

DHS, 2005

IHS, 2009 IHS, 2009

DHS, 2006

ES/BS, 1994 IS, 2000 ES/BS, 2008

DHS, 2007

IHS, 2005

MICS, 2005 MICS, 2006 DHS, 2006

ES/BS, 2009 IHS, 2000 ES/BS, 1999 CWIQ, 2003

DHS, 2008 MICS, 2006

IHS, 2003 IHS, 2007 LSMS, 2007 ES/BS, 2003 LFS, 2008

1964–65 Yes Yes Yes Yes Yes Yes Yes

2002

2001 1999–2000

Yes

Bermuda

2010

Bhutan Bolivia Bosnia and Herzegovina

2005 2001 1991

Botswana Brazil Brunei Darussalam Bulgaria

2001 2010 2001 2001

MICS, 2000 DHS, 1996

Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Channel Islands Chile China Hong Kong SAR, China Macao SAR, China Colombia Comoros Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Djibouti

2006 2008 2008 2005 2006 2010 2010 2003 2009 2001 2002 2010 2006 2006 2006 2003 1984 2007 2000 1998 2001 2002 2001 2001 2001 2009

MICS, 2006 MICS, 2005 DHS, 2005 MICS, 2006

2005 Yes Yes Yes

1994 1999–2000d 1992

2009 2009 2009 2009

Yes

ES/BS, 2007

DHS, 2005

CWIQ, 2003 CWIQ, 2007 IHS, 2007 PS, 2007 LFS, 2000 ES/BS, 2007

MICS, 2006 DHS, 2004

PS, 2008 PS, 2002/03

NSS, 2007

IHS, 2009 IHS, 2005

Yes Yes 1993

Yes

1984 1996/2001 2004

Yes Yes DHS, 2005 MICS, 2000 MICS, 2010 DHS, 2005; AIS, 2009 RHS, 1993 MICS, 2006

IHS, 2009 IHS, 2004 1-2-3, 2005/06 CWIQ/PS, 2005 LFS, 2009 IHS, 2008 ES/BS, 2008

MICS, 2006 RHS, 1993 MICS, 2006

IS, 1996 ITR, 1997 PS, 2002

2001

Yes Yes Yes Yes Yes Yes

2000 2000 2005 2003 2000 2000 2000 2000 2001

2009

Yes

Yes Yes Yes

2000 1990 2000 2000

1990 1985–86 1973 2001 2003

2000 1999–2000

2011 World Development Indicators

2000

2000 2000 2002 2000

2000 2000 2000 2000 2000

395

PRIMARY DATA DOCUMENTATION Currency

National accounts

SNA System of price Reference National Accounts valuation year

Base year

Dominica Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Faeroe Islands Fiji Finland France French Polynesia Gabon Gambia, The Georgia Germany Ghana Gibraltar Greece Greenland Grenada Guam Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hungary Iceland India

East Caribbean dollar 1990 Dominican peso 1991 U.S. dollar 2000 Egyptian pound 1991/92 U.S. dollar 1990 CFA franc 2000 Eritrean nakfa 1992 Estonian kroon 2000 Ethiopian birr 1999/2000 Danish krone Fijian dollar 2005 Euro 2000 a Euro CFP franc CFA franc 1991 Gambian dalasi 1987 a Georgian lari Euro 2000 New Ghanaian cedi 2006 Gibraltar pound a Euro Danish krone East Caribbean dollar 1990 U.S. dollar Guatemalan quetzal 2001 Guinean franc 1996 CFA franc 2005 Guyana dollar 2006 Haitian gourde 1986/87 Honduran lempira 2000 a Hungarian forint Iceland krona 2000 Indian rupee 2004/05

Indonesia Iran, Islamic Rep. Iraq Ireland Isle of Man Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kiribati Korea, Dem. Rep.

Indonesian rupiah Iranian rial Iraqi dinar Euro Pound sterling Israeli new shekel Euro Jamaican dollar Japanese yen Jordanian dinar Kazakh tenge Kenyan shilling Australian dollar Democratic People’s Republic of Korean won Korean won Euro Kuwaiti dinar Kyrgyz som Lao kip Latvian lats Lebanese pound Lesotho loti

Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho

396

2011 World Development Indicators

2000 1997/98 1997 2000 2005 2005 2000 2003 2000 1994 a

b

b

b b

b

2000

1996

b

b b

2000

VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAP VAB VAB VAB VAB

Balance of payments and trade

Alternative conversion factor

Balance of Payments Manual in use

External debt

System of trade

Accounting concept

BPM5 BPM5 BPM5 BPM5 BPM5

Actual Actual Actual Actual Actual

S G S G G

C B C C

G G S S S

Actual C B

S G

2005 2005 2005

BPM4 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5

B C C

G S S

2005 2005 2005 2005 2005

BPM5 BPM5 BPM5 BPM5 BPM5

Preliminary Estimate Actual

C C C B

G G S S G

2005

BPM5

PPP survey year

2005 2005 1965–84

2005

1987–95

2005 2005

1993 1990–95 1973–87

VAB

Actual

G G G G S S S S G G S G

C

S

BPM5

Actual

S G S

B

G

Actual Estimate Estimate Actual Actual Actual

G S

B B

G G G

2005 2005 2005

BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5

2005 2005 2005 2005

BPM5 BPM4 BPM5 BPM5

2005 2005

BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5

VAB b

b

2000

b

b

b

VAB VAB VAB VAB VAB VAB VAB VAB VAB VAP VAB VAB VAB

2005 2005 1991 1988–89

1980–2002 1997, 2004

Government IMF data finance dissemination standard

Actual Actual

S G G S S S

Actual

S S S G

C C C C

Actual Actual

G S

B C

G

C

S S G G S G G S

C C C C B C B

S S G S S S G G

G

C

S

S G

B B B C B C

G S

S G S

2003 b b

2000

b

2001 2006

b

2000

b

VAP VAB VAB VAB VAB VAB VAB VAB

1987–95

2005 2005 2005 2005

Actual Actual Actual Actual

BPM4 VAB

2005

BPM5 Actual

1995 a

1990 2000 1997 1995

1995

b

b

b

VAP VAB VAB VAB VAB VAB

1990–95 1987–95

2005 2005 2005 2005 2005 2005

BPM5 BPM5 BPM5 BPM5 BPM5 BPM5

Actual Preliminary Actual Actual

S S G

S G G

PRIMARY DATA DOCUMENTATION Latest population census

Dominica Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Faeroe Islands Fiji Finland France French Polynesia Gabon Gambia, The Georgia Germany Ghana Gibraltar Greece Greenland Grenada Guam Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hungary Iceland India

2001 2010 2010 2006 2007 2002 1984 2000 2007

Latest demographic, education, or health household survey

Source of most recent income and expenditure data

DHS, 2007 RHS, 2004 DHS, 2008 RHS, 2008

IHS, 2007 LFS, 2009 ES/BS, 2004/05 IHS, 2008

Yes

DHS, 2005

ES/BS, 2004 ES/BS, 2005 ES/BS, 2009 IS, 2000 ES/BS, 1994/95

DHS, 2000 MICS, 2005/06 MICS, 2005; RHS, 2005

c

2010 2001 2001 2010 2001 2010 2002 1996 2009 2002 2003 2001 2001

Yes Yes

2001 2001–02

2009 2009 2009 2009 2009 2009 2009 2009 2009

1999–2000 1999–2000

2009 2009 2009

1974–75 2001–02 2004 1999–2000 1984

2009 2009 2009 2009 2009

1999–2000

2009

1971 1999–2000 1999–2000 1970–71

DHS, 2002

c

2007 2010 2006e 2007 2003 2003 2002

Vital Latest Latest registration agricultural industrial complete census data

DHS, 2008

CWIQ/IHS, 2005 IHS, 2003 IHS, 2008 IHS, 2000 LSMS, 2006 IHS, 2000

RHS, 2002 DHS, 2005 MICS, 2010 MICS, 2006 DHS, 2005/06 DHS, 2005/06

LSMS, 2006 CWIQ, 2007 CWIQ, 2002 IHS, 1998 IHS, 2001 IHS, 2007 ES/BS, 2007

c

2001

DHS, 2005/06

IHS, 2004/05

Indonesia Iran, Islamic Rep. Iraq Ireland Isle of Man Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kiribati Korea, Dem. Rep.

2010 2006 1997 2006 2006 2009 2001 2001 2010 2004 2009 2009 2005 2009

DHS, 2007 DHS, 2000 MICS, 2006

IHS, 2007 ES/BS, 2005 IHS, 2007 IHS, 2000

Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho

2005 1981 2010 2009 2005 2000 1970 2006

MICS, 2005 DHS, 2009 MICS, 2006 SPA, 2004; DHS, 2008/09

ES/BS, 2001 ES/BS, 2000 LSMS, 2007 IS, 1993 ES/BS, 2006 ES/BS, 2007 IHS, 2005-06

Yes Yes Yes Yes Yes Yes

Yes Yes Yes Yes Yes Yes Yes Yes

Yes Yes

Yes Yes Yes Yes Yes Yes Yes

1971 1993 2000 1995–96/ 2000–01 2003 2003 1981 2000 1981 2000 1996 2000 1997

Yes 1977–79

2008 2009 2009 2008 2009 2003 2009 2009 2009 2009 2009 2009 2010 2006 2009 2008 2009 2008

2000 2000 2000 2000 2000 2005 2000 2000

2009 2008 2008 2009

2009 2008 2005 2009 1997 2009 2009 2009 2009

2000 2000 2000 2000 2000 2000 2000 2000 2000

2009 2007 2002 2009

2009 2006 2008 2009

2000 2004 2000 2000

2009 2009 2008 2009 2009 2009 2009

2009 2009 2009 2009 2009 2009 2009 2009

2004 2000 2000 2000 2005 2000 2003

2009 2009 2002 2009

2000 ES/BS, 1998 IHS, 2006

MICS, 2000 DHS, 2009/10

2000 2000 2000 2000 2000 2004 2000 2002

2000

MICS, 2010

FHS, 1996 MICS, 2005/06 MICS, 2006

Latest water withdrawal data

2009 2007 2009

2009 2003 2000–01 1988

Latest trade data

ES/BS, 2007 ES/BS, 2008 IHS, 2008 ES/BS, 2002/03

Yes

2000

Yes Yes

1970 2002 1998–99 2001 1998–99 1999–2000

Yes Yes

2009 2009 2003 2008 2008 2009 2009 2009

2009

2000

2009 2009 1975 2009 2009 2004

2002 2000 2000 2000 2005 2000

2011 World Development Indicators

397

PRIMARY DATA DOCUMENTATION Currency

National accounts

Base year

Liberia Libya Liechtenstein Lithuania Luxembourg Macedonia, FYR Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Mauritania Mauritius Mayotte Mexico Micronesia, Fed. Sts. Moldova Monaco Mongolia Montenegro Morocco Mozambique

Liberian dollar 1992 Libyan dinar 1999 Swiss franc Lithuanian litas 2000 Euro Macedonian denar 1997 Malagasy ariary 1984 Malawi kwacha 1994 Malaysian ringgit 2000 Maldivian rufiyaa 1995 CFA franc 1987 Euro 2005 U.S. dollar 1991 Mauritanian ouguiya 1998 Mauritian rupee 2006 Euro Mexican peso 2003 U.S. dollar 1998 a Moldovan leu Euro Mongolian tugrik 2005 Euro 2000 Moroccan dirham 1998 New Mozambican 2003 metical Myanmar Myanmar kyat 1985/86 Namibia Namibian dollar 2004/05 Nepal Nepalese rupee 2000/01 Netherlands Antilles Netherlands Antilles guilder a Netherlands Euro New Caledonia CFP franc New Zealand New Zealand dollar 2000/01 Nicaragua Nicaraguan gold cordoba 1994 Niger CFA franc 1987 Nigeria Nigerian naira 2002 Northern Mariana Islands U.S. dollar a Norway Norwegian krone Oman Rial Omani 1988 Pakistan Pakistani rupee 1999/2000 Palau U.S. dollar 1995 Panama Panamanian balboa 1996 Papua New Guinea Papua New Guinea kina 1998 Paraguay Paraguayan guarani 1994 Peru Peruvian new sol 1994 Philippines Philippine peso 1985 a Poland Polish zloty Portugal Euro 2000 Puerto Rico U.S. dollar 1954 Qatar Qatari riyal 2001 a Romania New Romanian leu Russian Federation Rwanda Samoa San Marino

398

Russian ruble Rwandan franc Samoan tala Euro

2011 World Development Indicators

2000 1995 2002 1995

SNA System of price Reference National Accounts valuation year

b

2000 1995

b

b

1996

b

b b

b

2000

b

b

2000

b

b

b

2002

b b

2005

b

b

2000

b

VAP VAB VAB VAB VAB VAB VAB VAB VAP VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB

Balance of payments and trade

Alternative conversion factor

VAB VAP VAB VAB

External debt

BPM5 BPM5

Estimate

2005 2005 2005 2005 2005 2005 2005 2005 2005

BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM4 BPM5

Actual

2005 2005

BPM4 BPM5

Actual Preliminary

2005

BPM5

Actual

1990–95

2005

BPM5

Actual

1992–95

2005 2005 2005 2005

BPM5 BPM5 BPM5 BPM5

Actual Actual Actual Actual

BPM5 BPM5 BPM5 BPM5

Estimate

2005 2005

1990–95

VAB

VAB VAP VAB VAB VAB VAB VAP VAB VAP VAB VAB VAP VAP VAB

Balance of Payments Manual in use

2005 1986

VAP VAB VAB

VAB VAB VAP VAB

PPP survey year

1965–95 1993 1971–98

1987–89, 1992 1987–95 1994

G G G G G S G S G

Actual

Accounting concept

B

G G

C C

S S G G G S

C B C B C

G S

C

G G

C

S

C

S

C

G

C

S G

C B C

G G

S

BPM5

2005 2005 2005

BPM5 BPM5 BPM4 BPM5

2005 2005 2005

BPM5 BPM5 BPM5

2005 2005 2005 2005 2005

BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5

Actual Actual Actual Actual Actual

2005 2005

BPM5 BPM5 BPM5 BPM5

2005 2005

S S S S G G G G G G

G

2005

1989 1985–90

Actual Actual Actual Estimate Actual Preliminary

System of trade

Government IMF data finance dissemination standard

C

S

Actual Actual Actual

S S G S G G

C B B B

G G G

Actual

G G G

C B B

S G G

C B B C B C C

G

Actual

S G G S G S S G S S

B C

G S

Preliminary Estimate Actual

G G G

C C

S G

C

G

G S S S S

PRIMARY DATA DOCUMENTATION Latest population census

Latest demographic, education, or health household survey

Source of most recent income and expenditure data

CWIQ, 2007

Liberia Libya Liechtenstein Lithuania Luxembourg Macedonia, FYR Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Mauritania Mauritius Mayotte Mexico Micronesia, Fed. Sts. Moldova Monaco Mongolia Montenegro Morocco Mozambique

2008 2006 2010 2001 2001 2002 1993 2008 2010 2006 2009 2005 1999 2000 2000 2007 2010 2000 2004 2008 2010 2003 2004 2007

DHS, 2007; MIS, 2009 MICS, 2000

Myanmar Namibia Nepal Netherlands Antilles

1983 2001 2001 2001

Netherlands New Caledonia New Zealand Nicaragua Niger Nigeria Northern Mariana Islands Norway Oman Pakistan Palau Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar Romania

2001 2009 2006 2005 2001 2006 2010 2001 2010 1998 2010 2010 2000 2002 2007 2010 2002 2001 2010 2010 2002

Russian Federation Rwanda Samoa San Marino

2010 2002 2006 2010

DHS, 2009 DHS, 2006

ES/BS, 2008 PS, 2005 LSMS, 2004/05 ES/BS, 2009 IHS, 2004 IHS, 2006

Yes Yes Yes Yes

Latest water withdrawal data

2008 2008

1985 2004

2000 2000

2008 2009 2009 2009 2009 2009 2009 2007 2009 1999 2009 2009

2009 2009 2009 2009 2010 2009 2008 2008 2009

2000

2000 2003

2009

2008 2009 2009 2009

2000

2009

2009

2000

2007

2000

1996 1999–2000

2009 2009 2009 2009

2009 2009

2000 2000

2003 1996–97 2002

2009 2009

2001 2008 2009 2008

2000 2000 2000 2000

2009 1997 2009 2009 2003 2006

2009 2008 2010 2009 2008 2009

2000 2000 2000 2000

2009 2004 2009 2007 2009 2009 2009 2009 2009 2009 2009 2001

2010 2009 2009

2000 2003 2000

2009 2004 2009 2009 2009 2009 2009

2000 2000 2000 2000 2000 2000 2000

2009

2008 2009

2005 2000

2009 2009 2009

2009 2009 2009

2000 2000

2003 1999–2000d 1994 2004 1993

Yes Yes Yes

MICS, 2007

Latest trade data

2001 ES/BS, 2008

MICS, 2005 DHS, 2008/09 MICS, 2006

Vital Latest Latest registration agricultural industrial complete census data

IHS, 2000

1984 2001 1984–85

Yes Yes ENPF, 1995 DHS, 2005

LFS, 2008 IHS, 2000 ES/BS, 2008

MICS, 2005 MICS, 2005/06 MICS, 2006 DHS, 2003; AIS, 2009

LSMS, 2007/08 ES/BS, 2008 ES/BS, 2007 ES/BS, 2008

MICS, 2000 DHS, 2006/07 DHS, 2006

ES/BS, 1993/94 LSMS, 2003/04

1991 Yes Yes Yes Yes

Yes IHS, 1999

RHS, 2006/07 DHS, 2006 DHS, 2008

IS, 1997 LSMS, 2005 CWIQ/PS, 2005 IHS, 2003/04 IS, 2000

FHS, 1995 DHS, 2006/07

Yes Yes Yes

Yes

IHS, 2006

1999–2000d 2002 2001 1980 1960 1999 1978–79 2000

Yes LSMS, 2003 DHS, 1996 RHS, 2004 DHS, 2007/08 DHS, 2008

LFS, 2009 IHS, 1996 IHS, 2008 IHS, 2009 ES/BS, 2009 ES/BS, 2008 IS, 1997

RHS, 1995/96 RHS, 1999

LFS, 2008

RHS, 1996 DHS, 2007/08 DHS, 2009

IHS, 2008 IHS, 2005

2001

Yes Yes Yes Yes Yes Yes Yes

1991 1994 2002 1996/2002 1999 1997/2002 2000–01 2002 1994–95 1984 1999

2000 2000 2000 2000 2000

Yes

2011 World Development Indicators

399

PRIMARY DATA DOCUMENTATION Currency

National accounts

SNA System of price Reference National Accounts valuation year

Base year

São Tomé & Príncipe

São Tomé & Príncipe dobra

2001

Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovak Republic Slovenia Solomon Islands Somalia South Africa Spain Sri Lanka St. Kitts and Nevis St. Lucia St. Vincent & Grenadines Sudan Suriname Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania

Saudi Arabian riyal CFA franc Serbian dinar Seychelles rupee Sierra Leonean leone Singapore dollar Euro Euro Solomon Islands dollar Somali shilling South African rand Euro Sri Lankan rupee East Caribbean dollar East Caribbean dollar East Caribbean dollar Sudanese pound Suriname dollar Swaziland lilangeni Swedish krona Swiss franc Syrian pound Tajik somoni Tanzanian shilling

1999 1999

Thailand Timor-Leste Togo Tonga Trinidad and Tobago

Thai baht U.S. dollar CFA franc Tongan pa’anga Trinidad and Tobago dollar

Tunisia Turkey Turkmenistan

Tunisian dinar New Turkish lira New Turkmen manat

Turks and Caicos Islands Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Vanuatu Venezuela, R.B. Vietnam Virgin Islands (U.S.) West Bank and Gaza Yemen, Rep. Zambia Zimbabwe

U.S. dollar Australian dollar Ugandan shilling 2001/02 a Ukrainian hryvnia U.A.E. dirham 1995 Pound sterling 2000 a U.S. dollar Uruguayan peso 2005 a Uzbek sum Vanuatu vatu 2006 Venezuelan bolivar fuerte 1997 Vietnamese dong 1994 U.S. dollar 1982 Israeli new shekel 1997 Yemeni rial 1990 Zambian kwacha 1994 U.S. dollar 2009

400

2011 World Development Indicators

a

1986 1990 2000 2000 a

1990 1985 2005 2000 2002 1990 1990 1990 1981/82 f 1990 2000 a

1987 2002

b b

b b

1995 2000

b b

b b

b

1996 b

2000

2000 2000 a a

2000 2001

1988 2000 1978 2000/01 2000

b

b

1990 1998 a

2007

b

Balance of payments and trade

Alternative conversion factor

PPP survey year

VAP

2005

VAP VAB VAB VAP VAB VAB VAB VAB VAB VAB VAB VAB VAP VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB

2005 2005 2005 2005 2005 2005 2005

Balance of Payments Manual in use

BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5

1977–90 2005 2005 2005

External debt

System of trade

Preliminary

S

Actual Actual Actual Preliminary

Actual Estimate Preliminary

2005 2005 2005 2005 2005 2005

BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM4 BPM5

VAP VAP VAP VAB VAB

2005

BPM5

Actual

2005

BPM5 BPM5 BPM5

Actual Actual

VAP VAB VAB

2005 2005

BPM5 BPM5 BPM4

Actual Actual Estimate

2005

1970–2008 1990–95

1987–95, 1997–2007

Actual Preliminary Actual Actual Actual Actual

Actual Actual Actual

G G G G G S S S G S G S G S G G G S S S

Government IMF data finance dissemination standard

Accounting concept

G

B C C B C C C

C C B C B B B C C C C

G

G G G G G S S S

S S G G G G G G G S S G G G

S G S G S

C

S

B C

G G G

G S

C B

S S

G G S G G S

B C B C C C

G S G S S S

G G S G S S S G

C C

G G G

B B B C

G G G G

G

2003

b

b

2000 1997

b

b

VAB VAB VAB VAB VAB VAB VAB VAP VAB VAP VAB VAP VAB VAB

1991

2005 2005

BPM5 BPM5 BPM4 BPM5 BPM5 BPM5 BPM4 BPM5 BPM5 BPM4

Actual Actual Actual Actual Preliminary

1990–96 1990–92 1991, 1998

2005 2005 2005

BPM5 BPM5 BPM5 BPM4

Actual Preliminary Actual

1987–95

2005 2005 2005 2005 2005

1990–95

Actual Actual

PRIMARY DATA DOCUMENTATION Latest population census

Latest demographic, education, or health household survey

Source of most recent income and expenditure data

PS, 2000/01

São Tomé & Príncipe

2001

DHS, 2008/09

Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovak Republic Slovenia Solomon Islands Somalia South Africa Spain Sri Lanka St. Kitts and Nevis St. Lucia St. Vincent & Grenadines Sudan Suriname Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania

2010 2002 2002 2010 2004 2010 2001 2002 2009 1987 2001 2001 2001 2001 2010 2001 2008 2004 2007

Demographic survey, 2007 DHS, 2005; MIS, 2008/09 MICS, 2005/06

2010 2004 2010 2002

Thailand Timor-Leste Togo Tonga Trinidad and Tobago

2010 2010 2010 2006 2000

MICS, 2005/06 DHS, 2009 MICS, 2006 MICS, 2006

IHS, 1992

Tunisia Turkey Turkmenistan

2004 2000 1995

MICS, 2006 DHS, 2003 MICS, 2006

IHS, 2000 LFS, 2008 LSMS, 1998

Turks and Caicos Islands Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Vanuatu Venezuela, R.B. Vietnam Virgin Islands (U.S.) West Bank and Gaza Yemen, Rep. Zambia Zimbabwe

2001 2002 2002 2001 2010 2001 2010 2004 1989 2009 2001 2009 2010 2007 2004

DHS, 2008 General household, 2005

IS, 1996 ES/BS, 2004 MICS, 2006 DHS, 2003 DHS, 2006/07

ES/BS, 2005 IHS, 2000 ES/BS, 2007 IHS, 1995

MICS/PAPFAM, 2006 MICS, 2006 DHS, 2006/07

c

2000 2002

PS, 2005 IHS, 2008 IHS, 2007 IHS, 2003

MICS, 2006 MICS, 2005 DHS, 2004/05; AIS, 2007/08

ES/BS, 1999 ES/BS, 2000/01 IS, 2000 ES/BS, 2000 ES/BS, 2004 LSMS, 2004 ES/BS, 2007

Vital Latest Latest registration agricultural industrial complete census data

Yes Yes Yes Yes Yes

Yes Yes Yes Yes Yes

IHS, 2009 LSMS, 2007 CWIQ, 2006

2009

1999 1998–99

2009 2009

1998 1984–85

2009 2003 2009 2009 2008 2009 1990 2009 2009 2009 2009 2009 2009 2009 2008 2009 2009 2009 2009 2009 2009

2009 2009 2008 2008 2002 2009 2009 2009 2007 1982 2009 2009 2009 2008 2008 2009 2009 2008 2007 2009 2009 2008 2000 2009

2009 2000 2005 2009 2009

2009 2005 2007 2007 2009

2000

2009 2009 2009

2009 2009 2000

2000 2003 2000

2001 2000

2000 1999 2002

2003 1999–2000 2000 1981 1994 2002–03 2003

Yes Yes

1996 2001 2004 2004 2001

Yes Yes

DHS, 2006; MIS, 2009/10 DHS, 2007

CPS (monthly) MICS, 2006 MICS, 2007 MICS, 2000 MICS, 2006 PAPFAM, 2006 MICS, 2006 DHS, 2007 DHS, 2005/06

PS, 2005 ES/BS, 2008

1991

Yes Yes Yes Yes

IHS, 2009 IHS, 2008

Yes Yes Yes

IHS, 2009 ES/BS, 2005 IHS, 2004/05 IHS, 2003

1997 2001

2009 2009 2007 2009 2009 2009 2009 2008 2005 2009

1971 2002 1990 1960

2003 2009 2009

Yes

IS, 1999 LFS, 2000 IHS, 2009 ES/BS, 2003

Latest water withdrawal data

2005

Yes Yes Yes

Latest trade data

1998 1999–2000d 1997/2002 2000

2009 2008 2008 2009 2009 2009 2009 2009

2006 2002 2003 2000

2003 2000 2000 2000

2000 2000 2000 2000 2000 2003 2000 2002

2002 2000

2000 2005 2000 2000 2000 2000

2007 2009 2008

2000

2008 2009 2009 2009

2000 2000 2002

Note: For explanation of the abbreviations used in the table see notes following the table. a. Original chained constant price data are rescaled. b. Country uses the 1993 System of National Accounts methodology. c. Register based. d. Conducted annually. e. Rolling census. f. Reporting period switch from fiscal year to calendar year from 1996. Pre-1996 data converted to calendar year.

2011 World Development Indicators

401

Primary data documentation notes • Base year is the base or pricing period used for

estimates. • System of trade refers to the United

age and sex, as well as the detailed definition of count-

constant price calculations in the country’s national

Nations general trade system (G) or special trade sys-

ing, coverage, and completeness. Countries that hold

accounts. Price indexes derived from national accounts

tem (S). Under the general trade system goods entering

register-based censuses produce similar census

aggregates, such as the implicit deflator for gross

directly for domestic consumption and goods entered

tables every 5 or 10 years. Germany’s 2001 census is

domestic product (GDP), express the price level relative

into customs storage are recorded as imports at

a register-based test census using a sample of 1.2

to base year prices. • Reference year is the year in

arrival. Under the special trade system goods are

percent of the population. A rare case, France has been

which the local currency, constant price series of a

recorded as imports when declared for domestic con-

conducting a rolling census every year since 2004; the

country is valued. The reference year is usually the

sumption whether at time of entry or on withdrawal

1999 general population census was the last to cover

same as the base year used to report the constant

from customs storage. Exports under the general sys-

the entire population simultaneously (www.insee.fr/

price series. However, when the constant price data

tem comprise outward-moving goods: (a) national

en/recensement/page_accueil_rp.htm). • Latest

are chain linked, the base year is changed annually, so

goods wholly or partly produced in the country; (b)

demographic, education, or health household survey

the data are rescaled to a specific reference year to

foreign goods, neither transformed nor declared for

indicates the household surveys used to compile the

provide a consistent time series. When the country has

domestic consumption in the country, that move out-

demographic, education, and health data in section 2.

not rescaled following a change in base year, World

ward from customs storage; and (c) nationalized goods

AIS is HIV/AIDS Indicator Survey, CPS is Current Popu-

Bank staff rescale the data to maintain a longer histori-

that have been declared for domestic consumption and

lation Survey, DGHS is Demographic and General

cal series. To allow for cross-country comparison and

move outward without being transformed. Under the

Health Survey, DHS is Demographic and Health Survey,

data aggregation, constant price data reported in World

special system of trade, exports are categories a and

ENPF is National Family Planning Survey (Encuesta

Development Indicators are rescaled to a common ref-

c. In some compilations categories b and c are classi-

Nacional de Planificacion Familiar), FHS is Family

erence year (2000) and currency (U.S. dollars). • Sys-

fied as re-exports. Direct transit trade—goods entering

Health Survey, LSMS is Living Standards Measurement

tem of National Accounts identifies countries that use

or leaving for transport only—is excluded from both

Survey, MICS is Multiple Indicator Cluster Survey, MIS

the 1993 System of National Accounts (1993 SNA),

import and export statistics. See About the data for

is Malaria Indicator Survey, NSS is National Sample

the terminology applied in World Development Indica-

tables 4.4, 4.5, and 6.2 for further discussion. • Gov-

Survey on Population Change, PAPFAM is Pan Arab

tors since 2001, to compile national accounts.

ernment ﬁnance accounting concept is the account-

Project for Family Health, RHS is Reproductive Health

Although more countries are adopting the 1993 SNA,

ing basis for reporting central government financial

Survey, and SPA is Service Provision Assessments.

many still follow the 1968 SNA, and some low-income

data. For most countries government finance data have

Detailed information for AIS, DHS, MIS, and SPA are

countries use concepts from the 1953 SNA. • SNA

been consolidated (C) into one set of accounts captur-

available at www.measuredhs.com/aboutsurveys; for

price valuation shows whether value added in the

ing all central government fiscal activities. Budgetary

MICS at www.childinfo.org; and for RHS at www.cdc.

national accounts is reported at basic prices (VAB) or

central government accounts (B) exclude some central

gov/reproductivehealth/surveys. • Source of most

producer prices (VAP). Producer prices include taxes

government units. See About the data for tables 4.12,

recent income and expenditure data shows household

paid by producers and thus tend to overstate the actual

4.13, and 4.14 for further details. • IMF data dissemi-

surveys that collect income and expenditure data.

value added in production. However, VAB can be higher

nation standard shows the countries that subscribe to

Names and detailed information on household surveys

than VAP in countries with high agricultural subsidies.

the IMF’s Special Data Dissemination Standard (SDDS)

can be found on the website of the International House-

See About the data for tables 4.1 and 4.2 for further

or General Data Dissemination System (GDDS). S

hold Survey Network (www.surveynetwork.org). Core

discussion of national accounts valuation. • Alterna-

refers to countries that subscribe to the SDDS and

Welfare Indicator Questionnaire Surveys (CWIQ), devel-

tive conversion factor identifies the countries and

have posted data on the Dissemination Standards Bul-

oped by the World Bank, measure changes in key social

years for which a World Bank–estimated conversion

letin Board at http://dsbb.imf.org. G refers to countries

indicators for different population groups—specifically

factor has been used in place of the official exchange

that subscribe to the GDDS. The SDDS was estab-

indicators of access, utilization, and satisfaction with

rate (line rf in the International Monetary Fund’s [IMF]

lished for member countries that have or might seek

core social and economic services. Expenditure sur-

International Financial Statistics). See Statistical meth-

access to international capital markets to guide them

vey/budget surveys (ES/BS) collect detailed informa-

ods for further discussion of alternative conversion

in providing their economic and financial data to the

tion on household consumption as well as on general

factors. • Purchasing power parity (PPP) survey year

public. The GDDS helps countries disseminate com-

demographic, social, and economic characteristics.

is the latest available survey year for the International

prehensive, timely, accessible, and reliable economic,

Integrated household surveys (IHS) collect detailed

Comparison Program’s estimates of PPPs. See About

financial, and sociodemographic statistics. IMF mem-

information on a wide variety of topics, including

the data for table 1.1 for a more detailed description

ber countries elect to participate in either the SDDS or

health, education, economic activities, housing, and

of PPPs. • Balance of Payments Manual in use refers

the GDDS. Both standards enhance the availability of

utilities. Income surveys (IS) collect information on the

to the classification system used to compile and report

timely and comprehensive data and therefore contrib-

income and wealth of households as well as various

data on balance of payments items in table 4.17. BPM4

ute to the pursuit of sound macroeconomic policies.

social and economic characteristics. Labor force sur-

refers to the 4th edition of the IMF’s Balance of Pay-

The SDDS is also expected to improve the functioning

veys (LFS) collect information on employment, unem-

ments Manual (1977), and BPM5 to the 5th edition

of financial markets. • Latest population census

ployment, hours of work, income, and wages. Living

(1993). • External debt shows debt reporting status

shows the most recent year in which a census was

Standards Measurement Studies (LSMS), developed

for 2009 data. Actual indicates that data are as

conducted and in which at least preliminary results

by the World Bank, provide a comprehensive picture of

reported, preliminary that data are based on reported

have been released. The preliminary results from the

household welfare and the factors that affect it; they

or collected information but include an element of staff

very recent censuses could be reflected in timely revi-

typically incorporate data collection at the individual,

estimation, and estimate that data are World Bank staff

sions if basic data are available, such as population by

household, and community levels. Priority surveys (PS)

402

2010 World Development Indicators

Primary data documentation notes are a light monitoring survey, designed by the World

Economies with exceptional reporting periods

Bank, for collecting data from a large number of house-

Nations Statistics Division. The new base year is 1990, and the SNA price valuation has been changed

Economy

Fiscal year end

Reporting period for national accounts data

Afghanistan

Mar. 20

FY

Australia

Jun. 30

FY

Bangladesh

Jun. 30

FY

Botswana

Jun. 30

FY

Canada

Mar. 31

CY

Egypt, Arab Rep.

Jun. 30

FY

Jul. 7

FY

Gambia, The

Jun. 30

CY

Haiti

Sep. 30

FY

India

Mar. 31

FY

Indonesia

Mar. 31

CY

Iran, Islamic Rep.

Mar. 20

FY

Japan

Mar. 31

CY

Kenya

Jun. 30

CY

Kuwait

Jun. 30

CY

Lesotho

Mar. 31

CY

Malawi

Mar. 31

CY

Myanmar

Mar. 31

FY

Namibia

Mar. 31

CY

Nepal

Jul. 14

FY

New Zealand

Mar. 31

FY

Pakistan

Jun. 30

FY

Puerto Rico

Jun. 30

FY

Sierra Leone

Jun. 30

CY

Singapore

Mar. 31

CY

withdrawal data show the most recent year for which

South Africa

Mar. 31

CY

upward adjustment to estimates of output, particularly

data on freshwater withdrawals have been compiled

Swaziland

Mar. 31

CY

in mining, services, and manufacturing. The constant

from a variety of sources. See About the data for table

Sweden

Jun. 30

CY

price series were rebased from 1995 to 2004 prices.

3.5 for more information.

Thailand

Sep. 30

CY

GDP in current prices average 14 percent higher than

Uganda

Jun. 30

FY

previous estimates. • South Africa. The base year

Exceptional reporting periods

United States

Sep. 30

CY

has been changed from 2000 to 2005. Data are

In most economies the fiscal year is concurrent with

Zimbabwe

Jun. 30

CY

revised from 2000 onward with official government

holds cost-effectively and quickly. Income tax registers (ITR) provide information on a population’s income and allowance, such as gross income, taxable income, and taxes by socioeconomic group. 1-2-3 surveys (1-2-3) are implemented in three phases and collect sociodemographic and employment data, data on the informal sector, and information on living conditions and household consumption. • Vital registration complete identifies countries which report to have at least 90 percent complete registries of vital (birth and death) statistics to the United Nations Statistics Division and reported in Population and Vital Statistics Reports. Countries with complete vital statistics registries may have more accurate and more timely demographic indicators than other countries. • Latest agricultural census shows the most recent year in which an agricultural census was conducted and reported to the Food and Agriculture Organization of the United Nations. • Latest industrial data show the most recent year for which manufacturing value added data at the three-digit level of the International Standard Industrial Classification (ISIC, revision 2 or 3) are available in the United Nations Industrial Development Organization database. • Latest trade data show the most recent year for which structure of merchandise trade data from the United Nations Statistics Division’s Commodity Trade (Comtrade) database are available. • Latest water

Ethiopia

to basic prices. • Fiji. The new base year is 2005. Data are revised from 2005 onward based on official government data. • Ghana. The Ghana Statistical Service revised Ghana’s national accounts series from 1993 to 2006. New GDP data are about 60 percent higher than previously reported and incorporate improved data sources and methodology. • Guinea-Bissau. National accounts data for 2003– 09 are revised. The new data have broader coverage of all sectors of the economy, and the new base year is 2005. GDP in current prices average 89 percent higher than previous estimates. • Guyana. The Bureau of Statistics has introduced a new series of GDP rebased to year 2006. Current price GDP average 63 percent higher than previous estimates. • India. The base year has been changed from 1999 to 2004. Data are revised from 2004 onward with official government data. GDP at current prices average 4 percent higher than previous estimates. • Kazakhstan. National accounts data have been revised by the National Statistical Office. The new base year is 2000. • Kiribati. The base year has been changed from 2005 to 2006. Data are revised from 2000 onward with official government data. • Namibia. The Central Bureau of Statistics has revised national accounts data for 2000–07. An expanded data survey has resulted in a substantial

the calendar year. Exceptions are shown in the table

Revisions to national accounts data

data. • Tonga. Data are revised from 1995 onward

at right. The ending date reported here is for the fis-

National accounts data are revised by national sta-

with official government data. GDP in current prices

cal year of the central government. Fiscal years for

tistical offices when methodologies change or data

average 20 percent higher than previous estimates.

other levels of government and reporting years for

sources improve. National accounts data in World

• Vanuatu. The base year has been changed from

statistical surveys may differ.

Development Indicators are also revised when data

1983 to 2006. Data are revised from 1998 onward

The reporting period for national accounts data

sources change. The following notes, while not com-

with official government data. GDP in current prices

is designated as either calendar year basis (CY) or

prehensive, provide information on revisions from

average 11 percent higher than previous estimates.

fiscal year basis (FY). Most economies report their

previous data. • Bulgaria. The National Statistical

national accounts and balance of payments data

Office has revised national accounts data from 1995

Changes to national currencies

using calendar years, but some use fiscal years. In

onward. GDP in current prices are about 4 percent

• Malta. On January 1, 2008, the euro replaced

World Development Indicators fiscal year data are

higher than previous estimates. • Colombia. The

the Maltese liri as Malta’s currency. • Zimbabwe.

assigned to the calendar year that contains the larger

base year has been changed from 2000 to 2005,

As of January 2009, multiple hard currencies, such

share of the fiscal year. If a country’s fiscal year ends

and data from 2000 onward are new. GDP in cur-

as rand, pound sterling, euro and U.S. dollar are in

before June 30, data are shown in the first year of

rent prices average 2.8 percent higher than previous

use. Data are reported in U.S. dollars, the most-

the fiscal period; if the fiscal year ends on or after

estimates. • Croatia. The Statistical Bureau revised

used currency.

June 30, data are shown in the second year of the

national accounts for 1995–2007. The new base

period. Balance of payments data are reported in

year is 2000. • Cuba. National accounts data for

World Development Indicators by calendar year.

1970–2008 are revised with data from the United

2011 World Development Indicators

403

STATISTICAL METHODS This section describes some of the statistical procedures used in preparing World

•

Aggregates of ratios are denoted by a w when calculated as weighted averages

Development Indicators. It covers the methods employed for calculating regional

of the ratios (using the value of the denominator or, in some cases, another

and income group aggregates and for calculating growth rates, and it describes the

indicator as a weight) and denoted by a u when calculated as unweighted

World Bank Atlas method for deriving the conversion factor used to estimate gross

averages. The aggregate ratios are based on available data, including data

national income (GNI) and GNI per capita in U.S. dollars. Other statistical procedures

for economies not shown in the main tables. Missing values are assumed

and calculations are described in the About the data sections following each table.

to have the same average value as the available data. No aggregate is calculated if missing data account for more than a third of the value of weights

Aggregation rules

in the benchmark year. In a few cases the aggregate ratio may be computed

Aggregates based on the World Bank’s regional and income classifications of

as the ratio of group totals after imputing values for missing data according to the above rules for computing totals.

economies appear at the end of most tables. The countries included in these classifications are shown on the flaps on the front and back covers of the book.

•

Aggregate growth rates are denoted by a w when calculated as a weighted

Most tables also include the aggregate euro area. This aggregate includes the

average of growth rates. In a few cases growth rates may be computed from

member states of the Economic and Monetary Union (EMU) of the European Union

time series of group totals. Growth rates are not calculated if more than half

that have adopted the euro as their currency: Austria, Belgium, Cyprus, Finland,

the observations in a period are missing. For further discussion of methods of computing growth rates see below.

France, Germany, Greece, Ireland, Italy, Luxembourg, Malta, Netherlands, Portugal, Slovak Republic, Slovenia, and Spain. Other classifications, such as the

•

Aggregates denoted by an m are medians of the values shown in the table.

European Union and regional trade blocs, are documented in About the data for

No value is shown if more than half the observations for countries with a

the tables in which they appear.

population of more than 1 million are missing.

Because of missing data, aggregates for groups of economies should be

Exceptions to the rules occur throughout the book. Depending on the judg-

treated as approximations of unknown totals or average values. Regional and

ment of World Bank analysts, the aggregates may be based on as little as 50

income group aggregates are based on the largest available set of data, includ-

percent of the available data. In other cases, where missing or excluded values

ing values for the 155 economies shown in the main tables, other economies

are judged to be small or irrelevant, aggregates are based only on the data

shown in table 1.6, and Taiwan, China. The aggregation rules are intended to

shown in the tables.

yield estimates for a consistent set of economies from one period to the next and for all indicators. Small differences between sums of subgroup aggregates and

Growth rates

overall totals and averages may occur because of the approximations used. In

Growth rates are calculated as annual averages and represented as percentages.

addition, compilation errors and data reporting practices may cause discrepan-

Except where noted, growth rates of values are computed from constant price

cies in theoretically identical aggregates such as world exports and world imports.

series. Three principal methods are used to calculate growth rates: least squares,

Five methods of aggregation are used in World Development Indicators:

exponential endpoint, and geometric endpoint. Rates of change from one period

For group and world totals denoted in the tables by a t, missing data are

to the next are calculated as proportional changes from the earlier period.

•

imputed based on the relationship of the sum of available data to the total in the year of the previous estimate. The imputation process works forward

Least squares growth rate. Least squares growth rates are used wherever

and backward from 2000. Missing values in 2000 are imputed using one of

there is a sufficiently long time series to permit a reliable calculation. No growth

several proxy variables for which complete data are available in that year. The

rate is calculated if more than half the observations in a period are missing.

imputed value is calculated so that it (or its proxy) bears the same relation-

The least squares growth rate, r, is estimated by fitting a linear regression trend

ship to the total of available data. Imputed values are usually not calculated

line to the logarithmic annual values of the variable in the relevant period. The

if missing data account for more than a third of the total in the benchmark

regression equation takes the form

year. The variables used as proxies are GNI in U.S. dollars, total population, exports and imports of goods and services in U.S. dollars, and value added

ln Xt = a + bt

in agriculture, industry, manufacturing, and services in U.S. dollars. •

Aggregates marked by an s are sums of available data. Missing values are not imputed. Sums are not computed if more than a third of the observations in the series or a proxy for the series are missing in a given year.

404

2011 World Development Indicators

which is the logarithmic transformation of the compound growth equation, Xt = Xo (1 + r ) t.

In this equation X is the variable, t is time, and a = ln Xo and b = ln (1 + r) are

States, and the euro area. A country’s infl ation rate is measured by the change

parameters to be estimated. If b* is the least-squares estimate of b, then the

in its GDP defl ator.

average annual growth rate, r, is obtained as [exp(b*) – 1] and is multiplied by

The inflation rate for Japan, the United Kingdom, the United States, and the

100 for expression as a percentage. The calculated growth rate is an average

euro area, representing international inflation, is measured by the change in the

rate that is representative of the available observations over the entire period.

“SDR deflator.” (Special drawing rights, or SDRs, are the International Monetary

It does not necessarily match the actual growth rate between any two periods.

Fund’s unit of account.) The SDR deflator is calculated as a weighted average of these countries’ GDP deflators in SDR terms, the weights being the amount of

Exponential growth rate. The growth rate between two points in time for cer-

each country’s currency in one SDR unit. Weights vary over time because both

tain demographic indicators, notably labor force and population, is calculated

the composition of the SDR and the relative exchange rates for each currency

from the equation

change. The SDR deflator is calculated in SDR terms first and then converted to U.S. dollars using the SDR to dollar Atlas conversion factor. The Atlas converr = ln(pn/p 0)/n

sion factor is then applied to a country’s GNI. The resulting GNI in U.S. dollars is divided by the midyear population to derive GNI per capita.

where pn and p0 are the last and first observations in the period, n is the number

When official exchange rates are deemed to be unreliable or unrepresenta-

of years in the period, and ln is the natural logarithm operator. This growth rate is

tive of the effective exchange rate during a period, an alternative estimate of the

based on a model of continuous, exponential growth between two points in time.

exchange rate is used in the Atlas formula (see below).

It does not take into account the intermediate values of the series. Nor does it correspond to the annual rate of change measured at a one-year interval, which

The following formulas describe the calculation of the Atlas conversion factor for year t:

is given by (pn – pn–1)/pn–1. Geometric growth rate. The geometric growth rate is applicable to compound growth over discrete periods, such as the payment and reinvestment of interest or dividends. Although continuous growth, as modeled by the exponential growth rate, may be more realistic, most economic phenomena are measured only at

and the calculation of GNI per capita in U.S. dollars for year t:

intervals, in which case the compound growth model is appropriate. The average growth rate over n periods is calculated as r = exp[ln(pn/p 0)/n] – 1.

Yt$ = (Yt /Nt)/et* where et* is the Atlas conversion factor (national currency to the U.S. dollar) for year t, et is the average annual exchange rate (national currency to the U.S. dollar)

Like the exponential growth rate, it does not take into account intermediate

for year t, pt is the GDP deflator for year t, ptS$ is the SDR deflator in U.S. dollar

values of the series.

terms for year t, Yt$ is the Atlas GNI per capita in U.S. dollars in year t, Yt is current GNI (local currency) for year t, and Nt is the midyear population for year t.

World Bank Atlas method In calculating GNI and GNI per capita in U.S. dollars for certain operational

Alternative conversion factors

purposes, the World Bank uses the Atlas conversion factor. The purpose of the

The World Bank systematically assesses the appropriateness of official exchange

Atlas conversion factor is to reduce the impact of exchange rate fluctuations in

rates as conversion factors. An alternative conversion factor is used when the

the cross-country comparison of national incomes.

official exchange rate is judged to diverge by an exceptionally large margin from the

The Atlas conversion factor for any year is the average of a country’s

rate effectively applied to domestic transactions of foreign currencies and traded

exchange rate (or alternative conversion factor) for that year and its exchange

products. This applies to only a small number of countries, as shown in Primary data

rates for the two preceding years, adjusted for the difference between the rate

documentation. Alternative conversion factors are used in the Atlas methodology

of infl ation in the country and that in Japan, the United Kingdom, the United

and elsewhere in World Development Indicators as single-year conversion factors.

2011 World Development Indicators

405

CREDITS 1. World view

resources to the book, for which the team is very grateful. Other contributors were

Section 1 was prepared by a team led by Eric Swanson. Eric Swanson

Brian Blankespoor, Lopamudra Chakraborti, Susmita Dasgupta, Olivier Dupriez,

wrote the introduction with input from Uranbileg Batjargal and Neil Fan-

Kirk Hamilton, Esther Grace Lee, Craig Meisner, Kiran Pandey, Giovanni Ruta,

tom. Bala Bhaskar Naidu Kalimili coordinated tables 1.1 and 1.6. Shota

and Akiko Saesaka.

Hatakeyama, Mehdi Akhlaghi, Buyant Khaltarkhuu, and Masako Hiraga prepared tables 1.2, 1.3, and 1.5. Uranbileg Batjargal prepared table 1.4, with

4. Economy

input from Azita Amjadi. Signe Zeikate of the World Bank’s Economic Policy

Section 4 was prepared by Bala Bhaskar Naidu Kalimili, Mahyar Eshragh-Tabary,

and Debt Department provided the estimates of debt relief for the Heavily

and Soong Sup Lee in close collaboration with the Sustainable Development and

Indebted Poor Countries Debt Initiative and Multilateral Debt Relief Initiative.

Economic Data Team of the World Bank’s Development Data Group. Soong Sup Lee wrote the introduction with valuable suggestions from Eric Swanson and the

2. People

IMF’s Financial Institutions Division, Statistics Department. Contributions to the

Section 2 was prepared by Masako Hiraga and Shota Hatakeyama, in partner-

section were provided by Azita Amjadi, Lopamudra Chakraborti, Kirk Hamilton,

ship with the World Bank’s Human Developmebnt Network and the Development

Barbro Hexeberg, Esther Grace Lee, Giovanni Ruta, and from Justin Thyme Matz

Research Group in the Development Economics Vice Presidency. The introduc-

and Yutong Li of the IMF’s Statistical Information Management Division, Statistics

tion was written by Sulekha Patel and Masako Hiraga, with valuable inputs and

Department. The national accounts data for low- and middle-income economies

comments from Eric Swanson. The poverty estimates at national poverty lines

were gathered by the World Bank’s regional staff through the annual Unified Sur-

were compliled by the Global Poverty Working Group: a team of poverty experts

vey. Maja Bresslauer, Mahyar Eshragh-Tabary, Bala Bhaskar Naidu Kalimili, and

from the Poverty Reduction and Equality Network, the Development Research

Buyant Khaltarkhuu worked on updating, estimating, and validating the databases

Group, and the Development Data Group. The poverty estimates at international

for national accounts. The team is grateful to Eurostat, the International Monetary

poverty lines were prepared by Shaohua Chen and Prem Sangraula of the World

Fund, Organisation for Economic Co-operation and Development, United Nations

Bank’s Development Research Group. The data on children at work were prepared

Industrial Development Organization, and World Trade Organization for access

by Lorenzo Guarcello and Furio Rosati from the Understanding Children’s Work

to their databases.

project. Other contributions were provided by Emi Suzuki (population, health, and nutrition); Montserrat Pallares-Miralles and Carolina Romero Robayo (vulnerability

5. States and markets

and security); Sara Elder of the International Labour Organization (labor force);

Section 5 was prepared by David Cieslikowski and Buyant Khaltarkhuu, in partner-

Amelie Gagnon, Said Ould Voffal, and Weixin Lu of the United Nations Educa-

ship with the World Bank’s Financial and Private Sector Development Network,

tional, Scientific, and Cultural Organization Institute for Statistics (education and

Poverty Reduction and Economic Management Network, Sustainable Develop-

literacy); the World Health Organization’s Chandika Indikadahena (health expen-

ment Network, the International Finance Corporation, and external partners.

diture), Charu Garg (national health account), Monika Bloessner and Mercedes

David Cieslikowski wrote the introduction to the section with input from Eric

de Onis (malnutrition and overweight), Neeru Gupta and Teena Kunjument (health

Swanson. Other contributors include Ada Karina Izaguirre (privatization and

workers), Jessica Ho (hospital beds), Rifat Hossain (water and sanitation), and

infrastructure projects); Leora Klapper and Inessa Love (business registration);

Hazim Timimi (tuberculosis); Delice Gan of the International Diabetes Federation

Federica Saliola and Joshua Wimpey (Enterprise Surveys); Sylvia Solf and Carolin

(diabetes); and Nyein Nyein Lwin of the United Nations Children’s Fund (health).

Geginat (Doing Business); Alka Banerjee and Michael Orzano (Standard & Poor’s

Eric Swanson provided valuable comments and suggestions on the introduction

global stock market indexes); Oya Pinar Ardic Alper (financial access); Satish

and at all stages of production.

Mannan (public policies and institutions); Henry Boyd and James Hackett of the International Institute for Strategic Studies (military personnel); Sam Perlo-

3. Environment

Freeman and Siemon Wezeman of the Stockholm International Peace Research

Section 3 was prepared by Mehdi Akhlaghi in partnership with the World Bank’s

Institute (military expenditures and arms transfers); Kacem Iaych of the Interna-

Sustainable Development Network. The introdcution was prepared by Soong Sup

tional Road Federation, Narjess Teyssier and Zubair Anwar of the International

Lee and Neil Fantom. The guidance of Glenn-Marie Lange is gratefully acknowl-

Civil Aviation Organization, and Hélène Stephan (transport); Jane Degerlund of

edged. Carola Fabi and Edward Gillin of the Food and Agriculture Organization

Containerisation International (ports); Vanessa Grey, Esperanza Magpantay, and

of the United Nations; Ricardo Quercioli and Karen Treanton of the International

Susan Teltscher of the International Telecommunication Union; Georges Boade of

Energy Agency; Laura Battlebury of the World Conservation Monitoring Centre;

the United Nations Educational, Scientific, and Cultural Organization Institute for

and Gerhard Metchies and Armin Wagner of German International Cooperation

Statistics (research and development, researchers, and technicians); and Ryan

(GIZ). The World Bank’s Environment Department devoted substantial staff

Lamb of the World Intellectual Property Organization (patents and trademarks).

406

2011 World Development Indicators

6. Global links

Client services

Section 6 was prepared by Uranbileg Batjargal in partnership with the Finan-

The Development Data Group’s Client Services and Communications Team (Azita

cial Data Team of the World Bank’s Development Data Group, Development

Amjadi, Buyant Erdene Khaltarkhuu, Alison Kwong, Beatriz Prieto-Oramas, Jomo

Research Group (trade), Development Prospects Group (commodity prices and

Tariku, and Vera Wen) contributed to the design and planning and helped coordi-

remittances), International Trade Department (trade facilitation), and external

nate work with the Office of the Publisher.

partners. Uranbileg Batjargal wrote the introduction, with substantial input from Ingo Borchert (Services Policy Restrictiveness Database), Caglar Ozden (bilateral

Administrative assistance, ofﬁce technology, and systems support

migration matrix), and Evis Rucaj (public sector debt). Eric Swanson provided

Awatif Abuzeid, Elysee Kiti, Premi Ratham Raj and Estela Zamora provided admin-

valuable comments. Substantial input for the data and tables came from Azita

istrative assistance. Jean-Pierre Djomalieu, Gytis Kanchas, and Nacer Megherbi

Amjadi (trade and tariffs) and Yasue Sakuramoto (external debt and financial

provided information technology support. Ramvel Chandrasekaran, Ugendran

data). Other contributors include Frederic Docquier (emigration rates); Flavine

Machakkalai, Atsushi Shimo, and Malarvizhi Veerappan provided systems support

Creppy and Yumiko Mochizuki of the United Nations Conference on Trade and

on the Development Data Platform application.

Development (trade); Betty Dow (commodity prices); Thierry Geiger of the World Economic Forum (trade facilitation); Jeff Reynolds and Joseph Siegel of DHL

Publishing and dissemination

(freight costs); Yasmin Ahmad and Elena Bernaldo of the Organisation for Eco-

The Office of the Publisher, under the direction of Carlos Rossel, provided valu-

nomic Co-operation and Development (aid); Hiroko Maeda and Ibrahim Levent

able assistance throughout the production process. Denise Bergeron, Nazim

(external debt); Henrik Pilgaard of the United Nations Refugee Agency (refugees);

Aziz Gokdemir, Stephen McGroarty, and Nora Ridolfi coordinated printing and

Costanza Giovannelli and Bela Hovy of the United Nations Population Division

supervised marketing and distribution. Merrell Tuck-Primdahl of the Develop-

(migration); Sanket Mohapatra and Ani Rudra Silwal (remittances); and Teresa

ment Economics Vice President’s Office managed the communications strategy.

Ciller of the World Tourism Organization (tourism). Ramgopal Erabelly, Shelley Lai Fu, and William Prince provided valuable technical assistance.

World Development Indicators CD-ROM Software preparation and testing was managed by Vilas Mandlekar with the assis-

Other parts of the book

tance of Ramgopal Erabelly, Buyant Erdene Khaltarkhuu, Parastoo Oloumi, and

Jeff Lecksell of the World Bank’s Map Design Unit coordinated preparation of the

William Prince. Systems development was undertaken by the Data and Informa-

maps on the inside covers. William Prince prepared Users guide. Eric Swanson

tion Systems Team led by Reza Farivari. William Prince coordinated user interface

wrote Statistical methods. Maja Bresslauer, Buyant Khaltarkhuu, and William

design and overall production and provided quality assurance, with assistance

Prince prepared Primary data documentation. Alison Kwong prepared Partners

from Jomo Tariku. Photo credits belong to the World Bank photo library.

and Index of indicators.

Open Data and Online Access Database management

Coordination of the Open Data website (data.worldbank.org/) was provided by

William Prince coordinated management of the World Development Indicators

Neil Fantom and Nicole Frost. Design, programming, and testing were carried

database. Operation of the database management system was made possible

out by Reza Farivari and his team: Azita Amjadi, Ramvel Chandrasekaran, Shelley

by Ramgopal Erabelly, Shelley Fu, and Shahin Outadi in the Data and Information

Fu, Buyant Erdene Khaltarkhuu, Ugendran Machakkalai, Shanmugam Natarajan,

Systems Team under the leadership of Reza Farivari.

Atsushi Shimo, Lakshmikanthan Subramanian, Jomo Tariku, Malarvizhi Veerappan, and Vera Wen. William Prince coordinated production and provided quality

Design, production, and editing

assurance. Support from the Corporate Communications Unit in External Affairs

Azita Amjadi, Alison Kwong, and Jomo Tariku coordinated all stages of production.

was provided by a team including Livia Barton, George Gongadze and Jeffrey

Jomo Tariku prepared the cover. Deborah Arroyo, Jomo Tariku, and Elaine Wilson

Mccoy. The multilingual web team was led by Valerie Hufbauer.

typeset the book. Communications Development Incorporated provided overall design direction and editing, led by Meta de Coquereaumont, Bruce Ross-Larson,

Client feedback

and Christopher Trott. Katrina Van Duyn proofread of the book. Staff from External

The team is grateful to the many people who have taken the time to provide

Affairs Office of the Publisher oversaw printing and dissemination of the book.

feedback and suggestions, which have helped improve this year’s edition. Please contact us at [email protected].

2011 World Development Indicators

407

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———. n.d. Performance Assessments and Allocation of IDA Resources Online

———. 2010. Progress on Sanitation and Drinking Water. Geneva: World Health Organization and United Nations Children’s Fund. ———. 2011. Global Atlas of the Health Workforce. Geneva: World Health Organization and United Nations Children’s Fund. ———. Various years. WHO-UNICEF estimates of national immunization coverage database. Online database. [www.who.int/immunization_monitoring/routine/ immunization_coverage/en/index4.html]. Geneva. WHO (World Health Organization), UNICEF (United Nations Children’s Fund), UNFPA (United Nations Population Fund), and World Bank. 2010. Trends in Maternal Mortality: 1990–2008 Estimates Developed by WHO, UNICEF, UNFPA, and the World Bank. Geneva: World Health Organization. WIPO (World Intellectual Property Organization). 2010. WIPO Patent Report: Statistics on Worldwide Patent Activity. Geneva: World Intellectual Property Organization. World Bank. 1990. World Development Report 1990: Poverty. Washington, D.C.: World Bank. ———. 2000. Trade Blocs. New York: Oxford University Press. ———. 2001. World Development Report 2000/2001: Attacking Poverty. New York: Oxford University Press. ———. 2002. Global Economic Prospects 2002: Making Trade Work for the World’s Poor. Washington, D.C.: World Bank. ———. 2007. Healthy Development: The World Bank Strategy for Health, Nutrition, and Population Results. Washington, D.C.: World Bank. ———. 2008a. “Brazil Country Partnership Strategy 2008–2011.” World Bank, Latin America and the Caribbean Region, Washington, D.C. ———. 2008b. “Improving Trade and Transport for Landlocked Developing Coun-

database. [www.worldbank.org/ida]. Washington, D.C. ———. n.d. PovcalNet oOnline database. [http://iresearch.worldbank.org/PovcalNet]. Washington, D.C. ———. n.d. Private Participation in Infrastructure Database. Online database. [http://ppi.worldbank.org/]. Washington, D.C. ———.n.d. Quarterly Public Sector Debt. Online database. [http://databank.worldbank.org/ddp/home.do?Step=12&id=4&CNO=3009]. Washington, D.C. ———. n.d. World Trade Indicators. Online database. [www.worldbank.org/wti]. Washington, D.C. ———. Various issues. Commodity Market Review. Washington, D.C.: World Bank, Development Prospects Group. ———. Various issues. Food price watch. Washington, D.C.: World Bank, Poverty Reduction and equity Group. ———. Various issues. Commodity Price Data. Washington, D.C.: World Bank, Development Prospects Group. ———. Various issues. Migration and Development Briefs. Washington, D.C.: World Bank, Development Prospects Group. ———. Various years. Global Development Finance: External Debt of Developing Countries. Washington, D.C.: World Bank. ———. Various years. Global Development Finance: Volumes I and II. Washington, D.C.: World Bank. ———. Various years. World Debt Tables. Washington, D.C.: World Bank. ———. Various years. World Development Indicators. Washington, D.C.: World Bank. World Bank and Dartmouth College. n.d. Global Preferential Trade Agreements Database. Online database. [http://wits.worldbank.org/gptad/]. Washington, D.C.

tries: World Bank Contributions to Implementing the Almaty Programme of

World Bank and IFPRI (International Food Policy Research Institute). 2006.

Action: A Report for the Mid-Term Review October 2008.” World Bank, Interna-

Agriculture and Achieving the Millennium Development Goals. Report 32729-GLB.

tional Trade Department, Washington, D.C.

Washington, D.C.: World Bank.

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415

BIBLIOGRAPHY World Bank and IMF (International Monetary Fund). 2011. Global Monitoring Report 2011: Improving the Odds of Achieving the MDGs: Heterogeneity, Gaps, and Challenges. Washington, DC: World Bank. World Economic Forum. 2010. The Global Competitiveness Report 2010–2011. Geneva: World Economic Forum. World Tourism Organization. Various years. Compendium of Tourism Statistics. Madrid: World Tourism Organization. ———. Various years. Yearbook of Tourism Statistics. Vols. 1 and 2. Madrid: World Tourism Organization. WTO (World Trade Organization). n.d. Regional Trade Agreements Gateway. [www. wto.org/english/tratop_e/region_e/region_e.htm]. Geneva. ———. n.d. Regional Trade Agreements Information System. Online database. [http://rtais.wto.org/]. Geneva. ———. Various years. Annual Report. Geneva.

416

2011 World Development Indicators

2011 World Development Indicators

417

INDEX OF INDICATORS References are to table numbers.

A

Agriculture

4.1

as share of GDP

4.2

Aid

agricultural raw materials commodity prices

by recipient 6.6

exports

aid dependency ratios

6.16

per capita

6.16 6.16

as share of total exports

4.4

total

from high-income economies as share of total exports

6.4

net flows

imports

from bilateral sources

6.13

as share of total imports

4.5

from international financial institutions

6.13

by high-income economies as share of total imports

6.4

from multilateral sources

6.13

6.4

from UN agencies

6.13

tariff rates applied by high-income countries cereal

official development assistance by DAC members

area under production

3.2

exports from high-income economies as share of total exports

6.4

imports, by high-income economies as share of total imports

6.4

administrative costs, as share of net bilateral ODA disbursements bilateral aid

tariff rates applied by high-income countries

6.4

by purpose

yield

3.3

by sector

employment, as share of total

3.2

fertilizer

6.15a 6.15a, 6.15b, 6.17

commitments

6.15a 6.15b 6.14, 6.15b

debt-related aid, as share of net bilateral ODA disbursements

commodity prices

6.6

consumption, per hectare of arable land

3.2

food

6.15a

development projects, programs, and other resource provisions, as share of net bilateral ODA disbursements

6.15a

disbursements net

commodity prices

6.6

gross disbursements

exports

4.4

humanitarian assistance, as share of net bilateral ODA

6.14

exports from high-income economies as share of total exports

6.4

disbursements

imports

4.5

net disbursements

imports by high-income economies as share of total imports

6.4

as share of general government disbursements

tariff rates applied by high-income countries

6.4

as share of donor GNI

3.5

from major donors, by recipient

6.17

per capita of donor country

6.14

freshwater withdrawals for, as share of total land agricultural, as share of land area

3.2

arable, as share of land area

3.1

gross

arable, per 100 people

3.1

tfor basic social services, as share of sector-allocable

area under cereal production

3.2

Irrigated

3.2

permanent cropland, as share of land area

3.1

machinery tractors per 100 square kilometers of arable land

total

bilateral ODA commitments net disbursements

3.2

6.14, 6.15a 6.14 1.4 6.15a

net disbursements

6.15a

total sector allocable, as share of bilateral ODA 3.3

commitments

food

3.3

untied aid 6.15b

livestock

3.3

AIDS—see HIV, prevalence

2011 World Development Indicators

6.14 1.4, 6.14

humanitarian assistance, as share of bilateral ODA

crop

value added

6.15a

technical cooperation, as share of bilateral ODA

production indexes

418

annual growth

6.15b

Air pollution—see Pollution

Bonds—see Debt flows; Private financial flows

Air transport

Brain drain—see Emigration of people with tertiary education to OECD

air freight

5.10

passengers carried

5.10

registered carrier departures worldwide

5.10

Asylum seekers—see Migration; Refugees

B

countries Breastfeeding, exclusive

2.20

Broad Money

4.15

Business environment businesses registered

Balance of payments

entry density

current account balance as share of GDP

new

5.1

4.a

total

5.1

exports and imports of goods and services

4.17

net current transfers

4.17

net income

4.17

total reserves

4.a, 4.17

See also Exports; Imports; Investment; Private financial flows; Trade

Battle-related deaths

closing a business time to resolve insolvency

5.3

corruption informal payments to public officials

5.2

crime

Base metal commodity prices and price index

5.1

4.17

losses due to theft, robbery, vandalism, and arson 6.6 5.8

5.2, 5.8

dealing with construction permits to build a warehouse number of procedures

5.3

time required

5.3

enforcing contracts Beverages commodity prices

6.6

number of procedures

5.3

time required

5.3

finance firms using banks to finance investment

Biodiversity—see Biological diversity

5.2

gender female participation in ownership

Biological diversity assessment, date prepared, by country

3.15

GEF benefits index

3.4

threatened species

3.4

firms formally registered when operations started value lost due to electrical outages

3.4

fish

3.4

innovation

higher plants

3.4

internationally recognized quality certification ownership

mammals

3.4

permits and licenses

3.15

5.2, 5.8

infrastructure

birds

treaty

5.2

informality

time required to obtain operating license

5.2 5.2 5.2

protecting investors Birth rate, crude

2.1

disclosure, index

5.3

registering property

See also Fertility rate Births attended by skilled health staff

2.19

Birthweight, low

2.20

number of procedures

5.3

time to register

5.3

regulation and tax average number of times firms spend meeting with tax officials

5.2

time dealing with officials

5.2

2011 World Development Indicators

419

INDEX OF INDICATORS starting a business

fixed capital

cost to start a business

5.3

number of start-up procedures

5.3

time to start a business

5.3

trade

4.10, 4.11

government, general final expenditure annual growth as share of GDP

4.9 4.8

household final expenditure

average time to clear direct exports

5.2

multilateral, as share of public and publicly

workforce firms offering formal training

guaranteed debt service 5.2

average annual growth per capita

C

as share of GDP

6.11 4.9 4.9 4.8

See also Purchasing power parity (PPP)

Carbon dioxide damage, as share of GNI

4.11

Contraceptives condom use, male and female

emissions

2.21

3.8

prevalence rate

1.3, 2.19

per capita

1.3, 3.8

unmet need for

2.19

total

1.6, 3.8

intensity

3.8

per unit of GDP

Children at work by economic activity

2.6

male and female

2.6

study and work

2.6

status in employment total

2.6 2.6, 5.8

work only

2.6

Contract enforcement number of procedures

5.3

time required for

5.3

Corruption, informal payments to public officials

5.2

Country Policy and Institutional Assessment (CPIA)—see Economic management; Social inclusion and equity policies; Public sector management and institutions; Structural policies Credit

Cities—see Urban environment;

getting credit Closing a business—see Business environment Commercial bank and other lending

6.12

See also Debt flows; Private financial flows Commodity prices and price indexes

6.6

Communications—see Internet; Newspapers, daily; Telephones; Television,

depth of credit information index

5.5

strength of legal rights index

5.5

provided by banking sector

5.5

to private sector

5.1

Crime intentional homicide rate

5.8

losses due to

5.2

households with Current account balance of central government employees

4.13 Customs

See also Remittances Computers (personal) per 100 people Consumption distribution—see Income distribution

420

2011 World Development Indicators

4.17

See also Balance of payments

Compensation

5.12

average time to clear

5.2

burden of procedures

6.9

D

E

Economic management (Country Policy and Institutional Assessment)

DAC (Development Assistance Committee)—see Aid Death rate, crude

2.1

See also Mortality rate

6.11

total, as share of exports of goods and services and income

6.11

5.9

children out of school

to grade 5, male and female

2.13

to last grade of primary education, male and female

2.13

completion rate, primary 6.10

male and female

2.14, 2.15

poorest and richest wealth quintiles

IBRD loans and IDA credits

6.10

total

6.10

total

as share of GNI

6.11

as share of exports of goods and services and income

6.11

enrollment ratio

6.11

as share of total reserves

6.11

1.2

gross

short-term as share of total debt

2.15 1.2, 2.14

girls to boys enrollment in primary and secondary education

present value

total

2.15

cohort survival rate

6.10

public and publicly guaranteed

total

2.12

6.11

long-term private nonguaranteed

5.9

macroeconomic management

poorest and richest wealth quintile

multilateral, as share of public and publicly guaranteed debt

IMF credit, use of

5.9

fiscal policy

male and female

debt service service

5.9

economic management cluster average

Education

Debt, external as share of GNI

debt policy

by level

2.12

primary

5.8

secondary

2.4

net

6.10

by level

2.12

6.10

primary, adjusted

2.12

intake ratio, gross first grade of primary education

2.13

bonds

6.12

grade 1

2.15

commercial banks and other lending

6.12

primary participation rate, gross

2.15

Debt flows

public expenditure on

See also Private financial flows

as share of GDP Deforestation, average annual Demand—see Comsumption; Imports; Exports; Savings Density—see Population, density Dependency ratio—See Population, age dependency ratio

3.4

2.11

as share of GNI

4.11

as share of total government expenditure

2.11

per student, as share of GDP per capita, by level

2.11

pupil–teacher ratio, primary

2.11

repeaters, primary, male and female

2.13

teachers, primary, trained

2.11

transition to secondary school, male and female

2.13

unemployment by level of educational attainment Development assistance—see Aid Disease—see Health risks

years of schooling, average Electricity consumption

Distribution of income or consumption—see Income distribution

2.5 2.15

5.11

production

2011 World Development Indicators

421

INDEX OF INDICATORS total

3.10

combustible renewables and waste, as share of total

sources

3.10

fossil fuel consumption, as share of total

3.7

5.11

GDP per unit of energy use

3.8

per capita

3.7

total

3.7

transmission and distribution losses value lost due to outages

5.2

3.7

See also Electricity; Fuels Emissions carbon dioxide average annual growth intensity per capita per unit of GDP total

Enforcing contracts—see Business environment 3.9 3.8

Enrollment—see Education

1.3, 3.8 3.8

Entry regulations for business—see Business environment

1.6, 3.8

methane

Environmental strategy or action plans, year adopted 3.15

agricultural, as share of total

3.9

from energy processes, as share of total

3.9

total

3.9

nitrous oxide agricultural, as share of total

3.9

energy and industry, as share of total

3.9

total

3.9

other greenhouse gases

3.9

Employment

Equity flows foreign direct investment

6.12

portfolio equity

6.12

See also Private financial flows European Commission distribution of net aid from

6.17

Exchange rates

children in employment

2.6, 5.8

in agriculture as share of total employment female male

3.2 1.5, 2.3 2.3

in industry, male and female

2.3

in services, male and female

2.3

to population ratio

2.4

vulnerable

official, local currency units to U.S. dollar

4.16

purchasing power parity conversion factor

4.16

ratio of PPP conversion factor to official exchange rate

4.16

real effective

4.16

See also Purchasing power parity (PPP) Export credits private, from DAC members

6.14

1.2, 2.4

See also Labor force; Unemployment

Exports

Endangered species—see Biological diversity; Plants, higher

arms

5.7

documents required for

6.9

goods and services Energy

as share of GDP

commodity price index

6.6

consumption, road sector

3.13

depletion, as share of GNI

4.11

emissions—see Pollution

total

3.8

production

3.7

use

total information and communications technology lead time

alternative and nuclear energy

3.7

average annual growth

3.7

2011 World Development Indicators

4.8 4.a, 4.9 4.17

high-technology share of manufactured exports

imports, net

422

average annual growth

5.13 5.13 5.12 6.9

merchandise annual growth

6.2, 6.3

from high-income countries, by product

6.4

from developing countries, by recipient

6.5

private

6.14

by regional trade blocs

6.7

total

6.14

direction of trade

6.3

See also Aid

structure

4.4

total

4.4

value, average annual growth

6.2

volume, average annual growth

6.2

services

6.14

Financing through international capital markets

6.1

See also Private financial flows Food—see Agriculture, production indexes; Commodity prices and price

structure

4.6

total

4.6

travel

other official flows

4.6, 6.19

indexes Foreign direct investment, net—see Investment; Private financial flows

See also Trade Forest

F

area

Female-headed households Female participation in ownership

2.10 5.2

crude birth rate desired total

3.1

total

3.4

deforestation, average annual

3.4

net depletion, as share of GNI

4.11

Fuels

Fertility rate adolescent

as share of total land area

consumption

2.19

road sector

2.1

3.13

exports

2.19

as share of total merchandise exports

2.18, 2.21

4.5

crude petroleum, from high-income economies, Finance, firms using banks to finance investment

as share of total exports

5.2

6.4

from high-income economies, as share of total exports Financial access, stability, and efficiency automated teller machines

6.4

petroleum products, from high-income economies, as share of total exports

5.5

6.4

imports

bank capital to asset ratio

5.5

bank nonperforming loans, ratio to total gross loans

5.5

as share of total imports

commercial bank branches

5.5

crude petroleum, by high-income economies, as share of total

4.4

imports

6.4

deposit accounts at commercial banks

5.5

loan accounts at commercial banks

5.5

by high-income economies, as share of total imports

point-of-sale terminals

5.5

petroleum products, by high-income economies, as share of total imports

from DAC members from bilateral sources

6.13

from international financial institutions

6.13

from multilateral sources

6.13

from UN agencies

6.13

total

6.13

net grants by NGOs

3.13

tariff rates applied by high-income countries

6.14

official

official development assistance

6.4

prices

Financial flows, net

6.4

G

GEF benefits index for biodiversity

6.4

3.4

Gender differences

6.14

in children in employment

6.14

in condom use

2.6, 5.8 2.21

2011 World Development Indicators

423

INDEX OF INDICATORS in education

1.2, 2.12, 2.13, 2.14

in employment by economic activity

Gross domestic product (GDP)

2.3

annual growth

2.3

contribution of natural resources

unemployment

3.16

implicit deflator—see Prices

total

2.5

youth

2.10

nonagricultural wage employment

1.5

unpaid family workers

1.5

vulnerable employment

2.4

in HIV prevalence

1.1, 1.6, 4.1, 4.10

2.21

in labor force participation

2.2

in life expectancy at birth

1.5

per capita, annual growth

1.1, 1.6

total

4.2, 4.10

Gross enrollment—see Education Gross national income (GNI) adjusted net national income

in literacy

annual growth

4.10

total

4.10

adult

2.14

annual growth

youth

2.14

per capita

in mortality

4.10

PPP dollars

adult

2.22

rank

child

2.22

U.S. dollars

1.1, 1.6 1.1 1.1, 1.6

in ownership of firms

5.2

in parliaments

1.5

PPP dollars

1.1

in smoking

2.21

U.S. dollars

1.1

in survival to age 65

2.22

Gini index

rank

total

2.9

Government, central cash surplus or deficit

4.12

debt

PPP dollars

1.1, 1.6

U.S. dollars

1.1, 1.6, 4.10

H

Health care

as share of GDP

4.12

children sleeping under treated nets

2.18

interest, as share of revenue

4.12

children with acute respiratory infection taken to health provider

2.18

expense

children with diarrhea who received oral rehydration and

as share of GDP

4.12

by economic type

4.13

children with fever receiving antimalarial drugs

4.12

HIV, prevalence

net incurrence of liabilities, as share of GDP, domestic and foreign revenue

continued feeding

2.18 2.18 1.3

hospital beds per 1,000 people

2.16

as share of GDP

4.12

immunization rate, child

2.18

grants and other

4.14

nurses and midwives per 1,000 people

2.16

social contributions

4.14

outpatient visits per capita

2.16

physicians per 1,000 people

2.16

taxes as share of GDP by source, as share of revenue

5.6 4.14

reproductive anemia, prevalence of, pregnant women births attended by skilled health staff

Greenhouse gases—see Emissions

contraceptive prevalence rate

2.20 2.19 1.3, 2.19

fertility rate Gross capital formation annual growth

4.9

as share of GDP

4.8

424

2011 World Development Indicators

adolescent

2.19

total

2.19

low-birthweight babies

2.20

maternal mortality ratio lifetime risk of maternal death pregnant women receiving prenatal care unmet need for contraception

1.3, 2.19, 5.8

prevalence

2.19

female

1.5, 2.19 2.19

total

tuberculosis incidence treatment success rate

2.21

population ages 15–24, male and female

2.21 1.3, 2.21

prevention 1.3, 2.20

condom use, male and female

2.21

2.18 Homicide rate, intentional

5.8

Health expenditure as share of GDP

2.16

external resources

2.16

out of pocket

2.16

per capita

2.16

durable dwelling units

3.12

public

2.16

home ownership

3.12

household size

3.12

multiunit dwellings

3.12

overcrowding

3.12

vacancy rate

3.12

Hospital beds—see Health care Housing conditions, national and urban

Health information census, year last completed

2.17

completeness of vital registration birth registration

2.17

infant death

2.17

Hunger, depth

total death

2.17

number completed

2.17

I

year last completed

2.17

Immunization rate, child

health survey, year last completed

2.17

national health account

Health risks

5.8

DPT, share of children ages 12–23 months

2.18

measles, share of children ages 12–23 months

2.18

anemia, prevalence of children under age 5

2.20

pregnant women

2.20

arms

5.7

documents required for

6.9

2.21

energy, net, as share of total energy use

3.8

2.21

goods and services

1.3, 2.21

as share of GDP

child malnutrition, prevalence

1.2, 2.20

condom use, male and female diabetes, prevalence HIV, prevalence

Imports

low-birthweight babies

2.20

average annual growth

overweight children, prevalence

2.20

total

smoking, prevalence, male and female tuberculosis, incidence undernourishment, prevalence

2.21 1.3, 2.21 2.20

4.8 4.9 4.17

information and communications technology goods lead time

6.9

merchandise annual growth

Heavily indebted poor countries (HIPCs)

HIV

5.12

6.3

by high-income countries, by product

6.4 6.5

assistance

1.4

by developing countries, by partner

completion point

1.4

structure

decision point

1.4

tariffs

Multilateral Debt Relief Initiative (MDRI) assistance

1.4

total

4.5

value, average annual growth

6.2

volume, average annual growth

6.2

4.5 6.4, 6.8

2011 World Development Indicators

425

INDEX OF INDICATORS services structure

4.7

total

4.7

travel

4.7, 6.18

net financial flows from

6.13

use of IMF credit

6.10

Internet

See also Trade

broadband subscribers fixed broadband access tariff

Income distribution Gini index percentage of

international Internet bandwidth 2.9 1.2, 2.9

Industry

5.12 5.12 5.12, 6.1

secure servers

5.12

users

5.12

Investment

annual growth

4.1

as share of GDP

4.2

foreign direct, net inflows as share of GDP

employment, male and female

2.3

from DAC members

6.14

freshwater withdrawals for output

3.5

total

6.12

6.1

foreign direct, net outflows as share of GDP

Inflation—see Prices

6.1

infrastructure, private participation in Informal economy, firms formally registered when operations started Information and communications technology trade

5.11

Innovation, internationally recognized certification ownership

5.2

Integration, global economic, indicators

6.1

real

5.1

transport

5.1

water and sanitation

5.1

See also Gross capital formation; Private financial flows Iodized salt, consumption of

4.15

annual growth

4.15

armed forces

5.7

4.15

children at work

2.6

female

2.2

risk premium on lending

5.5

spread

5.5

nonagricultural part-time

Internally displaced persons

2.20

Labor force

Interest rates lending

5.1

telecommunications

L

Interest payments—see Government, central, debt

deposit

energy

5.2

5.8

2.2

1.5 1.5

participation of population ages 15 and older, male and female

2.2

total

2.2

See also Employment; Migration; Unemployment

International Bank for Reconstruction and Development (IBRD) IBRD loans and IDA credits

6.10

net financial flows from

6.13

Land area arable—see Agriculture, land; Land use See also Protected areas; Surface area

International Development Association (IDA) IBRD loans and IDA credits

6.10

net concessional flows from

6.13

Resource Allocation Index International Monetary Fund (IMF)

426

2011 World Development Indicators

5.8, 5.9

Land use arable land, as share of total land

3.1

per 100 people

3.1

area under cereal production

3.2

by type

3.1

clothing

1.4

forest area, as share of total land

3.1

textiles

1.4

irrigated land

3.2

permanent cropland, as share of total land

3.1

total area

3.1

Merchandise exports agricultural raw materials

Life expectancy at birth male and female total

1.5 1.6, 2.22

6.5

cereals

6.4

chemical products

6.4 6.4

food

adult total

6.7

from developing countries, by partner

crude petroleum

Literacy male and female

4.4

from regional trade blocs

2.14 1.6

4.4, 6.4

footwear

6.4

fuels

4.4

mathematics, PISA mean score

2.14

furniture

youth, male and female

2.14

information and communications technology goods

5.12

information and communications technology services

5.12

Logistics Performance Index

M

Malnutrition, in children under age 5

6.9

1.2, 2.20

Malaria

6.4

iron and steel

6.4

machinery and transport equipment

6.4

manufactures

4.4

ores and metals

4.4

ores and nonferrous materials

6.4

petroleum products

6.4

structure

4.4 6.4 4.4

children sleeping under treated bednets

2.18

textiles

children with fever receiving antimalarial drugs

2.18

total

to low-income economies from high-income economies, by product 6.4 Management time dealing with officials

5.2

to middle-income economies from high-income economies, by product

Manufacturing annual growth as share of GDP 4

4.1 .2

6.4

value, average annual growth

6.2

volume, average annual growth

6.2

within regional trade blocs

6.7

imports

value added chemicals

4.3

agricultural raw materials

4.5

food, beverages, and tobacco

4.3

by developing countries, by partner

6.5

machinery and transport equipment

4.3

cereals

6.4

other

4.3

chemicals

6.4

structure

4.3

crude petroleum

6.4

textiles and clothing

4.3

food

4.5

total

4.3

footwear

6.4

to low-income economies by high-income economies, by product

6.4

See also Merchandise Market access to high-income countries

to middle-income economies by high-income economies, by product

6.4

fuels

4.5

goods admitted free of tariffs

1.4

furniture

support to agriculture

1.4

information and communications technology goods

tariffs on exports from least developed countries agricultural products

1.4

6.4 5.12

iron and steel

6.4

machinery and transport equipment

6.4

2011 World Development Indicators

427

INDEX OF INDICATORS manufactures

4.5

ores and metals

4.5

ores and nonferrous materials

6.4

births attended by skilled health staff

petroleum products

6.4

carbon dioxide emissions per capita

textiles

6.4

children sleeping under treated bednets

total

4.5

contraceptive prevalence rate

value, average annual growth

6.2

employment to population ratio

volume, average annual growth

6.2

enrollment ratio, net, primary

trade

average tariff imposed by developed countries on exports of least developed countries

1.4 2.19 1.3, 3.8 2.18 1.3, 2.19 2.4 2.12

female to male enrollments, primary and secondary

as share of GDP

1.2

6.1

fertility rate, adolescent

2.19

by developing countries, by partner

6.5

goods admitted free of tariffs from least developed countries

direction

6.3

heavily indebted poor countries (HIPCs)

growth

6.3

assistance

1.4

regional trade blocs

6.7

completion point

1.4

decision point

1.4

1.4

Multilateral Debt Relief Initiative (MDRI) assistance

Metals and minerals commodity prices and price index

6.6

nominal debt service relief committed

1.4

immunization rate, child Methane emissions

DPT

2.18

measles

2.18

agricultural as share of total

3.9

industrial as share of total

3.9

income or consumption, national share of poorest quintile

total

3.9

infant mortality rate

1.2, 2.9 2.22

Internet users per 100 people labor productivity, GDP per person employed

Migration emigration of people with tertiary education to OECD countries

6.1

as share of total population total

6.1 6.18

net

2.4

literacy rate of 15- to 24-year-olds

2.14

malnutrition, prevalence

international migrant stock

6.1, 6.18

1.2, 2.20

malaria children under age 5 sleeping under insecticide treated bednets

maternal mortality ratio

2.18 1.3, 2.19, 5.8

national parliament seats held by women

Military

Mobile cellular subscriptions per 100 people

armed forces personnel 5.7 5.7

for basic social services as share of total sector allocable

exports

5.7

net disbursements, as share of donor GNI

imports

5.7

ODA commitments

as share of central government expenditure as share of GDP

untied commitments poverty gap

military expenditure 5.7 5.7, 5.8

pregnant women receiving prenatal care share of cohort reaching last grade of primary education support to agriculture telephone lines, fixed, per 100 people

Millennium Development Goals, indicators for access to improved sanitation facilities access to improved water source

1.3, 2.18, 3.11, 5.8 2.18, 3.5, 5.8

1.4 1.4, 6.14 6.15b 2.7, 2.8 1.5, 2.19 2.13 1.4 5.11

tuberculosis case detection rate incidence

2011 World Development Indicators

1.5 5.11

official development assistance

as share of labor force total arms transfers

2.18

children under age 5 with fever who are treated with appropriate antimalarial drugs

See also Refugees; Remittances

428

1.3, 5.12

2.18 1.3, 2.21

treatment success rate under-five mortality rate

2.18

undernourishment, prevalence

2.20

unmet need for contraception

2.19

vulnerable employment women in wage employment in the nonagricultural sector Minerals depletion, as share of GNI

total

3.9

1.2, 2.22, 5.8 Nutrition anemia, prevalence of

1.2, 2.4 1.5 4.11

children ages under 5

2.20

pregnant women

2.20

breastfeeding, exclusive

2.20

iodized salt consumption

2.20

malnutrition, child

1.2, 2.20

overweight children, prevalence

2.20

4.15

undernourishment, prevalence

2.20

claims central government

4.15

vitamin A supplementation

2.20

other claims on domestic economy

4.15

Monetary indicators broad money

2.22

O

2.22

Official development assistance—see Aid

Mortality rate adult, male and female child, male and female children under age 5 crude death rate infant life expectancy at birth maternal lifetime risk of maternal death survival to age 65

1.2, 2.22, 5.8 2.1 2.22 2.22 1.3, 2.19, 5.8 2.19

Official flows—see Aid; Financial flows, net

P

Passenger cars per 1,000 people

3.13

2.22 Particulate matter

Motor vehicles passenger cars

3.13

per 1,000 people

3.13

per kilometer of road

3.13

road density

3.13

See also Roads; Traffic MUV G-5 index

selected cities

3.14

urban-population-weighted PM10

3.13

Patent applications filed

5.13

Peacebuilding and peacekeeping operations 6.6

N

operation name

5.8

troops, police, and military observers

5.8

Pension average, as share of per capita income

2.10

contributors

Natural resource depletion, as share of GNI

4.10

Net enrollment—see Education Newspapers, daily

5.12

as share of labor force

2.10

as share of working age population

2.10

public expenditure on, as share of GDP

2.10

Permits and licenses, time required to obtain operating license

5.2

Physicians—see Health care

Nitrous oxide emissions agricultural as share of total

3.9

industrial as share of total

3.9

Plants, higher

2011 World Development Indicators

429

INDEX OF INDICATORS threatened species

3.4

Pollution

3.14

in urban agglomerations

3.11

total

3.11

See also Migration

carbon dioxide damage, as share of GNI

in selected cities

4.11

emissions

Portfolio—see Equity flows; Private financial flows

average annual growth

3.9

intensity

3.8

per unit of GDP

3.8

container traffic in

5.9

per capita 1.3,

3.8

quality of infrastructure

6.9

total

Ports

1.6, 3.8

local damage 4.11

Poverty

methane emissions

international poverty line

agricultural, as share of total

3.9

local currency

from energy processes, as share of total

3.9

population living below

total

3.9

nitrogen dioxide, selected cities

3.14

nitrous oxide emissions

$1.25 a day

2.8

$2 a day

2.8

national poverty line

agricultural, as share of total

3.9

population living below, national, rural, and urban

2.7

energy and industry, as share of total

3.9

poverty gap, national, rural, and urban

2.7

total

3.9

organic water pollutants, emissions

Power—see Electricity, production

by industry

3.6

per day

3.6

per worker

3.6

particulate matter concentration

Prenatal care, pregnant women receiving

3.14

commodity prices and price indexes

total

3.13

consumer, annual growth

3.14

fuel

Population age dependency ratio, young and old

2.1

average annual growth

2.1

by age group, as share of total

6.6 4.16 3.8

GDP implicit deflator, annual growth

0–14

1.5, 2.19

Prices

selected cities sulfur dioxide, selected cities

4.16

net barter terms of trade

6.2

wholesale, annual growth

4.16

Primary education—see Education 2.11

5–64

2.1

65 and older

2.1

debt flows

1.1, 1.6

bonds

6.12

commercial bank and other lending

6.12

density female, as share of total

1.5

rural

Private financial flows

equity flows

annual growth

3.1

foreign direct investment, net inflows

6.12

as share of total

3.1

portfolio equity

6.12

total

1.1, 1.6, 2.1

urban

430

2.8

financing through international capital markets, as share of GDP from DAC members

as share of total

3.11

average annual growth

3.11

in largest city

3.11

2011 World Development Indicators

See also Investment Productivity

6.1 6.14

agricultural

3.3

labor

2.4

management time dealing with officials

5.2

water

3.5

meeting with tax officials, number of times

5.2

Protected areas

Regulation and tax administration

Relative prices (PPP)—see Purchasing power parity (PPP)

marine as share of total surface area

3.4

total

3.4

Remittances workers’ remittances and compensation of employees

terrestrial

as share of GDP

6.1

as share of total surface area

3.4

paid

6.18

total

3.4

received

6.18

Protecting investors disclosure index

5.3

Research and development

Public sector management and institutions (Country Policy and Institutional Assessment)

expenditures

5.13

researchers

5.13

technicians

5.13

efficiency of revenue mobilization

5.9

property rights and rule-based governance

5.9

public sector management and institutions cluster average

5.9

quality of budgetary and financial management

5.9

quality of public administration

5.9

goods hauled by

5.10

transparency, accountability, and corruption in the public sector

5.9

passengers carried

5.10

Reserves, gross international—see Balance of payments Roads

Purchasing power parity (PPP) conversion factor gross national income

4.16

paved, as share of total

5.10

sectoral energy consumption

3.13

total network

5.10

1.1, 1.6 Royalty and license fees

R

payments

5.13

receipts

5.13

Railways Rural environment

goods hauled by

5.10

lines, total

5.10

access to improved sanitation facilities

passengers carried

5.10

access to an improved water source

3.11 3.5

population Refugees by country of asylum

5.8, 6.18

by country of origin

5.8, 6.18

internally displaced persons Regional development banks, net financial flows from

5.8 6.13

annual growth

3.1

as share of total

3.1

S S&P/Global Equity Indices

5.4

Regional trade agreements—see Trade blocs, regional Sanitation, access to improved facilities, population with Registering property

total

number of procedures

5.3

time to register

5.3

1.3, 2.18, 5.8

urban and rural

3.11

Savings

2011 World Development Indicators

431

INDEX OF INDICATORS adjusted net

4.11

gross, as share of GDP

4.8

gross, as share of GNI

4.11

Stock markets listed domestic companies

Schooling—see Education Science and technology scientific and technical journal articles

5.4

market capitalization

5.13

as share of GDP

5.4

total

5.4

market liquidity

5.4

S&P/Global Equity Indices

5.4

turnover ratio

5.4

See also Research and development Steel products, commodity prices and price index

6.6

Secondary education—see Education Structural policies (Country Policy and Institutional Assessment) Services employment, male and female

2.3

exports computer, information and communications, and other commercial services

business regulating environment

5.9

financial sector

5.9

structural policies cluster average

5.9

trade

5.9

4.6

insurance and financial services

4.6

structure

4.6

total commercial

4.6

transport

4.6

travel

4.6

imports

Sulfur dioxide emissions—see Pollution Surface area

1.1, 1.6

See also Land use Survival to age 65, male and female

2.22

computer, information and communications, and 4.7

Suspended particulate matter—see Pollution

insurance and financial services

other commercial services

4.7

structure

4.7

total commercial

4.7

transport

4.7

T

travel

4.7

trade, as share of GDP

6.1

value added

Tariffs all products binding coverage

6.8

simple mean bound rate

6.8

annual growth

4.1

simple mean tariff

6.8

as share of GDP

4.2

weighted mean tariff

6.8

applied rates on imports from low- and middle-income economies Smoking, prevalence, male and female

2.21

Social inclusion and equity policies (Country Policy and Institutional Assessment)

6.4

manufactured products simple mean tariff

6.8

weighted mean tariff

6.8

building human resources

5.9

on exports of least developed countries

equity of public resource use

5.9

primary products

gender equity

5.9

simple mean tariff

6.8

policy and institutions for environmental sustainability

5.9

weighted mean tariff

6.8

social inclusion and equity cluster average

5.9

share of tariff lines with international peaks

6.8

social protection and labor

5.9

share of tariff lines with specific rates

6.9

Starting a business—see Business environment Taxes and tax policies

432

2011 World Development Indicators

1.4

business taxes

tourists

average number of times firms meet with tax officials

5.2

inbound

6.19

labor tax, as share of commercial profits

5.6

outbound

6.19

number of payments

5.6

other taxes, as share of commercial profits

5.6

profit tax, as share of commercial profits

5.6

arms

5.7

time to prepare, file, and pay

5.6

barriers

6.8

5.6

facilitation

total tax rate, as share of commercial profits

Trade

goods and services taxes, domestic

4.14

burden of customs procedures

income, profit, and capital gains taxes

4.14

documents

international trade taxes

4.14

to export

6.9

other taxes

4.14

to import

6.9

social contributions

4.14

tax revenue collected by central government, as share of GDP

5.6

Technology—see Computers; Exports, high-technology; Internet; Research and development; Science and technology

per 100 people

5.11

residential tariff

5.11 5.11, 6.1

mobile cellular per 100 people

to export

6.9

to import

6.9

liner shipping connectivity index

6.9

logistics performance index

6.9

information and communications technology

fixed line

1.3, 5.11

6.9

lead time

quality of port infrastructure

Telephones

international voice traffic

freight costs to the United States

6.9

6.9 5.12

merchandise as share of GDP

6.1

direction of, by developing countries

6.5

direction of, by region

6.3

high-income economy with low- and middle-income economies,

population covered

5.11

by product

6.4

prepaid tariff

5.11

nominal growth, by region

6.3

mobile cellular and fixed-line subscribers per employee

5.11

regional trading blocs

total revenue

5.11

structure

4.4, 4.5

total

4.4, 4.5

Television, households with

5.12

6.7

services as share of GDP

Terms of trade index, net barter

6.2

6.1

structure

4.6, 4.7

total

4.6, 4.7

See also Balance of payments; Exports; Imports; Manufacturing;

Tertiary education—see Education

Merchandise; Terms of trade; Trade blocs Threatened species—see Animal species; Biological diversity; Plants, higher Trade blocs, regional exports within bloc

6.7

tourism expenditure

total exports, by bloc

6.7

inbound

type of agreement

6.7

Tourism, international

as share of exports

6.19

year of creation

6.7

total

6.19

year of entry into force of the most recent agreement

6.7

outbound as share of imports

6.19

total

6.19

Trademark applications filed

5.13

Trade policies—see Tariffs

2011 World Development Indicators

433

INDEX OF INDICATORS Traffic—see Fuels; Motor vehicles; Roads

sulfur dioxide

3.14

housing conditions Transport—see Air transport; Ports; Railways; Roads Travel—see Tourism, international Treaties, participation in biological diversity

3.15

durable dwelling units

3.12

home ownership

3.12

household size

3.12

multiunit dwellings

3.12

overcrowding

3.12

vacancy rate

3.12

population

CFC control

3.15

climate change

3.15

as share of total

Convention on International Trade on Endangered Species (CITES)

3.15

average annual growth

3.11

Convention to Combat Desertification (CCD)

3.15

in largest city

3.11

Kyoto Protocol

3.15

in selected cities

3.14

Law of the Sea

3.15

in urban agglomerations

3.11

Ozone layer

3.15

total

3.11

Stockholm Convention on Persistent Organic Pollutants

3.15

Tuberculosis case detection rate incidence treatment rate

U

2.18 1.3, 2.21 2.18

See also Pollution; Population; Sanitation; Water

V

Value added as share of GDP in agriculture

4.2

in industry

4.2

in manufacturing

UN agencies, net official financial flows from

3.11

in services

6.13

4.2 4.1, 4.2

per worker Undernourishment, prevalence of Unemployment incidence of long-term, total, male, and female

2.5

by level of educational attainment, primary, secondary, tertiary

2.5

total, male, and female

2.5

youth, male, and female

in agriculture

2.20

1.3, 2.10

W

Water access to improved source of, population with total urban and rural

UNICEF, net official financial flows from

6.13

3.3

2.18, 5.8 3.5

freshwater annual withdrawals

UNTA, net official financial flows from

6.13

UNRWA net official financial flows from

6.13

access to an improved water source

3.11 3.5

nitrogen dioxide

3.14

particulate matter

3.14

2011 World Development Indicators

3.5

for domestic use

3.5

for industry

3.5

total

3.5

flows

3.5

per capita

3.5

pollution—see Pollution, organic water pollutants

emissions, selected cities

434

3.5

for agriculture

internal renewable resources

Urban environment access to improved sanitation facilities

as share of internal resources

productivity

3.5

Women in development female-headed households

2.10

female population, as share of total

1.5

life expectancy at birth pregnant women receiving prenatal care

1.5 1.5, 2.19

teenage mothers

1.5

unpaid family workers

1.5

vulnerable employment

2.4

women in nonagricultural sector

1.5

women in parliaments

1.5

Workforce, firms offering formal training World Bank, net financial flows from

5.2 6.13

See also International Bank for Reconstruction and Development; International Development Association

2011 World Development Indicators

435

REGION MAP

The world by region

East Asia and Pacific American Samoa Cambodia China Fiji Indonesia Kiribati Korea, Dem. Rep. Lao PDR Malaysia Marshall Islands Micronesia, Fed. Sts. Mongolia Myanmar Palau Papua New Guinea Philippines Samoa Solomon Islands Thailand Timor-Leste Tonga Tuvalu Vanuatu Vietnam

Colombia Costa Rica Cuba Dominica Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Mexico Nicaragua Panama Paraguay Peru St. Kitts and Nevis St. Lucia St. Vincent and the Grenadines Suriname Uruguay Venezuela, RB

Europe and Central Asia Albania Armenia Azerbaijan Belarus Bosnia and Herzegovina Bulgaria Georgia Kazakhstan Kosovo Kyrgyz Republic Lithuania Macedonia, FYR Moldova Montenegro Romania Russian Federation Serbia Tajikistan Turkey Turkmenistan Ukraine Uzbekistan

Middle East and North Africa Algeria Djibouti Egypt, Arab Rep. Iran, Islamic Rep. Iraq Jordan Lebanon Libya Morocco Syrian Arab Republic Tunisia West Bank and Gaza Yemen, Rep.

Latin America and the Caribbean Antigua and Barbuda Argentina Belize Bolivia Brazil Chile

South Asia Afghanistan Bangladesh Bhutan India Maldives Nepal Pakistan Sri Lanka Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon

Cape Verde Central African Republic Chad Comoros Congo, Dem. Rep. Congo, Rep. Côte d'Ivoire Eritrea Ethiopia Gabon Gambia, The Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritania Mauritius Mayotte Mozambique Namibia Niger Nigeria Rwanda São Tomé and Principe Senegal Seychelles Sierra Leone Somalia South Africa Sudan Swaziland Tanzania Togo Uganda Zambia Zimbabwe High-income OECD Australia Austria * Belgium * Canada Czech Republic Denmark Estonia * Finland * France * Germany * Greece * Hungary Iceland Ireland * Israel Italy *

Japan Korea, Rep. Luxembourg * Netherlands * New Zealand Norway Poland Portugal * Slovak Republic * Slovenia * Spain * Sweden Switzerland United Kingdom United States Other high income Andorra Aruba Bahamas, The Bahrain Barbados Bermuda Brunei Darussalam Cayman Islands Channel Islands Croatia Cyprus * Equatorial Guinea Faeroe Islands French Polynesia Gibraltar Greenland Guam Hong Kong SAR, China Isle of Man Kuwait Latvia Liechtenstein Macao SAR, China Malta * Monaco Netherlands Antilles New Caledonia Northern Mariana Islands Oman Puerto Rico Qatar San Marino Saudi Arabia Singapore Taiwan, China Turks and Caicos Islands Trinidad and Tobago United Arab Emirates Virgin Islands (U.S.) * Member of the Euro area

The World Bank 1818 H Street N.W. Washington, D.C.

ISBN 978-0-8213-8709-2

20433 USA Telephone: 202 473 1000 Fax: 202 477 6391 Web site: data.worldbank.org SKU 18709

Email: [email protected]

The World Development Indicators • Includes more than 800 indicators for 155 economies • Provides definitions, sources, and other information about the data • Organizes the data into six thematic areas

WORLD VIEW

PEOPLE

ENVIRONMENT

Living standards and development progress

Gender, health, and employment

Natural resources and environmental changes

ECONOMY

STATES & MARKETS

GLOBAL LINKS

New opportunities for growth

Elements of a good investment climate

Evidence on globalization

Saved: 91 trees 29 million Btu of total energy 8,609 pounds of net greenhouse gases 41,465 gallons of waste water 2,518 pounds of solid waste