World Development Indicators 2007 (World Development Indicators)

07

WORLD DEVELOPMENT INDICATORS WORLD VIEW

STATES & MARKETS

ENVIRONMENT

GLOBAL LINKS

ECONOMY PEOPLE

Low income Afghanistan Bangladesh Benin Bhutan Burkina Faso Burundi Cambodia Central African Republic Chad Comoros Congo, Dem. Rep. Côte d'Ivoire Eritrea Ethiopia Gambia, The Ghana Guinea Guinea-Bissau Haiti India Kenya Korea, Dem. Rep. Kyrgyz Republic Lao PDR Liberia Madagascar Malawi Mali Mauritania Mongolia Mozambique Myanmar Nepal Niger Nigeria Pakistan Papua New Guinea Rwanda São Tomé and Principe Senegal Sierra Leone Solomon Islands Somalia Sudan Tajikistan Tanzania Timor-Leste Togo Uganda Uzbekistan Vietnam Yemen, Rep. Zambia Zimbabwe

Armenia Azerbaijan Belarus Bolivia Bosnia and Herzegovina Brazil Bulgaria Cameroon Cape Verde China Colombia Congo, Rep. Cuba Djibouti Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Fiji Georgia Guatemala Guyana Honduras Indonesia Iran, Islamic Rep. Iraq Jamaica Jordan Kazakhstan Kiribati Lesotho Macedonia, FYR Maldives Marshall Islands Micronesia, Fed. Sts. Moldova Morocco Namibia Nicaragua Paraguay Peru Philippines Samoa Serbia and Montenegro Sri Lanka Suriname Swaziland Syrian Arab Republic Thailand Tonga Tunisia Turkmenistan Ukraine Vanuatu West Bank and Gaza

Lower middle income Albania Algeria Angola

Upper middle income American Samoa Argentina Barbados

Belize Botswana Chile Costa Rica Croatia Czech Republic Dominica Equatorial Guinea Estonia Gabon Grenada Hungary Latvia Lebanon Libya Lithuania Malaysia Mauritius Mayotte Mexico Northern Mariana Islands Oman Palau Panama Poland Romania Russian Federation Seychelles Slovak Republic South Africa St. Kitts and Nevis St. Lucia St. Vincent and the Grenadines Trinidad and Tobago Turkey Uruguay Venezuela, RB High income Andorra Antigua and Barbuda Aruba Australia Austria Bahamas, The Bahrain Belgium Bermuda Brunei Darussalam Canada Cayman Islands Channel Islands Cyprus Denmark Faeroe Islands Finland France French Polynesia Germany

Greece Greenland Guam Hong Kong, China Iceland Ireland Isle of Man Israel Italy Japan Korea, Rep. Kuwait Liechtenstein Luxembourg Macao, China Malta Monaco Netherlands Netherlands Antilles New Caledonia New Zealand Norway Portugal Puerto Rico Qatar San Marino Saudi Arabia Singapore Slovenia Spain Sweden Switzerland United Arab Emirates United Kingdom United States Virgin Islands (U.S.)

INCOME MAP

The world by income

The world by income Low ($875 or less) Lower middle ($876–$3,465) Upper middle ($3,466–$10,725) High ($10,726 or more) No data

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

Classified according to World Bank estimates of 2005 GNI per capita

2007

WORLD DEVELOPMENT INDICATORS

Copyright 2007 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 2007 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, clockwise from top left, World Bank photo library, Alfredo Caliz/Panos Pictures, World Bank photo library, and Digital Vision. 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 www.worldbank.org/data ISBN 0-8213-6959-8 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 2007 on recycled paper with 30 percent post-consumer waste, 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: 93 trees 4,354 pounds of solid waste 33,908 gallons of waste water 8,169 pounds of net greenhouse gases 65 million BTUs of total energy

2007

WORLD DEVELOPMENT INDICATORS

PREFACE You can’t monitor development progress without good data. The point may seem obvious, but it bears repeating. What we know about development—successes and failures—depends on the availability and quality of data. Data are the evidence for evidence-based decisionmaking. When we talk about managing for development results, we are talking about using data to plan, implement, guide, and evaluate development programs. We won’t know when we have achieved the Millennium Development Goals unless we have the data to measure progress. Strong statistical systems, based on institutional autonomy, professional integrity, and commitment to high standards, provide the basis for producing credible statistics for informed decisionmaking. That is why we are working with our partners to improve international databases, which provide the data for World Development Indicators, and to strengthen national statistical systems, the ultimate source of the data. Three years ago in Marrakech, Morocco, the Second Roundtable on Managing for Development Results endorsed a new strategy for improving development statistics, the Marrakech Action Plan for Statistics (MAPS). Since then, countries and donor agencies have united behind those joint goals. Much has been accomplished. With support from the Partnership for Statistics in Development in the 21st Century (PARIS21), regional bodies, international agencies, and bilateral donors, 88 countries have adopted National Statistical Development Strategies to guide the maturation of their statistical systems. Many are also subscribers to the General Data Dissemination System. Based on these plans, countries and donors have begun to increase their investments in statistics. MAPS also called for actions to improve the quality and availability of data needed in the near term to measure progress on national development plans and the Millennium Development Goals. An Accelerated Data Program, piloted in six African countries, is demonstrating that even existing data sets can yield valuable information. Work on the next round of population and housing censuses has begun. The United Nations Statistics Division has initiated an intergovernmental process to increase support for censuses in developing countries. Along with censuses, surveys are a major source of development statistics. In 2005 the International Household Survey Network was formed to coordinate activities and provide tools for documenting and archiving surveys, thus ensuring that investments in surveys will continue to pay dividends into the future. All of these are important steps in building national and international statistical systems that respond to the demand for evidence to guide development. But more remains to be done, and the need is urgent. The challenges to us—national and international statisticians, donors, data users, and everyone concerned with measuring results—are threefold: • How to accelerate investment in statistics. • How to produce statistics that meet the needs of users. • And how to harmonize donor efforts in support of developing countries as they build their statistical systems. Building statistical systems is a long-term process. So is our commitment. As we plan for the future, we are learning from our experience and realizing the results of past investments.

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PREFACE This year the preliminary results of the International Comparison Program are being released, providing new comparisons of price levels for more than 140 countries. The program, the largest single data collection effort ever undertaken, is a salutary example of what can be accomplished through global partnership, technical innovation, and systematic attention to building local statistical capacity. When the final results become available in next year’s World Development Indicators, we will know more about the size of the world’s economy and the welfare of its people than ever before. And what we have learned by working together through the program will help us to manage new large-scale efforts to improve development statistics. As always, we welcome your comments and suggestions for making World Development Indicators, its databases, and related publications more useful to you. Shaida Badiee Director Development Data Group

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ACKNOWLEDGMENTS This book and its companion volumes, The Little Data Book and The Little Green Data Book, are prepared by a team led by Eric Swanson and comprising Awatif Abuzeid, Mehdi Akhlaghi, Azita Amjadi, Uranbileg Batjargal, David Cieslikowski, Sebastien Dessus, Richard Fix, Masako Hiraga, Kiyomi Horiuchi, Raymond Muhula, M.H. Saeed Ordoubadi, Brian Pascual, Sulekha Patel, Changqing Sun, and K.M. Vijayalakshmi, working closely with other teams in the Development Economics Vice Presidency’s Development Data Group. The CD-ROM development team included Azita Amjadi, Ramgopal Erabelly, Saurabh Gupta, Reza Farivari, and William Prince. The work was carried out under the management of Shaida Badiee. 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 provided overall design direction, editing, and layout, led by Meta de Coquereaumont, Bruce Ross-Larson, and Christopher Trott. Elaine Wilson created the graphics and typeset the book. Amy Ditzel, Laura Peterson Nussbaum, and Zachary Schauf provided copyediting, proofreading, and production assistance. Communications Development’s London partner, Peter Grundy of Peter Grundy Art & Design, provided art direction and design. Staff from External Affairs oversaw printing and dissemination of the book.

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TABLE OF CONTENTS FRONT Preface Acknowledgments Partners Users guide

v vii xii xx

1. WORLD VIEW Introduction Goal, targets, and indicators for the Millennium Development Goals

1 12

1s

Tables

1.1 1.2 1.3 1.4 1.5 1.6

Size of the economy Millennium Development Goals: eradicating poverty and improving lives Millennium Development Goals: protecting our common environment Millennium Development Goals: overcoming obstacles Women in development Key indicators for other economies

14

1t

18

1u 1v 1w

22 26 28 32

Text figures, tables, and boxes 1a Faster growth, less dispersion among developing economies in the last decade 1b Growth accelerated in low- and middle-income countries 1c Poor developing countries are not systematically catching up with richer ones 1d Countries that opened up to trade also performed better on growth 1e Price inflation dropped in most developing countries in the last decade 1f The worst growth performers have much higher costs to start a business 1g Best and worst growth performers in annual per capita GDP growth, 1995–2005 1h The number of poor people declined, mostly in East Asia and Pacific 1i Poverty rates are on the decline in South and East Asia 1j Inequality has increased in many countries, with or without growth 1k Changes in income growth and distribution both affect poverty reduction 1l Poverty reduction and per capita income growth performances are correlated 1m The worst poverty reduction performers record very poor income growth 1n Best and worst poverty reduction performers 1o Under-five mortality rates have improved almost everywhere 1p The proportion of births attended by skilled staff increased greatly in many countries

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1x 1y

2 2

1z 1aa

2 2 3 3 3 4 4 4 4

1bb 1cc 1dd 1ee 1ff 1gg 1hh 1ii 1.1a

5 5 5 6 6

1.2a 1.3a 1.4a

Countries with high initial mortality rates progress more slowly Under-five mortality reduction performance is associated with good growth performance Important synergies between health- and education-related Millennium Development Goals Performance in maternal health and under-five mortality are associated Best and worst performers in reducing child mortality Most countries are progressing in primary school completion The number of countries with large gender disparity gaps in school is falling rapidly Countries starting from low levels progress faster in primary school completion Countries starting from low levels improve gender parity more rapidly The worst gender parity performance is associated with poor school completion performance The worst performers on school completion were poor growth performers Best and worst primary school completion performers More than a billion people still lack access to safe drinking water Carbon dioxide emissions are mounting and accumulating in the atmosphere Access to water improved almost everywhere Growth and water access performance are not systematically associated Growth and carbon content reduction performance are correlated . . . . . . But not enough to claim that growth is good for mitigating growth in carbon emissions Best and worst water access performers Developing countries produce slightly less than half the world’s output Location of indicators for Millennium Development Goals 1–5 Location of indicators for Millennium Development Goals 6–7 Location of indicators for Millennium Development Goal 8

6 6 7 7 7 8 8 8 8 9 9 9 10 10 10 10 11 11 11 17 21 25 27

2. PEOPLE Introduction

3. ENVIRONMENT 35

Tables

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

Introduction

121

Rural population and land use Agricultural inputs Agricultural output and productivity Deforestation and biodiversity Freshwater Water pollution Energy production and use Energy efficiency and emissions Sources of electricity Urbanization Urban housing conditions Traffic and congestion Air pollution Government commitment Toward a broader measure of savings

126 130 134 138 142 146 150 154 158 162 166 170 174 176 180

Tables Population dynamics Labor force structure Employment by economic activity Children at work Unemployment Poverty 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 expenditure, services, and use Disease prevention coverage and quality Reproductive health Nutrition Health risk factors and public health challenges Health gaps by income and gender Mortality

40 44 48 52 56 60 66 70 74 78 82 86 90 92 96 100 104 108 112 116

Text figures, tables, and boxes 2a Child mortality has fallen in the past 25 years for countries at all incomes 35 2b Under-five mortality is 15 times higher in low-income countries than in high-income countries 36 2c Little reduction in risks for poor children 36 2d In Sierra Leone most deaths occur before age 5 36 2e A child born in Denmark can expect to live to be 78 36 2f A health gap becomes a life gap 37 2g Health inequalities by social, cultural, and geographic factors 37 2h Under-five mortality falls with rising income 37 2i Health inequalities in developing countries 37 2j Why do the poor receive and seek less healthcare than the rich? 38 2k Rich people use health services more than poor people 38 2l Some countries have reduced inequalities in use of professional healthcare in childbirth 38 2m Differences in healthcare spending contribute to global disparities 39 2n Where are healthcare workers hiding? 39 2o Public health spending benefits the rich most 39 2p Health shocks can push households into poverty 39 2.3a Lower wages and less rewarding employment opportunities mean higher risk of poverty for women 51 2.4a Child labor is an obstacle to education for all 55 2.6a Regional poverty estimates 63 2.12a Children from poorer families are less likely to complete their schooling 89 2.14a Differences in healthcare expenditures contribute to global disparities in health outcomes 95 2.20a Under-five mortality rates improve as mothers’ education levels rise 119

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

Text figures, tables, and boxes 3a Agriculture’s share in GDP—declining, but still more than a fifth in low-income economies 122 3b Agricultural productivity has increased, yielding more output for all 122 3c More people will experience water scarcity and water stress 123 3d Agriculture is the biggest consumer of water . . . 123 3e . . . and the least productive user 123 3f Irrigation has increased, demanding more water 123 3g Cereal yields have increased in most regions—East Asia has almost reached the high-income economies 124 3h Forested areas are shrinking in Latin America and Sub-Saharan Africa—recovering in East Asia 124 3i Agriculture accounts for a seventh of all greenhouse gas emissions 125 3j Less rain is falling in the Sahel, with dire consequences 125 3k Horn of Africa suffers floods after parching drought 125 3.1a What is rural? Urban? 129 3.2a Nearly 40 percent of land globally is used for agriculture 133 3.3a The five countries with the highest agricultural productivity 137 3.3b The 10 countries with the highest cereal yield in 2003–05— and the 10 with the lowest 137 3.5a The rural-urban divide in access to an improved water source 145 3.6a Emissions of organic water pollutants declined in most countries from 1990 to 2003, even among the top emitters 149 3.7a Energy use per capita varies widely among the top energy users 153 3.8a High-income countries contribute more than half of global carbon dioxide emissions 157 3.8b The five largest contributors to carbon dioxide emissions differ considerably in per capita emissions 157 3.9a Coal is still the major source of electricity in all income groups, with low-income countries increasingly relying on this source 161 3.10a Population of the world’s largest metropolitan areas in 1000, 1900, 2000, and 2015 165 3.11a Selected housing indicators for smaller economies 169 3.12a The 15 economies with the most expensive gasoline— and the 15 with the cheapest, 2006 173

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TABLE OF CONTENTS 4. ECONOMY

5. STATES AND MARKETS

Introduction

185

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, investment, and trade Central government finances Central government expenses Central government revenues Monetary indicators Exchange rates and prices Balance of payments current account External debt Debt ratios

188 190 194 198 202 206 210 214 218 222 226 230 234 238 242 246 250 254

Tables

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

259

Private sector in the economy Investment climate: enterprise surveys Business environment: Doing Business indicators Stock markets Financial access, stability, and efficiency Tax policies Defense expenditures and arms transfers Public policies and institutions Transport services Power and communications The information age Science and technology

264 268 272 276 280 284 288 292 296 300 304 308

Tables

Text figures, tables, and boxes 4a Developing economies increase their share of global output 4b Growth is accelerating in the low-income economies 4c Patterns of regional growth vary widely 4d Inflation is now less than 10 percent in all developing regions 4e Economies with high growth rates generally have lower rates of inflation 4f Top 10 economies with largest reserves 4g More reserves to cover debt 4.3a Manufacturing continues to show strong growth in East Asia 4.4a Developing economies’ share of world merchandise exports continues to expand 4.5a Top 10 developing country exporters of merchandise in 2005 4.6a Top 10 developing country exporters of commercial services in 2005 4.7a The mix of commercial service imports by developing countries is changing 4.9a Investment is rising rapidly in Asia 4.10a Fourteen developing economies had a cash deficit greater than 4 percent of GDP 4.11a Interest payments are a large part of government expenses for some developing countries 4.12a Rich countries rely more on direct taxes 4.15a Top 15 economies with the largest current account surplus— and top 15 economies with the largest current account deficit in 2005 4.16a External debt started to decline in the Sub-Saharan African economies in 2005 4.17a The debt burden of Sub-Saharan Africa rose slightly in 2005, after falling

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185 186 186 186 186 187 187 201 205 209 213 217 225 229 233 237

249 253 257

5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5.11 5.12

Text figures, tables, and boxes 5a Governance and growth go together 5b Criteria for measuring economic and sector policies and governance system 5c The IDA Resource Allocation Index is a key element of a country’s IDA performance rating 5d On public sector management, countries bunch around the middle 5e Strong performance on economic management, weaker on public sector management 5f Worldwide Governance Indicators—Six key dimensions of governance 5g Other selected sources of data for monitoring governance

260 260 261 262 262 262 263

6. GLOBAL LINKS

BACK

Introduction

313

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 Primary commodity prices Regional trade blocs Tariff barriers Global private 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 Net financial flows from multilateral institutions Movement of people Travel and tourism

316 320 324

Tables

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

Text figures, tables, and boxes 6a Trade growth outpaces GDP growth 6b Exports from developing countries have grown fast 6c Foreign direct investment leads resource flows to developing economies 6d Developing economies differ greatly in external resource flows 6e Aid flows are rising 6f Only 41 percent of aid finances development projects and general budget support 6g Fast growth in tourism, especially for low-income countries 6h Migration to developing economies accounts for almost half of all migrants 6.1a Private capital flows are rising, but they remain below the peak of 2000 6.2a Terms of trade are deteriorating for non-oil-exporting developing countries 6.3a Three regions account from more than 75 percent of exports to other developing regions, 2005 6.4a Imports from low- and middle-income economies to high-income economies vary considerably 6.6a Preferential regional trade agreements have a mixed impact on trade 6.8a Private capital flows to developing countries are rising 6.11a Official development assistance from non-DAC donors, 2001–05 6.12a The flow of bilateral aid from DAC members reflects global events and priorities 6.13a Maintaining financial flows from multilateral institutions to developing countries 6.14a High-skill workers in developing countries are increasingly emigrating to high-income countries 6.15a International tourism generated more than $2 billion a day in 2005

Primary data documentation Statistical methods Credits Bibliography Index of indicators

369 378 380 382 389

327 330 332 336 340 344 346 348 352 356 360 364 314 314 314 314 315 315 315 315 319 323 326 329 335 343 351 355 359 363 367

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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, 2007. 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 Technische Zusammenarbeit The Deutsche Gesellschaft für Technische Zusammenarbeit (GTZ) GmbH is a German government-owned corporation for international cooperation with worldwide operations. GTZ’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.gtz.de/.

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/.

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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 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. 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) was established to promote international monetary cooperation, facilitate the expansion and balanced growth of international trade, promote exchange rate stability, help establish a multilateral payments system, make the general resources of the IMF temporarily available to its members under adequate safeguards, and shorten the duration and lessen the degree of disequilibrium in the international balance of payments of members. For more information, see www.imf.org/.

International Telecommunication Union The International Telecommunication Union (ITU), a specialized agency of the United Nations, covers all aspects of telecommunication, from setting standards that facilitate seamless interworking of equipment and systems on a global basis to adopting operational procedures for the vast and growing array of wireless services and designing programs to improve telecommunication infrastructure in the developing world. The ITU is also a catalyst for forging development partnerships between government and private industry. 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. It is responsible for promoting science and engineering through almost 20,000 research and education projects. In addition, the NSF fosters the exchange of scientific information among scientists and engineers in the United States and other countries, supports programs to strengthen scientific and engineering research potential, and evaluates the impact of research on industrial development and general welfare. For more information, see www.nsf.gov/. 2007 World Development Indicators

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PARTNERS Organisation for Economic Co-operation and Development The Organisation for Economic Co-operation and Development (OECD) includes 30 member countries sharing a commitment to democratic government and the market economy. With active relationships with some 70 other countries, nongovernmental organizations, and civil society, 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 Labor 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 the 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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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 works with other UN bodies and with governments and nongovernmental organizations to improve children’s lives in more than 140 developing countries through community-based services in primary health care, basic education, and safe water and sanitation. 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 Educational, Scientific, and Cultural Organization, Institute for Statistics The United Nations Educational, Scientific, and Cultural Organization is a specialized agency of the United Nations that promotes “collaboration among nations through education, science, and culture in order to further universal respect for justice, for the rule of law, and for the human rights and fundamental freedoms . . . for the peoples of the world, without distinction of race, sex, language, or religion.” 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,

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PARTNERS 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/.

World Bank Group The World Bank Group is the world’s largest source of development assistance. Its mission is to fight poverty and improve the living standards of people in the developing world. It is a development bank, providing loans, policy advice, technical assistance, and knowledge sharing services to low- and middle-income countries to reduce poverty. The Bank promotes growth to create jobs and to empower poor people to take advantage of these opportunities. It uses its financial resources, trained staff, and extensive knowledge base to help each developing country onto a path of stable, sustainable, and equitable growth in the fight against poverty. The World Bank Group has 185 member countries. For more information, see www.worldbank.org/data/.

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. The WHO carries out a wide range of functions, including coordinating international health work; helping governments strengthen health services; providing technical assistance and emergency aid; working for the prevention and control of disease; promoting improved nutrition, housing, sanitation, recreation, and economic and working conditions; promoting and coordinating biomedical and health services research; promoting improved standards of teaching and training in health and medical professions; establishing international standards for biological, pharmaceutical, and similar products; and standardizing diagnostic procedures. For more information, see www.who.int/.

World Intellectual Property Organization The World Intellectual Property Organization (WIPO) is an international organization dedicated to helping to ensure that the rights of creators and owners of intellectual property are protected worldwide and that inventors and authors are thus recognized and rewarded for their ingenuity. WIPO’s main tasks include harmonizing national intellectual property legislation and procedures, providing services for international applications for industrial property rights, exchanging intellectual property information, providing legal and technical assistance to developing and other countries facilitating the resolution of private intellectual property disputes, and marshalling information technology as a tool for storing, accessing, and using valuable intellectual property information. A substantial part of its activities and resources is devoted to development cooperation with developing countries. 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.world-tourism.org/.

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2007 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/.

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 with a mission to encourage and promote development and maintenance of better and safer roads and road networks. It helps put in place technological solutions and management practices that provide maximum economic and social returns from national road investments. The IRF has a major role to play in all aspects of road policy and development worldwide. For governments and fi nancial institutions, the IRF provides a wide base of expertise for planning road development strategy and policy. For its members, the IRF is a business network, a link to external institutions and agencies and a business card of introduction to government offi cials and decisionmakers. For the community of road professionals, the IRF is a source of support and information for national road associations, advocacy groups, companies, and institutions dedicated to the development of road infrastructure. For more information, see www.irfnet.org/.

2007 World Development Indicators

xvii

PARTNERS Netcraft Netcraft’s work includes the provision of network security services and research data and analysis of the Internet. It is an 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 www.netcraft.com/.

PricewaterhouseCoopers PricewaterhouseCoopers provides industry-focused assurance, tax, and advisory services for public and private clients in corporate accountability, risk management, structuring and mergers and acquisitions, and performance and process improvement. For more information, see www.pwcglobal.com/.

Standard & Poor’s Emerging Markets Data Base Standard & Poor’s Emerging Markets Data Base (EMDB) is the world’s leading source for information and indices on stock markets in developing countries. It currently covers 53 markets and more than 2,600 stocks. Drawing a sample of stocks in each EMDB market, Standard & Poor’s calculates indices to serve as benchmarks that are consistent across national boundaries. Standard & Poor’s calculates one index, the S&P/IFCG (Global) index, that refl ects the perspective of local investors and those interested in broad trends in emerging markets and another, the S&P/IFCI (Investable) index, that provides a broad, neutral, and historically consistent benchmark for the growing emerging market investment community. 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/.

World Information Technology and Services Alliance The World Information Technology and Services Alliance (WITSA) is the global voice of the information technology industry. It is dedicated to advocating policies that advance the industry’s growth and development; facilitating international trade and investment in information technology products and services; strengthening WITSA’s national industry associations; and providing members with a broad network of contacts. WITSA also hosts the World Congress on Information Technology and other worldwide events. For more information, see www.witsa.org/.

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2007 World Development Indicators

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/.

2007 World Development Indicators

xix

USERS GUIDE Tables

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

coverage, practices, and definitions differ widely; and

The tables are numbered by section and display the

ing of amounts not allocated to countries may result

cross-country and intertemporal comparisons involve

identifying icon of the section. Countries and econo-

in discrepancies between subgroup aggregates and

complex technical and conceptual problems that can-

mies are listed alphabetically (except for Hong Kong,

overall totals. For further discussion of aggregation

not be resolved unequivocally. Data coverage may

China, which appears after China). Data are shown

methods, see Statistical methods.

not be complete because of special circumstances

for 152 economies with populations of more than

affecting the collection and reporting of data, such

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

as problems stemming from conflicts.

tables. Table 1.6 presents selected indicators for

Aggregate measures for regions

56 other economies—small economies with popu-

The aggregate measures for regions include only

the sources thought to be most authoritative, they

For these reasons, although data are drawn from

lations between 30,000 and 1 million and smaller

low- and middle-income economies (note that these

should be construed only as indicating trends and

economies if they are members of the International

measures include developing economies with popu-

characterizing major differences among economies

Bank for Reconstruction and Development (IBRD) or,

lations of less than 1 million, including those listed

rather than as offering precise quantitative mea-

as it is commonly known, the World Bank. The term

in table 1.6).

sures of those differences. Discrepancies in data

country, used interchangeably with economy, does

The country composition of regions is based on

presented in different editions of World Development

not imply political independence, but refers to any

the World Bank’s analytical regions and may differ

Indicators reflect updates by countries as well as

territory for which authorities report separate social

from common geographic usage. For regional clas-

revisions to historical series and changes in meth-

or economic statistics. When available, aggregate

sifications, see the map on the inside back cover and

odology. Thus readers are advised not to compare

measures for income and regional groups appear at

the list on the back cover flap. For further discussion

data series between editions of World Development

the end of each table.

of aggregation methods, see Statistical methods.

Indicators or between different World Bank publica-

Indicators are shown for the most recent year

tions. Consistent time-series data for 1960–2005

or period for which data are available and, in most

are available on the World Development Indicators

tables, for an earlier year or period (usually 1990 in

Statistics

this edition). Time-series data are available on the

Data are shown for economies as they were con-

Except where otherwise noted, growth rates are

World Development Indicators CD-ROM and in WDI

stituted in 2005, and historical data are revised to

in real terms. (See Statistical methods for information

Online.

reflect current political arrangements. Exceptions are

on the methods used to calculate growth rates.) Data

noted throughout the tables.

for some economic indicators for some economies

Known deviations from standard definitions or

CD-ROM and in WDI Online.

breaks in comparability over time or across countries

Additional information about the data is provided

are presented in fiscal years rather than calendar

are either footnoted in the tables or noted in About

in Primary data documentation. That section sum-

years; see Primary data documentation. All dollar fig-

the data. When available data are deemed to be

marizes national and international efforts to improve

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

too weak to provide reliable measures of levels and

basic data collection and gives country-level informa-

The methods used for converting national currencies

trends or do not adequately adhere to international

tion on primary sources, census years, fiscal years,

are described in Statistical methods.

standards, the data are not shown.

statistical methods and concepts used, and other background information. Statistical methods provides

Aggregate measures for income groups

technical information on some of the general calcula-

Country notes

tions and formulas used throughout the book.

•

Unless otherwise noted, data for China do not include data for Hong Kong, China; Macao, China;

The aggregate measures for income groups include 208 economies (the economies listed in the main

or Taiwan, China.

tables plus those in table 1.6) whenever data are

Data consistency, reliability, and comparability

available. To maintain consistency in the aggregate

Considerable effort has been made to standardize

measures over time and between tables, missing

the data, but full comparability cannot be assured,

data are imputed where possible. The aggregates

and care must be taken in interpreting the indicators.

from Serbia and Montenegro on June 3, 2006,

are totals (designated by a t if the aggregates include

Many factors affect data availability, comparability,

this edition of World Development Indicators con-

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

and reliability: statistical systems in many develop-

tinues to list and show data for Serbia and Monte-

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

ing economies are still weak; statistical methods,

negro together; any exceptions are noted. Data

xx

2007 World Development Indicators

•

Data for Indonesia include Timor-Leste through

•

Although Montenegro declared independence

1999 unless otherwise noted.

from 1999 onward for Serbia and Montenegro

Symbols

for most indicators exclude data for Kosovo, a

..

territory within Serbia that is currently under inter-

means that data are not available or that aggregates

national administration pursuant to UN Security

cannot be calculated because of missing data in the

Council Resolution 1244 (1999); any exceptions

years shown.

are noted. 0 or 0.0 means zero or small enough that the number would

Classification of economies

round to zero at the displayed number of decimal

For operational and analytical purposes the World

places.

Bank’s main criterion for classifying economies is gross national income (GNI) per capita (calculated

/

by the World Bank Atlas method). Every economy is

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

classified as low income, middle income (subdivided

time, usually 12 months, straddles two calendar

into lower middle and upper middle), or high income.

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

For income classifications see the map on the inside

fiscal year.

front cover and the list on the front cover fl ap. Lowand middle-income economies are sometimes

$

referred to as developing economies. The term is

means current U.S. dollars unless otherwise noted.

used for convenience; it is not intended to imply that all economies in the group are experiencing

>

similar development or that other economies have

means more than.

reached a preferred or final stage of development. Note that classifi cation by income does not neces-

<

sarily refl ect development status. Because GNI per

means less than.

capita changes over time, the country composition of income groups may change from one edition of World Development Indicators to the next. Once the

Data presentation conventions

classifi cation is fi xed 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 (2005 in this edition), all historical

•

A billion is 1,000 million.

data presented are based on the same country

•

A trillion is 1,000 billion.

grouping.

•

capita of $875 or less in 2005. Middle-income economies are those with a GNI per capita of more than $875 but less than $10,726. Lower middle-income

Figures in italics refer to years or periods other than those specified or to growth rates calculated

Low-income economies are those with a GNI per

for less than the full period specified. •

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

and upper middle-income economies are separated at a GNI per capita of $3,465. High-income econo-

The cutoff date for data is February 1, 2007.

mies are those with a GNI per capita of $10,726 or more. The 13 participating member countries of the European Monetary Union (EMU) are presented as a subgroup under high-income economies. Note that Slovenia joined the EMU on January 1, 2007.

2007 World Development Indicators

xxi

1

WORLD VIEW

Introduction

M

easuring development—in ways familiar and new To achieve the Millennium Development Goals by 2015 many countries need to quickly improve their economic growth and their education and health systems, their management of environmental resources, and their infrastructure for water, sanitation, telecommunications, and transportation. Over the last 10 years developing economies have grown faster than in any period since 1965—and even faster since 2000. While the global picture is dominated by the larger economies—Brazil, China, India, Russia, and South Africa, recently joined by the major oil exporters—more are now doing well and fewer have suffered severe recessions, raising average growth rates. Economic growth is a clear marker of development, and countries that grow usually reduce poverty. But if the fruits of growth are not widely shared many poor people can be left behind even as average incomes rise. Nor does economic growth guarantee that access to water will improve or that more children will attend school. But failing to grow almost always makes matters worse. In considering the recent progress of developing countries on many social, economic, and environmental indicators, the Millennium Development Goals set one standard for all countries. But country performance is influenced by many factors. One is the starting point. Countries starting from worse positions have the potential to make faster progress, as they may benefit from the experience and technologies of more advanced economies. But poor countries may also face unusual obstacles in reaching their development goals. In either case, comparing a country’s progress over the last decade with the average progress of those starting from a similar position can help to identify countries that have made exceptional progress—and those whose progress has been unexpectedly slow. This section compares the progress of developing countries measured by the rate of change of selected indicators after first taking into account countries’ starting points. The difference between actual progress and the average progress of countries starting from a similar position is referred to as country performance, and countries are classified as follows:

• • • •

Best performers are significantly above the average of countries with similar starting points. Good performers are above average, yet not significantly so in a statistical sense. Poor performers are below the average, yet not significantly so in a statistical sense. Worst performers are significantly below the average of countries with similar starting points.

Those that perform well on one indicator may not perform well on another. The patterns are complex, but they begin to highlight more of the diversity—and sometimes the commonality— of outcomes in development.

2007 World Development Indicators

1

Economic growth Per capita GDP growth accelerated in low- and middle-income

Although developing economies as a whole are catching

countries in the last decade (1995–2005), as more coun-

up with high-income economies, there is little evidence of

tries grew at a moderate pace and fewer experienced severe

convergence between low- and middle-income economies.

recessions (figure 1a). And it was systematically faster in de-

For them, the relationship between per capita growth rates

veloping countries than in high-income countries in the last

and initial levels of per capita GDP shows that lower initial

five years—for the first time since the de-colonization period

per capita GDP was not systematically associated with higher

(figure 1b).

per capita GDP growth (figure 1c). This tells us that coun-

Current projections suggest that developing countries

tries start out with roughly the same potential for economic

will continue to grow more rapidly than high-income ones in

growth. Differences in performance are likely to be associ-

the next 25 years. Based on these scenarios, the develop-

ated with policies and institutions that encourage productive

ing country share of the global economy could rise from 23

investment in human, social, and physical capital. But luck

percent of world GDP today to 31 percent in 2030, and devel-

also plays an important role, particularly in the small and

oping country average incomes could increase from 16 per-

poor countries, which are more sensitive to external shocks,

cent to 24 percent of those of high-income countries (World

good and bad: conflicts, terms of trade, and the like.

Bank, Global Economic Prospects 2007). But the income gap

Globalization’s intense pace in the last decade—in trade,

between developing and high-income economies will remain

finance, technology, ideas, and migration—has changed the

substantial, and the absolute difference in per capita incomes

external environment for countries. Most developing countries

will continue to widen.

have further integrated into world markets, notably through a reduction in trade barriers and transport costs. Here, trade integration is measured by the ratio of imports and exports of goods and services to GDP. For countries starting from

Faster growth, less dispersion among developing economies in the last decade Number of countries

1a

1985–95

1995–2005

12

20

8

15

4

10

0

5

–4

–7

–6

–5

–4

–3

–2 –1 0 1 2 3 4 Per capita GDP growth rate (%) Note: Based on 100 country observations. Source: World Bank staff calculations.

5

Growth accelerated in low- and middle-income countries

6

7 More than 7

1b

Annual growth in GDP per capita (%) 6 Low-income

Middle-income

4 3 2 1 High-income

0 –1 –2 1980

–8 100

1,000 10,000 Per capita GDP, 1995 (PPP $, log) Note: Based on 125 country observations. Source: World Bank staff calculations.

Countries that opened up to trade also performed better on growth

1990

Note: Based on market exchange rates. Source: World Bank staff calculations.

2007 World Development Indicators

1995

1d

–0.2 –0.4 –0.6 –0.8 –1.0 -–1.2 Worst

1985

100,000

Per capita GDP growth performance (percentage points) 0.6 0.4 0.2 0.0

7 5

1c

Per capita GDP growth rate, 1995–2005 (%)

25

0

2

Poor developing countries are not systematically catching up with richer ones

2000

2005

Poor Good Best Trade integration performance Note: Based on 109 country observations. Performance is the difference between actual rate of change and average rate of change of countries starting from similar positions in trade integration or per capita GDP. Trade integration is measured by the ratio of imports and exports of goods and services to GDP. Source: World Bank staff calculations.

similar positions, countries integrating less rapidly recorded

Country growth performance is benchmarked against the

much lower per capita GDP growth (figure 1d). But that does

average growth rate for countries that started with a similar

not mean that trade integration necessarily causes growth.

per capita GDP in 1995 (in purchasing power parity terms).

Other factors, such as gains in competitiveness caused

Because initial levels of per capita GDP had little influence

by domestic policies, can cause both faster growth and

on growth rates over the period, potential average growth is

increased trade.

almost identical for all countries (figure 1g). The best and

Macroeconomic management also improved in the developing world, reflected in the sharp drop in the number of coun-

worst performers, which significantly deviated from averages in one direction or the other, are marked with an asterisk.

tries with very high price inflation (figure 1e). The best growth

Among rapidly growing countries, many are in Eastern

performers recorded average annual inflation of 12 percent

Europe or are oil exporters. One can also find some post-

over the last decade—worst performers, 29 percent.

conflict countries. At the slow end of the spectrum are counhamper

tries that experienced major conflicts or financial crises in

growth. The cost of starting a private business, as a percent-

Cumbersome

business

environments

also

the last decade, are landlocked, or are far from major trade

age of per capita income, is an indicator of the opportunity

routes. Most of them are located in Sub-Saharan Africa.

for entrepreneurs to develop new economic activities and to compete with existing businesses, an important force driving economic growth. That cost varies from less than 5 percent to a striking 1,440 percent—or 14 years of per capita income in 2005. Countries that performed worst on growth in the last decade also had much higher startup costs than other countries in 2005 (figure 1f). Price inflation dropped in most developing countries in the last decade Number of countries

1e

1985–95

1995–2005

50

Best and worst growth performers in annual per capita GDP growth, 1995–2005 Per capita GDP growth performance (percentage points)

Actual growth

1g

Average growth of countries starting from similar position

10 40 8 30 6 20 4 10 2 0 Hungary

Tajikistan

Russian Federation

Botswana

Albania

Vietnam* Eritrea*

Cambodia

Trinidad and Tobago

Djibouti*

Chad*

Mozambique* Côte d’Ivoire*

Vanuatu*

Lithuania* Paraguay*

Venezuela, RB*

Belarus*

Georgia*

Burundi

China* Haiti

Gabon*

Latvia* Central African Rep.

Estonia*

Armenia*

Kazakhstan*

Uruguay

Business startup costs as share of per capita income, 2005 (%) 350

4

Madagascar

1f

Azerbaijan*

0 Bosnia and Herzegovina*

The worst growth performers have much higher costs to start a business

More than 20

Niger

5–10 11–20 Average annual inflation (%) Note: Based on 107 country observations. Source: World Bank staff calculations.

Honduras

Less than 5

2

0 Worst

Poor Good Best Per capita GDP growth performance, 1995–2005 Note: Based on 109 country observations. Performance is the difference between actual growth and average growth of countries starting from similar positions in per capita GDP. Source: World Bank staff calculations.

Solomon Islands*

–6

50

Zimbabwe*

–4

100

Guinea-Bissau*

150

Congo, Rep.*

–2

200

Congo, Dem. Rep.*

250

Togo

0

300

Note: Based on 125 country observations. Asterisks indicate performers that significantly deviated, positively or negatively, from average per capita GDP growth of countries with similar starting points. Source: World Bank staff calculations.

2007 World Development Indicators

3

Poverty reduction The number of people living on less than $1 a day in develop-

The responsiveness of poverty to growth depends on the

ing countries fell by more than 260 million over 1990–2004,

distribution of income (or consumption) and how it changes.

thanks in large part to massive poverty reduction in China. In

Many factors influence how the benefits of growth are shared:

contrast, the number of poor people continued to increase in

health, education, infrastructure, gender parity, social safety

Sub- Saharan Africa, rising by almost 60 million (figure 1h). In

nets, rule of law, political voice and participation, and access

turn, the share of the population in Sub-Saharan Africa living

to markets, technology, information, and credit (World Bank

on less than $1 a day dropped from 47 percent in 1990 to

2005d). In the last decade poverty reduction was not always

41 percent in 2004 (figure 1i).

or everywhere commensurate with income growth. In some

The Millennium Development Goal of halving the pro-

countries and regions, inequality worsened, as poor people

portion of poor people is still within reach at the worldwide

did not reap the fruits of economic expansion, lacking oppor-

level—with a projected decline from 29 percent to 10 percent

tunities to do so.

between 1990 and 2015. But many countries will most likely

Fifty-nine countries with comparable $1 or $2 a day pov-

not reach it, particularly those in Sub-Saharan Africa, where

erty data measured at two points in time (with a gap of at

average poverty rates remain above 40 percent, raising con-

least 10 years) over the last two decades show that growth

cerns of widening inequalities between regions.

and changes in income distribution can reinforce or offset their effects on poverty reduction (figures 1j and 1k). In 26 cases income growth was accompanied by increased inequality, and in 20 more income distribution worsened as average incomes fell.

The number of poor people declined, mostly in East Asia and Pacific

1h

Inequality has increased in many countries, with or without growth

Change in the number of poor people, 1990–2004 (millions) 100 50

Negative income growth and increasing inequalities 20 countries (34%)

0

1j

Positive income growth and decreasing inequalities 10 countries (17%)

–50 –100 –150 –200 –250 –300

Negative income growth and decreasing inequalities 3 countries (5%)

–350 East Asia & Pacific

Europe & Central Asia

Latin America Middle East & & Caribbean North Africa

South Asia

Positive income growth and increasing inequalities 26 countries (44%)

Sub-Saharan Africa Note: Based on 59 country observations. Source: World Bank staff calculations.

Source: World Bank staff calculations.

Poverty rates are on the decline in South and East Asia

1i

Share of population living on less than $1 a day (%)

Changes in income growth and distribution both affect poverty reduction

1k

Change in poverty due to change in income distribution (percentage points) 2

60

Poverty increase

50

Sub-Saharan Africa

40

1

South Asia

0

30 East Asia & Pacific

20

–1

Latin America & Caribbean

10

Poverty decrease

Middle East & North Africa

Europe & Central Asia

0

–2 –1 0 1 2 3 4 Change in poverty due to growth (percentage points) Note: Based on 59 country observations. Source: World Bank staff calculations. –4

1981

1984

1987

1990

1993

Source: World Bank staff calculations.

4

–2

2007 World Development Indicators

1996

1999

2002

2004

–3

5

6

But this is not to say that growth is bad for poverty reduc-

Countries are ranked here by poverty reduction in the

tion. In 17 cases the contribution of growth to poverty reduc-

most recent 10-year period with data (figure 1n; periods vary

tion surpassed the negative impact of worsening inequality,

from country to country depending on the availability of pov-

and in another 11 cases reduction in inequality added to the

erty surveys). Also shown is the average poverty reduction

poverty-reducing effect of positive growth. In only one case—

of countries starting from a similar initial poverty rate. The

out of 60—was poverty reduced despite negative income

best and worst performers, which significantly deviated from

growth.

expectations in one direction or the other, are marked with

Looking at the relationship between countries’ per capita

an asterisk.

income growth and performance in reducing $1 a day pov-

There is great diversity in the characteristics of good per-

erty (controlling for starting points) also suggests a posi-

formers. Among them are low- and middle-income countries

tive and significant statistical relationship between the two

from most regions and with varying population sizes. Note

(figure 1l).

too that the best and worst performers are not necessarily

The worst poverty reduction performers recorded particu-

the countries that recorded the largest absolute changes in

larly weak income growth performance (figure 1m). But the

poverty rates. Mauritania, for example, recorded a substan-

distinction among the three other groups of performers (poor,

tial reduction but still fell short of the average performance

good, and best) is less pronounced. This suggests that the

of countries with similar initial poverty rates. Mexico experi-

relationship between income growth and poverty reduction is

enced a smaller poverty reduction but significantly exceeded

more diverse when the economy is not in deep recession. In

the average benchmark.

other words, income growth is necessary but may not be sufficient for sustained poverty reduction.

Poverty reduction and per capita income growth performances are correlated

1l

Poverty reduction performance (percentage points) 15

Best and worst poverty reduction performers

1n

Absolute changes in the poverty headcount index ($1 a day, PPP) per year (%) 2

10

Actual progress

Average progress of countries starting from similar position

5 0

1

–5

Brazil 1993–2003

Kyrgyz Republic 1993–2003* Lao PDR 1992–2002*

Nepal 1985–2004

India 1987–99

Indonesia 1987–2002 Turkmenistan 1988–98*

Honduras 1986–99

China 1990–2001 Paraguay 1990–2002

–3

2

The worst poverty reduction performers record very poor income growth

1m

–3 Best

Note: Based on 41 country observations. Performance is the difference between actual rate of change and average rate of change of countries starting from similar positions in poverty rates or per capita incomes. Source: World Bank staff calculations.

Moldova 1988–2001*

–2

Uzbekistan 1988–2000*

–1

Nigeria 1993–2003

0

Rwanda 1985–2000

1

Peru 1990–2002

Per capita income growth performance (percentage points) 3 2 1 0 –1 –2 –3 –4 –5 –6 –7 Worst Poor Good Poverty reduction performance

–2

Mauritania 1987–2000

–8 –6 –4 –2 2 4 6 8 0 Per capita income growth performance (percentage points) Note: Based on 41 country observations. Performance is the difference between actual rate of change and average rate of change of countries starting from similar positions in poverty rates or per capita incomes. Source: World Bank staff calculations.

Guatemala 1989–2002

–1 –10

Madagascar 1980–2001

–20 –12

Mexico 1992–2002*

–15

Bolivia 1991–2002

0

–10

Note: Based on 41 country observations. Asterisks indicate performers that signifi cantly deviated, positively or negatively, from average rate of change in poverty rate of countries with similar initial position over the period indicated. Source: World Bank staff calculations.

2007 World Development Indicators

5

Health More than 10 million children in developing countries die be-

Performance in reducing child mortality is measured by

fore the age of five every year, mostly from preventable ill-

progress from a given starting position. Worrying—and unlike

nesses. Child mortality has declined in every region since

other development goals—countries with high initial mortal-

1990 (figure 1o), but progress is slow: only 35 countries

ity rates face greater difficulties in reducing them (in rela-

are on track to meet the Millennium Development Goal of

tive terms) than do countries starting from more favorable

reducing under-five mortality by two-thirds between 1990 and

positions (figure 1q). HIV/AIDS and other communicable dis-

2015. Progress is particularly slow in Sub-Saharan Africa,

eases are probably behind this, as countries with higher HIV

where AIDS, malaria, and malnutrition are driving up mortal-

prevalence rates record significantly lower reductions in child

ity rates.

mortality. Countries with high under-five mortality rates are

Improving maternal health, itself a goal, is a powerful instrument for reducing child mortality. More than 500,000

to curb.

women in developing countries die in childbirth each year,

Economic growth is associated with improving mortality

and at least 10 million suffer injuries, infections, and disabili-

outcomes. On average, good and best performers in reduc-

ties. High mortality results from malnutrition, frequent preg-

ing under-five mortality had significantly higher growth per-

nancies, and inadequate healthcare during pregnancy and

formance than did poor and worst performers (figure 1r).

delivery. Women are receiving better care during childbirth,

Accordingly, country case studies emphasize the influence of

with the proportion of births attended by skilled health staff

poverty in determining child mortality. Because poor children

going up from 60 percent to 70 percent between 1990 and

are more likely to be malnourished and to receive less health-

2004 (figure 1p). Countries in Africa and South Asia neverthe-

care, they are more exposed to the risk of dying before the

less lag behind, with much lower ratios.

age of five.

Under-five mortality rates have improved almost everywhere

1o

Number of countries

1990

2004

40

Countries with high initial mortality rates progress more slowly

1q

Change in under-five mortality rate (percentage points) 10 8 6 4 2

35 30 25 20

0 –2 –4 –6 –8 –10

15 10 5 0 Less than 10 10–25

26–50 51–100 101–150 151–200 More than 200 Under-five mortality rate (per 1,000) Note: Based on 147 country observations. Source: World Bank staff calculations.

The proportion of births attended by skilled staff increased greatly in many countries Number of countries

1986–95

1p 1999–2005

25 20 15 10 5 0 Less than 10

10–30 31–50 51–70 71–90 More than 90 Share of births attended by skilled health staff (%) Note: Based on 66 country observations. Source: World Bank staff calculations.

6

also often countries where malaria is prevalent and difficult

2007 World Development Indicators

1

10 100 Initial under-five mortality rate (per 1,000, log) Note: Based on 147 country observations. Source: World Bank staff calculations.

1,000

Under-five mortality reduction performance is associated with good growth performance Per capita GDP growth performance (percentage points) 10 8 6 4 2 0 –2 –4 –6 –8 –10 Worst Poor Good Under-five mortality reduction performance

1r

Best

Note: Based on 116 country observations. Performance is the difference between actual rate of change and average rate of change of countries starting from similar positions in under-fi ve mortality rates or per capita GDP. Source: World Bank staff calculations.

Performance in reducing under-five mortality rates is sig-

Countries are ranked here by their reduction in under-five

nificantly associated with education (primary school comple-

mortality rates over 1990–2004 (figure 1u). Also shown is

tion) and gender (equal access to schooling), suggesting

the average reduction of countries starting from a similar

that there are synergies among the Millennium Development

position. The best and worst performers, which far exceeded

Goals (figure 1s).

averages in one direction or the other, are marked with an

The relationship between per capita GDP growth perfor-

asterisk.

mance and improvements in maternal healthcare performance

Most of the worst performers are in Sub-Saharan Africa,

(as measured by the proportion of births attended by skilled

where HIV is rampant, particularly in the east and south. But

health staff) is not straightforward—no direct statistical rela-

Sub-Saharan Africa also hosts some of the countries that

tionship can be observed between the two. But performance

recorded the largest drops in under-five mortality. In South

in improving maternal healthcare is strongly associated with

Asia 4 of the 8 countries are among the 10 countries that

performance in reducing under-five mortality (figure 1t). This

recorded the largest improvements in mortality rates. Three

might not reflect any direct causal relationship between these

of them are among the best performers, after accounting for

two indicators. Rather, it could reflect the impact of health

their starting positions. Iraq, starting from a favorable initial

infrastructure and policies on these two indicators.

position, saw its under-five mortality rate grow from 50 to 125 per 1,000 over the period 1990–2004.

Important synergies between health- and education-related Millennium Development Goals

1s

Under-five mortality reduction performance (percentage points) 10

Best and worst performers in reducing child mortality

1u

Absolute annual changes in mortality (per 1,000 children ages 1–5), 1990–2004 0

8 6

Actual progress

Average progress of countries starting from similar position

–1

4

–2

2

Malawi

Maldives* Iraq*

Nepal

Bangladesh*

Botswana*

–9

Egypt, Arab Rep.*

–8

Lao PDR*

–7

Mozambique

–6

–6 –4 –2 2 4 6 0 Primary school completion performance (percentage points) Note: Based on 70 country observations. Performance is the difference between actual rate of change and average rate of change of countries starting from similar positions in under-fi ve mortality rates or primary school completion rates. Source: World Bank staff calculations.

Guinea

–5

–6 –8

Bhutan*

–4

–4

Timor-Leste*

–3

0 –2

6 5

Performance in maternal health and under-five mortality are associated

1t

4 3

Under-five mortality reduction performance (percentage points) 1.0

2

0.5

1

0.0

0

–0.5

–3.0 Worst

Poor Good Maternal health performance

Best

Note: Based on 66 country observations. Performance is the difference between actual rate of change and average rate of change of countries starting from similar positions in maternal healthcare or under-fi ve mortality rates. Source: World Bank staff calculations.

Zimbabwe*

Swaziland*

Côte d'Ivoire*

Equatorial Guinea

–2.5

Rwanda

–3

–2.0

Cambodia*

–2

–1.5

Central African Republic

–1.0

Kenya*

–1

Note: Based on 147 country observations. Asterisks indicate performers that significantly deviated, positively or negatively, from average rate of change in under-fi ve mortality rate of countries with similar initial position. Source: World Bank staff calculations.

2007 World Development Indicators

7

Education and gender As a result of significant progress over the last decade, the

The ability of countries to raise their primary school

average primary completion rate has risen from 62 percent

completion rates in the last decade was determined largely

to 72 percent (figure 1v). But even at this pace Sub-Saharan

by their starting point. Countries with lower initial primary

Africa and South Asia may not reach the Millennium Devel-

completion rates made faster progress (figure 1x), probably

opment Goals target of having all children of relevant age

reflecting the fact that it becomes more difficult and costly to

complete primary school by 2015. In 2001–02 it was esti-

enroll and keep all children in school as the number of those

mated that about 100 million primary-school-age children

left out falls. Country case studies suggest that girls, poor

were not attending school, three-quarters of them in these

children, and children living in rural areas are less likely to

two regions.

complete schooling. These are the areas where faster prog-

Beyond the necessity of educating all children, eliminat-

ress must be made to achieve education for all.

ing discrimination against girls’ participation in school is a

Improvements in gender parity in school are also signifi -

powerful instrument for empowering half the world’s people,

cantly associated with initial conditions. On average coun-

improving the health of children, and reducing poverty. Prog-

tries starting with greater initial gender disparity have made

ress in eliminating gender disparities in primary and second-

faster progress (figure 1y).

ary school has been remarkable in the last decade (figure

When all children are enrolled and complete school, there

1w). On average the deviation from perfect parity (a gender

will be no gender disparity in school. Over the last decade the

parity index of 100 percent) shrank from 14 percent in 1991

number of countries in which the number of boys in primary

to 8 percent in 2003–05.

and secondary schools exceed that of girls by more than 40 percent (a gender parity index below 60 percent) fell—from

Most countries are progressing in primary school completion

1v

Number of countries

1994

2004

Countries starting from low levels progress faster in primary school completion

1x

Growth in school completion rate (percentage points) 15

35 30

10

25 5

20 15

0

10 –5 5 –10

0 Less than 20

20–40 41–60 61–80 Primary school completion rate (%) Note: Based on 68 country observations. Source: World Bank staff calculations.

More than 80

The number of countries with large gender disparity gaps in school is falling rapidly Number of countries

1991

1w 2003–05

60

40 60 80 Initial primary school completion rate (%) Note: Based on 70 country observations. Source: World Bank staff calculations. 0

20

Countries starting from low levels improve gender parity more rapidly

100

1y

Growth in gender parity index (percentage points) 4 3

50

2

40

1

30

0 20

–1

10

–2

0 Less than 60

60–70 71–80 81–90 91–95 More than 95 Gender parity index at school (%) Note: Based on 97 country observations. The gender parity index is equal to 100 minus the relative excess or deficit of boys over girls in primary and secondary school. Source: World Bank staff calculations.

8

2007 World Development Indicators

–3 40

60 80 100 Initial gender parity index at school (%) Note: Based on 97 country observations. The gender parity index is equal to 100 minus the relative excess or deficit of boys over girls in primary and secondary school. Source: World Bank staff calculations.

17 (of 97) to 3. And the number of countries with gender par-

Countries are ranked here by their primary school com-

ity index above 90 percent increased from 54 to 69. But the

pletion progress in the last decade (figure 1bb). Also shown

relationship between school completion and improvements in

is the average progress of countries starting from a similar

gender parity performance (accounting for initial conditions)

position. The best and worst performers, which far exceeded

appears to be more pronounced and uniform on the negative

averages in one direction or the other, are marked with an

side than it is on the positive side (figure 1z). Countries that

asterisk.

most improved their gender parity index did not record sig-

The two groups of performers, best and worst, both

nificantly higher school completion performances. But coun-

include a large number of Sub-Saharan African countries,

tries in which gender parity declined the most were countries

illustrating the diversity of performance in the region. Devel-

where school completion performance was also particularly

oping countries improved their primary completion rates by 1

poor, possibly reflecting the fact that dropout rates are higher

percentage point every year on average over the last decade

for girls than for boys during difficult periods.

or so. The best performers all recorded yearly increases

There is not a statistically significant correlation between

exceeding 2.8 percentage points.

performance in per capita GDP growth and primary school completion. While the relationship shows up at the extremes— the best and worst school completion performers record very distinct growth performances—the growth performance of poor school completion performers cannot be clearly distinguished from that of good performers (figure 1aa).

The worst gender parity performance is associated with poor school completion performance 1z School completion performance (percentage points) 0.5

Best and worst primary school completion performers Absolute changes in school completion rate per year, various periods (percentage points) 4

0.0

1bb

Actual progress

Average progress of countries starting from similar position

–0.5 3

–1.0 –1.5

2

–2.0 1

–2.5 Best

Lao PDR, 1994–2004

Nicaragua, 1994–2004

Benin, 1994–2004

Guinea, 1994–2004* Trinidad and Tobago, 1993–2003

Dominican Republic, 1994–2004 Ukraine, 1991–2001

Morocco, 1994–2004

Mali, 1994–2004* Tanzania, 1994–2005

2

Jamaica, 1993–2004

Cape Verde, 1994–2004

Per capita GDP growth performance (percentage points) 0.2

Macedonia, FYR, 1994–2004

1aa

Ethiopia, 1995–2005*

The worst performers on school completion were poor growth performers

Turkey, 1991–2004

0

Note: Based on 58 country observations. Performance is the difference between actual rate of change and average rate of change of countries starting from similar positions in gender parity or primary school completion rates. Source: World Bank staff calculations.

Togo, 1994–2004*

Poor Good Gender parity performance

Iran, Islamic Rep., 1994–2004

Worst

1

0.1

–0.3

–2

–0.4 –0.5 Worst

Poor Good Primary school completion performance

Best

Note: Based on 67 country observations. Performance is the difference between actual rate of change and average rate of change of countries starting from similar positions in primary school completion rates or per capita GDP. Source: World Bank staff calculations.

Burundi, 1993–2004*

–1

–0.2

Rwanda, 1992-2004*

–0.1

Zimbabwe, 1993–2003

0

0.0

Note: Based on 70 country observations. Asterisks indicate performers that signifi cantly deviated, positively or negatively, over the period indicated from the average rate of change in primary school completion rate of countries with similar initial position. Source: World Bank staff calculations.

2007 World Development Indicators

9

Environment Access to improved water sources and emissions of car-

Between 1990 and 2004 the proportion of people in

bon dioxide are among the indicators that the international

developing countries with access to an improved water source

community uses to monitor progress toward environmental

increased from 73 percent to 80 percent, and the number of

sustainability.

countries with more than half the population lacking access

Today, more than a billion people in developing countries lack access to an adequately protected source of water close

fell from 24 to 11 (figure 1ee). Countries starting from lower positions advanced faster.

to their dwellings (figure 1cc). Progress to improve access has

Economic activity, agriculture, and industry in particular

been significant in the last decade, but probably insufficient

compete with human needs for access to water sources. But

in Africa to meet the 2015 Millennium Development Goal tar-

greater wealth and urbanization allow more of the popula-

get of halving the proportion of people in 1990 without sus-

tion to connect to safe drinking water networks. The data

tainable access to safe drinking water.

do not reveal a statistically significant correlation between

The role of carbon dioxide in climate change is now well

water access and growth performance overall. But the worst

documented, but the use of carbon-based energy has addi-

growth performers distinctively record poor water access

tional effects on human health through local air pollution. Yet

performance (figure 1ff). Such countries may also be those

emissions mount as countries grow economically, unless they

with degraded water infrastructure and poor management

reduce the carbon content of their economic activity through

capacity.

technological progress or shift away from carbon-intensive production and consumption (figure 1dd).

More than a billion people still lack access to safe drinking water

1cc

Millions of people

1990

2004

500

Access to water improved almost everywhere Number of countries 80

1ee 1990

2004

70 400

60 50

300

40 200

30 20

100

10 0

0 Europe & Central Asia

East Asia & Pacific

Latin America Middle East & Caribbean & North Africa

South Asia

Sub-Saharan Africa

Source: World Bank staff calculations.

Carbon dioxide emissions are mounting and accumulating in the atmosphere

1dd

Billions of tons

Less than 20 20–40 41–60 61–80 More than 80 Share of population with access to improved water source (%) Note: Based on 113 country observations. Source: World Bank staff calculations.

Growth and water access performance are not systematically associated

1ff

Water access performance (percentage points) 0.2

25

0.1 20 Low- and middle-income countries

0.0 –0.1

15

–0.2 –0.3

10

High-income countries

–0.5

5

Worst

0 1960

1965

1970

1975

1980

Source: World Bank staff calculations.

10

–0.4

2007 World Development Indicators

1985

1990

1995

2002

Poor Good Per capita GDP growth performance

Best

Note: Based on 84 country observations. Performance is the difference between actual rate of change and average rate of change of countries starting from similar positions in per capita GDP or water access. Source: World Bank staff calculations.

In the next decades all countries need to make impor-

Countries are ranked here by their progress in water

tant efforts to reduce their carbon emissions. In developing

access in 1990–2004. Also shown is the average progress

economies such a commitment might be perceived as at

of countries starting from a similar position (figure 1ii). The

odds with that of fostering growth. But recent history sug-

best and worst performers, which far exceeded averages in

gests that developing countries that have grown the fastest

one direction or the other, are marked with an asterisk.

also made the greatest reductions in the carbon content of

A number of poor performers suffered from particularly

their economic activities (measured by carbon dioxide emis-

difficult geographical constraints—small Pacific island or des-

sions per unit of GDP in PPP terms; figure 1gg). It is likely

ert countries with low rainfall, for instance. But others, also

that growth was accompanied by more rapid adoption of new,

facing difficult geographical constraints, greatly improved

more energy efficient technologies and a shift toward less

access to safe water. The best and worst performers are

carbon-intensive production and consumption.

not necessarily countries that registered the largest abso-

This is not enough, however, to claim that growth is good

lute changes. Indeed, the initial rate of access to improved

for mitigating carbon dioxide emissions: the best growth

water sources can alone explain almost half the differences

performers recorded much higher growth in carbon dioxide

in progress across countries. Accounting for starting points

emissions than other groups (figure 1hh). Technical efficiency

thus portrays a different picture of relative performances

gains were not sufficient to compensate for the growth in

across countries.

output.

Growth and carbon content reduction performance are correlated . . .

1gg

Carbon content performance (percentage points) 15

Best and worst water access performers

1ii

Annual changes in share of people with permanent access to improved water source, 1990–2004 (%) 2.8

10

Actual progress

Average progress of countries starting from similar position

5 2.1

0 –5 –10

1.4

–15 –10

0.7

Myanmar

Ecuador

Ghana

Uzbekistan

Maldives

Chad Yemen, Rep.

Marshall Islands

Paraguay Samoa

Central African Rep.

Namibia* Philippines

1hh

Algeria

Malawi* Iraq

Burkina Faso

Afghanistan*

. . . But not enough to claim that growth is good for mitigating growth in carbon emissions

0.0

Trinidad and Tobago

–5 5 10 0 GDP growth performance (percentage points) Note: Based on 122 country observations. Performance is the difference between actual rate of change and average rate of change of countries starting from similar positions in carbon content levels or GDP. Source: World Bank staff calculations.

1.4

Carbon dioxide emissions growth performance (percentage points) 5 4

0.7

3 2

0 –0.7

–1 –2 Worst

Poor Good GDP growth performance

Best

Note: Based on 122 country observations. Performance is the difference between actual rate of change and average rate of change of countries starting from similar positions in GDP or carbon dioxide emissions. Source: World Bank staff calculations.

–1.4

Comoros

0.0

1

Note: Based on 102 country observations. Asterisks indicate performers that signifi cantly deviated, positively or negatively, from average rate of change in water access of countries with similar initial position. Source: World Bank staff calculations.

2007 World Development Indicators

11

Goals, targets, and indicators Goals and targets from the Millennium Declaration Indicators for monitoring progress Goal 1

Eradicate extreme poverty and hunger

Target 1

Halve, between 1990 and 2015, the proportion of people whose income is less than $1 a day

1 Proportion of population below $1 (PPP) a daya 1a Poverty headcount ratio (percentage of population below the national poverty line) 2 Poverty gap ratio [incidence × depth of poverty] 3 Share of poorest quintile in national consumption

Target 2

Halve, between 1990 and 2015, the proportion of people who suffer from hunger

4 5

Goal 2

Achieve universal primary education

Target 3

Ensure that, by 2015, children everywhere, boys and 6 girls alike, will be able to complete a full course of 7 8 primary schooling

Prevalence of underweight children under five years of age Proportion of population below minimum level of dietary energy consumption Net enrollment ratio in primary education Proportion of pupils starting grade 1 who reach grade 5b Literacy rate of 15- to 24-year-olds

Goal 3

Promote gender equality and empower women

Target 3

Ensure that, by 2015, children everywhere, boys and 6 girls alike, will be able to complete a full course of 7 8 primary schooling

Target 4

Eliminate gender disparity in primary and secondary education, preferably by 2005, and in all levels of education no later than 2015

Goal 4

Reduce child mortality

Target 5

Reduce by two-thirds, between 1990 and 2015, the under-five mortality rate

Goal 5

Improve maternal health

Target 6

Reduce by three-quarters, between 1990 and 2015, 16 Maternal mortality ratio the maternal mortality ratio 17 Proportion of births attended by skilled health personnel

Goal 6

Combat HIV/AIDS, malaria, and other diseases

Target 7

Have halted by 2015 and begun to reverse the spread of HIV/AIDS

18 HIV prevalence among pregnant women ages 15–24 19 Condom use rate of the contraceptive prevalence ratec 19a Condom use at last high-risk sex 19b Percentage of 15- to 24-year-olds with comprehensive correct knowledge of HIV/AIDSd 19c Contraceptive prevalence rate 20 Ratio of school attendance of orphans to school attendance of nonorphans ages 10–14

Target 8

Have halted by 2015 and begun to reverse the incidence of malaria and other major diseases

21 Prevalence and death rates associated with malaria 22 Proportion of population in malaria-risk areas using effective malaria prevention and treatment measurese 23 Prevalence and death rates associated with tuberculosis 24 Proportion of tuberculosis cases detected and cured under directly observed treatment, short course (DOTS)

Goal 7

Ensure environmental sustainability

Target 9

Integrate the principles of sustainable development into country policies and programs and reverse the loss of environmental resources

Target 10 Halve, by 2015, the proportion of people without sustainable access to safe drinking water and basic sanitation

12

2007 World Development Indicators

Net enrollment ratio in primary education Proportion of pupils starting grade 1 who reach grade 5b Literacy rate of 15- to 24-year-olds

9

Ratios of girls to boys in primary, secondary, and tertiary education 10 Ratio of literate women to men ages 15–24 11 Share of women in wage employment in the nonagricultural sector 12 Proportion of seats held by women in national parliaments 13 Under-five mortality rate 14 Infant mortality rate 15 Proportion of one-year-old children immunized against measles

25 Proportion of land area covered by forest 26 Ratio of area protected to maintain biological diversity to surface area 27 Energy use (kilograms of oil equivalent) per $1 GDP (PPP) 28 Carbon dioxide emissions per capita and consumption of ozone-depleting chlorofluorocarbons (ODP tons) 29 Proportion of population using solid fuels 30 Proportion of population with sustainable access to an improved water source, urban and rural 31 Proportion of population with access to improved sanitation, urban and rural

Goals and targets from the Millennium Declaration Indicators for monitoring progress Target 11 By 2020, to have achieved a significant improvement in the lives of at least 100 million slum dwellers Goal 8

32 Proportion of households with access to secure tenure

Develop a global partnership for development

Target 12 Develop further an open, rule-based, predictable, nondiscriminatory trading and financial system Includes a commitment to good governance, development and poverty reduction—both nationally and internationally

Target 13 Address the special needs of the least developed countries Includes tariff and quota free access for the least developed countries’ exports; enhanced programme of debt relief for heavily indebted poor countries (HIPC) and cancellation of official bilateral debt; and more generous ODA for countries committed to poverty reduction

Some of the indicators listed below are monitored separately for the least developed countries (LDCs), Africa, landlocked countries and small island developing states. Official development assistance (ODA) 33 Net ODA, total and to the least developed countries, as a percentage of OECD/DAC donors’ gross national income 34 Proportion of total bilateral, sector-allocable ODA of OECD/ DAC donors to basic social services (basic education, primary healthcare, nutrition, safe water and sanitation) 35 Proportion of bilateral official development assistance of OECD/DAC donors that is untied 36 ODA received in landlocked countries as a proportion of their gross national incomes 37 ODA received in small island developing states as proportion of their gross national incomes

Market access 38 Proportion of total developed country imports (by value and excluding arms) from developing countries and from the Target 14 Address the special needs of landlocked countries least developed countries, admitted free of duty and small island developing states (through 39 Average tariffs imposed by developed countries on the Programme of Action for the Sustainable agricultural products and textiles and clothing from Development of Small Island Developing States developing countries and the outcome of the 22nd special session of the 40 Agricultural support estimate for OECD countries as a General Assembly) percentage of their gross domestic product 41 Proportion of ODA provided to help build trade capacity Target 15 Deal comprehensively with the debt problems of developing countries through national and international measures in order to make debt sustainable in the long term

Debt sustainability 42 Total number of countries that have reached their HIPC decision points and number that have reached their HIPC completion points (cumulative) 43 Debt relief committed under HIPC Debt Initiative 44 Debt service as a percentage of exports of goods and services

Target 16 In cooperation with developing countries, develop and implement strategies for decent and productive work for youth

45 Unemployment rate of 15- to 24-year-olds, male and female and totalf

Target 17 In cooperation with pharmaceutical companies, provide access to affordable essential drugs in developing countries

46 Proportion of population with access to affordable essential drugs on a sustainable basis

Target 18 In cooperation with the private sector, make available the benefits of new technologies, especially information and communications

47 Telephone lines and cellular subscribers per 100 people 48a Personal computers in use per 100 people 48b Internet users per 100 people

Note: Goals, targets, and indicators effective September 8, 2003. a. For monitoring country poverty trends, indicators based on national poverty lines should be used, where available. b. An alternative indicator under development is “primary completion rate.” c. Among contraceptive methods, only condoms are effective in preventing HIV transmission. Since the condom use rate is only measured among women in union, it is supplemented by an indicator on condom use in high-risk situations (indicator 19a) and an indicator on HIV/AIDS knowledge (indicator 19b). Indicator 19c (contraceptive prevalence rate) is also useful in tracking progress in other health, gender, and poverty goals. d. This indicator is defined as the percentage of 15- to 24-year-olds who correctly identify the two major ways of preventing the sexual transmission of HIV (using condoms and limiting sex to one faithful, uninfected partner), who reject the two most common local misconceptions about HIV transmission, and who know that a healthy-looking person can transmit HIV. However, since there are currently not a sufficient number of surveys to be able to calculate the indicator as defined above, UNICEF, in collaboration with UNAIDS and WHO, produced two proxy indicators that represent two components of the actual indicator. They are the percentage of women and men ages 15–24 who know that a person can protect herself from HIV infection by “consistent use of condom,” and the percentage of women and men ages 15–24 who know a healthy-looking person can transmit HIV. e. Prevention to be measured by the percentage of children under age fi ve sleeping under insecticide-treated bednets; treatment to be measured by percentage of children under age fi ve who are appropriately treated. f. An improved measure of the target for future years is under development by the International Labour Organization.

2007 World Development Indicators

13

Tables

1.1

Size of the economy Population Surface Population area density

millions 2005

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, 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

14

.. 3 33 16 39 3 20 8 8 142 10 10 8 9 4 2 186 8 13 8 14 16 32 4 10 16 1,305 7 46 58 4 4 18 4 11 10 5 9 13 74 7 4 1 71 5 61 1 2 4 82 22 11 13 9 2 9

thousand sq. km 2005

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 1,284 757 9,634f 1 1,139 2,345 342 51 322 57 111 79 43 49 284 1,001 21 118 45 1,104 338 552 268 11 70 357 239 132 109 246 36 28

2007 World Development Indicators

Gross national income

people per sq. km 2005

$ billions 2005b

Rank 2005

.. 114 14 13 14 107 3 100 101 1,090 47 347 76 8 76 3 22 71 48 294 80 35 4 6 8 22 140 6,664 41 25 12 85 57 79 103 132 128 184 48 74 332 44 32 71 17 111 5 152 64 236 97 86 116 38 56 309

7.0 8.0 89.6 22.5 173.1 4.4 673.2 306.2 10.4 66.7 27.0 378.7 4.3 9.3 10.5 9.9 662.0 26.7 5.2 0.7 6.1 16.4 1,052.6 1.4 3.9 95.7 2,269.7 192.1 104.5 7.0 3.8 20.3 15.7 36.9 .. 114.8 261.8 21.9 34.7 93.0 16.8 0.8 12.2 11.1 196.9 2,169.2j 6.9 0.4 5.9 2,875.6 10.0 220.3 30.3 3.9 0.3 3.9

114 109 49 80 34 137 13 21 102 55 69 18 138 105 101 104 14 70 131 188 121 86 9 168 143 47 5 30 45 115 144 82 87 61 .. 41 26 81 63 48 85 187 98 99 29 6 116 192 124 3 103 28 66 140 201 142

Gross national income per capita

PPP gross national incomea

$ 2005b

Rank 2005

$ billions 2005

Per capita $ 2005

Rank 2005

..c 2,570 2,730 1,410 4,470 1,470 33,120 37,190 1,240 470 2,760 36,140 510 1,010 2,700 5,590 3,550 e 3,450 400 100 430 1,000 32,590 350 400 5,870 1,740 27,670 2,290 120 950 4,700 870 8,290 ..h 11,220i 48,330 2,460 2,620 1,260 2,450 170 9,060 160 37,530 34,600j 5,010 290 1,320 34,870 450 19,840 2,400 420 180 450

.. 115 108 134 89 132 20 16 142 175 107 17 173 148 111 77 96 98 183 208 180 150 21 186 183 76 128 29 123 207 151 87 156 65 .. 56 6 117 113 140 119 201 63 202 14 19 81 192 137 18 176 38 120 182 200 176

.. 17.0 222.4 d 35.2d 539.4 15.3 622.3 272.9 41.0 296.4 77.1 342.0 9.4 25.2 30.4 18.1 1,534.1 66.8 16.1d 4.8d 35.0 d 35.1 1,040.7 4.6d 14.3d 186.9 8,609.7g 240.7 338.4 d 41.4d 3.2 41.9d 27.0 56.7 .. 206.1 181.8 63.6d 53.8 328.7 35.2d 4.4 d 20.8 71.3d 163.5 1,859.1 8.2 2.9d 14.6 2,408.9 52.4 d 262.3 55.6d 21.1 1.1d 15.7d

.. 5,420 6,770 d 2,210 d 13,920 5,060 30,610 33,140 4,890 2,090 7,890 32,640 1,110 2,740 7,790 10,250 8,230 8,630 1,220 d 640 d 2,490 d 2,150 32,220 1,140 d 1,470 d 11,470 6,600 g 34,670 7,420 d 720 d 810 9,680 d 1,490 12,750 .. 20,140 33,570 7,150 d 4,070 4,440 5,120 d 1,010 d 15,420 1,000 d 31,170 30,540 5,890 1,920 d 3,270 29,210 2,370 d 23,620 4,410 d 2,240 700 d 1,840 d

.. 121 103 160 64 127 21 12 130 165 95 14 189 151 96 80 89 86 186 208 154 162 16 188 182 76 107 9 98 204 200 83 181 69 .. 49 11 101 138 133 125 192 60 193 20 22 115 172 147 27 155 41 134 158 206 175

Gross domestic product

% growth 2004–05

14.0 5.5 5.3 20.6 9.2 14.0 2.8 1.8 26.2 6.0 9.2 1.2 3.9 4.1 5.0 6.2 2.3 5.5 4.8 0.9 13.4 2.0 2.9 2.2 5.6 6.3 10.2 7.3 5.1 6.5 9.2 5.9 1.8 4.3 5.4 6.1 3.1 9.3 4.7 4.9 2.8 0.5 9.8 8.7 2.1 1.2 2.2 5.0 9.3 1.0 5.9 3.7 3.2 3.3 3.5 2.0

Per capita % growth 2004–05

.. 4.9 3.7 17.2 8.1 14.4 1.6 1.1 25.0 4.0 9.8 0.7 0.7 2.1 5.1 6.4 0.9 6.1 1.6 –2.6 11.2 0.3 1.9 0.9 2.3 5.2 9.5 6.3 3.5 3.4 6.0 4.1 0.2 4.3 5.2 5.8 2.8 7.7 3.3 3.0 1.0 –3.4 10.0 6.8 1.7 0.6 0.6 2.3 10.3 1.0 3.8 3.3 0.8 1.1 0.5 0.5

Population Surface Population area density

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

Gross national income

millions 2005

thousand sq. km 2005

people per sq. km 2005

$ billions 2005b

7 10 1,095 221 68 .. 4 7 59 3 128 5 15 34 22 48 3 5 6 2 4 2 3 6 3 2 19 13 25 14 3 1 103 4 3 30 20 51 2 27 16 4 5 14 132 5 3 156 3 6 6 28 83 38 11 4

112 93 3,287 1,905 1,648 438 70 22 301 11 378 89 2,725 580 121 99 18 200 237 65 10 30 111 1,760 65 26 587 118 330 1,240 1,026 2 1,958 34 1,567 447 802 677 824 147 42 271 130 1,267 924 324 310 796 76 463 407 1,285 300 313 92 9

64 113 368 122 42 .. 60 320 199 245 351 62 6 60 187 489 142 27 26 37 350 59 34 3 54 80 32 137 77 11 3 612 54 128 2 68 25 77 2 190 482 15 42 11 144 15 8 202 43 13 15 22 279 125 115 441

8.0 101.6 804.1 282.2 177.3 .. 171.1 128.7 1,772.9 9.0 4,976.5 13.5 44.6 18.4 .. 765.0 77.7 2.3 2.6 15.6 22.6 1.7 0.4 32.4 24.6 5.8 5.4 2.1 125.9 5.2 1.8 6.5 753.4 3.2k 1.8 52.6 6.2 .. 6.1 7.3 642.0 106.3 4.9 3.3 74.0 281.5 23.0 107.3 15.0 2.8 6.1 74.0 109.7 273.1 181.3 ..

Rank 2005

110 46 10 23 32 .. 35 36 7 106 2 94 59 83 .. 11 51 157 154 88 79 165 193 64 76 126 130 161 38 132 163 118 12 148 164 56 119 .. 122 113 15 44 133 146 52 24 .. 43 90 141 120 53 42 25 31 ..

Gross national income per capita

PPP gross national incomea

$ 2005b

Rank 2005

$ billions 2005

Per capita $ 2005

Rank 2005

1,120 10,070 730 1,280 2,600 ..h 41,140 18,580 30,250 3,390 38,950 2,460 2,940 540 ..c 15,840 30,630 450 430 6,770 6,320 950 130 5,530 7,210 2,830 290 160 4,970 380 580 5,250 7,310 930k 690 1,740 310 ..c 2,990 270 39,340 25,920 950 240 560 60,890 9,070 690 4,630 500 1,040 2,650 1,320 7,160 17,190 ..l

145 59 158 139 114 .. 9 43 26 99 12 117 103 171 .. 49 25 176 180 74 75 151 206 78 72 106 192 202 82 185 169 79 71 154 160 128 191 .. 102 195 11 32 151 196 170 2 .. 160 88 162 146 112 137 73 47 ..

20.9d 170.9 3,787.3d 820.5 549.4 .. 144.4 175.0 1,690.2 10.9 4,013.4 28.9 117.1 40.1 .. 1,055.2 59.1d 9.6 12.0 31.0 20.5 6.1d .. .. 48.6 14.4 16.4 8.4 261.6 13.5 6.6d 15.5 1,034.0 9.0 5.6 131.5 25.1d .. 16.1d 41.5 530.1 94.4 18.8 d 11.2d 136.8 186.9 37.2 366.1 23.6 14.0 d 29.3d 163.1 440.2 514.9 208.1 ..

2,900 d 16,940 3,460 d 3,720 8,050 .. 34,720 25,280 28,840 4,110 31,410 5,280 7,730 1,170 .. 21,850 24,010d 1,870 2,020 13,480 5,740 3,410 d .. .. 14,220 7,080 880 650 10,320 1,000 2,150 d 12,450 10,030 2,150 2,190 4,360 1,270 d .. 7,910 d 1,530 32,480 23,030 3,650 d 800 d 1,040 40,420 14,680 2,350 7,310 2,370 d 4,970 d 5,830 5,300 13,490 19,730 ..

150 56 143 140 91 .. 8 37 28 137 18 123 97 187 .. 45 36 174 166 67 118 144 .. .. 62 102 197 207 79 193 162 71 81 162 161 135 184 .. 93 179 15 42 141 201 191 4 .. 157 99 155 129 117 122 66 50 ..

1.1

WORLD VIEW

Size of the economy

Gross domestic product

% growth 2004–05

4.0 4.1 9.2 5.6 4.4 46.5 5.5 5.2 0.0 1.8 2.6 7.3 9.7 5.8 .. 4.0 8.5 –0.6 7.0 10.2 1.0 1.2 5.3 3.5 7.5 4.0 4.6 2.6 5.2 6.1 5.4 4.6 3.0 7.1 6.2 1.7 7.7 5.0 3.5 2.7 1.1 1.9 4.0 4.5 6.9 2.3 3.1 7.8 6.4 3.3 2.9 6.4 5.0 3.4 0.4 ..

2007 World Development Indicators

Per capita % growth 2004–05

1.8 4.3 7.7 4.2 2.9 .. 3.2 3.3 –0.8 1.3 2.6 4.8 8.7 3.4 .. 3.5 5.3 –1.6 4.6 10.8 0.0 1.4 3.9 1.5 8.1 3.8 1.8 0.4 3.3 3.0 2.4 3.7 1.9 7.4 4.6 0.6 5.7 3.9 2.4 0.7 0.9 1.0 3.4 1.1 4.7 1.6 2.2 5.2 4.5 1.3 1.0 4.9 3.2 3.4 –0.1 ..

15

1.1

Size of the economy Population Surface Population area density

millions 2005

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 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 Europe EMU

thousand sq. km 2005

people per sq. km 2005

Gross national income

$ billions 2005b

22 238 94 84.6 143 17,098 9 638.1 9 26 366 2.1 12 289.2 23 2,000 m 12 197 61 8.2 8 102 79 26.3n 6 72 77 1.2 4 1 6,302 119.8 5 49 112 42.8 2 20 99 34.9 8 638 13 .. 47 1,219 39 223.5 43 505 87 1,095.9 20 66 304 22.8 36 2,506 15 23.1 1 17 66 2.6 9 450 22 369.1 7 41 186 411.4 19 185 104 26.3 7 143 46 2.2 38 945 43 12.7o 64 513 126 175.0 6 57 113 2.2 1 5 254 13.4 10 164 65 28.8 72 784 94 342.0 5 488 10 .. 29 241 146 8.0 47 604 81 71.7 5 84 54 103.5 60 244 249 2,272.7 296 9,629 32 12,912.9 3 176 20 15.1 26 447 62 13.6 27 912 30 128.1 83 332 268 51.3 4 6 602 4.5 21 528 40 12.6 12 753 16 5.8 13 391 34 4.5 6,438 s 133,841 s 50 w 45,135.2 t 2,352 29,265 83 1,377.2 3,074 70,081 45 8,137.8 2,475 39,946 63 4,759.9 600 30,135 21 3,379.3 5,427 99,346 56 9,514.8 1,885 16,301 119 3,073.0 472 24,238 20 1,954.7 551 20,418 27 2,227.9 306 8,984 34 672.7 1,470 5,140 307 1,016.9 743 24,265 31 554.4 1,011 34,595 31 35,643.4 314 2,506 128 10,075.3

Rank 2005

50 16 160 22 107 72 174 39 60 62 .. 27 8 78 77 153 19 17 71 158 96 33 159 95 68 20 .. 111 54 .. 4 1 89 93 37 57 136 97 125 135

Gross national income per capita

PPP gross national incomea

$ 2005b

Rank 2005

$ billions 2005

Per capita $ 2005

3,910 4,460 230 12,510 700 3,220 n 220 27,580 7,950 17,440 ..c 4,770 25,250 1,160 640 2,280 40,910 55,320 1,380 330 340o 2,720 350 10,300 2,880 4,750 ..h 280 1,520 23,950 37,740 43,560 4,360 520 4,820 620 1,230 600 500 350 7,011 w 585 2,647 1,923 5,634 1,753 1,630 4,143 4,045 2,198 692 746 35,264 32,098

93 90 197 55 159 100 199 30 68 45 .. 85 34 144 164 124 10 3 136 190 189 110 186 58 105 86 .. 194 131 .. 13 7 91 172 83 165 143 167 174 186

193.4 1,522.7 11.9d 340.8d 20.6 .. 4.3 129.3 84.9 44.3 .. 568.3d 1,120.5 88.7 72.5d 5.9 283.5 275.8 71.2 8.2 28.0 542.1 9.5d 17.2 79.2 606.8 .. 43.2d 316.3 104.1d 1,968.8 12,434.4 34.0 52.9 171.2 250.2 .. 19.3 11.1 25.2 60,669.6 t 5,848.6 22,133.7 15,624.3 6,557.0 27,972.5 11,149.9 4,317.9 4,469.9 1,861.9 4,618.6 1,489.4 32,899.9 9,076.2

8,940 10,640 1,320 d 14,740 d 1,770 .. 780 29,780 15,760 22,160 .. 12,120 d 25,820 4,520 2,000 d 5,190 31,420 37,080 3,740 1,260 730 8,440 1,550 d 13,170 7,900 8,420 .. 1,500 d 6,720 24,090d 32,690 41,950 9,810 2,020 6,440 3,010 .. 920 950 1,940 9,424 w 2,486 7,199 6,314 10,931 5,154 5,914 9,152 8,116 6,084 3,142 2,004 32,550 28,915

Gross domestic product

Rank 2005

% growth 2004–05

Per capita % growth 2004–05

85 78 183 61 176 .. 202 24 58 44 .. 73 33 132 169 124 17 5 139 185 203 87 178 68 94 88 .. 180 105 .. 13 3 82 166 110 149 .. 196 195 171

4.1 6.4 6.0 6.6 5.1 4.7 7.5 6.4 6.0 4.0 .. 4.9 3.4 5.3 8.0 1.8 2.7 1.9 5.1 7.5 7.0 4.5 2.8 7.0 4.2 7.4 .. 6.6 2.6 8.5 1.8 3.2 6.6 7.0 9.3 8.4 6.3 2.6 5.2 –6.5 3.5 w 8.0 6.4 7.0 5.5 6.6 8.9 6.0 4.5 4.3 8.7 5.7 2.7 1.3

4.3 6.9 4.2 3.8 2.7 5.0 3.8 3.9 5.9 3.8 .. 3.7 1.7 4.4 5.9 0.8 2.3 1.2 2.5 6.2 5.0 3.6 0.2 6.7 3.2 6.0 .. 2.9 3.4 3.4 1.2 2.2 5.8 5.8 7.5 7.2 2.8 –0.6 3.5 –7.0 2.3 w 6.1 5.5 6.0 4.9 5.3 8.0 5.9 3.1 2.4 6.9 3.4 1.9 0.7

a. PPP is purchasing power parity; see Definitions. b. Calculated using the World Bank Atlas method. c. Estimated to be low-income ($875 or less). d. Based on regression; others are extrapolated from the latest International Comparison Program benchmark estimates. e. Included in the aggregates for lower middle-income economies based on earlier data. f. Includes Taiwan, China; Macao, China; and Hong Kong, China. g. Based on a 1986 bilateral comparison between China and the United States (Ruoen and Kai 1995) employing a different methodology than that used for other countries. This interim methodology will be revised in the next few years. h. Estimated to be lower middle-income ($876–$3,465). i. Included in the aggregates for upper middle-income economies based on earlier data. j. Includes the French overseas departments of French Guiana, Guadeloupe, Martinique, and Réunion. k. Excludes data for Transnistria. l. Estimated to be high-income ($10,726 or more). m. Provisional estimate. n. Excludes data for Kosovo. o. Data are for mainland Tanzania only.

16

2007 World Development Indicators

About the data

1.1

WORLD VIEW

Size of the economy Definitions

Population, land area, income, output, and growth in

GNI measures the total domestic and foreign value

• Population is based on the de facto definition of

output are basic measures of the size of an economy.

added claimed by residents. GNI comprises GDP

population, which counts all residents regardless of

They also provide a broad indication of actual and

plus net receipts of primary income (compensation

legal status or citizenship—except for refugees not

potential resources. Population, land area, income

of employees and property income) from nonresident

permanently settled in the country of asylum, who

(as measured by gross national income, GNI) and out-

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

are generally considered part of the population of

put (as measured by gross domestic product, GDP)

dollars to classify countries for analytical purposes

their country of origin. The values shown are midyear

are therefore used throughout World Development

and to determine borrowing eligibility. For definitions

estimates for 2005. See also table 2.1. • Surface

Indicators to normalize other indicators.

of the income groups in World Development Indica-

area is a country’s total area, including areas under

Population estimates are generally based on

tors, see Users guide. For discussion of the useful-

inland bodies of water and some coastal water-

extrapolations from the most recent national census.

ness of national income and output as measures of

ways. • Population density is midyear population

For further discussion of the measurement of popula-

productivity or welfare, see About the data for tables

divided by land area in square kilometers. • Gross

tion and population growth, see About the data for

4.1 and 4.2.

national income (GNI) is the sum of value added by

When calculating GNI in U.S. dollars from GNI

all resident producers plus any product taxes (less

The surface area of an economy includes inland

reported in national currencies, the World Bank fol-

subsidies) not included in the valuation of output

bodies of water and some coastal waterways. Sur-

lows its Atlas conversion method, using a three-year

plus net receipts of primary income (compensation

face area thus differs from land area, which excludes

average of exchange rates to smooth the effects of

of employees and property income) from abroad.

bodies of water, and from gross area, which may

transitory fluctuations in exchange rates. (For fur-

Data are in current U.S. dollars converted using the

include offshore territorial waters. Land area is par-

ther discussion of the Atlas method, see Statistical

World Bank Atlas method (see Statistical methods).

ticularly important for understanding an economy’s

methods.) GDP and GDP per capita growth rates are

• GNI per capita is gross national income divided by

agricultural capacity and the environmental effects

calculated from data in constant prices and national

midyear population. GNI per capita in U.S. dollars is

of human activity. (For measures of land area and

currency units.

converted using the World Bank Atlas method. • PPP

table 2.1 and Statistical methods.

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

Because exchange rates do not always reflect dif-

GNI is gross national income converted to interna-

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

ferences in price levels between countries, this table

tional dollars using purchasing power parity rates. An

tions in satellite mapping and computer databases

also converts GNI and GNI per capita estimates into

international dollar has the same purchasing power

have resulted in more precise measurements of land

international dollars using purchasing power parity

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

and water areas.

(PPP) rates. PPP rates provide a standard measure

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

allowing comparison of real levels of expenditure

added by all resident producers plus any product

between countries, just as conventional price indexes

taxes (less subsidies) not included in the valuation

allow comparison of real values over time. The PPP

of output. Growth is calculated from constant price

conversion factors used here are derived from price

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

surveys covering 118 countries conducted by the

domestic product divided by midyear population.

Developing countries produce slightly less than half the world’s output 1.1a Share of PPP GNI, 2005 East Asia & Pacific 18%

International Comparison Program. For Organisation for Economic Co-operation and Development (OECD) countries data come from the most recent round of

South Asia 8%

High-income 54%

Europe & Central Asia 7% Latin America & Caribbean 7% Middle East & North Africa 3%

surveys, completed in 2002; the rest are from either the 1996 or the 1993 survey or earlier round and extrapolated to the 1996 benchmark. Estimates for

Data sources

countries not included in the surveys are derived

Population estimates are prepared by World Bank

from statistical models using available data.

staff from a variety of sources (see Data sources

All 208 economies shown in World Development

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

Indicators are ranked by size, including those that

from the Food and Agriculture Organization (see

When measured by purchasing power parities

appear in table 1.6. The ranks are shown only in

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

(PPPs), which take into account national dif-

table 1.1. No rank is shown for economies for which

GDP growth, and GDP per capita growth are esti-

ferences in the cost of living, developing coun-

numerical estimates of GNI per capita are not pub-

mated by World Bank staff based on national

tries produce a large part of the world’s output.

lished. Economies with missing data are included in

accounts data collected by World Bank staff dur-

Much of this is in the form of nontradable goods

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

ing economic missions or reported by national

and services, which are undervalued at market

ative order of other economies remains consistent.

statistical offices to other international organiza-

Sub-Saharan Africa 2%

exchange rates. For this reason PPPs are used in

tions such as the OECD. Purchasing power parity

international comparisons of well-being such as

conversion factors are estimates by World Bank

$1 and $2 a day measures of absolute poverty.

staff based on data collected by the International

Source: World Bank staff estimates.

Comparison Program.

2007 World Development Indicators

17

1.2

Millennium Development Goals: eradicating poverty and improving lives Eradicate extreme poverty and hunger

Achieve universal primary education

Share of poorest quintile in Prevalence of child national malnutrition consumption Underweight or income % of children % under age 5 1993– 1990–95b 2000–05b 2005b,c

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, 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

18

.. 8.2 7.0 .. 3.1e 8.5 5.9 8.6 7.4 9.0 8.5 8.5 7.4 1.5 9.5 3.2 2.8 8.7 6.9 5.1 6.8 5.6 7.2 2.0 .. 3.8 4.7 5.3 2.5 .. .. 3.5 5.2 8.3 .. 10.3 8.3 4.0 3.3 8.6 2.7 .. 6.7 9.1 9.6 7.2 .. 4.8 5.6 8.5 5.6 6.7 2.9 7.0 5.2 2.4

.. .. 13 31 2 .. .. .. .. 68 .. .. .. 15 .. .. .. .. 33 .. .. 15 .. 23 .. 1 13 .. 8 34 .. 2 24 1 .. 1 .. 10 .. 17 11 44 .. 48 .. .. .. .. .. .. 27 .. 27 27 .. 28

2007 World Development Indicators

39 14 10 35 4f 3 .. .. 7 48 .. .. 30 8 4 13 .. .. 38 45 36 18 .. 24 37 1 8 .. 7 31 .. .. 17 .. 4 .. .. 5 12 9 10 40 .. 38 .. .. 12 17 .. .. 22 .. 23 33 25 17

Primary completion ratea %

Promote gender equality

Ratio of female to male enrollments in primary and secondary schoola %

Reduce child mortality

Under-fi ve mortality rate per 1,000

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

Births attended by skilled health staff % of total

1991

2005d

1991

2005d

1990

2005d

2000

1990–95b

2000–05b

25 .. 79 .. .. 90 .. .. .. 49 95 79 21 .. .. 83 93 85 21 46 .. 56 .. 27 18 .. 103 102 70 46 54 79 43 85 96 .. 98 61 91 .. 41 19 93 26 97 104 58 44 .. 100 63 99 .. 17 .. 27

32 97 96 .. 100 91 .. .. 94 77 100 .. 65 101 .. 92 108 98 31 36 92 62 .. 23 32 95 98 110 98 39 57 92 .. 91 94 104 99 92 101 95 87 51 101 55 100 .. 66 .. 87 96 72 102 74 55 .. ..

.. 96 .. .. .. .. 103 94 96 .. .. 100 49 .. .. 108 .. 100 61 81 .. .. 106 59 .. .. 86 .. 107 .. 83 .. .. .. 109 97 103 .. .. 79 .. .. 105 68 110 104 .. .. 101 .. 78 98 .. 45 .. ..

55 99 102 260 111 108 102 102 98 101 105 103 73 93 .. 102 105 100 77 83 87 83 106 65 60 98 98 93 104 73 89 104 67 104 110 101 109 111 .. .. 100 70 114 76 107 105 .. 97 103 .. 91 105 91 74 .. ..

.. 45 69 260 29 54 10 10 105 149 19 10 185 125 22 58 60 19 210 190 115 139 8 168 201 21 49 .. 35 205 110 18 157 12 13 13 9 65 57 104 60 147 16 204 7 9 92 151 47 9 122 11 82 234 253 150

.. 18 39 1,700 18 29 6 5 89 73 12 5 150 65 15 120 33 15 191 190 87 149 6 193 208 10 27 .. 21 205 108 12 195 7 7 4 5 31 25 33 27 78 7 127 4 5 91 137 45 5 112 5 43 160 200 120

1,900 55 140 .. 82 55 8 4 94 380 35 10 850 420 31 100 260 32 1,000 1,000 450 730 6 1,100 1,100 31 56 .. 130 990 510 43 690 8 33 9 5 150 130 84 150 630 63 850 6 17 420 540 32 8 540 9 240 740 1,100 680

.. .. 77 45 96 .. 100 100 .. 10 .. .. .. 47 97 .. 72 .. 42 .. .. 58 98 46 .. 100 .. .. 86 .. .. 98 45 100 100 99 .. 93 .. 46 51 21 .. .. 100 99 .. 44 .. .. 44 .. 34 31 25 20

14 98 96 95 98 99 .. 88 13 100 .. 75 67 100 94 97 99 38 25 44 62 98 44 14 100 97 100 96 61 86 99 68 100 100 100 .. 99 75 74 92 28 100 6 100 .. 86 55 92 .. 47 .. 41 56 35 24

Eradicate extreme poverty and hunger

Achieve universal primary education

Share of poorest quintile in Prevalence of child national malnutrition consumption Underweight or income % of children % under age 5 1993– 1990–95b 2000–05b 2005b,c

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

3.4 9.5 8.9 8.4 5.1 .. 7.4 5.7 6.5 5.3 10.6 6.7 7.4 6.0 .. 7.9 .. 8.9 8.1 6.6 .. 1.5 .. .. 6.8 6.1 4.9 7.0 4.4 6.1 6.2 .. 4.3 7.8 7.5 6.5 5.4 .. 1.4 6.0 7.6 6.4 5.6 2.6 5.0 9.6 .. 9.3 2.5 4.5 2.4 3.7 5.4 7.5 5.8 ..

18 .. 53 34 16 12 .. .. .. 5 .. 6 8 23 .. .. .. .. 40 .. .. 21 .. 5 .. .. 34 30 20 .. 48 15 .. .. 12 10 27 43 26 49 .. .. 11 43 39 .. 23 38 6 .. 4 11 30 .. .. ..

17 .. .. 28 .. 16 .. .. .. 4 .. 4 .. 20 24 .. .. 7 40 .. 4 18 27 .. .. .. 42 22 11 33 32 .. .. 4 13 10 24 32 24 45g .. .. 10 40 29 .. .. 38 .. .. 5 7 28 .. .. ..

Primary completion ratea %

Promote gender equality

Ratio of female to male enrollments in primary and secondary schoola %

Reduce child mortality

Under-fi ve mortality rate per 1,000

WORLD VIEW

1.2

Millennium Development Goals: eradicating poverty and improving lives

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

Births attended by skilled health staff % of total

1991

2005d

1991

2005d

1990

2005d

2000

1990–95b

2000–05b

65 93 68 91 91 58 .. .. 104 90 101 72 .. .. .. 98 .. .. 43 .. .. 59 .. .. 89 98 33 28 91 11 33 107 86 .. .. 47 27 .. 78 51 .. 100 44 17 .. 100 74 .. 86 47 71 .. 86 98 95 ..

79 95 89 101 96 74 101 105 101 84 .. 97 114 95 .. 104 100 97 76 92 90 67 .. .. 98 96 58 61 94 38 45 97 99 92 97 80 42 79 75 76g 100 .. 76 28 82 101 93 63 97 54 91 100 97 100 104 ..

106 101 69 .. 83 .. 103 104 98 100 96 104 .. .. .. 89 .. .. .. 103 .. 121 .. .. .. 99 97 80 .. 58 65 101 95 .. 113 69 .. .. 108 58 95 102 108 .. .. 104 91 .. .. .. .. .. 104 103 105 ..

109 107 87 97 99 76 103 105 106 104 98 102 106 94 .. 87 110 105 84 115 104 103 .. 106 110 103 96 98 109 75 96 98 101 109 116 88 82 104 101 88 99 113 103 72 82 109 99 75 109 87 101 103 106 109 108 ..

59 17 123 91 72 50 9 12 9 20 6 40 63 97 55 9 16 80 163 18 37 101 235 41 13 38 168 221 22 250 133 23 46 35 108 89 235 130 86 145 9 11 68 320 230 9 32 130 34 94 41 78 62 18 14 ..

40 8 74 36 36 .. 6 6 4 20 4 26 73 120 55 5 11 67 79 11 30 132 235 19 9 17 119 125 12 218 125 15 27 16 49 40 145 105 62 74 5 6 37 256 194 4 12 99 24 74 23 27 33 7 5 ..

110 16 540 230 76 250 5 17 5 87 10 41 210 1,000 67 20 5 110 650 42 150 550 760 97 13 23 550 1,800 41 1,200 1,000 24 83 36 110 220 1,000 360 300 740 16 7 230 1,600 800 16 87 500 160 300 170 410 200 13 5 25

45 .. 34 37 .. .. .. .. .. .. 100 87 100 45 .. 98 .. .. .. 100 .. 50 .. 94 .. .. 57 55 .. .. 40 98 .. .. .. 40 .. .. 68 7 .. 100 .. 15 31 .. 91 19 86 .. 67 .. 53 .. .. ..

56 100 43 72 90 72 100 .. .. 97 .. 100 .. 42 97 100 100 99 19 100 93 55 51 .. 100 99 51 56 97 41 57 99 83 100 97 63 48 57 76 15 .. .. 67 16 35 .. 95 31 93 41 77 73 60 100 100 100

2007 World Development Indicators

19

1.2

Millennium Development Goals: eradicating poverty and improving lives Eradicate extreme poverty and hunger

Achieve universal primary education

Share of poorest quintile in Prevalence of child national malnutrition consumption Underweight or income % of children % under age 5 1993– 1990–95b 2000–05b 2005b,c

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 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

8.1 6.1 5.3 .. 6.6 .. 1.1 5.0 8.8 9.1 .. 3.5 7.0 7.0 .. 4.3 9.1 7.6 .. 7.9 7.3 6.3 .. .. 6.0 5.3 6.1 5.7 9.2 .. 6.1 5.4 5.0 d 7.2 3.3 9.0 .. 7.4 3.6 4.6

6 3 29 15 22 2 29 .. .. .. .. 9 .. 33 34 .. .. .. 13 .. 29 18 .. .. 9 10 .. 26 .. 14 .. 1 5 .. 5 45 .. 39 25 16 30 w 46 15 17 7 32 20 .. .. 16 53 32 ..

3 6 23 .. 23 71 27 3 .. .. 33g .. .. 29 41 10 .. .. 7 .. 22 .. .. 6 4 4 12 23 1 .. .. 2 .. 8 4 28 5 46 23 .. .. w .. 11 12 .. 22 15 5 .. 15 .. 30 ..

Primary completion ratea %

Promote gender equality

Ratio of female to male enrollments in primary and secondary schoola %

Reduce child mortality

Under-fi ve mortality rate per 1,000

1991

2005d

1991

2005d

1990

96 93 33 56 39 .. .. .. 96 95 .. 75 .. 97 41 60 96 53 94 .. 61 .. 35 100 74 90 .. .. 94 103 .. .. 94 .. 43 .. .. .. .. 99 .. w 60 93 94 87 79 100 91 83 77 76 50 ..

93 94 39 85 52 .. .. .. 99 102 .. 99 109 .. 50 64 .. 97 111 102 54 82 65 99 97 88 .. 57 114 76 .. .. 91 97 92 94 98 62 78 80 85 w 74 96 97 95 84 98 92 98 89 82 58 97

99 108 .. 86 .. .. .. 90 .. .. .. 103 104 101 78 94 105 92 83 .. 96 .. 58 101 84 79 .. 81 .. 120 96 105 .. .. .. .. .. .. .. 91 .. w .. .. 90 99 .. .. 98 99 79 69 .. 100

105 110 99 101 90 28 71 .. 104 109 .. 101 107 102 89 94 112 94 94 84 95 101 72 104 105 84 .. 96 102 126 107 109 114 96 104 94 104 61 92 95 94 w 87 99 99 99 93 99 96 102 99 87 86 100

31 27 173 44 149 15 302 8 14 10 225 60 9 32 120 110 7 9 39 115 161 37 152 33 52 82 97 160 26 15 10 11 23 79 33 53 40 139 180 80 95 w 147 58 62 41 103 59 48 54 80 129 185 11

2005d

19 18 203 26 119 11 282 3 8 4 225 68 5 14 90 160 4 5 15 71 122 21 139 19 24 29 104 136 17 9 6 7 15 68 21 19 23 102 182 132 75 w 114 37 39 27 82 33 32 31 53 83 163 7

Improve maternal health Maternal mortality ratio Modeled estimates per 100,000 live births 2000

49 67 1,400 23 690 .. 2,000 30 3 17 1,100 230 4 92 590 370 2 7 160 100 1,500 44 570 160 120 70 31 880 35 54 13 17 27 24 96 130 .. 570 750 1,100 410 w 684 150 163 91 450 117 58 194 183 564 921 14

Births attended by skilled health staff % of total 1990–95b

99 .. 26 .. 47 92 .. .. .. 100 .. 82 .. 94 86 56 .. .. 77 .. 44 .. .. .. 81 76 .. 38 .. 99 .. .. .. .. .. .. .. 16 51 69 .. w 33 .. .. .. .. .. .. .. .. 30 .. ..

2000–05b

99 99 39 93 58 42 100 99 100 25 92 .. 96 87 74 .. .. 70 71 43 99 61 96 90 83 97 39 100 100 .. 99 99 96 95 90 97 27 43 .. 63 w 41 87 86 92 61 87 94 87 74 37 45 ..

a. Because of the change from International Standard Classification of Education 1976 (ISCED76) to ISCED97 in 1998, data before 1998 are not fully comparable with data from 1998 onward. b. Data are for the most recent year available. c. See table 2.6 for survey year and whether share is based on income or consumption expenditure. d. Provisional data. e. Urban data. f. Data are for 2005–06. g. Data are for 2005.

20

2007 World Development Indicators

1.2

WORLD VIEW

Millennium Development Goals: eradicating poverty and improving lives About the data

This table and the following two present indicators

rate. Because many school systems do not record

see About the data for the tables listed there. For

for 17 of the 18 targets specified by the Millennium

school completion on a consistent basis, it is esti-

information about the indicators for goals 6, 7, and

Development Goals. Each of the eight goals com-

mated from the gross enrollment rate in the final

8, see About the data for tables 1.3 and 1.4.

prises one or more targets, and each target has

grade of primary school, adjusted for repetition.

associated with it several indicators for monitoring

Official enrollments sometimes differ significantly

progress toward the target. Most of the targets are

from actual attendance, and even school systems

• Share of poorest quintile in national consump-

set as a value of a specific indicator to be attained

with high average enrollment ratios may have poor

tion or income is the share of consumption or, in

by a certain date. In some cases the target value is

completion rates. Estimates of primary school

some cases, income that accrues to the poorest

set relative to a level in 1990. In others it is set at

completion rates are provided by the United Nations

20 percent of the population. • Prevalence of child

an absolute level. Some of the targets for goals 7

Educational, Scientifi c, and Cultural Organization

malnutrition is the percentage of children under age

and 8 have not yet been quantified

Institute of Statistics and national sources.

fi ve whose weight for age is more than two standard

Definitions

The indicators in this table relate to goals 1–5.

Eliminating gender disparities in education would

deviations below the median for the international ref-

Goal 1 has two targets between 1990 and 2015:

help to increase the status and capabilities of

erence population ages 0–59 months. The reference

to reduce by half the proportion of people whose

women. The ratio of girls’ to boys’ enrollments in

population, adopted by the World Health Organization

income is less than $1 a day and to reduce by half

primary and secondary school provides an imperfect

in 1983, is based on children from the United States,

the proportion of people who suffer from hunger.

measure of the relative accessibility of schooling for

who are assumed to be well nourished. • Primary

Estimates of poverty rates can be found in table 2.6.

girls. With a target date of 2005, this is the first of

completion rate is the percentage of students com-

The indicator shown here, the share of the poorest

the goals to fall due.

pleting the last year of primary school. It is calculated

quintile in national consumption, is a distributional

The targets for reducing under-five and maternal

as the total number of students in the last grade of

measure. Countries with more unequal distributions

mortality are among the most challenging. Although

primary school, minus the number of repeaters in

of consumption (or income) will have a higher rate of

estimates of under-five mortality rates are available

that grade, divided by the total number of children

poverty for a given average income. No single indica-

at regular intervals for most countries, maternal

of official graduation age. • Ratio of female to male

tor captures the concept of suffering from hunger.

mortality is difficult to measure, in part because it

enrollments in primary and secondary school is the

Child malnutrition is a symptom of inadequate food

is relatively rare.

ratio of female to male gross enrollment rate in pri-

supply, lack of essential nutrients, illnesses that

Most of the 48 indicators relating to the Millennium

mary and secondary school. • Under-five mortality

deplete these nutrients, and undernourished moth-

Development Goals can be found in World Develop-

rate is the probability that a newborn baby will die

ers who give birth to underweight children.

ment Indicators. Table 1.2a shows where to find the

before reaching age five, if subject to current age-

Progress toward achieving universal primary educa-

indicators for the first five goals. For more informa-

specific mortality rates. The probability is expressed

tion is measured by the primary school completion

tion about data collection methods and limitations,

as a rate per 1,000. • Maternal mortality ratio is the number of women who die from pregnancy-related

1.2a

Location of indicators for Millennium Development Goals 1–5

causes during pregnancy and childbirth, per 100,000 live births. The data shown here have been collected

Goal 1. Eradicate extreme poverty and hunger 1. Proportion of population below $1 a day 2. Poverty gap ratio 3. Share of poorest quintile in national consumption 4. Prevalence of underweight in children under age five 5. Proportion of population below minimum level of dietary energy consumption Goal 2. Achieve universal primary education 6. Net enrollment ratio 7. Proportion of pupils starting grade 1 who reach grade 5 8. Literacy rate of 15- to 24-year-olds Goal 3. Promote gender equality and empower women 9. Ratio of girls to boys in primary, secondary, and tertiary education 10. Ratio of literate females to males among 15- to 24-year-olds 11. Share of women in wage employment in the nonagricultural sector 12. Proportion of seats held by women in national parliament Goal 4. Reduce child mortality 13. Under-five mortality rate 14. Infant mortality rate 15. Proportion of one-year-old children immunized against measles Goal 4. Improve maternal health 16. Maternal mortality ratio 17. Proportion of births attended by skilled health personnel * Table shows information on related indicators.

Table 2.6 2.6 1.2, 2.7 1.2, 2.17 2.17 2.10 2.11 2.12 1.2* 2.12* 1.5, 2.2* 1.5

in various years and adjusted to a common 2000 base year. The values are modeled estimates (see About the data for table 2.16). • Births attended by skilled health staff are the percentage of deliveries attended by personnel trained to give the necessary supervision, care, and advice to women during pregnancy, labor, and the postpartum period; to conduct deliveries on their own; and to care for newborns.

Data sources The indicators here and throughout this book have been compiled by World Bank staff from primary

1.2, 2.20 2.20 2.15

and secondary sources. Efforts have been made to

1.2, 2.16 1.2, 2.16

Goals Web site (www.un.org/millenniumgoals), but

harmonize these data series with those published on the United Nations Millennium Development some differences in timing, sources, and definitions remain.

2007 World Development Indicators

21

1.3

Millennium Development Goals: protecting our common environment Combat HIV/AIDS and other diseases

Incidence of HIV prevalence tuberculosis % of population per 100,000 people ages 15–49 2005 2005

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, 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

22

<0.1 .. 0.1 3.7 0.6 0.1 0.1 0.3 <0.1 <0.1 0.3 0.3 1.8 0.1 0.1 24.1 0.5 0.1 2.0 3.3 1.6 5.5b 0.3 10.7 3.5 0.3 0.1c .. 0.6 3.2 5.3 0.3 7.1 0.1 0.1 <0.1 0.2 1.1 0.3 <0.1 0.9 2.4 1.3 .. 0.1 0.4 7.9 2.4 0.2 0.1 2.3 0.2 0.9 1.5 3.8 3.8

2007 World Development Indicators

168 20 55 269 41 71 6 11 76 227 62 13 88 211 52 654 60 39 223 334 506 174 5 314 272 15 100 75 45 356 367 14 382 41 9 10 7 91 131 25 51 282 43 344 6 13 308 242 83 7 205 17 78 236 206 305

Ensure environmental sustainability

Carbon dioxide emissions per capita metric tons 1990 2003

0.2 2.2 3.0 0.4 3.4 1.2 15.9 7.5 7.5 0.1 10.6 10.1 0.1 0.8 1.6 1.5 1.4 8.6 0.1 0.0 0.0 0.1 15.0 0.1 0.0 2.7 2.1 4.6 1.6 0.1 0.5 0.9 0.4 5.1 3.0 15.6 9.7 1.3 1.6 1.4 0.5 0.0 18.1 0.1 10.3 6.4 6.3 0.2 3.2 12.3 0.2 7.1 0.6 0.2 0.2 0.1

0.0 1.0 5.1 0.6 3.4 1.1 17.8 8.7 3.5 0.3 6.3 9.9 0.3 0.9 4.9 2.3 1.6 5.6 0.1 0.0 0.0 0.2 17.9 0.1 0.0 3.7 3.2 5.6 1.3 0.0 0.4 1.5 0.3 5.4 2.3 11.4 10.1 2.5 1.8 2.0 1.0 0.2 13.5 0.1 13.0 6.2 0.9 0.2 0.8 9.8 0.4 8.7 0.9 0.1 0.2 0.2

Access to an improved water source % of population 1990 2004

4 96 94 36 94 .. 100 100 68 72 100 100 63 72 97 93 83 99 38 69 .. 50 100 52 19 90 70 .. 92 43 .. .. 69 100 .. 100 100 84 73 94 67 43 100 23 100 100 .. .. 80 100 55 .. 79 44 .. 47

39 96 85 53 96 92 100 100 77 74 100 100 67 85 97 95 90 99 61 79 41 66 100 75 42 95 77 .. 93 46 58 97 84 100 91 100 100 95 94 98 84 60 100 22 100 100 88 82 82 100 75 .. 95 50 59 54

Develop a global partnership for development

Access to improved sanitation facilities % of population 1990 2004

3 .. 88 29 81 .. 100 100 .. 20 .. 100 12 33 .. 38 71 99 7 44 .. 48 100 23 7 84 23 .. 82 16 .. .. 21 100 98 99 100 52 63 54 51 7 97 3 100 .. .. .. 97 100 15 .. 58 14 .. 24

34 91 92 31 91 83 100 100 54 39 84 100 33 46 95 42 75 99 13 36 17 51 100 27 9 91 44 .. 86 30 27 92 37 100 98 98 100 78 89 70 62 9 97 13 100 .. 36 53 94 100 18 .. 86 18 35 30

Fixed-line and mobile phone Youth unemployment subscribers per 1,000 % ages peoplea 15–24 2005 2005

.. .. 43 .. 24 .. 11 10 .. 7 .. 18 .. .. .. .. 18 22 .. .. .. .. 12 .. .. 17 .. 11 25 .. .. 15 .. 33 .. 19 9 .. 16 27 12 .. 16 8 19 23 .. .. 28 15 .. 25 .. .. .. ..

44 493 494 75 797 260 1,470 1,441 397 71 755 1,337 98 334 656 541 587 1,128 51 18 40 102 1,080 27 14 859 570 1,798 648 48 102 575 108 1,097 87 1,465 1,628 508 601 325 492 18 1,402 14 1,401 1,376 498 192 337 1,628 143 1,472 457 20 8 64

Combat HIV/AIDS and other diseases

Incidence of HIV prevalence tuberculosis % of population per 100,000 people ages 15–49 2005 2005

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

1.5 0.1 0.9 0.1 0.2 .. 0.2 .. 0.5 1.5 <0.1 .. 0.1 6.1 .. <0.1 .. <0.1 0.1 0.8 0.1 23.2 .. .. 0.2 <0.1 0.5 14.1 0.5 1.7 0.7 0.6 0.3 1.1 <0.1 0.1 16.1 1.3 19.6 0.5 0.2 0.1 0.2 1.1 3.9 0.1 .. 0.1 0.9 1.8 0.4 0.6 <0.1 0.1 0.4 ..

78 22 168 239 23 56 12 8 7 7 28 5 144 641 178 96 24 121 155 63 11 696 301 18 63 30 234 409 102 278 298 62 23 138 191 89 447 171 697 180 7 9 58 164 283 5 11 181 45 250 68 172 291 26 33 5

Ensure environmental sustainability

Carbon dioxide emissions per capita metric tons 1990 2003

0.5 5.8 0.8 0.8 4.0 2.6 8.7 7.1 6.9 3.3 8.7 3.2 17.6 0.2 12.4 5.6 21.3 2.8 0.1 5.4 3.3 .. 0.2 8.7 6.6 8.1 0.1 0.1 3.1 0.0 1.3 1.4 4.5 5.5 4.7 1.0 0.1 0.1 0.0 0.0 9.3 6.8 0.7 0.1 0.5 8.3 5.6 0.6 1.3 0.6 0.5 1.0 0.7 9.1 4.3 3.3

0.9 5.7 1.2 1.4 5.7 2.7 10.4 10.2 7.7 4.1 9.6 3.3 10.7 0.3 3.5 9.5 32.7 1.1 0.2 2.9 5.4 .. 0.1 8.9 3.7 5.2 0.1 0.1 6.4 0.0 0.9 2.6 4.1 1.7 3.2 1.3 0.1 0.2 1.2 0.1 8.7 8.7 0.8 0.1 0.4 9.9 12.8 0.8 1.9 0.4 0.7 1.0 1.0 8.0 5.5 0.5

Access to an improved water source % of population 1990 2004

84 99 70 72 92 83 .. 100 .. 92 100 97 87 45 100 .. .. 78 .. 99 100 .. 55 71 .. .. 40 40 98 34 38 100 82 .. 63 75 36 57 57 70 100 97 70 39 49 100 80 83 90 39 62 74 87 .. .. ..

87 99 86 77 94 81 .. 100 .. 93 100 97 86 61 100 92 .. 77 51 99 100 79 61 .. .. .. 46 73 99 50 53 100 97 92 62 81 43 78 87 90 100 .. 79 46 48 100 .. 91 90 39 86 83 85 .. .. ..

1.3

WORLD VIEW

Millennium Development Goals: protecting our common environment

Develop a global partnership for development

Access to improved sanitation facilities % of population 1990 2004

50 .. 14 46 83 81 .. .. .. 75 100 93 72 40 .. .. .. 60 .. .. .. 37 39 97 .. .. 14 47 .. 36 31 .. 58 .. .. 56 20 24 24 11 100 .. 45 7 39 100 83 37 71 44 58 52 57 .. .. ..

69 95 33 55 .. 79 .. .. .. 80 100 93 72 43 59 .. .. 59 30 78 98 37 27 97 .. .. 32 61 94 46 34 94 79 68 59 73 32 77 25 35 100 .. 47 13 44 100 .. 59 73 44 80 63 72 .. .. ..

Fixed-line and mobile phone Youth unemployment subscribers per 1,000 % ages peoplea 15–24 2005 2005

7 19 11 .. 23 .. 8 18 23 28 9 30 14 .. .. 10 .. 20 .. 13 .. .. .. .. 16 63 .. .. .. .. .. 26 7 19 20 17 .. .. .. .. 10 9 13 .. .. 12 .. 12 23 .. .. 21 16 38 16 23

2007 World Development Indicators

246 1,257 128 271 384 57 1,501 1,544 1,659 1,146 1,202 423 350 143 41 1,286 1,140 190 120 1,131 554 163 .. 156 1,510 882 31 41 943 70 256 862 650 480 279 455 40 13 206 26 1,436 1,283 260 23 151 1,489 623 116 555 15 374 280 459 1,073 1,486 974

23

1.3

Millennium Development Goals: protecting our common environment Combat HIV/AIDS and other diseases

Incidence of HIV prevalence tuberculosis % of population per 100,000 people ages 15–49 2005 2005

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 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

0.1 1.1 3.1 .. 0.9 0.2 1.6 0.3 <0.1 <0.1 0.9 18.8 <0.1 0.1 1.6 33.4 0.2 0.4 .. <0.1 7.0 b 1.4 3.2 2.6 0.1 .. 0.1 6.4d 1.4 .. .. 0.6 0.5 0.2 0.7 0.5e .. .. 17.0 20.1 1.0 w 1.7 0.6 0.3 2.2 1.1 0.2 0.7 0.6 0.1 0.7 6.2 0.4

134 119 361 41 255 33 475 29 17 15 224 600 27 60 228 1,262 6 7 37 198 342 142 373 9 24 29 70 369 99 16 14 5 28 113 42 175 21 82 600 601 136 w 220 111 113 104 158 136 84 61 43 174 348 17

Ensure environmental sustainability

Carbon dioxide emissions per capita metric tons 1990 2003

6.7 15.3 0.1 12.0 0.4 6.2 0.1 14.8 9.7 9.0 0.0 8.1 5.5 0.2 0.2 0.6 5.8 6.4 2.8 4.4 0.1 1.8 0.2 13.9 1.6 2.6 8.7 0.0 13.2 30.8 9.9 19.3 1.3 6.3 5.9 0.3 .. 0.8 0.3 1.6 4.3 w 0.8 3.5 2.4 8.1 2.4 1.9 10.2 2.4 2.5 0.7 0.8 11.8

4.2 10.3 0.1 13.7 0.4 6.2 0.1 11.4 7.0 7.7 .. 7.9 7.4 0.5 0.3 0.9 5.9 5.5 2.7 0.7 0.1 3.9 0.4 22.1 2.1 3.1 9.2 0.1 6.6 33.4 9.4 19.9 1.3 4.8 5.6 0.9 .. 0.9 0.2 0.9 4.3 w 0.8 3.6 2.9 6.4 2.4 2.7 6.9 2.4 3.4 1.0 0.8 12.8

Access to an improved water source % of population 1990 2004

.. 94 59 90 65 93 .. 100 100 .. .. 83 100 68 64 .. 100 100 80 .. 46 95 50 92 81 85 .. 44 96 100 100 100 100 94 .. 65 .. 71 50 78 77 w 64 78 76 90 73 72 93 83 88 71 49 100

57 97 74 .. 76 93 57 100 100 .. 29 88 100 79 70 62 100 100 93 59 62 99 52 91 93 96 72 60 96 100 100 100 100 82 83 85 92 67 58 81 83 w 75 84 82 94 80 79 92 91 89 84 56 100

Develop a global partnership for development

Access to improved sanitation facilities % of population 1990 2004

.. 87 37 .. 33 87 .. 100 99 .. .. 69 100 69 33 .. 100 100 73 .. 47 80 37 100 75 85 .. 42 96 97 .. 100 100 51 .. 36 .. 32 44 50 45 w 21 48 42 79 37 30 86 67 70 17 31 100

.. 87 42 .. 57 87 39 100 99 .. 26 65 100 91 34 48 100 100 90 51 47 99 35 100 85 88 62 43 96 98 .. 100 100 67 68 61 73 43 55 53 57 w 38 62 57 84 52 51 85 77 76 37 37 100

Fixed-line and mobile phone Youth unemployment subscribers per 1,000 % ages peoplea 15–24 2005 2005

20 .. .. .. .. .. .. 5 30 13 .. 60 20 26 .. .. 14 9 26 .. .. 5 .. 21 31 19 .. .. 17 .. 12 11 30 .. 28 5 40 .. .. 25 .. w .. .. .. 25 .. .. .. 17 .. 11 .. 13

820 1,119 18 740 171 917 19 1,435 1,065 1,287 73 825 1,374 235 69 208 1,804 1,609 307 46 56 537 82 861 692 868 82 56 545 1,273 1,616 1,227 624 80 606 306 398 92 89 79 523 w 114 590 511 901 382 496 898 496 389 119 142 1,338

a. Data are from the International Telecommunication Union’s (ITU) World Telecommunication Development Report database. b. Survey data, 2004. c. Includes Hong Kong, China. d. Survey data, 2004–05. e. Survey data, 2005.

24

2007 World Development Indicators

1.3

WORLD VIEW

Millennium Development Goals: protecting our common environment About the data

The Millennium Development Goals address issues

collected from sentinel sites or through targeted

mies. Fixed telephone lines and mobile phones are

of common concern to all nations. Diseases and

surveys. In older, generalized epidemics antenatal

among the telecommunications technologies that

environmental degradation do not respect national

clinics are a key site for monitoring HIV and other

are changing the way the global economy works.

boundaries. Epidemic diseases, wherever they per-

sexually transmitted diseases. Recently, household

sist, pose a threat to people everywhere. And dam-

surveys have been used to track the disease. The

age to the environment in one location may affect

table shows the estimated prevalence among adults

• HIV prevalence is the percentage of people ages

the well-being of plants, animals, and humans far

ages 15–49. Prevalence rates in the older popula-

15–49 who are infected with HIV. • Incidence of

away. The indicators in the table relate to goals 6

tion can be affected by life-prolonging treatment.

tuberculosis is the estimated number of new tuber-

and 7 and the targets of goal 8 that address youth

The incidence of tuberculosis is based on data on

culosis cases (pulmonary, smear positive, and extra-

employment and access to new technologies. For the

case notifications and estimates of the proportion

pulmonary). • Carbon dioxide emissions are those

other targets of goal 8, see table 1.4.

of cases detected in the population.

stemming from the burning of fossil fuels and the

Definitions

Measuring the prevalence or incidence of a disease

Carbon dioxide emissions are the primary source

manufacture of cement. They include carbon dioxide

can be difficult. Much of the developing world lacks

of greenhouse gases, which contribute to global

produced during consumption of solid, liquid, and gas

reporting systems for monitoring diseases. Estimates

warming.

fuels and gas flaring. • Access to an improved water

are often derived from surveys and reports from sen-

Access to reliable supplies of safe drinking water

source refers to the percentage of the population

tinel sites that must be extrapolated to the general

and sanitary disposal of excreta are two of the most

with reasonable access to an adequate amount of

population. Tracking diseases such as HIV/AIDS,

important means of improving human health and

water from an improved source, such as piped water

which has a long latency between contraction of the

protecting the environment. There is no widespread

into a dwelling, plot, or yard; public tap or standpipe;

virus and the appearance of symptoms, or malaria,

program for testing the quality of water. The indicator

tubewell or borehole; protected dug well or spring;

which has periods of dormancy, can be particularly

shown here measures the proportion of households

and rainwater collection. Unimproved sources include

difficult. For some of the most serious illnesses inter-

with access to an improved source, such as piped

unprotected dug well or spring, cart with small tank or

national organizations have formed coalitions such

water or protected wells. Improved sanitation facili-

drum, bottled water, and tanker trucks. Reasonable

as the Joint United Nations Programme on HIV/AIDS

ties prevent human, animal, and insect contact with

access is defined as the availability of at least 20

and the Roll Back Malaria campaign to gather infor-

excreta but do not include treatment to render sew-

liters a person a day from a source within 1 kilome-

mation and coordinate global efforts to treat victims

age outflows innocuous.

ter of the dwelling. • Access to improved sanitation

The eighth goal—to develop a global partnership

facilities refers to the percentage of the population

The models and data used to estimate HIV preva-

for development—takes note of the need for decent

with at least adequate access to excreta disposal

lence depend on the nature of the epidemic in each

and productive work for youth. Labor market informa-

facilities (private or shared, but not public) that

country. In early stages infections are usually con-

tion, such as unemployment rates, is still generally

can effectively prevent human, animal, and insect

centrated in high-risk groups for which data are

unavailable for most low- and middle-income econo-

contact with excreta. Improved facilities range from

and prevent the spread of disease.

simple but protected pit latrines to flush toilets with a

1.3a

Location of indicators for Millennium Development Goals 6–7

sewerage connection. To be effective, facilities must be correctly constructed and properly maintained.

Goal 6. Combat HIV/AIDS, malaria, and other diseases 18. HIV prevalence among pregnant women ages 15–24 19. Condom use rate of the contraceptive prevalence rate 19a. Condom use at last high-risk sex 19b. Percentage of 15- to 24-year-olds with comprehensive correct knowledge of HIV/AIDS 19c. Contraceptive prevalence rate 20. Ratio of school attendance of orphans to school attendance of nonorphans ages 10–14 21. Prevalence and death rates associated with malaria 22. Proportion of population in malaria-risk areas using effective malaria prevention and treatment measures 23. Prevalence and death rates associated with tuberculosis 24. Proportion of tuberculosis cases detected and cured under DOTS Goal 7. Ensure environmental sustainability 25. Proportion of land area covered by forest 26. Ratio of area protected to maintain biological diversity to surface area 27. Energy use (kilograms of oil equivalent) per $1 of GDP (PPP) 28. Carbon dioxide emissions per capita and consumption of ozone-depleting chlorofluorocarbons 29. Proportion of population using solid fuels 30. Proportion of population with sustainable access to an improved water source, urban and rural 31. Proportion of population with access to improved sanitation, urban and rural 32. Proportion of population with access to secure tenure

Table 1.3*, 2.18* — — — 2.16

• Youth unemployment refers to the share of the labor force ages 15–24 without work but available for and seeking employment. Definitions of labor force and unemployment differ by country. • Fixedline and mobile phone subscribers are telephone mainlines connecting a customer’s equipment to

— —

the public switched telephone network, and users of portable telephones subscribing to an automatic public mobile telephone service using cellular tech-

2.15* 1.3* 2.15 3.4 3.4 3.8

nology that provides access to the public switched telephone network.

Data sources The indicators here and throughout this book have been compiled by World Bank staff from primary

3.8* 3.7*

and secondary sources. Efforts have been made to

2.15, 3.5 2.15, 3.10 3.11

on the United Nations Millennium Development

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

harmonize these data series with those published Goals Web site (www.un.org/millenniumgoals), but some differences in timing, sources, and definitions remain.

2007 World Development Indicators

25

1.4

Millennium Development Goals: overcoming obstacles

Development Assistance Committee members Official development assistance (ODA) by donor

Australia Canada European Union Austria Belgium Denmark Finland France Germany Greece Ireland Italy Luxembourg Netherlands Portugal Spain Sweden United Kingdom Japan New Zealandb Norway b Switzerland United States

Net % of donor GNI 2005

For basic social servicesa % of total sector-allocable ODA 2004–05

0.25 0.34

10.7 30.4

0.52 0.53 0.81 0.46 0.47 0.36 0.17 0.42 0.29 0.82 0.82 0.21 0.27 0.94 0.47 0.28 0.27 0.94 0.44 0.22

13.9 16.5 17.6 13.4 6.3 12.1 18.8 32.0 9.4 29.5 22.0 2.7 18.3 15.2 30.2 4.6 29.9 14.3 7.2 18.4

Least developed countries’ access to high-income markets

Goods (excluding arms) admitted free of tariffs % 1998 2004

Support to agriculture

Average tariff on exports of least developed countries Agricultural products % 1998 2004

Textiles %

Clothing %

1998

2004

1998

2004

% of GDP 2005

95.4 62.9 97.5

97.3 98.9 96.0

0.2 0.5 3.4

0.4 0.2 2.8

8.9 9.8 0.0

0.9 0.3 0.2

25.4 20.5 0.0

0.0 1.4 1.0

0.29 0.75 1.14

58.0 94.8 90.7 99.9 57.7

33.7 99.2 99.1 99.4 69.4

7.0 0.5 17.2 4.1 4.2

6.7 0.2 3.5 6.7 3.5

3.8 12.8 15.6 0.0 6.8

1.7 0.2 0.0 0.0 5.7

0.5 18.6 16.3 0.0 14.4

0.1 0.2 0.0 0.0 12.3

1.28 0.40 1.11 1.68 0.88

Heavily indebted poor countries (HIPCs) HIPC HIPC HIPC decision completion Inititaive pointd assistancee pointc

Benin Bolivia Burkina Faso Burundi Cameroon Chad Congo, Dem. Rep. Congo, Rep. Ethiopia Gambia, The Ghana Guinea Guinea-Bissau Guyana Haiti

Jul. 2000 Feb. 2000 Jul. 2000 Aug. 2005 Oct. 2000 May 2001 Jul. 2003 Apr. 2006 Nov. 2001 Dec. 2000 Feb. 2002 Dec. 2000 Dec. 2000 Nov. 2002 Nov. 2006

Mar. 2003 Jun. 2001 Apr. 2002 Floating Apr. 2006 Floating Floating Floating Apr. 2004 Floating Jul. 2004 Floating Floating Dec. 2003 Floating

MDRI assistancef

$ millions

$ millions

328 1,663 672 826 1,569 202 6,875 1,679 2,284 83 2,595 676 515 732 ..

571 1,004 573 .. 707 .. .. .. 1,383 .. 1,963 .. .. 140 ..

HIPC HIPC HIPC decision completion Inititaive pointc pointd assistancee

Honduras Madagascar Malawi Mali Mauritania Mozambique Nicaragua Niger Rwanda São Tomé & Principe Senegal Sierra Leone Tanzania Uganda Zambia

Jul. 2000 Dec. 2000 Dec. 2000 Sep. 2000 Feb. 2000 Apr. 2000 Dec. 2000 Dec. 2000 Dec. 2000 Dec. 2000 Jun. 2000 Mar. 2002 Apr. 2000 Feb. 2000 Dec. 2000

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

MDRI assistancef

$ millions

$ millions

688 1,035 1,211 667 770 2,599 4,098 798 814 120 605 683 2,511 1,282 3,096

733 1,219 618 985 424 1,004 466 489 206 .. 1,297 .. 1,919 1,705 1,522

a. Includes basic health, education, nutrition, and water and sanitation services. b. Estimates of market access to New Zealand and Norway for least developed countries are 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 System database. c. The date refers to the Enhanced Heavily Indebted Poor Countries (HIPC) initiative. The following countries reached their decision point under the original HIPC framework: Bolivia in September 1997, Burkina Faso in September 1997, Côte d’Ivoire in March 1998, Guyana in December 1997, Mali in September 1998, Mozambique in April 1998, and Uganda in April 1997. d. The date refers to the Enhanced HIPC framework. The following countries also reached completion points under the original framework: Bolivia in September 1998, Burkina Faso in July 2000, Guyana in May 1999, Mali in September 2000, Mozambique in July 1999, and Uganda in April 1998. e. HIPC debt relief is committed in net present value (NPV) terms as of the decision point (plus topping-up assistance at completion point in the cases of Burkina Faso, Ethiopia, Niger, and Rwanda) and is converted to end-2005 NPV terms. f. Multilateral Debt Relief Initiative (MDRI) assistance has been delivered in full to all post-completion point countries, shown in end-2005 NPV terms.

26

2007 World Development Indicators

1.4

WORLD VIEW

Millennium Development Goals: overcoming obstacles About the data

Achieving the Millennium Development Goals will require an open, rule-based global economy in which all countries, rich and poor, participate. Many poor countries, lacking the resources to finance their development, burdened by unsustainable levels of debt, and unable to compete in the global marketplace, need assistance from rich countries. For goal 8— develop a global partnership for development—many of the indicators therefore monitor the actions of members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD). Official development assistance (ODA) has risen in recent years as a share of donor countries’ gross national income (GNI), but the poorest countries will need additional assistance to achieve the Millennium Development Goals. Official aid rose to a record of $106 billion in 2005, and donor countries have pledged to increase ODA to more than $130 billion (in 2004 dollars) by 2010. However, this would still fall short of levels considered necessary to achieve the Millennium Development Goals. One of the most important actions that high-income economies can take to help is to reduce barriers to the exports of low- and middle-income economies. The European Union has launched a program to eliminate tariffs on developing country exports of “everything but arms,” and the United States offers special concessions to exports from Sub-Saharan Africa. However, there are still many restrictions built into these programs. Average tariffs in the table reflect tariff schedules applied by high-income OECD members to exports of countries designated least developed countries by the United Nations. Agricultural commodities, textiles, and clothing are three of the most important exports of

developing economies. Although average tariffs have been falling, averages may disguise high tariffs targeted at specific goods (see table 6.7 for estimates of the share of tariff lines with “international peaks” in each country’s tariff schedule). The averages in the table include ad valorem duties and ad valorem equivalents of non-ad valorem duties. Subsidies to agricultural producers and exporters in OECD countries are another form of barrier to developing economies’ exports. The table shows the value of total support to agriculture as a share of the economy’s gross domestic product (GDP). Agricultural subsidies in OECD economies are estimated at $385 billion in 2005. The Debt Initiative for Heavily Indebted Poor Countries (HIPCs) is the first comprehensive approach to reducing the external debt of the world’s poorest, most heavily indebted countries. It represents an important step in placing debt relief within an overall framework of poverty reduction. A major review in 1999 led to an enhancement of the original framework. The Multilateral Debt Relief Initiative (MDRI), proposed by the Group of Eight countries, was launched in 2005 to further reduce the debt of HIPCs and provide additional resources to help them meet the Millennium Development Goals. Under the MDRI three multilateral institutions—the International Development Association (IDA), International Monetary Fund (IMF), and African Development Fund (AfDF)—provide 100 percent debt relief on eligible debts due to them from countries having completed the HIPC Initiative process. Debt relief under the two initiatives is expected to reduce the debt stocks of the 29 HIPCs that have reached the decision point by almost 90 percent. Debt service paid by these countries declined by about 2 percent of GDP between 1999 and 2005 and is expected to decline further in the medium term as a result of MDRI debt

1.4a

Location of indicators for Millennium Development Goal 8 Goal 8. Develop a global partnership for development 33.

Net ODA as a percentage of DAC donors’ gross national income

Table 6.9

34.

Proportion of ODA for basic social services

1.4

35.

Proportion of ODA that is untied

6.10

36.

Proportion of ODA received in landlocked countries as a percentage of GNI

—

37.

Proportion of ODA received in small island developing states as a percentage of GNI —

38.

40.

Proportion of total developed country imports (by value, excluding arms) from developing countries admitted free of duty 1.4 Average tariffs imposed by developed countries on agricultural products and textiles and clothing from developing countries 1.4, 6.7* Agricultural support estimate for OECD countries as a percentage of GDP 1.4

41.

Proportion of ODA provided to help build trade capacity

—

42.

Number of countries reaching HIPC decision and completion points

1.4

39.

43.

Debt relief committed under new HIPC initiative

1.4

44.

Debt services as a percentage of exports of goods and services

4.17*

45.

Unemployment rate of 15- to 24-year-olds

1.3, 2.8

46.

Proportion of population with access to affordable, essential drugs on a sustainable basis Telephone lines and cellular subscribers per 100 people

—

47.

1.3, 5.10

48a. Personal computers in use per 100 people

5.11

48b. Internet users per 100 people

5.11

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

relief. Nineteen of these countries have reached the completion point and have received nearly $29 billion in HIPC Initiative assistance and have received or are expected to receive $18 billion in MDRI assistance. Definitions • Net official development assistance (ODA) comprises grants and loans (net of repayments of principal) that meet the DAC definition of ODA and are made to countries and territories on the DAC list of recipient countries. • ODA for basic social services is aid reported by DAC donors for basic health, education, nutrition, and water and sanitation services. • Goods admitted free of tariffs refer to the value of exports of goods (excluding arms) from least developed countries admitted without tariff, as a share of total exports from least developed countries. • Average tariff is the simple mean tariff, the unweighted average of the effectively applied rates for all products subject to tariffs.• Agricultural products comprise plant and animal products, including tree crops but excluding timber and fish products. • Textiles and clothing include natural and synthetic fibers and fabrics and articles of clothing made from them. • Support to agriculture is the annual monetary value of all gross transfers from taxpayers and consumers arising from policy measures that support agriculture, net of the associated budgetary receipts, regardless of their objectives and impacts on farm production and income, or consumption of farm products. • HIPC decision point is the date at which a heavily indebted poor country with an established track record of good performance under adjustment programs supported by the International Monetary Fund and the World Bank commits to undertake additional reforms and to develop and implement a poverty reduction strategy.• HIPC completion point is the date at which the country successfully completes the key structural reforms agreed on at the decision point, including developing and implementing its poverty reduction strategy. The country then receives the bulk of debt relief under the HIPC Initiative without further policy conditions. • HIPC Initiative assistance is the present value of debt relief committed as of the decision point and measured in end-2005 terms.• MDRI assistance is the present value of debt relief under the Multilateral Debt Relief Initiative from IDA, IMF, and AfDB delivered to countries having reached the HIPC completion point and measured in end-2005 terms. Data sources The indicators here, and where they appear throughout the rest of the book, are compiled by World Bank staff from primary and secondary sources. Data on ODA and support to agriculture are from the OECD. The World Trade Organization, in collaboration with the United Nations Conference on Trade and Development and the International Trade Centre, provided the estimates of goods admitted free of tariffs and average tariffs. Data on the HIPC Initiative and MDRI are from the August 2006 report “Heavily Indebted Poor Countries (HIPC) Initiative and Multilateral Debt Relief Initiative (MDRI)— Status of Implementation.”

2007 World Development Indicators

27

1.5

Women in development Female population

Life expectancy at birth

Pregnant women receiving prenatal care

28

Women in nonagricultural sector

Unpaid family workers

Women in parliaments

Male Female % of male % of female employment employment 2000–05a 2000–05a

% of total seats 1990 2006

% of total 2005

Male 2005

Female 2005

% 2000–05a

% of women ages 15–19 2000–05a

% of total 2004

.. 50.4 49.5 50.7 51.1 53.4 50.6 51.1 51.5 48.9 53.3 50.9 49.6 50.2 51.4 50.9 50.7 51.6 49.7 51.2 51.7 50.3 50.4 51.2 50.5 50.5 48.6 52.9 50.6 50.4 50.4 49.2 49.2 51.9 50.0 51.3 50.5 49.5 49.9 49.9 50.8 50.9 54.0 50.3 51.0 51.3 50.2 50.4 52.7 51.2 49.4 50.6 51.3 48.8 50.6 50.7

.. 73 70 40 71 70 78 77 70 63 63 77 54 63 72 35 67 69 48 44 54 46 78 39 43 75 70 79 70 43 52 77 45 72 75 73 76 65 72 68 68 53 67 42 76 77 53 55 68 76 57 77 64 54 44 52

.. 79 73 43 79 76 83 82 75 65 74 82 56 67 77 34 75 76 49 46 61 47 83 40 45 81 74 85 76 45 54 81 47 79 79 79 80 72 78 73 74 57 78 43 82 84 54 58 75 82 58 82 72 54 47 53

16 91 81 66 98 93 .. .. 70 49 .. .. 81 79 99 97 97 .. 73 78 38 83 .. 62 39 .. 90 .. 94 68 88 92 88 .. 100 .. .. 99 84 70 86 70 .. 28 .. .. 94 91 95 .. 92 .. 84 82 62 79

.. .. .. .. .. 6 .. .. .. 33b .. .. 22 16 .. .. .. .. 23 .. 8 28 .. .. 37 .. .. .. 21 .. .. .. .. .. .. .. .. 23 .. 10 .. 14 .. 17 .. .. 33 .. .. .. 14 .. .. .. .. 18

.. 31.7 17.0 .. 45.5 46.5 48.6 46.2 48.8 23.1 56.0 44.8 .. 36.5 .. 43.0 46.7 53.0 14.6 .. 51.3 21.6 49.4 .. 12.8 38.1 40.9 47.3 48.3 20.1 .. 38.5 .. 46.2 37.7 47.1 48.8 38.2 42.7 20.6 34.8 .. 52.2 40.6 50.7 47.2 .. .. 50.3 46.6 .. 40.7 38.8 .. .. ..

years

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, 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

Teenage mothers

2007 World Development Indicators

.. .. 7.1 .. 0.8 1.1 0.2 0.6 .. 9.9 .. .. .. 5.2 .. 1.4 5.5 1.2 .. .. 31.6 9.5 0.1 .. .. 1.4 .. 0.2 3.5 .. .. 1.8 .. 1.3 .. 0.3 0.2 .. 3.7 9.0 7.7 .. 0.5 5.2 0.5 .. .. .. 19.0 0.5 .. 4.3 21.3 .. .. ..

.. .. 13.6 .. 1.6 0.8 0.4 1.6 .. 48.0 .. .. .. 11.1 .. 1.2 9.3 2.6 .. .. 53.3 27.2 0.2 .. .. 3.2 .. 1.4 7.7 .. .. 3.5 .. 4.4 .. 1.3 1.3 .. 11.0 25.8 7.7 .. 0.5 9.9 0.4 .. .. .. 39.0 1.9 .. 14.7 24.5 .. .. ..

4 29 2 15 6 36 6 12 .. 10 .. 9 3 9 .. 5 5 21 .. .. .. 14 13 4 .. .. 21 .. 5 5 14 11 6 .. 34 .. 31 8 5 4 12 .. .. .. 32 7 13 8 .. .. .. 7 7 .. 20 ..

27 7 6 15 35 5 25 32 11 15 29 35 7 17 .. 11 .. 22 12 31 10 9 21 11 7 15 20 .. .. 8 9 39 9 22 36 16 37 20 .. 2 17 22 19 22 38 12 9 13 9 32 11 13 8 19 14 2

Female population

Life expectancy at birth

Pregnant women receiving prenatal care

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

Teenage mothers

Women in nonagricultural sector

Unpaid family workers

Women in parliaments

Male Female % of male % of female employment employment 2000–05a 2000–05a

% of total seats 1990 2006

% of total 2005

Male 2005

Female 2005

% 2000–05a

% of women ages 15–19 2000–05a

% of total 2004

49.6 52.4 48.7 50.1 49.3 .. 50.3 50.5 51.5 50.6 51.1 48.0 52.1 49.9 50.0 49.9 40.0 50.8 50.0 54.3 51.0 53.5 50.1 48.4 53.4 50.1 50.3 50.3 49.2 50.2 50.5 50.3 51.1 52.2 49.9 50.3 51.6 50.3 50.4 50.4 50.4 50.9 50.0 48.9 49.4 50.3 43.8 48.5 49.6 48.5 49.6 49.7 49.7 51.5 51.7 52.0

67 69 63 66 70 .. 77 78 78 69 79 71 61 50 61 74 75 65 54 66 70 34 42 72 65 71 55 41 71 48 52 70 73 65 65 68 41 58 47 62 77 78 68 45 44 78 73 64 73 56 69 68 69 71 75 74

71 77 64 70 73 .. 82 82 83 73 86 74 72 48 67 81 80 72 57 77 75 36 43 77 77 76 57 40 76 49 55 77 78 72 68 73 42 64 47 63 82 82 73 45 44 83 76 65 78 57 74 73 73 79 81 82

83 .. .. 92 .. 77 .. .. .. 98 .. 99 .. 88 .. .. .. .. 27 .. 96 90 85 .. .. 81 80 92 74 57 64 .. .. 98 94 68 85 76 91 28 .. .. 86 41 58 .. 100 36 .. .. 94 92 88 .. .. ..

.. .. .. 10 .. .. .. .. .. .. .. 4 .. 23 .. .. .. .. .. .. .. .. .. .. .. .. 34 33 .. 40 16 .. .. .. .. 7 41 .. 18 21 .. .. 25 .. 25 .. .. .. .. .. .. 13 8 .. .. ..

46.8 47.0 17.3 31.1 13.7 .. 47.6 49.6 41.3 47.0 41.2 25.0 49.4 38.7 .. 41.6 25.2 43.8 .. 53.2 .. .. .. .. 52.2 42.3 .. 12.4 36.9 .. .. 37.5 37.4 54.6 50.3 21.8 .. .. .. .. 45.4 50.5 .. 7.8 .. 49.2 25.7 8.6 43.5 35.4 43.9 34.6 40.4 47.2 46.6 39.3

years

1.5

WORLD VIEW

Women in development

12.1 0.3 .. .. .. .. 0.6 0.2 3.0 0.4 1.5 .. 1.0 .. .. 1.3 .. 6.5 .. 2.5 .. .. .. .. 2.6 6.4 29.7 .. 2.2 .. .. 0.8 5.5 0.5 18.4 21.6 .. .. 12.8 .. 0.2 0.4 .. .. .. 0.2 .. 16.4 2.8 .. .. 2.6 8.2 4.1 1.0 0.1

8.3 0.7 .. .. .. .. 0.9 0.5 5.8 2.5 8.6 .. 1.3 .. .. 14.8 .. 15.9 .. 2.1 .. .. .. .. 4.4 12.0 51.9 .. 9.6 .. .. 4.7 11.0 1.5 31.7 52.5 .. .. 22.0 .. 1.0 0.9 .. .. .. 0.3 .. 46.9 3.7 .. .. 7.4 17.4 7.3 2.3 0.9

10 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 ..

2007 World Development Indicators

23 10 8 11 4 26 13 14 17 12 9 6 10 7 20 13 2 0 25 19 5 12 13 8 22 28 7 14 9 10 .. 17 .. 22 7 11 35 .. 27 6 37 32 21 12 6 38 2 21 17 1 10 29 16 20 21 ..

29

1.5

Women in development Female population

Life expectancy at birth

Pregnant women receiving prenatal care

years % of total 2005

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 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 Europe EMU

51.3 53.6 51.5 46.0 50.8 50.2 50.7 49.7 51.5 51.2 50.4 50.9 50.9 49.2 49.7 51.8 50.4 51.6 49.7 50.4 50.2 50.9 50.6 50.7 49.6 49.6 50.8 50.0 54.2 31.9 51.1 50.8 51.5 50.3 49.7 50.1 49.1 49.3 49.9 50.4 49.7 w 49.2 49.8 49.5 51.4 49.6 49.1 52.1 50.6 49.5 48.8 50.1 50.7 51.1

Male 2005

68 59 43 71 55 70 40 78 70 74 47 47 77 72 55 42 78 79 72 61 46 68 53 67 72 69 59 49 62 77 77 75 72 64 71 68 71 60 39 38 66 w 58 68 68 66 64 69 65 69 68 63 46 76 77

Female 2005

75 72 46 75 58 76 43 82 78 81 49 49 84 77 58 41 83 84 76 67 47 74 57 73 76 74 67 51 74 82 81 81 79 71 77 73 76 63 38 37 70 w 60 73 73 74 67 73 74 76 72 64 47 82 83

% 2000–05a

94 .. 94 .. 79 .. 68 .. .. .. .. 92 .. 100 60 90 .. .. 71 71 78 92 85 92 92 81 98 92 .. .. .. .. .. 97 94 86 96 41 93 .. .. .. .. 89 .. .. 90 .. .. .. .. .. .. ..

Teenage mothers

Women in nonagricultural sector

Unpaid family workers

Women in parliaments

% of women ages 15–19 2000–05a

% of total 2004

Male Female % of male % of female employment employment 2000–05a 2000–05a

% of total seats 1990 2006

.. .. 4 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 26 .. .. .. .. .. 4 31 .. .. .. .. .. .. .. 3 .. .. 32 ..

a. Data are for the most recent year available. b. Refers to women 15–49. c. Less than 0.5.

30

2007 World Development Indicators

46.5 50.9 .. 13.5 .. 45.4 .. 47.0 52.0 47.6 .. 45.9 42.0 43.2 16.8 29.9 50.9 47.1 18.2 53.3 .. 46.4 .. 41.1 25.0 19.9 .. .. 55.1 14.5 49.4 48.5 46.8 39.5 41.5 49.1 17.9 .. .. 21.8 38.1 w 23.4 40.9 40.2 44.2 36.2 40.6 47.6 43.3 17.7 17.8 .. 46.0 45.1

7.7 0.1 .. .. .. .. .. 0.3 0.1 4.3 .. 0.5 0.8 4.2 .. .. 0.3 1.4 .. .. 3.0 14.7 .. 0.5 .. 8.9 .. 10.3 0.6 .. 0.3 0.1 0.9 .. 1.8 21.9 6.4 .. .. 10.4 .. w .. .. .. 3.5 .. .. 3.0 4.4 .. .. .. 0.6 1.4

21.2 0.0 .. .. .. .. .. 1.3 0.2 6.9 .. 1.1 2.4 20.9 .. .. 0.3 2.8 .. .. 4.6 31.4 .. 1.9 .. 49.8 .. 40.5 0.6 .. 0.5 0.1 2.0 .. 3.3 50.3 32.2 .. .. 13.6 .. w .. .. .. 7.5 .. .. 7.1 7.1 .. .. .. 2.7 3.2

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 11 14 14 .. 13 17 .. 12 4 6 .. 12 12

11 10 49 0 19 .. 15 21 20 12 8 33 36 5 15 11 47 25 12 18 30 .. 9 19 23 4 16 30 9 0 20 15 11 18 18 27 .. 0c 15 16 17 w 16 16 16 15 16 18 14 .. 8 13 16 22 24

About the data

1.5

WORLD VIEW

Women in development Definitions

Despite much progress in recent decades, gender

Women’s wage work is important for economic

• Female population is the percentage of the popu-

inequalities remain pervasive in many dimensions of

growth and the well-being of families. But restricted

lation that is female. • Life expectancy at birth is

life—worldwide. But while disparities exist through-

access to education and vocational training, heavy

the number of years a newborn infant would live if

out the world, they are most prevalent in poor devel-

workloads at home and in nonpaid domestic and

prevailing patterns of mortality at the time of its birth

oping countries. Gender inequalities in the alloca-

market activities, and labor market discrimination

were to stay the same throughout its life. • Pregnant

tion of such resources as education, health care,

often limit women’s participation in paid economic

women receiving prenatal care are the percentage

nutrition, and political voice matter because of the

activities, lower their productivity, and reduce their

of women attended at least once during pregnancy

strong association with well-being, productivity, and

wages. When women are in salaried employment,

by skilled health personnel for reasons related to

economic growth. This pattern of inequality begins

they tend to be concentrated in the nonagricultural

pregnancy. • Teenage mothers are the percentage

at an early age, with boys routinely receiving a larger

sector. However, in many developing countries

of women ages 15–19 who already have children

share of education and health spending than do girls,

women are a large part of agricultural employment,

or are currently pregnant. • Women in nonagricul-

for example.

often as unpaid family workers. Among people who

tural sector refers to women wage employees in the

Because of biological differences girls are expected

are unsalaried, women are more likely than men to

nonagricultural sector as a percentage of total non-

to experience lower infant and child mortality rates

be unpaid family workers, while men are more likely

agricultural employment. • Unpaid family workers

and to have a longer life expectancy than boys. This

than women to be self-employed or employers. There

are those who work without pay in a market-oriented

biological advantage, however, may be overshad-

are several reasons for this.

establishment or activity operated by a related per-

owed by gender inequalities in nutrition and medical

Few women have access to credit markets, capital,

son living in the same household.• Women in parlia-

interventions, and by inadequate care during preg-

land, training, and education, which may be required

ments are the percentage of parliamentary seats in

nancy and delivery, so that female rates of illness

to start up a business. Cultural norms may prevent

a single or lower chamber held by women.

and death sometimes exceed male rates, particularly

women from working on their own or from super-

during early childhood and the reproductive years. In

vising other workers. Also, women may face time

high-income countries women tend to outlive men by

constraints due to their traditional family respon-

four to eight years on average, while in low-income

sibilities. Because of biases and misclassification

countries the difference is narrower—about two to

substantial numbers of employed women may be

three years. The difference in child mortality rates

underestimated or reported as unpaid family workers

(table 2.20) is another good indicator of female

even when they work in association or equally with

social disadvantage because nutrition and medi-

their husbands in the family enterprise.

cal interventions are particularly important for the

Women are vastly underrepresented in decision-

1–5 age group. Female child mortality rates that are

making positions in government, although there is

as high as or higher than male child mortality rates

some evidence of recent improvement. Gender parity

might be indicative of discrimination against girls.

in parliamentary representation is still far from being

Having a child during the teenage years limits girls’

realized. In 2007 women represented 17 percent of

opportunities for better education, jobs, and income

parliamentarians worldwide, compared with 9 per-

and increases the likelihood of divorce and separa-

cent in 1987. Without representation at this level, it

tion. Pregnancy is more likely to be unintended dur-

is difficult for women to influence policy.

ing the teenage years, and births are more likely to

For information on other aspects of gender, see

be premature and are associated with greater risks

tables 1.2 (Millennium Development Goals: eradicat-

of complications during delivery and of death. In

ing poverty and improving lives), 2.3 (employment

many countries maternal mortality (tables 1.2 and

by economic activity), 2.4 (children at work), 2.5

2.16) is a leading cause of death among women of

(unemployment), 2.11 (education efficiency), 2.12

reproductive age. Most maternal deaths result from

(education completion and outcomes), 2.16 (repro-

Data on female population and life expectancy

preventable causes—hemorrhage, infection, and

ductive health), 2.18 (health risk factors and public

are from the World Bank’s population database.

complications from unsafe abortions. Prenatal care

health challenges), 2.19 (health gaps by income and

Data on pregnant women receiving prenatal care

is essential for recognizing, diagnosing, and promptly

gender), and 2.20 (mortality).

are from United Nations Children’s Fund’s State

Data sources

treating complications that arise during pregnancy.

of the World’s Children 2007. Data on teenage

In high-income countries most women have access

mothers are from Demographic and Health Sur-

to health care during pregnancy, but in developing

veys by Macro International. Data on labor force

countries an estimated 8 million women suffer preg-

and employment are from the International Labour

nancy- related complications every year, and over

Organization’s Key Indicators of the Labour Market,

half a million die (WHO 2004). This is reflected in

fourth edition. Data on women in parliaments are

the differences in maternal mortality ratios between

from the Inter-Parliamentary Union.

high- and low-income countries.

2007 World Development Indicators

31

1.6

Key indicators for other economies Population Surface Population area density

American Samoa Andorra Antigua and Barbuda Aruba Bahamas, The Bahrain Barbados Belize Bermuda Bhutan Brunei Darussalam Cape Verde Cayman Islands Channel Islands Comoros Cyprus Djibouti Dominica Equatorial Guinea Faeroe Islands Fiji French Polynesia Greenland Grenada Guam Guyana Iceland Isle of Man

thousands 2005

thousand sq. km 2005

people per sq. km 2005

58 66 83 100 323 727 270 292 63 637 374 507 45 149 600 758 793 72 504 48 848 257 57 107 170 751 297 78

0.2 0.5 0.4 0.2 13.9 0.7 0.4 23.0 0.1 47.0 5.8 4.0 0.3 0.9 2.2 9.3 23.2 0.8 28.1 1.4 18.3 4.0 410.5 0.3 0.6 215.0 103.0 0.6

292 141 188 528 32 1,023 627 13 1,265 14 71 126 173 .. 269 82 34 96 18 35 46 70 0 313 308 4 3 136

About the data

Gross national income

Gross domestic product

Life Adult expectancy literacy rate at birth

$ millions 2005b

Per capita $ 2005b

PPPa Per capita $ millions $ 2005 2005

% growth 2004–05

Per capita % growth 2004–05

years 2005

.. .. 855 .. .. 10,288 .. 1,042 .. 798 .. 976 .. .. 389 13,633 803 271 .. .. 2,684 .. .. 408 .. 770 14,414 2,138

..d ..e 10,500 ..e ..e 14,370 ..d 3,570 ..e 1,250 f ..e 1,930 ..e ..e 650 18,430 1,010 3,800 ..d ..e 3,170 ..e ..e 3,860 ..e 1,020 48,570 27,590

.. .. .. .. 969 11,700 .. .. .. .. 15,470 21,290 .. .. 1,967 6,740 .. .. .. .. .. .. 6,000 g 3,041 g .. .. .. .. 2,000 g 1,201 g 16,446 22,230 2,240 g 1,776g 400 5,560 3,731g 7,580 g .. .. 5,052 5,960 .. .. .. .. 773 7,260 .. .. 4,230 g 3,178 g 10,315 34,760 .. ..

.. .. 7.2 1.6 .. 6.9 .. 3.1 .. 6.1 1.7 5.8 .. .. 4.2 3.7 3.2 6.4 10.0 .. 0.7 .. .. –4.1 .. –2.2 5.5 6.3

.. .. 5.4 .. .. 5.3 .. –0.2 .. 2.6 –0.5 3.4 .. .. 2.1 1.3 1.4 6.0 7.5 .. –0.1 .. .. –5.1 .. –2.4 3.9 6.0

.. .. .. .. 71 75 75 72 79 64 77 71 .. 79 63 79 53 .. 42 .. 68 74 .. .. 75 64 81 ..

Carbon dioxide emissions

% ages 15 thousand and older metric tons 2006c 2003

.. .. .. 97 .. 87 .. .. .. 60 93 .. .. .. .. 97 .. .. 87 .. .. .. .. .. .. .. .. ..

293 .. 399 2,154 1,868 21,872 1,190 780 498 385 4,549 143 304 .. 88 7,278 366 139 165 659 1,117 692 568 220 4,081 1,630 2,187 ..

Definitions

This table shows data for 55 economies—small

• Population is based on the de facto definition of

plus net receipts of primary income (compensation

economies with populations between 30,000 and

population, which counts all residents regardless of

of employees and property income) from abroad.

1 million and smaller economies if they are members

legal status or citizenship—except for refugees not

Data are in current U.S. dollars converted using the

of the World Bank. Where data on gross national

permanently settled in the country of asylum, who

World Bank Atlas method (see Statistical methods).

income (GNI) per capita are not available, the esti-

are generally considered part of the population of

• GNI per capita is gross national income divided by

mated range is given. For more information on the

their country of origin. The values shown are midyear

midyear population. GNI per capita in U.S. dollars is

calculation of GNI (gross national product, or GNP, in

estimates for 2005. See also table 2.1. • Surface

converted using the World Bank Atlas method. • PPP

the System of National Accounts 1968) and purchas-

area is a country’s total area, including areas under

GNI is gross national income converted to interna-

ing power parity (PPP) conversion factors, see About

inland bodies of water and some coastal water-

tional dollars using purchasing power parity rates. An

the data for table 1.1. Since 2000 this table has

ways. • Population density is midyear population

international dollar has the same purchasing power

excluded France’s overseas departments—French

divided by land area in square kilometers. • Gross

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

Guiana, Guadeloupe, Martinique, and Réunion—for

national income (GNI) is the sum of value added by

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

which GNI and other economic measures are now

all resident producers plus any product taxes (less

added by all resident producers plus any product

included in the French national accounts.

subsidies) not included in the valuation of output

taxes (less subsidies) not included in the valuation

32

2007 World Development Indicators

Population Surface Population area density

Kiribati Liechtenstein Luxembourg Macao, China Maldives Malta Marshall Islands Mayotte Micronesia, Fed. Sts. Monaco Netherlands Antilles New Caledonia Northern Mariana Islands Palau Qatar Samoa São Tomé and Principe Seychelles Solomon Islands San Marino St. Kitts and Nevis St. Lucia St. Vincent & Grenadines Suriname Timor-Leste Tonga Vanuatu Virgin Islands (U.S.)

Gross national income

Gross domestic product

PPPa Per capita $ millions $ 2005 2005

thousands 2005

thousand sq. km 2005

people per sq. km 2005

$ millions 2005b

Per capita $ 2005b

99 35 457 460 329 404 63 180 110 33 183 234 79 20 813 185 157 84 478 28 48 165 119 449 976 102 211 109

0.7 0.2 2.6 0.0 0.3 0.3 0.2 0.4 0.7 0.0 0.8 18.6 0.5 0.5 11.0 2.8 1.0 0.5 28.9 0.1 0.4 0.6 0.4 163.3 14.9 0.8 12.2 0.4

136 217 176 16,318 1,097 1,261 351 481 158 17,128 228 13 166 44 74 65 163 184 17 470 133 270 305 3 66 142 17 311

119 .. 26,315 .. 762 5,491 185 .. 254 .. .. .. .. 154 .. 373 68 691 297 .. 369 744 421 1,141 588 178 331 ..

1,210 .. .. ..e 58,050 29,841 .. ..e 2,320 .. 13,610 7,650 2,930 .. .. ..d 2,300 .. .. ..e .. ..e .. ..e .. ..d 7,670 .. .. ..e 2,020 1,199 g 440 .. 8,180 1,347g 620 898 g .. ..e 7,840 600 4,580 985 3,530 769 2,540 .. 600 .. 1,750 823g 1,560 670 g .. ..e

.. .. 65,340 .. .. 18,960 .. .. .. .. .. .. .. .. .. 6,480 g .. 15,940 g 1,880 g .. 12,500 5,980 6,460 .. .. 8,040 g 3,170 g ..

1.6

Life Adult expectancy literacy rate at birth

% growth 2004–05

Per capita % growth 2004–05

years 2005

0.3 .. 4.0 6.7 –5.2 2.5 3.5 .. 0.3 .. .. .. .. 5.5 6.1 5.4 3.2 –2.3 5.0 .. 8.8 5.8 2.2 5.1 2.5 2.3 2.8 ..

–0.9 .. 3.3 6.0 –7.5 1.9 0.1 .. –0.4 .. .. .. .. 5.0 1.4 4.7 0.9 –3.3 2.4 .. 8.2 4.6 1.7 4.5 –2.8 2.0 0.8 ..

.. .. 79 80 68 80 .. .. 68 .. 76 75 .. .. 74 71 63 .. 63 .. .. 74 72 70 57 73 69 79

WORLD VIEW

Key indicators for other economies

Carbon dioxide emissions

% ages 15 thousand and older metric tons 2006c 2003

.. .. .. 91 96 88 .. .. .. .. .. 96 .. .. 89 .. .. 92 .. .. .. .. .. 90 .. 99 74 ..

29 .. 9,927 1,864 443 2,462 .. .. .. .. 4,051 1,868 .. 242 46,172 150 92 546 179 .. 125 326 194 2,238 161 114 88 13,524

a. PPP is purchasing power parity; see Definitions. b. Calculated using the World Bank Atlas method. c. Actual reference year varies by country; for more information see the original source. d. Estimated to be upper middle-income ($3,466–$10,725). e. Estimated to be high-income ($10,726 or more). f. Included in the aggregates for low-income economies based on earlier data. g. Based on regression; others are extrapolated from the latest International Comparison Program benchmark estimates.

of output. Growth is calculated from constant price GDP data in local currency. • 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

Data sources

15 and older who can, with understanding, read and

The indicators here and throughout the rest of

write a short, simple statement about their everyday

the book are compiled by World Bank Group staff

life. • Carbon dioxide emissions are those stemming

from primary and secondary sources. More infor-

from the burning of fossil fuels and the manufacture

mation about the indicators and their sources can

of cement. They include carbon dioxide produced

be found in the About the data, Definitions, and

during consumption of solid, liquid, and gas fuels

Data sources entries that accompany each table

and gas flaring.

in subsequent sections.

2007 World Development Indicators

33 Text figures, tables, and boxes

2

PEOPLE

ntroduction

Introduction

T

he wide health divide

Advances in technology and knowledge for health and hygiene have transformed life over the past 50 years. In 1960 more than 20 percent of children in developing countries died before reaching their fifth birthday; by 2005 this had fallen to just over 8 percent. The declines are large, even for the poorest countries (figure 2a). But this reassuring picture, painted by rising global averages, obscures substantial disparities among the world’s regions and among the poor within countries. For millions of people health services and modern medicines are still out of reach, and many die prematurely from diseases that are easily prevented or cured. More than 25 years after the Health for All declaration, improving the health of the poorest people in developing countries remains a challenge. What can improve all this? There is no consensus on which determinants are most important across countries. But there is agreement on the need to reduce extreme income poverty, the major risk for poor health and premature death. The World Health Organization (WHO) concurs, noting that a poverty-oriented health strategy requires complementary policies in other sectors (WHO 2003). These include improving access to education, enhancing the position of women and other marginalized groups, shaping development policies in agriculture and rural development, and promoting open and participatory governance. Priorities in healthcare are also clear: focus on health problems and diseases that affect the poor disproportionately. Health gains require directing program benefits toward the poor and increasing the quality and availability of health services, especially where they are least available. This section looks at the rich-poor health divide between and within countries—and at the factors behind that divide.

Child mortality has fallen in the past 25 years for countries at all incomes 1980

Under-five mortality (log) 2.75 2005

2a 2005

1980

2.25 1.75 1.25 0.75 0.25 1.75 2.00 2.25 2.50 2.75 3.00 3.25 3.50 3.75 4.00 4.25 4.50 4.75 GDP per capita (constant $, log) Source: World Bank estimates.

2007 World Development Indicators

35

The divide between rich and poor countries Differences in the health of rich and poor countries remain

The differing patterns of mortality and well-being reflected

large and in some cases are increasing. Under-five mortal-

in the age distributions of death for developing and high-

ity fell more than 36 percent in high-income countries from

income countries show their impact on life expectancies at

1990 to 2005, but only 20 percent in developing countries,

birth (figures 2d, 2e, and 2f). In developing countries, where

as preventable diseases continue to take a toll on the world’s

deaths of children under age five are still the major health

poorest people. But more important than the changes in pro-

issue, average life expectancy at birth is 65 years. But several

portions are the levels: under-five mortality is five times high-

countries—such as Lesotho, Zambia, and Zimbabwe, with high

er in middle-income countries than in high-income countries

AIDS-related mortality—have life expectancies of less than 40

and 15 times higher in lower-income countries (figure 2b).

years. In high-income countries, by contrast, noncommunica-

What accounts for these disparities? Child mortality

ble illnesses—such as cardiovascular diseases, diabetes, and

from malaria doubled from 1990 to 2001, with the largest

related conditions of high blood pressure, high cholesterol, and

increase in Sub-Saharan Africa (Lopez and others 2006).

excessive body weight—cluster deaths at older ages, and the

Other increases in child mortality in developing countries

average life expectancy at birth is 79 years. Indeed, in Canada,

came from HIV/AIDS, again with the largest increase in Sub-

France, Japan, Norway, Sweden, and Switzerland life expec-

Saharan Africa, and problems in the first months of life, which

tancies of 80 years and above are the norm. So any efforts

depend strongly on the quality and availability of prenatal ser-

to improve health and increase life expectancy in developing

vices. Child deaths from these causes are far less common in

countries will have to focus on diseases that kill children.

high-income countries, just as they are from acute respiratory

Why are there health gaps between rich and poor coun-

infections, diarrheal diseases, and measles. But for develop-

tries? Poverty makes people in developing countries more vul-

ing countries, these diseases, along with malnutrition, remain

nerable to disease. Nearly a third of the people in South Asia

significant causes of avoidable child deaths (figure 2c).

and half those in Sub-Saharan Africa lived on less than $1

Under-five mortality is 15 times higher in lowincome countries than in high-income countries Under-five mortality (per 1,000)

1990

2b 2005

In Sierra Leone most deaths occur before age 5

160 140

Age group

120 100 80 60 40 20 0 Low-income

All developing

Middle-income

2d

Age distribution of death, Sierra Leone around 2005

Female

95–99 90–94 85–89 80–84 75–79 70–74 65–69 60–64 55–59 50–54 45–49 40–44 35–39 30–34 25–29 20–24 15–19 10–14 5–9 0–4

60

High-income

Male

50

40

30

20

10

0

10

20

30

40

50

60

Share of total deaths (%) Source: Harmonized estimates from WHO, UNICEF, and World Bank.

Little reduction in risks for poor children Risk of death by cause for children under five, per 1,000

Source: World Bank 2006f.

2c Developing High income 1990 2001 1990 2001

A child born in Denmark can expect to live to be 78

20

Age group

15 10 5 0 Perinatal causes

Acute respiratory infection

Diarrheal diseases

Source: Lopez and others 2006.

36

2007 World Development Indicators

Malaria

Measles

HIV/AIDS

2e

Age distribution of death, Denmark around 2005

Male

Female

95–99 90–94 85–89 80–84 75–79 70–74 65–69 60–64 55–59 50–54 45–49 40–44 35–39 30–34 25–29 20–24 15–19 10–14 5–9 0–4

60

50

40

30

20

10

0

10

20

Share of total deaths (%) Source: World Bank 2006f.

30

40

50

60

The health divide within countries: the rich-poor gap a day in 2002. The majority of them typically lack access to

Inequalities in health within countries are pervasive. Even in

safe drinking water and adequate sanitation, food, education,

healthy countries such as Finland, the Netherlands, and the

employment, health information, and professional healthcare.

United Kingdom, the poor die 5–10 years before the rich (Carr

Almost half the people in Sub-Saharan Africa cannot obtain

2004). But the inequalities are most apparent in poorer devel-

essential drugs (Jamison and others 2006). Many developing

oping countries. Studies from many developing countries show

countries experienced little increase in immunization coverage

that the poorest 20 percent of the population fares far worse

between 1990 and 2005, and in 2005 only 75 percent of chil-

than the richest 20 percent on a range of health outcomes,

dren ages 12–23 months were vaccinated against measles

including child mortality and nutritional status (box 2g, figures

and diphtheria, pertussis, and whooping cough, compared

2h and 2i). On average a child in the poorest 20 percent is

with almost 95 percent for high-income countries.

twice as likely to die before age 5 as a child in the richest

Several barriers beyond low income exclude people in

20 percent. The disparity is similar for maternal nutrition, with

developing countries from getting appropriate care, and these

women in the poorest 20 percent almost twice as likely to be

can be related to services, clients, and institutions. Service

malnourished as those in the richest 20 percent.

factors include the high cost of care and transportation, poor

Severe malnutrition among children reveals more pro-

quality and inappropriate care, and negative staff attitudes.

nounced inequality, with children in the poorest 20 percent

Client factors include social and cultural constraints on wom-

more than three times as likely to be underweight as children

en’s movements and limited information about their health

in the richest 20 percent. The inequality is largest in South

needs and availability of services. And institutional factors

Asia, where 21 percent of children in the poorest 20 percent

include men’s control over decisionmaking and budgets, local

were underweight, compared with 6 percent in the richest.

perceptions about illness and treatment norms, and discrimi-

Demographic and Health Surveys find that gaps in the use of health services are closely related to economic

nation in health settings. A health gap becomes a life gap

2f

Life expectancy at birth (years) 80

1990

2005

Under-five mortality falls with rising income

2h

Under-five mortality (per 1,000, average for 56 countries) 150

70 120

60 50

90

40 60

30 20

30

10 0

0 Low-income

All developing

Middle-income

High-income

Source: World Bank estimates.

Health inequalities by social, cultural, and geographic factors

Second

Third

Fourth

Richest 20%

Source: Gwatkin and others 2007.

Box 2g

Inequalities in health go beyond income to such sociocultural, demographic, and geographic factors as sex, race, religion, ethnic group, language, and residence. In parts of India and China infant girls are more likely to die than infant boys because the cultural preference for male children puts girls at a disadvantage in nutrition and healthcare early in life. Women and girls also face discrimination in healthcare because cultural norms restrict them from traveling long distances, especially alone. Poor communities—rural, remote, and in urban slums—typically face multiple health risks related to gaps in infrastructure, services, and trained personnel. For example, ethnic minorities, especially in isolated regions in Bangladesh, were less likely to be vaccinated for childhood diseases. Source: Carr 2004; Ashford, Gwatkin, and Yazbeck 2006.

Poorest 20%

Health inequalities in developing countries

2i

Ratio of poorest to richest 20% of the population (average for 56 countries) 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 Severe child underweight

Under-five mortality

Women malnourished

Source: Gwatkin and others 2007.

2007 World Development Indicators

37

Main determinant of health status: health spending status (box 2j and figure 2k). On average, children ages

Differences in health spending contribute to global dispari-

12–23 months in the richest 20 percent of the population

ties in health outcomes (figure 2m). In rich countries, total

are more than twice as likely as those in the poorest 20 per-

health spending, at 6 percent of GDP, is almost twice that

cent to have received basic immunizations. Inequality in

of developing countries, and childhood vaccinations, skilled

immunization is especially high in Sub-Saharan Africa: only

attendants at birth, and access to effective health interven-

32 percent of children in the poorest 20 percent have been

tions are almost universal. In developing countries, where

fully immunized, compared with 60 percent in the richest

access to free health services is seen as a basic human

20 percent.

right, public spending on health is less than 3 percent of

Use of professional healthcare during childbirth also var-

GDP. In low-income countries the annual per capita spend-

ies by income. Rich women are four times more likely to use

ing on healthcare in 2004 was just $32, well below the $60

modern methods of birth control than their poorer counter-

that the WHO deems sufficient for an adequately performing

parts and nearly five times more likely to be attended by a

health system (WHO, World Health Report 2000). By contrast,

skilled health professional during childbirth. Several coun-

annual per capita health spending in high-income countries

tries, such as Benin, Morocco, Nicaragua, and Vietnam, have

was $3,724.

reduced inequalities and increased the coverage of trained

The most obvious barrier to expanding health coverage

medical staff attending childbirths for the poorest women (fig-

in developing countries is the current low level of spending.

ure 2l). Childbirths attended by trained staff among the poor-

Expanding access to successful interventions will require more

est 20 percent more than doubled in Nicaragua from 1997

funds, a situation made more difficult as HIV/AIDS spreads

to 2001, from 33 percent to 78 percent. In a few countries,

and more spending is allocated to the treatment of AIDS and

such as Chad and Ghana, inequalities increased because of

AIDS-related opportunistic infections, such as tuberculosis

lack of progress in coverage among poor women.

and pneumonia. As public funds for general health shrink, the

Why do the poor receive and seek less health care than the rich?

Box 2j

According to World Development Report 2006: Equity and Development (World Bank 2005d), inequities occur when some groups of people have less say and fewer opportunities to shape events and institutions around them, resulting in institutions that favor the privileged, who are often the rich. In health this translates into a lower likelihood of the poor taking preventive measures and seeking and using healthcare. Government actions affect the health of poor people. Public spending on health can influence the type and quality of services available to the poor. Governments may allocate high proportions of their health budgets to urban hospitals, leaving rural residents without adequate health facilities. Income is another important constraint. In South Africa people in the poorest 20 percent have to travel an average of nearly two hours to obtain medical attention, compared with 34 minutes for those in the wealthiest 20 percent. Additional barriers that lower demand for health services include a lack of knowledge about hygiene, nutrition, and the availability of treatment options, particularly among the uneducated. This can keep people from seeking care when they need it, even when price is not an issue. In India immunization rates are low, even though immunization is free: mothers cited lack of knowledge of the benefits of vaccination and of the clinic location as the main reasons why their children had not been immunized. Lack of knowledge can also lead people to pay for inappropriate healthcare. Unqualified providers can overprescribe treatment to patients who do not know what is in their best interest. For example, instead of effective and inexpensive oral rehydration therapy, a poor child in Indonesia gets more than four (often useless) drugs per diarrhea episode. Poorer members of a community often have less say in whether to seek care than wealthier members, and this can affect the level of resources used in their interest. Similarly, within a family, women and children have less voice than men and older family members.

38

2007 World Development Indicators

Rich people use health services more than poor people

2k

Ratio of richest to poorest 20% of the population, 1992–2002 5 4 3 2 1 0 Use of oral rehydration therapy

Child vaccinations

Prenatal care (three visits)

Skilled assistance at delivery

Source: Ashford, Gwatkin, and Yazbeck 2006.

Some countries have reduced inequalities in use of professional healthcare in childbirth

2l

Lowest 20 percent

Highest 20 percent

Childbirth attended by a medically trained person (%) 100 80 60 40 20 0 1997 2002 1997 2001 Vietnam Nicaragua Source: Demographic and Health Surveys.

1996

2001 Benin

costs are borne more by households and the private sector.

thus designed to give everyone equal access to care, and

In 2004 more than 80 percent of the people in developing

this rationale is typically invoked to justify direct government

countries paid out of pocket for health services, compared

involvement in service provision. In reality, equal access is

with just 37 percent in high-income countries.

elusive, and research confirms that publicly financed health-

Greater public spending is not, however, always associ-

care benefits the rich more than the poor (figure 2o). In 21

ated with better outcomes, and performance varies across

countries the richest 20 percent received more than 26 per-

countries based on the capabilities of government and health

cent of government health spending, compared with 16 per-

systems. In many countries staff ostensibly delivering ser-

cent for the poorest 20 percent. Even health programs that

vices do not, and absenteeism is high (figure 2n). Corrup-

address illnesses affecting the poor tend to favor the rich.

tion in the form of informal payments, coupled with the low

In Sub- Saharan Africa the rich benefited more (53 percent)

technical quality of service providers and the poor attitudes

from prophylactic treatment for malaria than did the poor

of health staff, especially to the poorer population, often

(34 percent).

discourage a second visit. According to World Development

Primary healthcare is often free in the public health sys-

Report 2006: Equity and Development (World Bank 2005d),

tem, but treatment for major illnesses can be costly if pay-

more than 70 percent of patients in Azerbaijan, Poland, and

ment is required for drugs and services on top of transport

the Russian Federation, and more than 90 percent in Arme-

costs and time off from work. Indeed, health shocks can push

nia, made “informal payments” for services.

a high proportion of households into poverty because of out-

To improve health conditions among the poor and vul-

of-pocket health expenditures (figure 2p). This underscores

nerable in developing countries, governments support free

the need for policymakers to maintain and improve the health

or subsidized health services, often as part of a national

status of the poor through effective interventions—and to

policy to reduce poverty. Government spending on health is

protect households from falling into poverty.

Differences in healthcare spending contribute to global disparities Health expenditure (% of GDP)

2m High income

Developing

Public health spending benefits the rich most Share of health expenditure captured (%)

12

30

10

25

8

20

6

15

4

10

2

5

0

2o

0 Total

Public

Poorest 20%

Source: WHO and World Bank.

Source: Filmer 2003.

Where are healthcare workers hiding?

2n Absence rates among healthcare workers in primary health facilities

Health shocks can push households into poverty

2p

Share of individuals experiencing catastrophic personal expenditure, Colombia 2003 (%)

Inpatient

Outpatient

8

Country

(%)

7

India (14 states)

43

6

Indonesia

42

5

Bangladesh

35

Uganda

35

Peru

26

Papua New Guinea

19

4 3 2 1 0 Poorest 20%

Source: World Bank 2003c.

Richest 20%

Second

Third

Fourth

Richest 20%

Source: Baeza and Packard 2006.

2007 World Development Indicators

39

Tables

2.1

Population dynamics Total population

Average annual population growth rate

Population age composition

Dependency ratio

%

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, 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

40

.. 3.3 25.3 10.5 32.6 3.5 17.1 7.7 7.2 104.0 10.2 10.0 5.2 6.7 4.3 1.4 149.4 8.7 8.5 5.7 9.7 11.7 27.8 3.0 6.1 13.2 1,135.2 5.7 35.0 37.8 2.5 3.1 12.7 4.8 10.5 10.4 5.1 7.1 10.3 55.7 5.1 3.0 1.6 51.2 5.0 56.7 1.0 0.9 5.5 79.4 15.5 10.2 8.9 6.2 1.0 6.9

millions 2005

.. 3.1 32.9 15.9 38.7 3.0 20.3 8.2 8.4 141.8 9.8 10.5 8.4 9.2 3.9 1.8 186.4 7.7 13.2 7.5 14.1 16.3 32.3 4.0 9.7 16.3 1,304.5 6.9 45.6 57.5 4.0 4.3 18.2 4.4 11.3 10.2 5.4 8.9 13.2 74.0 6.9 4.4 1.3 71.3 5.2 60.9 1.4 1.5 4.5 82.5 22.1 11.1 12.6 9.4 1.6 8.5

2007 World Development Indicators

2015

.. 3.2 38.0 20.9 42.5 3.0 22.3 8.3 9.2 168.0 9.2 10.5 11.2 10.8 3.9 1.7 208.8 7.1 17.3 10.6 17.1 20.2 34.9 4.6 12.5 17.9 1,378.1 7.6 51.5 77.9 5.2 5.0 21.6 4.3 11.4 10.1 5.5 10.1 15.1 88.1 8.0 5.8 1.3 86.8 5.3 62.4 1.6 1.9 4.2 81.8 26.5 11.2 15.8 11.8 2.1 9.7

% 1990–2005 2005–15

.. –0.3 1.7 2.8 1.2 –1.1 1.2 0.4 1.1 2.1 –0.3 0.3 3.3 2.1 –0.7 1.4 1.5 –0.8 2.9 1.9 2.5 2.2 1.0 2.0 3.2 1.4 0.9 1.3 1.8 2.8 3.2 2.3 2.4 –0.5 0.4 –0.1 0.3 1.5 1.7 1.9 2.0 2.5 –1.0 2.2 0.3 0.5 2.5 3.2 –1.3 0.3 2.4 0.6 2.3 2.8 3.0 1.4

.. 0.4 1.5 2.7 0.9 –0.2 0.9 0.1 0.9 1.7 –0.6 0.1 2.8 1.7 –0.1 –0.4 1.1 –0.8 2.7 3.4 1.9 2.1 0.8 1.4 2.5 0.9 0.5 0.9 1.2 3.0 2.7 1.4 1.7 –0.2 0.1 –0.2 0.2 1.3 1.3 1.7 1.5 2.8 –0.3 2.0 0.2 0.2 1.5 2.2 –0.7 –0.1 1.8 0.0 2.3 2.3 2.9 1.3

Ages 0–14 2005

Ages 15–64 2005

Ages 65+ 2005

.. 27.0 29.6 46.5 26.4 20.8 19.6 15.5 25.8 35.5 15.2 16.8 44.2 38.1 16.5 37.6 27.9 13.8 47.2 45.0 37.1 41.2 17.6 43.0 47.3 24.9 21.4 14.4 31.0 47.3 47.1 28.4 41.9 15.5 19.1 14.6 18.8 32.7 32.4 33.6 34.0 44.8 15.2 44.5 17.3 18.2 40.0 40.1 18.9 14.3 39.0 14.3 43.2 43.7 47.5 37.5

.. 64.7 65.8 51.1 63.4 67.1 67.7 67.8 67.1 60.9 70.2 65.6 53.1 57.4 69.5 59.0 66.0 69.4 50.1 52.3 59.5 55.1 69.3 53.0 49.7 67.0 71.0 73.6 63.9 50.1 49.9 65.8 54.9 67.3 70.1 71.2 66.2 63.1 61.8 61.7 60.7 52.9 68.3 52.5 66.8 65.2 55.6 56.1 66.8 66.9 57.3 67.5 52.5 52.7 49.4 58.5

.. 8.3 4.5 2.5 10.2 12.1 12.7 16.7 7.1 3.6 14.7 17.6 2.7 4.5 14.0 3.3 6.1 16.8 2.7 2.7 3.4 3.7 13.1 4.1 3.0 8.1 7.6 12.0 5.1 2.7 2.9 5.8 3.3 17.2 10.8 14.2 15.0 4.1 5.8 4.8 5.4 2.3 16.5 2.9 15.9 16.6 4.4 3.7 14.3 18.8 3.7 18.2 4.3 3.5 3.1 4.0

dependents as proportion of workingage population Young Old 2005 2005

.. 0.4 0.5 0.9 0.4 0.3 0.3 0.2 0.4 0.6 0.2 0.3 0.8 0.7 0.2 0.6 0.4 0.2 0.9 0.9 0.6 0.7 0.3 0.8 1.0 0.4 0.3 0.2 0.5 0.9 0.9 0.4 0.8 0.2 0.3 0.2 0.3 0.5 0.5 0.5 0.6 0.8 0.2 0.8 0.3 0.3 0.7 0.7 0.3 0.2 0.7 0.2 0.8 0.8 1.0 0.6

.. 0.1 0.1 0.0a 0.2 0.2 0.2 0.2 0.1 0.1 0.2 0.3 0.1 0.1 0.2 0.1 0.1 0.2 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.1 0.1 0.1 0.3 0.2 0.2 0.2 0.1 0.1 0.1 0.1 0.0a 0.2 0.1 0.2 0.3 0.1 0.1 0.2 0.3 0.1 0.3 0.1 0.1 0.1 0.1

Crude death rate

Crude birth rate

per 1,000 people 2005

per 1,000 people 2005

.. 6 5 22 8 8 6 9 6 8 15 10 12 8 9 27 7 15 16 18 10 17 7 22 20 5 6 6 5 20 13 4 17 11 7 11 10 6 5 6 6 11 13 19 9 9 13 11 10 10 10 9 6 13 19 13

.. 13 21 48 18 12 13 10 17 26 9 11 41 29 9 26 20 9 46 45 30 34 11 37 49 16 12 8 21 50 44 17 36 9 11 10 12 24 22 26 24 39 11 39 11 13 30 34 11 8 31 9 34 41 50 30

Total population

Average annual population growth rate

Population age composition

Dependency ratio

%

1990

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

4.9 10.4 849.5 178.2 54.4 18.5 3.5 4.7 56.7 2.4 123.5 3.2 16.3 23.4 19.7 42.9 2.1 4.4 4.1 2.7 2.7 1.6 2.1 4.3 3.7 1.9 12.0 9.5 17.8 8.9 2.0 1.1 83.2 4.4 2.1 23.9 13.4 40.8 1.4 19.1 15.0 3.4 4.0 8.5 90.6 4.2 1.8 108.0 2.4 4.1 4.2 21.8 61.1 38.1 9.9 3.5

millions 2005

7.2 10.1 1,094.6 220.6 68.3 .. 4.2 6.9 58.6 2.7 127.8 5.5 15.1 34.3 22.5 48.3 2.5 5.1 5.9 2.3 3.6 1.8 3.3 5.9 3.4 2.0 18.6 12.9 25.3 13.5 3.1 1.2 103.1 4.2 2.6 30.2 19.8 50.5 2.0 27.1 16.3 4.1 5.1 14.0 131.5 4.6 2.6 155.8 3.2 5.9 5.9 28.0 83.1 38.2 10.5 3.9

2015

8.8 9.8 1,248.5 244.0 78.4 .. 4.7 8.1 58.0 2.7 124.9 6.7 15.0 44.1 23.3 49.2 3.4 5.7 7.3 2.2 4.0 1.7 4.4 7.0 3.3 2.1 23.8 16.0 29.5 18.0 4.0 1.3 114.3 4.1 2.9 34.2 23.5 54.9 2.2 32.7 16.8 4.4 6.2 19.2 160.8 4.8 3.2 190.5 3.8 7.0 7.1 32.1 98.7 37.6 10.9 4.1

% 1990–2005 2005–15

2.6 –0.2 1.7 1.4 1.5 .. 1.1 2.6 0.2 0.7 0.2 3.6 –0.5 2.5 0.9 0.8 1.2 1.0 2.4 –1.0 1.8 0.8 2.9 2.0 –0.5 0.4 2.9 2.1 2.3 2.8 2.8 1.1 1.4 –0.2 1.3 1.5 2.6 1.4 2.5 2.3 0.6 1.2 1.8 3.3 2.5 0.6 2.2 2.4 2.0 2.4 2.2 1.7 2.0 0.0a 0.4 0.7

2.0 –0.3 1.3 1.0 1.4 .. 1.1 1.5 –0.1 0.3 –0.2 2.0 –0.1 2.5 0.3 0.2 2.8 1.0 2.1 –0.6 1.0 –0.3 2.9 1.8 –0.5 0.2 2.5 2.2 1.5 2.9 2.6 0.7 1.0 –0.3 1.2 1.3 1.7 0.8 1.0 1.9 0.3 0.6 1.8 3.2 2.0 0.5 2.1 2.0 1.5 1.7 1.8 1.4 1.7 –0.2 0.3 0.4

Ages 0–14 2005

Ages 15–64 2005

Ages 65+ 2005

39.2 15.7 32.1 28.3 28.7 .. 20.2 27.8 14.0 31.2 14.0 37.2 23.1 42.8 25.0 18.6 24.3 31.5 40.9 14.7 28.6 38.6 47.1 30.1 16.7 19.6 44.0 47.3 32.4 48.2 43.0 24.6 31.0 18.3 30.5 31.1 44.0 29.5 41.5 39.0 18.2 21.3 38.9 49.0 44.3 19.6 34.5 38.3 30.4 40.3 37.6 32.2 35.1 16.3 15.9 22.3

56.9 69.1 62.7 66.2 66.8 .. 68.9 62.1 66.0 61.2 66.3 59.6 68.3 54.4 68.2 72.0 73.9 62.4 55.5 68.4 64.0 56.2 50.7 65.9 67.8 69.3 52.9 49.6 63.0 49.1 53.6 68.8 63.7 71.6 65.8 64.1 52.7 65.6 55.0 57.3 67.7 66.4 57.8 49.0 52.7 65.4 63.0 57.9 63.6 57.3 58.7 62.5 61.0 70.7 67.0 65.7

3.9 15.2 5.3 5.5 4.5 .. 10.9 10.1 20.0 7.6 19.7 3.2 8.5 2.8 6.8 9.4 1.8 6.1 3.7 16.9 7.3 5.3 2.2 4.1 15.5 11.1 3.1 3.0 4.6 2.7 3.4 6.6 5.3 10.1 3.8 4.8 3.3 4.9 3.5 3.7 14.1 12.3 3.3 2.0 3.0 15.0 2.6 3.8 6.0 2.4 3.7 5.3 3.9 12.9 17.1 12.1

dependents as proportion of workingage population Young Old 2005 2005

0.7 0.2 0.5 0.4 0.4 .. 0.3 0.4 0.2 0.5 0.2 0.6 0.3 0.8 0.4 0.3 0.3 0.5 0.7 0.2 0.4 0.7 0.9 0.5 0.2 0.3 0.8 1.0 0.5 1.0 0.8 0.4 0.5 0.3 0.5 0.5 0.8 0.4 0.8 0.7 0.3 0.3 0.7 1.0 0.8 0.3 0.5 0.7 0.5 0.7 0.6 0.5 0.6 0.2 0.2 0.3

0.1 0.2 0.1 0.1 0.1 .. 0.2 0.2 0.3 0.1 0.3 0.1 0.1 0.1 0.1 0.1 0.0a 0.1 0.1 0.2 0.1 0.1 0.0a 0.1 0.2 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.1 0.0a 0.1 0.2 0.0a 0.1 0.1 0.0 a 0.1 0.1 0.1 0.2 0.3 0.2

2.1

PEOPLE

Population dynamics

Crude death rate

Crude birth rate

per 1,000 people 2005

per 1,000 people 2005

6 14 8 7 4 .. 7 6 10 6 9 3 10 14 11 5 2 7 12 14 7 25 20 4 13 9 12 21 5 17 13 7 4 12 6 6 20 9 6 8 8 7 5 20 19 9 3 7 5 10 5 6 5 10 10 8

28 10 24 20 15 .. 15 21 10 16 8 28 18 39 15 9 19 21 34 9 18 28 50 23 9 11 38 43 21 49 40 15 18 11 18 23 39 19 22 29 12 14 28 53 41 12 25 26 22 29 29 22 24 9 11 13

2007 World Development Indicators

41

2.1

Population dynamics Total population

Average annual population growth rate

Population age composition

Dependency ratio

%

1990

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 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 Europe EMU

millions 2005

23.2 21.6 148.3 143.1 7.1 9.0 16.4 23.1 8.0 11.7 8.1 10.5b 4.1 5.5 3.0 4.3 5.3 5.4 2.0 2.0 6.7 8.2 35.2 46.9 38.8 43.4 17.0 19.6 26.1 36.2 0.8 1.1 8.6 9.0 6.7 7.4 12.8 19.0 5.3 6.5 26.2 38.3 54.6 64.2 4.0 6.1 1.2 1.3 8.2 10.0 56.2 72.1 3.7 4.8 17.8 28.8 51.9 47.1 1.8 4.5 57.6 60.2 249.6 296.4 3.1 3.5 20.5 26.2 19.8 26.6 66.2 83.1 2.0 3.6 12.1 21.0 8.4 11.7 10.6 13.0 5,256.3 s 6,437.7 s 1,739.4 2,352.4 2,613.4 3,074.5 2,083.6 2,474.6 529.8 599.8 4,352.8 5,426.9 1,596.1 1,885.5 466.1 471.8 437.6 550.8 225.5 306.0 1,113.1 1,469.8 514.4 743.1 903.5 1010.8 295.3 313.9

2015

20.7 136.0 11.3 28.9 14.5 8.0 c 6.9 4.8 5.4 2.0 11.0 47.3 44.4 21.0 44.1 1.1 9.3 7.5 23.8 7.6 47.1 69.0 7.8 1.3 11.0 80.7 5.5 41.8 42.3 5.6 61.7 322.5 3.7 30.1 31.1 92.1 4.9 28.4 13.8 13.8 7,165.8 s 2,787.8 3,322.7 2,694.1 628.6 6,110.5 2,027.8 471.5 620.1 365.1 1,703.4 922.6 1055.3 316.7

% 1990–2005 2005–15

–0.5 –0.2 1.6 2.3 2.5 0.1d 2.0 2.4 0.1 0.0a 1.4 1.9 0.7 1.0 2.2 2.6 0.4 0.7 2.6 1.4 2.5 1.1 2.9 0.5 1.4 1.7 1.8 3.2 –0.6 6.3 0.3 1.1 0.7 1.6 2.0 1.5 4.1 3.7 2.2 1.4 1.4 w 2.0 1.1 1.1 0.8 1.5 1.1 0.1 1.5 2.0 1.9 2.5 0.7 0.4

–0.4 –0.5 2.2 2.2 2.2 –0.1c 2.2 1.1 –0.1 –0.2 2.9 0.1 0.2 0.7 2.0 –0.4 0.3 0.0a 2.2 1.5 2.1 0.7 2.4 0.2 1.0 1.1 1.3 3.7 –1.1 2.2 0.2 0.8 0.5 1.4 1.6 1.0 3.0 3.0 1.7 0.6 1.1 w 1.7 0.8 0.8 0.5 1.2 0.7 0.0a 1.2 1.8 1.5 2.2 0.4 0.1

Ages 0–14 2005

Ages 15–64 2005

Ages 65+ 2005

15.4 15.3 43.5 37.3 42.6 18.3 42.8 19.5 16.7 13.9 44.1 32.6 14.3 24.1 39.2 41.0 17.5 16.5 36.9 39.0 42.6 23.8 43.5 21.5 25.9 28.4 31.8 50.5 14.9 22.0 17.9 20.8 24.3 33.2 31.2 29.5 45.5 46.4 45.8 40.0 28.1 w 36.4 25.0 25.3 24.2 30.0 23.9 19.7 30.0 33.5 33.4 43.5 18.2 15.5

69.8 70.9 54.0 59.8 54.3 67.6 53.8 72.0 71.5 70.5 53.3 63.1 69.2 68.6 57.2 55.5 65.3 67.6 60.0 57.2 54.2 69.1 53.4 71.1 67.8 65.7 63.6 47.1 69.0 76.9 66.1 66.9 62.5 62.1 63.7 65.0 51.4 51.4 51.2 56.4 64.5 w 59.3 67.7 67.9 66.7 64.0 69.2 68.6 63.9 62.3 61.7 53.4 67.0 66.8

14.8 13.8 2.5 2.9 3.1 14.1 3.3 8.5 11.8 15.6 2.6 4.2 16.5 7.3 3.6 3.5 17.2 16.0 3.1 3.9 3.2 7.1 3.1 7.4 6.3 5.9 4.7 2.5 16.1 1.1 16.0 12.3 13.2 4.7 5.1 5.4 3.1 2.3 3.0 3.6 7.4 w 4.3 7.3 6.9 9.1 6.0 6.9 11.8 6.1 4.2 4.9 3.1 14.8 17.7

dependents as proportion of workingage population Young Old 2005 2005

0.2 0.2 0.8 0.6 0.8 0.3 0.8 0.3 0.2 0.2 0.8 0.5 0.2 0.4 0.7 0.7 0.3 0.2 0.6 0.7 0.8 0.3 0.8 0.3 0.4 0.4 0.5 1.1 0.2 0.3 0.3 0.3 0.4 0.5 0.5 0.5 0.9 0.9 0.9 0.7 0.4 w 0.6 0.4 0.4 0.4 0.5 0.3 0.3 0.5 0.5 0.5 0.8 0.3 0.2

0.2 0.2 0.0a 0.0a 0.1 0.2 0.1 0.1 0.2 0.2 0.0a 0.1 0.2 0.1 0.1 0.1 0.3 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.0a 0.2 0.2 0.2 0.1 0.1 0.1 0.1 0.0a 0.1 0.1 0.1 w 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.1 0.1 0.2 0.3

Crude death rate

Crude birth rate

per 1,000 people 2005

per 1,000 people 2005

12 16 18 4 11 14 23 4 10 9 17 21 9 6 11 20 10 8 3 7 16 7 12 8 6 6 8 15 17 1 10 8 9 6 5 6 4 8 22 23 9w 10 8 7 10 9 7 12 6 6 8 17 8 9

10 10 41 27 36 11 46 10 10 9 44 24 11 18 32 34 10 10 28 28 36 16 38 14 17 19 22 51 9 16 12 14 15 20 22 18 33 40 40 29 20 w 29 16 16 16 22 15 13 20 24 25 40 12 10

a. Less than 0.05. b. Includes population of Kosovo and Metahia until 1999. c. Projections are based on data for Serbia and Montenegro before it separated into two independent states in 2006. d. Data are for 1990–99.

42

2007 World Development Indicators

About the data

2.1

PEOPLE

Population dynamics Definitions

Population estimates are usually based on national

Dependency ratios take into account variations in

• Total population of an economy includes all resi-

population censuses, but the frequency and quality

the different age groups: the proportions of children,

dents regardless of legal status or citizenship—

vary by country. Most countries conduct a complete

elderly people, and working-age people in the popula-

except for refugees not permanently settled in the

enumeration no more than once a decade. Estimates

tion. Separate calculations of young-age and old-age

country of asylum, who are generally considered part

for the years before and after the censuses are inter-

dependency suggest the burden of dependency that

of the population of their country of origin. The values

polations or extrapolations based on demographic

the working-age population must bear in relation to

shown are midyear estimates for 1990 and 2005

models. Errors and undercounting occur even in

children and the elderly. But dependency ratios show

and projections for 2015. • Average annual popula-

high-income countries; in developing countries such

only the age composition of a population, not eco-

tion growth rate is the exponential change for the

errors may be substantial because of limits in the

nomic dependency. Some children and elderly people

transport, communications, and other resources

are part of the labor force, and many working-age

required to conduct and analyze a full census.

people are not.

The quality and reliability of official demographic

The vital rates shown in the table are based on

data are also affected by the public trust in the gov-

data derived from birth and death registration sys-

ernment, the government’s commitment to full and

tems, censuses, and sample surveys conducted by

accurate enumeration, the confidentiality and protec-

national statistical offices and other organizations,

tion against misuse accorded to census data, and

or on demographic analysis. The estimates for 2005

the independence of census agencies from undue

for many countries are national projections based

political influence. Moreover, the international com-

on extrapolations of levels and trends measured in

parability of population indicators is limited by dif-

earlier years.

period indicated. See Statistical methods for more information. • Population age composition refers to the percentage of the total population that is in specific age groups. • Dependency ratio is the ratio of dependents—people younger than 15 or older than 64—to the working-age population—those ages 15–64. • Crude death rate and crude birth rate are the number of deaths and the number of live births occurring during the year, per 1,000 population, estimated at midyear. Subtracting the crude death rate

ferences in the concepts, definitions, data collec-

Vital registers are the preferred source of these

from the crude birth rate provides the rate of natural

tion procedures, and estimation methods used by

data, but in many developing countries systems

increase, which is equal to the population growth

national statistical agencies and other organizations

for registering births and deaths do not exist or are

rate in the absence of migration.

that collect population data.

incomplete because of deficiencies in the coverage of

Of the 152 economies listed in the table, 130

events or of geographic areas. Many developing coun-

(about 86 percent) conducted a census between

tries carry out special household surveys that esti-

1995 and 2005. The currentness of a census, along

mate vital rates by asking respondents about births

with the availability of complementary data from sur-

and deaths in the recent past. Estimates derived in

veys or registration systems, is one of many objective

this way are subject to sampling errors as well as

ways to judge the quality of demographic data. In

errors due to inaccurate recall by the respondents.

some European countries registration systems offer

The United Nations Statistics Division monitors

complete information on population in the absence

the completeness of vital registration systems. The

of a census. See Primary data documentation for the

share of countries with at least 90 percent complete

most recent census or survey year and for the com-

vital registration increased from 45 percent in 1988

pleteness of registration.

to 62 percent in 2005. Still, some of the most popu-

Current population estimates for developing coun-

lous developing countries—China, India, Indonesia,

tries that lack recent census-based data, and pre-

Brazil, Pakistan, Bangladesh, Nigeria—do not have

and post-census estimates for countries with census

complete vital registration systems. Between 2003

data, are provided by the United Nations Population

and 2005, 51 percent of births and deaths and 48

Division and other agencies. The standard estimation

percent of infant deaths worldwide were registered

method requires fertility, mortality, and net migration

and reported.

Data sources The World Bank’s population estimates are compiled and produced by its Human Development Network and Development Data Group in consultation with its operational staff and country offices.

data, which are often collected from sample surveys,

International migration is the only other factor

some of which may be small or limited in coverage.

besides birth and death rates that directly determines

The population estimates are the product of demo-

a country’s population growth. From 1990 to 2000

graphic modeling and so are susceptible to biases

the number of migrants in high-income countries

Division’s World Population Prospects: The 2004

and errors because of shortcomings in the model as

increased by 23 million. About 190 million people

Revision; census reports and other statistical

well as in the data. Population projections are made

currently live outside their home country, accounting

publications from national statistical offi ces;

using the cohort component method.

for about 3 percent of the world’s population. Esti-

household surveys conducted by national agen-

The growth rate of the total population conceals

mating international migration is difficult. At any time

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

the fact that different age groups may grow at very

many people are located outside their home country

Disease Control and Prevention; Eurostat, Demo-

different rates. In many developing countries the pop-

as tourists, workers, or refugees or for other reasons.

graphic Statistics (various years); Centro Latino-

ulation under age 15 was previously growing rapidly

Standards relating to the duration and purpose of

americano de Demografía, Boletín Demográfico

but is now starting to shrink. Previously high fertility

international moves that qualify as migration vary,

(various years); and U.S. Bureau of the Census,

rates and declining mortality rates are now reflected

and accurate estimates require information on flows

International Database.

in the larger share of the working-age population.

into and out of countries that is difficult to collect.

Important inputs to the World Bank’s demographic work come from the United Nations Population

2007 World Development Indicators

43

2.2

Labor force structure Labor force participation rate

Labor force

% ages 15–64 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, 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

44

Ages 15 and older average annual % growth

Total millions

Female

Female % of labor force

1990

2005

1990

2005

1990

2005

1990–2005

1990

2005

.. 86.3 81.0 90.9 84.7 89.7 84.4 80.1 80.6 89.8 82.2 71.3 90.0 80.9 82.4 76.0 88.8 77.8 92.1 90.7 86.7 83.5 84.9 89.4 79.0 80.9 88.9 85.5 85.0 91.2 86.3 87.6 90.3 76.9 79.5 82.2 87.1 85.6 85.9 76.7 81.9 92.6 83.0 92.3 79.0 75.0 85.5 86.2 78.2 81.4 80.5 76.7 90.7 90.8 91.4 82.7

.. 75.7 83.5 92.2 82.4 65.9 80.8 77.4 78.1 88.1 72.3 72.5 86.5 84.3 78.3 68.2 83.6 62.6 90.2 93.2 81.4 81.1 82.6 89.4 77.0 76.0 87.8 81.1 85.2 91.1 86.6 84.8 89.1 71.0 82.3 77.4 82.6 84.0 85.4 76.9 78.7 90.7 73.6 90.7 76.8 73.5 83.9 86.6 76.1 79.3 75.7 78.8 84.7 88.6 93.0 83.3

.. 63.3 23.7 76.0 43.5 76.7 61.5 55.3 68.5 64.5 72.4 46.2 59.2 49.9 66.1 58.9 47.6 72.3 79.3 91.8 81.0 58.2 68.3 71.7 64.7 35.2 79.1 53.0 48.5 62.6 57.7 35.3 44.5 55.0 43.5 74.1 77.6 37.8 33.6 27.6 53.5 63.1 76.0 74.5 72.2 57.0 65.5 63.3 79.1 56.8 77.5 43.1 30.2 82.8 60.5 59.1

.. 54.7 38.0 75.6 61.1 55.4 67.4 63.8 66.2 55.2 66.4 57.3 54.8 64.5 70.5 46.7 61.0 52.4 79.5 92.8 78.0 53.9 72.8 70.8 66.0 40.9 75.8 62.2 65.9 63.1 56.1 48.6 40.1 57.5 50.8 64.0 74.2 48.5 64.1 21.6 50.4 59.8 64.4 73.5 72.8 62.4 64.1 60.3 52.4 67.4 71.8 56.0 35.2 82.6 63.1 57.9

.. 1.6 7.2 4.5 13.0 1.9 8.4 3.5 3.3 46.9 5.3 3.9 2.0 2.5 2.3 0.5 62.4 4.4 3.8 2.8 4.4 4.4 14.7 1.4 2.3 5.0 650.1 2.9 14.1 15.0 1.0 1.2 4.6 2.2 4.5 5.5 2.9 2.6 3.7 16.6 2.0 1.2 0.9 22.6 2.6 24.8 0.4 0.4 2.9 38.3 6.7 4.2 2.9 3.0 0.4 2.6

.. 1.4 13.4 7.0 18.4 1.3 10.3 4.0 4.1 63.9 4.8 4.5 3.3 4.2 2.1 0.6 91.3 3.1 5.8 3.8 6.8 6.3 17.6 1.8 3.7 6.5 776.0 3.7 22.3 22.9 1.5 2.0 6.8 2.0 5.4 5.2 2.8 3.8 6.4 22.9 2.8 1.8 0.7 31.6 2.7 27.1 0.6 0.7 2.3 41.0 9.8 5.1 4.1 4.4 0.6 3.7

.. –0.9 4.1 2.9 2.3 –2.8 1.3 0.8 1.5 2.1 –0.7 0.9 3.3 3.4 –0.7 1.2 2.5 –2.4 2.9 2.1 2.9 2.4 1.2 2.0 3.0 1.8 1.2 1.7 3.1 2.8 3.0 3.5 2.6 –0.8 1.1 –0.3 –0.2 2.5 3.6 2.1 2.3 2.5 –1.7 2.2 0.2 0.6 2.7 3.4 –1.7 0.4 2.5 1.4 2.3 2.6 2.9 2.2

.. 40.2 22.6 46.4 34.4 47.7 41.3 40.8 47.4 40.2 48.6 39.0 40.8 39.2 44.7 45.2 35.0 48.0 46.3 52.6 52.6 41.5 44.0 47.0 46.0 30.5 44.8 36.3 36.9 41.6 41.5 27.6 30.2 42.1 34.8 47.4 46.1 29.6 27.8 26.3 41.2 42.4 49.9 44.9 47.2 43.3 43.9 43.4 52.3 40.4 48.9 36.2 24.7 46.2 40.3 43.3

.. 42.1 30.7 45.8 42.9 49.2 45.5 44.6 47.7 36.9 49.3 43.5 38.3 43.6 48.1 41.8 42.9 46.0 46.6 51.9 51.4 39.9 46.4 46.1 46.9 35.1 44.5 46.6 44.3 41.2 40.3 35.1 29.3 45.0 37.4 45.2 46.8 35.9 42.4 21.7 40.2 41.1 49.4 44.9 47.8 45.9 43.3 41.6 43.4 45.2 48.0 40.9 31.2 46.6 40.9 41.7

2007 World Development Indicators

Labor force participation rate

Labor force

% ages 15–64 Male

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

2.2

PEOPLE

Labor force structure

Ages 15 and older average annual % growth

Total millions

Female

Female % of labor force

1990

2005

1990

2005

1990

2005

1990–2005

1990

2005

89.0 74.4 86.6 82.9 82.3 77.8 77.9 68.1 76.7 83.0 83.1 71.3 81.6 90.6 84.0 75.3 83.1 78.0 81.6 83.4 81.5 86.8 85.2 81.4 81.7 77.5 83.6 91.7 82.7 90.7 87.6 86.6 85.4 81.5 83.7 83.9 88.0 89.2 67.1 82.5 80.0 83.0 87.0 94.7 86.9 82.5 83.8 88.1 82.7 75.9 85.7 82.0 83.7 79.2 82.6 67.4

90.5 66.8 84.3 87.1 75.5 .. 80.4 65.6 74.3 78.0 84.8 79.7 80.1 89.6 80.4 77.3 86.4 77.5 82.3 71.9 83.9 73.8 83.8 82.8 72.4 73.2 86.3 89.9 83.7 85.1 85.1 84.1 83.0 76.0 83.3 83.8 82.7 87.7 64.5 80.6 84.5 83.3 87.4 95.5 85.8 83.6 82.7 85.7 83.0 75.2 86.9 83.5 84.7 68.8 79.7 67.7

34.6 57.3 40.3 52.1 22.5 16.4 42.3 46.8 44.6 71.3 57.1 18.6 68.0 76.2 56.4 49.7 35.6 65.0 56.3 75.0 34.4 59.4 55.9 19.9 70.4 52.8 79.5 86.2 45.3 75.1 57.8 45.2 36.2 70.4 59.3 25.6 88.1 71.2 50.6 50.4 53.1 63.2 36.8 72.4 49.0 69.9 15.7 28.8 41.6 72.3 54.4 48.6 48.7 65.1 59.2 35.0

56.5 53.5 36.0 53.0 40.5 .. 62.2 58.7 50.1 59.3 60.5 28.9 73.6 71.3 49.9 54.2 50.4 59.9 56.4 63.0 35.7 48.7 55.7 33.9 65.9 47.9 79.8 86.2 48.1 74.8 56.5 46.9 42.6 65.4 56.2 28.7 84.9 70.0 48.4 52.5 69.5 71.2 36.9 73.0 46.6 77.3 23.6 33.7 54.9 72.8 68.6 61.2 56.5 57.6 67.8 44.4

1.6 4.5 335.1 75.3 15.6 4.7 1.3 1.6 23.9 1.1 63.9 0.8 7.7 9.8 9.7 19.1 0.9 1.8 1.5 1.5 0.9 0.6 0.8 1.3 1.9 0.9 5.4 4.5 7.1 3.8 0.8 0.5 29.5 2.1 0.8 7.5 6.3 20.0 0.4 7.1 6.9 1.7 1.3 3.6 32.7 2.2 0.6 35.2 0.9 1.8 1.6 8.5 23.4 18.6 4.8 1.2

3.1 4.2 435.0 107.2 27.5 .. 2.1 2.7 24.4 1.2 66.6 1.8 8.1 15.5 10.7 24.4 1.4 2.3 2.4 1.1 1.4 0.6 1.2 2.3 1.6 0.9 8.6 5.9 11.0 5.5 1.2 0.6 42.3 2.2 1.2 11.1 9.3 27.4 0.6 10.5 8.6 2.2 1.9 5.9 47.9 2.5 1.0 56.5 1.5 2.6 2.8 13.3 37.1 17.3 5.6 1.5

4.4 –0.5 1.7 2.4 3.8 .. 3.0 3.4 0.1 0.3 0.3 6.0 0.3 3.0 0.6 1.6 3.2 1.4 2.8 –1.9 2.7 0.3 2.8 4.1 –1.1 0.1 3.1 1.9 2.9 2.5 2.7 1.4 2.4 0.1 2.4 2.6 2.6 2.1 2.5 2.6 1.4 1.7 2.7 3.4 2.5 0.9 3.5 3.2 3.1 2.5 3.4 3.0 3.1 –0.5 1.0 1.6

27.7 44.5 29.9 38.4 20.2 16.8 34.3 40.5 37.1 46.8 40.6 18.8 46.3 46.0 39.3 39.3 21.8 46.2 41.3 49.5 31.8 46.5 39.4 17.3 48.1 40.0 49.2 50.3 34.8 46.0 40.7 33.9 30.6 48.6 41.0 23.7 54.0 44.6 44.1 37.9 39.1 43.1 30.1 42.6 36.2 44.7 11.1 23.3 32.5 46.4 38.3 37.0 36.6 45.8 42.7 35.8

37.7 45.1 28.4 37.9 33.8 .. 43.0 47.0 40.1 43.6 41.1 24.4 49.6 43.8 38.7 40.8 25.4 44.2 40.6 48.7 30.4 44.5 39.9 27.1 49.2 39.2 48.4 49.8 35.8 47.5 40.4 35.7 35.2 47.7 40.1 25.5 53.5 45.0 43.6 40.5 44.2 46.6 29.8 42.0 34.7 47.3 16.4 27.0 38.8 47.6 43.5 42.0 39.8 45.7 46.5 41.5

2007 World Development Indicators

45

2.2

Labor force structure Labor force participation rate

Labor force

% ages 15–64 Male

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 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 Europe EMU

Female

1990

2005

1990

2005

77.2 81.6 88.3 81.3 87.8 77.0 90.2 83.9 82.5 76.9 95.8 81.6 80.3 82.9 78.9 79.6 86.0 90.2 83.7 77.6 92.1 90.6 90.8 79.7 79.2 84.5 80.0 92.4 79.7 92.4 87.9 85.1 85.9 78.5 82.4 85.5 67.0 76.1 90.4 81.0 85.5 w 87.0 85.8 86.7 82.2 86.3 87.8 80.8 85.9 79.9 86.9 87.8 82.1 78.5

69.5 75.3 84.9 80.4 83.4 76.0 94.5 82.8 76.4 75.5 95.1 81.9 80.7 81.9 72.5 74.5 79.0 87.6 89.2 65.8 90.7 84.5 90.4 82.5 78.4 76.0 76.5 87.3 72.4 92.0 81.9 81.5 86.1 75.7 85.7 82.4 68.8 77.5 91.5 85.2 83.8 w 85.0 84.0 85.4 77.9 84.4 87.0 74.0 83.5 79.3 84.8 86.3 80.4 77.4

61.1 71.7 87.4 15.6 63.4 54.9 55.6 54.2 70.6 63.3 63.1 57.4 41.9 48.2 27.8 39.6 81.9 62.8 29.7 56.2 90.2 79.2 55.2 45.9 22.1 36.2 69.1 82.0 70.7 25.9 67.2 67.5 54.3 64.4 39.8 79.4 9.5 28.6 67.8 69.9 58.9 w 50.6 63.9 66.2 55.0 59.0 74.3 65.1 43.8 24.5 41.7 65.1 58.6 51.7

55.3 67.1 82.0 18.5 58.4 54.7 58.4 56.7 62.4 66.6 61.0 49.3 57.2 38.5 24.2 32.9 74.9 75.3 39.9 49.5 88.2 71.0 51.7 51.4 31.1 27.2 65.1 81.2 62.9 39.0 69.3 70.1 66.3 60.6 61.9 77.4 10.9 30.8 68.3 64.5 57.9 w 47.8 62.7 65.1 52.6 56.7 71.4 57.9 56.0 31.1 38.1 62.6 63.8 61.1

a. Includes population of Kosovo and Metahia until 1999. b. Data are for 1990–99.

46

2007 World Development Indicators

Ages 15 and older average annual % growth

Total millions 1990

2005

11.0 77.2 3.1 5.1 3.1 4.9a 1.7 1.6 2.6 1.0 2.8 14.4 16.0 7.3 7.8 0.2 4.7 3.7 3.7 1.9 12.8 30.4 1.5 0.5 2.4 21.0 1.5 7.8 26.3 0.9 29.4 129.3 1.4 8.2 7.3 31.3 0.4 3.0 3.5 4.3 2,390.7 t 699.6 1,263.5 1,031.9 231.6 1,963.1 856.8 223.8 171.0 64.9 437.0 209.7 427.6 131.5

10.3 73.2 4.2 7.5 4.6 3.9 2.4 2.2 2.7 1.0 3.5 19.6 20.9 8.4 10.5 0.3 4.7 4.2 7.6 2.1 19.3 35.7 2.4 0.6 3.8 26.6 2.2 11.9 22.3 2.7 30.6 155.5 1.8 11.3 12.9 44.0 0.8 5.9 4.9 5.8 3,027.5 t 965.3 1,569.4 1,301.1 268.3 2,534.7 1,063.4 219.1 252.9 108.3 585.0 306.0 492.8 147.2

1990–2005

–0.4 –0.4 2.1 2.5 2.6 0.0 b 2.2 2.4 0.1 0.4 1.4 2.1 1.8 1.0 2.0 2.7 –0.1 0.9 4.8 0.8 2.7 1.1 3.1 1.9 3.0 1.6 2.4 2.8 –1.1 7.3 0.3 1.2 1.6 2.2 3.8 2.3 4.5 4.6 2.4 2.0 1.6 w 2.1 1.4 1.5 1.0 1.7 1.4 –0.1 2.6 3.4 1.9 2.5 0.9 0.8

Female % of labor force 1990

2005

44.3 48.3 51.0 11.4 43.4 41.7 38.5 38.8 46.3 45.5 39.9 41.6 34.3 34.8 26.0 38.0 47.7 40.4 26.2 42.2 50.2 46.6 38.5 36.1 21.5 29.4 46.9 47.5 49.2 9.8 44.0 44.4 39.9 45.4 31.8 48.3 11.9 27.3 43.2 47.2 39.9 w 35.7 41.8 42.0 40.6 39.6 44.1 45.6 34.0 22.9 30.6 43.0 41.3 39.6

46.2 49.0 51.2 15.2 42.5 42.2 38.5 39.9 45.1 46.2 39.2 38.2 41.0 30.4 24.8 32.9 47.4 46.6 30.6 43.8 49.4 46.2 36.9 38.9 27.6 26.4 46.7 48.3 49.1 13.4 46.0 46.2 44.2 44.6 40.9 48.5 13.1 27.9 42.2 44.0 40.1 w 35.0 42.1 42.3 40.9 39.4 43.8 44.9 40.7 27.6 29.4 42.1 43.7 43.7

About the data

2.2

PEOPLE

Labor force structure Definitions

The labor force is the supply of labor available for

methods to ensure comparability across countries

• Labor force participation rate is the proportion

the production of goods and services in an economy.

and over time, including collection and tabulation

of the population ages 15–64 that is economically

It includes people who are currently employed and

methodologies as well as for country-specific factors

active: all people who supply labor for the produc-

people who are unemployed but seeking work as well

such as military service requirements. The estimates

tion of goods and services during a specified period.

as first-time job-seekers. Not everyone who works is

are based mainly on labor force surveys. Some popu-

included, however. Unpaid workers, family workers,

lation census estimates are also included in the esti-

and students are among those usually omitted, and

mates, but only when no labor force survey data are

in some countries members of the military are not

available. Data from official government estimates

counted. The size of the labor force tends to vary dur-

are not included as these methodologies can differ

ing the year as seasonal workers enter and leave it.

significantly across countries and over time. Data

Data on the labor force are from labor force sur-

with limited age group and geographic coverage are

endpoint method (see Statistical methods for more

also excluded.

information). • Females as a percentage of the labor

veys, censuses, establishment censuses and surveys, and various types of administrative records

The labor force participation rate of the population

such as employment exchange registers and unem-

ages 15–64 provides an indication of the relative

ployment insurance schemes. For some countries

size of the labor supply. But in many developing coun-

a combination of these sources is used. While the

tries children under age 15 work full or part time.

resulting statistics may provide rough estimates of

And in some high-income countries many workers

the labor force, they are not comparable across coun-

postpone retirement past age 65. As a result, labor

tries or sometimes within countries because of the

force participation rates calculated in this way may

noncomparability of the original data, differences in

systematically over- or under-estimate actual rates.

concepts and methodologies, and the different ways

For further information on the labor force participa-

the original sources may be combined.

tion rate, consult the original source.

Labor force surveys are the most comprehensive

The labor force estimates in the table were cal-

source for internationally comparable labor force

culated by World Bank staff by applying labor force

data. They can be designed to cover all noninstitu-

participation rates from the ILO database to World

tionalized civilians, all branches and sectors of the

Bank population estimates to create a series consis-

economy, and all categories of workers, including

tent with these population estimates. This procedure

people who hold multiple jobs. By contrast, labor

sometimes results in estimates of labor force size

force data obtained from population censuses are

that differ slightly from those in the ILO’s Yearbook

often based on a limited number of questions on the

of Labour Statistics and its database Key Indicators

economic characteristics of individuals, with little

of the Labour Market. The labor force estimates in

scope to probe. The resulting data are often contrary

this year’s World Development Indicators, as were last

to labor force survey data and often vary consider-

year’s, are for the population ages 15 and older. In

ably from country to country, depending on the scope

previous editions the labor force included children

and coverage of the census. Establishment censuses

under age 15. For this reason, labor force estimates

and surveys provide data only on the employed popu-

are not comparable across editions.

lation, leaving out unemployed workers, workers in

In general, estimates of women in the labor force

small establishments, and workers in the informal

are lower than those of men and are not compa-

sector (International Labour Organization, Key Indica-

rable internationally, refl ecting the fact that for

tors of the Labour Market 2001–2002).

women demographic, social, legal, and cultural

• Total labor force comprises people ages 15 and older who meet the ILO definition of the economically active population. It includes both the employed and the unemployed. • Average annual growth rate of the labor force is calculated using the exponential

force show the extent to which women are active in the labor force.

The reference period of the census or survey is

trends and norms determine whether their activities

another important source of differences: in some

are regarded as economic. In many countries large

countries data refer to people’s status on the day

numbers of women work on farms or in other fam-

of the census or survey or during a specific period

ily enterprises without pay, while others work in or

before the inquiry date, while in others the data are

near their homes, mixing work and family activities

recorded without reference to any period. In devel-

during the day. Countries differ in the criteria used

oping countries, where the household is often the

to determine the extent to which such workers are

basic unit of production and all members contribute

to be counted as part of the labor force. In most

to output, but some at low intensity or irregular inter-

economies the gap between male and female labor

vals, the estimated labor force may be significantly

force participation rates has been narrowing since

smaller than the numbers actually working.

1980. This stems from both falling rates for men and

economically active population in its Yearbook of

The labor force participation rates presented in the

rising rates for women. The largest gap between men

Labour Statistics. Labor force numbers were cal-

table are from the International Labour Organization’s

and women in labor force participation is observed in

culated by World Bank staff, applying labor force

(ILO) Estimates and Projections of the Economically

the Middle East and North Africa, where low partici-

participation rates from the ILO database to popu-

Active Population, 5th edition. These new estimates

pation of women in the work force also brings down

lation estimates.

used stricter data selection criteria and enhanced

the overall labor force participation rate.

Data sources The labor force participation rates are from the ILO database Estimates and Projections of the Economically Active Population, 1980–2020, 5th edition. The ILO publishes estimates of the

2007 World Development Indicators

47

2.3

Employment by economic activity Agriculture

Male % of male employment 1990–92a 2000–05a

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, 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

48

.. .. .. .. 0 b, c .. 6 6 .. 54 .. 3c .. 3c .. .. 31c .. .. .. .. 53 6c .. .. 24 .. 1 2c .. .. 32 .. .. .. 9 7 26 10 c 35 48 .. 23 .. 11 .. .. .. .. 4 66 20 c .. .. .. ..

.. .. 20 .. 2c .. 5c 6c 41 50 .. 3c .. 6c .. 26 25c 11 .. .. 61 .. 4c .. .. 17 .. 0b 32 .. .. 21 .. 16 28 5 4 23 11c 28 30 .. 7 .. 7 5 .. .. 52 3 60 12c 50 .. .. ..

2007 World Development Indicators

Industry

Female % of female employment 1990–92a

2000–05a

.. .. .. .. 0 b, c .. 4 8 .. 85 .. 2c .. 1c .. .. 25c .. .. .. .. 68 2c .. .. 6 .. 0b 1c .. .. 5 .. .. .. 7 3 3 2c 52 15 .. 13 .. 6 .. .. .. .. 4 59 26c .. .. .. ..

.. .. 22 .. 1b, c .. 3c 6c 37 59 .. 1c .. 3c .. 19 16c 7 .. .. 59 .. 2c .. .. 6 .. 0b 8 .. .. 5 .. 19 10 3 2 2 4c 39 3 .. 4 .. 3 3 .. .. 57 2 50 14 c 18 .. .. ..

Male % of male employment

Services

Female % of female employment

Male % of male employment

Female % of female employment

1990–92a

2000–05a

1990–92a

2000–05a

1990–92a

2000–05a

1990–92a

2000–05a

.. .. .. .. 40 c .. 32 47 .. 16 .. 41c .. 42c .. .. 27c .. .. .. .. 14 31c .. .. 32 .. 37 35c .. .. 27 .. .. .. 55 37 23 29 c 25 23 .. 42 .. 38 .. .. .. .. 50 10 32c .. .. .. ..

.. .. 26 .. 33c .. 31c 40 c 15 12 .. 35c .. 39 c .. 29 27c 39 .. .. 12 .. 32c .. .. 29 .. 22 21 .. .. 26 .. 37 23 49 34 24 27c 23 25 .. 44 .. 38 35 .. .. 14 41 14 30 c 18 .. .. ..

.. .. .. .. 18 c .. 12 20 .. 9 .. 16c .. 17c .. .. 10 c .. .. .. .. 4 11c .. .. 15 .. 27 25c .. .. 25 .. .. .. 33 16 21 17c 10 23 .. 30 .. 15 .. .. .. .. 24 10 17c .. .. .. ..

.. .. 28 .. 11c .. 9c 13c 9 18 .. 11c .. 14 c .. 13 13c 29 .. .. 13 .. 11c .. .. 12 .. 7 16 .. .. 13 .. 18 14 27 12 15 12c 6 22 .. 24 .. 12 12 .. .. 4 16 15 10 c 23 .. .. ..

.. .. .. .. 59 c .. 61 46 .. 25 .. 56c .. 55c .. .. 43c .. .. .. .. 26 64 c .. .. 45 .. 63 63c .. .. 41 .. .. .. 36 56 52 62c 41 29 .. 36 .. 51 .. .. .. .. 47 23 48 c .. .. .. ..

.. .. 54 .. 66c .. 65c 55c 44 38 .. 62c .. 55c .. 43 48 c 50 .. .. 27 .. 64 c .. .. 54 .. 77 48 .. .. 52 .. 47 50 46 62 53 62c 49 45 .. 49 .. 56 60 .. .. 34 56 27 58 c 27 .. .. ..

.. .. .. .. 81c .. 84 72 .. 2 .. 81c .. 82c .. .. 65c .. .. .. .. 23 87c .. .. 79 .. 73 74 c .. .. 69 .. .. .. 61 81 76 81c 37 63 .. 57 .. 78 .. .. .. .. 72 32 56c .. .. .. ..

.. .. 49 .. 88 c .. 88 c 81c 54 23 .. 82c .. 82c .. 58 71c 64 .. .. 27 .. 88 c .. .. 83 .. 93 76 .. .. 82 .. 63 76 71 86 83 84 c 55 75 .. 72 .. 84 84 .. .. 38 82 36 76c 56 .. .. ..

Agriculture

Male % of male employment 1990–92a 2000–05a

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

53c .. .. 54 .. .. 19 5 8 36 6 .. .. 19 c .. 14 .. .. .. .. .. .. .. .. 25 .. .. .. 23 .. .. 15c 33 .. .. .. .. .. 45 .. 5 13 .. .. .. 7 .. 45 35 .. 3c 1c 53 .. 10 5

51c 7c .. 43 23 .. 9 3 5 25 4 4 35 .. .. 7 .. 51 .. 15c .. .. .. .. 17 20 77 .. 16 .. .. 11 21 41 43 41 .. .. 33 .. 4 9 43 .. .. 5 7 38 22 .. 39 1c 45 18 c 12 3

Industry

Female % of female employment 1990–92a

6c .. .. 57 .. .. 3 2 9 16 7 .. .. 20 c .. 18 .. .. .. .. .. .. .. .. 15 .. .. .. 20 .. .. 13c 10 .. .. .. .. .. 52 .. 3 8 .. .. .. 3 .. 69 3 .. 0 b, c 0 b, c 32 .. 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

2000–05a

1990–92a

2000–05a

1990–92a

2000–05a

1990–92a

2000–05a

1990–92a

2000–05a

13c 3c .. 45 34 .. 1 1 3 9 5 2 32 .. .. 9 .. 55 .. 8c .. .. .. .. 11 19 79 .. 11 .. .. 9 5 40 38 63 .. .. 29 .. 2 5 10 .. .. 2 5 65 4 .. 20 0c 25 17c 13 0b

18 c .. .. 15 .. .. 33 38 37 25 40 .. .. 23c .. 40 .. .. .. .. .. .. .. .. 46 .. .. .. 31 .. .. 36c 25 .. .. .. .. .. 21 .. 33 31 .. .. .. 34 .. 20 20 .. 33c 30 c 17 .. 39 27

20 c 42c .. 20 31 .. 39 32 39 27 35 23 24 .. .. 34 .. 13 .. 35c .. .. .. .. 37 34 7 .. 35 .. .. 34 30 21 19 23 .. .. 17 .. 30 32 19 .. .. 32 11 22 22 .. 19 31c 17 39 c 42 25

25c .. .. 13 .. .. 18 15 22 12 27 .. .. 9c .. 28 .. .. .. .. .. .. .. .. 31 .. .. .. 32 .. .. 48 c 19 .. .. .. .. .. 8 .. 10 13 .. .. .. 10 .. 15 11 .. 19 c 13c 14 .. 24 19

23c 21c .. 15 28 .. 12 11 18 5 18 13 10 .. .. 17 .. 7 .. 16c .. .. .. .. 21 30 6 .. 27 .. .. 29 19 12 14 15 .. .. 7 .. 8 11 17 .. .. 8 14 16 9 .. 10 13c 12 17c 21 11

29 c .. .. 31 .. .. 48 57 55 39 54 .. .. 58 c .. 46 .. .. .. .. .. .. .. .. 29 .. .. .. 46 .. .. 48 c 41 .. .. .. .. .. 32 .. 60 56 .. .. .. 58 .. 35 45 .. 64 c 69 c 29 .. 51 67

29 c 51c .. 37 46 .. 51 64 56 48 59 73 41 .. .. 59 .. 36 .. 49 c .. .. .. .. 46 46 16 .. 49 .. .. 55 49 38 39 36 .. .. 49 .. 62 59 32 .. .. 63 82 40 56 .. 42 68 c 39 43c 46 72

69 c .. .. 31 .. .. 78 83 70 72 65 .. .. 71c .. 54 .. .. .. .. .. .. .. .. 54 .. .. .. 48 .. .. 39 c 62 .. .. .. .. .. 29 .. 82 80 .. .. .. 86 .. 16 85 .. 80 c 87c 55 .. 63 80

63c 76c .. 40 37 .. 86 88 79 86 77 83 58 .. .. 74 .. 38 .. 75c .. .. .. .. 68 51 15 .. 62 .. .. 62 76 48 49 22 .. .. 63 .. 86 84 52 .. .. 90 80 20 86 .. 70 86c 64 66c 66 89

2007 World Development Indicators

49

2.3

Employment by economic activity Agriculture

Male % of male employment 1990–92a 2000–05a

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 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 Europe EMU

29 .. .. .. .. .. .. 1 .. .. .. .. 11c .. .. .. 5 4 .. .. 78 c 59 .. 15 .. 33 .. 91 .. .. 3 4 7c .. 17 .. .. .. .. .. .. w .. .. .. .. .. .. .. 20 .. .. .. 6 7

31 12 .. 5 .. .. .. 0b 6c 9 .. 13 6c 32c .. .. 3 5c 24 .. 80 c 44 .. 10 .. 22 .. 60 21 9 2 2 7c .. 16c 56 12 .. .. .. .. w .. .. .. 16 .. .. 17 21 .. .. .. 4 5

Industry

Female % of female employment 1990–92a

38 .. .. .. .. .. .. 0b .. .. .. .. 8c .. .. .. 2 4 .. .. 90 c 60 .. 6 .. 72 .. 91 .. .. 1 1 1c .. 2 .. .. .. .. .. .. w .. .. .. .. .. .. .. 14 .. .. .. 5 7

2000–05a

33 8 .. 1 .. .. .. 0b 3c 9 .. 7 4c 40 c .. .. 1 3c 58 .. 84 c 41 .. 2 .. 52 .. 77 17 0b 1 1 2c .. 2c 60 34 .. .. .. .. w .. .. .. 12 .. .. 17 10 .. .. .. 2 3

Male % of male employment 1990–92a

2000–05a

44 .. .. .. .. .. .. 36 .. .. .. .. 41c .. .. .. 40 37 .. .. 7c 16 .. 34 .. 26 .. 4 .. .. 41 34 36c .. 32 .. .. .. .. .. .. w .. .. .. .. .. .. .. 30 .. .. .. 38 42

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

50

2007 World Development Indicators

35 38 .. 24 .. .. .. 36 50 c 47 .. 33 41c 40 c .. .. 34 32c 31 .. 4c 22 .. 37 .. 28 .. 11 38 36 33 30 29 c .. 25c 21 28 .. .. .. .. w .. .. .. 34 .. .. 35 27 .. .. .. 34 39

Services

Female % of female employment 1990–92a

30 .. .. .. .. .. .. 32 .. .. .. .. 16c .. .. .. 12 15 .. .. 1c 14 .. 14 .. 11 .. 6 .. .. 16 14 21c .. 16 .. .. .. .. .. .. w .. .. .. .. .. .. .. 14 .. .. .. 19 20

2000–05a

25 21 .. 1 .. .. .. 21 25c 25 .. 14 12c 35c .. .. 9 12c 7 .. 1c 19 .. 14 .. 15 .. 5 21 14 9 10 13c .. 11c 14 8 .. .. .. .. w .. .. .. 19 .. .. 20 14 .. .. .. 12 15

Male % of male employment 1990–92a

28 .. .. .. .. .. .. 63 .. .. .. .. 49 c .. .. .. 55 59 .. .. 15c 25 .. 51 .. 41 .. 5 .. .. 55 62 57c .. 52 .. .. .. .. .. .. w .. .. .. .. .. .. .. 50 .. .. .. 56 50

2000–05a

34 50 .. 71 .. .. .. 63 44 c 43 .. 54 52c 29 c .. .. 63 63c 45 .. 16c 34 .. 53 .. 50 .. 28 41 55 65 68 64 c .. 59 c 23 59 .. .. .. .. w .. .. .. 50 .. .. 47 52 .. .. .. 62 56

Female % of female employment 1990–92a

33 .. .. .. .. .. .. 68 .. .. .. .. 76c .. .. .. 86 81 .. .. 8c 25 .. 80 .. 17 .. 3 .. .. 82 85 78 c .. 82 .. .. .. .. .. .. w .. .. .. .. .. .. .. 72 .. .. .. 77 72

2000–05a

42 71 .. 98 .. .. .. 79 72c 65 .. 79 84 c 25c .. .. 90 85c 35 .. 15c 41 .. 84 .. 33 .. 17 62 86 90 90 86c .. 86c 26 56 .. .. .. .. w .. .. .. 69 .. .. 63 76 .. .. .. 96 82

2.3

PEOPLE

Employment by economic activity About the data

The International Labour Organization (ILO) classi-

underreported. Caution should be also used where

The distribution of economic activity by gender

fies economic activity using the International Stan-

the data refer only to urban areas, which record little

reveals some clear patterns. Men still make up the

dard Industrial Classification (ISIC) of All Economic

or no agricultural work. Moreover, the age group and

majority of people employed in all three sectors, but

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

area covered could differ by country or change over

the gender gap is biggest in industry. Employment in

Because this classification is based on where work

time within a country. For detailed information on

agriculture is also male-dominated, although not as

is performed (industry) rather than on what type of

breaks in series, consult the original source.

much as industry. Segregating one sex in a narrow

work is performed (occupation), all of an enterprise’s

Countries also take different approaches to the

range of occupations significantly reduces economic

employees are classified under the same industry,

treatment of unemployed people. In most countries

efficiency by reducing labor market flexibility and thus

regardless of their trade or occupation. The catego-

unemployed people with previous job experience are

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

ries should add up to 100 percent. Where they do

classified according to their last job. But in some

regation is particularly harmful for women, who have

not, the differences arise because of workers who

countries the unemployed and people seeking their

a much narrower range of labor market choices and

cannot be classified by economic activity.

first job are not classifiable by economic activity.

lower levels of pay than men (see box 2.3a). But

Data on employment are drawn from labor force

Because of these differences, the size and distribu-

it is also detrimental to men when job losses are

surveys, household surveys, official estimates, cen-

tion of employment by economic activity may not be

concentrated in industries dominated by men and

suses and administrative records of social insurance

fully comparable across countries.

job growth is centered in service occupations, where

schemes, and establishment surveys when no other

The ILO’s Yearbook of Labour Statistics and its data-

information is available. The concept of employment

base Key Indicators of the Labour Market report data

women have better chances, as has been the recent

generally refers to people above a certain age who

by major divisions of the ISIC revision 2 or revision

There are several explanations for the rising impor-

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

3. In this table the reported divisions or categories

tance of service jobs for women. Many service jobs—

Employment data include both full-time and part-time

are aggregated into three broad groups: agriculture,

such as nursing and social and clerical work—are

workers.

industry, and services. Such broad classification may

considered “feminine” because of a perceived simi-

There are many differences in how countries define

obscure fundamental shifts within countries’ industrial

larity to women’s traditional roles. Women often do

and measure employment status, particularly part-

patterns. A slight majority of countries report economic

not receive the training needed to take advantage of

time workers, members of the armed forces, and

activity according to the ISIC revision 2 instead of ISIC

changing employment opportunities. And the greater

household or contributing family workers. Where

revision 3. The use of one classification or another

availability of part-time work in service industries

the armed forces are included, they are allocated to

should not have a significant impact on the information

may lure more women, although it is unclear whether

the service sector, causing that sector to be some-

for the three broad sectors presented in this table.

this is a cause or an effect.

experience in many countries.

what overstated relative to the service sector in

The distribution of economic wealth in the world

economies where they are excluded. Where data are

remains strongly correlated with employment by

obtained from establishment surveys, they cover only

economic activity. The wealthier economies are

• Agriculture corresponds to division 1 (ISIC revi-

employees; thus self-employed and contributing fam-

those with the largest share of total employment in

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

ily workers are excluded. In such cases the employ-

services, whereas the poorer economies are largely

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

ment share of the agricultural sector is severely

agriculture based.

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

Definitions

sion 2) or tabulation categories C–F (ISIC revision 3) and includes mining and quarrying (including oil

Lower wages and less rewarding employment opportunities mean higher risk of poverty for women

Box 2.3a

Within any employment status, women’s earnings in Egypt tend to be lower than men’s (see table). A small- and micro-enterprise survey for Egypt found that while workers’ wages increased with firm size, women accounted for a decreasing share of total employment. Taken together, less rewarding employ-

production), manufacturing, construction, and public utilities (electricity, gas, and water). • Services correspond to divisions 6–9 (ISIC revision 2) or tabulation categories G–P (ISIC revision 3) and include wholesale and retail trade and restaurants and hotels;

ment opportunities and lower wages mean that women face a higher risk of poverty.

transport, storage, and communications; financing, Average wages per worker and women’s share of employment by firm size in Egypt, 2003 Average wages Size of firm 1 worker

(2002 Egyptian pounds)

Women as share of total employment 17.1

2–4 workers

172.1

9.4

5–9 workers

290.1

7.9

Total (firms of all sizes) Source: UNIFEM 2005.

community, social, and personal services.

(%)

112.8

10–24 workers

insurance, real estate, and business services; and

1,073.4

5.9

160.1

14.3

Data sources Data on employment are from the ILO database Key Indicators of the Labour Market, 4th edition.

2007 World Development Indicators

51

2.4

Children at work Survey year

% of economically active children ages 7–14

% 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 Fasoc Burundi Cambodia Cameroonc Canada Central African Republic Chad Chile China Hong Kong, 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

52

2000 2001 1997

2000 2003

2000 2000 2003 1998 2000 2001 2001 2000 2000 2003

2001 2000 2002 2000

2000 2001 1998 2003

2001

2000

2000 2000 1994 2000

Employment by economic activitya

Economically active children

Agriculture

% of economically active children ages 7–14 Manufacturing

Services

Total

Male

Female

Work only

Work and study

Male

Female

Male

Female

Male

.. 36.6 .. 30.1 20.7 .. .. .. 9.7 17.5 .. .. .. 19.2 20.2 .. 7.1 .. 66.5 37.0 52.3 15.9 .. 67.0 69.9 8.8 .. .. 12.2 39.8 .. 6.7 40.7 .. .. .. .. 12.5 17.9 6.4 12.7 .. .. 57.1 .. .. .. 25.3 .. .. 28.5 .. 20.1 48.3 67.5 ..

.. 41.1 .. 30.0 25.4 .. .. .. 12.0 20.9 .. .. .. 20.4 22.8 .. 9.5 .. 65.4 38.4 52.4 14.5 .. 66.5 73.5 10.5 .. .. 16.6 39.9 .. 9.7 40.9 .. .. .. .. 16.7 22.1 4.0 17.1 .. .. 67.9 .. .. .. 25.4 .. .. 28.5 .. 25.9 47.2 67.4 ..

.. 31.8 .. 30.3 16.0 .. .. .. 7.3 13.9 .. .. .. 18.0 17.6 .. 4.6 .. 67.7 35.7 52.1 17.4 .. 67.6 66.5 6.9 .. .. 7.7 39.8 .. 3.5 40.5 .. .. .. .. 8.1 13.6 8.9 8.1 .. .. 45.9 .. .. .. 25.3 .. .. 28.4 .. 13.9 49.5 67.5 ..

.. 43.1 .. 26.6 8.6 .. .. .. 4.2 63.3 .. .. .. 19.7 4.0 .. 5.8 .. 95.9 48.3 16.5 52.5 .. 54.9 44.6 4.0 .. .. 23.0 35.7 .. 20.8 46.4 .. .. .. .. 7.2 25.0 60.9 19.5 .. .. 63.5 .. .. .. 41.6 .. .. 36.4 .. 38.5 98.6 63.7 ..

.. 56.9 .. 73.4 91.4 .. .. .. 95.8 36.7 .. .. .. 80.3 96.0 .. 94.2 .. 4.1 51.7 83.5 47.5 .. 45.1 55.4 96.0 .. .. 77.0 64.3 .. 79.2 53.6 .. .. .. .. 92.8 75.0 39.1 80.5 .. .. 36.5 .. .. .. 58.4 .. .. 63.6 .. 61.5 1.4 36.3 ..

.. .. .. .. .. .. .. .. .. 61.4 .. .. .. 77.8 .. .. 64.3 .. 98.0 .. 78.5 90.4 .. .. .. 31.5 .. .. .. .. .. 56.5 .. .. .. .. .. .. 65.1 .. 66.4 .. .. 96.5 .. .. .. .. .. .. 81.0 .. 74.5 .. .. ..

.. .. .. .. .. .. .. .. .. 64.0 .. .. .. 72.9 .. .. 49.8 .. 98.2 .. 73.6 86.3 .. .. .. 11.9 .. .. .. .. .. 55.2 .. .. .. .. .. .. 69.2 .. 17.6 .. .. 88.7 .. .. .. .. .. .. 59.1 .. 39.8 .. .. ..

.. .. .. .. .. .. .. .. .. 11.6 .. .. .. 4.3 .. .. 6.5 .. 0.6 .. 4.7 1.9 .. .. .. 7.6 .. .. .. .. .. 8.7 .. .. .. .. .. .. 10.7 .. 10.8 .. .. 0.5 .. .. .. .. .. .. 4.5 .. 5.9 .. .. ..

.. .. .. .. .. .. .. .. .. 15.5 .. .. .. 3.5 .. .. 9.1 .. 0.5 .. 5.4 2.3 .. .. .. 5.8 .. .. .. .. .. 2.7 .. .. .. .. .. .. 8.6 .. 16.1 .. .. 2.8 .. .. .. .. .. .. 7.6 .. 20.1 .. .. ..

.. .. .. .. .. .. .. .. .. 25.2 .. .. .. 15.0 .. .. 26.8 .. 1.3 .. 15.7 5.1 .. .. .. 58.5 .. .. .. .. .. 28.0 .. .. .. .. .. .. 21.2 .. 21.2 .. .. 2.5 .. .. .. .. .. .. 13.8 .. 14.7 .. .. ..

2007 World Development Indicators

Female

.. .. ..

.. 18.3

23.6 .. 40.9 1.2 .. 20.4 8.8 .. .. 80.6

.. .. 42.1 ..

.. 22.1 .. 66.3

6.2

..

32.0 40.0 .. ..

Survey year

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexicod 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

2002 2000

2000

2000

1996 1999

1998

2000

2001 2000 2001

1996 2000 2000 1998–99

1999 1999

2001

2000 1999 1994 2001 2001

Employment by economic activitya

Economically active children

% of economically active children ages 7–14

% of children ages 7–14

2.4

PEOPLE

Children at work

Agriculture

% of economically active children ages 7–14 Manufacturing

Services

Total

Male

Female

Work only

Work and study

Male

Female

Male

Female

Male

Female

11.4 .. 5.2 .. .. 13.7 .. .. .. 1.1 .. .. 29.7 6.7 .. .. .. 8.6 .. .. .. 30.8 .. .. .. .. 25.6 10.6 .. 25.3 .. .. 14.7 33.5 22.0 13.2 .. .. 15.4 47.2 .. .. 12.1 .. .. .. .. .. 4.0 .. 8.1 17.7 13.3 .. 3.6 ..

16.5 .. 5.3 .. .. 17.4 .. .. .. 1.5 .. .. 30.3 6.9 .. .. .. 9.7 .. .. .. 34.2 .. .. .. .. 26.1 9.4 .. 32.3 .. .. 20.0 34.1 23.5 13.5 .. .. 16.2 42.2 .. .. 17.5 .. .. .. .. .. 6.4 .. 11.7 20.4 16.3 .. 4.6 ..

6.1 .. 5.1 .. .. 9.7 .. .. .. 0.6 .. .. 29.1 6.4 .. .. .. 7.6 .. .. .. 27.5 .. .. .. .. 25.1 11.6 .. 18.6 .. .. 9.5 32.8 20.6 12.8 .. .. 14.7 52.4 .. .. 6.5 .. .. .. .. .. 1.4 .. 4.4 15.2 10.0 .. 2.6 ..

41.9 .. 89.8 .. .. 51.7 .. .. .. 17.1 .. .. 4.4 44.8 .. .. .. 7.0 .. .. .. 17.6 .. .. .. .. 85.1 17.1 .. 68.7 .. .. 45.6 3.8 28.2 93.2 .. .. 9.5 35.6 .. .. 33.3 .. .. .. .. .. 37.5 .. 24.2 7.3 14.8 .. 3.6 ..

58.1 .. 10.2 .. .. 48.3 .. .. .. 82.9 .. .. 95.6 55.2 .. .. .. 93.0 .. .. .. 82.4 .. .. .. .. 14.9 82.9 .. 31.3 .. .. 54.4 96.2 71.8 6.8 .. .. 90.5 64.4 .. .. 66.7 .. .. .. .. .. 62.5 .. 75.7 92.7 85.2 .. 96.4 ..

73.6 .. 70.5 .. .. .. .. .. .. 36.8 .. .. .. 87.3 .. .. .. 93.0 .. .. .. .. .. .. .. .. 94.1 .. .. .. .. .. 61.3 .. .. 60.8 .. .. 91.5 89.0 .. .. 73.2 .. .. .. .. .. 71.1 .. 61.2 78.9 72.6 .. 52.7 ..

19.8 .. 76.6 .. .. .. .. .. .. 17.1 .. .. .. 74.4 .. .. .. 96.3 .. .. .. .. .. .. .. .. 93.9 .. .. .. .. .. 38.3 .. .. 60.3 .. .. 91.7 86.1 .. .. 32.0 .. .. .. .. .. 38.4 .. 30.9 76.3 53.6 .. 40.7 ..

5.9 .. 10.0 .. .. .. .. .. .. 6.2 .. .. .. 2.5 .. .. .. 0.0 .. .. .. .. .. .. .. .. 0.6 .. .. .. .. .. 11.4 .. .. 8.1 .. .. 0.4 1.2 .. .. 3.0 .. .. .. .. .. 1.4 .. 3.8 3.6 3.6 .. 11.4 ..

24.4 .. 15.4 .. .. .. .. .. .. 11.6 .. .. .. 0.3 .. .. .. 0.0 .. .. .. .. .. .. .. .. 1.4 .. .. .. .. .. 12.9 .. .. 8.5 .. .. 0.4 1.5 .. .. 10.2 .. .. .. .. .. 8.0 .. 4.6 3.4 5.3 .. 10.7 ..

18.6 .. 15.9 .. .. .. .. .. .. 43.6 .. .. .. 8.8 .. .. .. 7.0 .. .. .. .. .. .. .. .. 2.0 .. .. .. .. .. 22.6 .. .. 13.5 .. .. 8.1 9.7 .. .. 23.3 .. .. .. .. .. 27.2 .. 33.1 17.5 22.1 .. 25.6 ..

55.7

2007 World Development Indicators

6.5

..

71.3

.. 25.3

2.7

..

2.9 .. ..

48.2 .. .. 6.4

8.0 12.3

57.8

49.5 64.5 20.3 41.0 47.7

53

2.4

Children at work Survey year

% of economically active children ages 7–14

% of children ages 7–14

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 Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RBc Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe

2000 2000 2000 2000

1999 1998 2000 2000

2001 2000 2000 1999 2002–03

2000 2003

1999 1999 1999

Employment by economic activitya

Economically active children

Agriculture

% of economically active children ages 7–14 Manufacturing

Services

Total

Male

Female

Work only

Work and study

Male

Female

Male

Female

Male

Female

1.4 .. 33.1 .. 35.4 .. 74.0 .. .. .. .. 27.7 .. 17.0 19.1 11.2 .. .. .. .. 40.4 .. 72.5 3.9 .. 4.5 .. 13.1 .. .. .. .. .. 18.1 9.1 .. .. 13.1 14.4 14.3

1.7 .. 36.1 .. 43.2 .. 24.7 .. .. .. .. 29.0 .. 20.4 21.5 11.4 .. .. .. .. 41.5 .. 73.4 5.2 .. 5.2 .. 15.0 .. .. .. .. .. 22.0 11.4 .. .. 12.4 15.0 13.3

1.1 .. 30.3 .. 27.7 .. 72.7 .. .. .. .. 26.4 .. 13.4 16.8 10.9 .. .. .. .. 39.2 .. 71.6 2.8 .. 3.8 .. 11.3 .. .. .. .. .. 14.0 6.6 .. .. 14.0 13.9 15.3

20.7 .. 27.5 .. 56.2 .. 53.8 .. .. .. .. 5.1 .. 5.4 55.9 14.0 .. .. .. .. 40.0 .. 28.4 12.8 .. 66.8 .. 18.3 .. .. .. .. .. 4.1 17.6 .. .. 64.3 72.8 12.0

79.3 .. 72.5 .. 43.8 .. 46.2 .. .. .. .. 94.9 .. 94.6 44.1 86.0 .. .. .. .. 60.0 .. 71.6 87.2 .. 33.2 .. 81.7 .. .. .. .. .. 95.9 82.4 .. .. 35.7 27.2 88.0

96.4 .. .. .. .. .. .. .. .. .. .. .. .. 71.1 .. .. .. .. .. .. 83.5 .. .. .. .. 52.7 .. 94.3 .. .. .. .. .. .. 35.2 .. .. 87.2 92.7 ..

98.1 .. .. .. .. .. .. .. .. .. .. .. .. 71.4 .. .. .. .. .. .. 73.1 .. .. .. .. 83.4 .. 92.3 .. .. .. .. .. .. 9.2 .. .. 96.6 88.1 ..

0.0 .. .. .. .. .. .. .. .. .. .. .. .. 12.0 .. .. .. .. .. .. 0.1 .. .. .. .. 19.9 .. 1.5 .. .. .. .. .. .. 7.3 .. .. 1.2 0.3 ..

0.0 .. .. .. .. .. .. .. .. .. .. .. .. 15.0 .. .. .. .. .. .. 0.2 .. .. .. .. 10.2 .. 1.3 .. .. .. .. .. .. 9.5 .. .. 0.8 0.8 ..

2.6 .. .. .. .. .. .. .. .. .. .. .. .. 15.8 .. .. .. .. .. .. 16.3 .. .. .. .. 10.2 .. 3.2 .. .. .. .. .. .. 53.9 .. .. 10.8 6.6 ..

1.9 .. .. ..

.. 13.5 .. ..

26.7 .. .. 1.8 6.0

.. 81.0

1.8 11.0 ..

a. Shares by major industrial category do not sum to 100 percent because of a residual category not included in the table. b. The totals (urban and rural combined) represent what can be described as Angola-Secured Territory but not the nation as a whole. c. Data are for children ages 10–14. d. Data are for children ages 12–14.

54

2007 World Development Indicators

About the data

2.4

PEOPLE

Children at work Definitions

The data in the table refer to children’s economic activ-

working for pay in cash or in kind or is involved in

• Survey year is the year in which the underlying

ity, a broader concept than child labor. According to a

unpaid work, whether a child is working for someone

data were collected. • Economically active children

gradually emerging consensus, child labor is a subset

who is not a member of the household, whether a

refer to children involved in economic activity for at

of children’s economic activity or children’s work that is

child is involved in any type of family work (on the

least one hour in the reference week of the survey.

injurious and therefore targeted for elimination. There

farm or in a business), and the like. The ages used in

• Work only refers to children involved in economic

is also growing recognition that there are certain

country surveys to define child labor range from 5 to

activity and not attending school. • Work and study

intolerable, or “unconditionally worst,” forms of child

14 years old. The data in the table have been recalcu-

refer to children attending school in combination with

labor that constitute especially serious violations of

lated to present statistics for children ages 7–14.

economic activity. • Employment by economic activ-

Although efforts are made to harmonize the defini-

ity refers to the distribution of economically active

tion of employment and the questions on employment

children by the major industrial categories (ISIC revi-

In line with the international definition of employment,

used in survey questionnaires, some differences

sion 2 or revision 3). • Agriculture corresponds to

the threshold for classifying a child as economically

remain among the survey instruments used to col-

division 1 (ISIC revision 2) or categories A and B (ISIC

active is spending one hour on economic activity during

lect the information on working children. Differences

revision 3) and includes agriculture and hunting, for-

the reference week. Economic activity is as defined by

exist not only among different household surveys in

estry and logging, and fishing. • Manufacturing cor-

the 1993 United Nations System of National Accounts

the same country, but also within the same type of

responds to division 3 (ISIC revision 2) or category D

(revision 3) and corresponds to the international defini-

survey carried out in different countries.

(ISIC revision 3). • Services correspond to divisions

children’s rights, and these should be targeted as a priority for immediate action.

tion of employment adopted by the Thirteenth Interna-

Because of the differences in the underlying sur-

6–9 (ISIC revision 2) or categories G–P (ISIC revision

tional Conference of Labor Statisticians in 1982. Eco-

vey instruments and in survey dates, estimates of

3) and include wholesale and retail trade, hotels and

nomic activity covers all market production and certain

the economically active child population are not fully

restaurants, transport, financial intermediation, real

types of nonmarket production, including production of

comparable across countries. Caution should be

estate, public administration, education, health and

goods for own use. It excludes household chores per-

exercised in drawing conclusions concerning relative

social work, other community services, and private

formed by children in their own household. But some

levels of child economic activity across countries or

household activity.

forms of economic activity are not captured by house-

regions based on the published estimates.

hold surveys and so are not reflected in the estimates.

The table aggregates the distribution of working

These include unconditional forms of child labor, which

children by the industrial categories of the Interna-

require different data collection methodologies.

tional Standard Industrial Classification (ISIC): agri-

The data used to develop the indicators are from

culture, industry, and services. The residual category,

household surveys conducted by the International

which includes mining and quarrying; electricity, gas,

Labour Organization (ILO), the United Nations Chil-

and water; construction; extraterritorial organization;

dren’s Fund (UNICEF), the World Bank, and national

and other inadequately defined activities, is not pre-

statistical offices. These surveys yield a variety of

sented in the table, and so the broad groups do not

data in education, employment, health, expenditure,

add up to 100 percent. The use of either ISIC revision

and consumption that relate to child work. But they

2 or revision 3 is strictly related to the codification

do not provide information on unconditional forms

applied by each country in describing the economic

of children’s work.

activity. The use of two different classifications does

Household survey data generally include information on work type—for example, whether a child is

not affect the definition of the groups presented in

Data sources Estimates are produced by the Understanding

the table.

Children’s Work project based on household survey datasets made available by the ILO’s Inter-

Child labor is an obstacle to education for all

Box 2.4a

There is broad consensus that the single most effective way to stem the flow of school-age children into work is to extend and improve access to school, so that families have the opportunity to invest in their children’s education and it is worthwhile for them to do so. With no access to quality education, millions of children are left to work. More than one in five children ages 5–17 is economically active (see table).

national Programme on the Elimination of Child Labour under its Statistical Monitoring Programme on Child Labour, UNICEF under its Multiple Indicator Cluster Survey program, the World Bank under its Living Standards Measurement Study program, and national statistical offices. Informa-

Economically active children Age group

(% of age group)

tion on how the data were collected and some indication of their reliability can be found at www.

5–17

20.3

5–14

15.8

www.childinfo.org, and www.worldbank.org/lsms.

15–17

35.2

Detailed country statistics can be found at www.

Source: ILO 2006.

ilo.org/public/english/standards/ipec/simpoc/,

ucw-project.org.

2007 World Development Indicators

55

2.5

Unemployment Unemployment

Male % of male labor force 1990–92a 2000–05a

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, 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

56

.. .. .. .. 6.4b .. 11.3 3.5 .. 2.0 .. 4.8 .. 5.5b .. 11.7 5.4b .. .. 0.7 .. .. 12.1b .. .. 3.9 .. 2.0 6.7b .. .. 3.4 .. .. .. .. 8.3 11.7 6.0 b 6.4 3.9 b .. 3.9 .. 13.6 7.9 b .. .. .. 5.3 .. 4.9 2.6b .. .. 11.2

.. 13.2 19.8 .. 16.3b .. 5.3b 4.5 .. 4.2 .. 6.6 .. 4.3 .. 15.7 7.8 b 12.5 .. .. 0.8 8.2 7.5b .. .. 6.9 .. 7.8 10.6 .. .. 5.4 .. 11.7 .. 7.0 5.0 10.5 6.6b 7.3 8.7 .. 10.4 15.8b 8.8 9.0 b .. .. 13.4b 10.2 7.5 6.4 2.2 .. .. ..

2007 World Development Indicators

Female % of female labor force 1990–92a 2000–05a

.. .. .. .. 7.0 b .. 9.5 3.8 .. 1.9 .. 9.5 .. 5.6b .. 17.3 7.9 b .. .. 0.3 .. .. 10.2b .. .. 5.3 .. 1.9 13.0 b .. .. 5.4 .. .. .. .. 9.9 34.9 13.2b 17.0 4.9 b .. 3.5 .. 9.7 12.7b .. .. .. 8.4 .. 12.9 4.6b .. .. 13.6

.. 18.3 21.3 .. 14.7b .. 5.5b 5.4 .. 4.9 .. 8.3 .. 6.9 .. 22.3 12.3b 11.5 .. .. 0.9 6.7 6.8 b .. .. 9.5 .. 5.6 17.8 .. .. 8.5 .. 14.0 .. 9.9 5.4 30.7 11.4b 23.2 3.9 .. 8.9 31.2b 9.0 11.1b .. .. 11.8 b 9.3 8.7 15.9 3.7 .. .. ..

Total % of total labor force 1990–92a 2000–05a

.. .. .. .. 6.7b .. 10.5 3.6 .. 1.9 .. 6.7 .. 5.5b .. 13.9 6.4b .. .. 0.5 .. .. 11.2b .. .. 4.4 2.3b 2.0 9.4b .. .. 4.0 .. .. 4.6 .. 9.0 20.3 8.9 b 9.0 4.3b .. 3.7 .. 11.7 10.0 b .. .. .. 6.6 .. 7.8 3.2b .. .. 12.2

.. 15.2 20.1 .. 15.6b .. 5.4b 4.9 .. 4.3 .. 7.4 .. 5.5 .. 18.6 9.7b 12.1 .. .. 0.8 7.5 7.2b .. .. 7.8 4.2 6.8 13.7 .. .. 6.4 .. 12.7 3.3 8.3 5.2 18.4 8.6b 11.0 6.8 .. 9.6 23.1b 8.9 9.9 b .. .. 12.6b 9.8 8.2 10.2 2.8 .. .. ..

Long-term unemployment

Unemployment by educational attainment

% of total unemployment Male Female Total 2000–03a 2000–03a 2000–03a

% of total unemployment Primary Secondary Tertiary 2000–04a 2000–04a 2000–04a

.. .. .. .. .. 72.2 27.1b 25.0 .. .. .. 44.8 .. .. .. .. .. .. .. .. .. .. 11.4 .. .. .. .. .. .. .. .. 8.9 .. 52.9 c .. 47.4 21.8 2.2 .. .. .. .. .. .. 27.7 43.1b .. .. .. 48.3 .. 49.2 .. .. .. ..

.. .. .. .. .. 70.8 17.0 b 23.9 .. .. .. 48.2 .. .. .. .. .. .. .. .. .. .. 8.4 .. .. .. .. .. .. .. .. 13.3 .. 56.3c .. 51.9 17.9 1.3 .. .. .. .. .. .. 21.4 42.8 b .. .. .. 52.3 .. 61.0 .. .. .. ..

.. .. .. .. .. 71.6 22.5b 24.5 .. .. .. 46.3 .. .. .. .. .. .. .. .. .. .. 10.1 .. .. .. .. .. .. .. .. 10.9 .. 54.6c .. 49.9 19.9 1.6 .. .. .. .. .. .. 24.7 42.9 b .. .. .. 50.0 .. 56.5 .. .. .. ..

.. 56.4 .. .. 42.8 b 5.2 48.3 37.3 4.6 54.3 10.2 43.7 .. 60.2b .. 63.8 .. 37.8 46.8 .. .. .. 29.0 b .. .. 18.5 .. 48.6 26.9 .. .. 62.2 .. 21.5 .. 24.6 25.9 .. 28.8 .. .. .. 20.9 .. 35.8 40.6 .. .. 5.8 27.1 .. 34.2 .. .. .. ..

.. 38.4 .. .. 38.5b 81.5 32.7 55.7 31.4 22.7 40.6 38.1 .. 32.5b .. 23.8 .. 50.9 19.3 .. .. .. 30.8 b .. .. 59.0 .. 39.4 52.9 .. .. 24.1 .. 68.4 .. 71.8 46.6 .. 47.7 .. .. .. 62.1 .. 46.3 39.9 .. .. 57.6 60.5 .. 50.0 .. .. .. ..

.. 3.4 .. .. 17.7b 13.3 19.0 7.0 64.1 8.4 49.1 18.2 .. 4.4b .. .. .. 11.4 5.6 .. .. .. 40.2b .. .. 21.8 .. 10.1 16.5 .. .. 9.9 .. 9.8 .. 3.5 25.5 .. 21.9 .. .. .. 16.8 .. 17.5 17.7 .. .. 36.5 12.4 .. 15.1 .. .. .. ..

Unemployment

Male % of male labor force 1990–92a 2000–05a

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

3.3b 11.0 .. 2.7 9.5 .. 15.2 9.2 8.1 9.4 2.1b .. .. .. .. 2.8 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 3.2 2.7 .. .. 13.0 b .. 4.7 20.0 .. 4.3 11.0 b 11.3 .. .. 6.6 .. 3.8 10.8 9.0 6.4b 7.5b 7.9 12.2 3.5b 19.2

4.7b 6.1 4.9 b 8.1 10.1 29.4 4.9 10.2 6.4 8.1 4.9 b 11.8 7.0 .. .. 3.7 .. 11.2 .. 9.0 .. .. .. .. .. 36.7 3.5 .. 3.6 7.2 .. 5.8 2.9 10.0 14.3 11.0 .. .. 26.8 7.4 4.1 3.5b 7.6 .. .. 4.8 .. 6.6 9.4 4.3 6.7 9.4b 10.4 16.6 5.8 11.7

Female % of female labor force 1990–92a 2000–05a

3.0 b 8.7 .. 3.1 24.4 .. 15.2 13.9 17.3 22.2 2.2b .. .. .. .. 2.1 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 3.1 4.0 .. .. 25.3b .. 8.8 19.0 .. 7.3 9.6b 19.4 .. .. 5.1 .. 14.0 22.3 5.9 3.8 b 12.5b 9.9 14.7 5.0 b 13.3

8.3b 6.1 5.3b 12.9 20.4 15.0 3.7 11.3 10.5 15.7 4.4b 16.5 9.8 .. .. 3.1 .. 14.3 .. 8.4 .. .. .. .. .. 37.8 5.6 .. 3.6 10.9 .. 13.5 3.4 6.3 14.1 11.8 .. .. 35.9 10.7 4.4 4.4b 8.0 .. .. 3.8 .. 12.8 17.2 1.3 10.1 12.0 b 11.7 19.1 7.6 9.1

Total % of total labor force 1990–92a 2000–05a

3.2b 9.9 .. 2.9 11.1 .. 15.2 11.2 11.6 15.4 2.2b .. .. .. .. 2.5 .. .. .. .. .. .. .. .. .. .. .. .. 3.7 .. .. 3.1 3.1 .. .. 16.0 b .. 6.0 19.0 .. 5.5 10.4b 14.4 .. .. 5.9 .. 5.2 14.7 7.7 5.3b 9.4b 8.6 13.3 4.1b 17.0

5.9 b 6.1 5.0 b 9.9 11.6 26.8 4.4 10.7 8.0 11.4 4.7b 12.4 8.4 .. .. 3.5 1.7 12.5 .. 8.7 .. .. .. .. 8.3 37.2 4.5 .. 3.5 8.8 .. 8.5 3.0 8.1 14.2 11.2 .. .. 31.1 8.8 4.3 3.9 b 7.8 .. .. 4.4 .. 7.7 12.3 2.8 8.1 10.5b 10.9 17.7 6.7b 10.6

2.5

PEOPLE

Unemployment Long-term unemployment

Unemployment by educational attainment

% of total unemployment Male Female Total 2000–03a 2000–03a 2000–03a

% of total unemployment Primary Secondary Tertiary 2000–04a 2000–04a 2000–04a

.. 42.2 .. .. .. .. 40.9 .. 57.5 24.4 38.9 .. .. .. .. 0.7 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 1.1 .. .. .. .. .. .. .. 30.1 15.5 .. .. .. 7.1 .. .. 24.0 .. .. .. .. 56.1c 31.2 ..

.. 42.2 .. .. .. .. 26.0 .. 58.9 36.2 24.6 .. .. .. .. 0.3 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 0.8 .. .. .. .. .. .. .. 28.1 11.0 .. .. .. 5.4 .. .. 35.7 .. .. .. .. 59.3c 32.7 ..

.. 42.2 .. .. .. .. 35.4 .. 58.2 31.7 33.5 .. .. .. .. 0.6 .. .. .. .. .. .. .. .. 57.8 .. .. .. .. .. .. .. 1.0 .. .. .. .. .. .. .. 29.2 13.3 .. .. .. 6.4 .. .. 29.3 .. .. .. .. 57.7c 32.0 ..

.. 33.5 27.0 46.0 38.3 .. 48.2 20.2 49.4 13.0 70.8 .. 7.9 .. .. 17.0 27.5 13.7 .. 22.4 .. .. .. .. 15.0 .. 42.7 .. 32.0 .. .. 71.5b 13.7 .. 35.0 51.5c .. .. .. .. 46.3 1.0 50.8 b .. .. 21.7 .. 14.7 35.9 .. .. 9.4b .. 18.0 70.7 ..

.. 61.2 41.1 36.6 37.1 .. 24.9 48.8 41.4 5.4 53.4 .. 53.2 .. .. 53.4 39.9 67.8 .. 68.5 .. .. .. .. 68.5 .. 18.8 .. 48.8 .. .. 28.2b 30.1 .. 45.8 20.1c .. .. .. .. 35.1 48.8 24.8 b .. .. 54.7 .. 12.3 37.3 .. .. 61.4b .. 75.4 14.6 ..

2007 World Development Indicators

.. 5.4 31.9 6.7 19.3 .. 24.0 27.0 7.5 6.1 29.2 .. 38.9 .. .. 29.6 6.1 18.5 .. 8.8 .. .. .. .. 16.5 .. 6.1 .. 15.6 .. .. .. 46.4 .. 18.4 19.8 c .. .. .. .. 17.4 16.0 19.7b .. .. 21.7 .. 24.1 26.0 .. .. 28.6b .. 6.7 8.8 ..

57

2.5

Unemployment Unemployment

Male % of male labor force 1990–92a 2000–05a

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 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 Europe EMU

.. 5.4 .. 7.4 .. .. .. 2.7 .. .. .. .. 13.9 10.1b .. .. 6.8 2.3 .. .. 2.7b 1.3 .. 17.0 b .. 8.8 .. .. .. .. 11.5 7.9 6.8 b .. 8.2 .. .. .. 16.3 .. .. w .. .. .. 6.3 .. .. .. 5.4 .. .. .. 7.0 7.5

9.0 7.8 .. 4.7 .. 14.4 .. 5.5 17.3 5.7 .. 23.5b 8.2 6.0 b .. .. 6.9 3.9 9.0 .. 4.4 1.6 .. 7.8 b .. 10.3 .. 2.5 8.9 2.2 5.0 5.6 13.5b .. 14.4b 1.9 28.1 .. .. 10.4 .. w .. .. .. 9.6 .. .. 9.9 8.1 13.4 5.1 .. 6.2 8.2

Female % of female labor force 1990–92a 2000–05a

.. 5.2 .. 4.9 .. .. .. 2.6 .. .. .. .. 25.8 19.8 b .. .. 4.6 3.5 .. .. 4.2b 1.5 .. 23.9 b .. 7.8 .. .. .. .. 7.3 7.0 11.8 b .. 6.8 .. .. .. 22.4 .. .. w .. .. .. 6.8 .. .. .. 8.5 .. .. .. 7.9 12.6

6.9 8.0 .. 14.7 .. 16.4 .. 5.3 19.1 6.5 .. 31.6b 15.0 13.5b .. .. 6.2 4.8 28.3 .. 5.8 1.4 .. 14.5b .. 10.3 .. 3.9 8.3 2.6 4.2 5.4 20.8 b .. 20.3b 2.4 20.1 .. .. 6.1 .. w .. .. .. 10.8 .. .. 9.9 12.0 21.3 6.2 .. 6.6 10.6

Total % of total labor force 1990–92a 2000–05a

.. 5.3 .. .. .. .. .. 2.7 .. .. .. .. 18.1 13.3b .. .. 5.7 2.8 .. .. 3.5b 1.4 .. 19.6b .. 8.5 .. .. .. .. 9.7 7.5 9.0 b .. 7.7 .. .. .. 18.9 .. .. w .. 3.9 3.4 6.4 .. 2.5 .. 6.6 .. .. .. 7.4 9.5

a. Data are for the most recent year available. b. Limited coverage. c. Data are for 2005.

58

2007 World Development Indicators

8.0 7.9 .. .. .. 15.2 .. 5.4 18.1 6.1 .. 27.1b 11.0 8.5b .. .. 6.5 4.3 12.3 .. 5.1b 1.5 .. 10.4b 14.7 10.3 .. 3.2 8.6 2.3 4.6 5.5 16.8 b .. 16.8 b 2.1 26.8 .. .. 8.2 6.4 w .. 6.6 5.9 9.8 6.4 4.2 9.9 9.6 14.8 5.4 .. 6.4 9.2

Long-term unemployment

Unemployment by educational attainment

% of total unemployment Male Female Total 2000–03a 2000–03a 2000–03a

% of total unemployment Primary Secondary Tertiary 2000–04a 2000–04a 2000–04a

.. .. .. .. .. .. .. .. 60.2 .. .. .. 34.3 .. .. .. 19.6 21.6 .. .. .. .. .. 20.3 .. 36.5c .. .. .. .. 26.5 12.5 .. .. .. .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. .. 27.3 44.1

.. .. .. .. .. .. .. .. 62.1 .. .. .. 43.9 .. .. .. 15.3 32.6 .. .. .. .. .. 34.7 .. 46.9 c .. .. .. .. 17.1 11.0 .. .. .. .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. .. 23.9 46.4

.. .. .. .. .. .. .. .. 61.1 .. .. .. 39.8 .. .. .. 17.8 27.0 .. .. .. .. .. 27.6 .. 39.2c .. .. .. .. 23.0 11.8 .. .. .. .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. .. 26.0 45.5

26.0 .. 60.7 12.0 c .. .. .. 22.4 24.1b 26.2 .. 50.2 56.0 47.2 .. .. 23.2 28.7 75.2 .. .. 40.0 .. 55.5 43.4 55.7c .. .. 13.5 .. 30.3 18.4 54.8 b .. .. .. 57.5 .. .. .. .. w .. .. .. 37.7 .. .. .. .. .. 30.0 .. 34.8 40.3

66.9 .. 24.1 49.0 c .. .. .. 25.0 71.7b 63.9 .. 41.0 20.4 .. .. .. 58.1 54.5 10.3 .. .. 47.2 .. 40.5 37.4 28.1c .. .. 54.3 .. 44.4 34.3 31.3b .. .. .. 14.5 .. .. .. .. w .. .. .. 48.2 .. .. .. .. .. 34.8 .. 39.3 42.5

5.4 .. 5.9 40.0 c .. .. .. 38.8 4.3b 8.2 .. 5.1 22.7 52.8 .. .. 17.5 16.9 9.8 .. .. 0.2 .. 1.8 10.0 11.4 c .. .. 32.2 .. 14.6 47.3 13.9 b .. .. .. 17.6 .. .. .. .. w .. .. .. 11.3 .. .. .. .. .. 27.4 .. 29.7 16.3

About the data

2.5

PEOPLE

Unemployment Definitions

Unemployment and total employment in an economy

closely than that used by other sources and there-

• Unemployment refers to the share of the labor

are the broadest indicators of economic activity as

fore generate statistics that are more comparable

force without work but available for and seeking

refl ected by the labor market. The International

internationally. But the age group, geographic cover-

employment. Definitions of labor force and unem-

Labour Organization (ILO) defines the unemployed as

age, and collection methods could differ by country

ployment may differ by country (see About the data).

members of the economically active population who

or change over time within a country. For detailed

• Long-term unemployment refers to the number of

are without work but available for and seeking work,

information on breaks in series, consult the original

people with continuous periods of unemployment

including people who have lost their jobs and those

source.

extending for a year or longer, expressed as a per-

who have voluntarily left work. Some unemployment

Women tend to be excluded from the unemploy-

centage of the total unemployed. • Unemployment

is unavoidable in all economies. At any time some

ment count for various reasons. Women suffer more

by educational attainment shows the unemployed

workers are temporarily unemployed—between jobs

from discrimination and from structural, social, and

by level of educational attainment as a percent-

as employers look for the right workers and workers

cultural barriers that impede them from actively seek-

age of the total unemployed. The levels of educa-

search for better jobs. Such unemployment, often

ing work. Also, women are often responsible for the

tional attainment accord with the ISCED97 of the

called frictional unemployment, results from the nor-

care of children and the elderly or for other household

United Nations Educational, Cultural, and Scientific

mal operation of labor markets.

affairs. They may not be available for work during

Organization.

Changes in unemployment over time may reflect

the short reference period, as they need to make

changes in the demand for and supply of labor, but

arrangements before starting work. Furthermore,

they may also reflect changes in reporting practices.

women are considered to be employed when they are

Ironically, low unemployment rates can often disguise

working part-time or in temporary jobs in the informal

substantial poverty in a country, while high unemploy-

sector, despite the instability of these jobs or their

ment rates can occur in countries with a high level of

active searching for more secure employment.

economic development and low incidence of poverty.

Long-term unemployment is measured by the

In countries without unemployment or welfare ben-

length of time that an unemployed person has been

efits, people eke out a living in the informal sector.

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

In countries with well-developed safety nets, workers

table are from labor force surveys. The underlying

can afford to wait for suitable or desirable jobs. But

assumption is that shorter periods of joblessness

high and sustained unemployment indicates serious

are of less concern, especially when the unemployed

inefficiencies in the allocation of resources.

are covered by unemployment benefi ts or similar

The ILO definition of unemployment notwithstand-

forms of welfare support. The length of time that a

ing, reference periods, the criteria for those consid-

person has been unemployed is difficult to measure,

ered to be seeking work, and the treatment of people

because the ability to recall that time diminishes as

temporarily laid off and those seeking work for the

the period of joblessness extends. Women’s long-

first time vary across countries. In many developing

term unemployment is likely to be lower in countries

countries it is especially difficult to measure employ-

where women constitute a large share of the unpaid

ment and unemployment in agriculture. The timing of

family workforce. Women in such countries have

a survey, for example, can maximize the effects of

more access than men to nonmarket work and are

seasonal unemployment in agriculture. And informal

more likely to drop out of the labor force and not be

sector employment is difficult to quantify where infor-

counted as unemployed.

mal activities are not registered and tracked.

Unemployment by level of educational attainment

Data on unemployment are drawn from labor force

provides insights into the relationship between the

sample surveys and general household sample

educational attainment of workers and unemploy-

surveys, censuses, and offi cial estimates, which

ment and may be used to draw inferences about

are generally based on information from different

changes in employment demand. Information on

sources and can be combined in many ways. Admin-

education attainment is the best available indicator

istrative records, such as social insurance statistics

of skill levels of the labor force.

and employment office statistics, are not included

Besides the limitations to comparability raised

in this table because of their limitations in cover-

for measuring unemployment, the different ways of

age. Labor force surveys generally yield the most

classifying the level of education across countries

comprehensive data because they include groups

may also cause inconsistency. The level of educa-

not covered in other unemployment statistics, par-

tion is supposed to be classified according to Inter-

ticularly people seeking work for the first time. These

national Standard Classification of Education 1997

Data on unemployment are from the ILO database

surveys generally use a definition of unemployment

(ISCED97). For more information on ISCED97, see

Key Indicators of the Labour Market, 4th edition.

that follows the international recommendations more

About the data for table 2.9.

Data sources

2007 World Development Indicators

59

2.6

Poverty National poverty line

Population below the poverty line Survey year

Afganistan 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, 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

60

2002 1988 1995 1998–99

1995 1995–96 2000 1995 1997 2001–02 1998 1997 1998 1990 1997 1996

1995–96 1996 1996 1995

1992

2000 1995 1995–96 1995 1993–94 1995 1995–96

1992 2002 1992 1989 1994 1987

Population below the poverty line

Rural %

Urban %

National %

.. 29.6 16.6 .. .. 50.8 .. .. .. 55.2 .. .. 25.2 77.3 19.9

.. 19.8 7.3 .. 28.4 58.3 .. .. .. 29.4 .. .. 28.5 53.8 13.8

.. 25.4 12.2 .. 55.1 .. .. 68.1 51.0 41.9 .. 26.5 63.2 19.5

51.4 .. 61.1 36.0 40.1 59.6 .. .. 67.0 .. 7.9 .. 79.0 .. .. 25.5 .. .. .. .. .. 45.3 56.0 23.3 64.8 .. 14.7 47.0 .. .. .. .. 55.4 .. .. .. 71.9 .. .. ..

14.7 .. 22.4 43.0 21.1 41.4 .. .. 63.0 .. <2 .. 48.0 .. .. 19.2 .. .. .. .. .. 18.2 19.0 22.5 38.9 .. 6.8 33.3 .. .. .. .. 48.5 .. .. .. 33.7 .. .. ..

22.0 36.0 54.6 36.4 36.1 53.3 .. .. 64.0 19.9 6.0 .. 60.0 .. .. 22.0 .. .. .. .. .. 27.7 34.0 22.9 50.6 53.0 8.9 45.5 .. .. .. 64.0 52.1 .. 50.0 .. 57.9 40.0 .. 65.0

2007 World Development Indicators

International poverty line

Survey year

1995 1998 2001

2001 2000

1999 1999

2002–03 2001 2003 2004 2001

1998 1998 1999

2004 1998 1999–00 2002

1999–00

1998 2003 1998–99 2000

1995

Rural %

Urban %

National %

.. .. 30.3 ..

.. .. 22.6 ..

48.7 .. .. 42.0 53.0

.. .. 14.7 .. 29.9 51.9 .. .. 55.0 36.6

50.9 .. .. 49.6 49.8

.. 33.0 81.7 ..

.. 23.3 50.6 ..

.. 29.0 62.7 ..

41.0 .. 52.4 .. 38.0 49.9 .. .. .. .. 4.6 .. 79.0 .. .. .. .. .. .. .. .. 55.7 69.0 .. 49.8 .. .. 45.0 .. .. .. 61.0 52.7 .. 49.9 .. 74.5 .. .. 66.0

17.5 .. 19.2 .. 18.0 22.1 .. .. .. .. <2 .. 55.0 .. .. .. .. .. .. .. .. 34.7 30.0 .. 28.5 .. .. 37.0 .. .. .. 48.0 56.2 .. 18.6 .. 27.1 .. .. ..

1993a 21.5 2004b 12.8 2003a 46.4 2003a .. 1998a 35.0 1997a 40.2 2001a .. .. 1993a .. 17.0 2003b 4.6 2004 a .. 64.0 2003b .. .. .. 2003b .. 2002a .. 2001a .. .. 1996b .. 42.2 2004b 46.0 1998 b 16.7 1999–2000a 37.2 2002b .. .. 2003a 44.2 1999–2000a .. .. .. 57.6 1998 a 54.5 2003a .. 39.5 1998–99a .. 56.2 2002b .. .. .. 2001b

Survey year

2004 a 1995a 2004b 2003a

2001a 2000a 2002a 2003a 2002b

Population below $1 a day %

Poverty gap at $1 a day %

Population below $2 a day %

Poverty gap at $2 a day %

.. <2 <2 .. 6.6 <2 .. .. 3.7 41.3 <2 .. 30.9 23.2 .. 28.0 7.5 <2 27.2 54.6 34.1 17.1 .. 66.6 .. <2 9.9 .. 7.0 .. .. 3.3 14.8 <2 .. <2 .. 2.8 17.7 3.1 19.0 .. <2 23.0 .. .. .. 59.3 6.5 .. 44.8 .. 13.5 .. .. 53.9

.. <0.5 <0.5 .. 2.1 <0.5 .. .. 0.6 10.3 <0.5 .. 8.2 13.6 .. 9.9 3.4 <0.5 7.3 22.7 9.7 4.1 .. 38.1 .. <0.5 2.1 .. 3.1 .. .. 1.6 4.1 <0.5 .. <0.5 .. 0.5 7.1 <0.5 9.3 .. <0.5 4.8 .. .. .. 28.8 2.1 .. 17.3 .. 5.5 .. .. 26.6

.. 10.0 15.1 .. 17.4 31.1 .. .. 33.4 84.0 <2 .. 73.7 42.2 .. 55.5 21.2 6.1 71.8 87.6 77.7 50.6 .. 84.0 .. 5.6 34.9 .. 17.8 .. .. 9.8 48.8 <2 .. <2 .. 16.2 40.8 43.9 40.6 .. 7.5 77.8 .. .. .. 82.9 25.3 .. 78.5 .. 31.9 .. .. 78.0

.. 1.6 3.8 .. 7.1 7.1 .. .. 9.1 38.3 <0.5 .. 31.7 23.2 .. 26.5 8.5 1.5 30.4 48.9 34.5 19.3 .. 58.4 .. 1.3 12.5 .. 7.7 .. .. 4.0 18.4 <0.5 .. <0.5 .. 4.9 17.7 11.3 17.7 .. 1.9 29.6 .. .. .. 51.1 8.6 .. 40.8 .. 13.8 .. .. 47.4

National poverty line

Population below the poverty line

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

International poverty line

Population below the poverty line

Survey year

Rural %

Urban %

National %

Survey year

Rural %

Urban %

National %

Survey year

1998–99 1993 1993–94 1996

71.2 .. 37.3 .. .. .. .. .. .. 37.0 .. 27.0 39.0 47.0 .. .. .. 51.0 48.7 .. ..

28.6 .. 32.4 .. .. .. .. .. .. 18.7 .. 19.7 30.0 29.0 .. .. .. 41.2 33.1 .. ..

52.5 14.5 36.0 15.7 .. .. .. .. .. 27.5 .. 21.3 34.6 40.0 .. .. .. 47.6 45.0 .. ..

2004 1997 1999–00 1999

70.4 .. 30.2 34.4 .. .. .. .. .. 25.1 .. 18.7 .. 53.0 .. .. .. .. 41.0 .. ..

29.5 .. 24.7 16.1 .. .. .. .. .. 12.8 .. 12.9 .. 49.0 .. .. .. .. 26.9 .. ..

50.7 17.3 28.6 27.1 .. .. .. .. .. 18.7 .. 14.2 .. 52.0 .. .. .. 41.0 38.6 .. ..

2003b 2002a 2004–05a 2002a 1998a

.. .. .. 25.3 76.0 .. .. 75.9 65.5 .. 42.4 64.1 32.6 18.0 71.3 .. .. 43.3 .. .. 76.1 66.0 49.5 .. .. 33.4 64.9 41.3 28.5 77.1 53.1 .. .. ..

.. .. .. .. 63.2 .. .. 30.1 30.1 .. 12.6 58.0 39.4 7.6 62.0 .. .. 21.6 .. .. 31.9 52.0 31.7 .. .. 17.2 15.3 16.1 19.7 42.0 28.0 .. .. ..

.. .. .. 21.4 73.3 54.0 15.5 63.8 50.0 .. 24.2 62.4 35.6 13.1 69.4 .. .. 41.8 .. .. 50.3 63.0 43.0 .. .. 28.6 37.3 37.5 21.8 54.3 40.6 23.8 .. ..

.. .. .. 22.3 76.7 66.5 .. .. 61.2 .. 27.9 67.2 43.4 27.2 .. .. .. 34.6 .. .. 68.5 .. 36.4 .. .. 35.9 .. .. .. 72.1 50.7 .. .. ..

.. .. .. .. 52.1 54.9 .. .. 25.4 .. 11.3 42.6 30.3 12.0 .. .. .. 9.6 .. .. 30.5 .. 30.4 .. .. 24.2 .. .. .. 42.9 21.5 .. .. ..

.. .. .. 21.7 71.3 65.3 .. .. 46.3 .. 17.6 48.5 36.1 19.0 .. .. .. 30.9 .. .. 47.9 .. 34.1 .. .. 32.6 .. .. .. 53.1 36.8 .. .. ..

1995 1997 1996 1994

2001 1993

2000 2002 1997

2003 1997–98

2004 a 2002–03a 2003a 1997a 1998b 2003a 2002a 2003a 1995a

2002 1997 1990–91 1989 1998 1996 2000 2001 1998 1990–91 1996–97

1995–96

1993 1989–93 1985

1993 1997 1996 1991 2001 1994 1993

2003 1999 1997–98

2000 2004 2002 2002 1998–99

2003–04

1998 1992–93

1998–99

2004 1997

2.6

PEOPLE

Poverty

2003a 2003a 2001a 2004–05a 1997b 2001a 2000a 2004a 2003a 2002a 1998–99 2002–03 1993b 2003–04 a

2001a 1995a 2003a

2002a 2003b 2003b 2003b 2002a 2002a

Population below $1 a day %

Poverty gap at $1 a day %

14.9 <2 33.5 7.5 <2 .. .. .. .. <2 .. <2 <2 22.8 .. <2 .. <2 27.0 <2 .. 36.4 .. .. <2 <2 61.0 20.8 <2 36.1 25.9 .. 3.0 <2 10.8 <2 36.2 .. 34.9 24.1 .. .. 45.1 60.6 70.8 .. .. 17.0 7.4 .. 13.6 10.5 14.8 <2 .. ..

4.4 <0.5 7.6 0.9 <0.5 .. .. .. .. <0.5 .. <0.5 <.5 5.9 .. <0.5 .. <0.5 6.1 <0.5 .. 19.0 .. .. <0.5 <0.5 27.9 4.7 <0.5 12.2 7.6 .. 1.4 <0.5 2.2 <0.5 11.6 .. 14.0 5.4 .. .. 16.7 34.0 34.5 .. .. 3.1 2.1 .. 5.6 2.9 2.9 <0.5 .. ..

Population below $2 a day %

35.7 <2 80.0 52.4 7.3 .. .. .. .. 14.4 .. 7.0 16.0 58.3 .. <2 .. 21.4 74.1 4.7 .. 56.1 .. .. 7.8 <2 85.1 62.9 9.3 72.1 63.1 .. 11.6 20.8 44.6 14.3 74.1 .. 55.8 68.53 .. .. 79.9 85.8 92.4 .. .. 73.6 18.0 .. 29.8 30.6 43.0 <2 .. ..

2007 World Development Indicators

Poverty gap at $2 a day %

15.1 <0.5 34.6 15.7 1.5 .. .. .. .. 3.3 .. 1.5 3.8 23.9 .. <0.5 .. 4.4 30.2 1.2 .. 33.1 .. .. 1.8 <0.5 51.8 24.3 2.0 34.2 26.8 .. 4.2 4.7 15.1 3.1 34.9 .. 30.4 26.79 .. .. 41.2 54.6 59.5 .. .. 26.1 7.5 .. 13.8 11.9 16.3 <0.5 .. ..

61

2.6

Poverty National poverty line

Population below the poverty line Survey year

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 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

1994 1994 1993 1992 1989

1990–91

1991 1994 1987–89 1992 1990 1994 1999–2000 2000

1994 2000 1989 1998 1998 1998 1990–91

62

Population below the poverty line

Rural %

Urban %

National %

27.9 .. .. .. 40.4 .. .. .. .. .. .. .. .. 22.0 .. .. .. .. .. .. 40.8 .. .. 20.0 13.1 .. .. 37.4 34.9 .. .. .. .. 30.5 .. 45.5 .. 45.0 83.1 35.8

20.4 .. .. .. 23.7 .. .. .. .. .. .. .. .. 15.0 .. .. .. .. .. .. 31.2 .. .. 24.0 3.5 .. .. 9.6 .. .. .. .. 20.2 22.5 .. 9.2 .. 30.8 56.0 3.4

21.5 30.9 51.2 .. 33.4 .. 82.8 .. .. .. .. .. .. 20.0 .. .. .. .. .. .. 38.6 9.8 32.3 21.0 7.4 28.3 .. 33.8 31.5 .. .. .. .. 27.5 31.3 37.4 .. 41.8 72.9 25.8

a. Expenditure base. b. Income base.

2007 World Development Indicators

International poverty line

Survey year

1999–00

2003–04

1995–96

2000–01 1998

1995 2002 2002–03 2003

1998

2002

2004 1995–96

Rural %

Urban %

National %

.. .. 65.7 .. .. .. 79.0 .. .. .. .. .. .. 27.0 .. .. .. .. .. .. 38.7 .. .. .. 13.9 34.5 .. 41.7 28.4 .. .. .. .. .. .. 35.6 .. .. 78.0 48.0

.. .. 14.3 .. .. .. 56.4 .. .. .. .. .. .. 15.0 .. .. .. .. ..

.. .. 60.3 .. .. .. 70.2 .. .. .. .. .. .. 25.0 .. .. .. .. .. .. 35.7 13.6 .. .. 7.6 27.0 .. 37.7 19.5 .. .. .. .. .. .. 28.9 .. .. 68.0 34.9

..

29.5 ..

.. .. 3.6 22.0 .. 12.2 .. .. .. .. 24.7 .. .. 6.6 .. .. 53.0 7.9

Survey year

2003a 2002a 2000a 2001a 1989a 1996b 1998a 2000a 2002a 2000–01a

2003a 2000–01a 2002a 1992b 2000a 2003a

2003a

2003b 2003a 2003b

1998a 2004 a 1995–96a

Population below $1 a day %

Poverty gap at $1 a day %

Population below $2 a day %

Poverty gap at $2 a day %

<2 <2 60.3 .. 17.0 .. 57.0 .. <2 <2 .. 10.7 .. 5.6 .. 47.7 .. .. .. 7.4 57.8 <2 .. 12.4 <2 3.4 .. .. <2 .. .. .. <2 <2 18.5 .. .. 15.7 63.8 56.1

0.5 <0.5 25.6 .. 3.6 .. 39.5 .. <0.5 <0.5 .. 1.7 .. 0.8 .. 19.4 .. .. .. 1.3 20.7 <0.5 .. 3.5 <0.5 0.8 .. .. <0.5 .. .. .. <0.5 <0.5 8.9 .. .. 4.5 32.6 24.2

12.9 12.1 87.8 .. 56.2 .. 74.5 .. 2.9 <2 .. 34.1 .. 41.6 .. 77.8 .. .. .. 42.8 89.9 25.2 .. 39.0 6.6 18.7 .. .. 4.9 .. .. .. 5.7 <2 40.1 .. .. 45.2 87.2 83.0

3.0 3.1 51.5 .. 20.9 .. 51.8 .. 0.8 <0.5 .. 12.6 .. 11.9 .. 42.4 .. .. .. 13.0 49.3 6.2 .. 14.6 1.3 5.7 .. .. 0.9 .. .. .. 1.6 0.6 19.2 .. .. 15.0 55.2 48.2

2.6 2.6a

Regional poverty estimates

Region

PEOPLE

Poverty

1981

1984

1987

1990

1993

1996

1999

2002

2004a

People living on less than $1 a day (millions) East Asia & Pacific

796

564

429

476

420

279

277

227

169

634

425

310

374

334

211

223

177

128

3

2

2

2

17

21

18

6

4

Latin America & Caribbean

39

51

50

45

39

43

49

48

47

Middle East & North Africa

9

7

6

5

5

4

6

5

4

China Europe & Central Asia

South Asia

473

457

469

479

440

459

475

485

462

Sub-Saharan Africa

168

200

223

240

252

286

296

296

298

1,489

1,281

1,179

1,247

1,172

1,093

1,120

1,067

986

855

856

868

873

838

881

897

890

857

57.7

39.0

28.2

29.8

25.2

16.1

15.5

12.3

9.0

63.8

41.0

28.6

33.0

28.4

17.4

17.8

13.8

9.9

Total Excluding China

Share of people living on less than $1 a day (%) East Asia & Pacific China Europe & Central Asia Latin America & Caribbean

0.7

0.5

0.4

0.5

3.6

4.4

3.8

1.3

0.9

10.8

13.1

12.1

10.2

8.4

8.9

9.7

9.1

8.6

Middle East & North Africa

5.1

3.8

3.1

2.3

1.9

1.7

2.1

1.7

1.5

South Asia

51.6

46.6

44.9

43.0

37.1

36.6

35.8

34.7

32.0

Sub-Saharan Africa

42.3

46.2

47.2

46.7

45.5

47.7

45.8

42.6

41.1

Total

40.6

33.0

28.7

28.7

25.6

22.7

22.3

20.4

18.4

32.0

30.1

28.7

27.1

24.6

24.6

23.8

22.6

21.1

1,170

1,116

1,041

1,113

1,083

908

883

766

684

876

819

744

819

803

649

628

524

452

20

17

14

20

78

85

88

61

46

Latin America & Caribbean

104

126

122

115

111

122

128

131

121

Middle East & North Africa

51

49

50

49

52

55

64

61

59

818

853

904

954

976

1,035

1,073

1,124

1,124

Excluding China People living on less than $2 a day (millions) East Asia & Pacific China Europe & Central Asia

South Asia Sub-Saharan Africa Total Excluding China

295

333

365

396

422

458

491

513

522

2,457

2,494

2,496

2,647

2,722

2,664

2,727

2,665

2,556

1,581

1,675

1,752

1,828

1,919

2,014

2,099

2,131

2,104

84.8

77.2

68.5

69.7

65.0

52.5

49.3

41.7

36.6

88.1

79.0

68.6

72.2

68.1

53.3

50.1

40.9

34.9

Share of people living on less than $2 a day (%) East Asia & Pacific China Europe & Central Asia

4.6

3.9

3.1

4.3

16.5

18.0

18.6

12.9

9.8

Latin America & Caribbean

28.4

32.2

29.6

26.2

24.1

25.2

25.3

24.8

22.2

Middle East & North Africa

29.2

25.6

24.2

21.7

21.4

21.4

23.6

21.1

19.7

South Asia

89.1

87.1

86.6

85.7

82.4

82.4

80.8

80.3

77.7

Sub-Saharan Africa

74.5

77.0

77.4

77.1

76.1

76.4

75.8

73.8

72.0

67.1

64.3

60.7

60.8

59.4

55.5

54.4

50.8

47.7

59.3

58.9

57.9

56.8

56.4

56.2

55.8

54.1

51.8

Total Excluding China a. Preliminary estimate.

2007 World Development Indicators

63

2.6

Poverty

About the data

The World Bank produced its first global poverty

because of differences in timing or the quality and

when making comparisons over time. The commonly

estimates for developing countries for World Devel-

training of survey enumerators.

used $1 a day standard, measured in 1985 interna-

opment Report 1990 using household survey data

Comparisons of countries at different levels of

tional prices and adjusted to local currency using

for 22 countries (Ravallion, Datt, and van de Walle

development also pose a potential problem because

purchasing power parities (PPPs), was chosen for

1991). Incorporating survey data collected during

of differences in the relative importance of consump-

the World Bank’s World Development Report 1990:

the last 17 years, the database has expanded con-

tion of nonmarket goods. The local market value of

Poverty because it is typical of the poverty lines in

siderably and now includes more than 550 surveys

all consumption in kind (including own production,

low-income countries. PPP exchange rates, such as

representing about 100 developing countries. Some

particularly important in underdeveloped rural econo-

those from the Penn World Tables or the World Bank,

1.1 million randomly sampled households were inter-

mies) should be included in total consumption expen-

are used because they take into account the local

viewed in these surveys, representing 93 percent of

diture. Similarly, imputed profit from the production of

prices of goods and services not traded internation-

the population of developing countries. The surveys

nonmarket goods should be included in income. This

ally. But PPP rates were designed for comparing

asked detailed questions on sources of income and

is not always done, though such omissions were a far

aggregates from national accounts, not for making

how it was spent and on other household charac-

bigger problem in surveys before the 1980s. Most

international poverty comparisons. As a result, there

teristics such as the number of people sharing that

survey data now include valuations for consumption

is no certainty that an international poverty line mea-

income. Most interviews were conducted by staff of

or income from own production. Nonetheless, valu-

sures the same degree of need or deprivation across

government statistics offices. Along with improve-

ation methods vary. For example, some surveys use

countries.

ments in data coverage and quality, the underlying

the price in the nearest market, while others use the

methodology has also improved, resulting in better

average farmgate selling price.

Early editions of World Development Indicators used PPPs from the Penn World Tables. Recent editions

Whenever possible, the table uses consumption

use 1993 consumption PPP estimates produced by

data in deciding who is poor and income surveys only

the World Bank. Recalculated in 1993 PPP terms,

Data availability

when consumption data are unavailable. In recent

the original international poverty line of $1 a day in

Since 1979 there has been considerable expansion

editions there has been a change in how income

1985 PPP terms is now about $1.08 a day. The 2005

in the number of countries that field such surveys,

surveys are used. In the past, average household

round of the International Comparison Program will

the frequency of the surveys, and the quality of their

income was adjusted to accord with consumption

provide new consumption PPPs in the coming year.

data. The number of data sets rose dramatically from

and income data from national accounts. But when

Any revisions in the PPP of a country to incorporate

a mere 10 between 1979 and 1981 to 162 between

this approach was tested using data for some 20

better price indexes can produce dramatically differ-

2000 and 2004. The drop to 30 available surveys

countries for which income and consumption expen-

ent poverty lines in local currency.

after 2002 reflects the lag between the time data

diture data were both available from the same sur-

Issues also arise when comparing poverty mea-

are collected and the time they become available for

veys, income was found to yield a higher mean than

sures within countries. For example, the cost of living

analysis, not a reduction in data collection. Data cov-

consumption but also higher inequality. When pov-

is typically higher in urban than in rural areas. One

erage is improving in all regions, but the Middle East

erty measures based on consumption and income

reason is that food staples tend to be more expen-

and North Africa continues to lag, with only three

were compared, these two effects roughly cancelled

sive in urban areas. So the urban monetary poverty

countries having at least one data set available since

each other out: statistically, there was no significant

line should be higher than the rural poverty line. But it

2000. A complete overview of data availability by

difference. So recent editions use income data to

is not always clear that the difference between urban

year and country can be obtained at http://iresearch.

estimate poverty directly, without adjusting average

and rural poverty lines found in practice reflects only

worldbank.org/povcalnet/.

income measures.

differences in the cost of living. In some countries

Data quality

International poverty lines

real value—meaning that it allows the purchase of

The problems of estimating poverty and comparing

International comparisons of poverty estimates

more commodities for consumption—than does

poverty rates do not end with data availability. Sev-

entail both conceptual and practical problems.

the rural poverty line. Sometimes the difference

eral other issues, some related to data quality, also

Countries have different definitions of poverty, and

has been so large as to imply that the incidence of

arise in measuring household living standards from

consistent comparisons across countries can be

poverty is greater in urban than in rural areas, even

survey data. One relates to the choice of income

difficult. Local poverty lines tend to have higher pur-

though the reverse is found when adjustments are

or consumption as a welfare indicator. Income is

chasing power in rich countries, where more gen-

made only for differences in the cost of living. As with

generally more difficult to measure accurately, and

erous standards are used, than in poor countries.

international comparisons, when the real value of the

consumption comes closer to the notion of stan-

Is it reasonable to treat two people with the same

poverty line varies it is not clear how meaningful such

dard of living. And income can vary over time even

standard of living—in terms of their command over

urban-rural comparisons are.

if the standard of living does not. But consumption

commodities— differently because one happens to

data are not always available. Another issue is that

live in a better-off country?

and more comprehensive estimates.

the urban poverty line in common use has a higher

By combining all this information, a team in the World Bank’s Development Research Group cal-

household surveys can differ widely, for example, in

Poverty measures based on an international

culates the number of people living below various

the number of consumer goods they identify. And

poverty line attempt to hold the real value of the

international poverty lines, as well as other poverty

even similar surveys may not be strictly comparable

poverty line constant across countries, as is done

and inequality measures that are published in World

64

2007 World Development Indicators

2.6

WORLD VIEW

Poverty Definitions

Development Indicators. The database is updated

• Survey year is the year in which the underlying data

annually as new survey data become available, and

were collected. • Rural poverty rate is the percent-

a major reassessment of progress against poverty

age of the rural population living below the national

is made about every three years.

rural poverty line. • Urban poverty rate is the percentage of the urban population living below the

Do it yourself: PovcalNet

national urban poverty line. • National poverty rate

Recently, this research team developed PovcalNet,

is the percentage of the population living below the

an interactive Web-based computational tool that

national poverty line. National estimates are based

allows users to replicate the calculations by the

on population-weighted subgroup estimates from

World Bank’s researchers in estimating the extent

household surveys. • Population below $1 a day

of absolute poverty in the world. PovcalNet is self

and population below $2 a day are the percentages

contained and powered by reliable built-in software

of the population living on less than $1.08 a day and

that performs the relevant calculations from a pri-

$2.15 a day at 1993 international prices. As a result

mary database. The underlying software can also

of revisions in PPP exchange rates, poverty rates for

be downloaded from the site and used with distribu-

individual countries cannot be compared with poverty

tional data of various formats. The PovcalNet primary

rates reported in earlier editions. • Poverty gap is

database consists of distributional data calculated

the mean shortfall from the poverty line (counting

directly from household survey data. Detailed infor-

the nonpoor as having zero shortfall), expressed as a

mation for each of these is also available from the

percentage of the poverty line. This measure reflects

site.

the depth of poverty as well as its incidence.

Estimation from distributional data requires an interpolation method. The method chosen was Lorenz curves with fl exible functional forms, which have proved reliable in past work. The Lorenz curve can be graphed as the cumulative percentages of total consumption or income against the cumulative number of people, starting with the poorest individual. The empirical Lorenz curves estimated by PovcalNet are weighted by household size, so they are based on percentiles of population, not households. PovcalNet also allows users to calculate poverty measures under different assumptions. For example, instead of $1 a day, users can specify a different poverty line, say $1.50 or $3. Users can also specify different PPP rates and aggregate the estimates

Data sources

using alternative country groupings (for example, UN

The poverty measures are prepared by the World

country groupings or groupings based on average

Bank’s Development Research Group. The national

incomes) or a selected set of individual countries.

poverty lines are based on the World Bank’s

PovcalNet is available online at http://iresearch.

country poverty assessments. The international

worldbank.org/povcalnet/

poverty lines are based on nationally representative primary household surveys conducted by national statistical offices or by private agencies under the supervision of government or international agencies and obtained from government statistical offices and World Bank Group country departments. The World Bank Group has prepared an annual review of its poverty work since 1993. For details on data sources and methods used in deriving the World Bank’s latest estimates, see Chen and Ravallion’s “How Have the World’s Poorest Fared Since the Early 1980s?” (2004).

2007 World Development Indicators

65

2.7

Distribution of income or consumption Survey year

Gini index

Percentage share of income or consumption

Lowest 10%

Afghanistan Albania Algeria Angola Argentinab 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, 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

66

2004 a 1995a 2004 c 2003a 1994c 2000 c 2001a 2000a 2002a 2000 c 2003a 2002c 2001a 1993a 2004 c 2003a 2003a 1998 a 2004a 2001a 2000 c 1993a 2003c 2004 c 1996c 2003c

2003c 2002a 2001a 1996c 1997c 2004 c 1998c 1999–2000a 2002c 2003a 1999–2000a 2000 c 1995c 1998a 2003a 2000 c 1998–99a 2000 c 2002c 2003a 1993a 2001c

2007 World Development Indicators

.. 31.1 35.3 .. 51.3 33.8 35.2 29.1 36.5 33.4 29.7 33.0 36.5 60.1 26.2 60.5 57.0 29.2 39.5 42.4 41.7 44.6 32.6 61.3 .. 54.9 46.9 43.4 58.6 .. .. 49.8 44.6 29.0 .. 25.4 24.7 51.6 53.6 34.4 52.4 .. 35.8 30.0 26.9 32.7 .. 50.2 40.4 28.3 40.8 34.3 55.1 38.6 47.0 59.2

.. 3.4 2.8 .. 0.9 3.6 2.0 3.3 3.1 3.7 3.4 3.4 3.1 0.3 3.9 1.2 0.9 3.4 2.8 1.7 2.9 2.3 2.6 0.7 .. 1.4 1.6 2.0 0.74 .. .. 1.0 2.0 3.4 .. 4.3 2.6 1.4 0.9 3.7 0.7 .. 2.5 3.9 4.0 2.8 .. 1.8 2.0 3.2 2.1 2.5 0.9 2.9 2.1 0.7

Lowest 20%

.. 8.2 7.0 .. 3.1 8.5 5.9 8.6 7.4 8.6 8.5 8.5 7.4 1.5 9.5 3.2 2.8 8.7 6.9 5.1 6.8 5.6 7.2 2.0 .. 3.8 4.3 5.3 2.48 .. .. 3.5 5.2 8.3 .. 10.3 8.3 4.0 3.3 8.6 2.7 .. 6.7 9.1 9.6 7.2 .. 4.8 5.6 8.5 5.6 6.7 2.9 7.0 5.2 2.4

Second 20%

.. 12.6 11.6 .. 7.6 12.3 12.0 13.3 11.5 12.1 13.2 13.0 11.3 5.9 14.2 6.0 6.4 13.7 10.9 10.3 10.2 9.3 12.7 4.9 .. 7.3 8.5 9.4 6.20 .. .. 8.2 9.1 12.8 .. 14.5 14.7 7.8 7.5 12.1 7.5 .. 11.8 13.2 14.1 12.6 .. 8.7 10.5 13.7 10.1 11.9 7.0 10.8 8.8 6.2

Third 20%

Fourth 20%

.. 17.0 16.1 .. 12.8 15.7 17.2 17.4 15.3 15.6 17.3 16.3 15.4 10.9 17.9 9.7 11.0 17.2 14.5 15.1 13.7 13.7 17.2 9.6 .. 11.1 13.7 13.9 10.60 .. .. 13.1 13.7 16.8 .. 17.7 18.2 12.1 11.7 15.4 12.8 .. 16.3 16.8 17.5 17.2 .. 12.8 15.3 17.8 14.9 16.8 11.6 14.7 13.1 10.4

.. 22.6 22.7 .. 21.1 20.6 23.6 22.9 21.2 21.0 22.7 20.8 21.5 18.7 22.6 16.0 18.7 22.1 20.5 21.5 19.6 20.4 23.0 18.5 .. 17.8 21.7 20.7 18.05 .. .. 21.2 21.3 22.6 .. 21.7 22.9 19.3 19.4 20.4 21.2 .. 22.4 21.5 22.1 22.8 .. 20.3 22.3 23.1 22.9 23.0 19.0 21.4 19.4 17.7

Highest 20%

.. 39.5 42.6 .. 55.4 42.8 41.3 37.8 44.5 42.7 38.3 41.4 44.5 63.0 35.8 65.1 61.1 38.3 47.2 48.0 49.6 50.9 39.9 65.0 .. 60.0 51.9 50.7 62.67 .. .. 54.1 50.7 39.6 .. 35.9 35.8 56.7 58.0 43.6 55.9 .. 42.8 39.4 36.7 40.2 .. 53.4 46.4 36.9 46.6 41.5 59.5 46.1 53.4 63.4

Highest 10%

.. 24.4 26.8 .. 38.2 29.0 25.4 23.0 29.5 27.9 23.5 28.1 29.0 47.2 21.4 51.0 44.8 23.9 32.2 32.8 34.8 35.4 24.8 47.7 .. 45.0 34.9 34.9 46.90 .. .. 37.4 34.0 24.5 .. 22.4 21.3 41.1 41.6 29.5 38.8 .. 27.6 25.5 22.6 25.1 .. 37.0 30.3 22.1 30.0 26.0 43.4 30.7 39.3 47.7

Survey year

Gini index

Percentage share of income or consumption

Lowest 10%

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

2003c 2002a 2004–05a 2002a 1998a 2000 c 2001c 2000 c 2004 a 1993c 2002–03a 2003a 1997a 1998c 2003a 2002a 2003a 1995a

2003a 2003a 2001a 2004–05a 1997c 2001a 2000a 2004 a 2003a 2002a 1998–99a 2002–03a 1993c 2003–04 a 1999c 1997c 2001a 1995a 2003a 2000 c 2002a 2003c 1996a 2003c 2003c 2003a 2002a 1997c

53.8 26.9 36.8 34.3 43.0 .. 34.3 39.2 36.0 45.5 24.9 38.8 33.9 42.5 .. 31.6 .. 30.3 34.6 37.7 .. 63.2 .. .. 36.0 39.0 47.5 39.0 49.2 40.1 39.0 .. 46.1 33.2 32.8 39.5 47.3 .. 74.3 47.2 30.9 36.2 43.1 50.5 43.7 25.8 .. 30.6 56.1 50.9 58.4 52.0 44.5 34.5 38.5 ..

2.7

PEOPLE

Distribution of income or consumption

1.2 4.0 3.6 3.6 2.0 .. 2.9 2.1 2.3 2.1 4.8 2.7 3.0 2.5 .. 2.9 .. 3.8 3.4 2.5 .. 0.5 .. .. 2.7 2.4 1.9 2.9 1.7 2.4 2.5 .. 1.6 3.2 3.0 2.6 2.1 .. 0.5 2.6 2.5 2.2 2.2 0.8 1.9 3.9 .. 4.0 0.7 1.7 0.7 1.3 2.2 3.1 2.0 ..

Lowest 20%

3.4 9.5 8.1 8.4 5.1 .. 7.4 5.7 6.5 5.3 10.6 6.7 7.4 6.0 .. 7.9 .. 8.9 8.1 6.6 .. 1.5 .. .. 6.8 6.1 4.9 7.0 4.4 6.1 6.2 .. 4.3 7.8 7.5 6.5 5.4 .. 1.4 6.0 7.6 6.4 5.6 2.6 5.0 9.6 .. 9.3 2.5 4.5 2.4 3.7 5.4 7.5 5.8 ..

Second 20%

7.1 13.9 11.3 11.9 9.4 .. 12.3 10.5 12.0 9.2 14.2 10.8 11.9 9.8 .. 13.6 .. 12.8 11.9 11.2 .. 4.3 .. .. 11.6 10.8 8.5 10.8 8.1 10.2 10.6 .. 8.3 12.2 12.2 10.6 9.3 .. 3.0 9.0 13.2 11.4 9.8 7.1 9.6 14.0 .. 13.0 6.6 7.9 6.3 7.7 9.1 11.9 11.0 ..

Third 20%

11.6 17.6 14.9 15.4 14.1 .. 16.3 15.9 16.8 13.2 17.6 14.9 16.4 14.3 .. 18.0 .. 16.4 15.6 15.5 .. 8.9 .. .. 16.0 15.5 12.7 14.8 12.9 14.7 15.2 .. 12.6 16.5 16.8 14.8 13.0 .. 5.4 12.4 17.2 15.8 14.2 13.9 14.5 17.2 .. 16.3 11.4 11.9 10.8 12.2 13.6 16.1 15.5 ..

Fourth 20%

19.6 22.4 20.4 21.0 21.5 .. 21.9 23.0 22.8 20.6 22.0 21.3 22.8 20.8 .. 23.1 .. 22.5 21.1 22.0 .. 18.8 .. .. 22.3 22.2 20.4 20.7 20.3 22.2 22.3 .. 19.7 22.1 23.1 21.3 18.7 .. 11.5 18.0 23.3 22.6 21.1 23.1 21.7 22.0 .. 21.1 19.6 19.2 18.6 19.7 21.3 22.2 21.9 ..

Highest 20%

58.3 36.5 45.3 43.3 49.9 .. 42.0 44.9 42.0 51.6 35.7 46.3 41.5 49.1 .. 37.5 .. 39.4 43.3 44.7 .. 66.5 .. .. 43.2 45.5 53.5 46.6 54.3 46.6 45.7 .. 55.1 41.4 40.5 46.6 53.6 .. 78.7 54.6 38.7 43.8 49.3 53.3 49.2 37.2 .. 40.3 59.9 56.5 61.9 56.7 50.6 42.2 45.9 ..

2007 World Development Indicators

Highest 10%

42.2 22.2 31.1 28.5 33.7 .. 27.2 28.8 26.8 35.8 21.7 30.6 25.9 33.9 .. 22.5 .. 24.3 28.5 29.1 .. 48.3 .. .. 27.7 29.6 36.6 31.8 38.4 30.2 29.5 .. 39.4 26.4 24.6 30.9 39.4 .. 64.5 40.6 22.9 27.8 33.8 35.4 33.2 23.4 .. 26.3 43.0 40.5 46.1 40.9 34.2 27.0 29.8 ..

67

2.7

Distribution of income or consumption Survey year

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 Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay b Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe

2003a 2002a 2000a 2001a 2003a 1989a 1998c 1996c 1998 a 2000a 2000 c 2002a 2000–01c 2000 c 2000 c 2003a 2000–01a 2002a 1992c 2000a 2003a 1998a 2002a 2003a 1999 c 2000 c 2003c 2003a 2003c 2004 a 1998a 2004 a 1995–96a

Gini index

31.0 39.9 46.8 .. 41.3 30.0 62.9 42.5 25.8 28.4 .. 57.8 34.7 40.2 .. 50.4 25.0 33.7 .. 32.6 34.6 42.0 .. 38.9 39.8 43.6 40.8 45.7 28.1 .. 36.0 40.8 44.9 36.8 48.2 34.4 .. 33.4 50.8 50.1

Percentage share of income or consumption

Lowest 10%

Lowest 20%

3.3 2.4 2.1 .. 2.7 3.4 0.5 1.9 3.1 3.6 .. 1.4 2.6 3.0 .. 1.6 3.6 2.9 .. 3.3 2.9 2.7 .. 2.2 2.3 2.0 2.6 2.3 3.9 .. 2.1 1.9 1.9 2.8 0.7 4.2 .. 3.0 1.2 1.8

8.1 6.1 5.3 .. 6.6 8.3 1.1 5.0 8.8 9.1 .. 3.5 7.0 7.0 .. 4.3 9.1 7.6 .. 7.9 7.3 6.3 .. 5.9 6.0 5.3 6.1 5.7 9.2 .. 6.1 5.4 5.0 7.2 3.3 9.0 .. 7.4 3.6 4.6

Second 20%

12.9 10.5 9.1 .. 10.3 13.0 2.0 9.4 14.9 14.2 .. 6.3 12.1 10.5 .. 8.2 14.0 12.2 .. 12.3 12.0 9.9 .. 10.8 10.3 9.7 10.2 9.4 13.6 .. 11.4 10.7 9.1 11.7 8.7 11.4 .. 12.2 7.9 8.1

Third 20%

17.1 14.9 13.2 .. 14.2 17.3 9.8 14.6 18.7 18.1 .. 10.0 16.4 14.2 .. 12.3 17.6 16.3 .. 16.5 16.1 14.0 .. 15.3 14.8 14.2 14.7 13.2 17.3 .. 16.0 15.7 14.0 15.4 13.9 14.7 .. 16.7 12.6 12.2

Fourth 20%

22.7 21.8 19.4 .. 20.6 23.0 23.7 22.0 22.8 22.9 .. 18.0 22.5 20.4 .. 18.9 22.7 22.6 .. 22.4 22.3 20.8 .. 23.1 21.7 21.0 21.5 19.1 22.4 .. 22.5 22.4 21.5 21.0 22.0 20.5 .. 22.5 20.8 19.3

Highest 20%

39.2 46.6 53.0 .. 48.4 38.4 63.4 49.0 34.8 35.7 .. 62.2 42.0 48.0 .. 56.3 36.6 41.3 .. 40.8 42.4 49.0 .. 44.9 47.3 49.7 47.5 52.5 37.5 .. 44.0 45.8 50.5 44.7 52.1 44.3 .. 41.2 55.1 55.7

Highest 10%

24.4 30.6 38.2 .. 33.4 23.4 43.6 32.8 20.9 21.4 .. 44.7 26.6 32.7 .. 40.7 22.2 25.9 .. 25.6 26.9 33.4 .. 28.8 31.5 34.1 31.7 37.7 23.0 ` 28.5 29.9 34.0 29.6 35.2 28.8 .. 25.9 38.8 40.3

a. Refers to expenditure shares by percentiles of population, ranked by per capita expenditure. b. Urban data. c. Refers to income shares by percentiles of population, ranked by per capita income.

68

2007 World Development Indicators

About the data

2.7

PEOPLE

Distribution of income or consumption Definitions

Inequality in the distribution of income is reflected

countries in these respects may bias comparisons

• Survey year is the year in which the underlying data

in the percentage shares of income or consumption

of distribution.

were collected. • Gini index measures the extent

accruing to portions of the population ranked by

World Bank staff have made an effort to ensure

to which the distribution of income (or consump-

income or consumption levels. The portions ranked

that the data are as comparable as possible. Wher-

tion expenditure) among individuals or households

lowest by personal income receive the smallest

ever possible, consumption has been used rather

within an economy deviates from a perfectly equal

shares of total income. The Gini index provides a con-

than income. Income distribution and Gini indexes

distribution. A Lorenz curve plots the cumulative

venient summary measure of the degree of inequal-

for high-income countries are calculated directly from

percentages of total income received against the

ity. Data on the distribution of income or consump-

the Luxembourg Income Study database, using an

cumulative number of recipients, starting with the

tion come from nationally representative household

estimation method consistent with that applied for

poorest individual. The Gini index measures the area

surveys. Where the original data from the house-

developing countries.

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

For most countries the income distribution indi-

income or consumption is the share of total income

cators are based on the same data used to derive

or consumption that accrues to subgroups of popu-

the $1 and $2 a day poverty estimates in table 2.6.

lation indicated by deciles or quintiles. Percentage

This table contains additional countries for which

shares by quintile may not sum to 100 because of

poverty estimates are not provided in table 2.6,

rounding.

either because no reasonable purchasing power parity estimates are available or because the international poverty lines are not relevant for high-income economies. The distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. No adjustment 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 middle-income 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 table 2.6). Two sources of noncomparability 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

Data sources

unequal than the distribution of consumption. In

Data on distribution are compiled by the World

addition, the definitions of income used differ more

Bank’s Development Research Group using pri-

often among surveys. Consumption is usually a

mary household survey data obtained from govern-

much better welfare indicator, particularly in devel-

ment statistical agencies and World Bank country

oping countries. Second, households differ in size

departments. Data for high-income economies are

(number of members) and in the extent of income

estimated from the Luxembourg Income Study

sharing among members. And individuals differ in

database.

age and consumption needs. Differences among

2007 World Development Indicators

69

2.8 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, 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

70

Assessing vulnerability and security Urban informal sector employment

Youth unemployment

% of urban employment Male Female 1998– 1998– 2001 a 2001 a

Female Male % of male % of female labor force labor force ages 15–24 ages 15–24

.. .. .. .. .. .. .. .. .. .. .. .. 50 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 39 .. .. .. .. 21 .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. 41 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 65 .. .. .. .. 7 .. .. .. .. .. .. ..

2007 World Development Indicators

2000–05a

.. 42 43 .. 22b .. 11 11b .. 7 .. 16 .. .. .. 34 14b 23 .. .. .. .. 14b .. .. 15 .. 14 20 .. .. 11 .. 30 .. 19 9 16 12b 21 13 .. 16 4 18 22b .. .. 27 16 13 18 .. .. .. ..

2000–05a

.. 27 46 .. 28 b .. 10 10 b .. 6 .. 20 .. .. .. 46 23b 21 .. .. .. .. 11b .. .. 21 .. 8 32 .. .. 22 .. 36 .. 19 9 34 21b 40 9 .. 15 11 19 24b .. .. 31 14 19 35 .. .. .. ..

Female-headed households

Pension contributors

% of total 2000–05a

.. .. .. .. .. 29 .. .. .. 10 .. .. 21 20 .. .. .. .. 9 .. 25 24 .. .. 20 .. .. .. 30 .. .. .. .. .. .. .. .. 28 .. 12 .. 47 .. 24 .. .. 26 .. .. .. 34 .. .. .. .. 43

Year

2004 2002 2004 2002 2004 1996 2004 1992 1995 1996 2002 2004 2004 1994 1993 1993 1993 2003 1990 2003 2005 2000 1992 2004 1997 2004 2003 2003 2005 2004 2004 2003 2000 2003 2003 1995 2004 2003 2003 2002 2000 1993

% of labor force

% of workingage population

.. 48.8 36.7 .. 34.9 64.4 .. 80.8 52.0 2.8 97.0 86.2 4.8 10.1 35.9 .. 52.2 64.0 3.1 3.3 .. 13.7 57.0 .. 1.1 58.0 20.5 .. 19.0 .. 5.8 55.2 9.3 71.0 .. 86.0 92.0 27.2 27.0 55.4 18.0 .. 91.0 .. 90.3 90.0 15.0 .. 30.0 88.0 9.1 79.0 19.0 1.5 .. ..

.. 33.0 22.0 .. 25.8 48.3 .. 58.8 46.0 2.1 94.0 65.9 .. 7.8 24.6 .. 38.7 63.0 3.0 3.0 .. 11.5 63.0 .. 1.0 35.2 17.2 .. 14.0 .. 5.6 37.6 9.1 46.0 .. 61.0 74.0 18.6 20.7 27.7 12.0 .. 66.0 .. 67.0 62.0 14.0 .. 22.7 64.0 7.1 52.0 11.7 1.8 .. ..

Public expenditure on pensions

Year

2005 2004 2002 1994 2004 2003 2003 1996 1992 1997 2003 1993 2000 2004 1997 2005 1992 1991 2001 2003 1990 1997 2001 1996 1994 1992 1997 1997 2005 1992 2003 2003 2000 2002 2004 1997 2001 2003 1993 2003 2003

2004 2003 2002 2003 1995

% of GDP

0.5 4.6 3.2 .. 6.2 3.4 5.4 11.6 2.5 0.0 7.7 8.5 0.4 4.5 8.8 .. 9.8 8.9 0.3 0.2 .. 0.8 5.4 0.3 0.1 2.9 2.7 .. 1.1 .. 0.9 4.2 0.3 12.3 12.6 10.5 9.6 0.8 2.5 4.1 1.3 0.3 6.3 0.9 9.0 13.3 .. .. 3.0 13.2 1.3 12.8 0.7 .. .. ..

Year

2002 2002 2002 2002

2002

2002

2002

2002

2002

2002 2002 2002 2002 2002 2002 2002 2002 2002 2002

2002 2002

Average pension % of per capita income

.. .. 89.1 .. 73.7 .. 52.4 93.2 .. .. .. 62.8 .. .. .. .. .. 75.2 .. .. .. .. 57.1 .. .. 53.5 .. .. 54.4 .. .. 103.1 .. 61.6 .. 58.2 54.1 55.9 .. 119.8 39.3 .. 60.9 .. 78.8 65.0 .. .. .. 71.8 .. 99.9 .. .. .. ..

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

Urban informal sector employment

Youth unemployment

% of urban employment Male Female 1998– 1998– 2001 a 2001 a

Female Male % of male % of female labor force labor force ages 15–24 ages 15–24

.. .. 54 .. .. .. .. .. .. .. .. .. .. .. .. .. .. 33 .. .. .. .. .. .. 50 .. .. .. .. .. .. .. 18 .. .. .. .. .. .. 60 .. .. .. .. .. .. .. 64 .. .. .. .. .. .. .. ..

.. .. 41 .. .. .. .. .. .. .. .. .. .. .. .. .. .. 25 .. .. .. .. .. .. 27 .. .. .. .. .. .. .. 22 .. .. .. .. .. .. 76 .. .. .. .. .. .. .. 61 .. .. .. .. .. .. .. ..

2000–05a

5b 20 10 b .. 20 .. 9 17 21 22 10 28 13 .. .. 12 .. 19 .. 12 .. .. .. .. 16 63 .. .. 8 .. .. 21 6 19 20 17 .. .. 40 .. 10 9 11b .. .. 13 .. 11 19 .. 12 21b 15 37 14 25b

2000–05a

11b 19 11b .. 32 .. 7 19 27 36 7 43 16 .. .. 9 .. 21 .. 14 .. .. .. .. 15 62 .. .. 8 .. .. 34 7 18 21 16 .. .. 49 .. 10 10 16b .. .. 12 .. 15 30 5 17 21b 19 39 19 21b

Female-headed households

Pension contributors

% of total 2000–05a

Year

.. .. .. 12 .. .. .. .. .. .. .. 12 .. 32 .. .. .. .. .. .. .. .. .. .. .. .. 22 27 .. 11 29 .. .. .. .. 17 26 .. 42 16 .. .. 31 .. 17 .. .. .. .. .. .. 20 15 .. .. ..

1999 1996 2004 1995 2001 2002 1992 2003 2003 2003 2004 2005 1996 2004 2003 2003

2003 2004 2000 1993 1993 1990 1995 2000 2002 2000 2002 2003 1995

2003 2002 2005 1992 2000 2003 2004 1998 2004 2003 2000 2005 2003

PEOPLE

2.8

Assessing vulnerability and security

Public expenditure on pensions

% of labor force

% of workingage population

20.6 77.0 9.1 8.0 35.0 .. 93.0 82.0 90.0 .. 94.0 30.3 33.7 8.0 .. 58.0 .. 40.1 .. 90.0 32.1 .. .. 65.5 79.7 63.8 5.4 .. 48.7 2.5 5.0 51.3 34.6 60.0 61.4 22.4 2.0 .. .. 2.1 94.0 .. 17.9 1.3 1.9 92.0 .. 6.4 51.6 .. 11.6 16.3 27.0 84.8 92.0 ..

17.7 65.0 5.7 7.0 20.0 .. 64.7 63.0 56.0 .. 73.0 17.4 26.3 6.2 .. 43.0 .. 28.3 .. 64.0 19.6 .. .. 37.1 58.9 38.8 4.8 .. 37.8 2.0 4.0 33.6 22.6 43.0 49.1 12.3 2.1 .. .. 1.4 72.0 .. 11.5 1.5 1.3 75.0 .. 4.0 40.7 .. 9.1 12.3 18.6 54.5 71.0 ..

Year

1994 2003

2000 2003 1996 2003 2003 2001 2004 1993 2003 1990 1997 2002 2003

2001 2003 1998 1990 1999 1991 1992 1999 2003 2003 2002 2003 1996

2003 2003 2003 1996 1992 1991 2003 1993 1996 2001 2000 1993 2003 2003

% of GDP

0.6 11.0 .. .. 1.1 .. 4.7 5.9 15.5 .. 8.9 2.2 4.9 0.5 .. 1.3 3.5 6.4 .. 8.2 2.1 .. .. 2.1 6.2 8.7 0.2 .. 6.5 0.4 0.2 4.4 1.3 8.0 5.8 1.9 0.0 .. .. 0.3 9.0 7.4 2.5 0.1 0.1 10.7 .. 0.9 4.3 .. 1.2 2.6 1.0 15.8 11.9 ..

Year

2002

2002 2002 2002 2002 2002

2002

2002

2002 2002

2002

2002

2002 2002

2002

2002 2002 2002

2007 World Development Indicators

Average pension % of per capita income

.. 90.5 .. .. 124.2 .. 36.6 .. 88.8 .. 59.1 76.1 .. .. .. 43.3 .. .. .. 81.8 .. .. .. 91.2 71.3 .. .. .. .. .. .. .. 45.1 .. .. 74.1 .. .. .. .. 84.1 39.5 .. .. .. 65.1 .. .. .. .. .. 43.9 .. 69.7 79.8 ..

71

2.8 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 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 Europe EMU

Assessing vulnerability and security Urban informal sector employment

Youth unemployment

% of urban employment Male Female 1998– 1998– 2001 a 2001 a

Female Male % of male % of female labor force labor force ages 15–24 ages 15–24

.. 10 .. .. .. .. .. .. .. .. .. 16 .. .. .. .. .. .. .. .. .. .. .. .. .. 10 .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. 9 .. .. .. .. .. .. .. .. .. 28 .. .. .. .. .. .. .. .. .. .. .. .. .. 6 .. .. .. .. .. .. .. .. .. .. .. .. .. ..

2000–05a

21 .. .. .. .. .. .. 4 30 b 11 .. 56 17 20 .. .. 16 9 21 .. .. 5 .. 17b 31 19 .. .. 16 .. 13 12 25b .. 24b 4 39 .. .. 28 .. w .. .. .. 24 .. .. .. 14 .. 11 .. 13 16

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

72

2007 World Development Indicators

2000–05a

18 .. .. .. .. .. .. 6 29 b 12 .. 65 24 37 .. .. 13 9 39 .. .. 5 .. 26b 29 19 .. .. 17 .. 10 10 35b .. 35b 5 45 .. .. 21 .. w .. .. .. 27 .. .. .. 20 .. 12 .. 12 19

Female-headed households

Pension contributors

% of total 2000–05a

Year

.. .. 36 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 25 .. .. .. .. .. 27 28 .. .. .. .. .. .. .. 27 .. .. 23 ..

2005

2003 2003 2004 1995 2003 1995

2003 2004 1995 2003 2003

1996 1999 1997 2003 2002 2004 2005 2003 2003 2001 2001 1998 2000 1999 2000 1995

% of labor force

% of workingage population

57.5 .. .. .. 5.2 45.9 4.6 73.0 58.8 86.0 .. .. 92.0 35.6 12.1 .. 90.0 99.0 .. .. 2.0 18.0 15.9 .. 54.9 44.9 .. 1.8 76.0 .. 94.0 91.0 57.7 .. 20.8 8.4 19.0 15.0 5.9 12.0

39.1 .. .. .. 3.8 32.1 3.6 56.0 55.0 68.7 .. .. 63.0 22.2 12.0 .. 72.0 84.0 .. .. 2.0 17.0 15.0 .. 30.0 24.3 .. 1.6 52.0 .. 73.0 71.0 44.3 .. 14.6 10.0 6.4 7.0 4.9 10.0

Public expenditure on pensions

Year

2003 2004

2003 2003 1996 2003 2003

2003 1996

2003 2003 1991 1996

1997 1996 2003 2002 1996 2003 2005 2003 2003 1996 1995 2001 1998 2001 1994 1993 2002

% of GDP

6.9 5.8 .. .. 1.3 12.4 .. 1.4 8.5 10.1 .. .. 10.6 2.4 .. .. 14.0 12.1 0.5 3.0 .. .. 0.6 0.6 4.3 7.1 2.3 0.3 15.4 .. 8.3 7.5 15.0 5.3 2.7 1.6 0.8 0.1 0.1 2.3

Year

2002

2002

2002 2002

2002 2002

2002 2002 2002

2002

Average pension % of per capita income

.. .. .. .. .. .. .. .. 60.2 .. .. .. 88.3 .. .. .. 68.2 67.3 .. .. .. .. .. .. 72.7 103.3 .. .. .. .. 47.6 51.0 125.4 .. .. .. .. 106.3 .. ..

About the data

2.8

PEOPLE

Assessing vulnerability and security Definitions

As traditionally defined and measured, poverty is a

unemployment varies considerably over the year as a

• Urban informal sector employment is all persons

static concept, and vulnerability a dynamic one. Vul-

result of different school opening and closing dates.

who, during a given reference period, were employed

nerability reflects a household’s resilience in the face

The youth unemployment rate shares similar limita-

in at least one informal enterprise, irrespective of

of shocks and the likelihood that a shock will lead to a

tions on comparability as the general unemployment

their status in employment and whether it was their

decline in well-being. Thus, it depends primarily on the

rate. For further information, see About the data for

main or secondary job. • Youth unemployment refers

household’s asset endowment and insurance mecha-

table 2.5 and the original source.

to the share of the labor force ages 15–24 without

nisms. Because poor people have fewer assets and

The data on female-headed households are from

work but available for and seeking employment.

less diversified sources of income than the better-off,

recent Demographic and Health Surveys. The defini-

• Female-headed households refer to the percent-

fluctuations in income affect them more.

tion and concept of the female-headed household

age of households with a female head. • Pension

Enhancing security for poor people means reducing

differ greatly across countries, making cross-country

contributors refer to the share of the labor force

their vulnerability to such risks as ill health, providing

comparison difficult. In some cases it is assumed

or working-age population (here defined as ages

them the means to manage risk themselves, and

that a woman cannot be the head of any household

15–64) covered by a pension scheme. • Public

strengthening market or public institutions for man-

in which an adult male is present, because of sex-

expenditure on pensions includes all government

aging risk. The tools include microfinance programs,

biased stereotype. Users need to be cautious when

expenditures on cash transfers to the elderly, the

old age assistance and pensions, and public provi-

interpreting the data.

disabled, and survivors and the administrative costs

The data on pension contributors come from national

of these programs. • Average pension is estimated

sources, the International Labour Organization (ILO),

by dividing total pension expenditure by the number

Poor households face many risks, and vulnerability

and International Monetary Fund country reports.

of pensioners.

is thus multidimensional. The indicators in the table

Coverage by pension schemes may be broad or even

focus on individual risks—informal sector employ-

universal where eligibility is determined by citizenship,

ment, youth unemployment, female-headed house-

residency, or income status. In contribution-related

holds, income insecurity in old age, and the extent

schemes, however, eligibility is usually restricted to

to which publicly provided services may be capable

individuals who have made contributions for a mini-

of mitigating some of these risks. Poor people face

mum number of years. Definitional issues—relating to

labor market risks, often having to take up precari-

the labor force, for example—may arise in comparing

ous, low-quality jobs in the informal sector and to

coverage by contribution-related schemes over time

increase their household’s labor market participation

and across countries (for country-specific information,

by sending their children to work (see table 2.4).

see Palacios and Pallares-Miralles 2000). The share of

Income security is a prime concern for the elderly.

the labor force covered by a pension scheme may be

sion of education and basic health care (see tables 2.9 and 2.14).

For informal sector employment the data are from a variety of sources, including labor force and special

overstated in countries that do not attempt to count informal sector workers as part of the labor force.

informal sector surveys, household surveys, surveys

Public interventions and institutions can provide

of household industries or economic activities, sur-

services directly to poor people, although whether

veys of small enterprises and micro enterprises, and

these interventions and institutions work well for the

official estimates. The international comparability

poor is debated. State action is often ineffective,

of the data is affected by differences among coun-

in part because governments can influence only a

tries in definitions and coverage and in treatment of

few of the many sources of well-being and in part

Data on urban informal sector employment and

domestic workers. The data in the table are based

because of difficulties in delivering good and ser-

youth unemployment are from the ILO database

on national definitions of informal sector and urban

vices. The effectiveness of public provision is further

Key Indicators of the Labour Market, 4th edition.

areas established by countries and therefore data

constrained by the fiscal resources at governments’

Data on female-headed household are from Demo-

may not be comparable across countries. For details

disposal and the fact that state institutions may not

graphic and Health Surveys by Macro Interna-

on these definitions, consult the original source.

be responsive to the needs of poor people.

tional. Data on pension contributors and pension

Data sources

Youth unemployment is an important policy issue

The data on public pension spending are from

spending are from Robert Palacios and Montser-

for many economies. Experiencing unemployment

national sources and cover all government expendi-

rat Pallares-Miralles’s “International Patterns of

may permanently impair a young person’s produc-

tures, including the administrative costs of pension

Pension Provision” (2000) and updates, Edward

tive potential and future employment opportunities.

programs. They cover noncontributory pensions or

Whitehouse’s Pensions Panorama (2007), and

The table presents unemployment among youth ages

social assistance targeted to the elderly and dis-

the Organisation for Economic Co-operation and

15–24, but the lower age limit for young people in

abled and spending by social insurance schemes for

Development’s Social Expenditure database (forth-

a country could be determined by the minimum

which contributions had previously been made. The

coming). Further updates, notes, and sources will

age for leaving school, so age groups could dif-

pattern of spending in a country is correlated with

be available in the World Bank’s Progress Report

fer across countries. Also, since this age group is

its demographic structure—spending increases as

of Pensions Indicators (forthcoming).

likely to include school leavers, the level of youth

the population ages.

2007 World Development Indicators

73

2.9

Education inputs Public expenditure per studenta

1991

2005b

% of GDP per capita Secondary 1998 2005b

.. .. 26.5 .. .. .. .. 18.2 .. .. .. 16.3 .. .. .. .. .. .. .. 13.4 .. .. .. 11.9 8.0 .. .. .. .. .. .. 7.8 .. .. .. .. .. .. .. .. .. .. .. 22.1 21.9 11.8 .. 13.2 .. .. .. 7.6 .. .. .. 9.1

.. 7.8 11.3 .. 10.9 .. 16.4 23.2 6.3 7.0 14.1 20.2 11.5 16.2 .. 17.2 10.8 19.0 34.7 19.1 6.1 10.3 .. 11.8 7.3 12.8 .. 14.9 19.5 .. 4.0 17.0 .. 20.2 37.6 12.9 25.5 8.1 .. .. 9.2 11.3 20.1 .. 18.7 17.6 .. 7.4 .. 16.6 12.8 16.1 6.5 .. .. ..

.. .. .. .. 13.6 .. 14.5 29.9 15.4 12.4 .. .. .. 12.0 .. .. 10.4 .. .. .. 11.4 18.2 .. .. 27.5 13.8 11.5 .. 14.9 .. .. 23.2 54.5 .. 38.3 22.1 38.3 .. .. .. 8.2 .. 27.9 .. 26.4 28.6 .. .. .. 20.5 .. 15.5 .. .. .. ..

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 China Hong Kong, 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

74

2007 World Development Indicators

.. 12.0 17.1 .. 14.3 .. 14.4 28.6 10.2 14.7 25.3 23.1 21.2 13.0 .. 44.0 11.2 20.9 21.6 73.3 .. 16.0 .. .. 30.1 14.2 .. 19.9 18.4 .. 18.3 17.1 .. 26.0 41.1 23.4 35.0 5.5 .. .. 10.5 15.4 27.7 .. 28.1 29.6 .. 9.1 .. 22.3 34.5 22.5 3.5 .. .. ..

Public expenditure on education

Tertiary 1998

2005b

.. .. .. .. 20.2 .. 27.0 51.6 19.1 46.3 .. .. .. 52.2 .. .. 85.7 .. .. 1051.9 .. 69.7 49.0 .. .. 21.0 90.1 .. 35.6 .. 404.9 55.0 212.8 41.5 115.9 34.4 66.2 .. .. .. 10.5 .. 32.6 .. 41.3 29.7 .. .. .. .. .. 30.7 .. .. .. ..

.. 36.6 .. .. 10.3 .. 22.5 45.7 10.4 49.7 28.3 37.1 .. 36.0 .. 479.9 48.9 28.3 212.3 348.8 77.5 67.9 44.6 .. 359.9 15.5 .. 60.6 24.6 .. 245.9 36.1 .. 31.5 59.0 33.6 67.2 .. .. .. 17.2 1,101.3 23.2 .. 37.4 33.9 .. 238.0 .. .. 209.8 24.3 .. 244.1 .. ..

% of GDP 2005b

.. 2.9 .. .. 3.5 .. 4.8 5.5 2.5 2.5 6.0 6.2 3.5 6.4 .. 10.7 4.1 4.2 4.7 5.1 1.9 1.8 5.2 .. 2.1 3.7 .. 4.2 4.8 .. 2.2 4.9 .. 4.7 9.8 4.5 8.4 1.8 .. .. 2.8 5.4 5.7 5.0 6.5 5.9 .. 2.0 2.9 4.7 5.4 4.0 .. 2.0 .. ..

Primary Trained pupilteachers in primary teacher ratio education

% of total government expenditure

% of total

pupils per teacher

2005b

2005b

2005b

36.5 .. 98.5 .. .. 77.5 .. .. 100.0 48.0 99.8 .. 72.2 63.3 .. 92.5 .. 95.1 88.3 87.5 97.7 62.7 .. .. 26.8 96.4 84.7 93.2 .. .. 62.2 96.8 100.0 100.0 100.0 .. .. 88.3 70.9 .. 100.0 83.6 .. 97.1 .. .. 100.0 57.8 89.7 .. 53.2c .. .. 68.1 .. 40.5

83 21 25 .. 17 21 .. 13 13 51 16 12 47 24 .. 26 22 17 47 49 53 48 .. .. 63 27 21 18 29 34 83 21 42 15 10 18 .. 24 23 22 30 48 14 72 16 19 36 35 14 14 35c 11 31 45 .. ..

.. 8.4 .. .. 12.0 .. .. 10.8 19.6 14.2 11.3 11.8 14.1 18.1 .. 21.5 10.9 .. 16.6 17.7 .. 8.6 .. .. 10.1 18.5 .. 23.0 11.1 .. 8.1 18.5 21.6 10.0 16.6 8.5 15.1 9.7 .. .. 20.0 .. 15.4 19.4 12.8 11.0 .. 8.9 13.1 9.7 .. 8.0 .. .. .. ..

Public expenditure per studenta

1991

2005b

% of GDP per capita Secondary 1998 2005b

.. 21.2 .. .. .. .. 11.6 12.6 15.3 9.9 .. .. .. 12.9 .. 11.8 35.4 .. .. .. .. .. .. .. .. .. .. 7.2 10.1 .. .. 10.1 4.8 .. .. 15.3 .. .. .. .. 12.6 17.3 .. .. .. 32.7 10.5 .. 11.3 .. .. .. .. 12.9 17.2 ..

.. 21.9 11.1 2.6 9.7 .. 13.9 22.8 25.9 11.5 22.6 14.0 10.0 23.6 .. 18.6 12.2 7.6 8.6 20.6 7.2 24.2 .. .. 14.4 23.8 8.4 13.5 18.6 .. 9.8 11.8 15.5 16.6 14.3 22.9 14.1 2.7 20.1 12.4 18.7 19.4 8.8 19.0 .. 21.7 16.3 7.0 9.6 .. 12.6 6.7 11.7 22.9 24.4 ..

.. 18.6 21.2 .. .. .. 17.5 23.3 28.6 .. 19.9 15.7 .. .. .. 14.9 .. 11.9 4.3 24.0 .. 68.4 .. .. .. .. 39.9 .. .. 61.6 38.7 .. 14.2 .. .. 49.1 .. .. 36.4 13.1 21.8 24.5 .. .. .. 30.6 22.2 .. 19.1 .. .. 10.8 .. 16.5 29.1 ..

Primary

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

.. 26.8 19.8 4.9 11.0 .. 20.0 23.4 29.2 20.0 22.3 16.9 7.9 23.5 .. 25.1 18.1 14.3 4.0 24.5 7.6 49.0 .. .. 20.1 7.5 .. 28.6 26.3 .. 24.7 19.8 16.8 24.1 13.2 39.6 48.4 2.9 24.1 10.5 23.6 22.7 10.4 64.3 .. 33.0 15.5 11.0 12.3 .. 14.1 8.9 10.1 21.7 33.0 ..

Public expenditure on education

Tertiary 1998

2005b

.. 36.0 74.5 .. .. .. 29.4 32.9 28.4 .. 13.2 .. .. .. .. 7.0 .. 27.7 66.9 34.3 12.8 1237.4 .. 23.8 .. .. 180.9 .. .. 265.0 85.0 .. 47.8 .. .. 104.8 .. .. 157.6 .. 44.2 42.0 .. .. .. 47.8 .. .. 33.6 .. .. .. .. 36.3 29.7 ..

.. 31.9 68.6 13.3 22.8 .. 24.8 30.0 24.1 40.7 19.6 .. 5.7 262.6 .. 9.3 116.4 20.8 22.4 14.4 15.9 1104.8 .. .. 20.6 22.6 175.0 .. 93.7 .. 39.9 37.1 44.1 12.9 22.8 93.0 435.3 .. 106.6 71.1 43.0 34.1 .. .. .. 50.4 28.7 .. 26.5 .. 30.1 12.3 14.1 19.7 27.8 ..

% of GDP 2005b

.. 5.9 3.7 0.9 4.7 .. 4.5 7.3 4.9 4.5 3.7 .. 2.3 6.7 .. 4.6 5.1 4.4 2.3 5.3 2.6 13.4 .. .. 5.2 3.4 3.2 5.8 8.0 4.3 2.3 4.5 5.8 4.3 5.3 6.7 3.7 .. 6.9 3.4 5.3 6.8 3.1 2.3 .. 7.7 3.6 2.3 3.8 .. 4.3 2.4 3.2 5.6 5.9 ..

2.9

PEOPLE

Education inputs

Primary Trained pupilteachers in primary teacher ratio education

% of total government expenditure

% of total

pupils per teacher

2005b

2005b

2005b

87.2 .. .. 92.9 100.0 100.0 .. .. .. 100.0 .. .. 97.2 98.8 .. 100.0 100.0 58.0 83.4 .. 14.4 63.7 .. .. .. 100.0 36.5 85.8 .. .. 100.0 100.0 84.3 .. 96.4 100.0 59.8 76.0 16.7 95.8 .. .. 76.9 75.8 49.8 .. .. 85.5 89.6 100.0 67.0 .. 100.0 .. .. ..

33 10 40 20 19 21 18 12 11 28 19 20 17 40 .. 29 12 24 31 13 14 42 .. .. 15 20 54 64 18 54 40 22 28 18 34 27 66 31 33 40 c .. 16 34 44 37 11 .. 38 24 35 28 22 35 13 12 ..

.. 10.3 10.7 9.0 22.8 .. 13.1 13.7 9.5 9.5 10.7 .. .. 29.2 .. 15.0 12.7 .. 11.7 15.4 11.0 29.8 .. .. 15.7 16.4 25.3 .. 28.0 14.8 8.3 14.3 23.8 21.1 .. 27.2 19.5 .. .. 14.9 10.8 20.9 15.0 .. .. 15.7 24.2 10.9 8.9 .. 10.8 13.7 17.2 12.3 12.4 ..

2007 World Development Indicators

75

2.9

Education inputs Public expenditure per studenta

1991

2005b

% of GDP per capita Secondary 1998 2005b

.. .. .. .. 18.9 .. .. .. .. 17.4 .. 20.2 11.2 .. .. 6.5 46.2 36.1 .. .. .. 11.6 .. .. .. 10.7 .. .. .. .. 15.0 .. 7.8 .. .. .. .. .. .. 20.7 .. m .. .. .. .. .. .. .. .. .. .. .. 16.3 15.3

.. .. 11.3 .. 18.7 .. .. .. 13.0 30.0 .. 14.2 18.6 .. .. 12.4 24.0 24.9 14.2 8.7 .. 13.9 6.7 15.7 24.1 11.8 .. 11.3 14.8 7.1 18.4 21.5 6.5 .. .. .. .. .. 5.4 .. 15.4 m .. 14.1 11.7 14.7 .. 6.3 16.7 12.3 14.3 9.7 .. 18.7 18.7

.. .. .. .. .. .. .. .. 18.5 .. .. 21.2 24.4 .. .. 26.1 26.3 27.7 22.1 .. .. .. 30.9 12.2 .. .. .. .. .. 11.5 26.6 22.5 .. .. .. .. .. .. .. .. .. m .. .. .. .. .. .. .. .. .. 13.1 .. 24.4 25.4

Primary

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 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 Europe EMU

.. .. 18.6 .. 32.2 .. .. .. 17.8 25.7 .. 17.6 23.8 .. .. 30.9 26.8 29.2 26.3 11.3 .. 13.1 .. .. 24.1 14.8 .. 34.0 23.9 9.3 28.4 25.8 7.2 .. .. .. .. .. 8.2 .. 20.3 m .. 17.4 16.5 20.1 .. .. 20.5 14.9 17.5 12.1 .. 24.4 24.7

Public expenditure on education

Tertiary 1998

2005b

.. .. .. .. .. .. .. .. 33.0 .. .. 64.3 19.6 .. .. 388.4 53.3 54.5 .. .. .. .. 317.9 147.6 .. .. .. .. .. .. 32.8 27.5 .. .. .. .. .. .. .. .. .. m .. .. .. 33.6 .. .. .. .. .. .. .. 29.7 29.7

.. 12.1 408.8 .. 267.6 .. .. .. 29.3 26.4 .. 49.6 22.7 .. .. 341.5 46.9 64.8 .. 14.1 .. 23.0 .. .. 80.6 44.7 .. 188.8 34.1 28.9 28.1 26.7 19.5 .. .. .. .. .. .. .. 32.6 m .. 32.5 36.6 26.3 .. .. 23.2 31.3 .. 68.6 .. 29.4 27.8

% of GDP 2005b

3.6 3.7 3.8 6.8 5.4 .. 3.8 .. 4.4 6.0 .. 5.4 4.3 .. .. 6.2 7.5 6.1 .. 3.5 .. 4.2 2.6 4.2 8.1 4.0 .. 5.2 6.4 1.3 5.5 5.9 2.2 .. .. .. .. .. 2.0 .. 4.7 m .. 4.5 4.3 4.6 4.3 2.7 4.4 4.3 .. 2.9 4.3 5.9 5.4

Primary Trained pupilteachers in primary teacher ratio education

% of total government expenditure

% of total

pupils per teacher

2005b

2005b

2005b

.. 12.3 12.2 27.6 18.9 .. .. .. 11.2 12.6 .. 17.9 11.2 .. .. .. 12.8 13.0 .. 18.0 .. 27.5 13.6 .. .. 13.6 .. 18.3 18.9 27.4 11.9 15.2 7.9 .. .. .. .. .. 14.8 .. .. m .. 15.2 .. 15.4 .. .. 13.9 15.0 .. 12.8 .. 12.8 11.1

25.9 99.0 81.7 .. 100.0 .. 61.5 .. .. .. .. 78.7 .. .. 55.1 90.5 .. .. 88.4 84.1 100.0 79.3 36.8 81.0 .. .. .. 80.4 99.7 60.0 .. .. 100.0 .. 84.0 93.4 100.0 .. 100.0 .. .. m .. .. .. .. .. 95.7 .. .. .. .. .. .. ..

17 17 62 .. 47 .. 67 .. 18 15 .. 36 14 22 28 32 10 .. 25 21 56 21 34 18 21 .. .. 50 19 15 18 14 21 .. 19 22 25 26 51 39 29 m 42 22 22 22 31 22 17 24 23 41 48 16 14

a. Because of the change from International Standard Classification of Education (ISCED) 76 to ISCED 97 in 1998, data before 1998 are not fully comparable with data from 1998 onward. b. Provisional data. c. Data are for 2006.

76

2007 World Development Indicators

About the data

2.9

PEOPLE

Education inputs Definitions

Data on education are compiled by the United

contribute to the national economy (Hanushek

• Public expenditure per student is public current

Nations Educational, Scientific, and Cultural Organi-

2002).

spending on education divided by the number of stu-

zation (UNESCO) Institute for Statistics from official

The share of trained teachers in primary educa-

dents by level, as a percentage of gross domestic

responses to surveys and from reports provided by

tion measures the quality of the teaching staff. It

product (GDP) per capita. • Public expenditure on

education authorities in each country. Such data are

does not take account of competencies acquired by

education is current and capital public expenditure

used for monitoring, policymaking, and resource allo-

teachers through their professional experience or

on education, as a percentage of GDP and as a per-

cation. For a variety of reasons, however, education

self-instruction or of such factors as work experi-

centage of total government expenditure. • Trained

statistics generally fail to provide a complete and

ence, teaching methods and materials, or classroom

teachers in primary education are the percentage

accurate picture of a country’s education system.

conditions, which may affect the quality of teaching.

of primary school teachers who have received the

Statistics often lag by one to two years, though an

Since the training teachers receive varies greatly

minimum organized teacher training (pre-service

effort is being made to shorten the delay. Moreover,

(pre-service or in-service), care should be taken in

or in-service) required for teaching in their country.

coverage and data collection methods vary across

comparing across countries.

• Primary pupil-teacher ratio is the number of pupils

countries and over time within countries, so compari-

The primary pupil-teacher ratio reflects the average

enrolled in primary school divided by the number of

numbers of pupils per teacher. It is different from the

primary school teachers (regardless of their teaching

The data on education spending in the table

average class size because of the different practices

assignment).

for the majority of the countries refer to public

countries employ, such as part-time teaching, school

spending—government spending on public educa-

shifts, and multigrade classes. The comparability of

tion plus subsidies for private education. The data

pupil-teacher ratios across countries is affected by

generally exclude foreign aid for education. They may

the definition of teachers and by differences in class

also exclude spending by religious schools, which

size by grade and in the number of hours taught,

play a significant role in many developing countries.

as well as the different practices mentioned above.

Data for some countries and for some years refer to

Moreover, the underlying enrollment levels are sub-

spending by the ministry of education only (exclud-

ject to a variety of reporting errors (for further discus-

ing education expenditures by other ministries and

sion of enrollment data see About the data for table

sons should be interpreted with caution.

departments and local authorities).

2.10). While the pupil-teacher ratio is often used to

Many developing countries have sought to supple-

compare the quality of schooling across countries, it

ment public funds for education. Some countries

is often weakly related to the value added of school-

have adopted tuition fees to recover part of the cost

ing systems.

of providing education services or to encourage devel-

In 1998 UNESCO introduced the new International

opment of private schools. Charging fees raises dif-

Standard Classification of Education 1997. Thus the

ficult questions relating to equity, efficiency, access,

time-series data for the years through 1997 are not

and taxation, however, and some governments have

consistent with those for 1998 and later. Any time-

used scholarships, vouchers, and other methods of

series analysis should therefore be undertaken with

public finance to counter criticism. For most coun-

extreme caution.

tries, the data reflect only public spending. Data for

In 2006 the UNESCO Institute for Statistics also

a few countries include private spending, although

changed its convention for citing the reference year

national practices vary with respect to whether par-

of education data and indicators to the calendar year

ents or schools pay for books, uniforms, and other

in which the academic or financial year ends. Data

supplies. For greater detail, see the country- and

that used to be listed for 2004/05, for example, are

indicator-specific notes in the source.

now listed for 2005. This change was implemented

The share of public expenditure devoted to educa-

to present the most recent data available and to

tion allows an assessment of the priority a govern-

align the data reporting with that of other interna-

ment assigns to education relative to other public

tional organizations (in particular the Organisation

investments, as well as a government’s commit-

for Economic Co-operation and Development and

ment to investing in human capital development. It

Eurostat).

Data sources

also reflects the development status of a country’s

Data on education inputs are from the UNESCO

education system relative to that of others. How-

Institute for Statistics, which compiles inter-

ever, returns on investment to education, especially

national data on education in cooperation with

primary and lower secondary education, cannot be

national commissions and national statistical

understood simply by comparing current education

services. Data for latest years are provisional, as

indicators with national income. It takes a long time

of January 2007.

before currently enrolled children can productively

2007 World Development Indicators

77

2.10

Participation in education Gross enrollment ratio

% of relevant age group Primary Secondary

Tertiary

2005b

2005b

2005b

2005b

1 49 6 .. 62 33 102 89 29 11 105 116 5 50 .. .. 68 78 2 2 9 25 68 2 1 52 36 69 39 1 6 69 3 48 113 107 91 34 77 14 51 12 114 2 59 114 14 18 51 97 56c 66 28 7 .. ..

87 106 112 .. 112 94 103 106 96 109 101 104 96 113 .. 105 141 105 58 85 134 117 100 56 77 104 118 105 113 62 88 110 72 96 102 102 101 113 117 101 113 64 100 93 101 105 130 81 94 100 94 c 102 114 81 .. ..

16 78 83 .. 86 88 149 101 83 46 95 109 33 88 .. 75 102 102 14 13 29 44 109 12 16 89 73 87 79 22 39 79 25 88 94 96 124 71 61 87 63 31 98 31 109 111 50 47 83 100 45c 96 51 30 .. ..

1 19 20 1 64 28 72 50 15 6 62 63 .. 41 .. 5 22 41 2 2 3 6 60 2 1 43 19 31 28 .. 4 25 .. 42 61 43 74 33 .. 33 19 1 65 3 90 56 .. 1 46 .. 5c 79 10 3 .. ..

Preprimary

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, 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

78

Net enrollment ratioa

2007 World Development Indicators

Children out of school

thousand primary-school-age children Male Female

% of relevant age group Primary Secondary 1991

.. 95 89 50 .. .. 99 88 89 .. 86 96 41 .. .. 83 85 86 29 53 69 74 98 52 35 89 97 .. 69 54 79 87 45 79 93 87 98 57 98 84 .. 16 99 22 98 100 85 48 97 84 54 95 .. 27 38 22

2005b

1991

2005b

2005b

2005b

.. 94 97 .. 99 79 96 .. 85 93 89 99 78 94 .. 83 93 95 45 60 99 .. .. .. 61 .. .. 93 87 .. 44 .. 56 87 97 .. 98 88 98 95 93 47 94 61 99 99 .. .. 87 .. 69 c 99 94 66 .. ..

.. .. 53 .. .. .. 79 .. .. .. .. 87 .. .. .. 35 17 63 .. .. .. .. 89 .. .. 55 .. .. 34 .. .. 38 .. 63 70 .. 87 .. .. .. .. .. .. .. 93 .. .. .. .. .. .. 83 .. .. .. ..

.. 74 66 .. 79 84 85 .. 78 43 89 97 .. 73 .. 55 76 88 11 .. 24 .. .. .. 11 .. .. 80 55 .. .. .. 20 85 87 .. 92 53 52 79 53 25 90 28 94 96 .. 45 72 .. 38 c 87 34 24 .. ..

.. 7 0 .. 3 11 42 .. 45 .. 17 4 .. 28 .. 26 .. 5 553 222 .. .. .. .. .. .. .. 1 276 .. 203 .. 519 7 5 .. 5 67 11 58 26 144 2 .. 1 11 .. .. 26 .. 510 c 0 .. .. .. ..

.. 7 39 .. 19 7 35 .. 46 .. 21 3 .. 19 .. 25 .. 5 649 258 .. .. .. .. .. .. .. 12 257 .. 173 .. 705 7 14 .. 2 53 0 161 22 164 1 .. 1 4 .. .. 22 .. 480 c 3 .. .. .. ..

Gross enrollment ratio

% of relevant age group Primary Secondary

Tertiary

2005b

2005b

2005b

2005b

33 81 36 22 46 6 .. 112 103 92 85 30 34 54 .. 91 73 13 9 79 74 34 .. 8 64 32 8 .. 108 3 2 95 84 62 40 54 .. .. 29 64 c 89 92 37 1 15 85 7 50 62 59 31 60 40 53 76 ..

113 98 116 117 111 98 106 110 101 95 100 98 109 114 .. 105 98 98 116 93 106 132 .. 107 97 98 138 122 93 66 93 102 109 92 118 105 105 100 99 126c 107 102 112 47 103 99 84 87 111 75 106 114 112 99 116 ..

65 97 54 64 81 45 112 93 99 88 102 87 99 49 .. 93 95 86 47 97 89 39 .. 104 102 84 .. 28 76 24 21 89 80 82 94 50 14 40 61 43c 119 118 66 9 34 116 87 27 70 26 63 92 86 97 97 ..

16 60 12 17 24 15 59 56 63 19 54 39 53 3 .. 90 18 41 8 74 51 3 .. 56 73 28 3 0b 32 3 3 17 23 34 41 11 1 11 6 6 59 86 18 1 10 80 15 5 44 .. 24 33 29 61 57 ..

Preprimary

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

Net enrollment ratioa

Children out of school

% of relevant age group Primary Secondary 1991

2005b

89 91 .. 97 92 94 90 92 100 96 100 94 89 .. .. 100 49 92 63 92 73 71 .. 96 .. 94 64 48 .. 21 35 91 98 89 90 56 43 98 .. .. 95 98 73 22 58 100 69 33 .. .. 94 .. 96 97 98 ..

91 89 90 94 95 88 96 98 99 91 100 91 91 80 .. 99 87 87 84 .. 92 87 .. .. 89 92 92 95 93 51 72 95 98 86 89 86 79 90 72 78 99 99 87 40 91 99 76 68 98 .. .. 97 94 97 98 ..

1991

21 75 .. 39 .. .. 80 .. .. 64 97 .. .. .. .. 86 .. .. .. .. .. 15 .. .. .. .. .. .. .. 5 .. .. 44 .. .. .. .. .. .. .. 84 85 .. 5 .. 88 .. .. .. .. 26 .. .. 76 .. ..

2.10

2005b

.. 91 .. 57 77 38 87 89 92 79 100 81 92 42 .. 90 78 80 38 .. .. 25 .. .. 94 81 .. 24 76 .. 15 82 64 76 78 35 7 37 38 .. 89 91 43 8 27 96 75 21 64 .. .. 69 61 90 82 ..

thousand primary-school-age children Male Female 2005b

43 10 .. 0 307 .. 8 9 4 16 7 26 4 526 .. 4 14 13 55 .. 12 25 .. .. 7 2 93 83 112 505 65 3 22 12 13 216 331 267 62 .. 3 1 27 634 .. 2 41 2,328 1 .. .. 12 392 41 1 ..

2007 World Development Indicators

2005b

27 9 .. 246 0 .. 7 7 7 14 0 17 6 506 .. 10 14 11 71 .. 12 16 .. .. 6 1 95 30 110 607 65 2 7 12 9 309 468 221 50 .. 11 1 27 737 .. 2 38 3,975 2 .. .. 2 255 33 2 ..

79

PEOPLE

Participation in education

2.10

Participation in education Gross enrollment ratio

% of relevant age group Primary Secondary

Tertiary

2005b

2005b

2005b

2005b

76 85 3 10 8 .. 4 .. 92 79 2c 37 111 .. 25 18 85 95 10 9 29 90 2 86 22 8 .. 2 86 64 59 62 61 28 58 60 30 1 .. 43 38 w 27 39 35 59 33 36 50 62 21 33 16 76 101

107 123 120 91 88 .. 155 .. 99 99 17 104 108 98 60 107 99 102 124 101 106 97 100 106 110 93 .. 118 107 83 107 99 109 100 105 95 89 87 111 96 107 w 102 113 115 105 107 114 102 118 103 110 92 100 104

85 93 14 88 26 .. 30 .. 94 100 .. 93 119 83 34 45 103 93 68 82 .. 73 40 88 81 79 .. 16 89 64 105 95 108 95 74 76 99 48 28 36 65 w 45 77 76 86 61 71 90 86 73 50 30 100 106

Preprimary

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 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 Europe EMU

Net enrollment ratioa

40 68 3 28 5 .. 2 .. 36 74 .. 16 66 .. .. 4 84 47 .. 17 1 43 .. 12 29 29 .. 3 69 22 60 82 39 15 41 16 38 9 .. 4 24 w 9 26 22 43 18 19 49 28 22 10 5 67 62

Children out of school

thousand primary-school-age children Male Female

% of relevant age group Primary Secondary 1991

81 99 66 59 43 .. 43 .. .. 96 9 90 100 .. 40 77 100 84 91 77 49 76 64 91 94 89 .. .. 80 99 98 97 91 78 87 90 .. 51 .. .. 83 w .. 92 92 93 81 96 90 85 84 .. 50 95 95

2005b

92 91 74 78 76 .. .. .. .. 98 19c 87 99 97 .. 80 99 94 95 97 91 .. 78 95 97 89 .. .. 83 71 99 92 .. .. 91 88 80 75 89 82 .. w 78 .. 93 94 .. 93 91 95 90 86 66 94 99

1991

.. .. 7 31 .. .. .. .. .. .. .. 45 .. .. .. 31 85 80 43 .. .. .. 15 .. .. 42 .. .. .. 60 81 85 .. .. 18 .. .. .. .. .. .. w .. .. .. .. .. .. .. 30 .. .. .. .. ..

2005b

81 .. .. 66 21 .. .. .. .. 95 .. .. 97 .. .. 33 98 83 62 80 .. .. .. 75 67 .. .. 13 79 57 95 89 .. .. 63 69 95 .. 26 34 .. w 37 69 65 75 .. .. 84 67 65 .. 24 90 94

2005b

2005b

23 198 196 426 167 .. .. .. .. 0d .. 321 3 9 .. 21 4 5 0 2 273 .. .. 0d 11 354 .. .. 152 37 1 593 .. .. 130 .. 35 .. 119 224

24 171 177 367 195 .. .. .. .. 0 .. 248 10 13 .. 19 5 4 70 15 331 .. .. 0 6 546 .. .. 144 39 0d 1,028 .. .. 106 .. 35 .. 109 206

a. Because of the change from International Standard Classification of Education (ISCED) 76 to ISCED 97 in 1998, data before 1998 are not fully comparable with data from 1998 onward. b. Provisional data. c. Data are for 2006. d. Less than 0.5.

80

2007 World Development Indicators

About the data

2.10

Definitions

School enrollment data are reported to the United

the primary sources of data on school-age popula-

• Gross enrollment ratio is the ratio of total enroll-

Nations Educational, Scientific, and Cultural Organi-

tions, are commonly subject to underenumeration

ment, regardless of age, to the population of the age

zation (UNESCO) Institute for Statistics by national

(especially of young children) aimed at circumvent-

group that officially corresponds to the level of educa-

education authorities and statistical offices. Enroll-

ing laws or regulations. Errors are also introduced

tion shown. • Preprimary education refers to the ini-

ment ratios help to monitor two important issues

when parents round up children’s ages. While census

tial stage of organized instruction, designed primarily

for universal primary education: whether a country

data are often adjusted for age bias, adjustments

to introduce very young children to a school-type envi-

is on track to achieve the Millennium Development

are rarely made for inadequate vital registration

ronment. • Primary education provides children with

Goal of universal primary completion by 2015, which

systems. Compounding these problems, pre- and

basic reading, writing, and mathematics skills along

implies achieving a net primary enrollment ratio of

post-census estimates of school-age children are

with an elementary understanding of such subjects

100 percent, and whether an education system has

interpolations or projections based on models that

as history, geography, natural science, social sci-

sufficient capacity to meet the needs of universal

may miss important demographic events (see the

ence, art, and music. • Secondary education com-

primary education, as indicated in part by its gross

discussion of demographic data in About the data

pletes the provision of basic education that began

enrollment ratios.

for table 2.1).

at the primary level and aims at laying the founda-

Enrollment ratios, while a useful measure of partici-

Thus gross enrollment ratios indicate the capacity

tions for lifelong learning and human development

pation in education, also have some limitations. They

of each level of the education system, but a high ratio

by offering more subject- or skill-oriented instruction

are based on data collected during annual school sur-

does not necessarily mean a successful education

using more specialized teachers. • Tertiary educa-

veys, which are typically conducted at the beginning

system. The net enrollment ratio excludes overage

tion refers to a wide range of post-secondary educa-

of the school year. They do not reflect actual rates

and underage students in an attempt to capture

tion institutions, including technical and vocational

of attendance or dropouts during the school year.

more accurately the system’s coverage and internal

education, colleges, and universities, whether or not

And school administrators may report exaggerated

efficiency. It does not solve the problem completely,

leading to an advanced research qualification, that

enrollments, especially if there is a financial incen-

however, because some children fall outside the offi -

normally require as a minimum condition of admis-

tive to do so. Typically, the total number of teachers

cial school age because of late or early entry rather

sion the successful completion of education at the

allocated to a given school is related to enrollment.

than because of grade repetition. The difference

secondary level. • Net enrollment ratio is the ratio

This may create perverse incentives to inflate enroll-

between gross and net enrollment ratios shows the

of total enrollment of children of official school age

ment levels, particularly when enrollment is closely

incidence of overage and underage enrollments.

linked to government school funding formulas, such as student capitation grants.

based on the International Standard Classification

In using enrollment data, it is also important to con-

of Education 1997 to the population of the age group

sider repetition rates. These rates are quite high in

that officially corresponds to the level of education

Also as international indicators, the gross and

some developing countries, leading to a substantial

shown. • Children out of school are the number of

net primary enrollment ratios have an inherent

number of overage children enrolled in each grade

primary school age children not enrolled in primary

weakness: the length of primary education differs

and raising the gross enrollment ratio.

or secondary school.

significantly across countries, although the Interna-

Children out of school are children in the primary

tional Standard Classification of Education tries to

school age group who are not enrolled in primary or

minimize the difference. A relatively short duration

secondary education. The data are calculated by the

for primary education tends to increase the ratio,

UNESCO Institute for Statistics using administrative

whereas a relatively long duration tends to decrease

data. Children out of school include dropouts and

it (in part because there are more dropouts among

children who never enrolled as well as children of

older children).

primary age enrolled in preprimary education. The

Overage or underage enrollments frequently occur,

large number of children out of school creates pres-

particularly when parents prefer, for cultural or eco-

sure for the education system to enroll children and

nomic reasons, to have children start school at other

to provide classrooms, teachers, and educational

than the official age. Children’s age at enrollment

materials, a task made difficult in many developing

may be inaccurately estimated or misstated, espe-

countries by limited education budgets. However,

cially in communities where registration of births

getting these children into school is a high priority for

is not strictly enforced. Parents who want to enroll

countries and crucial for their prospects for achiev-

an underage child in primary school may do so by

ing the Millennium Development Goal of universal

overstating the child’s age. And in some education

primary education.

systems ages for children repeating a grade may be underreported. Other problems affecting cross-country comparisons of enrollment data stem from errors in estimates of school-age populations. Age-sex struc-

In 2006 the UNESCO Institute for Statistics

Data sources

changed its convention for citing the reference

Data on gross and net enrollment ratios and out

year. For more information, see About the data for

of school children are from the UNESCO Institute

table 2.9.

for Statistics. Data for latest years are provisional, as of January 2007.

tures from censuses or vital registration systems,

2007 World Development Indicators

81

PEOPLE

Participation in education

2.11 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, 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

82

Education efficiency Gross intake rate in grade 1

Share of cohort reaching grade 5a

Repeaters in primary school

Transition to secondary education

% of relevant age group Male Female

% of grade 1 students

% of enrollment Male Female

% of enrollment in last year of primary Male Female

Male

Female

2005b

2005b

1991

2004b

1991

2004b

96 99 102 .. 110 98 103 105 94 116 105 103 109 119 .. 108 127 107 81 92 137 120 97 69 112 99 95 93 126 72 62 103 75 99 105 97 98 118 136 99 129 55 101 148 98 .. 94 87 103 105 107e 103 125 87 .. ..

67 99 99 .. 110 102 102 105 93 131 103 104 97 119 .. 102 117 104 69 84 128 104 96 50 81 97 93 87 119 61 62 103 68 97 104 96 99 108 134 99 123 45 101 135 97 .. 94 99 105 105 113e 103 122 81 .. ..

.. .. 95 .. .. .. 98 .. .. .. .. 90 54 .. .. 81 .. 91 71 65 .. .. 95 24 56 94 58 .. .. 58 56 83 75 .. .. .. 94 .. .. .. 56 .. .. 16 100 69 .. .. .. .. 81 100 .. 64 .. ..

.. .. 94 .. 84 .. .. .. .. 63 .. .. 53 85 .. 89 .. .. 75 66 62 64 .. .. 34 99 .. 99 81 .. 65 84 .. .. 96 98 .. .. 75 98 67 83 99 .. 100 .. 68 .. 76 .. 62 .. 70 78 .. ..

.. .. 94 .. .. .. 99 .. .. .. .. 92 56 .. .. 87 .. 90 68 58 .. .. 98 22 41 91 78 .. .. 50 65 85 70 .. .. .. 94 .. .. .. 60 .. .. 23 100 95 .. .. .. .. 79 100 .. 48 .. ..

.. .. 97 .. 85 .. .. .. .. 67 .. .. 50 85 .. 92 .. .. 76 68 65 63 .. .. 32 99 .. 100 86 .. 67 90 .. .. 98 99 .. .. 77 99 72 74 99 .. 100 .. 71 .. 83 .. 65 .. 66 73 .. ..

2007 World Development Indicators

2005b

2005b

2004b

2004b

18 3 14 .. 8 0c .. .. 0c 7 0c .. 17 2 .. 6 .. 3 12 30 15 26 .. 30 22 3 0c 1 5 16 25 8 17 0c 1 1 .. 10 2 5 7 13 3 8 1 .. 35 10 0c 2 6 0 13 8 .. ..

14 2 8 .. 5 0c .. .. 0c 7 0c .. 17 1 .. 4 .. 2 12 30 12 25 .. 31 24 2 0c 1 4 17 23 6 18 0c 0c 1 .. 6 2 3 5 13 1 6 0c .. 34 9 0c 1 6 0 12 9 .. ..

.. 100 76 .. 92 93 100 .. 99 89d 99 .. 51 90 .. 97 .. 96 47 35 84 43 .. .. 56 95 .. 100 100 .. 58 92 42 100 98 99 100 83 76 83 93 91 94 84 100 .. .. .. 98 99 87 .. 97 68 .. ..

.. 99 83 .. 94 93 100 .. 99 96d 100 .. 51 90 .. 98 .. 96 44 30 80 47 .. .. 42 98 .. 100 100 .. 58 91 36 100 99 99 100 92 71 89 93 85 99 84 100 .. .. .. 99 99 87 .. 95 58 .. ..

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

2.11

PEOPLE

Education efficiency Gross intake rate in grade 1

Share of cohort reaching grade 5a

Repeaters in primary school

Transition to secondary education

% of relevant age group Male Female

% of grade 1 students

% of enrollment Male Female

% of enrollment in last year of primary Male Female

Male

Female

2005b

2005b

1991

2004b

1991

2004b

2005b

2005b

129 96 139 121 107 110 102 99 103 93 98 91 108 120 .. 105 93 97 121 90 102 128 .. .. 101 98 182 177 94 70 112 102 106 93 148 101 161 123 93 160 e 100 100 147 65 124 98 73 128 110 101 109 105 138 97 105 ..

127 94 130 116 139 103 101 102 103 92 98 92 107 116 .. 107 92 94 111 89 100 120 .. .. 102 97 176 188 94 59 113 102 105 91 149 97 150 122 94 160 e 99 99 137 51 107 98 74 103 109 90 106 106 129 97 106 ..

.. 77 .. 34 91 .. 99 .. .. .. 100 .. .. 75 .. 99 .. .. .. .. .. 58 .. .. .. .. 22 71 97 71 76 97 35 .. .. 75 36 .. 60 51 .. .. 11 61 .. 99 97 .. .. 70 73 .. .. 89 .. ..

.. .. 81 88 88 87 100 100 96 86 .. 99 .. 81 .. 98 .. .. 64 .. 91 58 .. .. .. .. 43 40 99 78 51 97 92 .. .. 81 66 68 84 75d 100 .. 51 66 71 99 98 68 85 68 80 90 71 .. .. ..

.. 98 .. 78 89 .. 100 .. .. .. 100 .. .. 78 .. 100 .. .. .. .. .. 73 .. .. .. .. 21 57 97 67 75 98 71 .. .. 76 32 .. 65 51 .. .. 37 65 .. 100 96 .. .. 68 75 .. .. 96 .. ..

.. .. 76 90 87 73 100 100 97 92 .. 99 .. 85 .. 98 .. .. 62 .. 96 69 .. .. .. .. 43 37 98 70 55 97 94 .. .. 77 58 72 85 83d 99 .. 56 64 75 100 98 72 86 68 83 90 80 .. .. ..

9 3 3 3 3 9 1 2 0c 3 .. 1 0c 6 .. 0 2 0c 20 4 12 21 .. .. 1 0c 19 9 .. 18 10 5 6 0c 0c 15 11 0c 15 21e .. .. 11 5 2 .. 1 3 7 0 9 8 3 1 13 ..

7 2 3 3 1 7 1 1 0c 2 .. 1 0c 6 .. 0 2 0c 18 2 8 17 .. .. 0c 0c 18 8 .. 19 10 4 4 0c 0c 10 10 0c 12 20 e .. .. 9 6 3 .. 1 3 5 0 6 7 1 0c 7 ..

2004b

2004b

.. 98 87 84 95 73 .. 74 100 .. .. 97 100 .. .. 99 93 98 80 97 83 67 .. .. 99 99 56 77 .. 53 48 60 95 99 96 79 51 72 90 79 96 .. .. 63 .. 100 98 67 64 77 91 96 97 .. .. ..

.. 99 82 84 86 66 .. 74 99 .. .. 97 100 .. .. 98 97 100 75 99 88 65 .. .. 99 98 53 72 .. 48 43 69 92 98 99 78 56 71 93 74 100 .. .. 53 .. 100 99 72 65 77 91 94 96 .. .. ..

2007 World Development Indicators

83

2.11 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 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 Europe EMU

Education efficiency Gross intake rate in grade 1

Share of cohort reaching grade 5a

Repeaters in primary school

Transition to secondary education

% of relevant age group Male Female

% of grade 1 students

% of enrollment Male Female

% of enrollment in last year of primary Male Female

Male

Female

2005b

2005b

1991

2004b

1991

126 98 178 85 101 .. .. .. 97 145 .. 117 103 99 72 122 92 89 123 101 125 .. 94 99 94 93 .. 164 104 89 .. 100 105 102 101 101 82 122 126 122 138 w 138 104 104 103 138 104 99 120 104 160 120 100 104

126 97 177 89 105 .. .. .. 96 144 .. 111 102 97 62 114 93 94 119 97 124 .. 88 98 96 88 .. 163 104 89 .. 99 106 102 98 95 82 97 123 118 140 w 140 102 102 100 140 101 97 115 103 160 110 100 104

.. .. 61 82 .. .. .. .. .. .. .. .. .. 92 90 74 100 .. 97 .. 81 .. 52 .. 94 98 .. .. .. 80 .. .. 96 .. 82 .. .. .. .. 72 .. w .. .. 59 .. .. 55 .. .. .. .. .. .. ..

.. .. 43 100 79 .. .. .. .. .. 66 82 .. .. 78 74 .. .. 93 .. 76 .. 79 66 96 95 .. 63 .. 96 .. .. 87 .. 88 87 .. 78 .. 68 .. w 77 .. .. .. .. .. .. .. 90 79 .. .. ..

.. .. 59 84 .. .. .. .. .. .. .. .. .. 93 99 80 100 .. 95 .. 82 .. 42 .. 77 97 .. .. .. 80 .. .. 98 .. 90 .. .. .. .. 81 .. w .. .. 79 .. .. 78 .. .. .. .. .. .. ..

2004b

.. .. 49 94 77 .. .. .. .. .. 52 83 .. .. 79 80 .. .. 92 .. 76 .. 70 76 97 94 .. 64 .. 97 .. .. 90 .. 95 86 .. 67 .. 71 .. w 75 .. .. .. .. .. .. .. 87 75 .. .. ..

2005b

3 .. 19 5 13 .. .. .. 3 1 .. 8 3 .. 1 18 .. 2 8 0c 4 .. 23 6 9 3 .. 14 0c 2 0 .. 10 0 8 3 1c 5 7 .. 5w 6 3 3 .. 5 1 .. .. 8 4 9 .. 2

2005b

2 .. 19 5 13 .. .. .. 2 0c .. 8 2 .. 2 14 .. 1 6 0c 4 .. 23 4 6 4 .. 14 0c 2 0 .. 7 0 5 2 1c 4 6 .. 4w 6 3 2 .. 4 1 .. .. 5 4 9 .. 1

2004b

2004b

98 .. .. 93 63 .. .. .. 98 100 .. 89 .. 96 88 91 .. 100 94 98 34 .. 70 95 86 93 .. 36 99 97 .. .. 76 100 98 95 100 .. 54 69 .. w 81 .. .. .. .. .. .. .. 85 85 .. .. ..

98 .. .. 97 59 .. .. .. 99 99 .. 91 .. 98 91 89 .. 100 95 97 33 .. 63 97 90 89 .. 36 100 98 .. .. 87 99 99 94 100 .. 57 70 .. w 77 .. .. .. .. .. .. .. 88 82 .. .. ..

a. Because of the change from International Standard Classification of Education (ISCED) 76 to ISCED 97 in 1998, data before 1998 are not fully comparable with data from 1998 onward. b. Provisional data. c. Less than 0.5. d. Data are for 2005. e. Data are for 2006.

84

2007 World Development Indicators

About the data

2.11

PEOPLE

Education efficiency Definitions

Indicators of students’ progress through school are

indirectly reflects the quality of schooling, and a high

• Gross intake rate in grade 1 is the number of

estimated by the United Nations Educational, Scien-

rate does not guarantee these learning outcomes.

new entrants in the first grade of primary education

tific, and Cultural Organization (UNESCO) Institute

Measuring actual learning outcomes requires set-

regardless of age, expressed as a percentage of the

for Statistics. These indicators measure an educa-

ting curriculum standards and measuring students’

population of the official primary school entrance

tion system’s success in extending coverage to all

learning progress against those standards through

age. • Share of cohort reaching grade 5 is the per-

students, maintaining the flow of students efficiently

standardized assessments or tests. Currently, many

centage of children enrolled in the first grade of pri-

from one grade to the next, and imparting a particular

countries do not systematically measure learning

mary school who eventually reach grade 5. The esti-

level of education.

progress and outcomes.

mate is based on the reconstructed cohort method

Gross intake rate indicates the general level of

The data on repeaters are often used to indicate

(see About the data). • Repeaters in primary school

access to primary education. It also indicates the

the internal effi ciency of the education system.

are the number of students enrolled in the same

capacity of the education system to provide access

Repeaters not only increase the cost of education

grade as in the previous year, as a percentage of all

to primary education. Low gross intake rates in grade

for the family and for the school system, but also use

students enrolled in primary school. • Transition to

1 reflect the fact that many children do not enter

limited school resources. Countries have different

secondary education refers to the number of new

primary school even though school attendance, at

policies on repetition and promotion; in some cases

entrants to the first grade of secondary school in

least through the primary level, is mandatory in all

the number of repeaters is controlled because of

a given year, as a percentage of the number of stu-

countries. Because the gross intake rate includes all

limited capacity. Care should be taken in interpret-

dents enrolled in the final grade of primary school in

new entrants regardless of age, it can be more than

ing this indicator.

the previous year.

100 percent. Once enrolled, students drop out for a

The transition rate from primary school to second-

variety of reasons, including low quality of schooling,

ary school conveys the degree of access or transition

relevance of curriculum (whether real or perceived by

between the two levels of education. As completing

parents or students), repetition and discouragement

primary education is a prerequisite for participat-

over poor performance, and the direct and indirect

ing in lower secondary school, growing numbers of

costs of schooling. Students’ progress to higher

primary completers will inevitably create pressures

grades may also be limited by the availability of

for expanding the number of places available at the

teachers, classrooms, and educational materials.

secondary level. A low transition rate can signal prob-

The share of cohort reaching grade 5 (cohort

lems such as an inadequate promotion and exami-

survival rate) is estimated as the proportion of an

nation system or insufficient capacity in secondary

entering cohort of grade 1 students that eventually

schools. The quality of data on the transition rate is

reaches grade 5. It measures the holding power and

affected when new entrants and repeaters are not

internal efficiency of an education system. Cohort

correctly distinguished in the first grade of secondary

survival rates approaching 100 percent indicate a

school. Students who interrupt their studies for one

high level of retention and a low level of dropout.

or more years after completing primary school could

Cohort survival rates are typically estimated from

also affect the quality of the data.

data on enrollment and repetition by grade for two

In 2006 the UNESCO Institute for Statistics

consecutive years, in a procedure called the recon-

changed its convention for citing the reference

structed cohort method. This method makes three

year. For more information, see About the data for

simplifying assumptions: dropouts never return to

table 2.9.

school; promotion, repetition, and dropout rates remain constant over the entire period in which the cohort is enrolled in school; and the same rates apply to all pupils enrolled in a given grade, regardless of whether they previously repeated a grade (Fredricksen 1993). Given these assumptions, cross-country comparisons should be made with caution, because other flows—caused by new entrants, reentrants, grade skipping, migration, or school transfers during the school year—are not considered. The UNESCO Institute for Statistics measures the

Data sources

share of cohort reaching grade 5 because research

Data on education efficiency are from the UNESCO

suggests that five to six years of schooling is a critical

Institute for Statistics. Data for latest years are

threshold for the achievement of sustainable basic

provisional, as of January 2007.

literacy and numeracy skills. But the indicator only

2007 World Development Indicators

85

2.12

Education completion and outcomes Primary completion rate

% of relevant age group Totala

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, 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

86

Malea

1991

2005b

1991

2005b

Femalea 1991 2005b

25 .. 79 35 .. 90 .. .. .. 49 95 79 21 .. .. 83 93 85 21 46 .. 56 .. 27 18 .. 103 102 70 46 54 79 43 85 96 .. 98 61 91 .. 41 19 93 26 97 104 58 44 .. 100 63 99 .. 17 .. 27

32 97 96 .. 100 91 .. .. 94 77 100 .. 65 101 .. 92 108 98 31 36 92 62 .. 23 32 95 98 110 98 39 57 92 .. 91 94 104 99 92 101 95 87 51 101 55 100 .. 66 .. 87 96 72 102 74 55 .. ..

37 .. 86 .. .. .. .. .. .. .. 95 76 28 .. .. 75 .. 87 26 49 .. 60 .. 35 30 .. .. .. 67 58 59 77 55 .. .. .. 98 .. 91 .. 38 22 93 32 98 .. 55 55 .. 99 70 99 .. 24 .. 29

46 97 96 .. 99 89 .. .. 95 74 102 .. 78 102 .. 90 .. 99 35 40 94 68 .. 29 42 96 .. 112 96 47 60 91 .. 92 95 104 99 88 100 96 86 58 103 61 99 .. 65 .. 86 96 75 103 79 64 .. ..

13 .. 73 .. .. .. .. .. .. .. 96 82 13 .. .. 90 .. 83 16 43 .. 52 .. 18 7 .. .. .. 73 34 49 81 32 .. .. .. 98 .. 92 .. 43 17 94 19 97 .. 61 33 .. 100 55 98 .. 9 .. 26

2007 World Development Indicators

18 97 95 .. 105 92 .. .. 93 79 97 .. 52 99 .. 94 .. 97 27 31 90 57 .. 16 21 95 .. 107 100 31 55 93 .. 91 93 104 100 96 101 93 87 44 100 49 100 .. 68 .. 87 96 69 101 69 45 .. ..

Youth literacy rate

Adult literacy rate

% ages 15–24

% ages 15 and older

Male 1990

.. 97 86 .. 98 100 .. .. .. 51 100 .. 57 96 .. 79 91 100 .. 58 81 86 .. 66 58 98 97 .. 94 80 95 97 65 100 99 .. .. 87 96 71 85 73 100 52 .. .. .. 50 .. .. 88 99 80 62 .. 56

2006c

Female 1990 2006c

Male 2006c

Female 2006c

51 99 94 84 99 100 .. .. 100 .. 100 .. 59 99 100 92 96 98 38 77 88 .. .. 70 56 99 99 .. 98 78 .. 97 71 100 100 .. .. 93 96 90 .. .. 100 .. .. .. .. .. .. .. 76 99 86 59 .. ..

.. 92 68 .. 98 99 .. .. .. 33 100 .. 25 89 .. 87 93 99 .. 45 66 76 .. 39 38 98 93 .. 96 58 90 98 40 100 99 .. .. 88 95 51 83 49 100 34 .. .. .. 34 .. .. 75 100 66 26 .. 54

43 99 80 83 97 100 .. .. 99 .. 100 .. 48 93 99 80 88 99 29 67 85 77 .. 65 41 96 95 .. 93 81 .. 95 61 99 100 .. .. 87 92 83 .. .. 100 .. .. .. .. .. .. .. 66 98 75 43 .. ..

13 98 60 54 97 99 .. .. 98 .. 99 .. 23 81 94 82 89 98 15 52 64 60 .. 33 13 96 87 .. 93 54 .. 95 39 97 100 .. .. 87 90 59 .. .. 100 .. .. .. .. .. .. .. 50 94 63 18 .. ..

18 99 86 63 99 100 .. .. 100 .. 100 .. 33 96 100 96 98 98 25 70 79 .. .. 47 23 99 99 .. 98 63 .. 98 52 100 100 .. .. 95 96 79 .. .. 100 .. .. .. .. .. .. .. 65 99 78 34 .. ..

Primary completion rate

% of relevant age group Totala

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

Malea

1991

2005b

1991

2005b

Femalea 1991 2005b

65 93 68 91 91 59 .. .. 104 90 101 72 .. .. .. 98 .. .. 43 .. .. 59 .. .. 89 98 33 28 91 11 33 107 86 .. .. 47 27 .. 78 51 .. 100 44 17 .. 100 74 .. 86 47 71 .. 86 98 95 ..

79 95 89 101 96 74 101 105 101 84 .. 97 114 95 .. 104 100 97 76 92 90 67 .. .. 98 96 58 61 94 38 45 97 99 92 97 80 42 79 75 76d 100 .. 76 28 82 101 93 63 97 54 91 100 97 100 104 ..

67 88 81 .. 97 64 .. .. 104 86 101 69 .. .. .. 98 .. .. 48 .. .. 42 .. .. .. .. 33 36 91 13 40 107 89 .. .. 55 33 .. 70 68 .. 101 43 21 .. 100 78 .. 86 52 70 .. 84 .. 94 ..

77 95 93 101 91 85 100 104 101 83 .. 97 115 96 .. 104 104 97 80 93 88 55 .. .. 99 96 58 62 91 45 46 97 98 93 94 84 49 78 71 80 d 101 .. 73 34 89 101 94 73 97 58 90 100 93 .. 102 ..

62 90 55 .. 85 53 .. .. 104 94 102 77 .. .. .. 98 .. .. 38 .. .. 76 .. .. .. .. 34 21 91 9 26 107 90 .. .. 38 22 .. 86 40 .. 99 59 12 .. 100 70 .. 86 42 71 .. 84 .. 95 ..

82 96 84 102 100 63 102 105 101 86 .. 96 113 94 .. 104 97 98 72 92 91 79 .. .. 97 97 58 59 91 31 43 98 100 91 99 77 35 80 80 72d 99 .. 80 22 74 101 93 52 97 50 91 99 100 .. 107 ..

Youth literacy rate

Adult literacy rate

% ages 15–24

% ages 15 and older

Male 1990

78 100 73 97 92 56 .. 99 .. 87 .. 98 100 93 .. .. 88 .. 79 .. 95 77 75 99 100 .. 78 76 95 .. 56 91 96 100 .. 68 66 90 86 67 .. .. 68 25 81 .. 95 63 96 74 96 97 97 .. .. ..

2.12

PEOPLE

Education completion and outcomes

2006c

Female 1990 2006c

Male 2006c

Female 2006c

87 .. 84 99 .. 89 .. 100 100 .. .. 99 100 80 .. .. 100 100 83 100 .. .. .. .. 100 99 73 82 97 32 68 94 98 99 97 81 .. 96 91 81 .. .. 84 52 .. .. 98 76 97 69 .. 98 94 .. .. ..

81 100 54 93 81 25 .. 98 .. 95 .. 95 100 87 .. .. 87 .. 61 .. 89 97 39 83 100 .. 67 51 94 .. 36 91 94 100 .. 42 32 86 89 27 .. .. 69 9 66 .. 75 31 95 62 95 92 97 .. .. ..

80 .. 73 94 84 84 .. 98 99 74 .. 95 100 78 .. .. 94 99 77 100 .. 74 .. .. 100 98 77 75 92 27 60 88 92 99 98 66 .. 94 87 63 .. .. 77 43 .. .. 87 63 93 63 .. 93 93 .. .. ..

80 .. 48 87 70 64 .. 96 98 86 .. 85 99 70 .. .. 91 98 61 100 .. 90 .. .. 100 94 65 54 85 12 43 81 90 98 98 40 .. 86 83 35 .. .. 77 15 .. .. 74 36 91 51 .. 82 93 .. .. ..

91 .. 68 99 .. 80 .. 100 100 .. .. 99 100 81 .. .. 100 100 75 100 .. .. .. .. 100 98 68 71 97 17 55 95 98 100 98 60 .. 93 93 60 .. .. 89 23 .. .. 97 55 96 64 .. 96 96 .. .. ..

2007 World Development Indicators

87

2.12

Education completion and outcomes Primary completion rate

% of relevant age group Totala

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 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 Europe EMU

Malea

1991

2005b

1991

2005b

Femalea 1991 2005b

96 93 33 56 39 71 .. .. 96 95 .. 75 .. 97 41 60 96 53 89 .. 61 .. 35 100 74 90 .. .. 94 103 .. .. 94 .. 43 .. .. .. .. 99 .. w 60 93 94 87 79 100 91 83 77 76 50 .. ..

93 94 39 85 52 .. .. .. 99 102 .. 99 109 .. 50 64 .. 97 111 102 54 82 65 99 97 88 .. 57 114 76 .. .. 91 97 92 94 98 62 78 80 85 w 74 96 97 95 84 98 92 98 89 82 58 97 ..

96 92 36 60 47 .. .. .. 95 .. .. 71 .. 98 46 57 96 53 94 .. 60 .. 48 97 79 93 .. .. 98 104 .. .. 91 .. 37 .. .. .. .. 100 .. w 70 96 99 86 85 105 93 82 83 86 55 .. ..

94 .. 40 85 56 .. .. .. 99 103 .. 99 110 .. 53 62 .. 96 112 104 55 83 76 97 97 93 .. 61 .. 78 .. .. 89 97 89 104 98 78 89 82 87 w 79 96 97 95 86 98 93 98 92 86 63 98 ..

96 93 30 51 30 .. .. .. 96 .. .. 80 .. 96 37 63 96 54 84 .. 62 .. 22 102 69 86 .. .. 97 103 .. .. 96 .. 49 .. .. .. .. 97 .. w 49 90 91 87 73 96 92 84 71 65 46 .. ..

93 .. 38 86 49 .. .. .. 100 102 .. 99 109 .. 46 66 .. 98 109 100 53 81 54 100 98 82 .. 53 .. 75 .. .. 93 96 95 98 98 46 66 79 83 w 69 95 96 95 81 98 91 99 86 77 53 97 ..

Youth literacy rate

Adult literacy rate

% ages 15–24

% ages 15 and older

Male 1990

99 100 78 91 50 .. .. 99 .. 100 .. 89 .. 96 76f 85 .. .. 92 100 89 .. 79 100 93 97 .. 80 100 82 .. .. 98 100 95 94 .. 74 86 97 .. w 73 95 95 97 86 97 99 93 80 70 76 .. ..

2006c

Female 1990 2006c

Male 2006c

98 100 79 98 58 99e 59 99 100 .. .. 93 .. 95 85f 87 .. .. 94 100 81 98 84 .. 96 98 100 83 100 .. .. .. .. .. 96 94 99 .. 73 .. 90 w 80 97 97 98 89 98 99 96 89 80 78 99 ..

99 100 67 79 30 .. .. 99 .. 100 .. 88 .. 94 54f 85 .. .. 67 100 77 .. 48 100 75 88 .. 60 100 89 .. .. 99 100 97 94 .. 25 76 91 .. w 55 91 90 95 77 93 97 93 59 50 61 .. ..

98 100 71 87 51 99e 47 97 100 .. .. 84 .. 92 71f 81 .. .. 86 100 78 95 69 .. 83 95 99 77 100 .. .. .. .. .. 93 94 97 .. 76 .. 87 w 71 93 93 96 85 95 99 91 81 70 70 99 ..

98 100 77 94 41 99e 37 100 100 .. .. 94 .. 96 71f 90 .. .. 90 100 76 98 64 .. 92 93 100 71 100 .. .. .. .. .. 98 94 99 .. 66 .. 84 w 67 95 95 98 82 97 99 96 77 63 68 99 ..

Female 2006c

96 99 60 69 29 94 e 24 89 100 .. .. 81 .. 89 52f 78 .. .. 74 99 62 91 38 .. 65 80 98 58 99 .. .. .. .. .. 93 87 88 .. 60 .. 77 w 50 87 85 93 72 87 96 89 61 45 53 98 ..

a. Because of the change from International Standard Classification of Education (ISCED) 76 to ISCED 97 in 1998, data before 1998 are not fully comparable with data from 1998 onward. b. Provisional data. c. Actual reference year varies by country. For more information, see the original source. d. Data are for 2006. e. Data exclude Kosovo and Metohia. f. Data are for North Sudan only.

88

2007 World Development Indicators

2.12

PEOPLE

Education completion and outcomes About the data

Many governments collect and publish statistics that

There are many reasons why the primary comple-

The reported literacy data are compiled by the

indicate how their education systems are working and

tion rate can exceed 100 percent. The numerator

UNESCO Institute for Statistics based on national

developing—statistics on enrollment and on such

may include late entrants and overage children who

censuses and household surveys during 1995–2005.

efficiency indicators as repetition rates, pupil-teacher

have repeated one or more grades of primary school

The data for 1991 are based on model estimations.

ratios, and cohort progression through school. The

but are now completing primary school as well as

Therefore the data for 1991 and later years may not

World Bank and the United Nations Educational, Sci-

children who entered school early, while the denomi-

be comparable. The estimation methodology can be

entific, and Cultural Organization (UNESCO) Institute

nator is the number of children of official completing

reviewed at www.uis.unesco.org.

for Statistics worked jointly to develop the primary

age. There are other data limitations that contribute

Literacy statistics for most countries cover the pop-

completion rate indicator. Increasingly used as a core

to completion rates exceeding 100 percent, such as

ulation ages 15 and older, by five-year age groups,

indicator of an education system’s performance, it

the use of estimates for the population with varying

but some include younger ages or are confined to age

reflects both the coverage of the education system

reliability for some countries, the conduct of school

ranges that tend to inflate literacy rates. The UNESCO

and the educational attainment of students. The

and population surveys at different times of year,

Institute for Statistics has reported the narrower age

indicator is vital as a key measure of educational

and other discrepancies in the numbers used in the

range of 15–24, which is better in capturing the abil-

outcome at the primary level and of progress on the

calculation.

ity of participants in the formal education system

Millennium Development Goals and the Education for

Basic student outcomes include achievements

and in reflecting recent progress in education. The

All initiative. However, because curricula and stan-

in reading and mathematics judged against estab-

youth literacy rate reported in the table measures

dards for school completion vary across countries, a

lished standards. In many countries national learning

the accumulated outcomes of primary education over

high rate of primary completion does not necessarily

assessments are enabling ministries of education to

the previous 10 years or so by indicating the propor-

mean high levels of student learning.

monitor progress in these outcomes. Internationally,

tion of people who have passed through the primary

The primary completion rate reflects the primary

the UNESCO Institute for Statistics has established

education system and acquired basic literacy and

cycle as defined by the International Standard Clas-

literacy as an outcome indicator based on an inter-

numeracy skills.

sification of Education, ranging from three or four

nationally agreed definition. Definitions

years of primary education (in a very small number

The literacy rate is defined as the percentage of

of countries) to five or six years (in most countries)

people who can, with understanding, both read and

• Primary completion rate is the percentage of stu-

and seven (in a small number of countries).

write a short, simple statement about their every-

dents completing the last year of primary school. It is

The data in the table are for the proxy primary

day life. In practice, literacy is difficult to measure.

calculated by taking the total number of students in

completion rate, calculated by taking the total num-

To estimate literacy using such a definition requires

the last grade of primary school, minus the number

ber of students in the last grade of primary school,

census or survey measurements under controlled

of repeaters in that grade, divided by the total num-

minus the number of repeaters in that grade, divided

conditions. Many countries estimate the number of

ber of children of official completing age. • Youth

by the total number of children of official graduation

literate people from self-reported data. Some use

literacy rate is the percentage of people ages 15–24

age. Data limitations preclude adjusting this number

educational attainment data as a proxy but apply

that can, with understanding, both read and write a

for students who drop out during the final year of

different lengths of school attendance or levels of

short, simple statement about their everyday life.

primary school. Thus proxy rates should be taken

completion. Because definition and methodologies

• Adult literacy rate is the literacy rate among peo-

as an upper-bound estimate of the actual primary

of data collection differ across countries, data need

ple ages 15 and older.

completion rate.

to be used with caution.

Children from poorer families are less likely to complete their schooling

2.12a

Share of children who have attained each grade, by wealth quintile, Zimbabwe, 1999 (%) 100 Richest 20% 80

60 Fourth 20%

Data sources

40

Third 20%

Second 20%

20

Data on the primary completion rate for 1991 and 2005 are primarily from the UNESCO Institute for

Poorest 20% 0 1

2

3

Source: Demographic and Health Survey.

4

5

6

7

8

9

>9

Statistics. The data for the latest years are provisional, as of January 2007. Data on literacy rates are from the UNESCO Institute for Statistics.

2007 World Development Indicators

89

2.13

Education gaps by income and gender Survey year

Armenia Bangladesh Benin Bolivia Burkina Faso Cambodia Cameroon Central African Republic Chad Colombia Comoros Côte d’Ivoire Dominican Republic Egypt, Arab Rep. Eritrea Ethiopia Gabon Ghana Guatemala Guinea Haiti India Indonesia Jordan

2000 2004 2001 2003 2003 2000 2004 1994–95 2004 2005 1996 1994 2002 2003 1995 2000 2000 2003 1995 1999 2000 1999 2002–03 2002

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 15–24 Poorest Richest quintile quintile

% of relevant age group Richest quintile Male

% of children ages 6–11 Poorest Richest quintile quintile

105 193 74 98 24 146 115 103 3 157 84 26 170 87 55 87 .. 90 114 13 141 99 85 ..

93 156 112 95 97 187 100 118 14 85 119 39 103 120 117 257 .. 90 124 39 200 72 92 ..

177 107 51 92 20 78 94 57 15 126 56 41 149 96 42 61 155 71 62 10 94 87 103 101

181 120 115 98 98 134 122 130 98 99 147 103 156 103 154 186 140 108 122 38 152 122 104 99

9 3 1 6 1 2 3 2 0a 6 2 2 6 6 1 1 5 4 2 1 3 3 7 10

11 8 6 11 6 7 9 6 5 11 6 6 11 11 7 5 8 9 9 5 8 10 11 12

Poorest quintile

96 26 7 48 8 4 12 0a 1 50 4 6 38 58 3 4 12 15 9 3 1 31 75 93

98 70 45 90 52 45 69 18 36 90 29 41 87 87 65 44 60 66 76 27 40 87 97 98

96 47 23 75 24 18 36 8 15 70 12 25 57 77 21 15 35 38 41 18 13 64 86 97

Female

98 58 15 75 20 17 37 6 8 77 12 17 69 71 24 12 40 41 40 9 18 55 89 97

14 25 66 24 87 50 42 65 91 8 72 70 14 24 84 87 8 57 58 95 64 35 19 11

13 10 21 5 32 12 4 21 36 1 26 23 4 5 10 42 3 20 8 77 21 2 6 9

About the data

The data in the table describe basic information on

section of the survey is not as robust and detailed

but do have detailed information on households’ own-

school participation and attainment by individuals

as the health section; however, it still provides useful

ership of consumer goods and access to a variety

in different socioeconomic groups within countries.

micro-level information on education that cannot be

of goods and services. Like income or consumption,

The data are from Demographic and Health Surveys

explained by aggregate national level data.

the asset index defines disparities in primarily eco-

conducted by Macro International with the support

The table defines socioeconomic status in terms

nomic terms. It therefore excludes other possibilities

of the U.S. Agency for International Development.

of a household’s assets, including ownership of con-

of disparities among groups, such as those based

These large-scale household sample surveys, con-

sumer items, features of the household’s dwelling,

on gender, education, ethnic background, or other

ducted periodically in developing countries, collect

and other characteristics related to wealth. Each

facets of social exclusion. To that extent the index

information on a large number of health, nutrition,

household asset on which information was collected

provides only a partial view of the multidimensional

and population measures as well as on respondents’

was assigned a weight generated through principal-

concepts of poverty, inequality, and inequity.

social, demographic, and economic characteristics

component analysis. The resulting scores were stan-

Creating one index that includes all asset indica-

using a standard set of questionnaires. The data

dardized in relation to a standard normal distribution

tors limits the types of analysis that can be per-

presented here draw on responses to individual and

with a mean of zero and a standard deviation of one.

formed. In particular, the use of a unified index does

household questionnaires.

The standardized scores were then used to create

not permit a disaggregated analysis to examine

break-points defining wealth quintiles, expressed as

which asset indicators have a more or less impor-

quintiles of individuals in the population.

tant association with health status or use of health

Typically, Demographic and Health Surveys collect basic information on educational attainment and enrollment levels from every household member

The choice of the asset index for defining socio-

services. In addition, some asset indicators may

ages 5 or 6 and older as background characteris-

economic status was based on pragmatic rather than

reflect household wealth better in some countries

tics. As the surveys are intended for the collection of

conceptual considerations: Demographic and Health

than in others—or reflect different degrees of wealth

demographic and health information, the education

Surveys do not provide income or consumption data

in different countries. Taking such information into

90

2007 World Development Indicators

Survey year

Kazakhstan Kenya Kyrgyz Republic Madagascar Malawi Mali Morocco Mozambique Namibia Nepal Nicaragua Niger Nigeria Pakistan Paraguay Peru Philippines Rwanda Tanzania Uganda Uzbekistan Vietnam Zambia Zimbabwe

1999 2003 1997 1997 2002 2001 2003–04 2003 1992 2001 2001 1998 2003 1990–91 1990 2000 2003 2000 1999 2000–01 1996 2002 2001–02 1994

PEOPLE

2.13

Education gaps by income and gender 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 15–24 Poorest Richest quintile quintile

% of relevant age group Richest quintile Male

% of children ages 6–11 Poorest Richest quintile quintile

.. 128 .. 84 180 45 109 104 .. 240 127 11 77 68 137 114 131 216 95 145 .. 121 83 138

.. 123 .. 87 226 89 85 134 .. 249 108 69 106 173 106 94 102 197 231 127 .. 105 119 114

125 104 133 59 103 36 98 79 138 116 79 15 67 45 103 112 103 100 63 106 102 139 74 104

130 118 138 134 126 101 116 150 116 160 104 77 111 127 114 109 102 126 119 120 114 127 112 109

10 5 10 2 4 1 2 2 5 3 3 1 4 2 5 6 6 3 4 4 10 5 4 7

11 9 10 7 8 5 9 5 8 7 10 4 10 8 10 11 11 6 7 8 10 10 9 10

Poorest quintile

98 14 86 1 10 3 17 2 15 18 14 8 16 11 29 41 46 7 27 7 84 58 16 36

100 57 88 47 52 37 78 17 65 59 88 46 70 55 77 93 88 28 55 43 87 97 79 80

98 30 85 13 32 16 47 8 25 37 47 22 39 32 49 72 67 14 34 19 84 84 38 51

Female

99 36 87 16 14 11 46 7 34 28 59 13 37 22 54 72 79 14 34 21 86 84 43 57

24 24 21 60 29 75 26 59 22 33 46 90 56 72 21 9 17 43 74 28 29 8 61 22

18 4 18 6 9 29 2 13 9 6 5 44 5 13 10 1 2 23 27 6 23 2 18 8

a. Less than 0.5.

Definitions

account and creating country-specific asset indexes

• Survey year is the year in which the underlying

not in school. These data differ from those in table

with country-specifi c choices of asset indicators

data were collected. • Gross intake rate in grade

2.10 because the definition and methodology are

might produce a more effective and accurate index

1 is the number of students in the first grade of

different.

for each country. The asset index used in the table

primary education, regardless of age, expressed

does not have this flexibility.

as a percentage of the population of the official pri-

The analysis was carried out for 48 countries. The

mary school entrance age. These data may differ

table shows the estimates for the poorest and rich-

from those in table 2.11. • Gross primary partici-

est quintiles only; the full set of estimates for 32

pation rate is the ratio of total students attending

indicators is available in the country reports (see

primary school, regardless of age, to the population

Data sources). The data in this table will differ from

of the age group that officially corresponds to pri-

data for similar indicators in preceding tables either

mary education. • Average years of schooling are

because the indicator refers to a period a few years

the years of formal schooling received, on average,

preceding the survey date or because the indica-

by adults ages 15–24. • Primary completion rate

tor definition or methodology is different. Findings

is the percentage of children of the official primary

should be interpreted with caution because of mea-

school completing age to the official primary school

Data on education gaps by income and gender are

surement error inherent in the use of survey data.

completing age plus four, who have completed the

from an analysis of Demographic and Health Sur-

last year of primary school or higher. These data

veys by Macro International and the World Bank.

differ from those in table 2.12 as the definition and

Country reports are available at http://devdata.

methodology are different. • Children out of school

worldbank.org/edstats/td16.asp.

Data sources

are the percentage of children ages 6–11 who are

2007 World Development Indicators

91

2.14

Health expenditure, services, and use 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, 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

92

Physicians

Total % of GDP

% of GDP

% of total

Out of pocket % of private

2004

2004

2004

2004

2004

2004

1990

97.7 99.8 94.6 100.0 48.7 89.2 61.6 67.9 93.6 88.3 72.7 83.5 99.9 82.5 100.0 27.9 64.2 98.0 97.9 100.0 85.4 94.5 49.4 95.4 95.8 45.9 86.5 .. 49.0 100.0 100.0 88.7 88.7 93.8 74.5 95.5 81.3 73.1 85.4 99.0 94.2 100.0 88.8 78.3 80.8 34.9 100.0 68.2 87.2 57.5 78.2 95.7 90.5 99.5 90.0 69.6

6.1 2.4 0.0 9.1 0.2 7.2 0.0 0.0 1.6 15.1 0.1 0.0 10.2 9.1 1.3 2.5 0.0 1.0 26.8 17.6 28.5 5.3 0.0 47.7 7.0 0.1 0.1 .. 0.1 19.1 3.6 0.8 5.0 0.4 0.3 0.0 0.0 1.5 0.8 0.8 1.2 59.6 0.5 35.2 0.0 0.0 1.3 23.0 9.8 0.0 29.9 .. 2.3 9.5 31.6 14.2

14 157 94 26 383 63 3,123 3,683 37 14 147 3,363 24 66 198 329 290 251 24 3 24 51 3,038 13 20 359 71 .. 168 5 28 290 33 609 230 771 3,897 148 127 64 184 10 463 6 2,664 3,464 231 19 60 3,521 27 1,879 127 22 9 33

0.1 1.4 0.9 0.0 c 2.7 3.9 2.2 2.2 3.9 0.2 3.6 3.3 0.1 0.4 1.6 0.2 1.4 3.2 0.0 c 0.1 0.1 0.1 2.1 0.0 c 0.0 c 1.1 1.5 .. 1.1 0.1 0.3 1.3 0.1 2.1 3.6 2.7 2.5 1.5 1.5 0.8 0.8 .. 3.5 0.0 c 2.0 3.1 0.5 .. 4.9 2.8 0.0 c 3.4 0.8 0.1 .. 0.1

4.4 6.7 3.6 1.9 9.6 5.4 9.6 10.3 3.6 3.1 6.2 9.7 4.9 6.8 8.3 6.4 8.8 8.0 6.1 3.2 6.7 5.2 9.8 4.1 4.2 6.1 4.7 .. 7.8 4.0 2.5 6.6 3.8 8.0 d 6.3 7.3 8.6 6.0 5.5 5.9 7.9 4.5 5.3 5.3 7.4 10.5 4.5 6.8 5.3 10.6 6.7 7.9 5.7 5.3 4.8 7.6

Public

0.7 3.0 2.6 1.5 4.3 1.4 6.5 7.8 0.9 0.9 4.6 6.9 2.5 4.1 4.1 4.0 4.8 4.6 3.3 0.8 1.7 1.5 6.8 1.5 1.5 2.9 1.8 .. 6.7 1.1 1.2 5.1 0.9 6.1d 5.5 6.5 7.1 1.9 2.2 2.2 3.5 1.8 4.0 2.7 5.7 8.2 3.1 1.8 1.5 8.2 2.8 4.2 2.3 0.7 1.3 2.9

2007 World Development Indicators

16.9 44.1 72.5 79.4 45.3 26.2 67.5 75.6 25.0 28.1 74.9 71.1 51.2 60.7 49.4 62.9 54.1 57.6 54.8 26.2 25.8 28.0 69.8 36.8 36.9 47.0 38.0 .. 86.0 28.1 49.2 77.0 23.2d 81.0 87.8 89.2 82.3 31.6 40.7 37.0 44.4 39.2 76.0 51.5 77.2 78.4 68.8 27.1 27.4 76.9 42.2 52.8 41.0 13.2 27.3 38.5

External resourcesa % of total

Per capita $

per 1,000 people 2000–05b

0.2 1.3 1.1 0.1 .. 3.6 2.5 3.4 3.5 0.3 4.6 3.9 0.0 c 1.2 1.3 0.4 2.1 3.6 0.1 0.0 c 0.2 0.2 2.1 0.1 0.0 c 1.1 1.5 .. 1.4 0.1 0.2 1.3 0.1 2.4 5.9 3.5 2.9 1.9 1.5 0.5 1.2 0.1 3.2 0.0 c 2.6 3.4 0.3 0.1 4.1 3.4 0.2 4.4 .. 0.1 0.1 ..

Health worker density index Physicians, nurses, and midwives per 1,000 people

Hospital beds

per 1,000 people

2000–03b

1990

2000–05b

0.4 5.4 .. .. .. 8.8 10.8 9.3 12.0 0.5 17.5 15.6 .. 1.1 5.7 .. 2.6 8.3 0.3 0.3 1.0 .. 12.2 .. 0.2 1.7 2.7 .. 1.9 .. .. 2.4 .. 7.7 13.4 13.4 13.6 3.7 3.1 4.9 2.0 .. 9.8 0.2 25.6 10.2 .. .. 7.9 13.2 0.9 7.5 .. 0.6 .. ..

0.2 4.0 2.5 1.3 4.6 9.1 9.2 10.2 10.1 0.3 13.2 8.0 0.8 1.3 4.5 1.6 3.3 9.8 0.3 0.7 2.1 2.6 6.0 0.9 0.7 3.2 2.6 .. 1.4 1.4 3.3 2.5 0.8 7.4 5.4 11.3 5.6 1.9 1.6 2.1 1.5 .. 11.6 0.2 12.5 9.7 3.2 0.6 9.8 10.4 1.5 5.1 1.1 0.6 1.5 0.8

0.4 3.1 .. .. 4.1 4.4 7.4 8.3 8.3 .. 11.3 6.9 .. 1.0 3.1 .. 2.7 6.3 .. .. 0.6 .. 3.7 .. .. 2.6 2.5 .. 1.1 .. .. 1.4 .. 5.5 4.9 8.8 4.0 2.1 1.5 2.2 .. .. 6.0 .. 7.2 7.7 .. .. 4.2 8.9 .. 4.7 0.5 .. .. 0.8

Health expenditure

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

Total % of GDP

% of GDP

% of total

Out of pocket % of private

2004

2004

2004

2004

7.2 7.9 5.0 2.8 6.6 5.3e 7.2 8.7 8.7 5.2 7.8 9.8 f 3.8 4.1 3.5 5.6 2.8 5.6 3.9 7.1 11.6 6.5 5.6 3.8 6.5 8.0 3.0 12.9 3.8 6.6 2.9 4.3 6.5 7.4 6.0 5.1 4.0 2.2 6.8 5.6 9.2 8.4 8.2 4.2 4.6 9.7 3.0 2.2 7.7 3.6 7.7 4.1 3.4 6.2 9.8 ..

Public

4.0 5.7 0.9 1.0 3.2 4.2e 5.7 6.1 6.5 2.8 6.3 4.7f 2.3 1.8 3.0 2.9 2.2 2.3 0.8 4.0 3.2 5.5 3.6 2.8 4.9 5.7 1.8 9.6 2.2 3.2 2.0 2.4 3.0 4.2 4.0 1.7 2.7 0.3 4.7 1.5 5.7 6.5 3.9 2.2 1.4 8.1 2.4 0.4 5.2 3.0 2.6 1.9 1.4 4.3 7.0 ..

54.9 71.6 17.3 34.2 47.8 78.5e 79.5 70.0 75.1 54.3 81.0 48.4f 59.8 42.7 85.6 51.4 77.6 40.9 20.5 56.6 27.4 84.2 63.9 74.9 75.0 71.0 59.1 74.7 58.8 49.2 69.4 54.7 46.4 56.8 66.6 34.3 68.4 12.9 69.0 26.3 62.4 77.4 47.1 52.5 30.4 83.5 81.4 19.6 66.9 84.3 33.7 46.9 39.8 68.6 71.6 ..

84.3 88.0 93.8 74.7 94.8 100.0 e 65.9 75.0 84.4 63.6 93.4 73.8 100.0 81.9 100.0 76.0 90.4 94.3 90.3 98.3 82.2 18.2 98.5 100.0 96.8 100.0 52.5 35.2 74.1 99.5 100.0 80.8 94.4 96.0 92.3 76.0 38.5 99.4 18.1 88.1 20.6 76.1 95.9 85.1 90.4 95.2 57.1 98.0 82.5 46.4 72.2 79.2 77.9 89.6 79.4 ..

Physicians

External resourcesa % of total

Per capita $

2004

2004

8.7 0.4 0.5 1.3 0.2 2.5e 0.0 3.2 0.0 1.4 0.0 7.1f 0.9 18.3 53.6 0.0 0.0 15.1 10.2 0.3 1.7 8.7 37.8 0.0 3.1 1.4 45.5 59.4 0.1 13.8 20.2 1.4 0.3 4.8 4.6 0.9 55.9 13.1 16.9 17.6 0.0 0.0 11.3 21.3 5.6 0.0 0.0 2.5 0.2 26.5 1.9 1.3 3.6 0.1 0.0 ..

77 800 31 33 158 58e 3,234 1,534 2,580 176 2,831 200 f 109 20 0g 787 633 24 17 418 670 49 9 195 424 212 7 19 180 24 15 222 424 46 37 82 12 5 190 14 3,442 2,040 67 9 23 5,405 295 14 343 30 88 104 36 411 1,665 ..

per 1,000 people 1990

0.7 2.8 0.5 0.1 0.3 0.6 2.0 3.2 .. 0.6 1.7 1.3 4.0 0.0 c .. 0.8 0.2 3.4 0.2 4.1 1.3 0.0 c .. 1.1 4.0 2.2 0.1 0.0 c 0.4 0.1 0.1 0.8 1.0 3.6 2.5 0.2 0.0 c 0.1 0.2 0.1 2.5 1.9 0.7 0.0 c 0.2 2.6 0.6 0.5 1.6 0.1 0.6 1.1 0.1 2.1 2.8 ..

2000–05b

0.6 3.2 0.6 0.1 0.4 0.7 2.8 3.8 4.2 0.8 2.0 2.0 3.5 0.1 3.3 1.6 1.5 2.5 .. 3.0 3.3 0.0 c 0.0 c .. 4.0 2.2 0.3 0.0 c 0.7 0.1 0.1 1.1 1.5 2.6 2.6 0.5 0.0 c 0.4 0.3 0.2 3.1 2.2 0.4 0.0 c 0.3 3.1 1.3 0.7 1.5 0.1 1.1 .. 1.2 2.5 3.3 ..

Health worker density index Physicians, nurses, and midwives per 1,000 people

2.14

PEOPLE

Health expenditure, services, and use

Hospital beds

per 1,000 people

2000–03b

1990

2000–05b

.. 11.9 .. 0.7 .. 3.6 19.0 10.3 10.5 2.5 10.4 4.8 9.5 .. .. 5.4 5.4 10.1 .. 8.2 4.4 .. .. .. 12.4 8.1 0.4 0.3 2.4 0.2 .. .. 3.9 9.2 6.0 1.5 0.3 0.8 .. 0.3 16.7 10.9 1.8 0.3 1.5 24.9 4.2 1.1 3.2 0.6 1.4 .. 7.4 7.7 7.0 ..

1.0 .. 0.8 0.7 1.4 1.7 6.1 6.2 7.2 2.2 .. 1.8 13.7 1.6 .. 3.1 3 12.0 2.6 14.1 1.7 .. .. 4.2 12.5 5.9 0.9 1.6 2.1 .. 0.7 2.9 1.0 13.1 11.5 1.3 0.9 0.6 .. 0.2 5.8 8.5 1.8 .. 1.7 4.6 2.1 0.6 2.5 4.0 0.9 1.4 1.4 5.7 4.1 ..

1.0 7.8 0.9 .. 1.6 1.3 4.3 6.1 4.4 1.4 14.3 1.7 7.7 .. .. 7.1 2.2 5.3 1.2 7.8 3.0 .. .. 3.9 8.7 4.8 0.4 .. 1.9 .. .. .. 1.0 6.7 .. 0.8 .. 0.6 .. .. 4.7 6.1 0.9 .. .. 3.8 2.0 0.7 2.5 .. 1.2 1.4 1.0 5.6 3.6 ..

2007 World Development Indicators

93

2.14

Health expenditure, services, and use Health expenditure

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 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 Europe EMU

Total % of GDP

% of GDP

% of total

Out of pocket % of private

2004

2004

2004

2004

Public

5.1 6.0 7.5 3.3 5.9 10.1h 3.3 3.7 7.2 8.7 .. 8.6 8.1 4.3 4.1 6.3 9.1 11.5 4.7 4.4 4.0 3.5 5.5 3.5 5.6 7.7 4.8 7.6 6.5 2.9 8.1 15.4 8.2 5.1 4.7 5.5 13.0 5.0 6.3 7.5 10.1 w 4.7 5.9 5.4 6.6 5.8 4.4 6.6 7.3 5.6 4.6 6.3 11.2 9.6

3.4 3.7 4.3 2.5 2.4 7.3h 1.9 1.3 5.3 6.6 .. 3.5 5.7 2.0 1.5 4.0 7.7 6.7 2.2 1.0 1.7 2.3 1.1 1.4 2.8 5.2e 3.3 2.5 3.7 2.0 7.0 6.9 3.6 2.4 2.0 1.5 7.8 1.9 3.4 3.5 5.9 w 1.1 3.1 2.6 3.8 2.8 1.7 4.5 3.7 2.7 0.9 2.6 6.7 7.2

66.1 61.3 56.8 75.4 40.3 72.1h 59.0 34.0 73.8 75.6 .. 40.4 70.9 45.6 35.4 63.8 84.9 58.5 47.4 21.6 43.6 64.7 20.7 38.9 50.0 72.3 68.9 32.7 56.7 69.9 86.3 44.7 43.5 46.6 42.0 27.1 60.0 38.3 54.7 46.1 59.1 w 23.8 52.6 47.7 57.8 49.3 39.8 67.8 51.9 48.9 18.8 41.8 60.4 74.7

93.4 76.7 36.9 31.0 94.5 88.2h 100.0 96.9 73.1 39.5 .. 17.2 81.0 84.0 98.1 40.2 92.0 76.7 100.0 97.3 83.2 74.7 84.9 88.5 .. 69.1 100.0 51.3 90.5 71.0 91.8 23.8 31.1 96.2 88.3 88.0 100.0 95.5 71.4 48.7 44.4 w 94.0 77.0 81.0 71.7 80.8 87.6 82.1 74.1 89.7 93.6 44.8 37.8 59.9

Physicians

External resourcesa % of total

Per capita $

2004

2004

25.0 0.1 37.1 .. 12.8 0.5h 35.4 0.0 0.0 0.1 .. 0.5 0.0 1.2 5.1 9.5 0.0 0.0 0.2 9.1 27.1 0.3 8.9 0.2 .. 0.0 0.4 25.2 0.7 0.0 0.0 0.0 0.3 3.9 0.0 2.0 42.0 15.0 36.3 13.1 0.1 w 5.4 0.6 0.5 0.7 1.2 0.5 1.1 0.4 1.3 1.5 6.8 0.0 0.0

178 245 16 348 39 219h 7 943 565 1,438 .. 390 1,971 43 25 146 3,532 5,572 58 14 12 88 18 329 126 325 124 19 90 711 2,900 6,096 315 23 196 30 .. 34 30 27 649 w 24 141 92 342 90 62 250 272 103 27 45 3,727 2,969

per 1,000 people 1990

1.8 4.1 0.0 c 1.4 0.1 2.0 .. 1.3 .. 2.0 .. 0.6 .. 0.1 .. 0.1 2.9 3.0 0.8 2.6 .. 0.2 0.1 0.7 0.5 0.9 3.6 0.0 c 4.3 0.8 1.6 1.8 3.7 3.4 1.6 0.4 .. 0.0 c 0.1 0.1 1.4 w 0.5 1.6 1.4 2.3 1.3 1.2 3.2 1.4 .. 0.5 .. 1.9 2.9

Health worker density index Physicians, nurses, and midwives per 1,000 people

2000–05b

2000–03b

1.9 4.3 0.0 c 1.4 0.1 2.1 0.0 c 1.4 3.1 2.3 .. 0.8 3.2 0.5 0.2 0.2 3.3 3.6 1.4 2.0 0.0 c 0.4 0.0 c .. 1.3 1.3 4.2 0.1 3.0 2.0 2.2 2.3 3.7 2.7 1.9 0.5 0.8 0.3 0.1 0.2 .. w 0.5 1.5 1.3 2.7 .. 1.5 3.1 .. .. 0.6 .. 2.6 3.5

6.2 12.5 0.2 4.4 .. .. .. 5.6 10.6 9.4 .. 4.6 6.8 1.2 1.0 3.4 13.5 12.1 3.3 7.2 0.4 .. 0.3 .. .. 4.2 .. 0.1 11.2 6.2 .. 13.2 4.5 13.7 2.6 1.3 .. 0.7 .. 0.6 .. w .. .. .. 7.5 .. 3.0 10.3 .. .. .. .. .. 12.2

Hospital beds

per 1,000 people 1990

8.9 13.1 1.7 2.5 0.7 5.9 .. 3.6 7.4 6.0 0.8 .. 4.6 2.7 1.1 .. 12.4 19.9 1.1 10.7 1.0 1.6 1.5 4.0 1.9 2.4 11.5 0.9 13.0 2.6 5.9 4.9 4.5 12.5 2.7 3.8 .. 0.8 .. 0.5 3.7 w .. 3.6 2.8 6.7 3.1 2.3 10.2 2.5 1.8 0.7 1.2 6.2 8.1

2000–05b

6.6 10.5 .. 2.2 .. 6.0 .. 2.9 7.2 5.0 .. .. 3.8 3.1 0.7 .. 3.6 6.0 1.5 6.1 .. .. .. 3.4 1.7 2.6 .. .. 8.8 2.2 4.2 3.3 1.9 5.5 0.8 2.4 .. 0.6 .. .. .. w .. .. .. 5.7 .. 2.5 7.6 .. .. 0.9 .. 6.4 6.6

a. 0 for category not applicable or less than 0.05. b. Data are for the most recent year available. c. Less than 0.05. d. Data are for 2005. e. Excludes northern Iraq. f. Includes contributions from the United Nations Relief and Works Agency for Palestine Refugees in the Near East to Palestinian refugees. g. Less than 0.5. h. Excludes Kosovo and Metahia.

94

2007 World Development Indicators

About the data

2.14

PEOPLE

Health expenditure, services, and use Definitions

National health accounts track financial flows in the

Organization (WHO), the Organisation for Economic

• Total health expenditure is the sum of public and

health sector, including public and private expendi-

Co-operation and Development (OECD), and the

private health expenditure. It covers the provision of

tures, by source of funding. In contrast with high-

World Bank to collect all available information on

health services (preventive and curative), family plan-

income countries, few developing countries have

health expenditures from national and local govern-

ning activities, nutrition activities, and emergency aid

health accounts that are methodologically consistent

ment budgets, national accounts, household sur-

with national accounting approaches. The difficulties

veys, insurance publications, international donors,

in creating national health accounts go beyond data

and existing tabulations.

designated for health but does not include provision of water and sanitation. • Public health expenditure consists of recurrent and capital spending from government (central and local) budgets, external

collection. To establish a national health accounting

Indicators on health services (physicians, health

system, a country needs to define the boundaries of

worker density, and hospital beds per 1,000 people)

the health care system and to define a taxonomy of

come from a variety of sources (see Data sources).

zations), and social (or compulsory) health insurance

health care delivery institutions. The accounting sys-

Data are lacking for many countries, and for oth-

funds. • Out of pocket health expenditure is any

tem should be comprehensive and standardized, pro-

ers comparability is limited by differences in defini-

direct outlay by households, including gratuities and

viding not only accurate measures of financial flows

tions. In estimates of health personnel, for example,

in-kind payments, to health practitioners and suppli-

but also information on the equity and efficiency of

some countries incorrectly include retired physicians

ers of pharmaceuticals, therapeutic appliances, and

health financing to inform health policy.

(because deletions to physician rosters are made

other goods and services whose primary intent is to

The absence of consistent national health account-

only periodically) or physicians working outside the

contribute to the restoration or enhancement of the

ing systems in most developing countries makes

health sector. There is no universally accepted defini-

health status of individuals or population groups. It

cross-country comparisons of health spending dif-

tion of hospital beds. Moreover, figures on physicians

ficult. Compiling estimates of public health expen-

and hospital beds are indicators of availability, not

ditures is complicated in countries where state or

of quality or use. They do not show how well trained

provincial and local governments are involved in

the physicians are or how well equipped the hospitals

financing and delivering health care, because the

or medical centers are. And physicians and hospital

eral arrangements, or foreign nongovernmental orga-

data on public spending often are not aggregated.

beds tend to be concentrated in urban areas, so

nizations. These resources are part of total health

There are a number of potential data sources related

these indicators give only a partial view of health

expenditure. • Health expenditure per capita is total

to external resources for health, including govern-

services available to the entire population.

health expenditure divided by number of people in the

borrowings and grants (including donations from international agencies and nongovernmental organi-

is a part of private health expenditure. • External resources for health are funds or services in kind that are provided by entities not part of the country in question. The resources may come from international organizations, other countries through bilat-

ment expenditure accounts, government records

The WHO receives data on health professionals

country. • Physicians are graduates of any faculty or

on external assistance, routine surveys of external

from ministries of health through its six regional

school of medicine who are working in the country in

financing assistance, and special surveys. Survey

offices, often in cooperation with national statisti-

any medical field (practice, teaching, or research).

data are the major source of information about out

cal offi ces. The data are scrutinized using such

• Health worker density index reflects a combined

of pocket expenditure on health. The data in the

additional resources as national and international

table are the product of an effort by the World Health

employment surveys, records from professional associations, and other publications. Signifi cant

Differences in healthcare expenditures contribute to global disparities in health outcomes Health expenditure as a share of GDP, 2004 (%)

Total spending

inconsistencies are returned to national authorities

density of physicians, nurses, and midwives per 1,000 people. • Hospital beds include inpatient beds available in public, private, general, and specialized hospitals and rehabilitation centers. In most cases beds for both acute and chronic care are included.

for validation and resubmission.

2.14a Public spending

12

10

8

6

The health worker density index indicates the overall level of health workers (physicians, nurses, and midwives) in the country. Dentists and pharmacists

Data sources Data on health expenditure come mostly from the WHO’s National Health Account database (www.

are not included. Comparability of the index across

who.int/nha/en) and from the OECD for its mem-

countries is affected by differences in the definition

ber countries, supplemented by World Bank poverty

of health workers. Many countries continue to use

assessments and country and sector studies. Data

national definitions and classifications for data col-

are also drawn from World Bank public expenditure

lection, and some countries provide information only

reviews, the International Monetary Fund’s Govern-

for public sector workers.

ment Finance Statistics database, and other studies. Data on physicians are from the WHO’s World

4

Health Report 2006 and Global Atlas of the Health Workforce database, OECD, and TransMONEE,

2

supplemented by country data. Data for the health worker density index are from the Joint Learning

0 Low-income

Middle-income

High-income

Initiative’s Human Resources for Health. Data on hospital beds are from the WHO’s World Health

Source: World Health Organization, Organisation for Economic Co-operation and Development, and World Bank.

Statistics 2006, OECD’s Health Data 2006, and TransMONEE, supplemented by country data.

2007 World Development Indicators

95

2.15

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, 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

96

Access to improved sanitation facilities

Child immunization rate

% of population 1990 2004

% of children ages 12–23 monthsb % of population Measles DPT 1990 2004 2005 2005

4 96 94 36 94 .. 100 100 68 72 100 100 63 72 97 93 83 99 38 69 .. 50 100 52 19 90 70 .. 92 43 .. .. 69 100 .. 100 100 84 73 94 67 43 100 23 100 100 .. .. 80 100 55 .. 79 44 .. 47

3 .. 88 29 81 .. 100 100 .. 20 .. 100 12 33 .. 38 71 99 7 44 .. 48 100 23 7 84 23 .. 82 16 .. .. 21 100 98 99 100 52 63 54 51 7 97 3 100 .. .. .. 97 100 15 .. 58 14 .. 24

39 96 85 53 96 92 100 100 77 74 100 100 67 85 97 95 90 99 61 79 41 66 100 75 42 95 77 .. 93 46 58 97 84 100 91 100 100 95 94 98 84 60 100 22 100 100 88 82 82 100 75 .. 95 50 59 54

2007 World Development Indicators

34 91 92 31 91 83 100 100 54 39 84 100 33 46 95 42 75 99 13 36 17 51 100 27 9 91 44 .. 86 30 27 92 37 100 98 98 100 78 89 70 62 9 97 13 100 .. 36 53 94 100 18 .. 86 18 35 30

64 97 83 45 99 94 94 75 98 81 99 88 85 64 90 90 99 96 84 75 79 68 94 35 23 90 86 81 89 70 56 89 51 96 98 97 95 99 93 98 99 84 96 59 97 87 55 84 92 93 83 88 77 59 80 54

76 98 88 47 92 90 92 86 93 88 99 97 93 81 93 97 96 96 96 74 82 80 94 40 20 91 87 85 87 73 65 91 56 96 99 97 93 77 94 98 89 83 96 69 97 98 38 88 84 90 84 88 81 69 80 43

Children with acute respiratory infection taken to health provider

Children with diarrhea who received oral rehydration and continued feeding

Children sleeping under treated bednetsa

Children with fever receiving antimalarial drugs

Tuberculosis treatment success rate

DOTS detection rate

% of children under age 5 with ARI 2000–05c

% of children under age 5 with diarrhea 1998–2005c

% of children under age 5 2000–05c

% of children under age 5 with fever 2000–05c

% of registered cases 2004

% of estimated cases 2005

28 83 52 58 .. 28 .. .. 36 20 .. .. 35 52 80 14 .. .. 36 40 37 40 .. 32 12 .. .. .. 57 36 38 .. 38 .. .. .. .. 63 .. 73 62 44 .. 19 .. .. 48 75 99 .. 44 .. 64 33 64 26

48 51 .. 32 .. 48 .. .. 40 53 .. .. 42 54 23 7 .. .. 47 16 59 43 .. 47 27 .. .. .. 39 17 .. .. 34 .. .. .. .. 42 .. 29 .. 54 .. 38 .. .. 44 38 .. .. 40 .. 22 44 23 41

.. .. .. 2 .. .. .. .. 1 .. .. .. 7 .. .. .. .. .. 2 1 .. 1 .. 2 1 .. .. .. 1 1 .. .. 4 .. .. .. .. .. .. .. .. 4 .. 2 .. .. .. 15 .. .. 4 .. .. 4 7 ..

.. .. .. 63 .. .. .. .. 1 .. .. .. 60 .. .. .. .. .. 50 31 .. 53 .. 69 44 .. .. .. .. 45 .. .. 58 .. .. .. .. .. .. .. .. 4 .. 3 .. .. .. 55 .. .. 63 .. .. 56 58 12

89 78 91 68 58 70 85 69 60 90 74 72 83 80 98 65 81 80 67 78 91 71 62 91 69 83 94 80 85 85 63 94 71 .. 93 73 88 80 85 70 90 85 71 79 .. .. 40 86 68 68 72 .. 85 72 75 80

44 25 106 85 67 60 42 56 55 59 46 64 83 72 71 69 53 90 18 30 66 106 64 40 22 112 80 53 26 72 57 118 38 .. 98 65 71 76 28 63 67 13 64 33 .. .. 57 69 91 52 37 .. 55 56 79 57

Access to an improved water source

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

Access to improved sanitation facilities

Child immunization rate

% of population 1990 2004

% of children ages 12–23 monthsb % of population Measles DPT 1990 2004 2005 2005

84 99 70 72 92 83 .. 100 .. 92 100 97 87 45 100 .. .. 78 .. 99 100 .. 55 71 .. .. 40 40 98 34 38 100 82 .. 63 75 36 57 57 70 100 97 70 39 49 100 80 83 90 39 62 74 87 .. .. ..

50 .. 14 46 83 81 .. .. .. 75 100 93 72 40 .. .. .. 60 .. .. .. 37 39 97 .. .. 14 47 .. 36 31 .. 58 .. .. 56 20 24 24 11 100 .. 45 7 39 100 83 37 71 44 58 52 57 .. .. ..

87 99 86 77 94 81 .. 100 .. 93 100 97 86 61 100 92 .. 77 51 99 100 79 61 .. .. .. 46 73 99 50 53 100 97 92 62 81 43 78 87 90 100 .. 79 46 48 100 .. 91 90 39 86 83 85 .. .. ..

69 95 33 55 .. 79 .. .. .. 80 100 93 72 43 59 .. .. 59 30 78 98 37 27 97 .. .. 32 61 94 46 34 94 79 68 59 73 32 77 25 35 100 .. 47 13 44 100 .. 59 73 44 80 63 72 .. .. ..

92 99 58 72 94 90 84 95 87 84 99 95 99 69 96 99 99 99 41 95 96 85 94 97 97 96 59 82 90 86 61 98 96 97 99 97 77 72 73 74 96 82 96 83 35 90 98 78 99 60 90 80 80 98 93 ..

91 99 59 70 95 81 90 95 96 88 99 95 98 76 79 96 99 98 49 99 92 83 87 98 94 97 61 93 90 85 71 97 98 98 99 98 72 73 86 75 98 89 86 89 25 91 99 72 85 61 75 84 79 99 93 ..

2.15

PEOPLE

Disease prevention coverage and quality Children with acute respiratory infection taken to health provider

Children with diarrhea who received oral rehydration and continued feeding

Children sleeping under treated bednetsa

Children with fever receiving antimalarial drugs

Tuberculosis treatment success rate

DOTS detection rate

% of children under age 5 with ARI 2000–05c

% of children under age 5 with diarrhea 1998–2005c

% of children under age 5 2000–05c

% of children under age 5 with fever 2000–05c

% of registered cases 2004

% of estimated cases 2005

.. .. .. 61 93 76 .. .. .. 39 .. 78 .. 49 93 .. .. .. 36 .. 74 54 70 .. .. .. 48 27 .. 36 41 .. .. 54 78 38 54 66 53 26 .. .. 57 27 33 .. .. .. .. .. .. 68 55 .. .. ..

.. .. 22 56 .. 54 .. .. .. 21 .. 44 22 33 .. .. .. .. 37 .. .. 53 .. .. .. .. 47 51 .. 45 28 .. .. 52 66 46 47 48 39 43 .. .. 49 43 28 .. .. .. .. .. .. 57 76 .. .. ..

.. .. 12 1 .. 1 .. .. .. .. .. .. .. 27 .. .. .. .. 9 .. .. .. .. .. .. .. 34 28 .. 38 33 .. .. .. .. .. 15 .. 14 .. .. .. 2 48 34 .. .. .. .. .. .. .. .. .. .. ..

85 54 86 90 84 85 .. 80 95 46 57 85 72 80 89 80 63 85 86 73 90 69 70 64 72 84 71 71 56 71 22 89 82 62 88 87 77 84 68 87 83 66 87 61 73 89 90 82 78 65 83 90 87 79 84 71

82 43 61 66 64 43 .. 42 72 61 57 63 72 43 99 18 66 67 68 83 74 85 50 178 100 66 67 39 73 21 28 32 110 65 82 101 49 95 90 67 47 51 88 50 22 44 108 37 131 21 33 86 75 62 85 74

.. .. .. 26 .. 0d .. .. .. .. .. .. .. 5 .. .. .. .. 18 .. .. .. .. .. .. .. .. 15 .. 8 2 .. .. .. .. .. .. .. 3 .. .. .. .. 6 1 .. .. .. .. .. .. .. .. .. .. ..

2007 World Development Indicators

97

2.15

Disease prevention coverage and quality Access to an improved water source

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 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 Europe EMU

Access to improved sanitation facilities

Child immunization rate

% of population 1990 2004

% of children ages 12–23 monthsb % of population Measles DPT 1990 2004 2005 2005

.. 94 59 90 65 93 .. 100 100 .. .. 83 100 68 64 .. 100 100 80 .. 46 95 50 92 81 85 .. 44 96 100 100 100 100 94 .. 65 .. 71 50 78 77 w 64 78 76 90 73 72 93 83 88 71 49 100 100

.. 87 37 .. 33 87 .. 100 99 .. .. 69 100 69 33 .. 100 100 73 .. 47 80 37 100 75 85 .. 42 96 97 .. 100 100 51 .. 36 .. 32 44 50 45 w 21 48 42 79 37 30 86 67 70 17 31 100 ..

57 97 74 .. 76 93 57 100 100 .. 29 88 100 79 70 62 100 100 93 59 62 99 52 91 93 96 72 60 96 100 100 100 100 82 83 85 92 67 58 81 83 w 75 84 82 94 80 79 92 91 89 84 56 100 100

.. 87 42 .. 57 87 39 100 99 .. 26 65 100 91 34 48 100 100 90 51 47 99 35 100 85 88 62 43 96 98 .. 100 100 67 68 61 73 43 55 53 57 w 38 62 57 84 52 51 85 77 76 37 37 100 ..

97 99 89 96 74 96 67 96 98 94 35 82 97 99 60 60 94 82 98 84 91 96 70 93 96 91 99 86 96 92 82 93 95 99 76 95 99 76 84 85 77 w 65 87 86 93 75 83 96 92 92 64 64 93 90

97 98 95 96 84 98 64 96 99 96 35 94 96 99 59 71 99 93 99 81 90 98 82 95 98 90 99 84 96 94 91 96 96 99 87 95 99 86 80 90 78 w 66 88 86 94 76 84 95 91 93 65 65 95 95

Children with acute respiratory infection taken to health provider

Children with diarrhea who received oral rehydration and continued feeding

Children sleeping under treated bednetsa

Children with fever receiving antimalarial drugs

Tuberculosis treatment success rate

DOTS detection rate

% of children under age 5 with ARI 2000–05c

% of children under age 5 with diarrhea 1998–2005c

% of children under age 5 2000–05c

% of children under age 5 with fever 2000–05c

% of registered cases 2004

% of estimated cases 2005

.. .. 27 .. 27 97 50 .. .. .. .. .. .. .. 57 60 .. .. 66 51 57 .. 30 74 43 41 51 67 .. .. .. .. .. 57 72 71 65 47 69 ..

.. .. 16 .. 33 .. 39 .. .. .. .. 37 .. .. 38 24 .. .. .. 29 53 .. 25 31 .. 19 .. 28 .. .. .. .. .. 33 51 39 .. .. 48 80

82 59 77 82 74 89 82 81 88 90 91 70 .. 85 77 50 64 .. 86 84 81 74 67 .. 90 91 86 70 .. 70 .. 61 86 78 81 93 50 82 83 54 84 w 83 85 88 70 84 90 73 82 84 86 74 66 ..

82 30 29 38 51 31 37 100 39 84 86 103 .. 86 35 42 56 .. 42 22 45 73 18 .. 82 3 43 45 .. 19 .. 85 83 39 73 84 2 41 52 41 60 w 52 74 74 73 61 76 42 64 71 58 49 40 35

31 .. 13 .. 14 .. 2 .. .. .. 9e .. .. .. 0d 0d .. .. .. 2 16 .. 54 .. .. .. .. 0d .. .. .. .. .. .. .. 16 .. .. 7 ..

.. .. 12 .. 29 .. 61 .. .. .. .. .. .. .. 50 26 .. .. .. 69 58 .. 60 .. .. .. .. .. .. .. .. .. .. .. .. 7 .. .. 52 ..

a. For malaria prevention only. b. Refers to children who were immunized before age 12 months or, in some cases, ages 12–23 months. c. Data are for the most recent year available. d. Less than 0.5. e. Data are for 2006.

98

2007 World Development Indicators

2.15

PEOPLE

Disease prevention coverage and quality About the data

People’s health is influenced by the environment in

over time based on scientific progress, so it is difficult

to improved sanitation facilities refers to the percent-

which they live. Lack of clean water and basic sani-

to accurately compare use rates among countries.

age of the population with at least adequate access

tation is the main reason diseases transmitted by

Until the current recommended method for home

to excreta disposal facilities that can effectively pre-

feces are so common in developing countries. The

management of diarrhea is adopted and applied in

vent human, animal, and insect contact with excreta.

data on access to an improved water source mea-

all countries, the data should be used with caution.

Improved facilities range from simple but protected pit

sure the percentage of the population using improved

Also, the prevalence of diarrhea may vary by season.

latrines to flush toilets with a sewerage connection. To

drinking water sources or delivery points. Access to

Since country surveys are administered at different

be effective, facilities must be correctly constructed

drinking water from an improved source and access to

times, data comparability is further affected.

and properly maintained. • Child immunization rate

improved sanitation do not ensure safety or adequacy,

Malaria is endemic to the poorest countries in the

is the percentage of children ages 12–23 months who

as these characteristics are not tested at the time of

world, mainly in tropical and subtropical regions of

received vaccinations before 12 months or at any time

the surveys. But improved drinking water technologies

Africa, Asia, and the Americas. An estimated 300–

before the survey for four diseases—measles and

and improved sanitation facilities are more likely than

500 million clinical malaria cases and more than 1

diphtheria, pertussis (whooping cough), and tetanus

those characterized as unimproved to provide safe

million malaria deaths occur each year—the vast

(DPT). A child is considered adequately immunized

drinking water and to prevent contact with human

majority in Sub-Saharan Africa and among children

against measles after receiving one dose of vac-

excreta. The data are derived by the Joint Monitoring

under age five. Insecticide-treated bednets, if properly

cine and against DPT after receiving three doses.

Programme (JMP) of the WHO and United Nations

used and maintained, are one of the most important

• Children with acute respiratory infection taken

Children’s Fund (UNICEF) based on national censuses

malaria-preventive strategies to limit human-mosquito

to a health provider refer to the percentage of chil-

and nationally representative household surveys. The

contact. Studies have emphasized that mortality rates

dren under age five with acute respiratory infection

coverage rates for water and sanitation are based on

could be reduced by about 25–30 percent if every

in the two weeks prior to the survey who were taken

information from service users on the facilities their

child under age five in malaria-risk areas such as

to an appropriate health provider, including hospital,

households actually use rather than on information

Africa slept under a treated bednet every night.

health center, dispensary, village health worker, clinic,

from service providers, who may include nonfunction-

Prompt and effective treatment of malaria is a criti-

and private physician • Children with diarrhea who

ing systems. While the estimates are based on use,

cal element of malaria control. It is vital that suffer-

received oral rehydration and continued feeding refer

the JMP reports use as access, because access is

ers, especially children under age five, start treat-

to the percentage of children under age five with diar-

the term used in the Millennium Development Goal

ment within 24 hours of the onset of symptoms, to

rhea in the two weeks prior to the survey who received

target for drinking water and sanitation.

prevent progression—often rapid—to severe malaria

either oral rehydration therapy or increased fluids, with

and death.

continued feeding. • Children sleeping under treated

Governments in developing countries usually finance immunization against measles and diphthe-

Data on the success rate of tuberculosis treatment

bednets refer to the percentage of children under age

ria, pertussis (whooping cough), and tetanus (DPT)

are provided for countries that have implemented

five who slept under an insecticide-treated bednet

as part of the basic public health package. In many

DOTS, the internationally recommended tuberculosis

to prevent malaria. • Children with fever receiving

developing countries, lack of precise information on

control strategy. Countries that have not adopted

antimalarial drugs refer to the percentage of children

the size of the cohort of one-year-old children makes

DOTS or have only recently done so are omitted

under age five who were ill with fever in the last two

immunization coverage diffi cult to estimate from

because of lack of data or poor comparability or reli-

weeks and received any appropriate (locally defined)

program statistics. The data shown here are based

ability of reported results. The treatment success

antimalarial drugs. • Tuberculosis treatment success

on an assessment of national immunization cover-

rate for tuberculosis provides a useful indicator of

rate is the percentage of new, registered smear-posi-

age rates by the WHO and UNICEF. The assessment

the quality of health services. A low rate or no suc-

tive (infectious) cases that were cured or in which a full

considered both administrative data from service

cess suggests that infectious patients may not be

course of treatment was completed. • DOTS detec-

providers and household survey data on children’s

receiving adequate treatment. An essential comple-

tion rate is the percentage of estimated new infec-

immunization histories. Based on the data available,

ment to the tuberculosis treatment success rate is

tious tuberculosis cases detected under the directly

consideration of potential biases, and contributions

the DOTS detection rate, which indicates whether

observed treatment, short course case detection and

of local experts, the most likely true level of immuni-

there is adequate coverage by the recommended

treatment strategy.

zation coverage was determined for each year.

case detection and treatment strategy. A country

Acute respiratory infection continues to be a leading cause of death among young children, killing

challenges if its DOTS detection rate remains low.

Data on water and sanitation are from the WHO and UNICEF’s Meeting the MDG Drinking Water and

about 2 million children under age five in developing countries in 2000. An estimated 60 percent of these

Data sources

with a high treatment success rate may still face big

Definitions

Sanitation Target (www.who.int/water_sanitation_

deaths can be prevented by the selective use of anti-

• Access to an improved water source refers to the

health/monitoring/jmp2006). Data on immuni-

biotics by appropriate health care providers. Data are

percentage of the population with reasonable access

zation are from WHO and UNICEF estimates of

drawn mostly from household health surveys in which

to an adequate amount of water from an improved

national immunization coverage (www.who.int/

mothers report on number of episodes and treatment

source, such as piped water into a dwelling, plot, or

immunization_monitoring/en). Data on children

for acute respiratory infection.

yard; public tap or standpipe; tubewell or borehole;

with acute respiratory infection, children with diar-

Since 1990 diarrhea-related deaths among children

protected dug well or spring; and rainwater collection.

rhea, children sleeping under treated bednets, and

have declined tremendously. Most diarrhea-related

Unimproved sources include unprotected dug well or

children receiving antimalarial drugs are from UNI-

deaths are due to dehydration, and many of these

spring, cart with small tank or drum, bottled water,

CEF’s State of the World’s Children 2007, Childinfo,

deaths can be prevented with the use of oral rehy-

and tanker trucks. Reasonable access is defined as

and Demographic and Health Surveys by Macro

dration salts at home. However, recommendations

the availability of at least 20 liters a person a day from

International. Data on tuberculosis are from the

for the use of oral rehydration therapy have changed

a source within 1 kilometer of the dwelling. • Access

WHO’s Global Tuberculosis Control Report 2007.

2007 World Development Indicators

99

2.16

Reproductive health Total fertility Adolescent rate fertility rate

births per 1,000 women births per woman ages 15–19 1990 2005 2005

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, 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

100

.. 2.9 4.6 7.1 3.0 2.5 1.9 1.5 2.7 4.3 1.9 1.6 6.7 4.9 1.7 4.4 2.8 1.8 6.9 6.8 5.5 5.9 1.8 5.6 6.7 2.6 2.1 1.3 3.1 6.7 6.3 3.2 6.5 1.6 1.7 1.9 1.7 3.3 3.6 4.3 3.7 6.2 2.0 6.9 1.8 1.8 5.3 5.8 2.1 1.5 5.7 1.4 5.6 6.5 7.1 5.2

.. 1.8 2.4 6.6 2.3 1.4 1.8 1.4 2.3 3.0 1.2 1.7 5.6 3.7 1.2 3.0 2.3 1.3 5.9 6.8 3.9 5.0 1.5 4.7 6.3 2.0 1.8 1.0 2.4 6.7 5.6 2.0 4.7 1.4 1.5 1.3 1.8 2.7 2.7 3.1 2.8 5.2 1.5 5.3 1.8 1.9 3.7 4.4 1.4 1.4 4.1 1.3 4.3 5.6 7.1 3.8

2007 World Development Indicators

.. 16 8 140 58 30 14 12 31 118 26 8 127 81 22 74 89 43 156 50 46 110 13 122 191 60 5 5 75 225 144 74 117 14 50 11 7 91 83 41 83 92 23 87 10 7 102 116 32 10 61 9 110 186 192 61

Unmet Contraceptive Tetanus Pregnant need for prevalence rate vaccinations women contraception receiving prenatal care

Births attended by skilled health staff

Maternal mortality ratio

per 100,000 live births

% of married women ages 15–49 2000–05a

% of married women ages 15–49 2000–05a

% of pregnant women 2005

% 2000–05a

.. .. .. .. .. 12 .. .. .. 11 .. .. 27 23 .. .. .. .. 29 .. 30 20 .. .. 21 .. .. .. 6 .. .. .. .. .. .. .. .. 11 .. 11 .. 27 .. 35 .. .. 28 .. .. .. 34 .. .. .. .. 40

10 75 57 6 .. 61 .. .. 55 58 .. .. 19 58 48 48 .. .. 14 16 24 26 .. 28 3 .. 87 .. 78 31 44 .. .. .. 73 .. .. 70 73 59 67 8 .. 15 .. .. 33 18 47 .. 25 .. 43 7 8 28

55 .. .. 75 .. .. .. .. .. 89 .. .. 69 .. .. .. .. .. 75 45 53 65 .. 56 39 .. .. .. 86 66 65 .. 73 .. .. .. .. .. .. 80 .. 62 .. 45 .. .. 60 .. .. .. 84 .. .. 76 54 52

16 91 81 66 98 93 .. .. 70 49 .. .. 81 79 99 97 97 .. 73 78 38 83 .. 62 39 .. 90 .. 94 68 88 92 88 .. 100 .. .. 99 84 70 86 70 .. 28 .. .. 94 91 95 .. 92 .. 84 82 62 79

National estimates % of total a a 1990–92 2000–05 1990–2005a

.. .. 77 .. 96 .. 100 .. .. .. .. .. .. .. 97 .. 72 .. .. .. .. 58 .. .. .. .. .. .. 82 .. .. 98 .. 100 .. .. .. 93 .. 41 .. .. .. .. .. .. .. 44 .. .. .. .. .. 31 .. ..

14 98 96 45 95 98 99 .. 88 13 100 .. 75 67 100 94 97 99 38 25 44 62 98 44 14 100 97 100 96 61 86 99 68 100 100 100 .. 99 75 74 92 28 100 6 100 .. 86 55 92 .. 47 .. 41 56 35 24

1,600 16 120 .. 39 16 .. .. 19 320 17 .. 500 30 8 330 72 6 480 .. 440 670 .. 1,100 1,100 17 51 .. 84 1,300 .. 36 600 8 37 4 10 180 80 84 170 1,000 8 673 6 10 520 730 52 8 .. 1 150 530 910 520

Modeled estimates 2000

1,900 55 140 1,700 82 55 8 4 94 380 35 10 850 420 31 100 260 32 1,000 1,000 450 730 6 1,100 1,100 31 56 .. 130 990 510 43 690 8 33 9 5 150 130 84 150 630 63 850 6 17 420 540 32 8 540 9 240 740 1,100 680

Total fertility Adolescent rate fertility rate

births per 1,000 women births per woman ages 15–19 1990 2005 2005

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

5.1 1.8 3.8 3.1 4.8 5.9 2.1 2.8 1.3 2.9 1.5 5.4 2.7 5.8 2.4 1.6 3.5 3.7 6.0 2.0 3.1 4.8 6.9 4.7 2.0 2.1 6.2 7.0 3.8 7.4 6.1 2.3 3.3 2.4 4.0 4.0 6.2 4.0 5.9 5.1 1.6 2.2 4.8 8.2 6.7 1.9 6.5 5.8 3.0 5.1 4.7 3.9 4.3 2.0 1.4 2.2

3.5 1.3 2.8 2.3 2.1 .. 1.9 2.8 1.3 2.4 1.3 3.3 1.7 5.0 2.0 1.1 2.4 2.4 4.5 1.3 2.3 3.4 6.8 2.8 1.3 1.6 5.0 5.8 2.7 6.7 5.6 2.0 2.1 1.3 2.3 2.4 5.3 2.2 3.7 3.5 1.7 2.0 3.1 7.7 5.5 1.8 3.4 4.1 2.6 3.8 3.7 2.7 3.2 1.2 1.4 1.8

97 21 70 53 19 .. 13 14 7 78 4 26 29 95 2 4 23 32 88 17 26 36 222 7 21 23 121 155 18 197 97 32 66 30 53 42 101 18 51 110 5 23 118 255 137 9 45 69 85 57 63 53 35 14 18 53

Unmet Contraceptive Tetanus Pregnant need for prevalence rate vaccinations women contraception receiving prenatal care

Births attended by skilled health staff

2.16 Maternal mortality ratio

per 100,000 live births

% of married women ages 15–49 2000–05a

% of married women ages 15–49 2000–05a

% of pregnant women 2005

% 2000–05a

.. .. .. 9 .. .. .. .. .. .. .. 11 .. 25 .. .. .. .. .. .. .. .. .. .. .. .. 24 30 .. 29 32 .. .. .. .. 10 18 .. 22 28 .. .. 15 .. 17 .. .. .. .. .. .. 10 17 .. .. ..

62 .. 47 57 74 44 .. .. .. 69 56 56 .. .. .. .. .. .. 32 .. 63 30 10 .. .. .. 27 31 .. 8 8 76 73 68 69 63 26 34 44 38 .. .. 69 14 13 .. 32 28 .. .. 73 69 49 .. .. ..

.. .. 80 70 .. 70 .. .. .. .. .. .. .. 72 .. .. .. .. 30 .. .. .. 72 .. .. .. 45 70 .. 75 34 .. .. .. .. .. 70 85 67 42 .. .. .. 54 51 .. .. 57 .. 10 .. .. 70 .. .. ..

83 .. .. 92 .. 77 .. .. .. 98 .. 99 .. 88 .. .. .. .. 27 .. 96 90 85 .. .. 81 80 92 74 57 64 .. .. 98 94 68 85 76 91 28 .. .. 86 41 58 .. 100 36 .. .. 94 92 88 .. .. ..

National estimates % of total a a 1990–92 2000–05 1990–2005a

45 .. .. 32 .. .. .. .. .. .. 100 87 .. .. .. 98 .. .. .. .. .. .. .. .. .. .. 57 55 .. .. 40 96 .. .. .. 31 .. .. 68 7 .. .. .. 15 31 .. .. 19 .. .. 67 .. .. .. .. ..

56 100 43 72 90 72 100 .. .. 97 .. 100 .. 42 97 100 100 99 19 100 93 55 51 .. 100 99 51 56 97 41 57 99 83 100 97 63 48 57 76 15 .. .. 67 16 35 .. 95 31 93 41 77 73 60 100 100 100

110 7 540 310 37 290 6 5 7 110 8 41 42 410 110 20 5 49 410 14 .. 760 .. 77 3 21 470 980 30 580 750 22 63 22 93 230 410 230 270 540 7 15 83 590 .. 6 23 530 40 .. 180 190 170 4 8 ..

2007 World Development Indicators

Modeled estimates 2000

110 16 540 230 76 250 5 17 5 87 10 41 210 1,000 67 20 5 110 650 42 150 550 760 97 13 23 550 1,800 41 1,200 1,000 24 83 36 110 220 1,000 360 300 740 16 7 230 1,600 800 16 87 500 160 300 170 410 200 13 5 25

101

PEOPLE

Reproductive health

2.16

Reproductive health Total fertility Adolescent rate fertility rate

Unmet Contraceptive Tetanus Pregnant need for prevalence rate vaccinations women contraception receiving prenatal care

births per 1,000 women births per woman ages 15–19 1990 2005 2005

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 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 Europe EMU

1.8 1.9 7.4 5.9 6.4 2.1 6.5 1.9 2.1 1.5 6.8 3.3 1.3 2.5 5.6 5.3 2.1 1.6 5.2 5.1 6.1 2.2 6.4 2.4 3.5 3.0 4.2 7.2 1.8 4.3 1.8 2.1 2.5 4.1 3.4 3.6 6.3 7.9 6.5 5.2 3.1 w 4.7 2.6 2.7 2.6 3.4 2.5 2.3 3.2 4.8 4.1 6.2 1.8 1.5

1.3 1.3 5.8 3.8 4.9 1.6 6.5 1.2 1.3 1.2 6.2 2.8 1.3 1.9 4.1 3.9 1.8 1.4 3.2 3.5 5.2 1.9 5.0 1.6 2.0 2.2 2.6 7.1 1.2 2.4 1.8 2.1 2.0 2.2 2.7 1.8 4.6 5.9 5.4 3.3 2.6 w 3.6 2.1 2.1 1.9 2.7 2.0 1.6 2.4 3.0 3.1 5.3 1.7 1.5

34 29 46 32 80 23 172 5 20 6 68 65 9 18 50 35 7 4 32 29 106 47 95 35 7 40 16 207 28 19 25 50 69 35 91 19 .. 91 126 89 57 w 92 32 29 46 60 16 29 77 32 76 132 24 9

a. Data are for the most recent year available. b. Data are for 2006.

102

2007 World Development Indicators

% of married women ages 15–49 2000–05a

.. .. 36 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 22 .. .. .. .. .. 10 35 .. .. .. .. .. .. .. 5 .. .. 27 ..

% of married women ages 15–49 2000–05a

70 .. 17 .. 11 58 4 .. .. .. 15b 60 .. 70 7 48 .. .. 48 34 26 72 26 38 66 71 62 23 89 .. 84 .. .. 68 .. 77 51 23 34 .. 60 w 40 76 77 .. 60 79 .. .. 59 46 23 .. ..

Births attended by skilled health staff

Maternal mortality ratio

per 100,000 live births % of pregnant women 2005

.. .. 76 .. 85 .. 76 .. .. .. 25 61 .. 76 41 .. .. .. .. .. 90 .. 61 .. .. 47 .. 56 .. .. .. .. .. .. .. 85 .. 24 98 70 .. w 69 .. .. .. .. .. .. .. .. 77 61 .. ..

% 2000–05a

94 .. 94 .. 79 .. 68 .. .. .. .. 92 .. 100 60 90 .. .. 71 71 78 92 85 92 92 81 98 92 .. .. .. .. .. 97 94 86 96 41 93 .. .. w .. 89 89 .. .. 89 .. 95 .. .. 70 .. ..

National estimates % of total a a 1990–92 2000–05 1990–2005a

.. .. 26 .. .. .. .. .. .. 100 .. .. .. .. 69 .. .. .. .. .. 44 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 16 51 .. .. w 33 .. .. .. .. .. .. .. .. 30 .. .. ..

99 99 39 93 58 92 42 100 99 100 33b 92 .. 96 87 74 .. .. 70 71 43 99 61 96 90 83 97 39 100 100 .. 99 99 96 95 90 97 27 43 .. 63 w 41 87 86 92 61 87 94 87 74 37 45 .. ..

17 .. 750 .. 430 7 1,800 6 4 17 1,000 150 6 43 .. 230 5 5 65 37 578 24 480 45 69 .. 14 510 13 3 7 8 26 30 58 170 .. 370 730 1,100

Modeled estimates 2000

49 67 1,400 23 690 11 2,000 30 3 17 1,100 230 4 92 590 370 2 7 160 100 1,500 44 570 160 120 70 31 880 35 54 13 17 27 24 96 130 .. 570 750 1,100 410 w 684 150 163 91 450 117 58 194 183 564 921 14 10

About the data

2.16

Definitions

Reproductive health is a state of physical and men-

generally two tetanus shots during the first pregnancy

• Total fertility rate is the number of children that

tal well-being in relation to the reproductive system

and one booster shot during each subsequent preg-

would be born to a woman if she were to live to the

and its functions and processes. Means of achieving

nancy, with five doses considered adequate for lifetime

end of her childbearing years and bear children in

reproductive health include education and services

protection. Information on tetanus shots during preg-

accordance with current age-specific fertility rates.

during pregnancy and childbirth, provision of safe and

nancy is collected through surveys in which pregnant

• Adolescent fertility rate is the number of births

effective contraception, and prevention and treatment

respondents are asked to show antenatal cards on

of sexually transmitted diseases. Complications of

which tetanus shots have been recorded. Because

pregnancy and childbirth are the leading cause of

not all women have antenatal cards, respondents are

death and disability among women of reproductive age

also asked about their receipt of these injections. Poor

in developing countries. Reproductive health services

recall may result in a downward bias in estimates of

will need to expand rapidly over the next two decades,

the share of births protected. But in settings where

practicing, or whose sexual partners are practicing,

when the number of women and men of reproductive

receiving injections is common, respondents may erro-

any form of contraception. • Tetanus vaccinations

age is projected to increase by about 500 million.

neously report having received tetanus shots.

refer to the percentage of pregnant women who

per 1,000 women ages 15–19. • Unmet need for contraception is the percentage of fertile, married women of reproductive age who do not want to become pregnant and are not using contraception. • Contraceptive prevalence rate is the percentage of women married or in-union ages 15–49 who are

Total and adolescent fertility rates are based on

The share of births attended by skilled health staff

receive two tetanus toxoid injections during their

data on registered live births from vital registration

is an indicator of a health system’s ability to provide

first pregnancy and one booster shot during each

systems or, in the absence of such systems, from

adequate care for pregnant women. Good antena-

subsequent pregnancy, with five doses considered

censuses or sample surveys. As long as the surveys

tal and postnatal care improve maternal health and

adequate for a lifetime. • Pregnant women receiving

are fairly recent, the estimated rates are generally

reduce maternal and infant mortality. But data may

prenatal care are the percentage of women attended

considered reliable measures of fertility in the recent

not reflect such improvements because health infor-

at least once during pregnancy by skilled health per-

past. Where no empirical information on age- specific

mation systems are often weak, maternal deaths are

sonnel for reasons related to pregnancy. • Births

fertility rates is available, a model is used to estimate

underreported, and rates of maternal mortality are

the share of births to adolescents. For countries with-

difficult to measure.

out vital registration systems, fertility rates are gener-

Maternal mortality ratios are generally of unknown

ally based on extrapolations from trends observed in

reliability, as are many other cause-specific mortality

censuses or surveys from earlier years.

indicators. Household surveys such as the Demo-

attended by skilled health staff are the percentage of deliveries attended by personnel trained to give the necessary supervision, care, and advice to women during pregnancy, labor, and the postpartum period; to conduct deliveries on their own; and to care for newborns. • Maternal mortality ratio is the

An increasing number of couples in the develop-

graphic and Health Surveys attempt to measure

number of women who die from pregnancy-related

ing world want to limit or postpone childbearing

maternal mortality by asking respondents about sur-

causes during pregnancy and childbirth, per 100,000

but are not using effective contraceptive methods.

vivorship of sisters. The main disadvantage of this

live births.

These couples have an unmet need for contracep-

method is that the estimates of maternal mortality

tion, shown in the table as the percentage of mar-

that it produces pertain to 12 years or so before the

ried women of reproductive age who do not want

survey, making them unsuitable for monitoring recent

to become pregnant but are not using contracep-

changes or observing the impact of interventions.

tion (Bulatao 1998). Information on this indicator is

In addition, measurement of maternal mortality is

collected through surveys and excludes women not

subject to many types of errors. Even in high-income

exposed to the risk of unintended pregnancy because

countries with vital registration systems, misclassi-

of menopause, infertility, or postpartum anovulation.

fication of maternal deaths has been found to lead

Common reasons for not using contraception are

to serious underestimation.

lack of knowledge about contraceptive methods and

Data sources Data on fertility rates are compiled and estimated by the World Bank’s Development Data Group. Important inputs come from the following sources: the United Nations Population Division’s World Population Prospects: The 2004 Revision; census reports and other statistical publications from national statistical offices; and household

The maternal mortality ratios shown in the table as

surveys such as Demographic and Health Surveys.

national estimates are based on national surveys,

Data on women with unmet need for contracep-

Contraceptive prevalence reflects all methods—

vital registration records, and surveillance data or

tion and contraceptive prevalence rates are from

ineffective traditional methods as well as highly

are derived from community and hospital records.

household surveys, including Demographic and

effective modern methods. Contraceptive prevalence

The ratios shown as modeled estimates are based on

Health Surveys by Macro International and Mul-

rates are obtained mainly from household surveys,

an exercise by the World Health Organization (WHO),

tiple Indicator Cluster Surveys by UNICEF. Data on

including Demographic and Health Surveys, Multiple

United Nations Children’s Fund (UNICEF), and the

tetanus vaccinations, pregnant women receiving

Indicator Cluster Surveys, and contraceptive preva-

United Nations Population Fund (UNFPA). For coun-

prenatal care, births attended by skilled health

lence surveys (see Primary data documentation for

tries with national data, reported maternal mortal-

the most recent survey year). Unmarried women are

ity was adjusted by a factor of under- or over-enu-

often excluded from such surveys, which may bias

meration and misclassification. For countries with no

the estimates.

national data, maternal mortality was estimated with

concerns about possible health side-effects.

staff, and national estimates of maternal mortality ratios are from UNICEF’s State of the World’s Children 2007 and Childinfo, and Demographic and Health Surveys by Macro International. Modeled estimates for maternal mortality ratios are from

Neonatal tetanus is an important cause of infant

a regression model using information on fertility, birth

mortality in some developing countries. It can be

attendants, and GDP. Neither set of ratios can be

Mortality in 2000: Estimates Developed by WHO,

prevented through immunization of the mother during

assumed to provide an accurate estimate of maternal

UNICEF, and UNFPA” (2003).

pregnancy. Recommended doses for full protection are

mortality for any of the countries in the table.

Carla AbouZahr and Tessa Wardlaw’s “Maternal

2007 World Development Indicators

103

PEOPLE

Reproductive health

2.17 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, 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

104

Nutrition Prevalence of undernourishment

Prevalence of child malnutrition

% of population 1990–92 2002–04a

% of children under age 5 Underweight Stunting 2000–05b 2000–05b

.. 5c 5 58 <2.5 52c <2.5 <2.5 34 c 35 <2.5c <2.5 20 28 9c 23 12 8c 21 48 43 33 <2.5 50 58 8 16 .. 17 31 54 6 18 16c 7 <2.5c <2.5 27 8 4 12 70 c 9c 69 c <2.5 <2.5 10 22 44 c <2.5 37 <2.5 16 39 24 65

2007 World Development Indicators

.. 6 4 35 3 24 <2.5 <2.5 7 30 4 <2.5 12 23 9 32 7 8 15 66 33 26 <2.5 44 35 4 12 .. 13 74 33 5 13 7 <2.5 <2.5 <2.5 29 6 4 11 75 <2.5 46 <2.5 <2.5 5 29 9 <2.5 11 <2.5 22 24 39 46

39.3 14.0 10.4 30.5 3.8d 2.6 .. .. 6.8 47.5 .. .. 30.0 7.6 4.1 12.5 .. .. 37.7 45.1 36.0 18.1 .. 24.3 36.7 0.7 7.8 .. 7.0 31.0 .. .. 17.2 .. 3.9 .. .. 5.3 11.6 8.6 10.3 39.6 .. 38.4 .. .. 11.9 17.2 .. .. 22.1 .. 22.7 32.7 25.0 17.2

53.7 35.1 19.1 45.2 4.2d 12.9 .. .. 13.3 43.0 .. .. 30.7 26.7 9.7 23.1 .. .. 38.7 56.8 44.6 31.7 .. 38.9 40.9 1.4 14.2 .. 12.0 38.1 .. .. .. .. 4.6 .. .. 8.9 .. 15.6 18.9 37.6 .. 46.5 .. .. 20.7 19.2 .. .. 29.9 .. 49.3 .. 30.5 22.7

Prevalence LowExclusive Consumption of overweight birthweight breastfeeding of iodized children babies salt

% of children under age 5 2000–05b

.. 22.4 10.1 .. .. 10.4 .. .. 2.6 0.8 .. .. 1.8 5.6 13.2 6.9 .. .. 2.9 0.7 2.0 5.2 .. .. 1.5 8.1 2.6 .. 3.7 3.9 .. .. .. .. .. .. .. 6.5 .. 6.7 3.6 0.7 .. 1.2 .. .. 3.7 1.5 .. .. 2.9 .. 5.4 .. 3.3 2.0

% of births 2000–05b

% of children under 6 months 2000–05b

% of households 2000–05b

.. 5 7 12 8 7 7 7 12 36 5 .. 16 7 4 10 8 10 19 16 11 13 6 14 22 6 4 5 6 12 .. 7 17 6 5 7 5 11 .. 12 7 14 4 14 4 .. 14 17 7 .. 16 .. 12 16 22 21

.. 6 13 11 .. 33 .. .. 7 36 .. .. 38 54 .. 34 .. .. 19 62 12 24 .. 17 2 63 51 .. 47 24 19 .. 5 .. 41 .. .. 10 .. 38 24 52 .. 49 .. .. 6 26 .. .. 53 .. 51 27 37 24

28 62 69 35 .. 97 .. .. 26 70 55 .. 72 90 62 66 88 98 45 96 14 88 .. 86 56 .. 93 .. .. 72 .. .. 84 .. 88 .. .. 18 .. 78 62 68 .. 28 .. .. 36 8 68 .. 28 .. 67 68 2 11

Vitamin A supplementation

% of children 6–59 months 2004

96 .. .. 77 .. .. .. .. 14 83 .. .. 94 42 .. 62e .. .. 95 94 72 81 .. 79 84 .. .. .. .. 81 94 60 .. .. .. .. .. .. .. .. .. 50 .. 52 .. .. .. 27 .. .. 95 .. 18e 95 64 ..

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

Prevalence of undernourishment

Prevalence of child malnutrition

% of population 1990–92 2002–04a

% of children under age 5 Underweight Stunting 2000–05b 2000–05b

23 <2.5c 25 9 4 .. <2.5 <2.5 <2.5 14 <2.5 4 <2.5c 39 18 <2.5 24 21c 29 3c <2.5 17 34 <2.5 4c 15c 35 50 3 29 15 6 5 5c 34 6 66 10 34 20 <2.5 <2.5 30 41 13 <2.5 .. 24 21 .. 18 42 26 <2.5c <2.5 ..

23 <2.5 20 6 4 .. <2.5 <2.5 <2.5 9 <2.5 6 6 31 33 <2.5 5 4 19 3 3 13 50 <2.5 <2.5 5 38 35 3 29 10 5 5 11 27 6 44 5 24 17 <2.5 <2.5 27 32 9 <2.5 .. 24 23 .. 15 12 18 <2.5 <2.5 ..

16.6 .. .. 28.2 .. 15.9 .. .. .. 3.6 .. 4.4 .. 19.9 23.9 .. .. 6.7 40.4 .. 3.9 18.0 26.5 .. .. .. 41.9 21.9 10.6 33.2 31.8 .. .. 4.3 12.7 10.2 23.7 31.8 24.0 45.0 g .. .. 9.6 40.1 28.7 .. .. 37.8 .. .. 4.6 7.1 27.6 .. .. ..

29.2 .. .. .. .. 22.1 .. .. .. .. .. 8.5 .. 30.3 38.6 .. .. .. 42.4 .. 11.0 46.1 39.5 .. .. .. 47.7 49.0 .. 38.2 34.5 .. .. 8.4 24.6 18.1 41.0 32.2 23.6 43.0 g .. .. 20.2 39.7 38.3 .. .. 36.8 .. .. .. 25.4 .. .. .. ..

2.17

Prevalence LowExclusive Consumption of overweight birthweight breastfeeding of iodized children babies salt

% of children under age 5 2000–05b

2.2 .. .. .. .. 3.0 .. .. .. .. .. 3.5 .. 3.7 0.6 .. .. .. 1.2 .. .. 12.1 2.3 .. .. .. .. 4.3 .. 1.5 .. .. .. .. .. 9.2 3.0 1.6 2.2 0.2 .. .. 4.7 0.8 3.6 .. .. 2.1 .. .. .. 7.6 .. .. .. ..

% of births 2000–05b

14 9 .. 9 .. 15 .. 8 .. 10 8 12 .. 10 7 4 .. .. 14 5 6 13 .. .. 4 6 17 16 9 23 .. 14 8 5 7 15 15 15 14 21 .. 6 12 13 14 5 8 .. 10 .. 9 11 20 6 8 ..

% of children under 6 months 2000–05b

35 .. 37f 40 44 12 .. .. .. .. .. 27 .. 13 65 .. .. .. 23 .. 27f 36 35 .. .. 99 67 53 .. 25 20 21f .. 46 51 31 30 15f 19 68g .. .. 31 1 17 .. .. .. .. .. 22 64 34 .. .. ..

% of households 2000–05b

.. .. 57 73 .. 40 .. .. .. .. .. 88 83 91 40 .. .. 42 75 .. 92 91 .. .. .. 94 75 49 .. 74 2 .. 91 59 75 59 54 60 63 63 .. .. 97 15 97 .. .. 17 .. .. 88 91 56 .. .. ..

2007 World Development Indicators

Vitamin A supplementation

% of children 6–59 months 2004

40 .. 51e 73e .. .. .. .. .. .. .. .. .. 63 95 .. .. 95 48 .. .. 71 95 .. .. .. 89 57 .. 97 95 .. .. .. 93 .. 26 96 .. 97 .. .. 98 .. 85 .. 95 95 .. 32 .. .. 85 .. .. ..

105

PEOPLE

Nutrition

2.17 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 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 Europe EMU

Nutrition Prevalence of undernourishment

Prevalence of child malnutrition

% of population 1990–92 2002–04a

% of children under age 5 Underweight Stunting 2000–05b 2000–05b

<2.5c 4c 43 4 23 5c 46 .. 4c 3c .. <2.5 <2.5 28 31 14 <2.5 <2.5 5 22c 37 30 33 13 <2.5 <2.5 12c 24 <2.5c 4 <2.5 <2.5 7 8c 11 31 .. 34 48 45 17 w 27 14 16 .. 20 17 6c 13 6 26 29 3 3

<2.5 3 33 4 20 9 51 .. 7 3 .. <2.5 <2.5 22 26 22 <2.5 <2.5 4 56 44 22 24 10 <2.5 3 7 19 <2.5 3 <2.5 <2.5 <2.5 25 18 16 16 38 46 47 14 w 24 10 11 4 16 12 6 10 7 21 30 3 3

3.2 5.5 22.5 .. 22.7 1.9 27.2 3.4 .. .. 33.0 g .. .. 29.4 40.7 10.3 .. .. 6.9 .. 21.8 .. .. 5.9 4.0 3.9 12.0 22.9 1.0 .. .. 1.6 .. 7.9 4.4 28.4 4.9 45.6 23.0 .. .. w .. 11.5 12.5 .. 21.7 14.9 4.9 .. 14.6 .. 29.6 .. ..

10.1 10.6 45.3 .. 25.4 5.1 33.8 2.2 .. .. 23.3 .. .. 13.5 43.3 30.2 .. .. 18.8 36.2 37.7 .. .. 3.6 12.3 .. 22.3 39.1 2.7 .. .. 1.1 .. 21.1 12.8 36.5 9.9 53.1 46.8 .. .. w .. 15.6 16.4 .. .. 17.7 .. .. 22.2 .. 39.2 .. ..

Prevalence LowExclusive Consumption of overweight birthweight breastfeeding of iodized children babies salt

% of children under age 5 2000–05b

5.5 .. 4.0 .. 2.2 .. .. 2.2 .. .. .. .. .. .. 3.4 .. .. .. .. .. .. .. .. .. .. .. .. 2.6 20.1 .. .. 5.6 .. .. 3.2 2.7 .. .. 3.0 ..

% of births 2000–05b

8 6 9 .. 18 4 23 8 7 6 .. .. .. 22 .. 9 .. .. 6 15 10 9 18 23 7 .. 6 12 5 .. 8 8 8 7 9 9 9 .. 12 .. 11 w .. 8 8 8 11 7 7 9 11 .. 14 .. ..

% of children under 6 months 2000–05b

16 .. 88 .. 34 11f 4 .. .. .. .. 7 .. 53 16 24 .. .. 81f 50 41 .. 18 2 47 21 13 63 22 .. .. .. .. 19 .. 15 29 f 12 40 .. 37 w 33 42 44 .. 37 44 .. .. 34 37 29 .. ..

% of households 2000–05b

53 35 90 .. 41 73 23 .. .. .. .. .. .. 94 1 59 .. .. 79 28 43 63 67 1 97 64 100 95 32 .. .. .. .. 57 .. 83 64 30 77 .. 69 w 56 80 83 .. 69 85 49 84 66 54 63 .. ..

Vitamin A supplementation

% of children 6–59 months 2004

.. .. 95 .. 95 .. 95 .. .. .. 6 37 .. 57e 70 86 .. .. .. 98 94 .. 95 .. .. .. .. 68 .. .. .. .. .. 86 .. 95e .. 20 50 20 .. w 68 .. .. .. .. .. .. .. .. 62 73 .. ..

a. Preliminary data. b. Data are for the most recent year available. c. Data are for 1993–95. d. Data are for 2005–06. e. Country’s vitamin A supplementation programs do not target children all the way up to 59 months of age. f. Refers to exclusive breastfeeding of children under four months. g. Data are for 2006.

106

2007 World Development Indicators

2.17

About the data

Data on undernourishment are produced by the Food and Agriculture Organization (FAO) of the United Nations based on the calories available from local food production, trade, and stocks; the number of calories needed by different age and gender groups; the proportion of the population represented by each age group; and a coefficient of distribution to take account of inequality in access to food (FAO, State of Food Insecurity in the World 2000). From a policy and program standpoint, however, this measure has its limits. First, food insecurity exists even where food availability is not a problem because of inadequate access of poor households to food. Second, food insecurity is an individual or household phenomenon, and the average food available to each person, even corrected for possible effects of low income, is not a good predictor of food insecurity among the population. And third, nutrition security is determined not only by food security but also by the quality of care of mothers and children and the quality of the household’s health environment (Smith and Haddad 2000). Estimates of child malnutrition, based on weight for age (underweight) and height for age (stunting), are from national survey data. The proportion of children who are underweight is the most common indicator of malnutrition. Being underweight, even mildly, increases the risk of death and inhibits cognitive development in children. Moreover, it perpetuates the problem from one generation to the next, as malnourished women are more likely to have low-birthweight babies. Height for age reflects linear growth achieved pre- and postnatally, and a deficit indicates long-term, cumulative effects of inadequacies of health, diet, or care. It is often argued that stunting is a proxy for multifaceted deprivation and is a better indicator of long-term changes in malnutrition. Estimates of children who are overweight are also from national survey data. Overweight in children has become a growing concern in developing countries. Researchers show an association between obesity in childhood and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2000). The survey data were analyzed in a standardized way by the World Health Organization (WHO) to allow comparisons across countries. New international child growth standards for infants and young children, called the Child Growth Standards, were released in 2006 by the WHO. The new standards confirm that children born anywhere in the world, raised in healthy environments, and following recommended feeding practice have the potential to develop to within the same range of height and weight. Naturally, there are individual differences among children, but the differences in children’s growth to age five are influenced more by nutrition, feeding practices, environment, and healthcare than by genetics or ethnicity. The new standards are the result of a community-based, multicountry project involving more than 8,000 children from Brazil, Ghana, India, Norway, Oman, and the United States. The children were selected based on an optimal environment for growth, including breastfeeding,

good healthcare, and mothers who did not smoke. Previously, the U.S. National Center for Health Statistics–WHO growth reference has been used to chart children’s growth. This reference was based on data from a limited sample of a random mix of breastfed and artificially fed children from the United States only, and the growth reference describes only how children grow in a particular region and time. Thus it does not provide a sound basis for evaluation against international standards and norms. Adoption of the new standards will have important implications for monitoring children’s growth. A study based on the new standards shows that the underweight rates increased during the first six months and decreased thereafter and that stunting and overweight rates increased for all age groups (birth to five years). Differences are particularly important during infancy, likely due to the inclusion of only breast-fed infants in the new standards (de Onis and others 2006). The new standards are expected to be widely used as a tool for monitoring the nutritional status of communities and alerting practitioners and policymakers to unhealthy trends in the population. They are also expected to play a key role in measuring and monitoring health targets for the Millennium Development Goals. Currently, national surveys are being reanalyzed with the new standards to update the global database, but the updated data are not yet available. The data on malnutrition and overweight presented in the table are still based on the old standard. Low birthweight, which is associated with maternal malnutrition, raises the risk of infant mortality and stunts growth in infancy and childhood. There is also emerging evidence that low-birthweight babies are more prone to noncommunicable diseases such as diabetes and cardiovascular diseases. Estimates of low-birthweight infants are drawn mostly from hospital records and household surveys. Many births in developing countries take place at home, and these births are seldom recorded. A hospital birth may indicate higher income and therefore better nutrition, or it could indicate a higher-risk birth, possibly skewing the data on birthweights downward. The data should therefore be treated with caution. It is estimated that improved breastfeeding practice can save some 1.3 million children a year. Breast milk alone contains all the nutrients, antibodies, hormones, and antioxidants an infant needs to thrive. It protects babies from diarrhea and acute respiratory infections, stimulates their immune systems and response to vaccination, and according to some studies confers cognitive benefits as well. The data on breastfeeding are derived from national surveys. Iodine defi ciency is the single most important cause of preventable mental retardation, and it contributes significantly to the risk of stillbirth and miscarriage. Iodized salt is the best source of iodine, and a global campaign to iodize edible salt is signifi cantly reducing the risks (UNICEF, State of the World’s Children 1999). Vitamin A is essential for the functioning of the immune system. A child deficient in vitamin A faces a 23 percent greater risk of dying from a range of

childhood ailments such as measles, malaria, and diarrhea. Improving the vitamin A status of pregnant women helps reduce anemia, improves their resistance to infection, and may reduce their risk of dying during pregnancy and childbirth. Giving vitamin A to new mothers who are breastfeeding helps to protect their children during the first months of life. Food fortification with vitamin A is being introduced in many developing countries. Definitions • Prevalence of undernourishment is the percentage of the population that is undernourished—whose dietary energy consumption is continuously below a minimum dietary energy requirement for maintaining a healthy life and carrying out light physical activity. • Prevalence of child malnutrition is the percentage of children under age five whose weight for age (underweight) or height for age (stunting) is more than two standard deviations below the median for the international reference population ages 0–59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The new Child Growth Standards were released by the WHO in 2006, but the data using these standards are not yet available • Prevalence of overweight children is the percentage of children under age five whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age, established by the U.S. National Center for Health Statistics and the WHO. The new Child Growth Standards were released by WHO in 2006, but the data using these standards are not yet available. • Lowbirthweight babies are the percentage of newborns weighing less than 2,500 grams, with the measurement taken within the first hours of life, before signifi cant postnatal weight loss has occurred. • Exclusive breastfeeding refers to the percentage of children less than six months old who are fed breast milk alone (no other liquids) in the past 24 hours. • Consumption of iodized salt refers to the percentage of households that use edible salt fortified with iodine. • Vitamin A supplementation refers to the percentage of children ages 6–59 months old who received at least one high-dose vitamin A capsule in the previous six months.

Data sources Data on undernourishment are from www.fao.org/ faostat/foodsecurity/index_en.htm. Data on malnutrition and overweight are from WHO’s Global Database on Child Growth and Malnutrition. Data on low-birthweight babies, breastfeeding, iodized salt consumption, and vitamin A supplementation are from the WHO’s World Health Report 2006 and the United Nations Children’s Fund’s State of the World’s Children 2007.

2007 World Development Indicators

107

PEOPLE

Nutrition

2.18

Health risk factors and public health challenges Prevalence of smoking

% of adults Male Female 2000–05a 2000–05a

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, 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

108

.. 60 32 .. 32 62 19 .. .. 55 53 30 .. .. 49 .. 22 44 .. .. .. .. 22 .. .. 48 67 22 .. .. .. 29 .. 34 .. 31 31 16 .. 40 42 .. 45 6 26 30 .. .. 53 37 7 47 21 .. .. 15

.. 18 0b .. 25 2 16 .. 1 27 7 25 .. .. 30 .. 14 23 .. .. .. .. 17 .. .. 37 4 4 .. .. .. 10 .. 27 .. 20 25 11 .. 18 15 .. 18 0b 19 21 .. .. 6 28 1 29 2 .. .. 6

2007 World Development Indicators

Prevalence of HIV

Incidence of Prevalence Mortality tuberculosis of diabetes caused by road traffic injury per 100,000 people

% of population ages 20–79

2005

2007

168 20 55 269 41 71 6 11 76 227 62 13 88 211 52 654 60 39 223 334 506 174 5 314 272 15 100 75 45 356 367 14 382 41 9 10 7 91 131 25 51 282 43 344 6 13 308 242 83 7 205 17 78 236 206 305

9.7 4.5 8.4 3.3 5.6 7.7 5.0 7.9 7.3 5.3 7.6 5.2 4.4 5.8 7.0 5.2 6.2 7.6 3.7 1.7 5.0 3.7 7.4 4.4 3.6 5.6 4.1 8.2 5.0 3.0 5.0 9.3 4.6 7.1 9.3 7.6 5.5 8.7 5.7 11.0 9.0 2.3 7.6 2.3 5.9 5.9 4.9 4.1 7.4 7.9 4.2 5.9 8.6 4.1 3.8 9.0

per 100,000 people 1998–2003a

.. 11.1 .. .. .. 5.6 8.2 11.5 6.9 .. 14.3 13.1 .. .. .. .. .. 10.2 .. .. .. .. 8.7 .. .. 10.7 19.0 .. 24.2 .. .. 20.1 .. 11.4 13.9 14.2 8.0 41.1 16.9 7.5 41.7 .. 14.8 .. 7.3 10.2 .. .. 6.2 8.0 .. 19.3 .. .. .. ..

Cause of death % of total deaths

Total % of population ages 15–49

Female % of population with HIV

Communicable diseases and maternal, Nonperinatal, and nutrition communicable diseases conditions

Injuries

2003

2005

2003

2005

2002

2002

2002

<0.1 .. 0.1 3.7 0.6 0.1 0.1 0.3 <0.1 <0.1 0.3 0.2 2.0 0.1 .. 24.0 0.5 .. 1.8 c 3.3 2.0 5.5 0.3 10.8 3.4 0.3 0.1e .. 0.5 3.2 5.4 0.3 7.0 .. 0.1 <0.1 0.2 1.0 f 0.3 <0.1 0.9 2.4 1.1 .. 0.1 0.4 7.7 2.2 0.1 0.1 2.2c 0.2 0.9 1.6 3.8 3.8

<0.1 .. 0.1 3.7 0.6 0.1 0.1 0.3 <0.1 <0.1 0.3 0.3 1.8 0.1 0.1 24.1 0.5 0.1 2.0 3.3 1.6 5.5d 0.3 10.7 3.5 0.3 0.1e .. 0.6 3.2 5.3 0.3 7.1 0.1 0.1 <0.1 0.2 1.1 0.3 <0.1 0.9 2.4 1.3 .. 0.1 0.4 7.9 2.4 0.2 0.1 2.3 0.2 0.9 1.5 3.8 3.8

.. .. 20.6 59.3 26.7 .. .. 19.2 .. .. 24.4 45.5 59.3 27.0 .. 56.0 34.5 .. 59.2 60.8 46.4 62.2 12.2 59.1 54.7 26.4 24.5e .. 26.4 59.0 58.6 27.0 57.8 .. 54.8 .. 24.0 49.2 52.4 .. 27.1 59.2 22.1 .. .. 33.3 59.6 58.8 .. 29.5 60.7 20.7 26.4 68.9 59.3 52.9

.. .. 21.6 60.7 27.7 .. .. 19.2 .. 12.7 25.5 38.6 58.4 27.9 .. 53.8 36.1 .. 57.1 60.8 45.4 61.7 16.3 56.5 56.3 27.1 27.7e .. 28.1 58.4 61.0 27.4 58.8 .. 55.3 .. 23.6 50.0 54.5 .. 28.3 58.5 24.0 .. .. 34.6 58.9 57.9 .. 30.6 60.0 21.5 27.1 67.9 58.6 53.3

65 8 33 75 13 5 4 3 17 46 3 7 69 38 3 87 19 3 78 71 61 68 5 73 74 12 12 .. 16 73 67 12 67 3 11 3 4 35 24 18 29 70 3 71 6 6 53 59 4 4 59 4 50 68 75 69

29 83 54 17 80 90 89 92 79 44 85 88 23 54 92 10 70 94 16 17 34 26 89 21 19 79 77 .. 60 17 23 77 23 91 80 91 90 58 63 78 57 22 84 23 86 85 39 32 93 92 33 92 40 24 19 29

6 9 13 8 7 5 6 6 4 10 12 6 7 8 5 2 11 4 6 12 5 7 6 6 6 9 11 .. 24 11 9 11 9 5 9 6 6 8 13 4 14 7 12 6 8 8 7 8 2 4 8 4 10 9 6 2

Prevalence of smoking

% of adults Male Female 2000–05a 2000–05a

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

.. 41 47 58 22 .. 28 32 31 .. 47 51 65 21 .. .. .. 51 59 51 42 .. .. .. 44 .. .. 21 43 .. .. 32 13 34 68 29 .. 36 23 49 36 24 .. .. .. 27 .. .. .. .. 23 .. 41 40 .. 17

.. 28 17 3 2 .. 26 18 17 .. 15 8 9 1 .. .. .. 5 13 19 31 .. .. .. 13 .. .. 5 2 .. .. 1 5 2 26 0b .. 12 10 24 28 22 5 .. 1 25 .. .. .. .. 7 .. 8 25 .. 10

Prevalence of HIV

Incidence of Prevalence Mortality tuberculosis of diabetes caused by road traffic injury per 100,000 people

% of population ages 20–79

2005

2007

78 22 168 239 23 56 12 8 7 7 28 5 144 641 178 96 24 121 155 63 11 696 301 18 63 30 234 409 102 278 298 62 23 138 191 89 447 171 697 180 7 9 58 164 283 5 11 181 45 250 68 172 291 26 33 5

9.1 7.6 6.7 2.3 7.8 10.0 5.1 6.9 5.8 10.3 4.9 9.8 5.6 3.3 5.2 7.8 14.4 5.1 3.1 7.6 7.7 3.8 4.6 4.4 7.6 7.1 3.0 2.1 10.7 4.1 4.6 11.1 10.6 7.6 1.9 8.1 3.7 3.2 4.2 4.2 5.2 6.4 10.1 3.7 4.5 3.6 13.1 9.6 9.7 2.9 4.8 6.0 7.6 7.6 5.7 10.7

per 100,000 people 1998–2003a

.. 13.1 .. .. .. 8.4 10.1 5.9 10.5 .. 7.0 .. .. .. .. 15.1 23.7 12.9 .. 22.7 .. .. .. .. 19.3 5.1 .. .. .. .. .. 14.7 11.8 14.1 .. .. .. .. .. .. 6.4 11.5 20.1 .. .. 6.1 .. .. 16.4 .. .. 17.6 .. 14.8 14.8 ..

2.18 Cause of death % of total deaths

Total % of population ages 15–49

Female % of population with HIV

Communicable diseases and maternal, Nonperinatal, and nutrition communicable diseases conditions

Injuries

2003

2005

2003

2005

2002

2002

2002

1.5 0.1 0.9 0.1 0.1 .. 0.2 .. 0.5 1.5 <0.1 .. 0.1 6.7c .. <0.1 .. <0.1 0.1 0.6 0.1 23.7 .. .. 0.1 <0.1 0.5 14.2 0.4 1.8g 0.7 0.2 0.3 0.9 <0.1 0.1 16.0 1.4 19.5 0.5 0.2 0.1 0.2 1.1 3.7 0.1 .. 0.1 0.9 1.6 0.4 0.5 <0.1 0.1 0.4 ..

1.5 0.1 0.9 0.1 0.2 .. 0.2 .. 0.5 1.5 <0.1 .. 0.1 6.1 .. <0.1 .. <0.1 0.1 0.8 0.1 23.2 .. .. 0.2 <0.1 0.5 14.1 0.5 1.7 0.7 0.6 0.3 1.1 <0.1 0.1 16.1 1.3 19.6 0.5 0.2 0.1 0.2 1.1 3.9 0.1 .. 0.1 0.9 1.8 0.4 0.6 <0.1 0.1 0.4 ..

25.0 .. 28.8 13.6 13.0 .. 32.0 .. 33.6 27.1 56.5 .. 56.0 64.2 .. 59.1 .. .. .. 20.3 .. 56.0 .. .. .. .. 28.2 59.3 25.0 57.3 59.2 .. 20.0 56.5 .. 18.2 57.5 31.6 60.0 20.3 33.8 .. 22.4 59.7 58.3 .. .. 13.3 26.0 59.2 27.3 26.8 20.2 30.0 3.9 ..

26.2 .. 28.6 17.1 16.7 .. 36.0 .. 33.3 27.6 58.2 .. 56.7 61.7 .. 56.9 .. .. .. 22.0 .. 60.0 .. .. .. .. 27.7 58.8 25.4 60.0 57.3 .. 23.3 57.1 .. 21.1 60.0 31.4 61.9 21.6 34.7 .. 23.6 59.2 61.5 .. .. 16.7 25.3 59.6 26.9 28.6 28.3 30.0 4.1 ..

32 2 41 29 12 43 10 6 4 14 12 18 8 72 32 6 13 17 55 4 10 81 76 17 2 3 65 79 20 78 65 7 16 5 23 23 83 45 71 49 8 3 30 80 71 8 13 53 21 52 28 32 35 3 9 ..

59 91 49 61 70 43 85 88 92 84 81 65 79 22 61 83 72 74 36 86 77 16 15 73 85 89 27 17 71 16 27 86 72 86 66 69 14 47 24 42 89 91 58 14 22 87 75 39 69 38 62 58 56 90 86 ..

9 7 10 10 18 13 5 6 4 2 8 16 13 6 7 12 15 9 9 11 13 3 10 10 13 8 8 4 9 6 8 6 11 9 11 8 2 9 5 9 4 6 12 6 7 5 12 8 10 9 10 9 9 7 4 ..

2007 World Development Indicators

109

PEOPLE

Health risk factors and public health challenges

2.18

Health risk factors and public health challenges Prevalence of smoking

% of adults Male Female 2000–05a 2000–05a

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 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 Europe EMU

32 60 .. 19 .. 48 .. 24 .. 28 .. 23 39 23 .. 11 17 27 .. .. .. 49 .. .. 50 49 .. 25 53 17 27 24 35 24 .. 35 .. .. 16 20 .. w .. .. .. .. .. 67 .. .. .. 47 .. .. ..

10 16 .. 8 .. 34 .. 4 .. 20 .. 8 25 2 .. 3 18 23 .. .. .. 3 .. .. 2 18 .. 3 11 1 25 19 24 1 .. 2 .. .. 1 2 .. w 15 .. .. .. .. 4 .. .. .. 18 .. .. ..

Prevalence of HIV

Incidence of Prevalence Mortality tuberculosis of diabetes caused by road traffic injury per 100,000 people

% of population ages 20–79

2005

2007

134 119 361 41 255 33 475 29 17 15 224 600 27 60 228 1,262 6 7 37 198 342 142 373 9 24 29 70 369 99 16 14 5 28 113 42 175 21 82 600 601 136 w 220 111 113 104 158 136 84 61 43 174 348 17 13

7.6 7.6 1.5 16.7 4.6 7.1 4.3 10.1 7.6 7.6 2.8 4.4 5.7 8.4 4.0 4.0 5.2 7.9 10.6 4.9 2.9 6.9 4.1 11.5 5.2 7.8 5.2 2.0 7.6 19.5 2.9 7.8 5.6 5.1 5.4 2.9 8.4 2.9 3.8 4.0

per 100,000 people 1998–2003a

16.8 19.4 .. .. .. .. .. 5.2 11.3 12.1 .. .. 12.8 .. .. .. 5.9 7.5 .. 5.6 .. .. .. .. .. .. 10.3 .. 10.8 .. 6.1 14.7 10.0 9.8 23.1 .. .. .. .. .. .. w .. .. .. .. .. 19.0 .. .. .. .. .. 10.9 10.0

Cause of death % of total deaths

Total % of population ages 15–49 2003

2005

Female % of population with HIV 2003

.. 0.1 .. 0.9 1.1 21.1 3.8 3.1 52.6 .. .. .. 0.9 0.9 58.5 0.2 0.2 22.2 1.6 1.6 60.0 0.3 0.3 25.5 <0.1 <0.1 .. <0.1 <0.1 .. 60.5 0.9 0.9h 18.8 56.9 15.6f <0.1 <0.1 22.9 0.1 0.1 .. 1.6 1.6 56.7 32.4 33.4 63.2 0.2 0.2 31.3 0.4 0.4 36.0 .. .. .. <0.1 <0.1 .. 52.3 6.6 7.0 d 1.4 1.4 38.6 3.2 3.2 58.9 2.6 2.6 56.0 0.1 0.1 .. .. .. .. .. 0.1 .. 57.6 6.8 6.4i 1.3 1.4 47.4 .. .. .. .. .. .. 0.6 0.6 25.5 0.4 0.5 55.6 0.1 0.2 .. 0.6 0.7 27.7 30.5 0.4 0.5j .. .. .. .. .. .. 17.0 56.3 15.6k 22.1 20.1 58.1 0.9 w 1.0 w 30.3 w 1.7 1.7 35.6 0.6 0.6 26.1 0.3 0.3 25.9 2.2 2.2 27.1 1.1 1.1 29.8 0.2 0.2 24.3 0.6 0.7 .. 0.5 0.6 30.3 0.1 0.1 .. 0.7 0.7 26.9 6.4 5.8 57.6 0.4 0.4 33.1 0.4 0.3 29.3

2005

.. 22.3 56.9 .. 58.9 20.0 60.5 27.3 .. .. 57.5 58.5 22.9 .. 56.3 57.1 31.3 36.9 .. .. 54.6 39.3 61.0 57.7 22.1 .. .. 57.8 48.8 .. .. 25.0 55.8 13.2 28.2 33.6 .. .. 57.0 59.3 31.3 w 34.2 28.7 28.7 28.6 31.0 27.4 .. 32.0 .. 25.6 58.4 33.2 29.7

Communicable diseases and maternal, Nonperinatal, and nutrition communicable diseases conditions 2002

5 4 76 15 64 3 78 12 4 4 66 65 5 13 45 84 5 6 17 27 77 31 66 23 9 14 19 75 4 12 12 6 7 14 15 24 .. 48 86 83 32 w 54 18 18 15 36 19 6 22 24 43 72 7 5

2002

90 81 18 69 26 93 14 82 90 87 23 28 90 76 41 13 90 89 73 67 17 58 26 71 80 79 73 18 87 67 85 88 86 80 66 66 .. 43 12 14 59 w 37 72 71 74 54 71 84 67 65 47 21 87 90

Injuries 2002

5 15 6 16 10 4 8 5 6 8 10 7 5 10 14 3 5 5 9 6 6 11 8 6 11 6 8 7 9 21 3 6 7 7 18 9 .. 10 2 3 9w 9 11 11 11 10 10 11 11 11 10 7 6 5

a. Data are for the most recent year available. b. Less than 0.5. c. Survey data, 2003. d. Survey data, 2004. e. Includes Hong Kong, China. f. Survey data, 2002. g. Survey data, 2001. h. Survey data, 2006. i. Survey data, 2004–05. j. Survey data, 2005. k. Survey data, 2001/02.

110

2007 World Development Indicators

2.18

About the data

The limited availability of data on health status is a

is considerable difference in completeness of the

and Related Health Problems, 10th revision. Cause

major constraint in assessing the health situation in

vital registry data. In some countries the vital registry

of death data have been carefully analyzed to take

developing countries. Surveillance data are lacking

system covers only part of the country. In some, not

into account incomplete coverage of vital registration

for many major public health concerns. Estimates

all deaths are registered. In addition, mortality from

and the likely differences in cause of death patterns

of prevalence and incidence are available for some

different causes is difficult to measure. For countries

that would be expected in the uncovered and often

diseases but are often unreliable and incomplete.

with incomplete vital registry systems, the WHO has

poorer subpopulations. Special attention has also

National health authorities differ widely in their

used demographic techniques to estimate deaths.

been paid to problems of misattribution or miscoding

capacity and willingness to collect or report infor-

Adult HIV prevalence rates reflect the rate of HIV

of causes of death in cardiovascular diseases, can-

mation. To compensate for the paucity of data and

infection in each country’s population. Low national

cer, injuries, and general ill-defined categories. For

ensure reasonable reliability and international com-

prevalence rates can be very misleading, however.

further information, consult the original source.

parability, the World Health Organization (WHO) pre-

They often disguise serious epidemics that are ini-

pares estimates in accordance with epidemiological

tially concentrated in certain localities or among spe-

models and statistical standards.

Definitions

cific population groups and threaten to spill over into

• Prevalence of smoking is the percentage of men

Smoking is the most common form of tobacco use

the wider population. In many parts of the developing

and women who smoke cigarettes. The age range var-

in many countries, and the prevalence of smoking is

world most new infections occur in young adults, with

ies, but in most countries is 18 and older or 15 and

therefore a good measure of the extent of the tobacco

young women especially vulnerable.

older. • Incidence of tuberculosis is the estimated

epidemic (Corrao and others 2000). While the preva-

Estimates from recent Demographic and Health

lence of smoking has been declining in some high-

Surveys that have collected data on HIV/AIDS differ

number of new tuberculosis cases (pulmonary, smear positive, extrapulmonary). • Prevalence of diabetes

income countries, it has been increasing in many

from those of the Joint United Nations Programme on

refers to the percentage of people ages 20–79 who

developing countries. Tobacco use causes heart and

HIV/AIDS (UNAIDS) and the WHO, which are based on

have type 1 or type 2 diabetes. • Mortality caused

other vascular diseases and cancers of the lung and

surveillance systems that focus on pregnant women

by road traffic injury refers to the number of deaths

other organs. Given the long delay between starting

who attend sentinel antenatal clinics. There are rea-

per 100,000 people caused by road traffic injury.

to smoke and the onset of disease, the health impact

sons to be cautious about comparing the two sets

• Prevalence of HIV is the percentage of people

of smoking in developing countries will increase rap-

of estimates. Demographic and Health Surveys are

who are infected with HIV. • Cause of death refers

idly in the next few decades. Because the data pres-

household surveys that use a representative sample

to the share of all deaths by underlying causes.

ent a one-time estimate, with no information on the

from the whole population, whereas surveillance

• Communicable diseases and maternal, perina-

intensity or duration of smoking, and because the

data from antenatal clinics are limited to pregnant

tal, and nutrition conditions include infectious and

definition of adult varies across countries, the data

women. Representative household surveys also fre-

parasitic diseases, respiratory infections, and nutri-

should be interpreted with caution.

quently provide better coverage of rural populations.

tional deficiencies such as underweight and stunt-

Tuberculosis is one of the main causes of death from

However, the fact that some respondents refuse to

ing. • Noncommunicable diseases include cancer,

a single infectious agent among adults in developing

participate or are absent from the household adds

diabetes mellitus, cardiovascular diseases, digestive

countries. In high-income countries tuberculosis has

considerable uncertainty to survey-based HIV esti-

diseases, skin diseases, musculoskeletal diseases,

reemerged largely as a result of cases among immi-

mates, because the possible association of absence

and congenital anomalies. • Injuries include uninten-

grants. The estimates of tuberculosis incidence in the

or refusal with higher HIV prevalence is unknown.

tional and intentional injuries.

table are based on a new approach in which reported

UNAIDS and WHO estimates are generally based on

cases are adjusted using the ratio of case notifications

surveillance systems that focus on pregnant women

to the estimated share of cases detected by panels of

who attend sentinel antenatal clinics. UNAIDS and

Data on smoking are from J. McCay, M. Erkson,

80 epidemiologists convened by the WHO.

the WHO use a methodology to estimate HIV preva-

and O. Shafey’s Tobacco Atlas, 2nd edition (2006).

Diabetes, an important cause of ill health and a

lence for the adult population (ages 15–49) that

Data on tuberculosis are from the WHO’s Global

risk factor for other diseases in developed countries,

assumes that prevalence among pregnant women is

Tuberculosis Control Report 2007. Data on diabe-

is spreading rapidly in developing countries. While

a good approximation of prevalence among men and

tes are from the International Diabetes Federa-

diabetes is most common among the elderly, preva-

women. However, this assumption might not apply to

tion’s Diabetes Atlas, 3rd edition. Data on mortal-

lence rates are rising among younger and productive

all countries or over time. There are also other poten-

ity caused by road traffic injury are from the WHO

populations in developing countries. Economic devel-

tial biases associated with the use of antenatal clinic

and the World Bank’s World Report on Road Traffic

opment has led to the greater adoption of Western

data, such as differences among women who attend

Injury Prevention (2004) and the Organisation for

lifestyles and diet in developing countries, resulting

antenatal clinics and those who do not.

Economic Co-operation and Development. Data on

Data sources

in a substantial increase in diabetes. Without effec-

The data on cause of death are compiled by WHO,

HIV are from UNAIDS and the WHO’s 2006 Report

tive prevention and control programs, diabetes will

based mainly on data from national vital registry sys-

on the Global AIDS Epidemic. Data on cause of

likely continue to increase. Data are estimated based

tems, as well as sample registration systems, popu-

death are from the Disease Control Priorities

on sample surveys.

lation laboratories and epidemiological analyses of

Project’s (2006) Global Burden of Disease and Risk

Data for mortality caused by road traffic injury are

specific conditions. Data are classified based on the

Factors (www.dcp2.org/pubs/GBD).

collected by the WHO based on vital registries. There

International Statistical Classification of Diseases

2007 World Development Indicators

111

PEOPLE

Health risk factors and public health challenges

2.19

Health gaps by income and gender Survey year

Armenia Bangladesh Benin Bolivia Brazil Burkina Faso Cambodia Cameroon Central African Republic Chad Colombia Comoros Côte d’Ivoire Dominican Republic Egypt, Arab Rep. Eritrea Ethiopia Gabon Ghana Guatemala Guinea Haiti India Indonesia Jordan Kazakhstan Kenya Kyrgyz Republic Madagascar Malawi Mali Mauritania Morocco Mozambique Namibia Nepal Nicaragua Niger Nigeria Pakistan Paraguay Peru Philippines Rwanda Senegal South Africa Tanzania Togo Turkey Turkmenistan Uganda Uzbekistan Vietnam Yemen, Rep. Zambia Zimbabwe

112

Prevalence of child malnutrition

Child immunization rate

Underweight % of children under age 5

% of children ages 12–23 monthsa Measles

Poorest quintile

Richest quintile

Poorest quintile

3 41 22 10 10 26 35 22 25 27 11 22 21 9 5 27 32 15 22 26 22 18 33 .. 7 5 22 10 29 24 26 23 13 21 22 40 13 30 24 33 5 13 .. 19 .. .. 20 23 13 12 21 15 .. 36 24 16

1 24 9 1 3 16 28 5 15 19 3 14 10 1 2 19 29 7 10 10 13 6 21 .. 3 6 7 7 24 11 13 15 3 7 10 26 2 26 10 19 1 1 .. 12 .. .. 11 10 3 10 10 10 .. 24 17 6

68 60 57 62 78 48 44 57 31 8 70 51 31 83 95 37 18 34 74 80 33 43 28 59 90 74 54 82 32 80 40 42 83 61 76 61 76 23 16 28 48 81 70 84 .. 74 65 35 64 91 49 96 64 16 81 80

2000 2004 2001 2003 1996 2003 2000 2004 1994–95 2004 2005 1996 1994 2002 2000 1995 2000 2000 2003 1998–99 1999 2000 1998–99 2002–03 1997 1999 2003 1997 1997 2000 2001 2000–01 2003–04 2003 2000 2001 2001 1998 2003 1990–91 1990 2000 2003 2000 1997 1998 2004 1998 1998 2000 2000–01 1996 2002 1997 2001–02 1999

2007 World Development Indicators

Infant mortality rate

DPT

Richest quintile

74b 91 83 74 90 71 82 86 80 38 91 86 79 94 99 92 52 71 88 91 73 63 81 85 93 76b 88 81 79 90 77 86 98 96 86 83 94 66 71 75 69 92 89 89 .. 85 91 63 89 80 65 93 98 73 88 86

Poorest quintile

89 71 63 64 66 45 39 55 27 5 73 58 26 46 94 30 14 18 64 74 30 31 36 42 98 90 56 82 32 79 28 18 89 52 76 62 77 9 7 24 40 76 64 80 .. 64 34 29 45 97 35 89 53 14 74 81

Under-five mortality rate

per 1,000 live births Richest quintile

84b 91 89 85 82 73 75 86 76 42 91 92 74 66 93 89 43 49 87 76 69 58 85 72 93 82b 73 87 81 93 71 61 98 96 83 85 83 68 61 64 69 93 92 89 .. 85 36 68 81 86 55 82 94 71 89 86

per 1,000

Poorest quintile

Richest quintile

Poorest quintile

Richest quintile

52

27 65 50 32 29 78 50 52 54 101 14 65 63 20 30 68 95 36 58 39 70 97 38 17 23 42 62 46 58 86 90 62 24 71 23 53 16 86 52 63 16 14 19 88 45 17 64 66 30 58 60 46 14 60 57 44

61 121 198 119 99 206 155 189 193 176 39 129 190 66 98 152 159 93 128 78 230 164 141 77 42 82 149 96 195 231 248 98 78 196 55 130 64 282 257 125 57 93 66 246 181 87 137 168 85 106 192 70 53 163 192 100

30 71 93 37 33 144 64 88 98 187 16 87b 97 22 34 104 147 55 88 39 133 109 46 22 25 45 91 49 101 149 148 79 26 108 31 68 19 184 79 74 20 18 21 154 70 22 93 97 33 70 106 50 16 73 92 62

90 112 87 83 97 110 101 132 109 32 87 117 50 76 74 93 57 61 58 119 100 97 61 35

68 96 83 119 132 137 61 62 143 36 86 50 131 133 89 43 64 42 139 85 62 88 84 68 89 106 54 39 109 115 59

Survey year

Armenia Bangladesh Benin Bolivia Brazil Burkina Faso Cambodia Cameroon Central African Republic Chad Colombia Comoros Côte d’Ivoire Dominican Republic Egypt, Arab Rep. Eritrea Ethiopia Gabon Ghana Guatemala Guinea Haiti India Indonesia Jordan Kazakhstan Kenya Kyrgyz Republic Madagascar Malawi Mali Mauritania Morocco Mozambique Namibia Nepal Nicaragua Niger Nigeria Pakistan Paraguay Peru Philippines Rwanda Senegal South Africa Tanzania Togo Turkey Turkmenistan Uganda Uzbekistan Vietnam Yemen, Rep. Zambia Zimbabwe

2000 2004 2001 2003 1996 2003 2000 2004 1994–95 2004 2005 1996 1994 2002 2000 1995 2000 2000 2003 1998–99 1999 2000 1998–99 2002–03 1997 1999 2003 1997 1997 2000 2001 2000–01 2003–04 2003 2000 2001 2001 1998 2003 1990–91 1990 2000 2003 2000 1997 1998 2004 1998 1998 2000 2000–01 1996 2002 1997 2001–02 1999

Prevalence of child malnutrition

Child immunization rate

Underweight % of children under age 5

% of children ages 12–23 monthsa Measles

Infant mortality rate

DPT

2.19 Under-five mortality rate

per 1,000 live births

per 1,000

Male

Female

Male

Female

Male

Female

Male

Female

Male

Female

2 34 19 6 6 25 32 14 21 23 6 19 19 5 4 26 32 10 17 21 17 14 28 .. 4 4 18 11 27 20 24 22 9 18 19 35 9 29 19 27 3 6 .. 19 .. .. 18 19 7 11 18 15 .. 33 21 12

3 35 17 6 5 23 33 15 19 23 6 17 16 5 3 27 31 9 17 18 19 13 30 .. 5 4 14 8 27 19 21 22 8 17 18 36 7 30 20 27 4 6 .. 19 .. .. 18 18 7 10 17 13 .. 30 21 11

71 76 69 65 87 54 57 65 52 23 83 63 54 89 97 52 28 55 82 82 52 54 52 73 90 79 73 84 47 83 49 61 88 77 79 73 87 36 34 55 56 84 78 86 .. 84 80 45 79 87 56 91 84 45 83 77

79 76 67 63 87 58 54 66 53 23 82 64 52 88 97 50 26 55 83 87 52 54 50 71 90 78 72 85 45 83 48 63 92 76 82 69 86 34 38 46 61 85 81 88 .. 81 80 40 78 88 57 92 82 40 86 81

90 81 74 70 82 57 50 65 49 20 84 68 49 54 94 49 22 40 81 73 46 43 56 58 96 89 71 83 48 84 41 39 95 73 78 74 84 25 19 45 50 85 78 85 .. 74 37 43 60 93 45 87 72 41 78 80

89 81 71 73 80 57 47 68 46 21 81 69 45 61 94 49 19 33 77 74 47 43 54 59 96 88 74 81 49 85 38 41 95 71 81 70 81 25 24 40 57 84 80 87 .. 78 33 41 57 92 48 90 73 39 82 82

46 80 98 71 52 95 103 88 109 122 26 93 99 38 55 82 124 74 70 50 112 97 75 46 34 62 84 72 109 117 136 74 51 127 45 79 39 141 116 102 39 46 35 123 74 49 83 89 51 83 93 50 25 98 95 63

42 64 92 64 44 89 82 74 94 108 18 75 83 31 55 69 101 49 59 48 101 83 71 40 23 47 67 60 90 108 116 59 37 120 34 75 32 131 102 86 33 40 25 112 65 35 82 71 46 60 85 37 25 80 93 56

51 102 162 94 60 195 133 154 165 207 30 122 163 46 69 163 197 103 111 64 202 143 98 58 38 72 122 81 176 207 250 110 59 181 67 105 48 299 222 122 49 64 48 215 144 66 135 156 61 101 164 65 34 128 176 95

45 91 163 91 53 192 110 141 152 198 21 103 137 40 70 141 178 80 108 65 188 132 105 51 30 53 103 70 152 199 226 94 48 176 54 112 41 306 212 119 45 57 34 198 134 48 130 132 58 76 149 46 31 114 160 85

2007 World Development Indicators

113

PEOPLE

Health gaps by income and gender

2.19

Health gaps by income and gender Survey year

Pregnant women receiving prenatal care

%

Armenia Bangladesh Benin Bolivia Brazil Burkina Faso Cambodia Cameroon Central African Republic Chad Colombia Comoros Côte d’Ivoire Dominican Republic Egypt, Arab Rep. Eritrea Ethiopia Gabon Ghana Guatemala Guinea Haiti India Indonesia Jordan Kazakhstan Kenya Kyrgyz Republic Madagascar Malawi Mali Mauritania Morocco Mozambique Namibia Nepal Nicaragua Niger Nigeria Pakistan Paraguay Peru Philippines Rwanda Senegal South Africa Tanzania Togo Turkey Turkmenistan Uganda Uzbekistan Vietnam Yemen, Rep. Zambia Zimbabwe

Poorest quintile

Richest quintile

85 25 73 62 72 56 22 65 39 9 84 67 62 97 31 34 15 85 83 37 58 65 44 78 93 97 75 96 67 89 42 33 40 67 81 30 69 24 37 8 73 41 72 90 67 96 91 69 38 98 88 93 68 17 89 94

97 81 100 98 98 96 80 97 91 77 99 95 98 99 84 90 60 98 98 97 97 91 93 99 97 91 94 99 96 98 92 89 93 98 96 80 97 85 96 72 98 74 97 95 97 94 97 97 96 97 98 96 100 68 99 97

2000 2004 2001 2003 1996 2003 2000 2004 1994–95 2004 2005 1996 1994 2002 2000 1995 2000 2000 2003 1998–99 1999 2000 1998–99 2002–03 1997 1999 2003 1997 1997 2000 2001 2000–01 2003–04 2003 2000 2001 2001 1998 2003 1990–91 1990 2000 2003 2000 1997 1998 2004 1998 1998 2000 2000–01 1996 2002 1997 2001–02 1999

Contraceptive prevalence

Births attended by skilled health staffc

Total fertility rated

Exclusive breastfeeding

% of married women ages 15–49

% of total

births per woman

% of children under 4 months

Poorest quintile

16 45 4 23 56 2 13 2 1 0 60 7 1 59 43 0e 3 6 9 55 1 17 29 49 28 49 12 44 2 20 4 0e 51 14 29 24 50 1 4 1 21 37 24 2 1 34 11 3 24 51 11 46 58 1 11 41

Richest quintile

Poorest quintile

Richest quintile

Poorest quintile

Richest quintile

Poorest quintile

29 50 15 49 77 27 25 27 9 7 72 19 13 70 61 19 23 18 26 32 9 24 55 58 47 55 44 54 24 40 18 17 57 37 64 55 71 18 21 23 46 58 35 15 24 70 36 13 48 50 41 52 52 24 53 67

93 3 50 27 72 19 15 29 14 1 72 26 17 94 31 5 1 67 21 9 12 4 16 40 91 99 17 96 30 43 22 15 29 25 55 4 78 4 13 5 41 13 25 17 20 68 31 25 53 97 20 92 58 7 20 57

100 39 99 98 99 84 81 95 82 51 99 85 84 100 94 74 25 97 90 92 82 70 84 94 99 99 75 100 89 83 89 93 95 89 97 45 99 63 85 55 98 88 92 60 86 98 87 91 98 98 77 100 100 50 91 94

2.5 4.1 7.2 6.7 4.8 6.6 4.7 6.5 5.1 5.1 4.1 6.4 6.4 4.5 4.0 8.0 6.3 6.3 6.4 7.6 5.8 6.8 3.4 3.0 5.2 3.4 7.6 4.6 8.1 7.1 7.3 5.4 3.3 6.3 6.0 5.3 5.6 8.4 6.5 5.1 7.9 5.5 5.9 6.0 7.4 4.8 7.3 7.3 3.9 3.4 8.5 4.4 2.2 7.3 7.3 4.9

1.6 2.2 3.5 2.0 1.7 3.6 2.2 3.2 4.9 6.0 1.4 3.0 3.7 2.1 2.9 3.7 3.6 3.0 2.8 2.9 4.0 2.7 1.8 2.2 3.1 1.2 3.1 2.0 3.4 4.8 5.3 3.5 1.9 3.8 2.7 2.3 2.1 5.7 4.2 4.0 2.7 1.6 2.0 5.4 3.6 1.9 3.3 2.9 1.7 2.1 4.1 2.2 1.4 4.7 3.6 2.6

.. 62 50 79 33 17 14 33 9 1 60 3b 0 18 72 64 63 6 62b 62 9 40 64 58 14 .. 22 18 b 57 53 38 28 53 47 100 b 76 53 1 15 36 7 88 60 89 13 15 58 7 10 11 73 .. 18 b 20 39 36

Richest quintile

.. 31 42b 31 60 b 28 18 30 b 4 2 64 .. 5 6 57 73 46 5b .. .. 8 15b 37 35 14b .. 17 .. 65 72 18 30 36 27 85b 67 15b 3 34 9 0 59 20 79 19 11b 55 34 4b 28 b 59 .. .. 13 70 b 46b

a. Refers to children who were immunized at any time before the survey. b. Data contain large sampling errors because of the small number of cases. c. Based on births in the fi ve years before the survey. d. Based on information in the three years before the survey. e. Less than 0.5.

114

2007 World Development Indicators

2.19

About the data

The data in the table describe the health status and

account and creating country-specific asset indexes

sexual partners are practicing, any modern method

use of health services by individuals in different

with country-specifi c choices of asset indicators

of contraception. These data may differ from those in

socioeconomic groups within countries. The data are

might produce a more effective and accurate index

table 2.16. • Births attended by skilled health staff

from Demographic and Health Surveys conducted

for each country. The asset index used in the table

are the percentage of deliveries attended by person-

by Macro International with the support of the U.S.

does not have this flexibility.

nel trained to give the necessary supervision, care,

Agency for International Development. These large-

The analysis was carried out for 56 countries,

and advice to women during pregnancy, labor, and

scale household sample surveys, conducted peri-

with the results issued in country reports. The table

the postpartum period; to conduct deliveries on their

odically in developing countries, collect information

shows the estimates for the poorest and richest quin-

own; and to care for newborns. Skilled health staff

on a large number of health, nutrition, and popula-

tiles and by sex only; the full set of estimates for up

include doctors, nurses, and trained midwives, but

tion measures as well as on respondents’ social,

to 117 indicators is available in the country reports

exclude trained or untrained traditional birth atten-

demographic, and economic characteristics using a

(see Data sources). The data in this table will differ

dants. Data in the tables are based on births in the

standard set of questionnaires. The data presented

from data for similar indicators in preceding tables

five years preceding the survey and may therefore dif-

here draw on responses to individual and household

either because the indicator refers to a period a few

fer from the estimates in table 2.16. • Total fertility

questionnaires.

years preceding the survey date or because the indi-

rate is the number of children that would be born to

cator definition or methodology is different.

a woman if she were to live to the end of her child-

The table defines socioeconomic status in terms of a household’s assets, including ownership of consumer items, features of the household’s dwelling,

bearing years and bear children in accordance with Definitions

current age-specific fertility rates. Data in the table

and other characteristics related to wealth. Each

• Survey year is the year in which the underlying

are based on the information in the three years pre-

household asset on which information was collected

data were collected. • Prevalence of child malnu-

ceding the survey and may therefore differ from the

was assigned a weight generated through principal-

trition is the percentage of children under age five

estimates in table 2.16. • Exclusive breastfeeding

component analysis. The resulting scores were stan-

whose weight for age is between two and three

refers to the percentage of children ages 0–3 months

dardized in relation to a standard normal distribution

standard deviations below the median reference

who received only the breast milk in the 24 hours

with a mean of zero and a standard deviation of one.

standard for their age as established by the World

preceding the survey. These data differ from those

The standardized scores were then used to create

Health Organization, the U.S. Centers for Disease

in table 2.17 because the definition differs.

break-points defining wealth quintiles, expressed as

Control and Prevention, and the U.S. National Cen-

quintiles of individuals in the population rather than

ter for Health Statistics. These data may differ from

quintiles of individuals at risk with respect to any

those in table 2.17. • Child immunization rate is the

one health indicator.

percentage of children ages 12–23 months at the

The choice of the asset index for defining socio-

time of the survey who received vaccinations at any

economic status was based on pragmatic rather than

time before the survey for four diseases—measles

conceptual considerations: Demographic and Health

and diphtheria, pertussis (whooping cough), and

Surveys do not provide income or consumption data

tetanus (DPT). These data may differ from those in

but do have detailed information on households’ own-

table 2.15. • Infant mortality rate is the number of

ership of consumer goods and access to a variety

infants dying before reaching one year of age, per

of goods and services. Like income or consumption,

1,000 live births in a given year. Data in the table

the asset index defines disparities in primarily eco-

are based on births in the 10 years preceding the

nomic terms. It therefore excludes other possibilities

survey and may therefore differ from the estimates

of disparities among groups, such as those based

in table 2.20. • Under-five mortality rate is the prob-

on gender, education, ethnic background, or other

ability that a newborn baby will die before reaching

facets of social exclusion. To that extent the index

age fi ve, if subject to current age-specific mortal-

provides only a partial view of the multidimensional

ity rates. The probability is expressed as a rate per

concepts of poverty, inequality, and inequity.

1,000. Data in the table are based on births in the

Creating one index that includes all asset indica-

10 years preceding the survey and may therefore

tors limits the types of analysis that can be per-

differ from the estimates in table 2.20. • Pregnant

formed. In particular, the use of a unified index does

women receiving prenatal care are the percentage

not permit a disaggregated analysis to examine

of women with one or more births during the fi ve

which asset indicators have a more or less impor-

years preceding the survey, who were attended at

Data on health gaps by income and gender are

tant association with health status or use of health

least once during pregnancy by skilled health per-

from an analysis of Demographic and Health Sur-

services. In addition, some asset indicators may

sonnel for reasons related to pregnancy. These data

veys by the World Bank and Macro International.

reflect household wealth better in some countries

may differ from those in table 2.16. • Contraceptive

Country reports are available at www.worldbank.

than in others—or reflect different degrees of wealth

prevalence is the percentage of women married or

org/povertyandhealth/countrydata.

in different countries. Taking such information into

in-union ages 15–49 who are practicing, or whose

Data sources

2007 World Development Indicators

115

PEOPLE

Health gaps by income and gender

2.20 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, 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

116

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 2005

per 1,000 1990 2005

per 1,000 Male Female 1997–2005a 1997–2005a

per 1,000 Male Female 2001–05a 2001–05a

% of cohort Male Female 2005 2005

1990

2005

.. 72 67 40 72 68 77 76 71 55 71 76 53 59 72 64 66 72 48 44 54 52 77 48 46 74 69 77 68 46 54 77 52 72 75 71 75 66 69 63 66 48 69 45 75 77 60 50 70 75 56 77 62 47 42 49

.. 75 72 41 75 73 81 79 72 64 68 79 55 65 74 35 71 73 48 45 57 46 80 39 44 78 72 82 73 44 53 79 46 76 77 76 78 68 75 71 71 55 73 43 79 80 54 57 71 79 57 79 68 54 45 53

2007 World Development Indicators

.. 37 54 154 26 46 8 8 84 100 16 8 111 89 18 45 50 15 113 114 80 85 7 102 120 18 38 .. 26 129 83 16 103 11 11 11 8 50 43 76 47 88 12 122 6 7 60 103 43 7 75 10 60 139 153 102

.. 16 34 154 15 26 5 4 74 54 10 4 89 52 13 87 31 12 96 114 68 87 5 115 124 8 23 .. 17 129 81 11 118 6 6 3 4 26 22 28 23 50 6 80 3 4 60 97 41 4 68 4 32 97 124 84

.. 45 69 260 29 54 10 10 105 149 19 10 185 125 22 58 60 19 210 190 115 139 8 168 201 21 49 .. 35 205 110 18 157 12 13 13 9 65 57 104 60 147 16 204 7 9 92 151 47 9 122 11 82 234 253 150

.. 18 39 260 18 29 6 5 89 73 12 5 150 65 15 120 33 15 191 190 87 149 6 193 208 10 27 .. 21 205 108 12 195 7 7 4 5 31 25 33 27 78 7 127 4 5 91 137 45 5 112 5 43 160 200 120

.. .. .. .. .. 5 .. .. .. 24 .. .. 72 25 .. .. .. .. 110 .. 34 73 .. .. 96 .. .. .. 4 .. .. .. 83 .. .. .. .. 9 .. 15 .. 55 .. 83 .. .. 32 .. .. .. 44 .. 15 101 .. 52

.. .. .. .. .. 3 .. .. .. 29 .. .. 79 29 .. .. .. .. 113 .. 30 72 .. .. 101 .. .. .. 3 .. .. .. 58 .. .. .. .. 9 .. 16 .. 50 .. 86 .. .. 33 .. .. .. 52 .. 18 98 .. 54

.. 96 132 505 174 204 89 120 226 238 357 125 309 253 152 841 252 216 407 512 372 508 97 658 495 131 141 79 182 486 450 117 474 164 121 161 121 267 184 171 221 455 288 451 136 135 438 320 214 119 344 112 296 324 465 454

.. 55 112 461 87 92 50 59 104 205 128 67 277 192 78 853 132 91 386 492 208 499 60 662 471 65 87 34 103 460 424 66 461 67 81 69 74 145 105 104 137 384 94 425 61 60 432 281 82 61 330 49 172 303 423 447

.. 81 76 29 73 67 86 83 61 61 52 83 50 61 75 11 65 70 40 32 47 34 86 22 32 80 75 87 72 32 40 82 35 74 81 76 82 62 73 71 68 39 60 37 82 82 44 51 67 83 51 83 61 49 34 41

.. 88 80 34 86 82 92 91 77 66 80 91 55 69 86 11 79 85 43 35 63 36 91 24 35 89 82 94 82 36 46 89 38 89 87 89 88 76 83 81 79 48 85 41 92 92 46 56 83 91 54 92 74 52 39 43

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

2.20

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 2005

per 1,000 1990 2005

per 1,000 Male Female 1997–2005a 1997–2005a

per 1,000 Male Female 2001–05a 2001–05a

% of cohort Male Female 2005 2005

1990

2005

65 69 59 62 65 62 75 77 77 71 79 68 68 58 65 71 75 68 50 69 69 57 43 68 71 72 51 46 70 46 49 69 71 68 63 64 43 56 62 55 77 75 64 40 46 77 70 59 72 52 68 66 66 71 74 75

69 73 64 68 71 .. 79 80 80 71 82 72 66 49 64 78 78 68 56 71 73 35 42 74 71 74 56 41 74 49 54 73 75 68 67 70 42 61 47 63 79 80 70 45 44 80 75 65 75 56 71 71 71 75 78 78

44 15 80 60 54 40 8 10 8 17 5 33 53 64 42 8 14 68 120 14 32 81 157 35 10 33 103 131 16 140 85 20 37 29 78 69 158 91 60 100 7 8 52 191 120 7 25 100 27 69 33 58 41 19 11 ..

31 7 56 28 31 .. 5 5 4 17 3 22 63 79 42 5 9 58 62 9 27 102 157 18 7 15 74 79 10 120 78 13 22 14 39 36 100 75 46 56 4 5 30 150 100 3 10 79 19 55 20 23 25 6 4 ..

59 17 123 91 72 50 9 12 9 20 6 40 63 97 55 9 16 80 163 18 37 101 235 41 13 38 168 221 22 250 133 23 46 35 108 89 235 130 86 145 9 11 68 320 230 9 32 130 34 94 41 78 62 18 14 ..

40 8 74 36 36 .. 6 6 4 20 4 26 73 120 55 5 11 67 79 11 30 132 235 19 9 17 119 125 12 218 125 15 27 16 49 40 145 105 62 74 5 6 37 256 194 4 12 99 24 74 23 27 33 7 5 ..

.. .. 25 13 .. .. .. .. .. .. .. 5 11 42 .. .. .. 10 .. .. .. .. .. .. .. .. 45 101 .. 132 38 .. .. .. .. 9 61 .. 22 28 .. .. 10 184 120 .. .. .. .. .. .. 19 14 .. .. ..

.. .. 37 11 .. .. .. .. .. .. .. 5 6 39 .. .. .. 11 .. .. .. .. .. .. .. .. 45 102 .. 125 38 .. .. .. .. 11 64 .. 20 40 .. .. 9 202 123 .. .. .. .. .. .. 17 9 .. .. ..

245 261 235 205 158 .. 94 86 92 237 92 165 343 479 305 138 88 264 318 300 151 853 535 137 303 139 337 635 154 358 341 207 155 276 237 162 600 301 620 248 90 99 219 368 499 92 114 180 152 388 159 186 169 189 139 186

201 111 154 155 104 .. 56 46 48 194 45 123 152 551 208 54 58 124 269 116 99 817 500 93 106 81 294 653 89 323 284 110 86 137 168 109 593 200 625 222 64 65 146 339 495 57 85 152 83 349 106 120 116 75 58 69

65 67 61 66 73 .. 84 86 85 68 86 73 49 38 53 79 83 60 50 62 74 10 28 76 62 76 49 25 75 42 46 68 76 60 60 72 26 54 30 60 84 85 68 39 32 86 80 64 77 44 72 69 72 71 81 74

2007 World Development Indicators

72 85 69 74 81 .. 90 92 92 73 94 79 73 35 67 91 88 77 55 83 83 14 32 84 86 85 54 25 84 46 52 82 85 77 69 80 28 65 31 63 90 90 76 41 33 91 85 67 86 49 81 78 80 87 91 89

117

PEOPLE

Mortality

2.20

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 2005

per 1,000 1990 2005

per 1,000 Male Female 1997–2005a 1997–2005a

per 1,000 Male Female 2001–05a 2001–05a

% of cohort Male Female 2005 2005

1990

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 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 Europe EMU

70 69 31 68 53 72 39 74 71 73 42 62 77 71 53 57 78 77 68 63 53 68 57 71 70 66 63 46 70 73 76 75 73 69 71 65 69 55 46 59 65 w 56 68 67 69 63 67 69 68 64 59 49 76 76

2005

72 65 44 73 56 73 41 80 74 78 48 48 81 75 57 41 81 81 74 64 46 71 55 70 73 71 63 50 68 79 79 78 76 67 74 71 73 62 38 37 68 w 59 70 71 70 65 71 69 72 70 63 47 79 80

a. Data are for the most recent year available.

118

2007 World Development Indicators

27 21 103 35 72 24 175 7 12 8 133 45 8 26 74 78 6 7 31 91 102 31 88 28 41 67 80 93 19 13 8 9 21 65 27 38 34 98 101 53 64 w 94 44 46 33 69 43 39 43 60 86 109 9 8

16 14 118 21 61 12 165 3 7 3 133 55 4 12 62 110 3 4 14 59 76 18 78 17 20 26 81 79 13 8 5 6 14 57 18 16 21 76 102 81 51 w 75 30 31 22 56 26 27 26 43 62 96 6 4

31 27 173 44 149 28 302 8 14 10 225 60 9 32 120 110 7 9 39 115 161 37 152 33 52 82 97 160 26 15 10 11 23 79 33 53 40 139 180 80 95 w 147 58 62 41 103 59 48 54 80 129 185 11 9

19 18 203 26 119 15 282 3 8 4 225 68 5 14 90 160 4 5 15 71 122 21 139 19 24 29 104 136 17 9 6 7 15 68 21 19 23 102 182 132 75 w 114 37 39 27 82 33 32 31 53 83 163 7 5

.. .. 105 3 76 .. .. .. .. .. .. 18 .. .. .. .. .. .. .. .. 56 .. 73 .. .. 10 19 78 .. .. .. .. .. .. .. 10 .. 33 89 35

.. .. 97 4 74 .. .. .. .. .. .. 13 .. .. .. .. .. .. .. .. 52 .. 65 .. .. 13 17 70 .. .. .. .. .. .. .. 7 .. 36 74 31

223 467 505 148 311 164 432 85 202 141 395 658 113 130 339 885 82 87 130 219 507 228 369 260 134 186 305 459 404 78 101 144 161 247 184 173 138 267 672 772 232 w 290 196 176 289 235 162 320 208 171 230 483 122 118

96 173 455 101 262 89 379 50 78 63 341 638 46 77 299 893 51 47 90 146 511 119 310 186 77 115 156 447 150 50 63 84 83 145 93 121 101 222 713 808 164 w 237 120 111 159 167 105 136 118 119 161 470 65 57

67 45 31 77 51 73 32 85 71 78 39 25 83 78 50 8 87 86 77 60 34 68 47 65 77 69 53 39 47 86 84 80 74 61 73 73 76 57 21 16 67 w 55 70 71 62 64 73 58 70 71 61 35 82 83

84 76 36 82 56 85 37 91 87 89 44 30 93 87 55 8 92 92 84 70 36 82 55 75 86 79 71 41 76 91 90 87 87 74 84 80 83 63 20 14 75 w 61 79 80 78 72 80 79 81 79 68 38 90 92

2.20

PEOPLE

Mortality About the data

Mortality rates for different age groups (infants, chil-

based on outdated surveys may not be reliable for

rent age-specific mortality rates. It shows that even

dren, and adults) and overall indicators of mortality

monitoring changes in health status or for compara-

in countries where mortality is high, a certain share

(life expectancy at birth or survival to a given age)

tive analytical work.

of the current birth cohort will live well beyond the life

are important indicators of health status in a country.

To produce harmonized estimates of infant and

Because data on the incidence and prevalence of

under-five mortality rates that use all available infor-

diseases (morbidity data) are frequently unavailable,

mation in a transparent way, the United Nations Chil-

mortality rates are often used to identify vulnerable

dren’s Fund (UNICEF) and the World Bank developed

populations. And they are among the indicators most

and adopted a methodology that fi ts a regression

• Life expectancy at birth is the number of years

frequently used to compare levels of socioeconomic

line to the relationship between mortality rates and

a newborn infant would live if prevailing patterns of

development across countries.

their reference dates using weighted least squares.

mortality at the time of its birth were to stay the

(For further discussion of methodology for childhood

same throughout its life. • Infant mortality rate is

mortality estimates, see Hill and others 1999.)

the number of infants dying before reaching one year

The main sources of mortality data are vital registration systems and direct or indirect estimates based

expectancy at birth, while in low-mortality countries close to 90 percent will reach at least age 65. Definitions

on sample surveys or censuses. A “complete” vital

Infant and child mortality rates are higher for boys

of age, per 1,000 live births in a given year. • Under-

registration system—one covering at least 90 percent

than for girls in countries in which parental gender

five mortality rate is the probability that a newborn

of vital events in the population—is the best source of

preferences are insignificant. Child mortality cap-

baby will die before reaching age five, if subject to

age-specific mortality data. But such systems are fairly

tures the effect of gender discrimination better than

current age-specific mortality rates. The probability

uncommon in developing countries. Thus estimates

does infant mortality, as malnutrition and medical

is expressed as a rate per 1,000. • Child mortality

must be obtained from sample surveys or derived by

interventions are more important in this age group.

rate is the probability of dying between the ages of

applying indirect estimation techniques to registra-

Where female child mortality is higher, as in some

one and five, if subject to current age-specific mortal-

tion, census, or survey data. Survey data are subject

countries in South Asia, girls probably have unequal

ity rates. The probability is expressed as a rate per

to recall error, and surveys estimating infant deaths

access to resources.

1,000. • Adult mortality rate is the probability of

require large samples because households in which a

Adult mortality rates have increased in many coun-

dying between the ages of 15 and 60—that is, the

birth or an infant death has occurred during a given year

tries in Sub-Saharan Africa and Europe and Central

probability of a 15-year-old dying before reaching age

cannot ordinarily be pre-selected for sampling. Indirect

Asia. In Sub-Saharan Africa the increase stems

60—if subject to current age-specific mortality rates

estimates rely on estimated actuarial “life” tables that

from AIDS-related mortality and affects both men

between those ages. • Survival to age 65 refers to

may be inappropriate for the population concerned.

and women. In Europe and Central Asia the causes

the percentage of a cohort of newborn infants that

Because life expectancy at birth is constructed using

are more diverse and affect men more. They include

would survive to age 65, if subject to current age-

infant mortality data and model life tables, similar reli-

a high prevalence of smoking, a high-fat diet, exces-

specific mortality rates.

ability issues arise for this indicator.

sive alcohol use, and stressful conditions related to

Life expectancy at birth and age-specific mortality

the economic transition. Data sources

rates are generally estimates based on vital registra-

The percentage of a cohort surviving to age 65

tion or the most recent census or survey available

reflects both child and adult mortality rates. Like life

Data on infant and under-fi ve mortality are the

(see Primary data documentation). Extrapolations

expectancy, it is a synthetic measure based on cur-

harmonized estimates of the World Health Organization, UNICEF, and the World Bank, based mainly

Under-five mortality rates improve as mothers’ education levels rise

on household surveys, censuses, and vital regis-

2.20a No education Some primary

Under-five mortality rate (per 1,000)

Complete primary/ some secondary

Complete secondary/ some higher

200

tration data, supplemented by the World Bank’s estimates based on household surveys and vital registration data. Other estimates are compiled and produced by the World Bank’s Human Development Network and Development Data Group in consultation with its operational staff and coun-

150

try offices. Important inputs to the World Bank’s demographic work come from the United Nations 100

Population Division’s World Population Prospects: The 2004 Revision, census reports and other statistical publications from national statistical

50

offices, Demographic and Health Surveys by Macro International, and the Human Mortality Database 0

by the University of California, Berkeley, and the Tanzania 2004–05

Bolivia 2003

Source: Demographic and Health Surveys.

Bangladesh 2004

Philippines 2003

Egypt 2005

Max Planck Institute for Demographic Research (www.mortality.org/).

2007 World Development Indicators

119 Text figures, tables, and boxes

3

ENVIRONMENT

Introduction

A

griculture is environment

For the 70 percent of the world’s poor in rural areas, agriculture is the major source of income and employment. It takes up more than one-third of the world’s area and more than two-thirds of the world’s water withdrawals. Competition for these resources is increasing with growth of population, cities, and demand for food. And climate change is altering the patterns of rainfall and temperature that agriculture depends on. The depletion and degradation of these resources thus pose serious challenges to the capacity of agriculture to produce enough food and other agricultural products to sustain the livelihoods of rural populations and accommodate the needs of urban populations. In the agriculture-based economies of Sub-Saharan Africa agriculture contributed a third to economic growth in 1990–2005. In the transforming economies of Asia, mainly China, India, and Indonesia, it contributed 8 percent to economic growth, while making up a fifth of the economy and employing half the labor force. And in the urbanizing economies of Latin America and some countries of Eastern Europe and Central Asia, it contributed 10 percent to the economy and to growth. Agriculture is a way of life throughout the world, with 2.5 billion of 3 billion rural people tied to agricultural activities, particularly to producing food. Producing food requires enormous amounts of water and cropland. In some parts of the world, the demand for water exceeds the supply. But in many places it appears that water scarcity is caused not by shortages of water but by its mismanagement. Not enough is known because data on the availability and productivity of water are limited. However, water is clearly central to the social, political, and economic affairs of a country and to cooperation or conflict across boundaries. Agricultural intensification—producing more crops on the same or smaller amounts of land— along with irrigation and the conversion of forest lands to cropland have helped meet the increasing demand for food. Food production has thankfully outpaced population growth in most regions. But this has too often been at the expense of soil degradation, water pollution, and added pressure on water resources. Turning forests into agricultural lands reduces biodiversity and contributes to global warming. Rising sea levels, warming temperatures, and changes in weather patterns affect millions of people. The impact is especially severe for those in developing countries, threatening their potential to move out of poverty.

2007 World Development Indicators

121

Agriculture, poverty reduction, and food security With economic growth the share of agriculture in the global

Agriculture’s changing role is underscored by rapid rural-

economy declines. Even so, agriculture remains important in

urban migration. The United Nations estimates that in 2007,

many developing economies and the source of income for

for the first time, the majority of the global population will be

many poor people. In some African countries more than half

urban (United Nations Population Division 2005, World Popu-

the GDP is in agriculture—in Liberia 64 percent, in Guinea-

lation Prospects 2004). And this will continue. Urban popula-

Bissau 60 percent, and in Central African Republic 54 per-

tion is expected to grow 1.8 percent a year through 2030,

cent. On average agriculture contributes more than 20 per-

almost twice as fast as the global population. Productivity

cent to value added in low-income economies (figure 3a).

must continue to rise, so that the shrinking rural population

Globally, about 40 percent of the active labor force is in agriculture, but in Sub-Saharan Africa and Asia and the Pacific

can provide more agricultural products for a rising urban population with higher incomes.

about 60 percent is in agriculture. Compare that with 18 per-

In recent years the increases in demand for food have

cent in Latin America and 4 percent in high-income econo-

been met by higher productivity through agricultural intensifi -

mies. Variations across countries are even greater, with agri-

cation, technological advance, mechanization, and irrigation

cultural employment’s share ranging from less than 2 percent

(figure 3b). However, continuing depletion and degradation

in the United Kingdom and the United States to 44 percent

of natural resources that constitute the agricultural sector’s

in China and 80 percent in Nepal. Agriculture is associated

main inputs—water and land—could slow the growth of pro-

with natural wealth— particularly in developing economies. A

ductivity and undermine food security.

recent World Bank study estimates that roughly two-thirds of the natural wealth in low-income countries is embodied in cropland and pastureland (World Bank 2006e). Agriculture’s share in GDP—declining, but still more than a fifth in low-income economies Share of GDP by income (%)

1990

3a 2005

35

Agricultural productivity has increased, yielding more output for all Value added per capita (2000 $)

3b 1970

1990

2005

500

30

400

25 20

300

15

200

10 100 5 0

0 Low-income

Lower middle-income

Upper middle-income

High-income

1990

Share of GDP by region (%) 35

2005

Low-income

Lower middle-income

Upper middle-income

Value added per agricultural worker (2000 $ thousands)

High-income

1990–92

2001–03

25

30 20 25 15

20 15

10

10 5

5

0

0 South Asia

Sub-Saharan East Asia Africa & Pacific

Source: Table 4.2.

122

2007 World Development Indicators

Middle East & North Africa

Europe & Latin America Central & Caribbean Asia

Low-income

Lower middle-income

Source: World Bank data files.

Upper middle-income

High-income

Water . . . water . . . Water is life. Water is health. Water is livelihood. But some

est consumer of water worldwide. Water productivity is much

1.1 billion people in developing countries have inadequate ac-

lower in agriculture than it is in industry (figure 3e).

cess to water, and about 700 million people in 43 countries

Globally, there is more than enough water for domestic

live below the water-stress threshold of 1,700 cubic meters

purposes, for agriculture, and for industry. But access to

per person per year (figure 3c). One billion people live in areas

water is very uneven across and within countries. Poor people

of economic water scarcity—where human, institutional, and

have limited access, not so much because of physical water

financial capital limit access to water even though water in

scarcity, but because of their lack of purchasing power and

nature is available locally to meet human demands, a situa-

because of inappropriate policies that limit their access to

tion especially prevalent in much of Sub-Saharan Africa and

infrastructure.

South Asia (CAWMA 2007).

Techniques to control soil moisture and intensify agri-

Water is needed for most economic activities, but agricul-

cultural production have been substantially improved in

ture is the most water-intensive sector (figure 3d), using 70

the last 50 years in many parts of the world. Irrigation is

percent of global water withdrawals (indicator table 3.5). Each

increasing globally, in all income groups and all regions (fig-

year some 7,100 cubic kilometers of water are consumed by

ure 3f). While the world’s cultivated land increased by about

crops to meet global food demand, the equivalent of 90 times

13 percent from 1961 to 2003, the irrigated area almost

the annual runoff of the Nile River, or more than 3,000 liters

doubled, from 10 percent to 18 percent of cropland. About

per person per day. Most of it (78 percent) comes directly

70 percent of the world’s irrigated land and 30 percent of

from rainfall, the remainder from irrigation (CAWMA 2007).

cultivated land are in Asia. By contrast, there is very little

Competition between water for food production and for other

irrigation in Sub- Saharan Africa, where agriculture is almost

sectors will intensify, but food production will remain the larg-

exclusively rainfed.

More people will experience water scarcity and water stress

3c

. . . and the least productive user

3e

GDP/water use (2000 $ per cubic meter)

Billions of people 6

Industry

Agriculture

35 30

Water scarcity

5 25 4

20

3

Water stress

15

2

10

1

5 0

0 1900

2005

2025

Source: UNDP 2006.

Lower Upper middle-income middle-income

High-income

World

Source: Table 3.5 and World Bank data files.

Agriculture is the biggest consumer of water . . . Share of total water withdrawals, 2002 (%) 100

Low-income

2050

3d Domestic

Industry

Agriculture

Irrigation has increased, demanding more water

3f

Share of cropland (%)

1990–92

2001–03

40 35

80

30 25

60

20 40

15 10

20

5 0

0 East Asia & Pacific

Europe & Latin Middle East Central Asia America & & North Caribbean Africa Source: Table 3.5.

South Asia

Sub-Saharan Africa

Highincome

South Asia

Middle East Latin America & North Africa & Caribbean

Europe & Central Asia

High-income

Sub-Saharan Africa

Source: Table 3.2.

2007 World Development Indicators

123

Land use and land loss Global demand for food is projected to double in the next 50

Perhaps more worrisome, productivity has declined sub-

years, as urbanization proceeds and income rises (CAWMA

stantially on approximately 16 percent of agricultural land in

2007). But arable land per capita is shrinking. In the last

developing countries, especially in Africa and Central Amer-

12 years it has fallen from 2,100 square meters per person

ica. One study estimates that global cropland production is

to 1,700 in low-income countries, and from 2,300 to 2,100

12.7 percent lower and pastoral production 3.8 percent lower

in high-income economies.

than would have been the case without soil degradation. This

Agricultural intensification has met global food demand.

implies a total agricultural production loss of 4.8 percent.

In Asia land under cereal production increased only 4 percent

Another estimate puts the global loss at 8.9 percent (Scherr

between 1970 and 1995, while cereal production doubled

1999, pp. 16–20).

due to the green revolution (Rosengrant and Hazel 2000).

In many countries soil degradation and the loss of agri-

More recently, the high-income economies, already the

cultural land combined with population growth have created

most intensified producers, realized an almost 20 percent

pressure that led to substantial deforestation. Global for-

increase—from 4,260 kilograms per hectare in 1990–92 to

ested area in 2005 was about 4 billion hectares, covering

5,040 in 2003–05 (figure 3g), substantially higher than their

30 percent of total land area (figure 3h). But deforestation

rate of population increase. In contrast, cereal yields in water-

continues at about 13 million hectares a year. Reforestation

stressed Sub-Saharan Africa increased by 10 percent—far

reduced the net loss of forest areas to 7.3 million hectares

less than the region’s population growth. The differences in

a year in 2000–05—an improvement from losses of 8.9 mil-

productivity are even starker among countries, ranging from

lion hectares a year in 1990–2000. Africa and Latin America

296 kilograms per hectare in Eritrea to 8,710 in Belgium.

continued to have the largest loss of forest after 1990.

Cereal yields have increased in most regions— East Asia has almost reached the high-income economies

3g

Thousands of kilograms per hectare

1990–92

2003–05

6 5 4 3 2 1 0 High-income

East Asia & Pacific

Latin America & Caribbean

South Asia

Middle East & North Africa

Europe & Central Asia

Sub-Saharan Africa

Source: Table 3.3.

Forested areas are shrinking in Latin America and Sub-Saharan Africa—recovering in East Asia

3h

Forested area (millions of hectares)

1990

2000

2005

10 8 6 4 2 0 High-income

Latin America & Caribbean

Source: Table 3.4.

124

2007 World Development Indicators

Europe & Central Asia

Sub-Saharan Africa

East Asia & Pacific

South Asia

Middle East & North Africa

Agriculture and climate change Agriculture and deforestation are estimated to be responsible for one-third of greenhouse gas emissions, which are the main contributors to climate change (figure 3i). In turn, climate change affects agriculture more than any other sector, increasing risks of crop failures and livestock losses and threatening food security. The decline in crop yields, especially in Africa, could leave hundreds of millions without the ability to produce or purchase sufficient food. Warming may also induce sudden shifts in regional weather patterns that would have severe consequences for water availability and flooding in tropical regions. And the impact of sea level rise could be catastrophic for many developing countries (Dasgupta and others 2007). Changes in climate patterns are already observed in some parts of the world. Average rainfall has fallen in the Sahel (figure 3j), with droughts in the 1970s and 1980s that resulted in more than 100,000 deaths (UNEP 2002, p. 219). Africa has had one major drought in each of the last three decades (box 3k). Ethiopia’s 1984 drought affected 8.7 million people—one million died and millions more faced malnourishment and famine (UNEP 2002). The 1991–92 drought

Agriculture accounts for a seventh of all greenhouse gas emissions

in South Africa reduced cereal harvests and exposed more than 17 million to the risk of starvation (UNEP 2002). Delay in addressing climate change could prove tremendously costly, while efforts to mitigate may be less expensive than commonly feared. A recent cost assessment argues that tackling climate change is a pro-growth strategy—and that ignoring it will ultimately undermine economic growth (Stern 2006). If action does not start now, the world may face far higher costs later. Efforts to stabilize emissions must aim not only at the energy sector, but also at reducing deforestation, encouraging reforestation, and fostering more sustainable agricultural practices. While all countries will be affected, the poorest countries and people will suffer earliest and most because they depend heavily on agriculture, the most climate-sensitive of all economic sectors. The developing regions are also at a geographic disadvantage. They are already warmer, on average, than developed regions. They suffer from high rainfall variability. And their low incomes and other vulnerabilities make their adaptation to climate change particularly difficult.

Horn of Africa suffers floods after parching drought

3i

Greenhouse gas emissions by source, 2000 Other energy-related 5%

In November 2006 thousands of Somalis trekked from flooded refugee camps to drier ground in northeast Kenya as UN agencies rushed emergency supplies to some 1.8 million people hit by the worst floods in the Horn of Africa in 50 years. The floods, which also affected Kenya and Ethiopia, began in late October. They worsened food insecurity caused by severe drought earlier this year. In some areas the soil was so parched that it was not able to absorb the rain, and the few crops that survived the drought were destroyed by floods. The flood displaced more than 100,000 of the estimated 160,000 mainly Somali refugees in Dadaab, who had fled the increasing violence in their country. At least 80 people died in floods in southern Somalia. The rain also dislodged landmines seeded during Somalia’s long-standing conflict, posing additional hazards.

Waste 3%

Buildings 8% Power 24% Agriculture 14% Deforestation 18%

Industry 14%

Box 3k

Transport 14%

Source: Stern Review.

Less rain is falling in the Sahel, with dire consequences

3j

Mean normalized rainfall, 1950–2000, June–October 2.5 2.0 1.5 1.0 0.5 0.0 –0.5 –1.0 –1.5 –2.0 1950

1955

1960

1965

1970

1975

1980

1985

1990

1995

2000

Note: The averages are standardized for the period 1950–2000 so that the mean of the series is zero and the standard deviation is one. Source: World Bank 2003c.

2007 World Development Indicators

125

Tables

3.1

Rural population and land use Rural population

% of total 1990 2005

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, 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

126

.. 63.6 47.9 62.9 13.0 32.5 14.6 34.2 46.3 80.2 33.6 3.6 65.5 44.4 60.8 58.1 25.2 33.6 86.2 93.7 87.4 59.3 23.4 63.2 79.2 16.7 72.6 0.5 31.3 72.2 45.7 49.3 60.3 46.0 26.6 24.8 15.2 44.8 44.9 56.5 50.8 84.2 28.9 87.4 38.6 25.9 30.9 61.7 44.8 26.6 63.5 41.2 58.9 72.0 71.9 70.5

.. 54.6 36.7 46.7 9.9 35.9 11.8 34.0 48.5 74.9 27.8 2.8 59.9 35.8 54.3 42.6 15.8 30.0 81.7 90.0 80.3 45.4 19.9 62.0 74.7 12.4 59.6 0.0 27.3 67.9 39.8 38.3 55.0 43.5 24.5 26.5 14.4 33.2 37.2 57.2 40.2 80.6 30.9 84.0 38.9 23.3 16.4 46.1 47.8 24.8 52.2 41.0 52.8 67.0 70.4 61.2

2007 World Development Indicators

Land area

average annual % growth 1990–2005

.. –1.4 –0.1 0.7 –0.7 –0.4 –0.3 0.3 1.4 1.6 –1.6 –1.4 2.6 0.7 –0.8 –0.5 –1.7 –1.5 2.6 1.4 1.8 0.5 –0.2 1.9 2.8 –0.6 –0.4 .. 1.0 2.3 2.3 0.7 1.8 –0.7 –0.2 0.3 0.0 –0.5 0.4 2.0 0.4 2.3 –0.6 2.0 0.4 –0.3 –1.7 1.3 –0.9 –0.2 1.1 0.6 1.6 2.2 2.9 0.5

Land use

% of land area thousand sq. km 2005

Forest area 1990 2005

652.1 27.4 2,381.7 1,246.7 2,736.7 28.2 7,682.3 82.5 82.7 130.2 207.5 30.2 110.6 1,084.4 51.2 566.7 8,459.4 108.6 273.6 25.7 176.5 465.4 9,093.5 623.0 1,259.2 748.8 9,598.1a 1.0 1,109.5 2,267.1 341.5 51.1 318.0 55.9 109.8 77.3 42.4 48.4 276.8 995.5 20.7 101.0 42.4 1,000.0 304.6 550.1 257.7 10.0 69.5 348.8 227.5 128.9 108.4 245.7 28.1 27.6

2.0 28.8 0.8 48.9 12.9 12.3 21.9 45.8 11.2 6.8 35.6 22.4 30.0 57.9 43.2 24.2 61.5 30.1 26.1 11.3 73.3 52.7 34.1 37.2 10.4 20.4 16.8 .. 55.4 62.0 66.5 50.2 32.1 37.8 18.7 34.0 10.5 28.4 49.9 0.0 18.1 .. 51.0 13.7 72.9 26.4 85.1 44.2 39.7 30.8 32.7 25.6 43.8 30.1 78.8 4.2

1.3 29.0 1.0 47.4 12.1 10.0 21.3 46.8 11.3 6.7 38.0 22.1 21.3 54.2 42.7 21.1 56.5 33.4 24.8 5.9 59.2 45.6 34.1 36.5 9.5 21.5 21.2 .. 54.7 58.9 65.8 46.8 32.7 38.2 24.7 34.3 11.8 28.4 39.2 0.1 14.4 15.4 53.9 13.0 73.9 28.3 84.5 47.1 39.7 31.8 24.2 29.1 36.3 27.4 73.7 3.8

Permanent cropland 1990 2005

0.2 4.6 0.2 0.4 0.4 4.3 0.0 1.0 6.0 2.3 1.4 0.5 0.9 0.1 2.0 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 1.4 7.4 3.1 0.2 9.3 4.8 0.4 12.5 .. 0.5 0.6 0.0 2.2 0.6 0.5 7.8 1.3 6.6 8.3 4.5 2.0 4.2 11.6

0.2 4.5 0.3 0.2 0.4 2.1 0.0 0.8 2.7 3.5 0.6 0.8 2.4 0.2 1.9 0.0 0.9 1.9 0.2 14.2 0.9 2.6 0.7 0.2 0.0 0.4 1.3 .. 1.5 0.5 0.2 5.9 11.3 2.1 6.6 3.1 0.2 10.3 4.4 0.5 12.1 0.0 0.3 0.7 0.0 2.1 0.7 0.5 3.8 0.6 9.7 8.8 5.6 2.6 8.9 11.6

Arable land 1990 2005

12.1 21.1 3.0 2.3 9.6 18.2 6.2 17.3 21.1 70.2 30.1 23.3 14.6 1.9 23.6 0.7 6.0 34.9 12.9 36.2 20.9 12.8 5.0 3.1 2.6 3.7 11.1 .. 3.0 2.9 1.4 5.1 7.6 30.8 27.6 41.1 60.4 21.7 5.8 2.3 26.5 .. 27.0 9.8 7.4 32.7 1.1 18.2 11.8 34.3 11.9 22.5 12.0 3.0 10.7 28.3

12.1 21.1 3.2 2.6 10.2 17.6 6.4 16.8 22.3 61.1 26.3 27.9 24.0 2.8 19.5 0.7 7.0 29.2 17.7 38.6 21.0 12.8 5.0 3.1 2.9 2.6 11.1 .. 1.8 3.0 1.4 4.4 10.4 19.8 27.9 39.4 52.7 22.7 4.9 3.0 31.9 5.6 13.9 11.1 7.3 33.6 1.3 31.5 11.5 34.1 18.4 20.4 13.3 4.5 10.7 28.3

Arable land hectares per 100 people 1990–92 2003–05

.. 18.8 24.6 21.3 74.7 16.2 249.0 17.3 22.6 6.1 58.4 8.2 33.1 34.9 25.8 21.4 33.2 43.4 37.8 14.7 28.5 39.3 147.4 50.4 42.0 12.7 8.1 .. 5.8 13.1 13.9 5.6 18.6 32.7 28.6 30.1 42.7 13.1 12.0 4.2 10.2 15.1 52.1 15.5 42.2 31.1 25.1 22.5 17.1 14.3 20.0 24.9 12.3 11.7 21.3 9.7

.. 18.6 23.7 21.9 73.4 16.4 241.1 17.0 22.2 5.7 56.2 8.1 33.5 34.5 25.9 21.3 32.5 42.0 39.0 14.1 26.8 37.8 144.4 49.0 39.4 12.4 8.0 .. 4.8 12.4 13.1 5.4 18.8 27.6 27.3 29.9 41.8 12.7 10.0 4.1 9.9 13.9 40.9 16.1 42.5 30.5 24.2 21.9 17.8 14.4 19.7 24.1 12.0 12.2 20.1 9.4

Rural population

% of total 1990 2005

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

59.7 34.2 74.5 69.4 43.7 30.3 43.1 9.6 33.3 50.6 36.9 27.8 43.7 81.8 41.6 26.2 2.0 62.2 84.6 30.7 16.9 82.8 54.7 21.4 32.4 42.2 76.4 88.4 50.2 76.7 60.3 56.1 27.5 53.2 43.0 51.6 78.9 75.1 72.3 91.1 31.3 15.3 46.9 84.6 65.0 28.0 34.6 69.4 46.1 86.9 51.3 31.1 51.2 38.7 52.1 27.8

53.5 33.7 71.3 51.9 33.1 .. 39.5 8.4 32.4 46.9 34.2 17.7 42.7 79.3 38.4 19.2 1.7 64.2 79.4 32.2 13.4 81.3 41.9 15.2 33.4 31.1 73.2 82.8 32.7 69.5 59.6 57.6 24.0 53.3 43.3 41.3 65.5 69.4 64.9 84.2 19.8 13.8 41.0 83.2 51.8 22.6 28.5 65.1 29.2 86.6 41.5 27.4 37.3 37.9 42.4 2.4

Land area

3.1

ENVIRONMENT

Rural population and land use Land use

average annual % growth 1990–2005

thousand sq. km 2005

Forest area 1990 2005

1.9 –0.2 1.4 –0.6 –0.4 .. 0.5 1.6 0.0 0.2 –0.2 0.4 –0.9 2.3 0.4 –1.1 1.7 1.3 2.0 –0.7 0.2 0.8 2.1 –0.3 –0.4 –1.6 2.7 1.7 –0.5 2.2 2.7 1.3 0.5 –0.3 1.3 0.0 1.4 0.9 1.8 1.8 –2.4 0.5 0.9 3.3 1.0 –0.9 1.1 2.0 –1.1 2.4 0.8 0.9 –0.1 –0.2 –0.9 –14.6

111.9 89.6 2,973.2 1,811.6 1,636.2 437.4 68.9 21.6 294.1 10.8 364.5 88.2 2,699.7 569.1 120.4 98.7 17.8 191.8 230.8 62.3 10.2 30.4 96.3 1,759.5 62.7 25.4 581.5 94.1 328.6 1,220.2 1,025.2 2.0 1,908.7 32.9 1,566.5 446.3 784.1 657.6 823.3 143.0 33.9 268.0 121.4 1,266.7 910.8 304.3 309.5 770.9 74.4 452.9 397.3 1,280.0 298.2 306.3 91.5 8.9

66.0 20.0 21.5 64.3 6.8 1.8 6.4 7.1 28.5 31.9 68.4 0.9 1.3 6.5 68.1 64.5 0.2 4.4 75.0 44.7 11.8 0.2 42.1 0.1 31.0 35.6 23.5 41.4 68.1 11.5 0.4 19.2 36.2 9.7 7.3 9.6 25.5 59.6 10.6 33.7 10.2 28.8 53.9 1.5 18.9 30.0 0.0 3.3 58.8 69.6 53.3 54.8 35.5 29.2 33.9 45.5

% of land area

41.5 22.1 22.8 48.8 6.8 1.9 9.7 7.9 33.9 31.3 68.2 0.9 1.2 6.2 51.4 63.5 0.3 4.5 69.9 47.2 13.3 0.3 32.7 0.1 33.5 35.6 22.1 36.2 63.6 10.3 0.3 18.2 33.7 10.0 6.5 9.8 24.6 49.0 9.3 25.4 10.8 31.0 42.7 1.0 12.2 30.8 0.0 2.5 57.7 65.0 46.5 53.7 24.0 30.0 41.3 46.0

Permanent cropland 1990 2005

3.2 2.6 2.2 6.5 0.8 0.7 0.0 4.1 10.1 9.2 1.3 0.8 0.1 0.9 1.5 1.6 0.1 0.6 0.3 0.6 11.9 0.1 2.2 0.2 1.1 1.5 1.0 1.2 16.0 0.0 0.0 3.0 1.0 22.9 0.0 1.6 0.3 0.8 0.0 0.5 0.9 5.1 1.6 0.0 2.8 .. 0.1 0.6 2.1 1.3 0.2 0.3 14.8 1.1 8.5 5.6

3.2 2.3 3.4 7.5 0.9 0.6 0.0 3.5 8.6 10.2 0.9 1.0 0.1 1.0 1.7 2.0 0.2 0.4 0.4 0.2 14.0 0.1 2.3 0.2 0.6 1.8 1.0 1.5 17.6 0.0 0.0 3.0 1.3 9.1 0.0 2.0 0.3 1.4 0.0 0.9 1.0 7.0 1.9 0.0 3.2 .. 0.1 1.0 2.0 1.4 0.2 0.5 16.8 1.2 8.5 4.7

Arable land 1990 2005

13.1 56.2 54.8 11.2 9.3 12.1 15.1 15.9 30.6 11.0 13.1 2.0 13.3 7.4 19.0 19.8 0.2 7.1 3.5 28.0 17.9 10.4 4.2 1.0 47.3 33.9 4.7 19.3 5.2 1.7 0.4 49.3 12.6 54.3 0.9 19.5 4.4 14.5 0.8 16.0 25.9 9.4 10.7 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

9.5 51.3 53.7 12.7 9.8 13.1 17.6 14.6 26.3 16.1 12.0 2.1 8.3 8.2 23.3 16.6 0.8 6.7 4.3 17.5 16.6 10.9 4.0 1.0 30.4 22.3 5.1 26.0 5.5 3.9 0.5 49.3 13.0 56.2 0.8 19.0 5.5 15.3 1.0 16.5 26.8 5.6 15.9 11.4 33.5 2.8 0.1 27.6 7.4 0.5 7.7 2.9 19.1 39.6 16.8 8.0

Arable land hectares per 100 people 1990–92 2003–05

16.2 45.2 15.5 10.3 23.3 21.9 29.7 5.3 14.7 6.7 3.5 3.9 148.7 14.6 12.0 3.6 0.6 27.2 16.7 41.0 5.2 18.4 12.1 33.5 58.8 27.9 17.6 18.7 7.7 38.9 17.9 8.3 25.1 43.3 49.1 30.4 22.0 20.5 42.3 9.4 5.7 38.5 38.5 118.8 24.1 19.6 1.5 15.2 18.1 3.9 54.3 14.0 7.4 35.3 16.4 1.7

2007 World Development Indicators

15.5 45.5 14.8 10.6 24.4 .. 29.5 4.8 13.6 6.6 3.4 3.5 149.3 14.2 12.3 3.4 0.6 25.9 17.2 44.1 4.9 18.3 11.9 32.3 49.0 27.9 16.7 19.9 7.4 36.6 16.9 8.1 24.6 43.9 48.3 29.4 22.8 20.4 41.0 8.9 5.6 37.4 37.8 111.0 24.2 18.9 1.5 14.1 17.6 4.0 53.6 13.6 7.1 32.6 14.9 1.8

127

3.1

Rural population and land use Rural population

% of total 1990 2005

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 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 Europe EMU

45.7 26.6 94.6 23.4 61.0 49.1 69.9 0.0 43.5 49.6 70.3 48.0 24.6 82.8 73.4 77.1 16.9 31.6 51.1 68.5 81.1 70.6 69.9 91.5 40.4 40.8 54.9 88.9 33.2 20.9 11.3 24.7 11.0 59.9 16.0 79.7 32.1 79.1 60.6 71.0 57.0 w 74.6 55.6 61.7 31.7 63.2 71.2 36.9 29.1 48.1 75.1 72.1 26.3 29.1

46.3 27.0 80.7 19.0 58.4 47.8 59.3 0.0 43.8 49.0 64.8 40.7 23.3 84.9 59.2 75.9 15.8 24.8 49.4 75.3 75.8 67.7 59.9 87.8 34.7 32.7 53.8 87.4 32.2 23.3 10.3 19.2 8.0 63.3 6.6 73.6 28.4 72.7 65.0 64.1 51.2 w 70.0 46.1 50.5 28.0 56.5 58.5 36.3 22.8 42.9 71.5 64.8 22.4 26.7

Land area

average annual % growth 1990–2005

–0.4 –0.2 1.8 1.0 2.3 –2.4 0.9 .. 0.2 –0.1 1.0 0.9 0.3 1.1 0.8 2.6 –0.1 –1.0 2.4 2.0 2.1 0.8 2.0 0.2 0.3 0.2 1.6 3.1 –0.9 7.4 –0.4 –0.5 –1.3 2.0 –3.9 1.0 3.4 3.1 2.8 0.7 0.6 w 1.6 –0.2 –0.2 0.0 0.7 –0.2 –0.1 –0.1 1.3 1.5 1.8 –0.3 –0.2

% of land area thousand sq. km 2005

230.0 16,381.4 24.7 2,000.0 b 192.5 102.0 71.6 0.7 48.1 20.1 627.3 1,214.5 499.2 64.6 2,376.0 17.2 410.3 40.0 183.8 140.0 883.6 510.9 54.4 5.1 155.4 769.6 469.9 197.1 579.4 83.6 241.9 9,161.9 175.0 425.4 882.1 310.1 6.0 528.0 743.4 386.9 129,606.2 s 28,184.8 68,517.7 39,305.8 29,211.9 96,702.5 15,869.9 23,367.1 20,126.9 8,960.9 4,781.3 23,596.5 32,903.7 2,455.3

a. Includes Taiwan, China; Macao, China; and Hong Kong, China. b. Provisional estimate.

128

2007 World Development Indicators

Land use

Forest area 1990 2005

27.8 49.4 12.9 1.4 48.6 25.1 42.5 3.0 40.0 59.0 13.2 7.6 27.0 36.4 32.1 27.4 66.7 28.9 2.0 2.9 46.9 31.2 12.6 45.8 4.1 12.6 8.8 25.0 16.0 2.9 10.8 32.6 5.2 7.2 59.0 28.8 1.5 1.0 66.1 57.5 31.5 w 26.3 34.8 32.6 37.7 32.3 28.8 38.2 48.9 2.2 16.5 29.2 29.1 33.5

27.7 49.4 19.5 1.4 45.0 26.4 38.5 2.9 40.1 62.8 11.4 7.6 35.9 29.9 28.4 31.5 67.1 30.5 2.5 2.9 39.9 28.4 7.1 44.1 6.8 13.2 8.8 18.4 16.5 3.7 11.8 33.1 8.6 7.7 54.1 41.7 1.5 1.0 57.1 45.3 30.5 w 23.9 33.8 31.2 37.2 30.9 28.4 38.3 45.5 2.4 16.8 26.5 29.4 37.3

Permanent cropland 1990 2005

2.6 0.2 12.4 0.0 0.1 2.4 0.8 1.5 1.0 1.2 0.0 0.7 9.7 15.9 0.1 0.7 0.0 0.5 4.0 1.4 1.0 6.1 1.7 9.0 12.5 3.9 0.2 9.4 3.1 0.2 0.3 0.2 0.3 0.6 0.9 3.2 19.1 0.2 0.0 0.3 1.0 w 1.0 1.1 1.4 0.6 1.0 2.2 0.6 0.9 0.8 1.8 0.8 0.7 4.7

2.3 0.1 10.9 0.1 0.2 3.1 1.0 0.3 0.5 1.3 0.0 0.8 9.9 15.5 0.2 0.8 0.0 0.6 4.7 0.9 1.2 7.0 2.2 9.2 13.8 3.6 0.1 10.9 1.6 2.3 0.2 0.3 0.2 0.8 0.9 7.6 19.1 0.3 0.0 0.3 1.1 w 1.3 1.2 1.6 0.6 1.2 2.8 0.4 1.0 0.9 2.8 0.9 0.7 4.4

Arable land 1990 2005

41.2 8.3 35.7 1.7 12.1 51.9 6.8 1.5 32.5 14.1 1.6 11.1 30.7 13.5 5.5 10.5 6.9 9.8 26.6 6.3 4.0 34.2 38.6 14.4 18.7 32.0 3.0 25.4 59.2 0.4 27.4 20.3 7.2 10.8 3.2 16.4 18.4 2.9 7.1 7.5 10.7 w 12.9 9.5 9.5 9.5 10.5 10.8 13.0 6.5 5.6 42.6 6.4 11.5 27.0

40.4 7.4 48.6 1.8 12.8 34.4 8.0 0.9 28.9 8.7 1.7 12.1 27.4 14.2 7.2 10.3 6.6 10.3 26.5 6.6 4.5 27.7 46.1 14.6 18.0 31.0 4.7 26.4 56.0 0.8 23.7 19.0 7.8 11.0 2.9 21.3 17.8 2.9 7.1 8.3 10.6 w 13.6 9.2 9.3 8.9 10.5 11.0 11.5 7.1 5.9 47.1 7.5 11.0 25.6

Arable land hectares per 100 people 1990–92 2003–05

42.4 85.0 12.0 17.0 22.9 41.9 10.8 0.0 27.1 8.6 14.5 33.0 32.2 4.7 48.4 16.7 30.3 5.7 26.6 14.9 11.3 25.6 45.5 5.8 29.0 34.8 40.5 20.3 66.9 2.0 9.7 61.6 40.7 18.0 10.5 8.2 3.4 8.2 48.3 25.4 22.3 w 17.3 21.2 15.3 45.1 19.5 9.5 56.8 27.3 18.0 14.6 25.1 37.5 20.5

43.2 84.9 13.7 16.3 22.1 42.4 11.1 0.0 26.0 8.7 13.6 32.2 32.0 4.8 48.8 16.1 29.8 5.5 25.6 14.6 10.8 22.4 43.0 5.8 28.4 33.2 46.8 19.4 68.5 1.6 9.6 59.7 40.1 18.4 10.1 8.0 3.1 7.8 46.6 25.0 21.9 w 16.9 20.8 15.1 43.9 19.1 9.4 58.1 26.6 18.0 13.8 24.8 36.9 20.2

3.1

ENVIRONMENT

Rural population and land use About the data

Three billion people, including 70 percent of the

(FAO), the primary compiler of these data, occasion-

assessments every 5–10 years since 1946. Global

world’s poor people, live in rural areas. Therefore,

ally adjusts its definitions of land use categories and

Forest Resources Assessment 2005 was carried out

adequate indicators to monitor progress in rural

sometimes revises earlier data. Because the data

between 2003 and 2005 and covered 229 countries

areas are essential However, indicators of rural

reflect changes in reporting procedures as well as

and territories at three points in time: 1990, 2000,

development are sparse, as few indicators are dis-

actual changes in land use, apparent trends should

and 2005. It is the most comprehensive assessment

aggregated between rural and urban areas (for some

be interpreted with caution.

of forests and forestry to date both in scope and in

that are, see tables 2.7, 3.5, and 3.10). This table

Satellite images show land use that differs from

number of countries and people involved. It exam-

shows indicators of rural population and land use.

that given by ground-based measures in both area

ines current status and recent trends for about 40

Rural population is approximated as the midyear non-

under cultivation and type of land use. Moreover,

variables covering the extent, condition, uses, and

urban population. While a practical means of identify-

land use data in countries such as India are based

values of forests and other wooded land with the aim

ing the rural population, it is not precise (see box 3a

on reporting systems that were designed for the col-

of assessing all benefits from forest resources.

for more discussion of the issue).

lection of tax revenue. Because taxes on land are Definitions

The data in the table show that land use patterns

no longer a major source of government revenue,

are changing. They also indicate major differences

the quality and coverage of land use data (except

• Rural population is calculated as the difference

in resource endowments and uses among countries.

for cropland) have declined. Data on forest area may

between the total population and the urban popula-

True comparability of the data is limited, however, by

be particularly unreliable because of differences

tion (see Definitions for tables 2.1 and 3.10). • Land

variations in definitions, statistical methods, and the

in definitions and irregular surveys (see About the

area is a country’s total area, excluding area under

quality of data collection. Countries use different def-

data for table 3.4). FAO’s Global Forest Resources

inland water bodies, national claims to the continental

initions of rural and urban population and land use,

Assessment 2005 aims to address this limitation.

shelf, and exclusive economic zones. In most cases

for example. The Food and Agriculture Organization

FAO has been coordinating global forest resources

the definitions of inland water bodies includes major rivers and lakes. (See table 1.1 for the total surface area of countries.) • Land use can be broken into sev-

What is rural? Urban?

Box 3.1a

eral categories, three of which are presented in this

The rural population identified in table 3.1 is approximated as the difference between total population

table (not shown are land used as permanent pasture

and the urban population, which is calculated on the basis of the urban share reported by the United

and land under urban developments). • Forest area is

Nations Population Division. However, there is no universal standard for distinguishing urban from rural

land under natural or planted stands of trees, whether

areas, and any urban-rural dichotomy is an oversimplification (see About the data for table 3.10). The

productive or not. • Permanent cropland is land culti-

two distinct images—isolated farm, thriving metropolis—represent poles on a continuum. Life changes

vated with crops that occupy the land for long periods

along a variety of dimensions, moving from the most remote forest outpost through fields and pastures,

and need not be replanted after each harvest, such

past tiny hamlets, through small towns with weekly farm markets, into intensively cultivated areas near

as cocoa, coffee, and rubber. This category includes

large towns and small cities, eventually reaching the center of a cosmopolitan megacity. The changes

land under flowering shrubs, fruit trees, nut trees, and

may sometimes be abrupt, but more often they are gradual. Along the way access to infrastructure,

vines, but excludes land under trees grown for wood

social services, and nonfarm employment all increase, and with them population density and income.

or timber. • Arable land includes land defined by the

For policy purposes, therefore, one needs to go beyond the rural-urban dichotomy presented in tables

FAO as under temporary crops (double-cropped areas

3.1 and 3.10, because rurality has many dimensions.

are counted once), temporary meadows for mowing

A recent World Bank Policy Research Paper proposes an operational definition of rurality based on two

or for pasture, land under market or kitchen gardens,

dimensions: population density and distance to large cities (population greater than 100,000; Chomitz,

and land temporarily fallow. Land abandoned as a

Buys, and Thomas 2005). The report argues that these criteria constitute important gradients along

result of shifting cultivation is excluded.

which economic behavior and appropriate development interventions vary substantially. Where population densities are low, markets of all kinds are thin, and the unit cost of delivering most social services

Data sources

and many types of infrastructure is high. Where large urban areas are distant, farm-gate (or factory-gate)

Data on urban population shares used to estimate

prices of outputs will be low and prices of inputs will be high, and it will be difficult to recruit skilled people

rural population come from the United Nations

to public service or private enterprises. Thus, remoteness and low population density together define a

Population Division’s World Urbanization Prospects:

set of rural areas that face special challenges in development.

The 2005 Revision. The total population figures are

Based on these criteria, and using the Gridded Population of the World (CIESIN 2005), the authors

World Bank estimates. Data on land area and land

produced estimates of the rural population for Latin America and the Caribbean that differ substantially

use are from the FAO’s electronic files. The FAO

from those presented in table 3.1. The range of these estimates are from 13 percent of the total popula-

gathers these data from national agencies through

tion, based on a population density of less than 20 people per square kilometer, to 64 percent, based

annual questionnaires and by analyzing the results

on a population density of more than 500 people per square kilometer. Taking remoteness into account,

of national agricultural censuses. Data on forest

the estimated rural population would be between 13 and 52 percent of the population. The estimate for

area are from the FAO’s Global Forest Resources

Latin America and the Caribbean in table 3.1 is 23 percent.

Assessment 2005.

2007 World Development Indicators

129

3.2 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, 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

130

Agricultural inputs Agricultural landa

Irrigated land

Land under cereal production

Fertilizer consumption

Agricultural employment

Agricultural machinery

% of land area 1990–92 2003–05

% of cropland 1990–92 2001–03

thousand hectares 1990–92 2003–05

hundred grams per hectare of arable land 1990–92 2000–02

% of total employment 1990–92 2001–03

Tractors per 100 sq. km of arable land 1990–92 2001–03

33.9 55.6 6.4 2.3 5.6 49.9 4.2 0.3 68.0 33.8 2.1 .. 0.6 5.5 0.2 0.2 4.6 29.6 0.6 1.2 6.6 0.3 1.4 0.0 0.5 57.1 43.6 .. 14.3 0.1 0.3 15.2 1.1 0.2 22.6 .. 16.9 14.9 27.9 100.0 4.9 .. 0.5 1.4 2.8 11.0 1.1 0.9 39.9 4.0 0.7 31.1 6.8 7.0 4.1 8.0

2,283 243 3,105 893 8,510 163 12,814 903 628 10,985 2,581 326 660 633 333 140 19,633 2,174 2,725 219 1,801 816 20,864 104 1,242 742 93,430 .. 1,598 1,868 9 83 1,434 647 235 .. 1,581 134 861 2,410 453 .. 454 4,586 1,050 9,212 14 90 249 6,673 1,078 1,455 768 627 112 406

59 903 144 29 73 493 275 1,995 432 1,136 2,251 4,937 78 42 .. 22 656 1,194 60 34 19 34 476 5 25 1,215 2,777 .. 1,822 8 35 4,522 151 1,514 1,288 .. 2,249 860 508 3,977 1,336 .. 993 .. 1,647 2,918 25 44 889 2,616 38 2,289 1,072 16 15 35

58.3 41.1 16.3 46.1 46.6 43.1 60.5 42.5 51.5 73.5 43.6 43.6 20.6 32.9 15.0 45.9 28.9 55.7 34.9 82.9 30.2 19.7 7.5 8.0 38.4 21.0 57.0 .. 40.5 10.1 30.8 55.7 59.8 15.0 61.5 55.4 65.4 74.7 28.6 2.7 71.1 .. 31.2 51.0 7.9 55.3 20.0 63.2 44.8 49.8 55.7 71.3 39.5 48.9 53.2 57.9

58.3 40.9 16.8 46.2 47.0 49.3 57.5 40.0 57.5 69.3 42.7 46.0 31.3 34.2 42.1 45.8 31.2 48.5 39.8 91.3 30.3 19.7 7.4 8.3 38.6 20.4 59.5 .. 38.2 10.1 30.9 56.1 62.6 50.8 60.6 55.2 62.0 76.4 26.9 3.5 82.2 74.6 19.1 31.8 7.4 53.9 20.0 77.9 43.3 48.8 64.8 65.2 42.9 50.7 58.0 57.7

2007 World Development Indicators

33.8 49.5 6.9 2.3 5.4 51.2 5.2 0.3 70.5 54.3 2.3 4.6 0.4 4.1 0.3 0.3 4.4 16.5 0.5 1.6 7.0 0.4 1.5 0.1 0.8 82.4 47.5 .. 23.3 0.1 0.4 20.6 1.1 0.4 22.5 0.7 19.6 17.2 33.0 100.0 4.9 3.7 0.6 2.6 2.9 13.3 1.4 0.6 44.1 4.0 0.5 37.4 6.4 5.6 4.6 8.4

2,788 148 2,751 1,306 9,391 198 19,905 810 788 11,511 2,148 314 963 792 326 67 19,806 1,721 3,249 210 2,332 1,077 17,272 187 2,096 687 79,896 .. 1,265 1,974 13 58 953 646 309 1,563 1,495 150 892 2,851 330 370 268 9,039 1,200 9,160 20 186 339 6,875 1,376 1,272 666 778 138 458

19 420 130 5 295 157 479 1,540 61 1,738 1,325 3,427 154 37 356 122 1,201 500 30 33 .. 75 549 3 49 2,386 3,519 .. 2,670 7 67 6,455 256 1,303 476 1,186 1,393 848 1,679 4,464 1,054 119 432 145 1,353 2,221 9 26 412 2,245 60 1,580 1,427 32 80 181

.. .. .. .. 0.4 .. 5.5 7.5 32.5 66.4 21.7 2.8 .. 1.7 .. .. 25.6 19.7 .. .. .. 60.6 4.2 .. .. 18.8 53.5 0.8 1.4 .. .. 25.2 .. .. 25.1 10.1 5.4 19.5 7.0 36.2 17.9 .. 19.5 .. 8.8 .. .. .. .. 4.0 62.2 22.7 .. .. .. ..

.. 62.7 21.1 .. 1.1 45.7 4.4 5.6 40.1 51.7 .. 1.7 .. .. .. 16.8 20.6 18.0 .. .. 70.2 .. 2.8 .. .. 13.6 44.7 0.2 21.2 .. .. 15.5 .. 15.8 21.7 4.7 3.2 15.4 8.5 28.6 19.9 .. 6.7 .. 5.3 4.3 .. .. 53.8 2.5 .. 15.6 38.7 .. .. ..

1 177 128 35 103 277 67 2,367 184 6 195 1,498 1 25 235 143 142 128 3 2 3 1 162 0 1 144 77 .. 98 4 15 259 15 35 250 .. 625 22 67 251 60 .. 395 .. 1,040 784 50 2 279 1,253 15 774 33 5 1 2

1 141 129 33 108 289 65 2,380 164 7 111 1,254 1 20 289 159 137 95 4 2 7 1 160 0 0 272 89 .. 91 4 14 311 12 25 249 305 540 17 106 309 52 8 889 3 882 685 46 1 254 801 9 939 30 5 1 2

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

3.2

ENVIRONMENT

Agricultural inputs Agricultural landa

Irrigated land

Land under cereal production

Fertilizer consumption

Agricultural employment

Agricultural machinery

% of land area 1990–92 2003–05

% of cropland 1990–92 2001–03

thousand hectares 1990–92 2003–05

hundred grams per hectare of arable land 1990–92 2000–02

% of total employment 1990–92 2001–03

Tractors per 100 sq. km of arable land 1990–92 2001–03

29.8 70.7 60.9 23.5 37.7 21.9 70.2 26.7 55.4 44.0 15.5 12.0 79.1 45.7 21.0 21.9 7.9 50.7 7.2 39.3 31.1 76.7 27.1 8.8 52.1 17.9 47.0 40.2 22.7 26.3 38.7 55.7 54.5 75.1 79.9 68.2 60.9 15.8 47.0 29.0 58.9 65.0 52.1 27.0 79.4 3.3 3.5 33.7 28.7 2.0 59.6 17.1 37.4 61.6 42.8 47.5

26.2 65.4 60.6 26.3 37.9 22.9 62.4 24.4 50.7 47.4 12.9 11.5 76.9 .. 24.9 19.2 8.6 56.2 8.5 26.5 32.2 76.9 27.0 8.8 42.5 48.8 47.4 47.2 24.0 32.4 38.8 55.7 56.2 76.7 83.3 68.1 62.0 17.2 47.2 29.5 56.8 64.3 57.5 30.4 79.7 3.4 3.5 35.2 30.0 2.3 62.5 16.6 40.9 52.8 41.7 25.1

3.8 4.1 28.3 14.5 39.7 63.0 .. 44.4 22.9 11.0 54.3 25.0 9.8 1.3 58.2 47.1 60.0 72.6 16.2 1.1 28.1 0.6 0.5 21.8 0.5 12.1 30.7 1.2 4.8 3.7 11.8 16.0 22.4 14.2 5.8 13.2 2.8 10.1 0.7 43.0 61.0 7.6 4.0 0.5 0.7 .. 71.6 78.5 4.8 .. 2.9 29.9 15.7 0.7 20.5 36.8

5.6 4.8 32.7 12.7 42.7 58.6 .. 45.4 24.9 5.9 54.7 27.3 15.7 1.8 50.9 47.1 77.0 76.0 17.2 2.1 33.2 0.9 0.5 21.9 0.4 9.0 30.6 2.3 4.8 5.0 6.5 20.1 23.2 13.9 7.0 15.5 2.7 17.9 1.0 47.2 60.0 8.5 2.8 0.5 0.8 .. 88.4 81.1 6.2 .. 2.1 27.9 14.5 0.7 27.2 37.1

502 2,803 100,760 13,861 9,612 3,506 298 108 4,347 3 2,439 112 22,174 1,766 1,569 1,368 0 579 630 699 41 178 135 355 1,135 257 1,321 1,443 699 2,393 133 1 10,075 676 620 5,374 1,509 5,283 215 2,957 185 153 299 7,011 16,417 361 2 11,777 182 2 455 683 6,957 8,523 780 0

391 2,940 97,872 15,140 8,983 3,221 294 92 4,142 1 2,008 53 13,697 2,017 1,272 1,093 1 596 758 451 61 177 120 341 899 195 1,457 1,544 701 3,292 176 0 10,126 927 180 5,565 2,071 7,215 244 3,346 217 121 502 8,111 17,799 326 2 12,587 195 3 782 1,116 6,613 8,290 435 0

203 796 758 1,330 750 347 6,591 2,836 2,195 1,737 3,779 969 133 255 3,522 4,932 2,000 238 31 977 1,639 167 8 458 531 .. 34 351 5,264 91 132 2,732 696 762 111 353 12 79 .. 340 6,298 1,892 270 1 142 2,362 2,441 962 666 622 92 246 935 895 1,123 ..

1,193 993 1,044 1,259 890 968 5,172 2,575 1,819 1,258 3,066 1,322 23 320 1,018 4,317 711 209 94 572 2,838 309 .. 349 903 502 31 400 6,548 88 59 3,035 727 86 31 440 53 146 4 333 4,286 5,704 177 3 66 2,086 2,491 1,378 545 556 319 759 1,313 1,151 1,258 ..

42.1 11.3 68.1 54.9 25.6 .. 14.1 3.7 8.4 27.3 6.8 .. .. 19.0 .. 16.7 .. 35.5 .. .. .. .. .. .. 18.8 .. .. .. 23.9 .. .. 14.3 24.2 .. .. .. .. 69.4 48.2 82.3 4.3 10.7 38.7 .. .. 5.9 .. 48.9 25.8 .. 1.7 1.0 45.3 25.2 15.6 3.5

35.1 6.0 .. 44.8 .. .. 6.8 2.0 5.0 20.4 4.7 3.9 35.4 .. .. 9.4 .. 52.8 .. 14.8 .. .. .. .. 17.7 23.0 78.0 .. 14.8 .. .. 10.5 17.2 47.9 45.0 44.8 .. .. .. .. 2.8 8.7 26.8 .. .. 3.8 .. 45.3 17.7 .. 32.2 3.5 37.3 18.9 12.5 2.0

31 158 65 18 136 72 1,667 763 1,619 242 4,297 352 59 24 297 275 215 179 11 343 188 57 8 187 242 730 11 8 161 11 8 36 129 293 73 46 16 12 47 23 2,056 322 20 0 8 1,723 42 133 103 59 72 36 20 821 569 ..

2007 World Development Indicators

49 247 141 41 161 80 1,324 714 2,031 177 4,588 308 22 28 241 1,239 69 167 12 580 488 61 9 219 641 954 12 6 241 6 8 37 131 221 42 58 14 10 39 24 1,645 507 15 0 10 1,486 50 149 148 53 55 36 20 1,034 1,044 ..

131

3.2 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 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 Europe EMU

Agricultural inputs Agricultural landa

Irrigated land

Land under cereal production

Fertilizer consumption

Agricultural employment

Agricultural machinery

% of land area 1990–92 2003–05

% of cropland 1990–92 2001–03

thousand hectares 1990–92 2003–05

hundred grams per hectare of arable land 1990–92 2000–02

% of total employment 1990–92 2001–03

Tractors per 100 sq. km of arable land 1990–92 2001–03

64.4 13.0 75.6 .. 41.9 21.1 38.3 2.2 50.9 9.8 70.2 80.2 60.8 36.2 52.0 75.8 8.2 46.9 73.7 30.9 53.7 41.9 58.7 25.7 58.4 51.8 66.1 61.0 69.8 3.7 75.0 46.6 84.7 62.8 24.7 21.0 .. 33.4 47.4 52.3 37.7 w 44.3 34.6 41.2 25.7 37.4 48.4 28.4 34.7 22.6 54.7 43.4 38.5 49.7

63.8 13.2 78.4 .. 42.4 54.8 39.7 1.2 42.3 25.0 70.3 82.0 58.3 36.5 56.6 80.9 7.8 38.1 75.6 30.4 54.4 36.2 66.7 25.9 63.0 53.3 70.2 .. 71.4 6.7 70.2 45.3 85.4 64.1 24.5 30.8 .. 33.6 47.5 53.1 38.3 w 45.2 35.5 42.6 25.9 38.2 50.7 27.6 35.8 23.2 54.5 44.1 38.6 47.5

31.0 4.2 0.3 44.2 3.3 1.9 5.2 .. .. 0.8 19.2 8.3 16.9 28.0 13.9 24.1 4.1 6.0 14.3 72.9 3.2 21.0 0.3 3.3 7.3 14.8 .. 0.1 7.6 .. 2.5 11.3 10.2 87.3 13.9 44.6 .. 24.3 0.7 3.6 17.7 w 21.8 20.1 24.4 11.8 20.7 .. 10.3 11.3 29.6 33.9 3.4 10.7 14.9

31.2 3.7 0.7 42.7 4.6 0.8 5.0 .. 12.6 1.5 18.7 9.5 20.7 34.4 11.0 26.0 4.3 5.8 24.0 68.2 3.5 26.6 0.3 3.3 8.0 19.5 89.1 0.1 6.8 29.2 3.0 12.5 14.3 87.4 16.9 33.9 .. 31.4 2.8 5.2 18.4 w 24.4 18.5 24.4 9.5 20.7 .. 11.2 11.4 32.7 38.9 3.6 11.8 17.0

5,842 59,600 258 1,062 1,154 2,618 452 .. 1 130 531 5,736 7,588 834 6,267 69 1,184 207 3,812 266 3,003 10,594 610 6 1,525 13,760 332 1,098 12,555 1 3,549 64,547 509 1,227 799 6,726 .. 738 813 1,431 704,675 s 211,290 350,107 228,729 121,378 561,397 142,270 140,517 47,720 30,593 129,690 70,608 143,278 32,976

a. Agricultural land includes permanent pastures, arable land, and land under permanent crops.

132

2007 World Development Indicators

5,675 788 355 39,471 410 121 332 20 48 669 1,446 1,067 1,202 65 140 1,987 265 732 253 23 5 .. 54,333 25,920 802 .. .. 99 3,168 4,239 682 26 5 4,429 549 558 6,573 1,186 1,653 882 2,016 2,862 10,005 51 40 61 688 371 1,098 1,112 1,010 164 4,032 2,272 3,195 621 718 393 1,461 175 3,340 136 31 11,134 598 1,039 767 56 74 2 1,111 631 1,450 330 372 13,817 757 768 983 1,273 543 1,550 1 14 13,082 792 154 0 4,810 5,149 3,040 3,323 3,141 57,163 1,015 1,096 528 610 849 1,692 1,602 1,614 1,042 1,388 1,132 8,392 1,299 3,172 .. .. .. 635 127 95 717 131 84 1,617 508 443 677,585 s 925 w 1,020 w 230,781 541 686 310,863 970 1,110 208,372 1,278 1,573 102,492 553 471 541,644 817 951 133,753 .. .. 114,042 581 347 49,696 587 896 29,108 643 833 129,043 767 1,067 86,002 136 125 135,941 1,213 1,212 31,419 2,332 2,059

30.6 14.5 .. .. .. .. .. 0.3 .. .. .. .. 10.7 44.3 .. .. 3.3 4.2 .. .. 84.2 61.5 .. 11.8 .. 46.5 .. 91.5 .. .. 2.2 2.9 4.5 .. 12.6 73.8 .. .. .. .. 41.8 w .. 44.2 48.9 20.8 50.6 54.2 22.4 18.1 .. 66.1 .. 5.7 7.3

38.1 11.4 .. 5.4 .. .. .. 0.3 6.0 9.3 .. 10.7 6.1 34.7 .. .. 2.2 4.2 30.3 .. 82.1 45.7 .. 7.4 .. 35.5 .. 69.1 19.5 .. 1.4 2.2 4.3 .. 10.0 61.9 14.1 .. .. .. .. w .. 35.8 40.1 15.5 .. 44.9 20.9 16.7 .. .. .. 3.8 4.9

146 92 1 20 2 1,067 3 637 .. .. 21 101 494 71 8 251 604 2,870 137 392 19 39 0 354 88 287 439 9 145 50 762 245 259 380 176 60 .. 40 11 61 186 w 52 127 99 164 100 63 172 123 115 67 19 417 992

179 52 1 28 3 1,023 2 794 .. .. 16 46 712 113 7 222 615 2,649 224 233 19 144 0 360 126 410 256 9 124 55 878 270 241 373 189 247 .. 43 11 75 200 w 84 137 112 173 117 89 185 123 142 129 13 431 1,002

About the data

Agriculture is still a major sector in many economies, and agricultural activities provide developing

Definitions

• Agricultural land refers to the share of land area

Nearly 40 percent of land globally is used for agriculture

3.2a

that is arable, under permanent crops, or under permanent pastures. Arable land includes land defined

countries with food and revenue. But they also can degrade natural resources. Poor farming practices

3.2

ENVIRONMENT

Agricultural inputs

Total land: 130 million square kilometers

by the FAO as land under temporary crops (double-

can cause soil erosion and loss of soil fertility.

cropped areas are counted once), temporary mead-

Efforts to increase productivity through the use of

ows for mowing or for pasture, land under market

chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts.

Permanent pastures 26.4%

Others 31.2%

or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is

Excessive use of chemical fertilizers can alter the

excluded. Land under permanent crops is land culti-

chemistry of soil. Pesticide poisoning is common in

Arable land 10.8%

developing countries. And salinization of irrigated land diminishes soil fertility. Thus inappropriate use of inputs for agricultural production has far-reach-

Forests 30.5%

agricultural production: land, fertilizer, labor, and agricultural machinery. There is no single correct mix

and need not be replanted after each harvest, such as cocoa, coffee, and rubber. This category includes

Permanent crops 1.1%

ing effects. This table provides indicators of major inputs to

vated with crops that occupy the land for long periods

land under flowering shrubs, fruit trees, nut trees, and vines, but excludes land under trees grown for

Note: Agricultural land includes permanent pastures, arable land, and land under permanent crops. Source: Tables 3.1 and 3.2.

wood or timber. Permanent pasture is land used for five or more years for forage, including natural and cultivated crops. • Irrigated land refers to areas

of inputs: appropriate levels and application rates

purposely provided with water, including land irri-

vary by country and over time, depending on the type

gated by controlled flooding. • Cropland refers to

of crops, the climate and soils, and the production

arable land and permanent cropland (see table 3.1).

process used.

• Land under cereal production refers to harvested

The data shown here and in table 3.3 are col-

areas, although some countries report only sown

lected by the Food and Agriculture Organization

or cultivated area. • Fertilizer consumption is the

(FAO) through annual questionnaires. The FAO tries to

quantity of plant nutrients used per unit of arable

impose standard definitions and reporting methods,

land. Fertilizer products cover nitrogenous, potash,

but exact consistency across countries and over time

and phosphate fertilizers (including ground rock

is not possible. For example, despite standard defini-

phosphate). Traditional nutrients—animal and plant

tions, data on agricultural land in different climates

manures—are not included. The time reference for

may not be comparable. For example, permanent

fertilizer consumption is the crop year (July through

pastures are quite different in nature and intensity

June). • Agricultural employment refers to employ-

in African countries and dry Middle Eastern coun-

ment in agriculture, forestry, hunting, and fishing (see

tries. Data on agricultural employment, in particular,

table 2.3 for more detail). • Agricultural machinery

should be used with caution. In many countries much

refers to wheel and crawler tractors (excluding gar-

agricultural employment is informal and unrecorded,

den tractors) in use in agriculture at the end of the

including substantial work performed by women and

calendar year specified or during the first quarter of

children.

the following year.

Fertilizer consumption measures the quantity of plant nutrients. Consumption is calculated as production plus imports minus exports. Because some chemical compounds used for fertilizers have other industrial applications, the consumption data may overstate the quantity available for crops. To smooth annual fluctuations in agricultural activity, the indicators in the table have been averaged over three years.

Data sources Data on agricultural inputs are from electronic files that the FAO makes available to the World Bank.

2007 World Development Indicators

133

3.3

Agricultural output and productivity Crop production index

1999–2001 = 100 1990–92 2002–04

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, 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

134

.. 86.2 85.4 60.5 67.2 106.5 59.7 93.4 137.4 75.4 107.3 77.6 57.7 63.6 107.2 96.2 77.2 149.2 67.0 112.4 65.2 71.2 87.9 74.4 69.0 78.2 69.6 .. 98.4 124.7 80.2 71.4 73.0 79.9 112.1 .. 103.5 119.1 80.1 69.2 102.2 .. 121.4 .. 97.5 94.0 87.2 55.8 120.6 83.7 59.1 86.9 77.6 73.7 71.1 108.5

2007 World Development Indicators

.. 100.6 122.9 119.4 106.4 119.2 81.6 99.1 122.7 104.7 124.7 106.0 125.4 116.4 101.1 111.5 119.6 110.9 126.6 107.0 105.8 103.0 93.8 97.7 110.9 110.5 110.6 .. 107.4 97.2 105.1 99.6 96.2 97.2 112.6 94.8 97.7 110.0 95.9 104.2 90.6 67.7 89.9 106.7 102.4 98.8 101.9 65.2 91.9 95.1 117.0 90.4 102.6 107.5 104.9 98.8

Food production index

1999–2001 = 100 1990–92 2002–04

.. 74.2 81.7 65.0 73.6 112.9 69.2 89.8 104.8 73.8 136.1 91.3 62.7 70.0 120.3 114.8 70.4 137.5 68.7 112.1 65.1 73.9 84.1 69.9 72.5 74.0 60.1 .. 83.9 121.4 79.0 72.2 72.9 99.0 111.5 .. 97.6 104.0 72.4 67.5 86.4 .. 181.3 .. 104.0 97.4 89.1 60.2 102.7 98.0 61.1 93.7 75.4 72.9 73.1 99.8

.. 105.9 113.2 113.0 101.4 121.8 91.7 99.0 118.6 104.6 107.2 101.3 126.3 111.6 96.0 104.8 118.0 101.7 116.2 106.3 106.3 104.2 94.8 106.9 110.2 108.6 113.2 .. 106.8 97.5 107.0 101.0 100.0 98.9 108.1 96.6 100.6 106.1 106.5 107.7 102.9 83.2 100.4 110.6 103.3 99.5 101.6 68.7 100.9 97.5 116.9 92.2 105.5 110.7 105.1 101.9

Livestock production index

1999–2001 = 100 1990–92 2002–04

.. 66.6 80.7 75.6 89.2 118.9 83.3 92.5 98.2 73.8 146.5 94.3 89.1 77.2 122.7 118.8 65.5 147.1 70.7 135.1 65.7 84.1 78.3 68.1 84.5 68.0 49.4 .. 80.6 100.8 76.0 79.9 74.9 126.6 130.0 .. 89.0 79.5 65.1 65.4 74.5 .. 193.3 .. 106.5 97.3 86.5 98.8 78.9 107.5 89.8 101.5 76.6 60.5 81.2 69.8

.. 108.9 103.3 100.0 92.0 123.2 96.9 99.6 113.6 103.2 99.7 99.7 109.2 107.9 86.6 103.4 116.8 96.2 108.1 100.2 103.5 103.1 103.6 113.5 105.4 107.0 116.1 .. 107.1 99.2 114.5 101.4 110.9 108.2 92.7 95.8 102.8 103.7 115.3 115.3 108.5 97.1 101.7 117.7 104.3 100.4 100.5 102.6 110.3 101.0 108.7 98.2 100.6 111.8 106.6 111.6

Cereal yield

Agricultural productivity

kilograms per hectare 1990–92 2003–05

Agriculture value added per worker 2000 $ 1990–92 2001–03

.. 2,372 915 378 2,652 1,826 1,739 5,400 2,113 2,567 2,739 6,122 880 1,385 3,548 312 1,916 3,639 783 1,370 1,356 1,166 2,559 884 636 3,949 4,307 .. 2,492 791 688 3,188 869 3,975 2,092 .. 5,448 4,078 1,724 5,738 1,871 .. 1,304 .. 3,246 6,370 1,712 1,120 1,998 5,578 1,084 3,589 1,882 1,064 1,529 997

.. 3,491 1,468 547 3,771 2,122 1,960 5,738 2,633 3,533 2,850 8,710 1,147 1,857 3,393 514 3,149 3,157 959 1,336 2,062 1,720 2,962 1,046 711 5,621 5,057 .. 3,567 767 806 4,001 1,266 4,179 3,076 4,816 6,080 4,177 2,485 7,528 2,462 296 2,274 1,261 3,284 6,876 1,641 1,155 2,152 6,497 1,437 3,699 1,747 1,476 1,149 824

.. 773 1,911 183 6,764 1,428 22,405 12,048 1,085 246 1,977 21,356 368 670 .. 571 1,700 2,493 143 110 .. 713 28,224 290 179 3,618 254 .. 3,406 186 314 3,143 605 4,751 .. .. 15,157 2,254 1,686 1,531 1,633 .. 3,002 .. 15,425 22,234 1,566 224 2,388 14,025 302 7,563 2,149 172 205 ..

.. 1,314 2,067 160 9,272 2,645 31,218 20,587 1,033 308 2,513 36,043 578 746 5,696 410 3,002 6,313 163 80 296 1,102 37,590 407 225 4,795 368 .. 2,951 154 329 4,283 798 8,957 .. 4,564 35,696 4,108 1,486 1,975 1,616 64 3,168 150 29,735 39,220 1,696 226 1,404 23,475 331 9,114 2,274 225 225 ..

Crop production index

1999–2001 = 100 1990–92 2002–04

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

92.9 114.0 79.6 82.8 73.8 .. 92.7 97.8 97.3 84.9 112.9 100.1 163.8 86.9 126.2 88.2 33.6 68.5 62.2 128.7 109.7 67.5 62.3 79.2 80.2 107.4 93.6 57.5 74.4 73.8 63.2 110.7 82.8 136.6 246.9 101.1 64.7 61.5 71.9 73.5 93.7 78.9 76.6 71.4 68.9 120.7 62.8 80.6 110.9 78.5 85.8 52.6 84.2 109.1 103.1 167.7

118.9 99.7 100.0 112.7 118.1 .. 100.3 103.3 92.6 96.7 95.0 136.6 108.4 103.2 108.4 91.3 110.6 102.9 115.3 119.4 94.1 100.8 97.7 96.9 113.1 93.3 103.5 84.3 114.0 107.4 97.2 101.6 103.8 112.2 107.3 133.4 106.1 114.7 111.4 111.2 97.9 101.9 115.3 119.5 103.4 103.4 87.3 102.5 104.2 101.6 120.7 108.1 109.6 91.6 98.6 114.6

Food production index

1999–2001 = 100 1990–92 2002–04

86.5 117.0 75.9 83.8 72.2 .. 95.3 82.8 97.0 85.7 108.4 85.4 163.0 85.7 119.6 79.8 26.4 74.0 59.1 222.3 100.4 87.8 80.5 77.1 159.9 107.8 90.4 49.6 70.5 78.6 84.2 101.1 77.7 153.3 98.3 94.3 70.5 62.3 99.5 75.2 105.5 77.8 64.0 75.4 69.1 104.1 60.2 70.6 94.8 79.9 77.4 57.1 77.9 110.0 98.7 127.6

111.9 100.9 102.5 113.1 113.3 .. 96.4 106.5 94.3 97.9 97.4 124.1 106.4 106.4 109.1 92.8 122.0 101.0 115.9 111.0 100.4 100.4 96.2 101.6 111.0 96.2 101.8 87.1 113.7 105.8 107.6 104.9 105.5 112.6 96.4 122.6 103.0 115.2 109.8 109.4 94.8 112.1 119.4 116.3 103.7 98.6 89.9 106.0 101.8 105.9 110.3 110.7 112.2 103.6 99.1 97.8

Livestock production index

1999–2001 = 100 1990–92 2002–04

69.3 125.5 69.4 85.8 68.8 .. 94.3 72.4 95.1 87.2 106.8 71.2 178.5 83.9 145.1 68.1 27.9 106.9 60.6 273.8 65.6 115.0 90.4 75.9 187.0 105.1 93.3 85.4 81.3 81.3 87.4 71.1 71.4 198.7 93.9 81.3 94.8 65.0 104.1 80.1 105.3 80.7 57.5 82.0 76.9 98.2 65.7 67.6 76.3 80.8 87.3 68.3 62.1 114.8 85.7 118.4

105.8 101.9 110.5 127.3 103.3 .. 96.1 113.1 99.4 102.8 100.2 94.1 111.6 110.4 114.2 100.4 115.7 98.4 107.5 101.1 120.4 100.0 107.8 101.0 107.8 103.3 97.1 101.8 115.1 112.9 109.3 116.8 107.8 103.2 95.9 102.0 100.9 115.1 109.3 107.3 92.6 112.1 119.9 104.7 106.6 97.3 94.0 109.1 101.1 110.1 98.2 114.1 120.7 105.0 98.2 94.1

3.3

ENVIRONMENT

Agricultural output and productivity Cereal yield

Agricultural productivity

kilograms per hectare 1990–92 2003–05

Agriculture value added per worker 2000 $ 1990–92 2001–03

1,371 4,551 1,947 3,826 1,523 .. 6,653 3,132 4,340 1,298 5,713 1,167 1,338 1,645 5,073 5,885 3,112 2,772 2,344 1,641 2,001 703 951 706 1,938 2,652 1,935 871 2,827 840 802 4,117 2,520 2,928 967 1,095 330 2,739 381 1,831 7,145 5,257 1,529 323 1,135 3,744 2,145 1,818 1,862 2,504 1,905 2,463 2,070 2,958 1,939 1,100

1,095 4,718 2,391 4,278 2,411 .. 7,390 3,725 5,057 1,162 5,807 1,354 994 1,409 3,408 6,233 1,975 2,984 3,180 2,225 2,377 906 889 626 3,183 3,053 2,321 1,150 3,293 872 1,075 3,436 2,872 2,572 808 1,282 921 3,420 441 2,284 8,036 7,360 1,778 394 1,057 4,121 2,332 2,438 1,958 3,539 2,245 3,399 2,946 3,191 2,683 1,731

976 3,268 332 483 1,953 .. .. .. 11,536 2,013 20,196 1,892 1,745 335 .. 5,677 .. 676 360 1,790 .. 476 .. .. .. 2,256 185 72 3,803 204 574 3,942 2,247 1,286 703 1,438 108 .. 811 196 24,056 20,180 .. 170 592 20,055 1,005 589 2,363 .. 1,596 930 899 1,502 4,640 ..

2007 World Development Indicators

1,110 4,120 381 556 2,330 2,256 .. .. 21,113 1,944 33,546 1,099 1,389 327 .. 9,948 13,048 929 459 2,442 24,436 503 .. .. 4,117 2,964 177 130 4,570 227 385 5,065 2,708 725 684 1,515 137 .. 1,057 208 37,337 26,310 1,901 172 843 32,649 1,128 690 3,557 614 1,939 1,428 1,010 1,967 5,925 ..

135

3.3

Agricultural output and productivity Crop production index

1999–2001 = 100 1990–92 2002–04

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 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 Europe EMU

136

92.2 125.8 111.4 120.7 73.0 97.6 128.1 157.1 .. 93.1 .. 79.6 87.9 86.2 68.9 106.6 102.2 112.4 73.6 123.6 92.7 82.0 73.4 116.3 104.6 88.0 111.4 78.0 130.6 23.4 104.9 88.4 70.4 107.8 79.5 60.1 .. 75.0 80.7 69.2 82.5 w 78.5 80.9 77.5 93.1 80.1 71.8 113.2 78.2 78.8 79.9 75.9 89.9 91.5

2007 World Development Indicators

112.2 116.0 117.6 114.8 68.3 110.0 113.5 100.0 .. 110.2 .. 102.4 106.1 98.8 110.8 100.1 102.1 95.3 117.1 132.9 103.6 106.1 110.3 91.9 104.2 104.0 116.5 106.6 114.0 56.0 100.3 101.5 112.7 109.0 96.0 116.6 .. 100.1 102.4 69.3 105.7 w 103.5 110.2 111.7 104.9 108.1 110.8 107.1 111.5 113.7 101.0 103.9 98.2 97.8

Food production index

1999–2001 = 100 1990–92 2002–04

97.7 132.6 107.3 105.2 71.9 109.2 118.9 352.1 .. 77.2 .. 84.2 87.1 88.9 66.7 108.9 97.9 104.9 75.1 138.1 88.7 84.1 74.1 88.7 91.2 89.5 57.1 79.5 146.0 26.5 107.2 84.8 76.7 91.3 73.9 63.1 .. 71.5 84.3 77.3 82.0 w 76.1 79.8 72.8 101.8 78.7 64.5 127.1 74.4 75.7 75.5 77.6 89.7 94.6

110.7 110.2 117.2 116.0 74.9 106.0 112.3 69.3 .. 105.8 .. 105.7 105.3 100.0 107.6 105.3 100.0 100.1 122.2 132.6 105.0 106.0 104.2 122.1 103.0 103.2 125.2 107.7 108.1 62.2 98.9 102.7 104.3 107.9 98.9 118.3 .. 107.4 104.1 85.7 106.2 w 105.2 110.5 112.5 104.2 108.9 112.4 106.1 110.4 112.5 103.5 105.1 99.9 98.8

Livestock production index

1999–2001 = 100 1990–92 2002–04

114.5 152.1 77.7 67.8 74.8 103.8 86.1 396.3 .. 73.6 .. 94.6 79.5 94.6 67.6 130.3 95.7 104.8 75.0 192.6 82.9 86.8 87.9 73.5 60.3 92.2 64.0 82.3 170.0 57.5 105.6 83.4 84.2 99.7 73.5 57.9 .. 66.3 80.1 90.1 83.4 w 73.5 81.2 67.9 115.8 79.3 52.4 149.3 72.9 70.4 69.1 84.5 90.1 97.9

107.6 103.2 107.3 104.9 98.2 94.9 105.2 74.2 .. 103.6 .. 108.2 107.2 109.9 106.3 111.9 97.7 101.9 115.6 139.2 109.4 105.5 106.7 142.6 99.9 101.6 121.7 112.9 108.1 116.9 98.5 102.6 98.3 104.7 100.4 124.9 .. 115.5 99.2 100.1 107.0 w 109.6 111.0 114.1 102.7 110.6 116.6 104.1 108.9 107.7 109.8 107.1 101.2 99.7

Cereal yield

Agricultural productivity

kilograms per hectare 1990–92 2003–05

Agriculture value added per worker 2000 $ 1990–92 2001–03

2,777 1,743 1,088 4,212 803 2,926 1,223 .. .. 3,609 .. 1,602 2,310 2,950 596 1,299 4,272 6,102 947 1,037 1,276 2,186 791 3,159 1,401 2,192 2,210 1,487 2,834 2,042 6,321 4,875 2,445 1,777 2,561 3,097 .. 906 1,251 1,123 2,868 w 1,753 2,987 3,206 2,453 2,452 3,816 2,657 2,234 1,632 1,992 986 4,263 4,656

3,255 1,839 989 4,430 1,013 4,056 1,223 .. .. 5,247 .. 2,907 3,040 3,428 481 1,114 4,835 6,150 1,786 2,252 1,469 2,725 1,040 2,722 1,539 2,399 3,011 1,667 2,436 3,119 7,097 6,444 4,279 3,461 3,329 4,651 .. 772 1,584 676 3,247 w 2,086 3,312 3,629 2,673 2,791 4,460 2,324 3,204 2,405 2,497 1,102 5,041 5,426

2,196 1,824 192 7,867 250 .. .. 25,421 .. 11,310 .. 1,796 9,515 705 346 1,239 21,463 22,228 2,247 391 245 501 354 1,666 2,431 1,788 1,222 187 1,194 9,390 22,506 20,797 5,714 1,274 4,548 215 .. 273 161 244 756 w 315 535 424 2,378 448 303 1,817 2,223 1,575 340 314 15,048 12,644

3,477 2,226 222 13,964 250 1,562 .. 34,911 3,475 32,311 .. 2,391 18,691 737 707 1,149 30,116 22,348 3,248 379 283 586 404 2,435 2,431 1,764 .. 230 1,433 34,155 25,876 36,216 6,743 1,524 5,899 290 .. 348 205 266 875 w 364 717 576 2,731 562 412 1,946 2,924 1,919 393 337 25,144 21,414

3.3

ENVIRONMENT

Agricultural output and productivity About the data

The agricultural production indexes in the table are

To ease cross-country comparisons, the FAO uses

excluded. But millet and sorghum, which are grown

prepared by the Food and Agriculture Organization

international commodity prices to value production.

as feed for livestock and poultry in Europe and North

(FAO). The FAO obtains data from official and semiof-

These prices, expressed in international dollars

America, are used as food in Africa, Asia, and coun-

ficial reports of crop yields, area under production, and

(equivalent in purchasing power to the U.S. dollar),

tries of the former Soviet Union. So some cereal

livestock numbers. If data are not available, the FAO

are derived using a Geary-Khamis formula applied

crops are excluded from the data for some countries

makes estimates. The indexes are calculated using

to agricultural outputs (see Inter-Secretariat Work-

and included elsewhere, depending on their use.

the Laspeyres formula: production quantities of each

ing Group on National Accounts 1993, sections

commodity are weighted by average international com-

16.93–96). This method assigns a single price to

modity prices in the base period and summed for each

each commodity so that, for example, one metric ton

year. Because the FAO’s indexes are based on the con-

of wheat has the same price regardless of where it

• Crop production index shows agricultural pro-

cept of agriculture as a single enterprise, estimates of

was produced. The use of international prices elimi-

duction for each period relative to the base period

the amounts retained for seed and feed are subtracted

nates fluctuations in the value of output due to transi-

1999–2001. It includes all crops except fodder

from the production data to avoid double counting. The

tory movements of nominal exchange rates unrelated

crops. The regional and income group aggregates for

resulting aggregate represents production available for

to the purchasing power of the domestic currency.

the FAO’s production indexes are calculated from the

Definitions

any use except as seed and feed. The FAO’s indexes

Data on cereal yield may be affected by a variety

underlying values in international dollars, normalized

may differ from other sources because of differences

of reporting and timing differences. Cereal crops

to the base period 1999–2001. The data in this table

in coverage, weights, concepts, time periods, calcula-

harvested for hay or harvested green for food, feed,

are three-year averages. • Food production index

tion methods, and use of international prices.

or silage, and those used for grazing, are generally

covers food crops that are considered edible and that contain nutrients. Coffee and tea are excluded because, although edible, they have no nutritive

The five countries with the highest agricultural productivity

3.3a

value. • Livestock production index includes meat and milk from all sources, dairy products such as

Agricultural value added per agricultural worker (2000 $ thousands)

1990–92

2001–03

40

cheese, and eggs, honey, raw silk, wool, and hides and skins. • Cereal yield, measured in kilograms per hectare of harvested land, includes wheat, rice,

35

maize, barley, oats, rye, millet, sorghum, buckwheat,

30

and mixed grains. Production data on cereals refer to

25

crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or

20

silage, and those used for grazing, are excluded. The 15

FAO allocates production data to the calendar year in

10

which the bulk of the harvest took place. But most of a crop harvested near the end of a year will be

5

used in the following year.• Agricultural productivity refers to the ratio of agricultural value added, mea-

0 France

Canada

Netherlands

United States

Belgium

Denmark

sured in 2000 U.S. dollars, to the number of workers in agriculture. Agricultural productivity is measured

Source: Table 3.3.

by value added per unit of input. (For further discussion of the calculation of value added in national The 10 countries with the highest cereal yield in 2003–05—and the 10 with the lowest

accounts, see About the data for tables 4.1 and 4.2.)

3.3b

and fishing. Thus interpretations of land productiv-

Country

Kilograms per hectare

Belgium

8,710

Eritrea

296

Netherlands

8,036

Niger

394

Egypt, Arab Rep.

7,528

Namibia

441

Ireland

7,390

Sudan

481

New Zealand

7,360

Botswana

514

United Kingdom

7,097

Angola

547

by the FAO. The FAO makes these data and the

France

6,876

Libya

626

data on cereal yield and agricultural employment

Germany

6,497

Zimbabwe

676

available to the World Bank in electronic files that

United States

6,444

Chad

711

may contain more recent information than pub-

Korea, Rep.

6,233

Congo, Dem. Rep.

767

lished versions. For sources of data on agricultural

Source: Table 3.3.

Country

Kilograms per hectare

Agricultural value added includes that from forestry ity should be made with caution. To smooth annual fluctuations in agricultural activity, the indicators in the table have been averaged over three years.

Data sources The agricultural production indexes are prepared

value added, see Data sources for table 4.2.

2007 World Development Indicators

137

3.4

Deforestation and biodiversity Forest area

Average annual deforestationa

thousand sq. km

% 1990– 2005

2005

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgiumd Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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

138

9 8 23 591 330 3 1,637 39 9 9 79 7 24 587 22 119 4,777 36 68 2 104 212 3,101 228 119 161 1,973 .. 607 1,336 225 24 104 21 27 26 5 14 109 1 3 16 23 130 225 156 218 5 28 111 55 38 39 67 21 1

2.3 0.0 –1.8 0.2 0.4 1.2 0.2 –0.2 0.0 0.1 –0.5 0.1 1.9 0.4 0.1 0.9 0.5 –0.6 0.3 3.2 1.3 0.9 0.0 0.1 0.6 –0.4 –1.7 .. 0.1 0.3 0.1 0.4 –0.1 –0.1 –2.1 0.0 –0.8 0.0 1.4 –3.5 1.4 0.3 –0.4 0.9 –0.1 –0.5 0.0 –0.4 0.0 –0.2 1.7 –0.9 1.1 0.6 0.4 0.6

2007 World Development Indicators

Mammals

Birds

Higher plantsb

Nationally GEF benefits protected areas index for biodiversity

Marine protected areas

% of thousand total land thousand area sq. km sq. km

Total known Threatened Total known Threatened Total known Threatened species species species species species species 2004

2004

2004

2004

2002

2002

2005

144 73 100 296 375 78 376 101 82 131 71 92 159 361 78 169 578 106 129 116 127 322 211 187 104 159 580 57 467 430 166 232 229 96 65 88 81 36 341 118 137 70 67 288 80 148 166 133 98 126 249 118 193 215 101 41

12 1 12 11 32 9 63 5 11 22 6 9 6 26 8 6 74 12 6 7 23 42 16 11 12 22 80 1 39 29 14 13 23 7 11 6 4 5 34 6 2 9 4 35 3 16 11 3 11 9 15 11 7 18 5 4

434 303 372 930 1,038 302 851 412 364 604 226 427 485 1,414 312 570 1,712 379 452 597 521 936 472 663 531 445 1,221 306 1,821 1,148 597 838 702 365 358 386 427 224 1,515 481 434 537 267 839 421 517 632 535 268 487 729 412 684 640 459 271

17 9 11 20 55 12 60 8 11 23 4 10 2 30 8 9 120 11 2 9 24 18 19 3 5 32 82 20 86 30 4 18 11 9 18 9 10 16 69 17 3 7 3 20 10 15 5 2 8 14 8 14 10 10 1 15

4,000 3,031 3,164 5,185 9,372 3,553 15,638 3,100 4,300 5,000 2,100 1,550 2,500 17,367 .. 2,151 56,215 3,572 1,100 2,500 .. 8,260 3,270 3,602 1,600 5,284 32,200 .. 51,220 11,007 6,000 12,119 3,660 4,288 6,522 1,900 1,450 5,657 19,362 2,076 2,911 .. 1,630 6,603 1,102 4,630 6,651 974 4,350 2,682 3,725 4,992 8,681 3,000 1,000 5,242

1 0 2 26 42 1 56 3 0 12 0 0 14 70 1 0 381 0 2 2 31 334 1 15 2 40 443 6 222 65 35 110 105 0 163 4 3 30 1 2 25 3 0 22 1 2 107 4 0 12 117 2 85 22 4 28

3.6 0.2 3.0 9.6 18.5 0.3 95.8 0.3 0.9 1.6 0.0 0.0 0.2 13.8 0.4 1.5 100.0 0.9 0.3 0.5 3.9 13.3 22.2 1.7 2.1 16.2 64.8 .. 57.3 17.0 3.4 11.1 3.9 0.5 13.5 0.1 0.2 6.8 30.0 3.2 0.8 0.9 0.0 8.5 0.2 3.9 3.4 0.1 0.7 0.7 2.0 3.0 8.9 2.6 0.7 5.8

% of surface area

2004 c

2004 c

2004 c

2004 c

2.2 0.7 118.6 125.5 174.5 3.0 745.3 23.5 4.0 0.7 13.2 1.0 26.4 211.0 0.3 174.9 1,532.6 11.2 42.1 1.5 41.5 37.4 628.7 103.3 119.8 26.9 1,100.7 0.3 825.3 194.4 61.3 12.1 54.5 3.6 1.5 14.4 10.9 11.9 67.2 56.0 0.4 5.0 8.9 186.2 29.5 16.2 8.8 0.3 3.0 111.5 36.9 4.3 25.4 15.6 0.0 0.1

0.3 2.7 5.0 10.1 6.4 10.6 9.7 28.5 4.8 0.5 6.3 3.5 23.9 19.5 0.5 30.9 18.1 10.3 15.4 5.7 23.5 8.0 6.9 16.6 9.5 3.6 11.8 24.7 74.4 8.6 18.0 23.6 17.1 6.5 1.4 18.7 25.7 24.6 24.3 5.6 1.9 5.0 21.1 18.6 9.7 3.0 3.4 3.5 4.3 32.0 16.2 3.3 23.4 6.4 0.0 0.3

.. 0.3 0.9 29.1 7.8 .. 680.8 .. 1.2 0.3 .. 0.0 .. .. .. .. 47.4 0.0 .. .. 1.9 3.9 362.7 .. .. 114.5 16.0 0.3 8.1 .. .. 4.8 0.3 2.5 31.7 .. 5.1 8.6 141.0 76.7 0.1 .. .. .. 1.1 0.5 1.0 0.2 0.0 9.1 .. 2.5 0.1 .. .. ..

.. 1.0 0.0 2.3 0.3 .. 8.8 .. 1.4 0.2 .. 0.0 .. .. .. .. 0.6 0.0 .. .. 1.1 0.8 3.6 .. .. 15.1 0.2 .. 0.7 .. .. 9.4 0.1 4.4 28.6 .. 11.8 17.6 49.7 7.7 0.4 .. .. .. 0.3 0.1 0.4 1.9 0.1 2.6 .. 1.9 0.1 .. .. ..

Forest area

Average annual deforestationa

thousand sq. km

% 1990– 2005

2005

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

46 20 677 885 111 8 7 2 100 3 249 1 33 35 62 63 0 9 161 29 1 0 32 2 21 9 128 34 209 126 3 0 642 3 103 44 193 322 77 36 4 83 52 13 111 94 0 19 43 294 185 687 72 92 38 4

2.5 –0.6 –0.4 1.6 0.0 –0.1 –3.4 –0.7 –1.3 0.1 0.0 0.0 0.2 0.3 1.6 0.1 –6.7 –0.3 0.5 –0.4 –0.8 –4.0 1.5 0.0 –0.5 0.0 0.4 0.8 0.4 0.7 2.4 0.3 0.5 –0.2 0.7 –0.1 0.2 1.2 0.8 1.6 –0.4 –0.5 1.4 2.3 2.4 –0.2 0.0 1.6 0.1 0.4 0.8 0.1 2.2 –0.2 –1.5 –0.1

Mammals

Birds

Higher plantsb

3.4

Nationally GEF benefits protected areas index for biodiversity

Marine protected areas

% of thousand total land thousand area sq. km sq. km

Total known Threatened Total known Threatened Total known Threatened species species species species species species 2004

2004

2004

2004

2002

2002

2005

201 88 422 667 158 102 63 115 132 35 171 93 145 407 105 89 23 58 215 68 70 59 183 87 71 89 165 207 337 134 94 14 544 50 140 129 228 288 192 203 95 73 181 123 290 83 74 195 241 260 168 441 222 110 105 38

10 7 85 146 21 9 4 13 12 5 37 7 15 33 12 12 1 6 30 4 5 3 20 5 5 9 49 7 50 12 7 3 72 4 13 12 12 39 10 29 9 8 6 10 25 9 12 17 17 58 11 46 50 12 15 2

699 367 1,180 1,604 498 396 408 534 478 298 592 397 497 1,103 369 423 358 207 704 325 377 311 576 326 227 291 262 658 746 624 521 137 1,026 203 387 430 685 1,047 619 274 444 351 632 493 899 442 483 625 904 720 696 1,781 590 424 501 310

6 9 79 121 18 18 8 18 15 12 53 14 23 28 22 34 12 4 21 8 10 7 11 7 4 9 34 13 40 5 5 13 57 8 22 13 23 41 18 31 11 74 8 2 9 6 14 30 20 33 27 94 70 12 15 12

5,680 2,214 18,664 29,375 8,000 .. 950 2,317 5,599 3,308 5,565 2,100 6,000 6,506 2,898 2,898 234 4,500 8,286 1,153 3,000 1,591 2,200 1,825 1,796 3,500 9,505 3,765 15,500 1,741 1,100 750 26,071 1,752 2,823 3,675 5,692 7,000 3,174 6,973 1,221 2,382 7,590 1,460 4,715 1,715 1,204 4,950 9,915 11,544 7,851 17,144 8,931 2,450 5,050 2,493

111 1 246 383 1 0 1 0 3 208 12 0 1 103 3 0 0 1 19 0 0 1 46 1 0 0 276 14 683 6 0 87 261 0 0 2 46 38 24 7 0 21 39 2 170 2 6 2 195 142 10 274 212 4 15 52

7.9 0.2 43.9 90.0 7.9 1.7 0.7 0.9 4.4 4.9 41.4 0.3 5.4 9.9 0.7 1.8 0.1 1.2 5.4 0.0 0.2 0.3 2.9 1.7 0.0 0.2 31.4 3.9 14.8 1.6 1.4 4.2 75.8 0.0 4.4 4.0 8.2 10.6 5.9 2.2 0.1 22.3 3.6 0.9 6.6 1.6 4.4 5.1 11.7 27.7 3.3 36.3 33.7 0.6 3.8 3.8

2004 c

23.4 8.3 156.3 259.9 105.5 0.0 0.8 4.6 32.4 1.8 52.2 9.7 77.4 71.9 3.2 3.5 0.0 7.2 37.4 9.7 0.1 0.1 15.2 1.2 5.9 2.0 18.3 19.4 100.8 46.7 2.5 0.1 99.0 0.5 217.9 4.7 45.3 35.3 46.0 26.6 9.5 64.7 28.1 96.9 55.0 19.7 0.2 73.1 13.1 7.3 16.6 216.1 24.3 70.3 4.7 0.3

ENVIRONMENT

Deforestation and biodiversity

% of surface area

2004 c

2004 c

2004 c

21.0 9.3 5.3 14.3 6.5 0.0 1.1 21.3 11.0 16.2 14.3 11.0 2.9 12.6 2.6 3.6 0.0 3.7 16.2 15.6 0.7 0.2 15.8 0.1 9.5 7.9 3.1 20.6 30.7 3.8 0.2 3.3 5.2 1.4 13.9 1.1 5.8 5.4 5.6 18.6 28.0 24.1 23.1 7.7 6.0 6.5 0.1 9.5 17.6 1.6 4.2 16.9 8.2 23.1 5.1 3.5

1.9 .. 16.1 130.1 6.2 .. 0.0 0.1 1.5 8.2 10.6 0.0 0.5 3.1 .. 3.5 0.3 .. .. 0.2 0.0 .. 0.6 0.5 0.5 .. 0.2 .. 5.0 .. 15.0 0.1 82.1 .. .. 0.5 22.5 0.2 74.0 .. 0.8 22.7 1.3 .. .. 1.3 29.6 2.2 10.0 3.5 .. 3.4 16.6 0.7 2.0 1.7

1.7 .. 0.5 6.8 0.4 .. 0.0 0.6 0.5 74.5 2.8 0.0 0.0 0.5 .. 3.5 1.5 .. .. 0.2 0.0 .. 0.5 0.0 0.8 .. 0.0 .. 1.5 .. 1.5 4.4 4.2 .. .. 0.1 2.8 0.0 9.0 .. 1.9 8.4 1.0 .. .. 0.4 9.6 0.3 13.3 0.8 .. 0.3 5.5 0.2 2.2 19.1

2007 World Development Indicators

139

3.4 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 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 Europe EMU

Deforestation and biodiversity Forest area

Average annual deforestationa

thousand sq. km 2005

% 1990– 2005

64 8,088 5 27 87 27 28 0 19 13 71 92 179 19 675 5 275 12 5 4 353 145 4 2 11 102 41 36 96 3 28 3,031 15 33 477 129 0 5 425 175 39,426 s 6,746 23,132 12,255 10,878 29,878 4,507 8,946 9,150 211 801 6,263 9,548 915

0.0 0.0 –3.4 0.0 0.5 –0.4 0.6 0.0 0.0 –0.4 0.9 0.0 –2.2 1.2 0.8 –1.0 0.0 –0.4 –1.6 0.0 1.0 0.6 2.9 0.3 –4.3 –0.3 0.0 1.8 –0.2 –1.8 –0.6 –0.1 –4.4 –0.5 0.6 –2.5 .. 0.0 0.9 1.4 0.1 w 0.5 0.1 0.2 0.1 0.2 –0.2 0.0 0.4 –0.5 –0.2 0.6 –0.1 –0.8

Mammals

Birds

Higher plantsb

Nationally GEF benefits protected areas index for biodiversity

% of thousand total land thousand area sq. km sq. km

Total known Threatened Total known Threatened Total known Threatened species species species species species species 2004

2004

2004

2004

101 296 206 94 191 96 197 73 87 87 182 320 132 123 302 124 85 93 82 76 375 300 175 116 78 145 103 360 120 30 103 468 118 91 353 279 .. 74 255 222

15 43 13 9 11 10 12 3 7 7 15 29 20 21 16 6 5 4 3 7 34 36 7 1 10 15 12 29 14 5 10 40 6 7 26 41 1 6 11 8

365 645 665 433 612 381 626 400 332 350 642 829 515 381 952 490 457 382 350 351 1,056 971 565 435 360 436 318 1,015 325 268 557 888 414 343 1,392 837 .. 385 770 661

13 47 9 17 5 10 10 10 11 7 13 36 20 16 10 6 9 8 11 9 37 42 2 2 9 14 13 15 13 11 10 71 24 16 25 41 1 14 12 10

Marine protected areas

2002

2002

2005

2004 c

3,400 11,400 2,288 2,028 2,086 4,082 2,090 2,282 3,124 3,200 3,028 23,420 5,050 3,314 3,137 2,715 1,750 3,030 3,000 5,000 10,008 11,625 3,085 2,259 2,196 8,650 .. 4,900 5,100 .. 1,623 19,473 2,278 4,800 21,073 10,500 .. 1,650 4,747 4,440

1 7 3 3 7 1 47 54 2 0 17 75 14 280 17 11 3 2 0 2 239 84 10 1 0 3 0 38 1 0 13 240 1 1 67 145 .. 159 8 17

.. 37.1 1.1 3.4 1.3 .. 1.5 0.1 0.1 0.2 6.7 23.5 6.6 6.6 5.5 0.1 0.3 0.2 0.9 0.7 15.1 8.0 0.4 2.4 0.5 6.0 2.0 3.3 0.4 0.2 2.1 90.3 1.4 1.2 26.8 11.7 .. 3.4 5.0 2.1

5.8 1,287.0 1.9 819.1 21.6 3.8 3.2 0.0 11.0 2.9 1.9 74.0 46.2 17.7 123.0 0.6 44.8 11.9 2.7 26.0 374.3 80.3 6.5 0.2 2.3 20.3 19.8 64.3 19.4 0.2 60.5 1,490.1 0.7 20.5 644.4 13.6 .. 0.0 312.3 57.5 15,048.4 s 2,806.6 7,988.4 5,196.6 2,791.8 10,795.0 1,924.7 1,657.2 3,966.3 301.1 288.6 2,657.1 4,253.5 283.1

2004 c

2004 c

2.5 6.1 7.9 301.8 7.9 .. 41.0 5.2 11.2 0.9 3.7 0.1 4.5 .. 4.2 0.0 22.8 .. 14.5 0.0 0.3 3.3 6.1 3.4 9.3 1.8 27.3 2.3 5.2 0.3 3.5 .. 10.9 4.3 29.6 .. 1.5 .. 18.6 .. 42.4 2.3 15.7 5.8 11.9 .. 4.7 0.1 1.5 0.2 2.6 4.5 4.2 .. 32.6 .. 3.3 3.1 0.2 .. 25.0 22.5 16.3 909.5 0.4 0.1 4.8 .. 73.1 21.3 4.4 0.7 .. .. 0.0 .. 42.0 .. 14.9 .. 11.6 w 4,348.9 s 10.0 73.8 11.7 1,233.1 13.2 632.6 9.6 600.6 11.2 1,307.0 12.1 192.1 7.1 321.6 19.7 495.7 3.4 114.7 6.0 20.9 11.3 162.0 12.9 3,042.0 11.5 19.5

a. Negative numbers indicate an increase in forest area. b. Flowering plants only. c. Data may refer to earlier years. They are the most recent reported by the World Conservation Monitoring Centre in 2004. d. Includes Luxembourg.

140

2007 World Development Indicators

% of surface area 2004 c

2.6 1.8 .. 0.2 0.4 0.1 .. 0.1 .. 0.0 0.5 0.3 0.4 3.5 0.0 .. 1.0 .. .. .. 0.2 1.1 .. 1.3 0.1 0.6 .. .. 0.5 .. 9.2 9.4 0.0 .. 2.3 0.2 .. .. .. .. 3.8 w .. 1.9 1.7 2.1 1.6 1.3 1.4 2.7 1.5 0.5 .. 8.8 0.8

About the data

Biological diversity is defined in terms of the variability in genes, species, and ecosystems. Faced with mounting threats to biodiversity, the international community has increasingly focused on conserving this diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts traditionally have focused on protecting areas of high biodiversity. The estimates of forest area are from the Food and Agriculture Organization’s (FAO) Global Forest Resources Assessment 2005, which provides detailed information on forest cover in 2005 and adjusted estimates of forest cover in 1990 and 2000. The current survey is the latest global forest assessment and uses a uniform global definition of forest. No breakdown of forest cover between natural forest and plantation is shown in the table because of space limitations. (This breakdown is provided by the FAO only for developing countries.) For this reason the deforestation data in the table may underestimate the rate at which natural forest is disappearing in some countries. Measures of species richness are among the most straightforward ways to indicate the importance of an area for biodiversity. The number of threatened species is also an important measure of the immediate need for conservation efforts in a geographic area. Global analyses of the status of threatened species have been carried out for few groups of organisms. Only for mammals, birds, and amphibians has the status of virtually all known species been assessed. Threatened species are defined according to the World Conservation Union’s (IUCN) classification categories: endangered (in danger of extinction and unlikely to survive if causal factors continue operating), vulnerable (likely to move into the endangered category in the near future if causal factors continue operating), rare (not endangered or vulnerable but at risk), indeterminate (known to be endangered, vulnerable, or rare but not enough information is available to say which), out of danger (formerly included in one of the above categories but now considered relatively secure because appropriate conservation measures are in effect), and insufficiently known (suspected but not definitely known to belong to one of the above categories). While the number of birds and mammals is fairly well known, it is difficult to make an accurate count of plants. The number of plant species is highly debated. The IUCN’s 2003 IUCN Red List of Threatened Plants provides the most comprehensive list of threatened species on a global scale, the result of more than 20 years’ work by botanists from around the world. Only 5 percent of plant species have been evaluated, and 70 percent of these are threatened with extinction. Plant species data should be interpreted with caution since they are not necessarily comparable across countries because of differences in taxonomic concepts and coverage. However, they do identify countries that are major sources of global biodiversity and that show national commitments to habitat protection. Setting priorities for conserving biodiversity requires a broader set of information than species richness.

3.4

ENVIRONMENT

Deforestation and biodiversity Definitions

With the support of the World Bank’s Development Research Group and in close collaboration with scientific nongovernmental organizations, the Global Environment Facility (GEF) developed the GEF benefits index for biodiversity, a comprehensive indicator of national biodiversity status, to guide its biodiversity priorities. This indicator incorporates information on individual species range maps available from the IUCN for virtually all mammals (4,612), amphibians (5,327), and endangered birds (1,098); country-level data from the World Resources Institute (WRI) for reptiles and vascular plants; country-level data from FishBase for 27,669 fish species; and the ecological characteristics of 867 terrestrial ecoregions of the world from WWF International. For each country the biodiversity indicator incorporates the best available and comparable information in four relevant dimensions: represented species, threatened species, represented ecoregions, and threatened ecoregions. To combine these dimensions into one measure, the indicator uses dimensional weights that reflect the consensus of conservation scientists in the GEF, IUCN, WWF International, and other nongovernmental organizations. The index shown in the table has been normalized so that values run from 0 (no biodiversity potential) to 100 (maximum biodiversity potential). The table shows information on protected areas, numbers of certain species, and numbers of those species under threat. The World Conservation Monitoring Centre (WCMC) compiles these data from a variety of sources. Because of differences in defi nitions and reporting practices, cross-country comparability is limited. Compounding these problems, available data cover different periods. Nationally protected areas are areas of at least 1,000 hectares that fall into one of six IUCN management categories: • Scientific reserves and strict nature reserves with limited public access. • National parks of national or international significance and not materially affected by human activity. • Natural monuments and natural landscapes with unique aspects. • Managed nature reserves and wildlife sanctuaries. • Protected landscapes (which may include cultural landscapes). • Areas managed mainly for the sustainable use of natural systems to ensure long-term protection and maintenance of biological diversity. Designating land as a protected area does not necessarily mean that protection is in force. And for small countries that may only have protected areas smaller than 1,000 hectares, this size limit in the definition will result in an underestimate of the extent and number of protected areas. Due to variations in consistency and methodology of collection, the quality of the data are highly variable across countries. Some countries update their information more frequently than others, some may have more accurate data on extent of coverage, and many underreport the number or extent of protected areas.

• Forest area is land under natural or planted stands of trees, whether productive or not. • Average annual deforestation refers to the permanent conversion of natural forest area to other uses, including shifting cultivation, permanent agriculture, ranching, settlements, and infrastructure development. Deforested areas do not include areas logged but intended for regeneration or areas degraded by fuelwood gathering, acid precipitation, or forest fires. Negative numbers indicate an increase in forest area. • Mammals exclude whales and porpoises. • Birds are listed for countries included within their breeding or wintering ranges. • Higher plants refer to native vascular plant species. • Threatened species are the number of species classified by the IUCN as endangered, vulnerable, rare, indeterminate, out of danger, or insufficiently known. • GEF benefits index for biodiversity is a composite index of relative biodiversity potential for each country based on the species represented in each country, their threat status, and the diversity of habitat types in each country. The index shown in the table has been normalized so that values run from 0 (no biodiversity potential) to 100 (maximum biodiversity potential). • Nationally protected areas are totally or partially protected areas of at least 1,000 hectares that are designated as scientifi c reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, and protected landscapes. Marine areas, unclassified areas, and littoral (intertidal) areas are not included. The data also do not include sites protected under local or provincial law. Total land area is used to calculate the percentage of total area protected (see table 3.1). • Marine protected areas are areas of intertidal or subtidal terrain—and overlying water and associated flora and fauna and historical and cultural features—that have been reserved by law or other effective means to protect part or all of the enclosed environment.

Data sources Data on forest area and deforestation are from the FAO’s Global Forest Resources Assessment 2005. Data on species are from the electronic files of the United Nations Environmental Program and WCMC and 2003 IUCN Red List of Threatened Plants. For China the number of mammals is from Princeton University Press Guide to the Mammals of China (forthcoming). The GEF benefi ts index for biodiversity is from Kiran Dev Pandey, Piet Buys, Ken Chomitz, and David Wheeler’s, “Biodiversity Conservation Indicators: New Tools for Priority Setting at the Global Environment Facility” (2006). Data on protected areas are from the United Nations Environment Programme and WCMC.

2007 World Development Indicators

141

3.5

Freshwater Renewable internal freshwater resourcesa

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, 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

142

Annual freshwater withdrawals

Water productivity

Access to an improved water source

Flows billion cu. m

Per capita cu. m

billion cu. m

% of internal resources

% for agriculture

% for industry

% for domestic

GDP/water use 2000 $ per cu. m

% of urban population

% of rural population

2005

2005

2002b

2002b

2002b

2002b

2002b

2002

2004

2004

23.3 1.7 6.1 0.4 29.2 3.0 23.9 2.1 17.3 79.4 2.8 .. 0.1 1.4 .. 0.2 59.3 10.5 0.8 0.3 4.1 1.0 46.0 0.0 0.2 12.6 630.3 .. 10.7 0.4 0.0 2.7 0.9 .. 8.2 2.6 1.3 3.4 17.0 68.3 1.3 0.3 0.2 5.6 2.5 40.0 0.1 0.0 3.6 47.1 1.0 7.8 2.0 1.5 0.2 1.0

42.3 6.4 54.2 0.2 10.6 32.4 4.9 3.8 213.0 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 19.5 21.2 16.1 3.9 3,794.4 7.2 10.7 1.2 4.6 2.3 22.4 0.1 1.0 6.2 44.0 3.2 13.4 1.8 0.7 1.1 7.6

98 62 65 60 74 66 75 1 68 96 30 .. 45 81 .. 41 62 19 86 77 98 74 12 4 83 64 68 .. 46 31 9 53 65 .. 69 2 43 66 82 86 59 97 5 94 3 10 42 65 59 20 66 80 80 90 82 94

0 11 13 17 9 4 10 64 28 1 47 .. 23 7 .. 18 18 78 1 6 0 8 69 16 0 25 26 .. 4 17 22 17 12 .. 12 57 25 2 5 6 16 0 38 0 84 74 8 12 21 68 10 3 13 2 5 1

2 27 22 23 17 30 15 35 5 3 23 .. 32 13 .. 41 20 3 13 17 1 18 20 80 17 11 7 .. 50 53 70 29 24 .. 19 41 32 32 12 8 25 3 57 6 14 16 50 23 20 12 24 16 6 8 13 5

.. 2.4 9.7 30.8 8.3 0.8 17.9 93.5 0.4 0.7 5.0 .. 19.0 6.1 .. 35.4 10.5 1.3 3.6 2.6 1.0 11.1 16.3 38.4 7.2 6.3 2.2 .. 8.1 12.1 76.0 6.2 11.0 .. .. 23.0 127.5 6.3 1.0 1.6 10.7 2.3 39.5 1.5 50.3 34.2 42.1 14.1 0.9 40.9 5.5 16.1 10.0 2.2 1.1 3.8

.. 99 88 75 98 99 100 100 95 82 100 100 78 95 99 100 96 100 94 92 64 86 100 93 41 100 93 .. 99 82 84 100 97 100 95 100 100 97 97 99 94 74 100 81 100 100 95 95 96 100 88 .. 99 78 79 52

.. 94 80 40 80 80 100 100 59 72 100 100 57 68 96 90 57 97 54 77 35 44 99 61 43 58 67 .. 71 29 27 92 74 100 78 100 100 91 89 97 70 57 99 11 100 100 47 77 67 100 64 .. 92 35 49 56

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 2,812 .. 2,112 900 222 112 77 38 38 13 6 21 432 2 18 3 13 122 107 179 164 3 58 107 30 58 109 226 16 13

.. 8,595 341 9,284 7,123 3,017 24,202 6,680 966 740 3,805 1,145 1,221 33,054 9,086 1,360 29,066 2,713 945 1,338 8,571 16,726 88,238 34,920 1,539 54,249 2,156 .. 46,316 15,639 55,515 25,975 4,231 8,485 3,381 1,290 1,108 2,361 32,657 24 2,587 636 9,435 1,712 20,396 2,932 118,511 1,977 12,985 1,297 1,370 5,223 8,667 24,037 10,086 1,524

2007 World Development Indicators

Renewable internal freshwater resourcesa

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

Annual freshwater withdrawals

Water productivity

3.5

ENVIRONMENT

Freshwater

Access to an improved water source

Flows billion cu. m

Per capita cu. m

billion cu. m

% of internal resources

% for agriculture

% for industry

% for domestic

GDP/water use 2000 $ per cu. m

% of urban population

% of rural population

2005

2005

2002b

2002b

2002b

2002b

2002b

2002

2004

2004

0.9 7.6 645.8 82.8 72.9 42.7 1.1 2.1 44.4 0.4 88.4 1.0 35.0 1.6 9.0 18.6 0.4 10.1 3.0 0.3 1.4 0.1 0.1 4.3 0.3 .. 15.0 1.0 9.0 6.5 1.7 0.6 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 1.4 169.4 0.8 .. 0.5 20.1 28.5 16.2 11.3 ..

0.9 127.3 51.2 2.9 56.7 121.3 2.3 256.3 24.3 4.4 20.6 144.3 46.4 7.6 13.5 28.6 .. 21.7 1.6 1.8 28.8 1.0 0.1 711.3 1.7 .. 4.4 6.3 1.6 10.9 425.0 21.8 19.1 231.0 1.3 43.4 0.6 3.8 4.8 5.1 72.2 0.6 0.7 62.3 3.6 0.6 136.0 323.3 0.6 .. 0.5 1.2 6.0 30.2 29.6 ..

80 32 86 91 91 92 0 62 45 49 62 75 82 64 55 48 52 94 90 13 67 20 55 83 7 .. 96 80 62 90 88 .. 77 33 52 87 87 98 71 96 34 42 83 95 69 11 90 96 28 .. 71 82 74 8 78 ..

12 59 5 1 2 5 77 7 37 17 18 4 17 6 25 16 2 3 6 33 1 40 18 3 15 .. 2 5 21 1 3 .. 5 58 27 3 2 1 5 1 60 9 2 0 10 67 2 2 5 .. 8 10 9 79 12 ..

8 9 8 8 7 3 23 31 18 34 20 21 2 30 20 36 45 3 4 53 33 40 27 14 78 .. 3 15 17 9 9 .. 17 10 20 10 11 1 24 3 6 48 15 4 21 23 7 2 67 .. 20 8 17 13 10 ..

7.3 6.7 0.8 2.2 1.5 0.5 95.9 55.5 25.3 20.2 52.9 9.3 0.7 8.4 .. 30.6 90.8 0.1 0.6 30.0 13.1 18.4 5.4 8.7 48.2 .. 0.2 1.7 10.5 0.4 0.7 7.9 7.5 0.6 2.3 2.9 7.3 .. 12.4 0.6 49.4 27.2 3.1 0.9 6.0 79.2 16.0 0.5 14.6 .. 14.7 2.8 2.8 10.8 10.3 ..

95 100 95 87 99 .. 100 100 100 98 100 99 97 83 100 97 .. 98 79 100 100 92 72 .. .. .. 77 98 100 78 59 100 100 97 87 99 72 80 98 96 100 100 90 80 67 100 .. 96 99 88 99 89 87 100 .. ..

81 98 83 69 84 .. .. 100 .. 88 100 91 73 46 100 71 .. 66 43 96 100 76 52 .. .. .. 35 68 96 36 44 100 87 88 30 56 26 77 81 89 100 .. 63 36 31 100 .. 89 79 32 68 65 82 .. .. ..

96 6 1,261 2,838 129 35 49 1 183 9 430 1 75 21 67 65 0 47 190 17 5 5 200 1 16 5 337 16 580 60 0 3 409 1 35 29 100 881 6 198 11 327 190 4 221 382 1 52 147 801 94 1,616 479 54 38 7

13,311 595 1,152 12,867 1,883 .. 11,781 116 3,114 3,541 3,365 128 4,978 604 2,979 1,344 0 9,041 32,140 7,259 1,342 2,897 60,915 103 4,569 2,655 18,113 1,250 22,882 4,438 130 2,252 3,967 238 13,626 961 5,068 17,431 3,052 7,305 674 79,778 36,840 251 1,680 82,625 390 336 45,613 136,059 15,936 57,780 5,767 1,404 3,602 1,815

2007 World Development Indicators

143

3.5

Freshwater Renewable internal freshwater resourcesa

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 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 Europe EMU

Annual freshwater withdrawals

Water productivity

Access to an improved water source

Flows billion cu. m

Per capita cu. m

billion cu. m

% of internal resources

% for agriculture

% for industry

% for domestic

GDP/water use 2000 $ per cu. m

% of urban population

% of rural population

2005

2005

2002b

2002b

2002b

2002b

2002b

2002

2004

2004

23.2 76.7 0.2 17.3 2.2 .. 0.4 .. .. .. 3.3 12.5 35.6 12.6 37.3 1.0 3.0 2.6 20.0 12.0 5.2 87.1 0.2 0.3 2.6 37.5 24.7 0.3 37.5 2.3 9.5 479.3 3.2 58.3 8.4 71.4 .. 6.6 1.7 4.2 3,807.4 s 1,240.7 1,667.0 1,337.3 329.6 2,907.6 958.8 383.2 265.3 239.8 941.1 119.3 899.7 199.7

54.8 1.8 1.6 721.7 8.6 .. 0.2 .. .. .. 54.8 27.9 32.0 25.2 124.4 40.1 1.7 6.4 285.0 18.0 6.2 41.5 1.5 8.2 62.9 16.5 1,760.7 0.8 70.7 1,150.0 6.6 17.1 5.3 357.9 1.2 19.5 .. 161.7 2.2 34.2 9.1 w 18.9 6.3 7.3 4.0 8.8 11.1 7.5 2.0 105.0 51.8 3.1 10.2 22.3

42 4,313 10 2 26 44 160 1 13 19 6 45 111 50 30 3 171 40 7 66 84 210 12 4 4 227 1 39 53 0 145 2,800 59 16 723 367 0 4 80 12 43,507 s 7,404 26,662 18,455 8,207 34,066 9,454 5,255 13,429 228 1,816 3,884 9,441 929

1,955 30,135 1,051 104 2,213 5,456 28,957 138 2,339 9,348 729 955 2,562 2,548 828 2,299 18,949 5,432 368 10,189 2,192 3,269 1,871 2,911 419 3,150 290 1,353 1,128 44 2,408 9,446 17,036 623 27,185 4,409 0 195 6,873 945 6,794 w 3,149 8,677 7,460 13,701 6,280 5,019 11,139 24,402 746 1,236 5,229 9,640 2,959

57 18 68 89 93 .. 92 .. .. .. 100 63 68 95 97 97 9 2 95 92 89 95 45 6 82 74 98 40 52 68 3 41 96 93 47 68 .. 95 76 79 70 w 89 71 75 54 78 74 59 71 89 90 87 42 38

34 63 8 1 3 .. 3 .. .. .. 0 6 19 2 1 1 54 74 2 5 0 2 2 26 4 11 1 17 35 9 75 46 1 2 7 24 .. 1 7 7 20 w 5 19 17 29 13 20 31 10 4 4 3 42 48

9 19 24 10 4 .. 5 .. .. .. 0 31 13 2 3 2 37 24 3 4 10 2 53 68 14 15 2 43 12 23 22 13 3 5 46 8 .. 4 17 14 10 w 6 10 8 18 8 7 10 19 7 6 10 15 15

1.8 3.7 14.1 11.0 2.1 .. 2.5 .. .. .. .. 11.3 17.3 1.3 0.4 1.4 84.3 97.0 1.0 0.1 2.0 1.5 8.2 29.6 7.9 5.3 .. 22.0 1.0 34.0 157.7 20.9 5.6 0.3 13.2 0.5 .. 1.5 2.0 1.6 8.6 w 0.8 3.3 2.5 6.8 2.3 2.1 2.7 7.6 2.0 0.7 3.1 28.2 30.5

91 100 92 97 92 99 75 100 100 .. 32 99 100 98 78 87 100 100 98 92 85 98 80 92 99 98 93 87 99 100 100 100 100 95 85 99 94 71 90 98 95 w 88 95 94 98 93 92 99 96 96 94 80 100 100

a. River flows from other countries are not included because of data unreliability. b. Data are for the most recent year available for 1987–2002 (see Primary data documentation).

144

2007 World Development Indicators

16 88 69 .. 60 86 46 .. 99 .. 27 73 100 74 64 54 100 100 87 48 49 100 36 88 82 93 54 56 91 100 100 100 100 75 70 80 88 65 40 72 72 w 70 92 71 82 71 70 80 73 81 81 43 99 100

About the data

3.5

ENVIRONMENT

Freshwater Definitions

The data on freshwater resources are based on esti-

different times and at different levels of quality and

• Renewable internal freshwater resources fl ows

mates of runoff into rivers and recharge of groundwa-

precision, requiring caution in interpreting the data,

refer to internal renewable resources (internal river

ter. These estimates are based on different sources

particularly for water-short countries, notably in the

flows and groundwater from rainfall) in the country.

and refer to different years, so cross-country com-

Middle East.

• Renewable internal freshwater resources per cap-

parisons should be made with caution. Because the

The data on access to an improved water source

ita are calculated using the World Bank’s population

data are collected intermittently, they may hide sig-

measure the percentage of the population with ready

estimates (see table 2.1). • Annual freshwater with-

nificant variations in total renewable water resources

access to water for domestic purposes. The data are

drawals refer to total water withdrawals, not count-

from one year to the next. The data also fail to distin-

based on surveys and estimates provided by govern-

ing evaporation losses from storage basins. With-

guish between seasonal and geographic variations

ments to the Joint Monitoring Program of the World

drawals also include water from desalination plants

in water availability within countries. Data for small

Health Organization (WHO) and the United Nations

in countries where they are a signifi cant source.

countries and countries in arid and semiarid zones

Children’s Fund (UNICEF). The coverage rates are

Withdrawals can exceed 100 percent of total renew-

are less reliable than those for larger countries and

based on information from service users on what

able resources where extraction from nonrenewable

countries with greater rainfall.

their households actually use rather than on infor-

aquifers or desalination plants is considerable or

Caution is also needed in comparing data on

mation from service providers, which may include

where there is significant water reuse. Withdrawals

annual freshwater withdrawals, which are subject

nonfunctioning systems. Access to drinking water

for agriculture and industry are total withdrawals

to variations in collection and estimation methods.

from an improved source does not ensure that the

for irrigation and livestock production and for direct

In addition, inflows and outflows are estimated at

water is safe or adequate, as these characteristics

industrial use (including withdrawals for cooling ther-

are not tested at the time of surveys. While informa-

moelectric plants). Withdrawals for domestic uses

tion on access to an improved water source is widely

include drinking water, municipal use or supply, and

used, it is extremely subjective, and such terms as

use for public services, commercial establishments,

safe, improved, adequate, and reasonable may have

and homes. • Water productivity is calculated as

different meaning in different countries despite offi -

GDP in constant prices divided by annual total water

cial WHO definitions (see Definitions). Even in high-

withdrawal. • Access to an improved water source

income countries treated water may not always be

refers to the percentage of the population with rea-

safe to drink. While access to an improved water

sonable access to an adequate amount of water from

source is equated with connection to a supply sys-

an improved source, such as piped water into a dwell-

tem, this does not take into account variations in

ing, plot, or yard; public tap or standpipe; tubewell or

the quality and cost (broadly defined) of the service

borehole; protected dug well or spring; and rainwater

once connected.

collection. Unimproved sources include unprotected

The rural-urban divide in access to an improved water source Access to improved water source by income group, 2004 (percent) 100

3.5a

Rural

Urban

80

60

40

20

0 Low-income

Lower middleincome

Upper middleincome

High-income

Water productivity is an indication only of the

dug wells or springs, cart with small tank or drum,

effi ciency by which each country uses its water

bottled water, and tanker trucks. Reasonable access

resources. Given the different economic struc-

is defined as the availability of at least 20 liters a

ture of each country, these indicators should be

person a day from a source within 1 kilometer of

used with proper caution, taking into account the

the dwelling.

countries’ sectoral activities and natural resource endowments.

Access to improved water source by developing region, 2004 (percent) 100

80

Data sources Data on freshwater resources and withdrawals are

60

compiled by the World Resources Institute from various sources and published in World Resources

40

2005 (produced in collaboration with the United Nations Environment Programme, United Nations

20

Development Programme, and World Bank). These data are supplemented by the Food and Agriculture

0

Organization’s AQUASTAT data. Data on access to East Asia & Pacific

Europe & Latin Middle Central America & East & Asia Caribbean North Africa

Source: Table 3.5.

South Asia

SubSaharan Africa

water are from WHO and UNICEF’s Meeting the MDG Drinking Water and Sanitation Target (www. unicef.org/wes/mdgreport).

2007 World Development Indicators

145

3.6

Water pollution Emissions of organic water pollutants

thousand kilograms per day 1990 2003a

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, 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

146

5.9 34.8 107.0 4.5 186.7 37.9 186.1 94.1 53.3 171.1 .. 118.0 .. 8.4 50.7 4.5 780.4 149.4 .. 1.6 11.8 14.0 321.5 1.0 .. 66.8 7,038.1 86.1 93.3 .. 2.5 27.2 7.9 80.0 173.0 205.1 91.9 47.9 25.6 211.5 5.5 .. .. 18.6 79.5 653.5 2.0 0.8 .. 835.0 16.5 63.5 21.6 .. .. 5.4

0.2 .. .. .. 164.3 7.1 111.7 36.9 15.5 303.3 .. 102.3 .. 11.5 .. 5.5 .. 101.9 2.6 .. .. 10.0 312.5 .. .. 72.9 6,088.7 34.3 93.9 .. .. 31.2 .. 42.9 .. 158.5 83.6 .. 40.2 186.1 22.8 .. .. 22.1 67.4 564.6 .. .. .. 966.7 .. 43.7 19.3 .. .. ..

2007 World Development Indicators

Industry shares of emissions of organic water pollutants

kilograms per day per worker

Primary metals

Paper and pulp

Chemicals

% of total Stone, Food and ceramics, beverages and glass

Textiles

Wood

Other

1990

2003a

2003a

2003a

2003a

2003a

2003a

2003a

2003a

2003a

0.16 0.14 0.25 0.19 0.20 0.11 0.18 0.15 0.15 0.17 .. 0.16 .. 0.24 0.14 0.19 0.19 0.11 .. 0.24 0.14 0.28 0.17 0.18 .. 0.22 0.14 0.12 0.19 .. 0.32 0.20 0.22 0.15 0.25 0.13 0.18 0.36 0.23 0.20 0.22 .. .. 0.23 0.18 0.15 0.25 0.34 .. 0.12 0.20 0.18 0.23 .. .. 0.20

0.21 .. .. .. 0.23 0.28 0.18 0.08 0.16 0.14 .. 0.17 .. 0.25 .. 0.19 .. 0.17 0.22 .. .. 0.19 0.16 .. .. 0.24 0.14 0.20 0.21 .. .. 0.22 .. 0.17 .. 0.14 0.17 .. 0.28 0.20 0.18 .. .. 0.23 0.16 0.15 .. .. .. 0.14 .. 0.19 0.28 .. .. ..

.. .. .. .. 5.6 .. .. 14.6 20.2 1.6 .. 13.6 .. 1.2 .. 1.9 .. 7.9 3.5 .. .. 0.4 9.6 .. .. 6.9 20.4 1.2 3.1 .. .. 1.6 .. 6.1 .. 15.6 4.4 .. 2.2 10.8 2.1 .. .. 2.3 8.7 7.2 .. .. .. 9.3 .. 8.1 4.9 .. .. ..

37.7 .. .. .. 14.6 .. .. 14.8 5.5 6.2 .. 18.4 .. 15.1 .. 10.8 .. 9.5 1.1 .. .. 5.2 22.1 .. .. 11.3 10.9 43.5 16.2 .. .. 10.0 .. 15.9 .. 7.0 29.1 .. 11.2 8.2 10.2 .. .. 11.0 40.1 13.8 .. .. .. 20.4 .. 9.7 7.2 .. .. ..

17.5 .. .. .. 8.6 .. 5.6 15.4 15.9 2.6 .. 11.2 .. 6.8 .. 1.6 .. 6.6 5.4 .. .. 36.1 8.6 .. .. 8.9 14.8 3.9 9.7 .. .. 8.2 .. 7.5 .. 7.9 7.9 .. 5.9 9.0 8.1 .. .. 5.5 7.6 12.9 .. .. .. 11.8 .. 9.0 6.1 .. .. ..

31.1 .. .. .. 58.9 77.6 77.1 34.9 39.2 23.8 .. 40.3 .. 64.9 .. 56.3 .. 46.1 73.8 .. .. 48.8 39.5 .. .. 62.7 28.1 30.5 53.2 .. .. 65.7 .. 48.4 .. 43.6 44.2 .. 72.3 50.7 43.5 .. .. 61.0 26.6 49.5 .. .. .. 38.7 .. 55.0 72.8 .. .. ..

0.4 .. .. .. 0.1 .. 0.2 0.6 0.3 0.1 .. 0.2 .. 0.2 .. 0.3 .. 0.2 0.1 .. .. 0.0 0.1 .. .. 0.1 0.5 0.1 0.2 .. .. 0.1 .. 0.2 .. 0.3 0.2 .. 0.1 0.3 0.1 .. .. 0.3 0.2 0.2 .. .. .. 0.2 .. 0.3 0.1 .. .. ..

13.2 .. .. .. 7.6 22.4 5.1 0.9 11.0 64.2 .. 5.9 .. 8.7 .. 25.0 .. 22.2 4.1 .. .. 3.8 5.8 .. .. 5.0 15.5 16.2 14.2 .. .. 10.2 .. 12.0 .. 10.4 2.2 .. 5.8 17.7 34.1 .. .. 17.3 2.4 2.9 .. .. .. 2.3 .. 12.4 6.9 .. .. ..

.. .. .. .. 1.1 .. 5.3 12.3 0.9 0.4 .. 2.2 .. 2.3 .. 1.7 .. 2.3 10.1 .. .. 5.0 5.4 .. .. 2.6 0.9 0.2 1.0 .. .. 1.3 .. 3.6 .. 3.9 3.5 .. 1.3 0.6 0.5 .. .. 2.0 3.9 2.3 .. .. .. 2.1 .. 1.6 0.8 .. .. ..

.. .. .. .. 3.5 .. .. 3.5 6.9 1.0 .. 8.2 .. 0.7 .. 2.5 .. 5.2 1.9 .. .. 0.8 8.9 .. .. 2.5 8.8 4.6 2.4 .. .. 2.9 .. 6.3 .. 11.4 8.6 .. 1.3 2.8 1.4 .. .. 0.7 10.6 11.1 .. .. .. 15.1 .. 4.0 1.0 .. .. ..

Emissions of organic water pollutants

thousand kilograms per day 1990 2003a

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

17.8 178.0 1,410.6 495.6 102.7 26.7 34.6 46.4 358.1 18.7 1,556.6 8.3 .. 42.6 .. 369.2 9.1 30.9 .. 39.9 .. 3.0 0.6 .. 53.8 32.4 11.0 10.0 104.7 .. .. 17.8 174.3 55.9 10.2 41.7 20.4 7.7 7.4 20.9 136.7 50.2 10.5 .. 70.8 55.0 0.4 104.1 9.7 5.7 3.3 56.1 228.3 428.9 147.9 19.0

.. 60.7 1,519.8 733.0 164.8 .. 11.6 54.0 488.9 .. 1,184.7 23.5 .. 56.1 .. 315.2 11.9 19.1 .. 29.2 14.9 3.1 .. .. 45.3 .. .. 11.8 183.8 .. .. 17.7 296.1 21.6 .. 72.1 10.2 6.2 .. 26.9 .. 46.1 .. 0.4 .. 51.7 5.8 .. 11.7 .. .. .. .. 329.4 127.5 9.2

3.6

ENVIRONMENT

Water pollution Industry shares of emissions of organic water pollutants

kilograms per day per worker

Primary metals

Paper and pulp

Chemicals

% of total Stone, Food and ceramics, beverages and glass

Textiles

Wood

Other

1990

2003a

2003a

2003a

2003a

2003a

2003a

2003a

2003a

2003a

0.23 0.16 0.20 0.19 0.16 0.19 0.18 0.16 0.13 0.29 0.14 0.19 .. 0.23 .. 0.12 0.16 0.12 .. 0.12 .. 0.16 0.30 .. 0.13 0.18 0.27 0.29 0.13 .. .. 0.16 0.18 0.15 0.18 0.14 0.27 0.17 0.35 0.13 0.18 0.22 0.27 .. 0.22 0.20 0.11 0.18 0.26 0.25 0.28 0.20 0.21 0.14 0.15 0.15

.. 0.10 0.20 0.18 0.15 .. 0.21 0.16 0.12 .. 0.15 0.18 .. 0.24 .. 0.12 0.17 0.21 .. 0.19 0.19 0.16 .. .. 0.17 .. .. 0.29 0.12 .. .. 0.15 0.20 0.45 .. 0.16 0.31 0.18 .. 0.16 .. 0.22 .. 0.32 .. 0.20 0.17 .. 0.32 .. .. .. .. 0.17 0.15 0.18

.. 11.8 12.2 2.5 15.6 .. 1.9 3.6 9.4 .. 7.1 5.1 .. .. .. 11.4 2.1 7.3 .. 4.1 0.9 1.2 .. .. 0.8 .. .. 0.0 7.8 .. .. 0.9 7.8 .. .. 2.1 1.1 56.5 .. 3.5 .. 3.2 .. .. .. 9.0 7.3 .. 1.5 .. .. .. .. 7.5 3.1 1.9

.. .. 7.6 8.2 8.0 .. .. 22.3 16.6 .. 19.0 12.7 .. 11.5 .. 18.9 16.6 7.8 .. 15.4 15.6 4.0 .. .. 10.6 .. .. 16.0 14.9 .. .. 6.6 12.5 2.2 .. 8.0 7.1 4.6 .. 9.7 .. 21.7 .. 17.0 .. 31.3 13.3 .. 13.2 .. .. .. .. 11.7 16.4 14.9

.. 12.8 9.2 9.2 10.7 .. 10.4 10.5 10.7 .. 9.4 10.8 .. 5.4 .. 13.0 11.1 3.5 .. 3.6 4.0 0.7 .. .. 4.9 .. .. 3.7 15.5 .. .. 2.6 10.4 .. .. 6.8 2.7 13.2 .. 5.9 .. 5.2 .. 4.4 .. 4.7 10.1 .. 4.6 .. .. .. .. 7.6 4.9 36.4

.. 49.1 53.7 53.7 46.7 .. 22.9 45.5 30.8 .. 45.7 53.4 .. 66.8 .. 25.8 50.2 65.4 .. 53.8 60.7 39.7 .. .. 54.0 .. .. 70.0 33.7 .. .. 32.8 55.6 97.7 .. 43.0 81.2 14.9 .. 55.1 .. 57.3 .. 76.9 .. 42.8 54.3 .. 76.6 .. .. .. .. 52.2 37.8 ..

.. 0.4 0.3 0.1 0.7 .. 0.7 0.1 0.3 .. 0.2 0.4 .. 0.1 .. 0.2 0.4 0.4 .. 0.1 0.5 0.1 .. .. 0.2 .. .. 0.0 0.2 .. .. 0.1 0.2 .. .. 0.3 0.1 0.4 .. 1.4 .. 0.1 .. 0.3 .. 0.1 0.9 .. 0.2 .. .. .. .. 0.2 0.4 0.2

.. .. 12.7 19.4 9.5 .. 3.1 6.0 15.0 .. 4.8 10.8 .. 12.8 .. 13.6 11.6 11.0 .. 9.6 10.2 51.3 .. .. 18.1 .. .. 7.8 8.3 .. .. 55.4 7.5 .. .. 35.3 5.8 2.9 .. 21.7 .. 4.6 .. .. .. 1.4 8.3 .. 3.2 .. .. .. .. 9.1 26.1 9.8

.. 5.5 0.3 4.5 0.9 .. 7.5 1.9 3.9 .. 1.6 3.3 .. 1.7 .. 1.5 2.8 0.9 .. 9.7 4.6 0.6 .. .. 7.1 .. .. 1.7 6.8 .. .. 0.6 0.9 .. .. 1.1 1.4 1.7 .. 1.7 .. 3.6 .. 0.8 .. 3.1 2.4 .. 0.4 .. .. .. .. 4.3 5.3 2.4

.. 9.8 3.9 2.4 8.1 .. 9.3 10.1 13.3 .. 12.3 3.4 .. .. .. 15.7 5.2 3.7 .. 3.7 3.4 2.3 .. .. 4.3 .. .. 0.7 12.8 .. .. 1.1 5.1 .. .. 3.4 0.7 5.8 .. 1.0 .. 4.2 .. .. .. 7.5 3.4 .. 0.4 .. .. .. .. 7.3 6.0 9.7

2007 World Development Indicators

147

3.6

Water pollution Emissions of organic water pollutants

thousand kilograms per day 1990 2003a

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 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

413.9 1,911.3 1.6 18.5 10.3 137.8 4.2 32.4 77.2 55.6 6.2 261.6 320.3 53.0 .. 6.6 109.6 146.0 21.7 .. 31.1 291.6 .. 10.0 44.6 177.3 .. 16.7 692.4 5.6 739.6 2,565.2 38.7 .. 96.5 .. .. 6.9 15.9 37.1

38.4 1,388.1 .. .. 6.6 98.7 .. 34.3 43.3 38.4 .. 221.3 352.9 78.4 38.6 .. 103.9 .. 15.1 .. 35.2 .. .. 7.9 55.8 172.2 .. .. 445.8 .. 331.0 1,805.9 15.8 .. 94.2 .. .. 15.4 .. ..

Industry shares of emissions of organic water pollutants

kilograms per day per worker

Primary metals

Paper and pulp

Chemicals

Textiles

Wood

Other

1990

2003a

2003a

2003a

2003a

2003a

2003a

2003a

2003a

2003a

0.12 0.13 0.25 0.15 0.32 0.15 0.32 0.09 0.13 0.16 0.38 0.17 0.17 0.19 .. 0.33 0.15 0.16 0.22 .. 0.24 0.17 .. 0.26 0.18 0.18 .. 0.30 0.14 0.14 0.15 0.15 0.23 .. 0.21 .. .. 0.27 0.23 0.20

0.07 0.18 .. .. 0.30 0.16 .. 0.10 0.14 0.16 .. 0.18 0.15 0.18 0.29 .. 0.14 .. 0.20 .. 0.25 .. .. 0.23 0.14 0.16 .. .. 0.18 .. 0.12 0.13 0.28 .. 0.21 .. .. 0.23 .. ..

.. 20.3 .. .. 5.8 9.9 .. 1.4 2.9 33.7 .. 15.1 7.5 0.5 0.7 .. 11.3 .. 4.1 .. 1.5 .. .. 6.5 2.5 11.4 .. .. 28.1 .. 9.0 9.6 1.2 .. 13.7 .. .. .. .. ..

17.6 8.1 .. .. 8.4 11.8 .. 24.6 16.9 14.7 .. 18.0 20.6 7.2 2.5 .. 35.0 .. 1.5 .. 9.4 .. .. 18.8 6.1 4.8 .. .. 4.2 .. 48.0 10.6 3.7 .. 10.4 .. .. 7.7 .. ..

.. 3.2 .. .. 10.7 8.2 .. 16.0 8.4 8.3 .. 10.5 9.5 6.6 3.1 .. 7.8 .. 3.9 .. 2.7 .. .. 11.9 5.5 8.0 .. .. 7.0 .. 17.5 14.0 6.6 .. 10.2 .. .. 6.8 .. ..

5.1 51.9 .. .. 70.1 47.4 .. 25.4 43.7 23.7 .. 36.0 39.6 51.5 88.6 .. 26.6 .. 69.8 .. 69.3 .. .. 55.3 35.8 43.7 .. .. 46.8 .. 0.6 42.1 79.2 .. 53.1 .. .. 74.6 .. ..

.. 0.4 .. .. 0.1 0.3 .. 0.1 0.3 0.2 .. 0.1 0.4 0.2 0.4 .. 0.1 .. 0.9 .. 0.1 .. .. 0.2 0.4 0.3 .. .. 0.4 .. 0.3 0.2 0.1 .. 0.3 .. .. 0.4 .. ..

28.7 5.9 .. .. 4.2 12.7 .. 3.9 12.2 10.8 .. 10.9 8.6 31.6 3.2 .. 1.3 .. 19.4 .. 14.0 .. .. 3.8 43.3 26.4 .. .. 5.4 .. 5.2 5.4 7.4 .. 7.5 .. .. 7.6 .. ..

12.5 2.6 .. .. 0.4 2.2 .. 1.6 4.0 2.0 .. 3.9 4.3 1.1 0.6 .. 3.0 .. 0.2 .. 1.5 .. .. 2.0 1.9 0.4 .. .. 1.1 .. 4.0 4.2 0.6 .. 1.5 .. .. 0.9 .. ..

.. 7.5 .. .. 0.3 7.6 .. 26.9 11.6 6.7 .. 5.5 9.6 1.2 1.1 .. 14.9 .. 0.2 .. 1.4 .. .. 1.5 4.6 5.0 .. .. 7.0 .. 15.4 13.9 1.2 .. 3.3 .. .. .. .. ..

Note: Industry shares may not sum to 100 percent because data may be for different years. a. Data are for most recent year available for 1993–2003.

148

2007 World Development Indicators

% of total Stone, Food and ceramics, beverages and glass

About the data

3.6

ENVIRONMENT

Water pollution Definitions

Emissions of organic pollutants from industrial activi-

pollution control programs start by regulating emis-

• Emissions of organic water pollutants are mea-

ties are a major cause of degradation of water qual-

sions of organic water pollutants. Such data are fairly

sured in terms of biochemical oxygen demand, which

ity. Water quality and pollution levels are generally

reliable because sampling techniques for measur-

refers to the amount of oxygen that bacteria in water

measured in terms of concentration or load—the

ing water pollution are more widely understood and

will consume in breaking down waste. This is a stan-

rate of occurrence of a substance in an aqueous

much less expensive than those for air pollution.

dard water treatment test for the presence of organic

solution. Polluting substances include organic mat-

Hettige, Mani, and Wheeler (1998) used plant- and

pollutants. Emissions per worker are total emissions

ter, metals, minerals, sediment, bacteria, and toxic

sector-level information on emissions and employ-

divided by the number of industrial workers. • Indus-

chemicals. This table focuses on organic water pol-

ment from 13 national environmental protection

try shares of emissions of organic water pollutants

lution resulting from industrial activities. Because

agencies and sector-level information on output

refer to emissions from manufacturing activities as

water pollution tends to be sensitive to local condi-

and employment from the United Nations Industrial

defined by two-digit divisions of the International

tions, the national-level data in the table may not

Development Organization (UNIDO). Their economet-

Standard Industrial Classification (ISIC) revision 2:

reflect the quality of water in specific locations.

ric analysis found that the ratio of BOD to employ-

primary metals (ISIC division 37); paper and pulp

The data in the table come from an international

ment in each industrial sector is about the same

(34); chemicals (35); food and beverages (31); stone,

study of industrial emissions that may be the first

across countries. This finding allowed the authors

ceramics, and glass (36); textiles (32); wood (33);

to include data from developing countries (Het-

to estimate BOD loads across countries and over

and other (38 and 39).

tige, Mani, and Wheeler 1998). These data were

time. The estimated BOD intensities per unit of

updated through 2003 by the World Bank’s Devel-

employment were multiplied by sectoral employ-

opment Research Group. Unlike estimates from

ment numbers from UNIDO’s industry database for

earlier studies based on engineering or economic

1980–98. The estimates of sectoral emissions were

models, these estimates are based on actual mea-

then totaled to get daily emissions of organic water

surements of plant-level water pollution. The focus is

pollutants in kilograms per day for each country and

on organic water pollution caused by organic waste,

year. The data in the table were derived by updating

measured in terms of biochemical oxygen demand

these estimates through 2003.

(BOD), because the data for this indicator are the most plentiful and the most 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 other emissions data because most industrial

Emissions of organic water pollutants declined in most countries from 1990 to 2003, even among the top emitters

3.6a

Countries with highest emissions of organic water pollutants (millions of kilograms per day)

1990

2003

8 7 6

Data sources 5

Data on water pollution come from a 1998 study 4

by Hemamala Hettige, Muthukumara Mani, and

3

David Wheeler, “Industrial Pollution in Economic

2

Development: Kuznets Revisited” (available at www.worldbank.org/nipr). The data were updated

1

through 2003 by the World Bank’s Development

0

Research Group using the same methodology as China

Source: Table 3.6.

United States

India

Russian Federation

Japan

Germany

the initial study. Sectoral employment numbers are from UNIDO’s industry database.

2007 World Development Indicators

149

3.7

Energy production and use Total energy production

million metric tons 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, 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

150

.. 2.4 104.4 28.7 48.5 0.3 157.5 8.1 18.2 10.8 3.5 13.1 1.8 4.9 3.6 0.9 98.1 9.6 .. .. .. 12.1 273.7 .. .. 7.6 889.3 0.0 48.5 12.0 9.0 1.0 3.4 4.3 6.5 40.1 10.0 1.0 16.5 54.9 1.7 .. 4.1 14.2 12.1 111.9 14.6 .. 1.5 186.2 4.4 9.2 3.4 .. .. 1.3

2004

.. 1.0 165.7 57.4 85.4 0.7 261.8 9.9 20.1 18.4 3.6 13.5 1.6 11.8 3.2 1.0 176.3 10.3 .. .. .. 12.5 397.5 .. .. 8.4 1,536.8 0.0 76.2 17.0 12.6 1.7 7.2 3.9 5.9 34.2 31.0 1.6 29.3 64.7 2.4 .. 3.6 19.4 15.9 137.4 12.1 .. 1.3 136.0 6.2 10.3 5.3 .. .. 1.7

2007 World Development Indicators

Energy use

Total million metric tons of oil equivalent 1990

.. 2.7 23.9 6.3 46.1 4.3 87.5 25.0 16.7 12.8 38.9 49.1 1.7 2.8 4.5 1.3 134.0 28.8 .. .. .. 5.0 209.4 .. .. 14.1 866.5 10.7 25.0 11.9 1.1 2.0 4.4 6.7 16.8 49.0 17.9 4.1 6.1 31.9 2.5 .. 6.3 15.2 29.2 227.3 1.2 .. 8.8 356.2 5.3 22.2 4.5 .. .. 1.6

2004

.. 2.4 32.9 9.5 63.7 2.1 115.8 33.2 12.9 22.8 26.8 57.7 2.5 5.0 4.7 1.9 204.8 18.9 .. .. .. 6.9 269.0 .. .. 27.9 1,609.3 17.1 27.7 16.6 1.1 3.7 6.9 8.8 10.7 45.5 20.1 7.7 10.1 56.9 4.5 .. 5.2 21.2 38.1 275.2 1.7 .. 2.8 348.0 8.4 30.5 7.6 .. .. 2.2

Net energy importsa

Per capita average annual % growth 1990–2004

.. 1.9 2.2 2.9 2.0 –1.5 2.2 1.9 –2.4 4.5 –1.9 1.3 2.7 4.1 5.8 2.9 3.2 –2.0 .. .. .. 2.5 1.7 .. .. 5.4 3.6 3.1 0.5 2.4 –0.6 4.7 3.6 2.2 –1.6 –0.3 0.4 5.1 3.9 4.4 4.1 .. –1.2 2.6 2.0 1.3 2.2 .. –7.9 0.0 3.6 2.5 4.3 .. .. 3.2

kilograms of oil equivalent 1990

2004

.. 809 943 597 1,415 1,246 5,130 3,246 2,259 123 3,810 4,927 324 416 1,130 890 897 3,306 .. .. .. 432 7,534 .. .. 1,067 763 1,869 716 315 425 658 348 1,502 1,594 4,728 3,481 584 597 573 496 .. 4,091 296 5,851 4,006 1,298 .. 1,642 4,485 345 2,183 504 .. .. 231

.. 760 1,017 613 1,661 704 5,762 4,060 1,559 164 2,725 5,536 303 553 1,203 1,055 1,114 2,434 .. .. .. 433 8,411 .. .. 1,732 1,242 2,488 616 297 274 870 388 1,985 950 4,460 3,716 873 773 783 664 .. 3,835 303 7,286 4,547 1,243 .. 626 4,218 386 2,755 616 .. .. 262

average annual % growth

Combustible renewables and waste % of total

1990–2004 1990

.. 2.5 0.5 0.2 0.8 –0.5 1.1 1.6 –3.3 2.3 –1.5 1.0 –0.5 1.9 5.0 1.3 1.7 –1.2 .. .. .. 0.2 0.7 .. .. 3.9 2.6 1.6 –1.2 –0.3 –3.8 2.3 1.0 2.5 –2.0 –0.2 0.0 3.6 2.2 2.5 2.0 .. –0.2 0.3 1.7 0.8 –0.4 .. –6.6 –0.2 1.2 1.9 1.9 .. .. 1.8

.. 13.6 0.1 68.8 3.7 0.0 4.5 9.9 0.0 53.5 0.6 1.4 93.2 27.2 3.6 33.1 31.1 0.6 .. .. .. 75.9 3.9 .. .. 19.0 23.1 0.5 23.2 84.0 69.4 36.6 72.1 3.8 34.8 1.2 6.4 24.2 13.5 3.3 48.2 .. 2.9 92.8 15.6 4.8 59.8 .. 7.7 1.3 73.1 4.0 67.9 .. .. 76.5

2004

.. 6.3 0.2 64.7 3.3 0.0 4.3 11.3 0.0 35.7 4.2 2.2 65.6 14.7 3.9 24.4 26.5 3.9 .. .. .. 77.8 4.4 .. .. 15.4 13.7 0.3 14.9 92.5 61.7 8.2 64.9 4.3 19.4 3.3 11.7 19.3 5.7 2.5 32.5 .. 11.7 90.4 20.3 4.3 58.8 .. 22.8 3.0 69.1 3.2 52.9 .. .. 74.0

% of energy use 1990

2004

.. .. 8 59 –338 –404 –356 –505 –5 –34 94 65 –80 –126 68 70 –9 –55 16 19 91 86 73 77 –6 34 –77 –137 19 31 28 46 27 14 67 46 .. .. .. .. .. .. –140 –80 –31 –48 .. .. .. .. 46 70 –3 5 100 100 –94 –175 –1 –3 –753 –1,084 49 53 23 –4 35 56 61 45 18 25 44 –55 75 79 –169 –191 –72 –14 32 46 .. .. 34 31 7 9 59 58 51 50 –1,077 –615 .. .. 83 54 48 61 18 25 59 66 24 30 .. .. .. .. 21 25

Total energy production

million metric tons of oil equivalent

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

1990

2004

1.7 14.3 333.4 164.7 179.7 104.9 3.5 0.4 25.3 0.5 76.8 0.2 89.0 10.3 28.7 21.9 50.4 1.8 .. 0.8 0.1 .. .. 73.2 4.4 1.7 .. .. 48.8 .. .. .. 194.8 0.1 .. 0.8 6.8 10.7 0.2 5.5 60.5 12.0 1.5 .. 150.5 120.3 38.3 34.4 0.6 .. 4.6 10.6 13.7 99.4 3.4 ..

1.7 10.2 466.9 258.0 278.0 103.4 1.9 1.7 30.1 0.5 96.8 0.3 118.6 13.7 19.2 38.0 132.8 1.5 .. 2.1 0.2 .. .. 85.4 5.2 1.5 .. .. 88.5 .. .. .. 253.9 0.1 .. 0.7 8.2 19.0 0.3 8.1 67.9 13.0 1.9 .. 229.4 238.6 58.1 59.0 0.8 .. 6.6 9.5 23.4 78.8 3.9 ..

3.7

Energy use

Total million metric tons of oil equivalent 1990

2004

2.4 28.6 361.6 97.6 68.8 19.1 10.4 12.1 148.0 2.9 446.0 3.5 79.7 12.5 32.9 92.7 8.5 5.1 .. 5.9 2.3 .. .. 11.5 11.1 2.9 .. .. 22.6 .. .. .. 124.3 6.9 .. 6.7 7.2 10.7 0.7 5.8 66.7 13.8 2.1 .. 70.9 21.5 4.6 43.4 1.5 .. 3.1 10.0 26.2 99.9 17.7 ..

3.9 26.4 572.9 174.0 145.8 29.7 15.2 20.7 184.5 4.1 533.2 6.5 54.8 16.9 20.4 213.0 25.1 2.8 .. 4.6 5.4 .. .. 18.2 9.2 2.7 .. .. 56.7 .. .. .. 165.5 3.4 .. 11.5 8.6 14.1 1.3 9.1 82.1 17.6 3.3 .. 99.0 27.7 11.8 74.4 2.5 .. 4.0 13.2 44.3 91.7 26.5 ..

Net energy importsa

Per capita average annual % growth 1990–2004

3.0 –0.2 3.3 4.0 5.3 3.5 3.5 4.0 1.6 2.6 1.2 3.7 –2.8 2.1 –3.4 6.0 8.4 –3.5 .. –1.8 5.9 .. .. 2.9 –1.0 –1.0 .. .. 6.3 .. .. .. 1.9 –6.0 .. 3.8 1.1 2.0 5.1 3.3 1.3 1.9 2.9 .. 2.3 1.6 6.8 3.7 4.2 .. 1.9 2.1 4.3 –0.9 3.3 ..

ENVIRONMENT

Energy production and use

kilograms of oil equivalent 1990

2004

496 2,753 426 548 1,264 1,029 2,969 2,599 2,610 1,231 3,610 1,104 4,846 533 1,670 2,161 3,985 1,114 .. 2,245 842 .. .. 2,663 3,002 1,515 .. .. 1,269 .. .. .. 1,494 1,575 .. 281 536 262 449 304 4,464 3,990 535 .. 783 5,067 2,475 402 618 .. 731 458 428 2,620 1,793 ..

548 2,608 531 800 2,167 .. 3,738 3,049 3,171 1,541 4,173 1,219 3,652 506 910 4,431 10,212 546 .. 1,988 1,525 .. .. 3,169 2,666 1,328 .. .. 2,279 .. .. .. 1,622 802 .. 384 441 283 665 341 5,045 4,344 643 .. 769 6,024 4,667 489 801 .. 694 479 542 2,403 2,528 ..

average annual % growth

Combustible renewables and waste % of total

1990–2004 1990

0.3 0.0 1.5 2.6 3.7 .. 2.5 1.3 1.5 1.8 1.0 0.3 –2.0 –0.4 –4.2 5.1 6.4 –4.6 .. –0.8 4.1 .. .. 0.9 –0.4 –1.4 .. .. 3.8 .. .. .. 0.4 –5.7 .. 2.3 –1.6 0.5 2.5 0.9 0.6 0.7 1.0 .. –0.3 1.0 4.4 1.2 2.2 .. –0.3 0.3 2.1 –0.8 2.9 ..

62.0 1.3 48.6 40.4 1.0 0.1 1.0 0.0 0.6 16.2 1.1 0.1 0.1 78.4 2.9 0.3 0.1 0.1 .. 8.2 4.5 .. .. 1.1 2.6 6.4 .. .. 9.4 .. .. .. 5.9 0.5 .. 4.7 94.4 84.4 16.0 93.4 1.4 4.0 53.2 .. 79.8 4.8 .. 43.2 28.3 .. 72.3 26.9 29.2 2.2 14.0 ..

2004

40.0 3.3 37.4 27.1 0.5 0.1 1.4 0.0 3.3 11.7 1.2 0.0 0.1 74.1 5.0 0.8 .. 0.1 .. 29.9 2.4 .. .. 0.8 7.6 6.3 .. .. 4.9 .. .. .. 5.0 2.3 .. 3.9 84.1 73.4 13.8 86.8 2.6 5.0 51.1 .. 80.2 4.9 .. 35.6 16.8 .. 53.8 17.7 23.9 5.0 10.9 ..

% of energy use 1990

30 50 8 –69 –161 –451 67 96 83 84 83 95 –12 18 13 76 –495 64 .. 87 94 .. .. –534 60 41 .. .. –115 .. .. .. –57 99 .. 89 5 0 67 5 9 13 29 .. –112 –460 –740 21 59 .. –48 –6 48 1 81 ..

2007 World Development Indicators

2004

55 61 19 –48 –91 –248 87 92 84 88 82 96 –116 19 6 82 –429 47 .. 53 96 .. .. –369 43 43 .. .. –56 .. .. .. –53 98 .. 94 4 –34 76 11 17 26 41 .. –132 –763 –391 21 70 .. –65 28 47 14 85 ..

151

3.7

Energy production and use Total energy production

million metric tons of oil equivalent 1990

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 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 Europe EMU

40.8 1,118.7 .. 376.9 1.4 13.2 .. .. 5.3 2.8 .. 114.5 34.6 4.2 8.8 .. 29.8 9.7 22.3 1.6 9.1 26.5 1.2 12.6 6.1 25.8 48.8 .. 101.3 109.4 208.0 1,650.5 1.1 40.5 148.9 24.7 .. 9.4 4.9 8.6 8,798.3 t 791.6 4,386.6 2,160.0 2,226.9 5,175.0 1,218.4 1,885.7 618.0 601.9 391.5 481.8 3,657.9 470.7

2004

28.1 1,158.5 .. 556.2 1.1 11.5 .. 0.1 6.5 3.4 .. 156.0 32.5 5.2 29.3 .. 35.1 11.8 29.5 1.5 17.5 50.1 1.9 29.4 6.8 24.1 58.2 .. 76.3 164.0 225.2 1,641.0 0.9 56.9 196.1 65.3 .. 20.6 6.4 8.6 11,171.2 t 1,173.8 5,604.9 3,257.8 2,347.3 6,767.1 2,079.8 1,721.9 910.5 823.7 562.2 715.4 4,450.0 462.9

a. Negative value indicates that a country is a net exporter.

152

2007 World Development Indicators

Energy use

Total million metric tons of oil equivalent

Net energy importsa

Per capita average annual % growth

1990

2004

1990–2004

62.4 774.8 .. 67.4 2.2 21.5 .. 13.4 21.3 5.0 .. 91.2 91.1 5.5 10.6 .. 47.6 25.0 11.7 9.1 9.8 43.9 1.4 6.0 5.5 53.0 11.3 .. 210.0 22.5 212.2 1,927.6 2.3 45.0 43.9 24.3 .. 2.6 5.5 9.4 8,609.9 t 773.3 3,502.9 1,923.6 1,579.4 4,267.1 1,135.3 1,733.4 459.8 194.4 432.8 317.4 4,369.4 1,053.1

38.6 641.5 .. 140.4 2.8 16.2 .. 25.6 18.3 7.2 .. 131.1 142.2 9.4 17.6 .. 53.9 27.1 18.4 3.3 18.7 97.1 2.7 11.3 8.7 81.9 15.6 .. 140.3 43.8 233.7 2,325.9 2.9 54.0 56.2 50.2 .. 6.4 6.9 9.3 11,026.3 t 1,136.6 4,431.1 2,889.6 1,541.6 5,548.9 2,085.8 1,335.7 644.6 356.7 694.3 452.2 5,512.8 1,245.1

–2.9 –1.2 .. 4.3 1.6 –1.1 .. 3.4 –0.3 2.7 .. 2.4 3.4 3.8 3.7 .. 0.7 0.7 3.2 –5.5 4.6 5.2 4.6 5.4 3.4 3.4 3.1 .. –3.0 4.6 0.6 1.4 1.1 1.9 1.6 5.1 .. 6.2 1.6 –0.1 1.7 w 2.8 1.4 2.5 –0.2 1.6 3.7 –1.9 2.5 4.3 3.4 2.4 1.7 1.3

kilograms of oil equivalent 1990

2004

2,689 1,778 5,211 4,460 .. .. 4,114 6,233 281 242 2,044 2,004 .. .. 4,384 6,034 4,035 3,407 2,508 3,591 .. .. 2,592 2,829 2,345 3,331 324 485 408 497 .. .. 5,557 5,998 3,724 3,672 909 993 1,647 519 374 498 803 1,524 365 449 4,968 8,675 679 876 943 1,151 2,912 3,265 .. .. 4,027 2,958 12,716 10,142 3,686 3,906 7,722 7,921 725 832 2,098 2,088 2,224 2,149 367 611 .. .. 212 313 653 605 888 719 1,685 w 1,793 w 464 513 1,349 1,451 953 1,175 2,980 2,583 1,008 1,068 722 1,124 3,726 2,847 1,050 1,187 861 1,189 394 486 693 703 4,842 5,511 3,568 3,990

average annual % growth

Combustible renewables and waste % of total

1990–2004 1990

–2.4 –0.9 .. 1.9 –0.9 1.1 .. 0.8 –0.4 2.7 .. 0.3 2.7 2.9 1.4 .. 0.4 0.1 0.5 –6.7 1.9 4.1 1.4 4.9 2.0 1.6 1.4 .. –2.1 –1.8 0.3 0.2 0.3 0.3 –0.5 3.5 .. 2.4 –0.7 –1.5 0.3 w 0.7 0.2 1.1 –1.1 0.2 2.5 –2.0 0.9 2.3 1.5 0.0 0.9 0.9

1.0 1.6 .. 0.0 60.6 1.8 .. .. 0.8 5.3 .. 11.4 4.5 71.0 81.7 .. 11.6 3.7 0.0 .. 91.0 33.4 82.6 0.8 18.7 13.6 .. .. 0.1 0.1 0.3 3.2 24.3 .. 1.2 77.7 .. 3.0 73.4 50.4 10.8 w 55.6 11.8 18.4 3.7 19.0 26.1 1.9 18.2 1.8 49.1 56.6 2.9 3.1

2004

% of energy use 1990

2004

8.4 35 27 1.1 –44 –81 .. .. .. 0.0 –459 –296 38.9 39 60 4.9 38 29 .. .. .. .. .. 99 2.2 75 65 6.7 45 52 .. .. .. 10.0 –26 –19 3.4 62 77 52.0 24 45 79.2 18 –66 .. .. .. 16.7 37 35 6.3 61 56 0.0 –91 –60 .. 83 55 91.6 8 7 16.4 40 48 70.6 17 29 0.2 –109 –160 12.4 –11 22 6.8 51 71 .. –332 –274 .. .. .. 0.2 52 46 0.0 –385 –274 1.3 2 4 3.0 14 29 15.4 49 70 .. 10 –5 1.0 –239 –249 47.2 –2 –30 .. .. .. 1.2 –266 –224 79.1 10 8 63.8 9 8 10.3 w –2 w –2 w 47.8 –2 –3 10.5 –25 –27 13.9 –12 –13 4.0 –41 –52 17.5 –21 –22 16.1 –7 0 2.4 –9 –30 14.8 –34 –41 1.2 –210 –131 38.0 10 19 55.7 –52 –58 3.1 16 19 4.2 55 63

3.7

ENVIRONMENT

Energy production and use About the data

In developing countries growth in energy use is closely

waste—solid biomass and animal products, gas

The IEA makes these estimates in consultation

related to growth in the modern sectors—industry,

and liquid from biomass, and industrial and munici-

with national statistical offices, oil companies, elec-

motorized transport, and urban areas—but energy

pal waste. Biomass is defined as any plant matter

tricity utilities, and national energy experts. The IEA

use also reflects climatic, geographic, and economic

used directly as fuel or converted into fuel, heat,

occasionally revises its time series to reflect politi-

factors (such as the relative price of energy). Energy

or electricity. (The data series published in World

cal changes. In addition, energy statistics for other

use has been growing rapidly in low- and middle-

Development Indicators 1998 and earlier editions did

countries have undergone continuous changes in

income countries, but high-income countries still

not include energy from combustible renewables and

coverage or methodology as more detailed energy

use more than five times as much energy on a per

waste.) Data for combustible renewables and waste

accounts have become available in recent years.

capita basis.

are often based on small surveys or other incomplete

Breaks in series are therefore unavoidable.

Energy data are compiled by the International Energy

information. Thus the data give only a broad impres-

Agency (IEA). IEA data for countries that are not mem-

sion of developments and are not strictly comparable

bers of the Organisation for Economic Co-operation

between countries. The IEA reports include country

• Total energy production refers to forms of primary

and Development (OECD) are based on national

notes that explain some of these differences (see

energy—petroleum (crude oil, natural gas liquids,

energy data adjusted to conform to annual question-

Data sources). All forms of energy—primary energy

and oil from non-conventional sources), natural gas,

naires completed by OECD member governments.

and primary electricity—are converted into oil equiv-

solid fuels (coal, lignite, and other derived fuels), and

Total energy use refers to the use of primary energy

alents. To convert nuclear electricity into oil equiva-

combustible renewables and waste—and primary

before transformation to other end-use fuels (such

lents, a notional thermal efficiency of 33 percent

electricity, all converted into oil equivalents (see

as electricity and refined petroleum products). It

is assumed; for hydroelectric power 100 percent

About the data). • Energy use refers to use of primary

includes energy from combustible renewables and

efficiency is assumed.

energy before transformation to other end-use fuels,

Definitions

which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied Energy use per capita varies widely among the top energy users

3.7a

to ships and aircraft engaged in international transport (see About the data). • Combustible renewables

Countries with highest energy use (millions of metric tons of oil equivalent)

1990

2004

2,500

and waste comprise solid biomass, liquid biomass, biogas, industrial waste, and municipal waste, measured as a percentage of total energy use. • Net

2,000

energy imports are estimated as energy use less production, both measured in oil equivalents. The

1,500

deviations from zero of the world net imports are from statistical errors and changes in stock.

1,000

500

0 United States

China

Russian Federation

India

Japan

Germany

France

Energy use per capita in countries with highest energy use (kilograms of oil equivalent per person) 8,000 7,000 6,000 5,000 4,000

Data sources 3,000

Data on energy production and use come from

2,000

IEA electronic files. The IEA’s data are published 1,000

in its annual publications, Energy Statistics and

0

Balances of Non-OECD Countries, Energy Statistics United States

Source: Table 3.7.

China

Russian Federation

India

Japan

Germany

France

of OECD Countries, and Energy Balances of OECD Countries.

2007 World Development Indicators

153

3.8

Energy efficiency and emissions GDP per unit of energy use

2000 PPP $ per kilogram of oil equivalent 1990 2004

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, 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

154

.. 3.8 5.7 3.7 6.4 1.6 4.0 7.1 .. 9.8 1.2 4.7 2.6 5.1 .. 6.2 7.2 2.1 .. .. .. 4.7 3.0 .. .. 5.5 2.1 10.8 8.4 5.0 2.3 9.7 5.2 5.0 .. 3.1 6.9 7.1 5.8 5.1 7.3 .. 1.6 2.6 3.8 5.5 4.8 .. 1.2 4.7 4.6 6.7 6.7 .. .. 10.4

.. 5.9 6.0 3.3 7.4 5.6 4.8 7.3 2.5 10.5 2.4 5.2 3.3 4.5 5.3 8.6 6.8 3.0 .. .. .. 4.5 3.4 .. .. 6.1 4.4 11.5 10.9 2.2 3.3 10.0 3.7 5.6 .. 4.0 7.9 7.6 4.8 4.9 7.0 .. 3.5 2.8 3.8 5.9 4.9 .. 4.1 6.2 5.4 7.4 6.4 .. .. 6.2

2007 World Development Indicators

Carbon dioxide emissions

Total million metric tons 1990 2003

2.6 7.3 77.0 4.6 109.7 4.2 272.2 57.7 53.7 15.4 107.8 100.6 0.7 5.5 6.9 2.2 202.6 75.3 1.0 0.2 0.5 1.6 415.7 0.2 0.1 35.3 2,398.2 26.2 56.8 4.0 1.2 2.9 5.4 24.6 32.0 161.7 49.7 9.6 16.6 75.4 2.6 .. 28.3 3.0 51.2 362.3 6.0 0.2 17.3 980.3 3.8 72.2 5.1 1.0 0.2 1.0

0.7 3.0 163.6 8.6 127.5 3.4 354.1 70.3 29.2 34.6 62.5 102.8 2.0 7.9 19.1 4.1 298.3 44.0 1.0 0.2 0.5 3.5 565.5 0.3 0.1 58.5 4,143.5 37.8 55.5 1.8 1.4 6.3 5.7 23.8 25.2 116.3 54.5 21.3 23.2 139.6 6.5 0.7 18.2 7.3 67.8 373.9 1.2 0.3 3.7 805.0 7.7 96.2 10.7 1.3 0.3 1.7

average annual % change 1990–2003

–9.0 –3.7 7.8 4.3 1.2 –0.9 2.7 1.2 –5.1 6.9 –4.4 –0.4 7.4 4.4 15.3 4.2 3.7 –3.6 1.2 1.9 1.4 4.7 2.2 2.3 2.8 5.2 2.5 2.5 –0.6 –6.5 –1.0 5.1 1.3 1.6 –1.8 –2.2 –1.2 6.8 2.4 5.6 6.4 .. –3.9 8.0 1.4 0.1 –9.1 3.4 –11.3 –1.1 5.7 2.6 6.7 2.3 2.0 7.2

Methane emissions

Per capita metric tons 1990 2003

0.2 2.2 3.0 0.4 3.4 1.2 15.9 7.5 7.5 0.1 10.6 10.1 0.1 0.8 1.6 1.5 1.4 8.6 0.1 0.0 0.0 0.1 15.0 0.1 0.0 2.7 2.1 4.6 1.6 0.1 0.5 0.9 0.4 5.1 3.0 15.6 9.7 1.3 1.6 1.4 0.5 .. 18.1 0.1 10.3 6.4 6.3 0.2 3.2 12.3 0.2 7.1 0.6 0.2 0.2 0.1

.. 1.0 5.1 0.6 3.4 1.1 17.8 8.7 3.5 0.3 6.3 9.9 0.3 0.9 4.9 2.3 1.6 5.6 0.1 0.0 0.0 0.2 17.9 0.1 0.0 3.7 3.2 5.6 1.3 0.0 0.4 1.5 0.3 5.4 2.3 11.4 10.1 2.5 1.8 2.0 1.0 0.2 13.5 0.1 13.0 6.2 0.9 0.2 0.8 9.8 0.4 8.7 0.9 0.1 0.2 0.2

kilograms per 2000 PPP $ of GDP 1990 2003

.. 0.9 0.7 0.3 0.5 0.5 1.0 0.4 1.9 0.2 2.4 0.5 0.2 0.5 .. 0.3 0.3 1.6 0.2 0.0 .. 0.1 0.8 0.1 0.0 0.6 1.6 0.3 0.4 0.1 0.5 0.2 0.3 0.6 .. 1.3 0.5 0.4 0.6 0.6 0.2 .. 2.5 0.1 0.6 0.4 1.2 0.2 0.8 0.7 0.2 0.6 0.2 0.1 0.3 0.1

.. 0.2 0.8 0.3 0.3 0.3 0.6 0.3 1.0 0.1 1.0 0.3 0.2 0.3 .. 0.2 0.2 0.7 0.1 0.0 0.0 0.1 0.6 0.1 0.0 0.4 0.6 0.2 0.2 0.1 0.3 0.2 0.2 0.5 .. 0.6 0.3 0.3 0.5 0.5 0.2 0.2 1.1 0.1 0.4 0.2 0.1 0.1 0.3 0.4 0.2 0.4 0.2 0.1 0.2 0.1

Nitrous oxide emissions

million million metric tons metric tons average of carbon average of carbon annual dioxide annual dioxide % change equivalent % change equivalent 2000 1990–2000 2000 1990–2000

13.2 0.5 28.5 15.8 86.7 2.8 113.2 9.7 11.9 47.6 21.6 11.7 3.3 21.3 1.4 7.0 297.2 10.0 8.8 1.8 68.0 11.8 123.4 6.6 9.6 14.5 802.9 .. 55.5 32.9 3.2 3.6 6.5 3.8 9.1 10.8 6.0 5.9 16.2 34.3 3.2 0.0 2.4 47.5 4.3 59.3 3.8 0.7 4.4 62.7 7.1 10.9 6.2 5.7 0.9 3.4

5.3 –3.8 4.0 1.6 0.7 –2.0 0.1 –1.6 –2.4 0.9 –1.1 –0.4 2.2 1.2 –3.0 1.3 0.9 –6.3 2.1 2.0 1.0 1.2 5.8 1.6 1.6 1.5 1.8 .. 1.2 0.6 1.9 –0.3 2.0 –0.5 –0.8 –3.5 –0.3 1.1 1.8 4.1 1.9 .. –4.4 2.0 –3.4 –1.1 2.3 1.7 –1.9 –4.4 3.4 2.4 0.5 1.9 0.0 1.7

7.5 0.1 9.2 6.1 63.4 0.3 27.0 2.8 0.8 44.8 8.3 13.3 2.7 5.8 0.6 4.8 207.7 18.5 11.7 1.2 0.1 9.8 57.5 5.1 8.7 7.5 644.7 .. 41.2 17.2 1.0 3.6 2.9 3.4 9.3 8.2 9.3 4.3 2.9 16.0 2.2 .. 0.4 12.2 7.3 72.3 1.8 0.5 1.1 60.5 7.4 11.2 5.2 2.4 0.8 2.6

3.4 –0.5 1.4 2.0 1.2 –4.1 3.4 1.0 –4.2 3.7 –3.4 0.1 2.7 –0.1 –5.1 1.0 1.1 –2.2 2.3 0.9 3.6 1.9 0.9 1.8 2.2 3.6 2.4 .. 4.8 0.0 2.6 –1.0 1.8 –1.2 –3.3 –4.8 –1.5 0.4 –0.3 3.9 0.7 .. –5.8 6.6 –1.5 –1.7 0.0 0.3 –4.4 –3.2 6.4 0.2 0.8 2.9 2.4 0.7

GDP per unit of energy use

2000 PPP $ per kilogram of oil equivalent 1990 2004

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

5.0 4.2 4.0 4.1 3.6 .. 5.2 7.0 8.4 3.0 6.5 3.5 1.0 2.2 .. 4.5 1.2 1.7 .. 2.5 2.7 .. .. .. 2.8 4.1 .. .. 4.4 .. .. .. 5.1 1.4 .. 11.9 1.3 .. 12.3 3.4 5.2 4.1 5.3 .. 1.1 5.1 4.3 3.9 7.4 .. 6.5 8.4 9.1 2.9 7.9 ..

4.8 5.9 5.5 4.1 3.1 .. 9.5 7.3 8.2 2.5 6.4 3.6 1.9 2.1 .. 4.2 1.9 3.3 .. 5.6 3.5 .. .. .. 4.5 4.6 .. .. 4.1 .. .. .. 5.5 2.0 .. 10.3 2.6 .. 10.2 4.0 5.8 5.1 5.5 .. 1.4 5.9 3.0 4.2 8.4 .. 6.6 10.9 7.9 5.1 7.1 ..

Carbon dioxide emissions

Total million metric tons 1990 2003

2.6 60.1 677.7 149.3 218.2 48.5 30.6 33.1 389.5 8.0 1,070.4 10.2 288.1 5.8 244.6 241.1 45.2 12.6 0.2 14.5 9.1 .. 0.5 37.8 24.3 15.5 0.9 0.6 55.3 0.4 2.6 1.5 375.1 23.8 10.0 23.5 1.0 4.3 0.0 0.6 139.7 23.6 2.6 1.0 45.3 35.3 10.3 68.0 3.1 2.4 2.3 21.0 43.9 347.5 42.3 11.8

6.5 58.2 1,273.2 295.0 381.4 72.9 41.4 68.3 445.5 10.7 1,231.3 17.1 159.2 8.8 77.5 455.9 78.5 5.3 1.3 6.7 19.0 .. 0.5 50.2 12.7 10.5 2.3 0.9 156.4 0.6 2.5 3.1 415.9 7.2 8.0 37.9 1.6 9.5 2.3 2.9 140.9 34.8 3.9 1.2 52.2 45.0 32.2 114.1 6.0 2.5 4.1 26.1 76.9 304.5 57.5 2.1

average annual % change 1990–2003

7.5 –0.4 4.9 4.6 3.7 3.8 2.9 5.8 1.0 2.4 1.0 3.8 –6.0 5.0 –11.9 4.6 11.0 –7.5 16.7 –6.9 5.1 .. 3.1 2.3 –5.2 –0.7 8.5 2.8 6.8 2.3 –1.2 6.4 0.7 –10.1 –2.6 3.7 3.2 6.5 .. 11.4 0.2 3.3 5.0 1.1 –0.1 2.0 8.7 4.3 5.5 –0.2 4.7 2.3 5.0 –1.3 3.0 –4.1

Methane emissions

Per capita metric tons 1990 2003

0.5 5.8 0.8 0.8 4.0 2.6 8.7 7.1 6.9 3.3 8.7 3.2 17.6 0.2 12.4 5.6 21.3 2.8 0.1 5.4 3.3 .. 0.2 8.7 6.6 8.1 0.1 0.1 3.1 0.0 1.3 1.4 4.5 5.5 4.7 1.0 0.1 0.1 0.0 0.0 9.3 6.8 0.7 0.1 0.5 8.3 5.6 0.6 1.3 0.6 0.5 1.0 0.7 9.1 4.3 3.3

0.9 5.7 1.2 1.4 5.7 .. 10.4 10.2 7.7 4.1 9.6 3.3 10.7 0.3 3.5 9.5 32.7 1.1 0.2 2.9 5.4 .. 0.1 8.9 3.7 5.2 0.1 0.1 6.4 0.0 0.9 2.6 4.1 1.7 3.2 1.3 0.1 0.2 1.2 0.1 8.7 8.7 0.8 0.1 0.4 9.9 12.8 0.8 1.9 0.4 0.7 1.0 1.0 8.0 5.5 0.5

kilograms per 2000 PPP $ of GDP 1990 2003

0.2 0.6 0.6 0.5 1.0 .. 0.7 0.5 0.4 1.3 0.4 1.0 3.8 0.3 .. 0.7 1.5 1.4 0.1 0.7 1.5 .. .. .. 0.7 1.4 0.1 0.2 0.7 0.1 0.9 0.3 0.7 1.9 3.3 0.4 0.1 .. 0.0 0.0 0.5 0.5 0.3 0.2 0.7 0.4 0.6 0.5 0.4 0.4 0.1 0.3 0.2 1.5 0.4 ..

0.3 0.4 0.4 0.4 0.8 .. 0.3 0.4 0.3 1.0 0.3 0.7 1.7 0.2 .. 0.5 1.4 0.6 0.1 0.3 1.1 .. .. .. 0.3 0.8 0.2 0.1 0.7 0.0 0.4 0.2 0.4 1.0 1.8 0.3 0.1 .. 0.2 0.1 0.3 0.4 0.2 0.1 0.4 0.3 0.9 0.4 0.3 0.2 0.2 0.2 0.2 0.7 0.3 ..

3.8

ENVIRONMENT

Energy efficiency and emissions

Nitrous oxide emissions

million million metric tons metric tons average of carbon average of carbon annual dioxide annual dioxide % change equivalent % change equivalent 2000 1990–2000 2000 1990–2000

4.9 11.3 445.3 169.2 96.9 14.4 12.9 11.4 37.0 1.3 21.8 7.9 27.3 21.5 33.5 25.0 9.9 2.2 6.2 2.6 1.3 1.2 1.2 9.6 5.9 1.3 18.9 3.6 30.4 12.0 4.4 0.3 111.7 2.6 8.2 10.0 11.1 61.1 4.5 16.4 21.6 36.2 5.3 6.5 72.5 7.1 3.7 94.7 3.3 3.9 12.3 19.6 34.2 47.2 14.3 ..

–0.2 –2.5 1.6 1.4 6.7 0.7 –0.1 3.3 –0.7 0.8 –1.7 1.0 –4.5 1.1 0.3 –0.2 6.5 –2.4 0.9 –4.0 8.6 2.0 –0.8 0.9 –4.2 0.0 1.5 1.6 4.3 0.9 1.3 5.0 0.0 –4.1 1.7 1.0 1.8 2.4 0.5 1.5 –2.3 –0.5 1.3 2.5 4.2 0.6 8.5 2.5 1.0 3.9 0.5 1.5 0.7 –2.2 0.3 ..

3.5 12.9 399.0 38.7 43.8 6.5 9.8 1.7 43.5 1.3 37.0 0.2 7.8 22.6 6.5 16.1 0.2 0.1 0.1 1.2 1.1 1.5 0.8 2.5 3.5 1.1 11.6 2.3 13.3 13.8 6.4 0.9 10.0 1.6 12.1 15.7 3.2 12.5 4.2 11.3 17.2 12.4 4.0 5.0 41.6 5.1 1.0 84.6 2.7 2.3 10.2 21.9 20.8 23.9 8.1 ..

2007 World Development Indicators

0.0 13.6 2.6 1.0 1.5 0.1 0.6 2.0 0.6 0.3 –0.6 9.2 –6.5 0.3 –4.0 4.8 14.1 1.2 2.7 –6.6 5.5 0.5 0.9 –1.1 16.8 1.5 1.2 1.3 1.5 2.4 1.3 1.7 1.1 –6.0 3.7 0.6 1.0 3.2 –0.2 1.5 0.3 0.5 0.8 2.8 1.9 0.0 1.9 3.4 0.7 1.8 0.2 8.0 3.3 –2.2 0.3 ..

155

3.8

Energy efficiency and emissions GDP per unit of energy use

2000 PPP $ per kilogram of oil equivalent 1990 2004

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 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 Europe EMU

156

Carbon dioxide emissions

Total million metric tons 1990 2003

average annual % change 1990–2003

2.5 4.5 155.0 91.1 –4.0 1.6 2.0 2,261.7 1,493.0 –3.3 .. .. 0.5 0.6 1.7 2.8 2.2 197.4 302.3 0.5 5.0 6.5 3.1 4.8 2.6 .. .. 65.4 49.9 –0.4 .. .. 0.3 0.7 4.3 3.4 4.4 45.1 47.8 1.2 2.7 3.9 51.4 37.5 –1.8 4.9 5.4 18.0 15.4 1.1 .. .. 0.0 .. .. 3.9 3.7 285.4 364.2 1.7 7.3 6.9 211.8 309.2 3.2 7.3 8.3 3.8 10.3 8.8 2.7 3.7 5.4 9.0 5.0 .. .. 0.4 1.0 12.0 4.0 4.5 49.4 52.7 0.0 8.2 8.3 42.7 40.4 –0.2 2.9 3.4 35.8 48.9 1.9 0.9 2.1 23.4 4.7 –12.5 1.4 1.3 2.3 3.8 2.8 5.7 4.9 95.7 245.9 6.4 4.3 3.1 0.8 2.2 7.8 1.4 1.3 16.9 28.6 3.5 6.7 8.2 13.3 20.9 3.2 5.8 6.2 146.2 220.0 3.6 1.6 .. 32.0 43.3 2.6 .. .. 0.8 1.7 7.2 1.8 2.0 684.0 314.4 –6.6 1.9 2.2 54.7 135.0 7.4 5.9 7.3 569.1 558.5 –0.4 3.7 4.6 4,816.2 5,788.2 1.7 9.9 10.4 3.9 4.4 0.7 0.7 0.8 129.2 123.6 0.1 2.6 2.6 117.3 144.0 2.0 3.3 4.2 21.4 76.1 11.5 .. .. .. .. .. 3.0 2.8 9.6 17.1 4.2 1.5 1.5 2.4 2.2 –2.0 3.0 2.6 16.6 11.5 –2.7 3.9 w 4.8 w 22,501.8 t 26,750.9 t 1.2 w 3.5 4.4 1,336.9 1,893.3 2.4 3.0 4.2 9,319.6 10,753.5 0.5 3.1 4.5 4,965.3 6,943.3 1.7 2.8 3.7 4,353.8 3,811.5 –1.1 3.0 4.3 10,656.6 12,646.8 0.8 2.6 4.4 3,030.6 5,100.6 2.5 2.1 2.8 4,821.9 3,265.3 –3.1 6.0 6.2 1,037.3 1,299.9 2.0 4.6 4.2 575.4 1,012.5 4.5 4.2 5.5 768.4 1,436.7 4.9 2.8 2.8 418.3 531.9 1.6 4.7 5.2 10,651.9 12,738.4 1.5 5.8 6.5 2,466.1 2,535.8 0.4

2007 World Development Indicators

Methane emissions

Per capita metric tons 1990 2003

6.7 15.3 0.1 12.1 0.4 6.2 0.1 14.8 9.7 9.0 0.0 8.1 5.5 0.2 0.2 0.6 5.8 6.4 2.8 4.4 0.1 1.8 0.2 13.9 1.6 2.6 8.7 0.0 13.2 30.8 9.9 19.3 1.3 6.3 5.9 0.3 .. 0.8 0.3 1.6 4.3 w 0.8 3.5 2.4 8.1 2.4 1.9 10.2 2.4 2.5 0.7 0.8 11.8 8.3

4.2 10.3 0.1 13.7 0.4 6.2 0.1 11.4 7.0 7.7 .. 7.9 7.4 0.5 0.3 0.9 5.9 5.5 2.7 0.7 0.1 3.9 0.4 22.1 2.1 3.1 9.2 0.1 6.6 33.4 9.4 19.9 1.3 4.8 5.6 0.9 .. 0.9 0.2 0.9 4.3 w 0.8 3.6 2.9 6.4 2.4 2.7 6.9 2.4 3.4 1.0 0.8 12.8 8.2

Nitrous oxide emissions

kilograms per 2000 PPP $ of GDP 1990 2003

million million metric tons metric tons average of carbon average of carbon annual dioxide annual dioxide % change equivalent % change equivalent 2000 1990–2000 2000 1990–2000

1.2 1.8 0.1 1.1 0.3 .. 0.1 1.2 1.1 0.8 .. 1.1 0.4 0.1 0.2 0.2 0.3 0.3 1.4 2.2 0.2 0.5 0.2 2.4 0.4 0.6 1.9 0.1 1.8 1.5 0.6 0.8 0.2 4.2 1.3 0.3 .. 1.4 0.4 0.7 0.7 w 0.5 1.1 1.0 1.2 1.0 1.2 1.6 0.5 0.8 0.5 0.6 0.6 0.5

36.1 298.7 2.2 54.4 8.4 9.5 2.6 1.2 4.2 2.5 .. 37.4 39.6 13.3 46.6 1.1 7.1 5.0 9.7 1.4 31.7 75.9 2.1 3.1 4.8 97.4 27.1 12.4 153.5 35.2 51.1 613.4 18.3 46.2 95.1 68.1 .. 8.7 11.2 11.0 5,893.6 t 1,344.6 3,033.6 2,080.8 952.9 4,377.7 1,365.5 844.1 800.9 233.0 631.7 504.6 1,450.2 287.0

0.6 1.2 0.1 0.9 0.3 .. 0.2 0.5 0.5 0.4 .. 0.8 0.3 0.1 0.1 0.2 0.2 0.2 0.8 0.7 0.2 0.5 0.3 1.8 0.3 0.4 .. 0.0 1.2 1.5 0.3 0.5 0.2 2.8 1.1 0.4 .. 1.0 0.2 0.4 0.5 w 0.4 0.6 0.5 0.7 0.5 0.6 0.9 0.3 0.7 0.4 0.4 0.4 0.3

–1.7 –4.6 –1.5 5.7 2.5 –2.6 0.8 7.1 –3.1 –0.7 .. 0.7 2.3 2.9 1.7 1.0 –1.0 –1.1 6.7 0.8 1.8 0.4 1.7 2.4 3.0 2.1 1.7 2.5 –2.2 7.1 –3.3 –0.5 2.0 1.5 2.4 1.5 .. 8.9 1.4 0.2 –0.6 w 1.8 0.8 1.5 –0.1 1.0 1.7 –2.0 0.8 4.2 1.7 1.5 –0.9 –1.8

7.2 51.5 1.2 8.7 6.6 6.1 0.9 0.9 3.2 2.0 .. 25.8 30.1 2.9 47.1 1.2 7.1 3.7 9.4 0.1 27.1 13.1 2.3 0.3 5.2 40.6 0.6 12.9 19.9 0.1 43.8 430.0 0.7 13.5 6.9 12.9 .. 5.6 5.5 8.6 3,454.4 t 910.8 1,545.4 1,242.3 303.4 2,455.6 780.0 236.3 418.8 118.3 550.3 353.8 961.9 278.2

–6.6 –3.7 –1.4 1.5 3.8 –3.5 3.0 46.1 –4.9 2.3 .. 0.1 1.5 2.0 2.0 1.2 –0.4 0.3 2.0 1.5 1.6 0.7 1.5 –1.6 1.4 –1.2 –1.4 2.7 –4.3 3.2 –3.5 0.8 3.0 1.9 –0.5 5.2 .. 0.9 1.3 –0.5 0.3 w 2.7 1.0 1.7 0.0 1.2 2.2 –1.9 1.4 1.9 2.8 1.2 0.1 –1.1

About the data

The ratio of GDP to energy use provides a measure of energy effi ciency. To produce comparable and consistent estimates of real GDP across countries

Definitions

High-income countries contribute more than half of global carbon dioxide emissions

• GDP per unit of energy use is the PPP GDP per kilogram of oil equivalent of energy use. PPP GDP

3.8a

international dollars using purchasing power parity

Share of carbon dioxide emissions, 2003

parity rates. An international dollar has the same

Other low-income 2%

purchasing power over GDP as a U.S. dollar has in

India 5%

(PPP) rates. Differences in this ratio over time and across countries reflect in part structural changes

Other middleincome 26%

in the economy, changes in the energy efficiency of particular sectors, and differences in fuel mixes. Because commercial energy is widely traded, it is

China 16%

necessary to distinguish between its production and

the United States. • Carbon dioxide emissions are

United States 23%

those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid,

Other high-income 28%

its use. Net energy imports show the extent to which

energy production and use (see table 3.7), account

are those stemming from human activities such as • Nitrous oxide emissions are those stemming from

Source: Table 3.8.

agriculture, biomass burning, industrial activities, and livestock management.

dle-income countries have been their main suppliers. Carbon dioxide emissions, largely byproducts of

and gas fuels and gas flaring. • Methane emissions agriculture and from industrial methane production.

an economy’s use exceeds its domestic production. High-income countries are net energy importers; mid-

is gross domestic product converted to 2000 constant international dollars using purchasing power

relative to physical inputs to GDP—that is, units of energy use—GDP is converted to 2000 constant

3.8

ENVIRONMENT

Energy efficiency and emissions

The five largest contributors to carbon dioxide emissions differ considerably in per capita emissions

3.8b

for the largest share of greenhouse gases, which are associated with global warming. Anthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combus-

Per capita carbon dioxide emissions of the five largest producers (metric tons) 20

1990

2003

tion, different fossil fuels release different amounts of carbon dioxide for the same level of energy use.

15

Burning oil releases about 50 percent more carbon dioxide than burning natural gas, and burning coal releases about twice as much. Cement manufactur-

10

ing releases about half a metric ton of carbon dioxide for each metric ton of cement produced.

5

Methane emissions, largely the result of agricultural activities and industrial production of methane, are expressed in carbon dioxide equivalents using global warming potential, which allows different gases to be compared on the basis of their effective contribu-

0 United States

China

Russian India Federation

Japan

Source: Table 3.8.

tions. A kilogram of methane is 23 times as effective at trapping heat in the earth’s atmosphere as a kilo-

dioxide by multiplying the carbon mass by 3.664 (the

gram of carbon dioxide within a time horizon of 100

ratio of the mass of carbon to that of carbon dioxide).

years. The global warming potential of a kilogram of

Although the estimates of global carbon dioxide emis-

nitrous oxide is nearly 300 times that of a kilogram of

sions are probably within 10 percent of actual emis-

carbon dioxide within the same time horizon.

sions (as calculated from global average fuel chemis-

The Carbon Dioxide Information Analysis Center

try and use), country estimates may have larger error

(CDIAC), sponsored by the U.S. Department of Energy,

bounds. The world totals shown in the table include

calculates annual anthropogenic emissions of carbon

the carbon dioxide emissions not allocated to specific

dioxide. These calculations are based on data on fos-

countries. Trends estimated from a consistent time

sil fuel consumption (from the World Energy Data Set

series tend to be more accurate than individual val-

The underlying data on energy use are from elec-

maintained by the United Nations Statistics Division)

ues. Each year the CDIAC recalculates the entire time

tronic files of the International Energy Agency. Data

and data on world cement manufacturing (from the

series from 1950 to the present, incorporating its

on carbon dioxide emissions are from the CDIAC,

Cement Manufacturing Data Set maintained by the

most recent findings and the latest corrections to its

Environmental Sciences Division, Oak Ridge

U.S. Bureau of Mines). Emissions of carbon dioxide

database. Estimates do not include fuels supplied to

National Laboratory, in the U.S. state of Tennes-

are often calculated and reported in terms of their

ships and aircraft engaged in international transport

see. Data on methane and nitrous oxide emissions

content of elemental carbon. For this table these

because of the difficulty of apportioning these fuels

are compiled by the World Resources Institute.

values were converted to the actual mass of carbon

among the countries benefiting from that transport.

Data sources

2007 World Development Indicators

157

3.9

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, 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

158

.. 3.2 16.1 0.8 51.0 9.0 154.3 49.3 19.7 7.7 37.6 70.3 0.0 2.1 6.5 0.9 222.8 42.1 .. .. .. 2.7 481.9 .. .. 18.4 621.2 28.9 36.2 5.7 0.5 3.5 2.0 8.9 15.0 62.3 26.0 3.7 6.3 42.3 2.2 .. 11.8 1.2 54.4 417.2 1.0 .. 11.2 547.7 5.7 34.8 2.3 .. .. 0.6

2004

.. 5.6 31.3 2.2 100.3 6.0 239.3 61.6 21.6 21.5 31.2 84.4 0.1 4.4 12.6 1.3 387.5 41.4 .. .. 0.8 4.1 598.4 .. .. 52.0 2,199.6 37.1 50.2 6.9 0.4 8.2 5.4 13.2 15.7 83.8 40.5 13.8 12.6 101.3 4.4 .. 10.3 2.5 85.8 567.1 1.5 .. 6.9 610.0 6.0 58.8 7.0 .. .. 0.5

2007 World Development Indicators

Coal

Gas

Oil

Hydropower

Nuclear power

1990

2004

1990

2004

1990

2004

1990

2004

1990

2004

.. .. .. .. 1.3 .. 77.1 14.2 .. .. .. 28.2 .. .. 47.8 88.1 2.0 50.3 .. .. .. .. 17.1 .. .. 34.3 71.2 98.3 9.8 .. .. .. .. .. .. 76.4 90.7 1.2 .. .. .. .. 90.0 .. 33.0 8.5 .. .. .. 58.8 .. 72.4 .. .. .. ..

.. .. .. .. 1.7 .. 79.3 14.8 .. .. 0.0 13.6 .. .. 52.1 95.7 2.7 46.1 .. .. .. .. 17.2 .. .. 16.1 77.9 68.5 6.0 .. .. .. .. 16.2 .. 60.3 46.1 15.2 .. .. .. .. 92.4 .. 27.5 5.0 .. .. .. 50.5 .. 60.2 17.1 .. .. ..

.. .. 93.7 .. 39.0 22.9 10.6 15.7 0.5 84.3 47.9 7.7 .. 37.6 .. .. 0.0 7.6 .. .. .. .. 2.0 .. .. 1.3 0.5 .. 12.4 .. .. .. .. 15.4 0.2 0.6 2.7 .. .. 39.6 .. .. 5.5 .. 8.6 0.7 16.4 .. 36.6 7.4 .. 0.3 .. .. .. ..

.. .. 97.0 .. 54.8 30.4 12.3 17.8 58.9 87.5 87.3 25.5 .. 29.2 .. .. 5.0 3.6 .. .. .. .. 5.4 .. .. 34.0 0.4 30.9 12.9 .. .. .. 67.5 18.6 0.0 2.6 24.7 0.1 8.5 70.8 .. .. 4.7 .. 14.9 3.2 16.7 .. 12.0 10.1 .. 15.3 .. .. .. ..

.. 10.9 5.4 13.8 9.7 43.3 2.7 3.8 91.1 4.3 52.1 1.9 100.0 5.3 .. 11.9 2.2 2.9 .. .. .. 1.5 3.4 .. .. 7.6 7.9 1.7 1.0 0.4 0.6 2.5 33.3 35.8 91.5 0.9 3.4 88.6 21.5 36.9 6.9 .. 4.5 11.6 3.1 2.1 11.2 .. 5.0 1.9 .. 22.3 9.0 .. .. 20.6

.. 1.7 2.2 33.5 4.0 .. 0.7 3.0 28.4 6.7 12.6 2.0 98.8 19.7 1.1 4.3 3.2 2.0 .. .. 49.3 4.6 3.6 .. .. 1.5 3.3 0.6 0.2 0.3 .. 1.8 0.1 12.4 95.3 0.4 4.0 72.6 32.6 16.2 45.6 .. 0.3 0.7 0.7 1.0 24.8 .. 0.6 1.7 12.6 14.3 35.7 .. .. 52.5

.. 89.1 0.8 86.2 35.6 33.8 9.2 63.9 8.9 11.4 0.0 0.4 .. 55.3 52.2 .. 92.8 4.5 .. .. .. 98.5 61.6 .. .. 55.3 20.4 .. 76.0 99.6 99.4 97.5 66.7 48.8 0.6 1.9 0.1 9.4 78.5 23.5 73.5 .. 0.0 88.4 20.0 12.9 72.1 .. 58.3 3.2 100.0 5.1 76.0 .. .. 76.5

.. 98.3 0.8 66.5 30.4 33.1 6.8 59.1 12.7 5.7 0.1 0.4 1.2 49.0 46.8 .. 82.8 7.6 .. .. 1.8 95.4 57.0 .. .. 45.4 16.1 .. 79.8 99.7 100.0 79.0 32.4 52.7 0.6 2.4 0.1 11.5 58.9 12.5 31.2 .. 0.2 99.3 17.6 10.5 58.1 .. 87.4 3.5 87.4 7.9 34.7 .. .. 47.5

.. .. .. .. 14.3 .. .. .. .. .. .. 60.8 .. .. .. .. 1.0 34.8 .. .. .. .. 15.1 .. .. .. 0.2 .. .. .. .. .. .. .. .. 20.2 .. .. .. .. .. .. .. .. 35.3 75.3 .. .. .. 27.8 .. .. .. .. .. ..

.. .. .. .. 7.8 36.5 .. .. .. .. .. 56.1 .. .. .. .. 3.0 40.6 .. .. .. .. 15.1 .. .. .. 2.3 .. .. .. .. .. .. .. .. 31.4 .. .. .. .. .. .. .. .. 26.5 79.0 .. .. .. 27.4 .. .. .. .. .. ..

Electricity production

3.9

ENVIRONMENT

Sources of electricity Sources of electricitya

% of total billion kilowatt hours

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

Coal

Gas

Oil

Hydropower

Nuclear power

1990

2004

1990

2004

1990

2004

1990

2004

1990

2004

1990

2004

2.3 28.4 289.4 33.3 59.1 24.0 14.2 20.9 213.1 2.5 838.2 3.6 82.7 3.0 27.7 105.4 18.5 11.9 .. 4.0 1.5 .. .. 10.2 18.7 6.1 .. .. 23.0 .. .. .. 124.1 11.2 .. 9.6 0.5 2.5 1.4 0.9 71.9 32.1 1.4 .. 13.5 121.6 4.5 37.7 2.7 .. 27.2 13.8 25.2 134.4 28.4 ..

4.9 33.7 667.8 120.2 164.5 32.3 25.2 49.1 293.0 7.2 1,071.0 9.0 66.9 5.6 22.0 366.6 41.3 15.1 .. 4.7 10.2 .. .. 20.2 18.8 6.7 .. .. 82.9 .. .. .. 224.1 3.6 .. 19.3 11.7 6.4 1.7 2.3 100.8 41.8 2.8 .. 20.2 110.1 11.5 85.7 5.8 .. 51.9 24.3 56.0 152.6 44.8 ..

.. 30.5 65.3 31.5 .. .. 57.4 50.1 16.8 .. 13.9 .. 72.3 .. 40.1 16.8 .. 9.1 .. 3.8 .. .. .. .. .. 85.0 .. .. 12.3 .. .. .. 6.3 34.4 .. 23.0 13.9 1.6 1.5 .. 38.3 1.5 .. .. 0.1 0.1 .. 0.1 .. .. .. .. 7.7 97.5 32.1 ..

.. 24.7 69.1 40.1 .. .. 30.6 75.3 17.4 .. 27.5 .. 69.9 .. 38.6 38.8 .. 3.5 .. 0.6 .. .. .. .. .. 77.6 .. .. 27.9 .. .. .. 10.7 .. .. 67.4 .. .. 0.4 .. 26.0 9.9 .. .. .. 0.1 .. 0.2 .. .. .. 3.8 28.9 94.1 33.1 ..

.. 15.7 3.4 2.3 52.5 .. 27.7 .. 18.6 .. 19.8 11.9 10.6 .. .. 9.1 45.7 13.6 .. 25.4 .. .. .. .. 6.7 .. .. .. 20.4 .. .. .. 11.6 36.9 .. .. 0.2 39.3 .. .. 50.9 17.7 .. .. 53.7 0.0 81.6 33.6 .. .. .. 1.7 .. 0.1 .. ..

.. 34.8 9.5 16.1 76.2 .. 51.1 8.8 44.3 .. 22.8 50.2 10.6 .. .. 16.2 20.5 3.5 .. 30.6 .. .. .. 19.3 14.4 .. .. .. 61.8 .. .. .. 38.8 97.9 .. .. 0.1 57.0 .. .. 60.5 16.7 .. .. 62.7 0.3 82.0 50.7 .. .. .. 8.2 22.1 2.1 26.1 ..

1.7 4.8 4.3 42.7 37.3 89.2 10.0 49.9 48.2 92.4 18.4 87.8 8.8 7.6 3.6 17.9 54.3 .. .. 7.6 66.7 .. .. 100.0 12.4 1.0 .. .. 50.0 .. .. .. 56.7 26.4 .. 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 46.7 1.2 33.1 ..

51.5 2.3 5.4 30.2 17.3 98.5 12.7 15.8 15.7 96.5 9.2 49.2 7.4 24.1 4.5 7.6 79.5 .. .. 1.3 89.0 .. .. 80.7 1.9 0.2 .. .. 3.3 .. .. .. 31.1 0.4 .. 23.2 0.2 6.8 2.7 0.2 2.8 0.1 75.2 .. 3.1 0.0 18.0 15.9 34.0 .. .. 15.1 15.2 1.6 12.7 ..

98.3 0.6 24.8 20.2 10.3 10.8 4.9 0.0 14.8 3.6 10.7 0.3 8.3 81.6 56.3 6.0 .. 77.4 .. 63.3 33.3 .. .. .. 2.5 14.0 .. .. 17.3 .. .. .. 18.9 2.3 .. 12.7 62.6 48.1 95.2 99.9 0.1 72.6 28.8 .. 32.6 99.6 .. 44.9 83.2 .. 99.9 75.8 24.0 1.1 32.3 ..

48.1 0.6 12.7 8.1 6.5 1.5 2.5 0.1 13.5 1.9 8.8 0.6 12.0 51.5 56.9 1.2 .. 93.1 .. 66.4 11.0 .. .. .. 2.2 22.2 .. .. 7.0 .. .. .. 11.2 1.6 .. 8.4 99.7 36.2 96.9 99.8 0.1 64.6 11.4 .. 34.2 98.8 .. 30.0 65.6 .. 100.0 72.3 15.4 1.4 22.0 ..

.. 48.3 2.1 .. .. .. .. .. 0.1 .. 24.1 .. .. .. .. 50.2 .. .. .. .. .. .. .. .. 78.2 .. .. .. .. .. .. .. 2.4 .. .. .. .. .. .. .. 4.9 .. .. .. .. .. .. 0.8 .. .. .. .. .. .. .. ..

.. 35.3 2.5 .. .. .. .. .. .. .. 26.4 .. .. .. .. 35.7 .. .. .. .. .. .. .. .. 80.5 .. .. .. .. .. .. .. 4.1 .. .. .. .. .. .. .. 3.8 .. .. .. .. .. .. 3.3 .. .. .. .. .. .. .. ..

2007 World Development Indicators

159

3.9

Sources of electricity Electricity production

Sources of electricitya

% of total billion kilowatt hours 1990

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 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 Europe EMU

2004

64.3 56.5 1,008.5 929.9 .. .. 69.2 159.9 0.9 2.4 36.5 35.4 .. .. 15.7 36.8 25.5 30.5 12.1 15.3 .. .. 165.4 242.2 151.2 277.1 3.2 8.0 1.5 3.9 .. .. 146.0 151.7 54.9 63.6 11.6 32.1 16.8 17.3 1.6 2.5 44.2 125.7 0.2 0.3 3.6 6.4 5.8 13.1 57.5 150.7 13.2 11.5 .. .. 252.5 182.0 17.1 52.4 317.8 393.2 3,202.8 4,147.7 7.4 5.9 50.9 51.0 59.3 98.5 8.7 46.0 .. .. 1.7 4.3 8.0 8.5 9.4 9.7 11,787.7 s 17,372.6 s 518.1 1,026.4 3,842.9 6,258.9 1,828.6 3,903.4 2,014.3 2,355.4 4,361.0 7,285.3 785.8 2,659.5 2,213.4 1,964.1 608.5 1,088.3 190.0 449.0 338.9 785.3 224.4 339.0 7,426.7 10,087.3 1,667.3 2,227.2

Coal

Gas

1990

2004

28.8 15.3 .. .. .. 65.6 .. .. 31.9 36.2 .. 94.3 40.1 .. .. .. 1.1 0.1 .. .. .. 25.0 .. .. .. 35.1 .. .. 41.8 .. 65.0 53.1 .. 4.9 .. 23.1 .. .. 0.5 53.3 38.1 w 40.9 35.8 41.5 30.6 36.4 61.3 33.0 3.8 1.2 55.8 72.1 39.1 34.5

38.5 17.3 .. .. .. 69.9 .. .. 20.0 34.0 .. 93.2 29.0 .. .. .. 1.7 .. .. .. 3.5 15.9 .. .. .. 22.9 .. .. 24.7 .. 34.1 50.4 .. 3.9 .. 15.3 .. .. 0.2 43.0 39.8 w 47.1 42.7 50.0 30.6 43.3 69.1 28.6 4.7 2.9 58.7 68.2 37.3 27.0

1990

35.1 45.7 .. 43.5 2.0 1.6 .. 11.8 7.1 0.2 .. .. 1.0 .. .. .. 0.3 0.6 20.5 5.3 .. 40.2 .. 99.0 63.7 17.7 100.0 .. 16.8 96.3 1.6 11.9 .. 75.9 26.2 0.1 .. .. .. .. 13.8 w 15.0 19.5 10.4 27.6 18.9 3.5 28.4 9.7 38.4 8.6 3.3 10.8 8.6

Oil 2004

18.5 45.3 .. 49.2 1.8 1.5 .. 68.8 7.9 2.3 .. 0.0 20.0 .. .. .. 0.5 1.5 41.2 2.3 .. 71.0 .. 99.5 90.2 41.3 100.0 .. 20.7 97.5 40.6 17.6 0.0 74.0 16.9 42.7 .. .. .. .. 19.7 w 19.9 20.6 13.3 32.6 20.5 7.6 33.8 19.6 60.2 16.0 4.9 19.1 18.3

a. Shares may not sum to 100 percent because some sources of generated electricty are not shown.

160

2007 World Development Indicators

1990

18.4 9.9 .. 56.5 98.0 1.7 .. 100.0 6.4 2.5 .. 0.0 5.7 0.2 36.8 .. 0.9 0.7 56.0 .. 4.9 23.5 39.9 0.1 35.5 6.9 .. .. 9.0 3.7 10.9 4.1 5.1 6.9 11.5 15.0 .. 100.0 0.3 .. 10.3 w 6.6 14.7 16.0 13.5 13.7 12.7 12.1 18.8 48.2 6.1 2.2 8.4 9.4

Hydropower 2004

3.9 2.7 .. 50.8 75.0 0.8 .. 31.2 2.4 0.3 .. .. 8.6 63.2 72.8 .. 1.3 0.3 45.6 .. 1.5 6.2 38.9 0.1 8.3 5.1 .. .. 0.3 2.5 1.3 3.4 18.3 9.2 12.1 3.7 .. 100.0 0.4 0.2 6.4 w 7.0 7.6 8.0 6.9 7.5 4.9 3.2 14.0 29.8 7.1 2.7 5.6 4.9

Nuclear power

1990

2004

1990

2004

17.7 17.0 .. .. .. 31.1 .. .. 7.4 28.2 .. 0.6 16.8 99.8 63.2 .. 49.7 54.3 23.5 94.7 95.1 11.3 60.1 .. 0.8 40.2 0.0 .. 3.2 .. 1.6 8.5 94.2 12.3 62.3 61.8 .. .. 99.2 46.7 18.1 w 34.8 21.6 27.5 16.2 23.1 21.7 13.3 63.5 12.2 27.6 18.4 15.2 11.1

29.2 18.9 .. .. 12.5 27.9 .. .. 13.5 26.8 .. 0.9 11.4 36.8 27.2 .. 39.6 53.1 13.2 97.7 95.1 4.8 61.1 .. 1.2 30.6 0.0 .. 6.5 .. 1.3 6.5 81.0 12.8 71.0 38.4 .. .. 99.4 56.8 16.0 w 23.4 21.5 23.4 18.3 21.8 15.6 17.4 56.3 7.0 14.9 19.5 11.9 10.0

.. 11.9 .. .. .. .. .. .. 47.2 32.9 .. 5.1 35.9 .. .. .. 46.7 43.1 .. .. .. .. .. .. .. .. .. .. 29.2 .. 20.7 19.1 .. .. .. .. .. .. .. .. 17.1 w 1.2 7.4 5.0 9.6 6.7 0.2 12.0 2.0 .. 1.9 3.8 23.2 35.4

9.8 15.6 .. .. .. .. .. .. 55.9 35.7 .. 5.5 23.0 .. .. .. 51.1 42.4 .. .. .. .. .. .. .. .. .. .. 47.8 .. 20.3 19.6 .. .. .. .. .. .. .. .. 15.8 w 1.9 6.7 4.3 10.7 6.0 1.9 16.6 2.6 .. 2.5 3.9 22.8 34.0

3.9

ENVIRONMENT

Sources of electricity About the data

Use of energy is important in improving people’s stan-

IEA data for countries that are not members of the

in coverage or methodology as more detailed energy

dard of living. But electricity generation also can dam-

Organisation for Economic Co-operation and Devel-

accounts have become available in recent years.

age the environment. Whether such damage occurs

opment (OECD) are based on national energy data

Breaks in series are therefore unavoidable.

depends largely on how electricity is generated. For

adjusted to conform to annual questionnaires com-

example, burning coal releases twice as much carbon

pleted by OECD member governments. In addition,

dioxide—a major contributor to global warming—as

estimates are sometimes made to complete major

• Electricity production is measured at the termi-

does burning an equivalent amount of natural gas

aggregates from which key data are missing, and

nals of all alternator sets in a station. In addition to

(see About the data for table 3.8). Nuclear energy

adjustments are made to compensate for differ-

hydropower, coal, oil, gas, and nuclear power genera-

does not generate carbon dioxide emissions, but it

ences in definitions. The IEA makes these estimates

tion, it covers generation by geothermal, solar, wind,

produces other dangerous waste products. The table

in consultation with national statistical offices, oil

and tide and wave energy as well as that from com-

provides information on electricity production by

companies, electricity utilities, and national energy

bustible renewables and waste. Production includes

source. Shares may not sum to 100 percent because

experts. It occasionally revises its time series to

the output of electricity plants designed to produce

some sources of generated electricity (such as wind,

reflect political changes. Since 1990, for example, it

electricity only, as well as that of combined heat and

solar, and geothermal) are not shown.

has constructed energy statistics for countries of the

power plants. • Sources of electricity refer to the

The International Energy Agency (IEA) compiles

former Soviet Union. In addition, energy statistics for

inputs used to generate electricity: coal, gas, oil,

data on energy inputs used to generate electricity.

other countries have undergone continuous changes

hydropower, and nuclear power. • Coal refers to all

Definitions

coal and brown coal, both primary (including hard coal and lignite-brown coal) and derived fuels (includ-

Coal is still the major source of electricity in all income groups, with low-income countries increasingly relying on this source

3.9a

ing patent fuel, coke oven coke, gas coke, coke oven gas, and blast furnace gas). Peat is also included in

1990

2004

Other 1.5% Nuclear power 1.2%

this category. • Gas refers to natural gas but not

Other 0.7% Nuclear power 1.9%

to natural gas liquids. • Oil refers to crude oil and petroleum products. • Hydropower refers to electric-

Hydropower 35%

Coal 41%

ity produced by hydroelectric power plants.• Nuclear

Hydropower 23%

Coal 47%

Low-income countries

plants.

Oil 7% Oil 7%

power refers to electricity produced by nuclear power

Gas 20%

Gas 15%

Other 1% Nuclear power 7%

Other 0.9% Nuclear power 7%

Coal 36%

Hydropower 22%

Oil 15%

Middle-income countries

Hydropower 22%

Oil 8%

Gas 20%

Coal 43%

Gas 21%

Other 3%

Other 3% Nuclear power 23%

Coal 39%

Nuclear power 23%

Data sources

Hydropower 12%

Hydropower 15% Oil 8%

Coal 37%

High-income countries

Gas 11%

Oil 6%

Data on electricity production are from the IEA’s Gas 19%

electronic files and its annual publications Energy Statistics and Balances of Non-OECD Countries, Energy Statistics of OECD Countries, and Energy

Note: Components may not sum to 100 percent because of rounding. Source: Table 3.9.

Balances of OECD Countries.

2007 World Development Indicators

161

3.10

Urbanization Urban population

millions 1990 2005

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, 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

162

.. 1.2 13.2 3.9 28.3 2.4 14.6 5.1 3.8 20.6 6.8 9.6 1.8 3.7 1.7 0.6 111.7 5.8 1.2 0.4 1.2 4.7 21.3 1.1 1.3 11.0 311.0 5.7 24.0 10.5 1.3 1.6 5.0 2.6 7.7 7.8 4.4 3.9 5.7 24.2 2.5 0.5 1.1 6.4 3.1 42.0 0.7 0.4 3.0 58.3 5.6 6.0 3.7 1.7 0.3 2.0

.. 1.4 20.8 8.5 34.9 1.9 17.9 5.4 4.3 35.6 7.1 10.2 3.4 5.9 1.8 1.0 157.0 5.4 2.4 0.8 2.8 8.9 25.9 1.5 2.5 14.3 527.0 6.9 33.2 18.5 2.4 2.7 8.2 2.5 8.5 7.5 4.6 5.9 8.3 31.7 4.1 0.9 0.9 11.4 3.2 46.7 1.2 0.8 2.3 62.0 10.6 6.6 5.9 3.1 0.5 3.3

2007 World Development Indicators

% of total population 1990 2005

.. 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 69 28 54 51 40 54 73 75 85 55 55 44 49 16 71 13 61 74 69 38 55 73 37 59 41 28 28 30

.. 45 63 53 90 64 88 66 52 25 72 97 40 64 46 57 84 70 18 10 20 55 80 38 25 88 40 100 73 32 60 62 45 57 76 74 86 67 63 43 60 19 69 16 61 77 84 54 52 75 48 59 47 33 30 39

average annual % growth 1990–2005

.. 1.0 3.0 5.2 1.4 –1.4 1.4 0.4 0.7 3.7 0.3 0.4 4.3 3.1 1.0 3.5 2.3 –0.5 4.9 4.9 5.6 4.3 1.3 2.2 4.6 1.8 3.6 1.4 2.1 3.7 4.0 3.7 3.3 0.0 0.7 –0.3 0.4 2.9 2.5 1.8 3.4 4.0 –1.2 3.9 0.3 0.7 3.8 5.7 –1.7 0.4 4.2 0.6 3.3 3.8 3.3 3.3

Population in urban agglomerations of more than 1 million

Population in largest city

% of total population 1990 2005

% of urban population 1990 2005

% of urban population 1990 2004

% of rural population 1990 2004

.. .. 8 15 39 33 60 27 24 9 16 10 .. 25 .. .. 34 14 .. .. 6 14 40 .. .. 35 13 100 30 15 28 24 17 .. 20 12 26 21 26 22 19 .. .. 3 17 23 .. .. 22 8 12 30 .. 14 .. 17

.. .. 14 40 37 49 25 41 45 32 24 10 .. 29 .. .. 13 21 50 .. 48 20 18 .. 38 42 3 100 20 35 52 47 42 .. 27 16 31 39 28 37 39 .. .. 28 28 22 .. .. 41 6 21 51 22 51 .. 56

.. 99 99 61 86 96 100 100 .. 55 .. 100 32 49 99 61 82 100 32 42 .. 59 100 34 28 91 64 .. 95 53 .. .. 37 100 99 99 100 60 77 70 70 44 97 13 100 .. .. .. 99 100 23 .. 73 27 .. 25

.. .. 77 18 45 .. 100 100 .. 12 .. 100 2 14 .. 21 37 96 3 44 .. 40 99 17 2 52 7 .. 52 1 .. 97 10 100 95 97 100 43 45 42 33 0 96 2 100 .. .. .. 94 100 10 .. 47 10 .. 23

.. .. 10 17 39 37 61 27 22 13 18 10 .. 31 .. .. 37 14 .. .. 10 20 44 .. .. 35 18 100 36 17 29 28 20 .. 19 11 20 23 29 20 22 .. .. 4 21 22 .. .. 23 8 16 29 .. 15 .. 25

.. .. 15 33 36 57 24 42 43 35 25 10 .. 26 .. .. 12 20 38 .. 49 20 21 .. 36 40 3 100 23 33 49 46 44 .. 26 16 23 34 29 35 37 .. .. 25 34 21 .. .. 45 5 19 49 17 46 .. 64

Access to improved sanitation facilities

.. 99 99 56 92 96 100 100 73 51 93 100 59 60 99 57 83 100 42 47 53 58 100 47 24 95 69 .. 96 42 28 89 46 100 99 99 100 81 94 86 77 32 97 44 100 .. 37 72 96 100 27 .. 90 31 57 57

.. 84 82 16 83 61 100 100 36 35 61 100 11 22 92 25 37 96 6 35 8 43 99 12 4 62 28 .. 54 25 25 97 29 100 95 97 100 73 82 58 39 3 96 7 100 .. 30 46 91 100 11 .. 82 11 23 14

Urban population

millions 1990 2005

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

2.0 6.8 216.6 54.5 30.6 12.9 2.0 4.2 37.8 1.2 78.0 2.3 9.2 4.3 11.5 31.6 2.1 1.7 0.6 1.9 2.3 0.3 1.0 3.4 2.5 1.1 2.8 1.1 8.9 2.1 0.8 0.5 60.3 2.0 1.2 11.6 2.8 10.1 0.4 1.7 10.3 2.9 2.1 1.3 31.7 3.1 1.2 33.0 1.3 0.5 2.1 15.0 29.8 23.4 4.7 2.6

3.4 6.7 314.1 106.1 45.7 .. 2.5 6.3 39.6 1.4 84.1 4.5 8.7 7.1 13.9 39.0 2.5 1.8 1.2 1.6 3.1 0.3 1.9 5.0 2.3 1.4 5.0 2.2 17.1 4.1 1.2 0.5 78.3 2.0 1.4 17.7 6.8 15.5 0.7 4.3 13.1 3.5 3.0 2.3 63.4 3.6 1.8 54.4 2.3 0.8 3.5 20.3 52.1 23.7 6.1 3.8

% of total population 1990 2005

40 66 26 31 56 70 57 90 67 49 63 72 56 18 58 74 98 38 15 69 83 17 45 79 68 58 24 12 50 23 40 44 73 47 57 48 21 25 28 9 69 85 53 15 35 72 65 31 54 13 49 69 49 61 48 72

47 66 29 48 67 .. 61 92 68 53 66 82 57 21 62 81 98 36 21 68 87 19 58 85 67 69 27 17 67 31 40 42 76 47 57 59 35 31 35 16 80 86 59 17 48 77 72 35 71 13 59 73 63 62 58 98

average annual % growth 1990–2005

3.6 –0.2 2.5 4.6 2.7 .. 1.5 2.6 0.2 1.2 0.5 4.1 –0.7 3.4 1.3 1.4 2.9 0.6 4.4 –1.2 2.0 1.4 5.7 2.5 –0.7 1.6 3.8 4.8 4.5 4.7 2.9 0.9 1.8 –0.3 1.2 2.7 6.0 2.9 4.2 6.4 1.6 1.2 2.6 4.0 4.7 1.1 2.7 3.4 3.9 2.6 3.5 2.0 3.8 0.1 1.7 2.6

Population in urban agglomerations of more than 1 million

Population in largest city

% of total population 1990 2005

% of urban population 1990 2005

.. 19 10 9 23 26 26 43 19 .. 46 27 7 6 16 51 65 .. .. .. 47 .. .. 49 .. .. 8 .. 6 8 .. .. 32 .. .. 16 6 7 .. .. 14 25 19 .. 11 .. .. 16 35 .. 22 27 14 4 37 44

.. 17 12 12 23 .. 25 44 17 .. 48 24 8 8 20 51 71 .. .. .. 50 .. .. 55 .. .. 9 .. 6 10 .. .. 35 .. .. 16 7 8 .. .. 14 28 23 .. 14 .. .. 18 38 .. 31 26 14 4 39 67

29 29 6 14 21 32 46 48 9 .. 42 37 12 32 22 33 67 38 .. .. 57 .. 55 44 .. .. 33 .. 13 36 .. .. 25 .. 48 23 27 29 .. 23 10 30 35 33 15 22 .. 22 65 .. 45 39 27 7 54 60

3.10

Access to improved sanitation facilities

28 25 6 12 16 .. 41 47 8 .. 42 29 13 39 24 25 73 43 .. .. 57 .. 49 42 .. .. 32 .. 8 33 .. .. 25 .. 60 18 19 27 .. 19 9 33 38 36 17 22 .. 21 53 .. 54 35 21 7 45 68

% of urban population 1990 2004

% of rural population 1990 2004

77 100 45 65 86 95 .. 100 .. 86 100 97 87 48 .. .. .. 75 .. .. 100 61 59 97 .. .. 27 64 95 50 42 95 75 .. .. 87 49 48 70 48 100 .. 64 35 51 100 97 82 89 67 72 69 66 .. .. ..

31 .. 3 37 78 48 .. .. .. 64 100 82 52 37 .. .. .. 51 .. .. .. 32 24 96 .. .. 10 45 .. 32 22 .. 13 .. .. 27 12 16 8 7 100 88 24 2 33 100 61 17 51 41 45 15 48 .. .. ..

87 100 59 73 .. .. .. 100 .. 91 100 94 87 46 58 .. .. 75 67 82 100 61 49 97 .. .. 48 62 95 59 49 95 91 86 75 88 53 88 50 62 100 .. 56 43 53 100 97 92 89 67 94 74 80 .. .. ..

2007 World Development Indicators

54 85 22 40 .. .. .. .. .. 69 100 87 52 41 60 .. .. 51 20 71 87 32 7 96 .. .. 26 61 93 39 8 94 41 52 37 52 19 72 13 30 100 .. 34 4 36 100 .. 41 51 41 61 32 59 .. .. ..

163

ENVIRONMENT

Urbanization

3.10

Urbanization Urban population

millions 1990 2005

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 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 Europe EMU

164

12.6 108.8 0.4 12.5 3.1 5.4 1.2 3.0 3.0 1.0 2.0 18.3 29.3 2.9 6.9 0.2 7.1 4.6 6.3 1.7 5.0 16.1 1.2 0.1 4.9 33.2 1.7 2.0 34.7 1.4 51.1 188.0 2.8 8.2 16.6 13.4 1.3 2.5 3.3 3.1 2,253.0 s 442.0 1,160.1 798.0 362.0 1,602.1 459.7 294.0 310.1 117.1 277.7 143.5 650.9 209.5

11.6 104.5 1.7 18.7 4.8 4.2 2.2 4.3 3.0 1.0 2.9 27.8 33.3 3.0 14.8 0.3 7.6 5.6 9.6 1.6 9.3 20.7 2.5 0.2 6.5 48.5 2.2 3.6 31.9 3.5 54.0 239.5 3.2 9.6 24.8 21.9 2.6 5.7 4.1 4.7 3,128.3 s 704.7 1,657.4 1,225.8 431.6 2,362.1 781.5 300.5 425.4 174.7 418.4 261.7 766.2 230.1

2007 World Development Indicators

% of total population 1990 2005

54 73 5 77 39 51 30 100 57 50 30 52 75 17 27 23 83 68 49 32 19 29 30 9 60 59 45 11 67 79 89 75 89 40 84 20 68 21 39 29 43 w 25 44 38 68 37 29 63 71 52 25 28 74 71

54 73 19 81 42 52 41 100 56 51 35 59 77 15 41 24 84 75 51 25 24 32 40 12 65 67 46 13 68 77 90 81 92 37 93 26 72 27 35 36 49 w 30 54 50 72 44 41 64 77 57 28 35 78 73

average annual % growth 1990–2005

–0.5 –0.3 12.4 2.7 3.0 –2.1 4.0 2.4 0.1 0.1 2.7 2.9 0.8 0.0 5.2 3.1 0.4 1.2 2.9 –0.3 4.2 1.7 5.1 2.9 2.0 2.6 1.9 4.1 –0.7 6.3 0.4 1.6 1.0 1.0 2.7 3.3 4.6 5.4 1.3 2.8 2.2 w 3.2 2.4 2.9 1.2 2.6 3.6 0.1 2.1 2.7 2.8 4.1 1.1 0.6

Population in urban agglomerations of more than 1 million

Population in largest city

% of total population 1990 2005

% of urban population 1990 2005

8 18 .. 30 17 11 .. 99 .. .. 14 25 22 .. 9 .. 17 14 25 .. 5 11 16 .. .. 22 .. 4 12 27 26 41 41 10 34 13 .. 5 9 10 18 w 10 17 16 .. 14 .. 15 32 20 10 .. .. 18

9 19 .. 36 19 14 .. 100 .. .. 16 30 24 .. 12 .. 19 15 25 .. 7 10 22 .. .. 26 .. 5 13 29 26 43 36 8 37 13 .. 9 11 12 20 w 12 20 19 .. 17 .. 16 34 20 12 .. .. 18

14 8 57 19 44 22 43 99 .. .. 48 10 15 .. 34 .. 21 20 25 .. 27 37 52 .. .. 20 .. 38 7 34 15 9 46 25 17 30 .. 26 23 34 17 w 17 15 14 18 16 9 13 24 27 10 26 20 15

17 10 45 22 45 26 36 100 .. .. 46 12 17 .. 31 .. 22 20 26 .. 29 32 54 .. .. 20 .. 36 8 38 16 8 40 23 12 23 .. 31 31 32 16 w 18 14 12 19 15 8 15 22 25 11 26 19 15

Access to improved sanitation facilities

% of urban population 1990 2004

% of rural population 1990 2004

.. 93 49 100 53 97 .. 100 100 .. .. 85 100 89 53 .. 100 100 97 .. 52 95 71 100 95 96 .. 54 98 98 .. 100 100 69 .. 58 .. 82 63 69 77 w 50 79 75 89 71 65 94 81 87 50 52 100 ..

.. 70 36 .. 19 77 .. .. 98 .. .. 53 100 64 26 .. 100 100 50 .. 45 74 24 100 47 70 .. 41 92 95 .. 100 99 39 .. 30 .. 19 31 42 23 w 12 25 22 58 19 15 72 36 52 6 24 100 ..

89 93 56 100 79 97 53 100 100 .. 48 79 100 98 50 59 100 100 99 70 53 98 71 100 96 96 77 54 98 98 .. 100 100 78 71 92 78 86 59 63 80 w 61 81 77 91 75 72 93 86 92 63 53 100 ..

.. 70 38 .. 34 77 30 .. 98 .. 14 46 100 89 24 44 100 100 81 45 43 99 15 100 65 72 50 41 93 95 .. 100 99 61 48 50 61 28 52 47 38 w 28 42 39 66 35 36 71 49 58 27 28 100 ..

3.10

About the data

There is no consistent and universally accepted stan-

Estimates of the world’s urban population would

effectively prevent human, animal, and insect con-

dard for making the distinction between urban and

change significantly if China, India, and a few other

tact with excreta. The rural population with access

rural. The wide variety of situations across countries

populous nations were to change their definition of

is included to allow comparison of rural and urban

makes it difficult to adopt uniform criteria for distin-

urban centers. According to China’s State Statis-

access. This definition and the definition of urban

guishing urban and rural areas. Most countries have

tical Bureau, by the end of 1996 urban residents

areas vary, however, so comparisons between coun-

adopted an urban classification related to the size or

accounted for about 43 percent of China’s popula-

tries can be misleading.

characteristics of settlements. Other countries have

tion, while in 1994 only 20 percent of the population

defined urban areas based on the presence of cer-

was considered urban. In addition to the continuous

tain infrastructure and services. And some countries

migration of people from rural to urban areas, one of

• Urban population is the midyear population of

have designated urban areas based on administrative

the main reasons for this shift was the rapid growth

areas defined as urban in each country and reported

arrangements. The population of a city or metropolitan

in the hundreds of towns reclassified as cities in

to the United Nations (see About the data). • Popula-

area depends on the boundaries chosen. For example,

recent years. Because the estimates in the table are

tion in urban agglomerations of more than 1 million

in 1990 Beijing, China, contained 2.3 million people in

based on national definitions of what constitutes a

is the percentage of a country’s population living in

87 square kilometers of “inner city” and 5.4 million in

city or metropolitan area, cross-country comparisons

metropolitan areas that in 2005 had a population

158 square kilometers of “core city.” The population

should be made with caution. To estimate urban

of more than 1 million. • Population in largest city

of “inner city and inner suburban districts” was 6.3

populations, UN ratios of urban to total population

is the percentage of a country’s urban population

million, and that of “inner city, inner and outer subur-

were applied to the World Bank’s estimates of total

living in that country’s largest metropolitan area.

ban districts, and inner and outer counties” was 10.8

population (see table 2.1).

• Access to improved sanitation facilities refers to

Definitions

million. (For most countries the last definition is used.)

The urban population with access to improved sani-

the percentage of the urban or rural population with

For further discussion of urban-rural issues see box

tation facilities is defined as people with access to

access to at least adequate excreta disposal facili-

3.1a in About the data for table 3.1.

at least adequate excreta disposal facilities that can

ties (private or shared but not public) that can effectively prevent human, animal, and insect contact with excreta. Improved facilities range from simple but

Population of the world’s largest metropolitan areas in 1000, 1900, 2000, and 2015 (millions)

3.10a

protected pit latrines to flush toilets with a sewerage connection. To be effective, facilities must be cor-

1000 City

rectly constructed and properly maintained.

1900 Population

City

Population

Cordova

0.45

London

6.5

Kaifeng

0.40

New York

4.2 3.3

Constantinopole

0.30

Paris

Angkor

0.20

Berlin

2.7

Kyoto

0.18

Chicago

1.7

Cairo

0.14

Vienna

1.7

Baghdad

0.13

Tokyo

1.5

Nishapur

0.13

St. Petersburg

1.4

Hasa

0.11

Manchester

1.4

Anhivada

0.10

Philadelphia

1.4

2000 City

2015 Population

City

Population

Tokyo

34.5

Tokyo

35.5

Mexico City

18.1

Mumbai

21.9

New York–Newark

17.9

Mexico City

21.6

São Paulo

17.1

São Paulo

20.5

Mumbai

16.1

New York–Newark

19.9

Shanghai

13.2

Delhi

18.6

Data on urban population and the population in

Kolkata

13.1

Shanghai

17.2

urban agglomerations and in the largest city are

Delhi

12.4

Kolkata

17.0

from the United Nations Population Division’s World

Buenos Aires

11.9

Dhaka

16.8

Urbanization Prospects: The 2005 Revision. The

Los Angeles–Long Beach–Santa Ana

11.8

Jakarta

16.8

Data sources

total population figures are World Bank estimates.

Source: O’Meara 1999; United Nations Population Division, 2005, World Urbanization Prospects: The 2005 Revision.

Data on access to sanitation in urban and rural areas are from the World Health Organization.

2007 World Development Indicators

165

ENVIRONMENT

Urbanization

3.11

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, China Colombia Congo Dem Rep Congo Rep Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti

166

2001 1998 2001 2001 2001 1991 1999 2001 1999 2001 1992 2001 2001 2000 2001 1996 1990 1998 1987 2001 2003 1993 2002 2000 1993 1984 1984 2000 1998 2001 1981 2001 2001 2002 2001 1996 1992 2000 1994 2000 1999 2003 1993 2002 2001 2000 2001 2002

1982

.. 4.2 4.9 .. 3.6 4.1 3.8 2.6 4.7 4.8 .. 2.6 5.9 4.2 .. 4.2 3.8 2.7 6.2 4.7 5.2 5.2 2.6 5.2 5.1 3.4 3.4 .. 4.8 5.4 10.5 4.0 5.4 3.0 4.2 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 .. .. 4.2

2007 World Development Indicators

.. 3.9 .. .. .. 4.0 .. .. 4.4 4.8 .. .. .. 4.3 .. 3.9 3.7 2.7 5.8 .. .. 5.1 .. 5.8 5.1 3.5 3.2 .. .. .. .. .. .. .. 4.2 .. .. .. 3.7 .. .. .. 2.3 4.7 .. .. .. .. 3.5 .. 5.1 .. 4.7 .. .. ..

Overcrowding

People living in overcrowded dwellingsa % of total National Urban

.. .. .. .. 19 4 1 2 .. .. .. 0b .. 40 .. 27 .. .. 30 .. .. 67 .. 32 .. .. .. .. 27b 55 .. 22 .. .. .. .. .. .. 30 .. 63 .. 3 .. .. .. .. .. .. .. .. 1 .. .. .. 26

.. .. .. .. .. 6 .. .. .. .. .. .. .. .. .. 47 .. .. 53 .. .. 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 .. .. .. 77 .. 78 .. 91 82 .. 83b .. .. 88 .. .. .. .. .. 97 81 .. 67 .. .. .. .. .. .. 18 .. .. 45 .. 67 .. .. ..

.. .. .. .. .. 93 .. .. .. 42b .. .. .. 58 .. 90 b .. 89 .. .. .. .. .. 92 .. 92 .. .. .. .. .. .. .. .. .. .. .. .. 88 .. 83 .. .. 23 .. .. .. .. .. .. .. .. 80 .. .. ..

.. 65b 67 .. .. 95 .. .. 74 88 b .. 67 59 70 .. 61 74 98 .. .. .. 73 64 85 .. 66 88 .. 68 b .. 76 72 .. .. .. 52 .. .. 68 b .. 70 .. .. .. 64 55 .. 68 .. 43 57 .. 81 .. .. 92

.. 30 b .. .. .. 90 .. .. 62 61b .. .. .. 59 .. 47 75 98 .. .. .. 48 .. 74 .. 65 74 .. .. .. .. .. .. .. .. .. .. .. 58 b .. 68 .. .. 54 .. .. .. .. .. .. .. .. 74 .. .. 68

Multiunit dwellings

Vacancy rate

% of total National Urban

Unoccupied dwellings % of total National Urban

.. .. .. .. 4 1 .. 50 4 .. .. 32b .. 3b .. 1 .. .. .. .. .. 27 32 .. .. 13 .. .. 13 .. .. 2 .. .. 15 49 .. 8 9 75 3 .. 72 .. 44 .. .. .. .. .. 53 .. 2 .. .. ..

.. .. .. .. .. 1 .. .. 5 .. .. .. .. 5b .. .. .. .. .. .. .. 42 .. .. .. 15 .. .. .. .. .. 3 .. .. 21 .. .. .. 14 .. 6 .. .. .. .. .. .. .. .. .. .. .. 4 .. .. ..

.. 12 19 .. 16b .. .. 13 .. .. .. .. .. 6 .. .. .. 23 .. .. .. .. 8 .. .. 11 1 .. 10 b .. .. 9 .. 12 0 12 .. 11 12 .. 11 .. 13 .. .. 7 .. .. .. 7 5 .. 13 .. .. 9

.. 13 .. .. .. .. .. .. .. .. .. .. .. 4 .. .. .. 17 .. .. .. .. .. .. .. 10 .. .. .. .. .. 6 .. .. 0 .. .. .. 7 .. 11 .. .. .. .. .. .. .. .. .. .. .. 11 .. .. 19

Census year

Household size

number of people National Urban

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem Rep Korea, Rep. 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

2001 1990 2001 2000 1996 1997 2002 1995 2001 2001 2000 1994 1999 2000 1993 1995 1999 1995 2000 2001 1974 2001 2002 1993 1998 2000 1998 1988 2000 2000 2003 2000 1982 1997 2001 2001 2001 1995 2001 1991 1980 2003 1998 2000 1990 2002 1993 1990 1988 2001 1990

4.4 2.7 5.3 4.0 4.8 7.7 3.0 3.5 2.8 3.5 2.7 6.2 .. 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 4.4 .. 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 4.5b 4.6 .. 5.3 3.2 2.8 3.3

.. .. 5.3 .. 4.6 7.2 .. .. .. .. .. 6.0 .. 3.4 .. .. .. 3.6 6.1 2.6 .. .. .. .. .. 3.6b 4.8 4.4 4.4 .. .. 3.8 .. .. 4.5 5.3 4.9 .. .. 4.9 .. .. .. 6.0 4.7 .. .. 6.8 .. 6.5 4.5 .. 5.3 .. .. ..

Overcrowding

People living in overcrowded dwellingsa % of total National Urban

.. .. 77 .. 33b .. .. .. .. .. .. 1 .. .. 23 .. .. .. .. 4 .. 10 b 31 .. 7 8b 64 30 .. .. .. 6 27b .. .. .. 37 .. .. .. .. 1b .. .. .. 1 .. .. 28 b .. 38 b .. .. .. .. ..

.. .. 71 .. 26b .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 57 .. .. .. .. 7 .. .. .. .. 28 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

Durable dwelling units

Home ownership

Buildings with durable structure % of total National Urban

Privately owned dwellings % of total National Urban

69 .. 83 .. 72 88 .. .. .. 98 b .. 97 .. 35 .. .. .. .. 49 88 .. .. 20 .. .. 95b .. 48 .. .. .. 91 87 .. .. .. 7 .. .. .. .. .. 79 .. .. .. .. 58 88 .. 95b 49 62 .. .. ..

85 .. 81 .. 76 96 .. .. .. .. .. 97 .. 72 .. .. .. .. 77 .. .. .. .. .. .. 95b .. 84 .. .. .. 94 .. .. .. .. 20 .. .. .. .. .. 87 .. .. .. .. 86 98 b .. 98 b 64 .. .. .. ..

.. .. 87 .. 73 70 .. .. .. 58 b 61 69 .. 72 50 .. .. .. 96 58 .. 84 1 .. .. 48 b 81 86 .. .. .. 87 78 .. .. .. 92 .. .. 88 .. 65 84 77 .. 67 .. 81 80 .. 79 .. 83 .. 76 72

.. .. 67 .. 67 66 .. .. .. .. .. 64 .. 25 .. .. .. .. 86 .. .. .. .. .. .. .. 59 47 .. .. .. 81 .. .. .. .. 83 .. .. .. .. .. 86 40 .. .. .. .. 66b 44 75 .. 76 .. .. ..

ENVIRONMENT

3.11

Urban housing conditions Multiunit dwellings

Vacancy rate

% of total National Urban

Unoccupied dwellings % of total National Urban

.. .. .. .. .. 4 8b .. .. 2b 37 57 .. .. 15 .. 9b .. .. 74 .. 0 .. .. .. .. .. .. 10 b .. .. .. 6 .. 48 .. 1 .. .. .. .. 17 0 .. .. 38 .. .. 10 b .. 1b .. 6 .. 86 ..

.. .. .. .. .. 5 .. .. .. .. .. 67 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 16b .. .. .. .. .. 56 .. 1 .. .. .. .. .. 0 .. .. .. .. .. 10 b 8 2b .. 11 .. .. ..

14 4 6 .. .. 13 .. .. 21 .. .. .. .. 39 .. .. 11 .. .. 0 .. .. .. 7 .. 7b .. .. .. .. .. 7 .. .. .. .. 0 .. .. 0 .. 10 8 .. .. .. .. .. 14 .. 6b 7 4 1 .. 11

2007 World Development Indicators

.. .. 9 .. .. 15 .. .. .. .. .. .. .. 17 .. .. .. .. .. .. .. .. .. .. .. 3b .. .. .. .. .. 6 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 6b 3 4 .. .. ..

167

3.11

Urban housing conditions Census year

Household size

number of people National Urban

Romania Russia 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 Zambia Zimbabwe

1992 2002 1991 1992 2001 1985 2000 1991 1975 2001 1991 2001 1993 1997 1990 1990 1981 2000 2002 2000 2000 1994 1990 1991 2003 2001 2000 1996 2001 1999 1997 1994 2000 1992

3.1 2.8 4.7 6.1 .. 2.9 6.8 4.4 .. 3.1 .. 4.0 3.3 3.8 5.8 5.4 2.0 2.4 6.3 .. 4.9 3.8 .. 3.7 8.0 5.0 .. 4.9 .. .. .. 2.7 3.3 .. 4.4 4.6 7.1 6.7 5.3 4.8

Overcrowding

People living in overcrowded dwellingsa % of total National Urban

3.1 2.7 .. .. .. 2.2 .. .. .. .. .. .. .. .. 6.0 3.7 .. 2.1 6.0 .. 4.5b .. .. .. .. .. .. 4.0 b .. .. 2.4 .. 3.4b .. .. 4.5 .. 6.8 5.9 4.2

a. More than two people per room. b. Data are from a previous census.

168

2007 World Development Indicators

.. 7 .. .. .. .. .. .. .. .. .. .. 0 .. .. .. .. .. .. .. 33b .. .. 9b .. .. .. .. .. .. .. .. 22b .. .. .. .. 54b .. ..

.. 5 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 7b .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 6b .. ..

Durable dwelling units

Home ownership

Buildings with durable structure % of total National Urban

Privately owned dwellings % of total National Urban

58 .. 79 92 .. .. 34 .. .. .. .. .. .. 93b .. .. .. .. .. .. .. 93 .. 98 b 99 .. .. 21b .. .. .. .. .. .. .. 77 .. .. .. ..

.. .. 78 .. .. .. .. .. .. .. .. .. .. 92b .. .. .. .. .. .. .. 93 .. .. .. .. .. .. .. .. .. .. .. .. .. 89 .. .. .. ..

87 .. 92 42 .. .. 68 .. .. 69 .. .. 78 70 b 86b .. .. 31 .. .. 82b 81 .. 74b 71 70 .. 80 b .. .. .. 66 57b .. 78 95 78 88 b 94 94

77 .. 73 .. .. .. .. .. .. .. .. .. .. 58 b 58 b .. .. 24 .. .. 43b 62 .. .. 89 b .. .. 24b .. .. 69 .. 57b .. .. 86 .. 68 b 30 30

Multiunit dwellings

Vacancy rate

% of total National Urban

Unoccupied dwellings % of total National Urban

39 73 19 .. .. .. .. .. .. 37 .. 7 .. 1 0b .. 54 28 .. .. .. 3 .. 17b 6 .. .. 0b .. .. .. .. .. .. 14 .. 45 3b .. 6

71 86 25 .. .. .. .. .. .. .. .. .. .. 14b 1b .. .. 32 .. .. .. .. .. .. 10 b .. .. 2b .. .. 19 .. .. .. .. .. .. 11b .. ..

6 .. .. .. .. .. .. .. .. 9 .. .. .. 13 .. .. 1 11 .. .. .. 3 .. .. 15 .. .. .. .. .. .. 9 13b .. 16 .. .. .. .. ..

4 .. .. .. .. .. .. .. .. .. .. .. .. 1b .. .. .. 7 .. .. .. .. .. .. 12b .. .. .. .. .. .. 7 13b .. .. .. .. .. .. ..

About the data

3.11

Definitions

Urbanization can yield important social benefi ts,

conditions thus requires an extensive set of indica-

• Census year is the year in which the underlying

improving access to public services and the job

tors within a reasonable framework.

data were collected. • Household size refers to the

market. At the same time it also leads to signifi -

There is a strong demand for quantitative indi-

average number of people within a household. It is

cant demands for services. Inadequate living quar-

cators that can measure housing conditions on a

calculated by dividing total population by the number

ters and demand for housing and shelter are major

regular basis to monitor progress. However, data

of households in the country and in urban areas.

concerns for policymakers. The unmet demand for

deficiencies and lack of rigorous quantitative analy-

• Overcrowding refers to the number of households

affordable housing, along with urban poverty, has led

sis hamper informed decision-making on desirable

living in dwellings with two or more people per room

to the emergence of slums in many poor countries.

policies to improve housing conditions. The data

as a percentage of the total number of households in

Improving the shelter situation requires a better

in the table are from housing and population cen-

the country and in urban areas. • Durable dwelling

understanding of the mechanisms governing hous-

suses, collected using similar definitions. The table

units refer to the number of housing units in struc-

ing markets and the processes governing housing

will incorporate household survey data in future edi-

tures made of durable building materials (concrete,

availability. That requires good data and adequate

tions. The table focuses attention on urban areas,

stone, cement, brick, asbestos, zinc, and stucco)

policy-oriented analysis so that housing policy can be

where housing conditions are typically most severe.

expected to maintain their stability for 20 years or

formulated in a global comparative perspective and

Not all the compiled indicators are presented in the

longer under local conditions with normal mainte-

drawn from the lessons learned in other countries.

table because of space limitations. Additional indica-

nance and repair, taking into account location and

Housing policies and outcomes affect such broad

tors for more countries will be available in the World

environmental hazards such as floods, mudslides,

socioeconomic conditions as the infant mortality

Bank’s central database.

and earthquakes as a percentage of total dwellings.

rate, performance in school, household saving, pro-

• Home ownership refers to the number of privately

ductivity levels, capital formation, and government

owned dwellings as a percentage of total dwellings

budget deficits. A good understanding of housing

or the number of households that own housing units as a percentage of total households. This category includes privately owned and owner-occupied units, depending on the definition used in the census data.

Selected housing indicators for smaller economies

3.11a

Census Household Overcrowding Durable Home Multiunit Vacancy year size dwelling ownership dwellings rate units

number of people

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

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

Privately People living Buildings owned in overcrowded with durable dwellings dwellingsa structure % of total % of total % of total

.. 12 .. 3 .. 28 .. 14 .. 2b 0 .. .. 24b .. 9b 8 15b 51 .. 4 2 ..

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

State- and community-owned units, rented, squatted, and rent-free units are not included. • Multiunit dwellings refer to the number of multiunit dwellings, such as apartments, flats, condominiums, barracks, boarding houses, orphanages, retirement houses,

% of total

Unoccupied dwellings % 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

hostels, hotels, and collective dwellings, as a percentage of total occupied dwellings. • Vacancy rate refers to 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.

2007 World Development Indicators

169

ENVIRONMENT

Urban housing conditions

3.12

Traffic and congestion Motor vehicles

per 1,000 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, 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

170

per kilometer of road

Passenger cars

Road density

Fuel prices

Particulate matter concentrations

per 1,000 people

km. of road per 100 sq. km. of land

$ per liter Super gasoline Diesel fuel

Urban-populationweighted PM10 micrograms per cubic meter

1990

2004a

1990

2004 a

1990

2004 a

2004a

2006

2006

1990

2004

3 11 55 19 181 5 530 421 52 1 61 423 3 41 114 18 88 163 4 .. .. 10 605 1 2 81 5 66 39 .. 18 87 24 .. 37 246 368 75 35 29 33 1 211 1 441 494 32 13 107 405 8 248 21 4 7 8

.. 70 .. .. 181 .. .. 599 66 1 168 529 .. 49 .. 105 170 360 .. .. 30 .. 577 .. .. 136 15 72 51 .. .. 198 .. 370 .. 391 424 .. 55 .. .. .. 459 2 515 597 .. 7 63 580 .. 476 57 .. .. ..

3 3 15 3 27 2 11 30 7 0 13 30 2 6 24 3 8 39 3 3 0 3 20 0 0 13 4 253 13 9 3 7 6 34 16 46 27 48 8 33 14 1 22 2 29 32 4 5 27 53 4 22 16 1 2 14

.. 12 .. .. 37 .. .. 33 9 1 18 37 .. 7 .. 8 18 63 .. .. 31 .. 34 .. .. 26 11 254 19 .. .. 24 .. 58 .. 31 32 .. 17 .. .. .. 11 4 34 38 .. 3 16 207 .. 46 45 .. .. ..

2 2 26 14 134 1 450 387 36 0 59 385 2 25 101 10 84 146 2 3 .. 6 468 1 1 52 1 42 21 17 12 55 15 185 18 228 320 21 31 21 17 1 154 1 386 405 19 6 89 386 5 171 11 2 4 5

9 47 .. .. 140 .. .. 503 53 0 174 468 .. 15 .. 42 136 314 .. .. 25 .. 561 .. .. 89 10 53 43 .. .. 146 .. 302 .. 358 360 .. 32 .. .. .. 349 1 446 495 .. 5 50 546 .. 368 52 .. .. ..

5 66 5 4 15 27 11 162 72 184 45 498 17 6 43 4 21 40 6 48 22 11 15 4 3 11 20 186 10 7 5 69 25 51 55 165 169 26 16 9 48 4 134 4 26 173 4 37 29 .. 21 89 13 18 12 15

0.68 1.44 0.32 0.50 0.62 0.96 0.93 1.32 0.46 0.79 0.79 1.63 0.81 0.54 1.34 0.78 1.26 1.05 1.15 1.20 1.01 1.14 0.84 1.37 1.31 1.09 0.69 1.69 0.98 0.94 0.96 0.98 1.20 1.34 1.10 1.30 1.58 1.03 0.47 0.30 0.82 1.90 1.23 0.93 1.55 1.48 0.64 1.08 0.86 1.55 0.86 1.16 0.78 0.79 .. 0.88

0.65 1.29 0.19 0.36 0.48 0.77 0.94 1.26 0.41 0.45 0.55 1.34 0.81 0.47 1.24 0.74 0.84 1.08 1.12 1.22 0.78 1.07 0.78 1.27 1.20 0.86 0.61 1.06 0.57 1.00 0.67 0.67 1.06 1.22 0.91 1.29 1.45 0.75 0.39 0.12 0.80 0.81 1.22 0.62 1.26 1.33 0.39 1.01 0.89 1.38 0.84 1.19 0.64 0.82 .. 0.60

75 92 115 142 105 .. 22 38 105 223 7 32 75 120 42 119 40 111 149 56 116 119 25 61 214 88 113b .. 38 73 130 45 94 60 44 73 30 44 38 221 46 122 19 134 24 18 9 141 .. 27 39 69 63 105 117 70

46 56 88 91 78 69 16 35 59 140 7 25 43 86 19 69 28 55 94 39 64 64 19 48 127 54 72b .. 23 52 85 39 38 31 19 23 20 30 25 135 35 85 16 76 19 14 6 95 45 19 35 41 67 71 78 42

2007 World Development Indicators

Motor vehicles

per 1,000 people

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

per kilometer of road

3.12

Passenger cars

Road density

Fuel prices

Particulate matter concentrations

per 1,000 people

km. of road per 100 sq. km. of land

$ per liter Super gasoline Diesel fuel

Urban-populationweighted PM10 micrograms per cubic meter

1990

2004a

1990

2004 a

1990

2004 a

2004a

2006

2006

1990

2004

22 212 4 16 34 14 270 210 529 52 469 60 76 12 .. 79 474 44 9 135 321 11 14 165 160 132 6 4 124 3 10 59 119 53 21 37 4 2 71 .. 405 524 19 6 30 458 130 6 75 27 27 128 10 168 222 295

61 313 9 .. .. .. 447 288 610 .. 586 106 100 18 .. 302 422 38 .. 348 .. .. .. .. 421 .. .. .. 254 .. .. 130 211 87 41 45 .. .. 82 .. 427 701 46 .. .. 527 .. 14 107 .. 88 47 34 354 463 ..

10 21 2 10 14 6 10 74 99 7 52 26 8 5 .. 60 165 10 3 6 183 4 4 10 12 30 2 4 26 2 3 35 41 17 1 15 2 3 1 .. 58 19 5 4 21 22 9 4 18 6 4 43 4 18 34 79

28 19 3 .. .. .. 11 112 73 .. 63 77 17 10 .. 145 181 10 .. 12 .. .. .. .. 18 .. .. .. 75 .. .. 79 93 29 2 23 .. .. 4 .. 58 31 13 .. .. 26 .. 8 27 .. 15 16 13 33 278 ..

5 188 2 7 25 1 227 174 476 43 283 44 50 10 .. 48 368 44 6 106 300 3 7 96 133 121 4 2 101 2 7 44 82 48 6 28 3 1 39 .. 368 436 10 5 12 380 83 4 60 7 16 62 7 138 162 242

52 274 6 .. .. .. 382 234 590 135 441 71 80 9 .. 218 349 39 .. 297 .. .. .. .. 383 .. .. .. 222 .. .. 96 142 65 26 45 .. .. 42 .. 429 592 18 .. 17 424 .. 10 76 .. 52 30 9 294 429 ..

12 178 114 20 11 10 140 81 165 194 323 8 3 11 26 102 32 10 14 112 71 20 11 5 127 34 9 16 30 2 1 99 18 39 3 13 4 4 5 12 372 35 15 1 21 30 11 34 16 4 7 6 67 138 86 289

0.89 1.30 1.01 0.57 0.09 .. 1.34 1.47 1.56 0.82 1.09 0.86 0.70 1.12 0.71 1.65 0.22 0.64 0.86 1.20 0.74 0.89 0.79 0.13 1.08 1.23 1.15 1.17 0.53 1.22 0.97 0.74 0.74 0.45 0.88 1.22 1.15 0.66 0.87 0.94 1.70 0.98 0.67 1.14 0.51 1.80 0.31 1.01 0.70 0.94 0.97 1.22 0.76 1.30 1.56 0.65

0.73 1.31 0.75 0.44 0.03 .. 1.35 1.27 1.49 0.75 0.90 0.45 0.45 0.98 0.79 1.33 0.21 0.54 0.73 1.15 0.62 0.88 0.85 0.13 1.09 1.09 1.00 1.12 0.40 1.04 0.84 0.56 0.52 0.31 0.87 0.87 1.06 0.75 0.87 0.73 1.32 0.70 0.58 1.11 0.66 1.66 0.39 0.64 0.60 0.64 0.77 0.86 0.67 1.30 1.10 0.78

45 36 110 139 86 146 26 71 42 58 43 110 12 66 184 82 82 65 73 40 45 85 59 107 30 38 77 74 37 264 146 26 69 110 65 32 110 116 74 67 45 16 49 216 179 24 148 224 58 34 105 98 55 59 52 27

47 18 72 102 58 138 19 38 27 42 31 50 19 39 79 38 108 24 47 16 42 54 44 98 10 20 45 46 29 165 103 16 39 39 68 20 39 69 43 39 34 15 31 144 67 12 120 128 37 19 101 65 32 38 26 20

2007 World Development Indicators

171

ENVIRONMENT

Traffic and congestion

3.12

Traffic and congestion Motor vehicles

per 1,000 people 1990

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 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 Europe EMU

72 87 2 165 11 137 10 130 194 306 2 139 360 21 9 66 464 491 26 3 5 46 24 117 48 50 .. 2 63 121 400 758 138 .. 93 .. .. 34 14 32 118 w 5 37 22 119 25 9 97 100 36 4 21 499 428

2004a

185 174 .. .. 14 199 4 134 256 505 .. 144 558 34 .. 83 504 559 36 .. .. .. .. .. 95 108 .. 5 138 .. 510 808 .. .. .. .. 35 .. .. 50 141 w 8 69 38 186 47 20 170 153 .. 10 .. 636 569

per kilometer of road 1990

2004 a

11 14 1 19 6 31 4 142 57 42 1 26 43 4 22 18 29 46 10 1 2 36 11 19 19 8 .. .. 20 52 64 30 45 .. 25 .. .. 8 3 4 .. .. .. .. .. .. .. .. .. .. .. .. .. ..

20 48 .. .. 9 102 2 179 32 26 .. 24 34 .. .. 24 11 58 7 .. .. .. .. .. 49 18 .. 4 39 .. 79 37 .. .. .. .. 24 .. .. 7 .. .. .. .. .. .. .. .. .. .. .. .. .. ..

Passenger cars

Road density

Fuel prices

Particulate matter concentrations

per 1,000 people

km. of road per 100 sq. km. of land

$ per liter Super gasoline Diesel fuel

Urban-populationweighted PM10 micrograms per cubic meter

1990

56 65 1 98 8 133 7 89 163 289 1 97 309 7 8 35 426 449 10 0 1 14 16 98 23 34 .. 1 63 97 341 573 122 .. 73 .. .. 14 8 29 91 w 3 24 10 90 16 4 79 72 24 2 14 390 379

2004 a

149 140 .. .. 11 181 2 99 222 456 .. 92 455 13 .. 40 457 516 12 .. .. .. .. .. 83 75 .. 2 115 .. 451 465 .. .. .. .. 27 .. .. 44 100 w 5 51 27 142 35 14 142 108 .. 6 .. 457 522

a. Data are for 2004 or most recent year available. b. Includes Taiwan, China; Macao, China; and Hong Kong, China.

172

2007 World Development Indicators

2004a

86 3 57 8 7 44 16 463 89 191 4 30 133 151 1 21 104 178 52 20 9 11 14 162 12 55 5 36 29 1 160 70 34 19 11 72 83 12 12 25 22 w 19 15 16 12 15 18 12 17 7 .. 6 41 139

2006

2006

1990

2004

1.26 0.77 1.11 0.16 1.31 1.48 0.98 0.92 1.35 1.23 0.74 0.85 1.15 0.88 0.72 0.80 1.46 1.27 0.60 0.80 1.04 0.70 1.03 0.43 0.83 1.88 .. 1.17 0.81 0.37 1.63 0.63 1.23 0.85 0.03 0.67 1.29 0.30 1.31 0.61 0.97 m 0.98 0.86 0.85 0.92 0.89 0.53 1.14 0.82 0.46 0.91 1.03 1.33 1.52

1.24 0.66 1.08 0.07 1.09 1.31 0.98 0.63 1.43 1.21 0.67 0.84 1.10 0.55 0.49 0.85 1.44 1.36 0.13 0.74 0.99 0.65 1.01 0.24 0.57 1.62 .. 1.01 0.87 0.53 1.73 0.69 0.94 0.54 0.02 0.53 0.98 0.28 1.22 0.65 0.84 m 0.84 0.74 0.70 0.79 0.79 0.40 1.09 0.67 0.34 0.65 0.98 1.24 1.29

36 13 49 163 95 28 91 106 40 40 78 34 41 95 288 60 15 37 158 148 57 88 49 142 71 75 185 27 47 264 25 30 237 79 22 124 .. 142 95 35 77 w 129 79 93 50 93 112 38 59 124 131 114 38 33

16 20 37 133 76 13 56 44 16 30 41 26 33 104 182 34 12 24 86 55 28 73 43 114 33 48 62 17 27 126 15 23 134 76 7 65 .. 91 58 28 54 w 77 56 64 36 63 72 30 38 84 84 64 28 24

3.12

About the data

Traffi c congestion in urban areas constrains eco-

compiled data are of uneven quality. The coverage of

pollution are emissions from traffic and industrial

nomic productivity, damages people’s health, and

each indicator may differ across countries because

sources, but nonanthropogenic sources such as dust

degrades the quality of their lives. The particulate

of differences in definitions. Comparability also is

storms may also be a significant contributor for some

air pollution emitted by motor vehicles—the dust and

limited when time series data are reported. The IRF

cities. Data on particulate matter for selected cities

soot in exhaust—is proving to be far more damaging

recently took steps to improve the quality of the data

are in table 3.13. Estimates of economic damages

to human health than was once believed. (For infor-

published in its 2006 World Road Statistics. However,

from death and illness due to particulate matter pol-

mation on particulate matter and other air pollutants,

this effort covers data only for 1999–2004. There-

lution are shown in table 3.15.

see table 3.13.)

fore, the data shown in this table for 1990 and 2004

In recent years ownership of passenger cars has

may not be comparable. Moreover, the data do not

increased, and the expansion of economic activity

capture the quality or age of vehicles. Road density

has led to the transport by road of more goods and

is a very rough indicator of accessibility and does

• Motor vehicles include cars, buses, and freight

services over greater distances (see table 5.9).

not capture the condition, type, or width of roads.

vehicles but not two-wheelers. Population figures

These developments have increased demand for

Thus comparisons over time and between countries

refer to the midyear population in the year for which

roads and vehicles, adding to urban congestion, air

should be made with caution.

data are available. Roads refer to motorways,

Definitions

pollution, health hazards, and traffic accidents and

The data on fuel prices are compiled by the Ger-

highways, main or national roads, and secondary

injuries. Congestion, the most visible cost of expand-

man Agency for Technical Cooperation (GTZ) from its

or regional roads. A motorway is a road specially

ing vehicle ownership, is reflected in the indicators in

global network of regional offices and representa-

designed and built for motor traffic that separates

the table. Other relevant indicators—such as aver-

tives and other sources, including the Allgemeiner

the traffic flowing in opposite directions. • Passenger

age vehicle speed in major cities or the cost of traffic

Deutscher Automobile Club (for Europe) and a project

cars refer to road motor vehicles, other than two-

congestion, which takes a heavy toll on economic

of the Latin American Energy Organization for Latin

wheelers, intended for the carriage of passengers

productivity—are not included because data are

America. Local prices are converted to U.S. dollars

and designed to seat no more than nine people

incomplete or difficult to compare.

using the exchange rate on the survey date listed

(including the driver). • Road density refers to the

The data in the table—except those on fuel prices

in the international monetary table of the Financial

ratio of the length of the country’s total road network

and particulate matter—are compiled by the Interna-

Times. For countries with multiple exchange rates the

to the country’s land area. The road network includes

tional Road Federation (IRF) through questionnaires

market, parallel, or black market rate is used rather

all roads in the country—motorways, highways, main

sent to national organizations. The IRF uses a hierar-

than the official exchange rate.

or national roads, secondary or regional roads, and

chy of sources to gather as much information as pos-

Significant uncertainties exist around estimates

other urban and rural roads. • Fuel prices refer to

sible. The primary sources are national road associa-

of particulate matter concentrations, and caution

the pump prices of the most widely sold grade of

tions. Where such an association lacks data or does

should be used in interpreting them. But they do allow

gasoline and of diesel fuel. Prices are converted

not respond, other agencies are contacted, including

for cross-country comparisons of the relative risk of

from the local currency to U.S. dollars (see About the

road directorates, ministries of transport or public

particulate matter pollution that urban residents face.

data). • Particulate matter concentrations refer to

works, and central statistical offices. As a result, the

Major sources of urban outdoor particulate matter

fine suspended particulates less than 10 microns in diameter (PM10) that are capable of penetrating deep into the respiratory tract and causing significant

The 15 economies with the most expensive gasoline—and the 15 with the cheapest, 2006

3.12a

health damage. Data for countries and aggregates for regions and income groups are urban-population-

Economy

$ per liter

Economy

$ per liter

weighted PM10 levels in residential areas of cities

Eritrea

1.90

Venezuela, RB

0.03

with more than 100,000 residents. The estimates

Turkey

1.88

Iran, Islamic Rep.

0.09

represent the average annual exposure level of the

Norway

1.80

Libya

0.13

average urban resident to outdoor particulate mat-

Netherlands

1.70

Saudi Arabia

0.16

Hong Kong, China

1.69

Kuwait

0.22

Korea, Rep.

1.65

Egypt, Arab Rep.

0.30

Belgium

1.63

Yemen, Rep.

0.30

United Kingdom

1.63

Oman

0.31

Denmark

1.58

Algeria

0.32

Italy

1.56

United Arab Emirates

0.37

Portugal

1.56

Trinidad and Tobago

0.43

Finland

1.55

Azerbaijan

0.46

Wheeler, Bart Ostro, Uwe Deichmann, Kirk Ham-

Germany

1.55

Ecuador

0.47

ilton, and Katie Bolt’s “Ambient Particulate Mat-

France

1.48

Angola

0.50

ter Concentrations in Residential and Pollution

Serbia and Montenegro

1.48

Nigeria

0.51

Hotspot Areas of World Cities: New Estimates

ter. The state of a country’s technology and pollution controls is an important determinant of particulate matter concentrations. Data sources Data on vehicles and traffic are from the IRF’s electronic files and its annual World Road Statistics. The data on fuel prices are from the GTZ’s electronic files. Data on particulate matter concentrations are from Kiran Dev Pandey, David

Based on the Global Model of Ambient ParticuSource: Table 3.12.

lates (GMAPS)” (2006).

2007 World Development Indicators

173

ENVIRONMENT

Traffic and congestion

3.13

Air pollution City

City population

Particulate matter

Sulfur dioxide

Nitrogen dioxide

About the data

Indoor and outdoor air pollution place a major burden on world health. More than half of the world’s thousands 2005

Argentina

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

174

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 Pingxiang Quingdao Shanghai Shenyang Taiyuan Tianjin Wulumqi Wuhan Zhengzhou Zibo Bogotá Zagreb Havana Prague Copenhagen Guayaquil Quito Cairo Helsinki Paris Berlin Frankfurt Munich Accra Athens Budapest Reykjavik Ahmadabad Bangalore

2007 World Development Indicators

1,423 3,626 1,474 4,331 2,260 1,012 11,469 18,333 1,093 3,640 5,312 2,188 5,683 1,611 10,717 3,046 4,065 6,363 3,073 8,425 3,447 3,695 2,743 2,837 2,411 1,149 2,188 905 2,817 14,503 4,720 2,794 7,040 2,025 7,093 2,590 2,982 7,747 908 b 2,189 1,171 1,088 2,387 1,514 11,128 1,091 9,820 3,389 668 b 1,263 1,981 3,230 1,693 164b 5,120 6,462

micrograms per micrograms per micrograms per cubic meter cubic meter cubic meter 2004 1995–2001 a 1995–2001 a

58 12 12 20 41 28 35 40 61 19 22 13 61 82 89 74 86 123 50 63 70 77 94 70 91 59 78 67 68 73 101 88 125 57 79 97 74 31 33 21 23 21 23 30 169 21 11 22 19 20 33 43 19 18 83 45

.. .. 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 75 190 53 99 211 82 60 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 .. 64 73 73 55 50 70 43 95 43 .. .. 5 33 54 .. .. .. 35 57 26 45 53 .. 64 51 42 21 ..

population rely on dung, wood, crop waste, or coal to meet their basic energy needs. Cooking and heating with such solid fuels on open fires or stoves without chimneys leads to indoor air pollution. Every year indoor air pollution is responsible for the deaths of 1.6 million people—one death every 20 seconds. In many urban areas exposure to air pollution is the main environmental threat to human health. Longterm exposure to high levels of soot and small particles in the air contributes to a wide range of health effects, including respiratory diseases, lung cancer, and heart disease. Particulate pollution, on its own or in combination with sulfur dioxide, leads to an enormous burden of ill health. Emissions of sulfur dioxide and nitrogen oxides lead to the deposition of acid rain and other acidic compounds over long distances. Acid deposition changes the chemical balance of soils and can lead to the leaching of trace minerals and nutrients critical to trees and plants. Where coal is the primary fuel for power plants, steel mills, industrial boilers, and domestic heating, the result is usually high levels of urban air pollution—especially particulates and sometimes sulfur dioxide—and, if the sulfur content of the coal is high, widespread acid deposition. Where coal is not an important primary fuel or is used in plants with effective dust control, the worst emissions of air pollutants stem from the combustion of petroleum products. The data on sulfur dioxide and nitrogen dioxide concentrations are based on reports from urban monitoring sites. Annual means (measured in micrograms per cubic meter) are average concentrations observed at these sites. Coverage is not comprehensive because not all cities have monitoring systems. The data on concentrations of particulate matter are estimates, for selected cities, of average annual concentrations in residential areas away from air pollution “hotspots,” such as industrial districts and transport corridors. The data are extracted from a complete set of estimates by the World Bank’s Development Research Group and Environment Department in a study of annual ambient concentrations of particulate matter in world cities with populations exceeding 100,000 (Pandey and others 2006). Pollutant concentrations are sensitive to local conditions, and even in the same city different monitoring sites may register different concentrations. Thus these data should be considered only a general

City

City population

Particulate matter

Sulfur dioxide

Nitrogen dioxide

3.13

indication of air quality in each city, and cross-country comparisons should be made with caution. The current World Health Organization (WHO) air quality guidelines are annual mean concentrations of 20

thousands 2005

India

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

Kolkata Madras Delhi Hyderabad Kanpur 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

14,277 6,916 15,048 6,115 3,018 2,566 18,196 2,350 4,409 13,215 7,314 1,037 2,953 3,348 1,660 11,268 35,197 3,366b 2,773 3,554 9,645 2,511 1,405 19,411 1,147 1,148 802 10,686 2,914b 776 1,680 2,761 1,934 10,654 1,132 4,326 456b 3,083 2,631 3,254 4,795 5,608 1,708 1,144 6,593 3,573 9,712 2,672 2,280 8,505 2,228 8,814 12,298 18,718 2,913

micrograms per micrograms per micrograms per cubic meter cubic meter cubic meter 2004 1995–2001 a 1995–2001 a

128 37 150 41 109 109 63 56 47 104 58 19 30 29 44 35 40 31 43 44 41 50 29 51 34 14 14 39 39 39 43 23 18 21 22 44 15 16 32 33 35 30 11 23 79 46 55 35 25 21 15 25 34 21 10

49 15 24 12 15 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

34 17 41 17 14 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

micrograms per cubic meter for particulate matter less than 10 microns in diameter (PM10) and 40 micrograms for nitrogen dioxide and daily mean concentrations of 20 micrograms per cubic meter for sulfur dioxide. 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 refers to fine suspended particulates less than 10 microns in diameter (PM10) that are capable of penetrating deep into the respiratory tract and causing significant health damage. Data are extracted from a larger study of urbanpopulation-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. The state of a country’s technology and pollution controls is an important determinant of particulate matter concentrations. • Sulfur dioxide is an air pollutant produced when fossil fuels containing sulfur are burned. It contributes to acid rain and can damage human health, particularly that of the young and the elderly. • 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. Nitrogen dioxide is emitted by bacteria, motor vehicles, industrial activities, nitrogenous fertilizers, combustion of fuels and biomass, and aerobic decomposition of organic matter in soils and oceans. Data sources Data on city population are from the United Nations Population Division. Data on particulate matter concentrations are from a recent World Bank study by Kiran D. Pandey, David Wheeler, Bart Ostro, Uwe Deichman, Kirk Hamilton, and Kathrine Bolt, “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 for 2000.

2007 World Development Indicators

175

ENVIRONMENT

Air pollution

3.14

Government commitment Environmental strategies or action 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, 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

176

.. 1993 2001 .. 1992 .. 1992 .. 1998 1991 .. .. 1993 1994 .. 1990 .. .. 1993 1994 1999 .. 1990 .. 1990 .. 1994 .. 1998 .. .. 1990 1994 2001 .. 1994 1994 .. 1993 1992 1994 1995 1998 1994 1995 1990 .. 1992 1998 .. 1992 .. 1994 1994 1993 1999

Biodiversity assessments, strategies, or action plans

.. .. .. .. .. .. 1994 .. .. 1990 .. .. .. 1988 .. 1991 1988 1994 .. 1989 .. 1989 1994 .. .. 1993 1994 .. 1988 1990 1990 1992 1991 2000 .. .. .. 1995 1995 1988 1988 .. .. 1991 .. .. 1990 1989 .. .. 1988 .. 1988 1988 1991 ..

2007 World Development Indicators

Participation in treatiesa

Climate changeb

Ozone layer

CFC control

Law of the Seac

Biological diversity b

Kyoto Protocol

CITES

CCD

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 .. 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

2004f 1999 f 1992f 2000 f 1990 1999 f 1987f 1987 1996f 1990 f 1986d 1988 1993f 1994f 1992g 1991f 1990 f 1990 f 1989 1997f 2001f 1989 f 1986 1993f 1989 f 1990 1989 f .. 1990 f 1994f 1994f 1991f 1993f 1991d 1992f 1993d 1988 1993f 1990 f 1988 1992 2005f 1996f 1994f 1986 1987e 1994f 1990 f 1996f 1988 1989 f 1988 1987f 1992f 2002f 2000 f

2004f 1999 f 1992f 2000 f 1990 1999 f 1989 1989 1996f 1990 f 1988d 1988 1993f 1994f 1992g 1991f 1990 f 1990 f 1989 1997f 2001f 1989 f 1988 1993f 1994 1990 1991f .. 1993f 1994f 1994f 1991f 1993f 1991d 1992f 1993d 1988 1993f 1990 f 1988 1992 2005f 1996f 1994f 1988 1988e 1994f 1990 f 1996f 1988 1989 1988 1989 f 1992f 2002f 2000 f

.. 2003f 1996 1994 1995 2002f 1994 1995 .. 2001 2006f 1998 1997 1995 1994g 1994 1994 1996 2005 .. .. 1994 2003 .. .. 1997 1996 .. 1994 1995 .. 1994 1994 1994g 1994 1996 2004 .. .. 1994 .. .. 2005f .. 1996 1996 1998 1994 1996f 1994f 1994 1995 1997 1994 1994 1996

2002 1994f 1995 1998 1994 1993d 1993 1994 2000 e 1994 1993 1996 1994 1994 2002f 1995 1994 1996 1993 1997 1995f 1994 1992 1995 1994 1994 1993 .. 2001f 1996 1994 1994 1994 1996 1994 1993e 1993 1996 1993 1994 1994 1996f 1994 1994 1994d 1994 1997 1994 1994f 1993 1994 1994 1995 1993 1995 1996

1985f 2005f 2005f .. 2001 2003f .. 2002 2000 f 2001f .. 2002 2002f 1999 .. 2003f 2002 2002 2005f 2001f 2002f 2002f 2002 .. .. 2002 2002e .. 1981 2005f .. 2002 .. .. 2002 2001e 2002 2002f 2000 2005f 1998 2005f 2002 2005f 2002 2002e .. 2001f 1999 f 2002 2003f 2002 1999 2000 f .. 2005f

1995f 2003f 1983f .. 1981 .. 1976 1982f 1998f 1981 1995f 1983 1984f 1979 2002 1977f 1975 1991f 1989 f 1988f 1997 1981f 1975 1980 f 1989 f 1975 1981f .. 1999 1976f 1983f 1975 1994f 2000 f 1990 f 993g 1977 1986f 1975 1978 1987f 1994f 1992f 1989 f 1976f 1978 1989 f 1977f 1996f 1976 1975 1992f 1979 1981f 1990 f ..

.. 2000 f 1996 1997 1997 1997 2000 1997f 1998f 1996 2001f 1997f 1996 1996 2002f 1996 1997 2001f 1996 1997 1997 1997 1995 1996 1996 1997 1997 .. .. 1997 1999 1998 1997 2000 d 1997 2000 f 1995f 1997f 1995 1995 1997f 1996 .. 1997 1995d 1997 1996f 1996 1999 1996 1996 1997 1998f 1997 1995 1996

Stockholm Convention

2004 2006 2006 2005 2003 2004 2002 2004f .. 2004f 2006 2004 2003 .. 2002f 2004 2004 2004 2005 2006 .. 2001 .. 2004 2005 2004 .. 2005f .. .. 2004 .. .. 2002 2003 .. 2004 2003 .. 2005f .. 2003 2002d 2004 e .. 2006 2006 2002 2003 2006 .. .. .. ..

Environmental strategies or action plans

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

1993 1995 1993 1993 .. .. .. .. .. 1994 .. 1991 .. 1994 .. .. .. 1995 1995 .. .. 1989 2003 .. .. .. 1988 1994 1991 .. 1988 1990 .. 2002 1995 .. 1994 .. 1992 1993 1994 1994 1994 .. 1990 .. .. 1994 1990 1992 .. .. 1989 1993 1995 ..

Biodiversity assessments, strategies, or action plans

.. .. 1994 1993 .. .. .. .. .. .. .. .. .. 1992 .. .. .. .. .. .. .. .. 1996f .. .. .. 1991 .. 1988 1989 .. .. 1988 .. .. 1988 .. 1989 .. .. .. .. .. 1991 1992 1994 .. 1991 .. 1993 .. 1988 1989 1991 .. ..

3.14

Participation in treatiesa

Climate changeb

Ozone layer

CFC control

Law of the Seac

Biological diversity b

Kyoto Protocol

CITES

CCD

Stockholm Convention

1996 1994 1994 1994 1996 .. 1994 1996 1994 1995 1994 1994 1995 1994 1995 1994 1995 2000 1995 1995 1995 1995 1996f 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 ..

1993f 1988f 1991f 1992f 1990 f .. 1988f 1992f 1988 1993f 1988f 1989 f 1998f 1988f 1995f 1992 1992f 2000 f 1998f 1995f 1993f 1994f .. 1990 f 1995f 1994g 1996f 1991f 1989 f 1994f 1994f 1992f 1987 1996f 1996f 1995 1994f 1993f 1993f 1994f 1988f 1987 1993f 1992f 1988f 1986 1999 f 1992f 1989 f 1992f 1992f 1989 1991f 1990 f 1988f ..

1993f 1989 f 1992f 1992 1990 f .. 1988 1992 1988 1993f 1988 1989 f 1998f 1988 1995f 1992 1992f 2000 f 1998f 1995f 1993f 1994f 2000 1990 f 1995f 1994g 1996f 1991f 1989 f 1994f 1994f 1992f 1988 1996f 1996f 1995 1994f 1993f 1993f 1994f 1988d 1988 1993f 1992f 1988f 1988 1999 f 1992f 1989 1992f 1992f 1993f 1991 1990 f 1988 ..

1994 2002 1995 1994 .. 1994 1996 .. 1995 1994 1996 1995f .. 1994 .. 1996 1994 .. 1998 2004f 1995 .. 2002f .. 2003f 1994g 2001 .. 1996 1994 1996 1994 1994 .. 1996 .. 1997 1996 1994 1998 1996 1996 2000 .. 1994 1996 1994 1997 1996 1997 1994 .. 1994 1998 1997 ..

1995 1994 1994 1994 1996 .. 1996 1995 1994 1995 1993d 1993 1994 1994 1994 e 1994 2002 1996e 1996e 1995 1994 1995 2005f 2001 1996 1997f 1996 1994 1994 1995 1996 1992 1993 1995 1993 1995 1995 1995 1997 1993 1994 d 1993 1995 1995 1994 1993 1995 1994 1995 1993 1994 1993 1993 1996 1993 ..

2000 2002f 2002f 2004 2005f .. 2002 2004 2002 1999 f 2002d 2003f .. 2005f 2005f 2002 2005f 2003f 2003f 2002 .. 2000 f 1998f .. 2003 2004f 2003f 2001f 2002 2002 2005f 2001f 2000 2003f 1999 f 2002f 2005f 2003f 2003f 2005f 2002f 2002 1999 2004 2004f 2002 2005f 2005f 1999 2002 1999 2002 2003 2002 2002e ..

1985f 1985f 1976 1978f 1976 .. 2002 1979 1979 1997f 1980 1978f 2000 f 1978 .. 1993f 2002 .. 2004f 1997f .. 2003 2002f 2003f 2001f 2000 f 1975 1982f 1977f 1994f 1998f 1975 1991f 2001f 1996f 1975 1981f 1997f 1990 f 1975f 1984 1989 f 1977f 1975 1974 1976 .. 1976f 1978 1975f 1976 1975 1981 1989 1980 ..

1997 1999 f 1996 1998 1997 .. 1997 1996 1997 1997f 1998d 1996 1997 1997 2003f 1999 1997 1997f 1996d 2002f 1996 1995

2005 .. 2006 .. 2006 .. .. .. .. .. 2002f 2004 .. 2004 2002f 2007 2006 2006 2006 2004 2003 2002

1996 2003f 2002f 1997 1996 1997 1995 1996 1996 1995 1999 f 1996 1996 1997 1997f 1997 1996 1995d 2000 f 1998 1996 1997 1996 1996f 1997 1996 2000 f 1997 1995 2000 2001f 1996 ..

2005f 2006 2004 .. .. .. 2003 2005 2004 2003 2004 2004 2004 2005 2004f 2005f .. 2002d 2004 .. 2006 2004 2002 2005 .. 2003 2003 2004 2005 2004 .. 2004 d ..

2007 World Development Indicators

177

ENVIRONMENT

Government commitment

3.14

Government commitment Environmental strategies or action plans

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 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 .. 1984 2001 1994 1993 .. 1994 .. 1993 .. 1994 .. 1997 .. .. 1999 .. 1994 .. 1991 .. 1994 1998 .. 1994 1999 .. 1995 1995 .. .. .. .. .. 1996 1994 1987

Biodiversity assessments, strategies, or action plans

.. 1994 .. .. 1991 2001 g .. 1995 .. .. 2001f .. .. 1991 .. 1992f .. .. .. .. 1988 .. .. .. 1988 .. .. 1988 .. .. 1994 1995 .. .. .. 1993 .. 1992 .. ..

Participation in treatiesa

Climate changeb

Ozone layer

CFC control

Law of the Seac

Biological diversity b

Kyoto Protocol

CITES

CCD

Stockholm Convention

1994 1995 1998 1995 1995 2001 g 1995 1997 1994 1996 2001f 1997 1994 1994 1994 1992f 1994 1994 1996 1998 1996 1995 1995 1994 1994 2004 1995 1994 1997 1996 1994 1994 1994 1994 1995 1995 .. 1996 1994 1994

1993f 1986d 2001f 1993f 1993f 2001 g 2001f 1989 f 1993g 1992g 1994 1990 f 1988f 1989 f 1993f .. 1986 1987 1989 f 1996f 1993f 1989 f 1991f 1989 f 1989 f 1991f 1993f 1988f 1986d 1989 f 1987 1986 1989 f 1993f 1988f 1994f .. 1996f 1990 f 1992f

1993f 1988d 2001f 1993f 1993 2002 2001f 1989 f 1993g 1992g .. 1990 f 1988 1989 f 1993f 1994 1988 1988 1989 f 1998f 1993f 1989 1991 1989 f 1989 f 1991f 1993f 1988 1988d 1989 f 1988 1988 1991f 1993f 1989 1994f .. 1996f 1990 f 1992f

1996 1997 .. 1996 1994 .. 1994 1994 1996 1995g .. 1997 1997 1994 1994 .. 1996 .. .. .. 1994 .. 1994 1994 1994 .. .. 1994 1999 .. 1997f .. 1994 .. .. 2006f .. 1994 1994 1994

1994 1995 1996 2001e 1994 2002 1994 e 1995 1994 e 1996 1985f 1995 1995 1994 1995 1997f 1993 1994 1996 1997e 1996 2004 1995d 1996 1993 1997 1996e 1993 1995 2000 1994 .. 1993 1995e 1994 1994 .. 1996 1993 1994

2001 2004 2004f 2005f 2001f .. 2006f 2006f 2002 2002 2002f 2002f 2002 2002f 2004f 1996 2002 2006f 2006f .. 2002f 2002 2004f 1999 2003f .. 1999 2002f 2004 2005f 2002 .. 2001 1999 .. 2002 .. 2004f 2006f ..

1994f 1992 1980 f 1996f 1977f 2002 1994f 1986f 1993 2000 f .. 1975 1986f 1979 f 1982 2006 1974 1974 2003f .. 1979 1983 1978 1984f 1974 1996f .. 1991f 1999 f 1990 f 1976 1974 1975 1997f 1977 1994f .. 1997f 1980 f 1981f

1998f 2003f 1998 1997f 1995

2004 .. 2002f .. 2003

1997 1999 f 2001f 2001f

2003f 2005 2002 2004

1997 1996 1998f 1995

2002 2004 .. 2006

1995 1996 1997 1997f 1997 2001f 1995d 2000 f 1995 1998 1996 1997 2002f 1998f 1996 2000 1999 f 1995 1998f 1998f .. 1997f 1996 1997

2002 2003 2005 .. 2004 2005 2004 2002f 2004 .. .. 2004f .. 2002 2005 .. 2004 .. 2005 2002 2004 2006 ..

a. Ratification of the treaty. b. Years shown refer to the year the treaty entered into force in that country. c. Convention became effective November 16, 1994. d. Acceptance. e. Approval. f. Accession. g. Succession.

178

2007 World Development Indicators

About the data

3.14

Definitions

National environmental strategies and participation

• The Framework Convention on Climate Change

• Environmental strategies or action plans provide

in international treaties on environmental issues pro-

aims to stabilize atmospheric concentrations of

a comprehensive, cross-sectoral analysis of conser-

vide some evidence of government commitment to

greenhouse gases at levels that will prevent human

vation and resource management issues to help inte-

sound environmental management. But the signing

activities from interfering dangerously with the

grate environmental concerns with the development

of these treaties does not always imply ratification,

global climate.

process. They include national conservation strate-

• The Vienna Convention for the Protection of the

gies, national environmental action plans, national

Ozone Layer aims to protect human health and the

environmental management strategies, and national

In many countries efforts to halt environmental

environment by promoting research on the effects

sustainable development strategies. The year shown

degradation have failed, primarily because govern-

of changes in the ozone layer and on alternative

for a country refers to the year in which a strategy

ments have neglected to make this issue a pri-

substances (such as substitutes for chlorofluoro-

or action plan was adopted. • Biodiversity assess-

ority, a refl ection of competing claims on scarce

carbon) and technologies, monitoring the ozone

ments, strategies, or action plans include biodiver-

resources. To address this problem, many countries

layer, and taking measures to control the activities

sity profiles (see About the data). • Participation in

are preparing national environmental strategies—

that produce adverse effects.

treaties covers nine international treaties (see About

nor does it guarantee that governments will comply with treaty obligations.

some focusing narrowly on environmental issues,

• The Montreal Protocol for Chlorofl uorocarbon

the data). • Climate change refers to the Framework

and others integrating environmental, economic,

Control requires that countries help protect the

Convention on Climate Change (signed in New York

and social concerns. Among such initiatives are

earth from excessive ultraviolet radiation by cut-

in 1992). • Ozone layer refers to the Vienna Conven-

conservation strategies and environmental action

ting chlorofluorocarbon consumption by 20 percent

tion for the Protection of the Ozone Layer (signed

plans. Some countries have also prepared country

over their 1986 level by 1994 and by 50 percent

in 1985). • CFC control refers to the Montreal Pro-

environmental profiles and biodiversity strategies

over their 1986 level by 1999, with allowances

tocol for Chlorofluorocarbon Control (formally, the

and profiles.

for increases in consumption by developing

Protocol on Substances That Deplete the Ozone

countries.

Layer, signed in 1987). • Law of the Sea refers to

National conservation strategies—promoted by the World Conservation Union (IUCN)—provide a

• The United Nations Convention on the Law of the

the United Nations Convention on the Law of the Sea

comprehensive, cross-sectoral analysis of conser-

Sea, which became effective in November 1994,

(signed in Montego Bay, Jamaica, in 1982). • Bio-

vation and resource management issues to help inte-

establishes a comprehensive legal regime for seas

logical diversity refers to the Convention on Biologi-

grate environmental concerns with the development

and oceans, establishes rules for environmental

cal Diversity (signed at the Earth Summit in Rio de

process. Such strategies discuss current and future

standards and enforcement provisions, and devel-

Janeiro in 1992). • Kyoto Protocol refers to the pro-

needs, institutional capabilities, prevailing technical

ops international rules and national legislation to

tocol on climate change adopted at the third confer-

conditions, and the status of natural resources in

prevent and control marine pollution.

ence of the parties to the United Nations Framework

a country.

• The Convention on Biological Diversity promotes

Convention on Climate Change, held in Kyoto, Japan,

National environmental action plans, supported

conservation of biodiversity through scientifi c

in December 1997. • CITES refers to the Conven-

by the World Bank and other development agencies,

and technological cooperation among countries,

tion on International Trade in Endangered Species of

describe a country’s main environmental concerns,

access to financial and genetic resources, and

Wild Fauna and Flora, an agreement among govern-

identify the principal causes of environmental prob-

transfer of ecologically sound technologies.

ments to ensure that the survival of wild animals and

lems, and formulate policies and actions to deal with

But 10 years after Rio the World Summit on Sus-

plants is not threatened by uncontrolled exploitation.

them. These plans are a continuing process in which

tainable Development in Johannesburg recognized

• CCD refers to the United Nations Convention to

governments develop comprehensive environmental

that many of the proposed actions have yet to mate-

Combat Desertification, an international convention

policies, recommend specific actions, and outline

rialize. To help developing countries comply with

dedicated to addressing the problems of land deg-

the investment strategies, legislation, and institu-

their obligations under these agreements, the Global

radation in the world’s drylands. Adopted in Paris on

tional arrangements required to implement them.

Environment Facility (GEF) was created to focus on

June 17, 1994, it entered into force on December 26,

Biodiversity profiles—prepared by the World Con-

global improvement in biodiversity, climate change,

1996. • Stockholm Convention is an international

servation Monitoring Centre and the IUCN—provide

international waters, and ozone layer depletion. The

legally binding instrument designed to protect human

basic background on species diversity, protected

UNEP, United Nations Development Programme, and

health and the environment from persistent organic

areas, major ecosystems and habitat types, and

the World Bank manage the GEF according to the poli-

pollutants. It was adopted on May 22, 2001, and

legislative and administrative support. In an effort

cies of its governing body of country representatives.

entered into force May 17, 2004.

to establish a scientific baseline for measuring prog-

The World Bank is responsible for the GEF Trust Fund

ress in biodiversity conservation, the United Nations

and is chair of the GEF.

Environment Programme (UNEP) coordinates global biodiversity assessments. To address global issues, many governments have also signed international treaties and agreements

Data sources Data on environmental strategies and participation in international environmental treaties are from the Secretariat of the United Nations Frame-

launched in the wake of the 1972 United Nations

work Convention on Climate Change, the Ozone

Conference on Human Environment in Stockholm and

Secretariat of the UNEP, the World Resources

the 1992 United Nations Conference on Environment

Institute, the UNEP, the Center for International

and Development (the Earth Summit) in Rio de

Earth Science Information Network, and the

Janeiro, which produced Agenda 21—an array of

United Nations Treaty Series.

actions to address environmental challenges:

2007 World Development Indicators

179

ENVIRONMENT

Government commitment

3.15 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, 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

180

Toward a broader measure of savings Gross savings

Consumption of fixed capital

Net savings

Education expenditure

Energy depletion

Mineral depletion

Net forest depletion

Carbon dioxide damage

Particulate emission damage

Adjusted net savings

% of GNI

% of GNI

% of GNI

% of GNI

% of GNI

% of GNI

% of GNI

% of GNI

% of GNI

% of GNI

2005

2005

2005

2005

2005

2005

2005

2005

2005

2005

23.8 15.6 53.8 23.4 24.8 25.7 20.7a 24.6 34.5 28.8 30.9 23.4 10.7 20.4 –1.9 49.2 23.0 17.0 .. 8.7 15.0 18.1 21.7a 14.0 25.4 19.1 50.4 31.9 19.0 14.1 37.6 19.2 13.3 24.0 .. 25.6 23.7 20.5 24.9 21.4 11.2 10.3 22.4 a 17.3 22.6 18.0 35.5 15.6 20.0 21.1 22.2 14.9 14.8 6.6 7.8 ..

7.6 10.7 11.6 12.0 12.1 10.1 15.0 14.3 11.6 8.2 11.0 15.4 8.7 10.0 10.3 12.9 11.9 11.1 8.3 6.7 8.8 9.9 14.6 8.1 10.8 13.4 10.2 13.9 11.4 7.0 12.8 6.1 9.9 12.9 .. 13.6 15.1 11.9 11.5 9.8 11.1 7.6 13.5 7.1 16.1 12.5 13.2 8.2 9.9 14.8 8.7 8.7 10.9 8.3 7.6 8.6

16.2 4.9 42.2 11.3 12.8 15.6 5.8 10.3 22.9 20.7 19.9 8.0 2.0 10.4 –12.2 36.3 11.1 5.9 .. 2.0 6.2 8.2 7.1 5.9 14.6 5.6 40.2 18.0 7.6 7.1 24.8 13.0 3.5 11.1 .. 12.0 8.6 8.6 13.3 11.6 0.0 2.8 8.9 10.2 6.5 5.5 22.2 7.4 10.1 6.3 13.6 6.2 3.8 –1.7 0.2 ..

.. 2.8 4.5 3.0 4.1 3.0 4.8 5.6 3.5 1.7 5.5 3.0 2.4 6.3 .. 5.6 4.1 3.5 2.4 3.9 1.8 3.2 5.2 1.6 1.4 3.9 2.0 3.7 4.9 0.9 3.8 4.0 4.6 4.1 8.1 4.2 8.1 1.2 1.4 4.4 2.8 2.7 5.1 3.0 6.0 5.2 3.3 2.0 2.9 4.3 2.8 3.1 1.6 2.0 2.3 1.5

0.0 1.9 46.9 51.3 10.4 0.0 3.1 0.2 60.4 3.8 2.4 0.0 0.0 33.7 1.2 0.4 4.1 1.2 0.0 0.0 0.0 13.8 6.8 0.0 73.3 0.4 6.8 0.0 10.2 4.3 74.9 0.0 5.4 1.6 .. 0.7 2.3 0.0 28.1 17.5 0.0 0.0 1.7 0.0 0.0 0.0 37.8 0.0 0.6 0.2 0.1 0.3 1.5 0.0 0.0 0.0

.. 0.0 0.1 0.0 0.4 0.8 3.1 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 2.1 2.4 1.0 0.0 0.1 0.0 0.0 0.4 0.0 0.0 13.6 0.8 0.0 0.7 1.6 0.0 0.0 0.0 0.0 .. 0.0 0.0 1.5 0.1 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 0.1 0.0 3.5 0.0 0.0

1.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.7 0.0 0.0 0.9 0.0 .. 0.0 0.0 0.0 1.2 11.3 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.2 .. 0.0 0.0 0.0 0.0 0.2 0.5 1.2 0.2 0.0 0.0 0.0 0.0 0.6 0.0 0.0 1.8 0.0 0.7 2.0 0.0 0.8

0.1 0.2 1.3 0.3 0.6 0.7 0.4 0.2 2.8 0.4 1.8 0.2 0.3 0.7 1.5 0.3 0.3 1.3 0.2 0.2 0.1 0.2 0.3 0.1 0.0 0.4 1.4 0.2 0.4 0.2 0.2 0.2 0.3 0.5 .. 0.8 0.1 0.6 0.5 1.2 0.3 0.5 1.2 0.5 0.2 0.1 0.2 0.5 0.5 0.2 0.5 0.3 0.2 0.3 0.6 0.2

0.7 0.2 0.3 1.8 1.6 1.8 0.1 0.3 1.1 0.5 .. 0.2 0.4 1.3 0.1 .. 0.3 1.4 1.4 0.1 0.4 0.8 0.2 0.4 1.1 0.6 1.4 .. 0.1 0.6 0.8 0.3 0.3 0.4 .. 0.1 0.1 0.3 0.1 0.9 0.2 0.7 0.0 0.3 0.1 0.0 .. 0.8 1.1 0.1 0.1 0.9 0.5 0.3 1.0 0.5

.. 5.4 –2.1 –39.1 3.9 15.3 3.9 15.2 –37.9 17.0 21.2b 10.6 2.7 –20.0 .. 39.1b 8.0 4.7 .. –5.8 7.3 –3.4 4.6 6.9 –58.4 –5.5 31.8 21.6b 1.1 1.2 –47.3 16.3 2.1 12.5 .. 14.6 14.2 7.5 –14.2 –4.0 1.8 3.2 11.0 12.3 12.2 10.5 –12.5b 7.5 10.7 10.1 13.3 7.7 2.5 –5.8 0.8 ..

2007 World Development Indicators

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

3.15

Gross savings

Consumption of fixed capital

Net savings

Education expenditure

Energy depletion

Mineral depletion

Net forest depletion

Carbon dioxide damage

Particulate emission damage

Adjusted net savings

% of GNI

% of GNI

% of GNI

% of GNI

% of GNI

% of GNI

% of GNI

% of GNI

% of GNI

% of GNI

2005

2005

2005

2005

2005

2005

2005

2005

2005

2005

30.8 16.5 32.2 24.7 41.6 .. 27.6a .. 19.9 19.6a 26.2a 6.5 28.5 12.2 .. 32.2 .. 5.7 1.7 22.7 –0.9 21.7 23.0 .. 18.5 20.3 11.6 –7.6 37.6 12.0 –5.2 19.8 21.5 20.8 38.2 29.1 4.7 .. 39.2 31.0 26.5 23.0a 12.9 10.3 34.1 37.1 .. 18.4 10.5 .. 16.4 19.9 28.2 18.2 13.1 ..

10.1 13.6 9.2 10.2 11.1 .. 10.9 17.6 13.4 7.6 14.0 10.4 12.5 8.8 .. 13.4 12.8 8.8 9.5 17.8 12.3 7.6 9.0 12.4 12.4 11.1 7.9 7.2 12.4 8.7 8.6 11.8 12.5 8.1 8.9 10.3 8.6 .. 10.9 7.8 15.0 13.7 9.6 7.7 10.5 13.4 .. 8.9 12.6 .. 9.8 11.7 8.1 12.8 17.1 ..

20.6 2.9 23.0 14.4 30.5 .. 16.7 .. 6.5 12.0 12.2 –3.9 16.0 3.4 .. 18.9 .. –3.1 –7.8 4.9 –13.2 14.1 14.0 .. 6.1 9.2 3.7 –14.8 25.2 3.3 –13.8 8.0 9.0 12.7 29.3 18.8 –3.9 .. 28.3 23.2 11.5 9.3 3.3 2.7 23.6 23.7 .. 9.5 –2.1 .. 6.6 8.2 20.1 5.4 –4.1 ..

3.5 5.8 4.0 0.9 4.4 .. 4.8 7.3 4.6 5.0 3.1 5.6 4.4 6.6 .. 3.7 6.9 4.4 1.4 5.6 2.4 6.7 .. .. 5.7 4.9 2.5 5.1 5.8 2.7 3.2 3.9 5.3 4.2 5.4 6.0 1.8 0.8 7.3 2.6 4.9 7.2 2.9 2.3 0.9 7.0 4.2 1.6 4.4 .. 4.2 2.9 2.8 5.6 5.7 ..

0.0 0.8 4.8 13.7 48.1 .. 0.0 0.2 0.2 0.0 0.0 0.5 53.6 0.0 .. 0.0 52.1 1.5 0.0 0.0 0.0 0.0 0.0 76.9 0.5 0.0 0.0 0.0 20.9 0.0 0.0 0.0 9.6 0.0 0.0 0.0 0.1 .. 0.0 0.0 1.6 1.0 0.0 0.0 54.4 16.0 .. 7.5 0.0 .. 0.0 2.2 0.5 1.7 0.0 ..

0.3 0.0 1.0 2.0 0.5 .. 0.1 0.0 0.0 1.7 0.0 0.1 1.7 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 28.1 0.0 0.2 0.0 13.0 0.3 0.0 .. 1.2 0.0 0.0 0.1 0.1 0.0 0.0 0.0 .. 0.0 0.0 .. 0.0 3.4 0.4 0.4 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 1.1 .. 0.0 0.0 0.0 0.0 0.8 0.0 1.4 6.1 0.0 0.1 0.2 0.0 0.9 0.0 0.0 0.6 0.0 0.0 0.0 0.0 0.0 0.5 .. 0.0 2.5 0.0 0.0 0.0 2.6 0.1 0.0 .. 0.4 0.0 .. 0.0 0.0 0.2 0.0 0.0 ..

0.6 0.4 1.3 0.8 1.4 .. 0.2 0.4 0.2 0.8 0.2 1.0 2.5 0.4 .. 0.4 0.7 1.5 0.4 0.4 0.6 0.0 0.6 1.0 0.4 1.4 0.3 0.3 0.9 0.1 1.0 0.4 0.4 1.8 3.6 0.5 0.2 .. 0.3 0.3 0.2 0.2 0.6 0.3 0.5 0.1 .. 0.8 0.3 .. 0.4 0.3 0.6 0.8 0.2 ..

0.4 0.1 0.7 1.1 0.8 .. 0.1 0.5 0.2 0.3 0.5 0.7 0.3 0.1 .. 0.6 1.3 0.3 0.7 0.1 1.1 0.3 0.5 .. 0.2 0.1 0.2 0.3 0.2 1.1 2.5 .. 0.4 0.7 1.2 0.1 0.3 .. 0.1 0.1 0.6 0.1 0.1 0.8 0.8 0.1 .. 1.5 0.2 .. 0.7 0.7 0.4 0.4 0.4 ..

22.9 7.4 18.6 –2.3 –16.0 .. 21.1 .. 10.5 14.3 14.6 –0.5 –37.6 8.3 .. 21.5 .. –1.9 –7.5 9.2 –12.5 19.1 .. .. 10.7 12.4 5.6 –11.3 9.0 4.8 –42.7 11.6b 3.6 14.4 16.9 23.9 –3.2 .. 34.1 23.0 14.1 15.1 5.4 1.3 –31.4 14.6 .. 0.9 1.8 .. 9.7 4.5 20.8 7.7 1.0 ..

2007 World Development Indicators

181

ENVIRONMENT

Toward a broader measure of savings

3.15 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 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 Europe EMU

Toward a broader measure of savings Gross savings

Consumption of fixed capital

Net savings

Education expenditure

Energy depletion

Mineral depletion

Net forest depletion

Carbon dioxide damage

Particulate emission damage

Adjusted net savings

% of GNI

% of GNI

% of GNI

% of GNI

% of GNI

% of GNI

% of GNI

% of GNI

% of GNI

% of GNI

2005

2005

2005

2005

2005

2005

2005

2005

2005

2005

14.2 32.7 19.5 .. 15.8 9.8 7.0 .. 21.2 25.2 .. 14.4 22.6 20.5 18.5 16.6 23.1 .. 14.6 7.3 9.3 30.1 9.9 .. 21.9 18.5 36.4 10.1 22.7 .. 13.9 13.0a 13.1 35.1 40.5 34.4 11.6 35.4 10.9 2.9 20.8 w 28.1 30.0 35.0 23.4 29.7 44.4 23.2 22.9 30.9 30.1 17.4 18.7 20.7

11.7 7.0 7.7 13.0 9.3 11.3 7.7 14.6 22.5 13.5 .. 12.0 14.6 9.9 9.9 10.6 12.1 13.7 10.3 8.5 8.0 11.2 8.3 12.4 11.6 11.8 11.0 8.1 10.4 .. 10.3 12.2 12.1 8.7 12.0 9.2 8.8 10.2 9.5 8.1 12.6 w 9.1 11.0 10.7 11.4 10.7 10.3 10.6 12.0 11.0 9.1 10.7 13.1 13.9

2.4 25.7 11.8 .. 6.5 –1.5 –0.6 .. –1.3 11.7 .. 2.4 8.1 10.6 8.6 6.0 11.0 .. 4.3 –1.2 1.3 18.9 1.6 .. 10.3 6.7 25.4 2.0 12.2 .. 3.6 0.8 1.0 26.4 28.5 25.3 2.8 25.2 1.4 –5.2 8.2 w 19.0 19.0 24.3 12.0 19.0 34.1 12.6 10.9 19.9 21.0 6.7 5.7 6.8

3.2 3.5 3.5 7.2 3.7 .. 1.0 2.7 4.1 5.4 .. 5.3 4.1 2.6 0.9 6.3 8.0 5.0 2.6 2.6 2.4 4.7 2.6 4.0 5.9 3.5 .. 4.0 4.4 .. 5.3 4.8 2.6 9.4 4.4 2.8 .. .. 2.9 6.9 4.4 w 3.3 3.6 2.9 4.5 3.5 2.2 4.1 4.4 4.5 3.6 3.8 4.6 4.6

4.2 36.8 0.0 61.4 0.0 2.4 0.0 0.0 0.1 0.1 .. 4.8 0.0 0.0 18.9 0.0 0.0 0.0 43.8 0.8 0.0 3.8 0.0 57.9 6.3 0.4 .. 0.0 9.0 .. 1.6 1.9 0.0 75.4 37.9 17.5 0.0 52.3 0.1 5.2 4.1 w 9.8 12.1 10.4 14.4 11.8 7.8 16.6 8.9 35.2 4.9 15.5 2.0 0.2

0.1 0.8 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 0.2 0.0 0.1 0.0 0.4 0.0 0.1 0.0 0.2 0.1 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 7.9 2.7 0.3 w 0.7 0.9 1.0 0.8 0.9 0.8 0.4 1.7 0.2 0.8 0.8 0.1 0.0

a. World Bank estimate based on preliminary data. b. Adjusted net savings do not include particulate emission damage.

182

2007 World Development Indicators

0.0 0.0 2.6 0.0 0.0 .. 1.9 0.0 0.5 0.2 .. 0.2 0.0 0.3 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.3 2.8 0.0 0.1 0.0 .. 4.6 0.0 .. 0.0 0.0 0.3 0.0 0.0 0.5 .. 0.0 0.0 0.0 0.0 w 0.6 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.1 0.6 0.3 0.0 0.0

0.7 1.6 0.2 0.7 0.4 1.4 0.5 0.4 0.7 0.3 .. 1.1 0.2 0.3 0.2 0.3 0.1 0.1 1.4 1.8 0.2 1.0 0.6 1.7 0.6 0.5 3.7 0.2 3.2 .. 0.2 0.3 0.2 7.1 0.8 1.1 .. 0.9 0.2 2.2 0.4 w 1.1 1.0 1.1 0.8 1.0 1.2 1.2 0.4 1.2 1.1 0.7 0.3 0.2

0.1 0.4 0.1 1.2 0.9 0.0 1.1 0.9 0.0 0.2 .. 0.1 0.4 0.4 0.4 0.1 0.0 0.2 0.9 0.4 0.2 0.4 0.3 0.2 0.3 1.3 1.1 0.0 0.7 .. 0.0 0.3 1.8 1.1 0.0 0.6 .. 0.8 1.0 0.1 0.4 w 0.7 0.7 0.9 0.5 0.7 1.2 0.5 0.5 0.6 0.8 0.5 0.3 0.2

0.6 –10.4 12.3 .. 8.8 .. –3.1 .. 1.5 16.3 .. 0.3 11.6 12.1 –10.1 11.9 18.6 .. –39.4 –1.6 2.8 18.2 0.4 .. 8.7 7.9 .. 1.1 3.7 .. 7.1 3.0 1.3 –47.9 –6.9 8.5 .. .. –4.9 –8.6 7.4 w 9.5 7.8 13.7 –0.1 8.0 25.3 –2.0 3.7 –13.0 16.4 –7.3 7.7 10.8

About the data

3.15

Definitions

Adjusted net savings measure the change in value of

rents because they are not produced; in contrast, for

• Gross savings are the difference between gross

a specified set of assets, excluding capital gains. If

produced goods and services competitive forces will

national income and public and private consumption,

a country’s net savings are positive and the account-

expand supply until economic profits are driven to

plus net current transfers. • Consumption of fixed

ing includes a sufficiently broad range of assets,

zero. For each type of resource and each country, unit

capital represents the replacement value of capital

economic theory suggests that the present value

resource rents are derived by taking the difference

used up in the process of production. • Net savings

of social welfare is increasing. Conversely, persis-

between world prices and the average unit extrac-

are gross savings minus the value of consumption of

tently negative adjusted net savings indicate that an

tion or harvest costs (including a “normal” return on

fixed capital. • Education expenditure refers to public

economy is on an unsustainable path.

capital). Unit rents are then multiplied by the physi-

current operating expenditures in education, including

The table provides a test to check the extent

cal quantity extracted or harvested in order to arrive

wages and salaries and excluding capital investments

to which today’s rents from a number of natural

at a depletion figure. This figure is one of a range

in buildings and equipment. • Energy depletion is the

resources and changes in human capital are bal-

of depletion estimates that are possible, depending

product of unit resource rents and the physical quanti-

anced by net savings, that is, this generation’s

on the assumptions made about future quantities,

ties of energy extracted. It covers coal, crude oil, and

bequest to future generations.

prices, and costs, and there is reason to believe that

natural gas. • Mineral depletion is the product of unit

Adjusted net savings are derived from standard

it is at the high end of the range. World prices are

resource rents and the physical quantities of miner-

national accounting measures of gross savings by

used in order to reflect the social opportunity cost

als extracted. It refers to tin, gold, lead, zinc, iron,

making four adjustments. First, estimates of capital

of depleting minerals and energy.

copper, nickel, silver, bauxite, and phosphate. • Net

consumption of produced assets are deducted to

A positive net depletion figure for forest resources

forest depletion is the product of unit resource rents

obtain net savings. Second, current public expen-

implies that the harvest rate exceeds the rate of

and the excess of roundwood harvest over natural

ditures on education are added to net savings (in

natural growth; this is not the same as deforesta-

growth. • Carbon dioxide damage is estimated to be

standard national accounting these expenditures

tion, which represents a change in land use (see

$20 per ton of carbon (the unit damage in 1995 U.S.

are treated as consumption). Third, estimates of

Definitions for table 3.4). In principle, there should

dollars) times the number of tons of carbon emitted.

the depletion of a variety of natural resources are

be an addition to savings in countries where growth

• Particulate emission damage is the willingness to

deducted to reflect the decline in asset values asso-

exceeds harvest, but empirical estimates suggest

pay to avoid mortality and morbidity attributable to

ciated with their extraction and harvest. And fourth,

that most of this net growth is in forested areas

particulate emissions.• Adjusted net savings are net

deductions are made for damages from carbon diox-

that cannot be exploited economically at present.

savings plus education expenditure and minus energy

ide and particulate emissions.

Because the depletion estimates reflect only timber

depletion, mineral depletion, net forest depletion, and

values, they ignore all the external and nontimber

carbon dioxide and particulate emissions damage.

The exercise treats public education expenditures as an addition to savings effort. However, because

benefits associated with standing forests. Data sources

of the wide variability in the effectiveness of govern-

Pollution damage from emissions of carbon dioxide

ment education expenditures, these figures cannot

is calculated as the marginal social cost per unit mul-

Gross savings are derived from the World Bank’s

be construed as the value of investments in human

tiplied by the increase in the stock of carbon dioxide.

national accounts data files, described in the

capital. The reader should bear in mind that current

The unit damage figure represents the present value

Economy section. Consumption of fi xed capital

expenditure of $1 on education does not necessarily

of global damage to economic assets and to human

is from the United Nations Statistics Division’s

yield $1 of human capital. The calculation should also

welfare over the time the unit of pollution remains

National Accounts Statistics: Main Aggregates

consider private education expenditure, but data are

in the atmosphere.

and Detailed Tables, 1997, extrapolated to 2005.

not available for a large number of countries.

Pollution damage from particulate emissions is

Data on education expenditure are from the United Nations Statistics Division’s Statistical Yearbook

While extensive, the accounting of natural resource

estimated by valuing the human health effects from

depletion and pollution costs still has some gaps.

exposure to particulate matter pollution in urban

Key estimates missing on the resource side include

areas. The estimates are calculated as willingness to

the value of fossil water extracted from aquifers, net

pay to avoid mortality and morbidity from cardiopul-

estimated. The wide range of data sources and

depletion of fish stocks, and depletion and degrada-

monary disease and lung cancer in adults and acute

estimation methods used to arrive at resource

tion of soils. Important pollutants affecting human

respiratory infections in children that is attributable

depletion estimates are described in Kunte and

health and economic assets are excluded because

to particulate emissions.

others’ “Estimating National Wealth” (1998). The

no internationally comparable data are widely available on damage from ground-level ozone or from sulfur oxides. Estimates of resource depletion are based on the calculation of unit resource rents. An economic rent represents an excess return to a given factor of production—in this case the returns from resource extraction or harvest are higher than the normal rate

ENVIRONMENT

Toward a broader measure of savings

For a detailed note on methodology, see www. worldbank.org/data.

1997 and from the United Nations Educational, Scientifi c, and Cultural Organization Institute for Statistics online database. Missing data are

unit damage figure for carbon dioxide emissions is from Frankhauser’s “Fractales, tissues urbains et reseaux de transport” (1994). The estimates of damage from particulate emissions are from Pandey and others’ “The Human Costs of Air Pollution: New Estimates for Developing Countries” (2006). The conceptual underpinnings of the savings measure appear in Hamilton and Clemens’ “Genuine Savings Rates in Developing Countries” (1999).

of return on capital. Natural resources give rise to

2007 World Development Indicators

183 Text figures, tables, and boxes

4

ECONOMY

Introduction

A

portrait of the global economy

A portrait of the global economy and the activity of more than 200 countries and territories that produce, trade, and consume the world’s output—that is what the data in this section provide. Timely and reliable macroeconomic statistics are important for three reasons. First, they provide a measure of the wealth of economies, reflecting the welfare of their residents and prospects for future growth. Second, because the design of sound macroeconomic policies requires an understanding of historical patterns and trends, they provide guidance in shaping development policies. Third, they inform consumers, workers, investors, taxpayers, voters, and citizens on how their economy is managed so that they can make appropriate choices and exert control over their governments. Developing economies grew faster over the last decade (1995–2005) than in the two previous decades and faster than high-income countries. World output in 2005 amounted to about $61 trillion, measured in purchasing power parities. This was a 45 percent increase over 1995, when the world output was $42.3 trillion (figure 4a). The share of developing economies in global output increased from 39 percent to 46 percent. The developing economies in the East Asia and the Pacific region grew the most, doubling their output and increasing their share of global output from 13 percent to 19 percent. Further integration into world markets, better functioning internal markets, and rising demand for many commodities all contributed to the acceleration of growth in developing countries. Past periods of growth were often interrupted by financial or balance of payments crises. Indeed, from 1997 to 1998 some of the fastest growing economies experienced a major financial crisis, which started in Asia and spread to the transition economies of Europe and Central Asia. But recovery from this crisis has been widespread and durable. Developing economies are running lower fiscal and external deficits, accumulating larger reserves, and adopting more cautious monetary and financial policies. These policies make economies less vulnerable to shocks and less volatile, increasing the confidence of investors. The financial shocks of the period also revealed the importance of reliable, publicly available data for monitoring the actions of governments and private agents. 4a

Developing economies increase their share of global output 1995

2005

$42.3 trillion

$61.3 trillion

East Asia & Pacific 13%

East Asia & Pacific 19%

Latin America & Caribbean 8%

High-income 60%

Europe & Central Asia 7% South Asia 6% Middle East & North Africa 3% Sub-Saharan Africa 2%

High-income 54%

Latin America & Caribbean 8%

Europe & Central Asia 7% South Asia 8%

Note: Global output is measured in 2005 international dollars (GDP in purchasing power parity terms). Source: World Bank staff estimates.

Middle East & North Africa 3% Sub-Saharan Africa 2%

2007 World Development Indicators

185

Better policies to achieve macroeconomic stability

Long-term trends Developing economies are expected to grow faster than highincome economies. The surprise is that they often don’t. Labor surpluses and higher returns to physical capital in developing countries, along with ready access to technology already developed and amortized in high-income countries, are among the reasons that developing economies are expected to grow faster and, in the long run, close the gap with richer economies. But until recently only a few developing economies enjoyed sustained periods of high growth. And even fewer have reached the average growth of the high-income economies. Poverty traps, exclusion from global markets, and government and market failures are some of the reasons put forward to explain the failure to converge. The last decade brought a change, however. The average growth of low- and middle-income economies surpassed that of high-income economies (figure 4b). The most successful are no longer counted as “developing.” During this period 13 countries graduated from the World Bank’s classification of low- and middle-income economies: Antigua and Barbuda, Bahrain, Greece, Guam, Isle of Man, Republic of Korea, Malta, New Caledonia, Northern Mariana Islands, Puerto Rico, Saudi Arabia, San Marino, and Slovenia. But these are only a few, and they account for less than 2 percent of the world’s population. Growth is still uneven (figure 4c). Global and regional averages are driven by a few large countries, which carry large weights in the aggregate measures. Growth is accelerating in the low-income economies Average annual growth (%) 6

4b 1985–95

1975–85

1995–2005

The high growth experienced in the developing world was due in part to expanding trade (section 6) and a better investment climate (section 5). The very rapid industrialization of large countries such as China and India also benefited the exporters of primary commodities—oil, metals and minerals, and agricultural produce. Macroeconomic stability also helped. Since the high inflation and the debt crises of the 1970s and 1980s, better fiscal, monetary, and exchange rate policies have brought inflation rates down in most developing countries. And the very rapid inflation in European and Central Asian countries after the collapse of the Soviet Union came back to earth after their transition from central planning to market economies (figure 4d). Macroeconomic stability, one factor in a favorable investment climate, promotes economic growth (figure 4e). But low inflation does not always lead to high economic growth. In general, developed economies have lower inflation and economic growth rates. The median inflation rate was below 10 percent in all developing regions, well below the median of around 15 percent or higher in 1990 in three regions.

Inflation is now less than 10 percent in all developing regions

4d

Average annual growth in GDP deflator (%) 50 Europe & Central Asia

5

40

4 30 3 2 1

10

0

0

Middle East & North Africa

Low-income

Middle-income

1970

High-income

Source: World Bank data files.

4c

Average annual growth (%)

1975–85

1985–95

1995–2005

10

High-income average, 1975–2005

4 2 0 –2 –4 –6 East Asia & Pacific

Europe & Central Asia

1980

1985

1990

1995

2000

Latin America Middle East & & Caribbean North Africa

Source: World Bank data files.

2007 World Development Indicators

Economies with high growth rates generally have lower rates of inflation

2005

South Asia

4e

Inflation, 1990–2005 (%) 200 175 150 125 100 Moldova 75 50 25 0 –25 –5

8 6

1975

East Asia & Pacific

Source: World Bank data files.

Patterns of regional growth vary widely

186

Latin America & Caribbean Sub-Saharan Africa

South Asia

20

Belarus

Brazil

China

0

Sub-Saharan Africa

5

10

GDP growth (%) Source: World Bank data files.

Uzbekistan

15

20

Rising reserves

External public debt relief

Trade surpluses and growing workers’ remittances have allowed many developing countries to accumulate large holdings of reserve assets over the past five years. One motive may be the desire to maintain larger precautionary reserves to protect against financial and balance of payments crises. Indeed, the globalization of financial transactions may have made countries with open capital accounts more vulnerable. China, India, and the Russian Federation are now among the top 10 economies with the largest reserves holdings (table 4f). Together they accounted for 25 percent of the world reserves in 2005. In contrast, the United States holds only 4 percent of the world reserves. With one exception in 1991, the current account deficit of the United States increased steadily from around $12 billion in 1982 to $792 billion in 2005. The U.S. current account deficit is financed largely by China’s current account surplus and growing investments by major oil exporters. Large reserve holdings also make economies less vulnerable to debt crises, reassuring lenders and lowering interest rates. Economies with large reserves are less likely to require assistance from lenders of last resort, such as the World Bank or International Monetary Fund (IMF). Since 1995 the ratio of reserves to external debt has increased for many economies (figure 4g).

Improvements in macroeconomic management of the poorest countries have also paved the way for more extensive debt relief. Since 1996 developing countries have benefited from debt writeoffs by official donors and will continue to do so. It makes sense to relieve debt when the causes of excessive indebtedness are being tackled at their roots and when the benefits of debt reduction are directed toward more effective poverty reduction programs. Making debt sustainable for poor countries is one of the Millennium Development Goals. Debt can bridge financing gaps and meet investment needs for projects with high social returns. But when unsustainable, it obliges countries to undertake policies that might be disruptive and harmful for growth and welfare, such as default, large fiscal adjustments, and devaluation. In 2005 the external debt of developing countries amounted to $2,730 billion, and related debt service (principal and interest) to $513 billion. The debt stock has been declining in most regions and, accordingly, debt service declined. The ratio of debt service to exports in 2005 was 13.8 percent, the lowest in the last 20 years. The ratio of total external debt to GDP declined from nearly 6.6 percent in 1999 to 5.4 percent in 2005. The debt crises of the 1980s and 1990s were the result of excessive borrowing with overly optimistic expectations. But cyclical global recessions, declining agricultural commodity prices, and conflicts also left many poor countries unable to service their debt. Traditional debt relief, based on rescheduling and restructuring of payments, proved ineffective for them. Special programs to address the problems of the poor countries with predominantly official creditors were started in 1996, when the World Bank and the IMF launched the Heavily Indebted Poor Countries (HIPC) Initiative. The initiative aims to provide permanent relief from unsustainable debt by redirecting the resources for debt service toward social expenditures aimed at poverty reduction. The initiative relieved $61 billion in total nominal debt service for 29 countries, and another 11 countries are eligible for additional debt relief. The debt stock of the 29 HIPCs was reduced by 90 percent and their debt service by 2 percent between 1999 and 2005. And as a direct result of debt relief, public expenditures in education and health have increased by 3 percent in these countries. The International Development Association (IDA), the IMF, and the African Development Fund have committed to cancel an additional debt stock of $49 billion for all HIPCs under the new Multilateral Debt Relief Initiative in 2006. IDA has since canceled $27 billion and the IMF $3 billion for 19 countries that have made progress in their economic and social reforms.

Top 10 economies with largest reserves

4f

International reserves Economy Japan China Taiwan, China Korea, Rep. United States Russian Federation India Hong Kong, China Singapore Germany

$ billions 2004 2005

844.7 622.9 247.7 199.2 190.5 126.3 131.6 123.6 112.2 97.2

846.9 831.4 260.3 210.6 188.3 182.3 137.8 124.3 115.8 101.7

Share Increase Reserves of world over (months total 2004 of import (%)

(%)

coverage)

18 18 6 5 4 4 3 3 3 2

0.3 33.5 5.1 5.7 –1.2 44.4 4.7 0.6 3.2 4.6

16 14 14 8 1 11 12 4 5 1

Source: International Monetary Fund and World Bank data files.

More reserves to cover debt

4g

Number of countries 60

1995

2005

50 40 30 20 10 0 Less than 10%

10%–20% 21%–50% 51%–100% More than 100% Foreign reserves as share of external debt

Source: World Bank and International Monetary Fund data files.

2007 World Development Indicators

187

Tables

4.a Recent economic performance

Algeria Argentina Armenia Azerbaijan Bangladesh Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Cameroon Chile China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Gabon Ghana Honduras India Indonesia Iran, Islamic Rep. Jamaica Jordan Kazakhstan Kenya Lesotho

188

Gross domestic product

Exports of goods and services

Imports of goods and services

average annual % growth 2004 2005

average annual % growth 2004 2005

average annual % growth 2004 2005

5.3 9.2 14.0 26.2 6.0 4.1 5.0 6.2 2.3 5.5 2.0 6.3 10.2 5.1 5.9 9.2 5.9 1.8 4.3 9.3 4.7 4.9 2.8 2.2 5.9 4.0 9.2 5.6 4.4 1.8 7.3 9.7 5.8 1.2

5.2 8.0 9.5 22.7 6.7 4.1 5.7 4.2 3.5 5.6 3.5 5.0 10.4 4.7 6.5 5.7 6.5 1.9 4.6 9.0 4.5 5.8 3.8 2.7 6.0 5.1 8.3 5.5 5.8 2.7 6.3 9.0 5.7 2.8

2007 World Development Indicators

5.8 13.5 15.9 58.5 15.6 9.6 20.8 22.0 11.6 7.2 –3.9 6.1 24.3 4.6 14.3 14.9 12.6 1.5 4.6 6.1 7.4 22.5 0.4 –5.8 9.3 6.0 21.9 8.6 –13.2 .. 5.8 1.4 4.7 –2.6

5.1 11.6 4.5 48.0 15.7 4.5 12.0 4.6 –3.1 11.8 1.7 4.8 14.6 4.4 2.4 4.2 5.6 9.0 5.2 –8.2 8.5 14.9 4.4 –8.0 10.3 8.9 5.2 8.9 0.8 .. 0.7 10.3 6.4 2.4

7.8 20.1 11.7 –0.6 19.1 13.5 13.2 5.2 9.5 14.6 23.1 20.4 11.4 25.2 20.0 16.4 11.7 7.3 3.5 14.2 13.5 23.8 0.8 1.8 6.7 9.3 22.1 12.3 –13.2 .. 21.2 13.3 14.3 –1.5

23.1 12.3 12.5 20.1 14.1 15.2 3.9 2.4 –1.8 8.9 9.3 8.9 16.5 2.4 5.3 6.5 6.2 2.9 5.0 11.3 17.6 21.2 8.4 0.2 13.7 15.7 1.1 8.6 13.7 .. 4.9 2.9 1.5 7.8

GDP deflator

% growth 2004 2005

16.1 8.8 3.2 10.3 5.1 4.7 1.6 8.8 7.2 3.8 4.7 4.8 3.9 6.2 21.5 7.2 11.1 3.6 3.2 4.2 6.7 5.4 4.4 8.9 15.0 10.3 4.4 13.7 16.0 9.6 4.0 17.9 4.2 3.2

17.8 9.3 4.0 17.7 5.2 4.6 –5.5 8.4 5.3 5.2 5.1 3.7 3.6 5.8 7.9 –5.6 11.5 3.3 2.9 8.6 7.0 4.5 3.9 2.3 14.8 5.5 4.4 14.5 23.5 13.0 5.8 7.0 4.0 5.0

Current account balance

% of GDP 2004 2005

.. 3.2 –3.9 1.3 –0.2 5.3 –21.7 14.2 1.8 –11.3 .. 0.6 7.2 –1.6 0.0 17.7 –4.8 –0.1 –6.7 –1.7 –0.2 2.4 –4.6 .. –7.6 –1.0 .. 0.3 .. –11.3 –18.2 –1.3 –2.6 –3.0

21.2 3.0 –4.7 –3.3 0.9 .. –15.4 10.8 1.3 –12.5 –1.2 3.9 5.6 –1.3 –9.7 1.4 –4.9 1.8 –6.8 –2.2 0.8 1.7 –4.6 1.8 –5.1 –0.6 –0.9 .. 5.6 –9.6 –22.8 7.0 –7.6 1.7

Total reservesa

$ millions 2005

months of import coverage 2005

.. .. 841 1,028 3,488 2,545 2,457 6,335 60,357 10,253 131 .. 1,046,465 13,659 470 .. 2,682 .. 10,101 2,325 3,923 26,660 1,922 .. 2,084 2,776 172,635 53,223 47,130 1,728 6,192 11,800 2,654 455

.. .. 3.8 1.9 2.5 7.7 5.0 13.5 5.0 4.6 0.3 .. 13.6 5.0 1.5 .. 2.5 .. 4.8 1.9 2.9 8.4 2.5 .. 3.0 4.9 8.2 5.2 9.0 2.7 5.3 4.2 4.3 3.9

ECONOMY

Macedonia, FYR Malawi Malaysia Mauritius Mexico Moldova Morocco Nicaragua Nigeria Pakistan Panama Paraguay Peru Philippines Poland Romania Russian Federation Senegal Serbia and Montenegro Slovak Republic South Africa Sri Lanka Swaziland Syrian Arab Republic Thailand Tunisia Turkey Ukraine Uruguay Uzbekistan Venezuela, RB Zambia Zimbabwe

Gross domestic product

Exports of goods and services

Imports of goods and services

average annual % growth 2004 2005

average annual % growth 2004 2005

average annual % growth 2004 2005

4.0 2.6 5.2 4.6 3.0 7.1 1.7 4.0 6.9 7.8 6.4 2.9 6.4 5.0 3.4 4.1 6.4 5.1 4.7 6.0 4.9 5.3 1.8 5.1 4.5 4.2 7.4 2.6 6.6 7.0 9.3 5.2 –6.5

3.2 9.2 5.5 3.5 4.5 3.0 7.0 3.7 6.3 6.3 7.0 3.5 6.6 5.4 5.0 5.8 6.5 5.1 6.1 6.7 4.2 6.0 2.3 4.0 4.5 5.3 5.0 5.2 5.0 6.0 8.5 6.0 –5.1

8.5 20.2 8.6 5.7 6.9 20.2 9.8 5.3 –1.8 7.6 13.8 2.7 14.9 4.2 8.1 4.2 6.3 3.1 10.0 10.9 6.7 7.5 6.0 3.9 4.3 3.2 8.6 –11.2 16.8 7.1 5.2 12.3 –4.3

13.7 2.1 5.5 7.0 5.7 –0.1 5.9 13.9 4.4 13.4 7.3 14.2 5.4 8.5 5.7 10.3 4.3 –15.5 41.7 10.2 5.2 5.4 6.0 –1.5 8.4 3.9 14.3 –1.3 8.5 2.0 8.5 6.3 3.5

2.4 11.0 8.0 4.8 8.7 21.2 5.7 6.2 21.3 44.1 14.2 4.6 10.6 2.4 4.9 3.7 17.3 1.9 –4.0 11.2 10.1 8.7 6.3 17.9 9.4 1.1 11.6 2.1 8.8 7.3 30.0 20.6 –3.1

18.2 –1.5 5.0 7.7 8.5 –2.0 4.8 9.8 16.0 20.0 7.0 33.3 9.9 6.1 6.9 8.6 20.2 –21.2 28.9 11.6 9.4 5.4 5.5 1.9 0.6 1.4 5.5 8.4 15.0 1.2 28.2 11.0 3.6

GDP deflator

% growth 2004 2005

3.0 15.5 4.6 4.8 5.4 7.3 1.4 10.3 26.9 9.8 2.4 5.9 3.3 6.2 2.8 12.0 19.7 2.6 17.3 2.4 4.7 10.4 4.9 5.8 4.6 1.9 5.4 20.0 1.7 15.9 29.1 19.0 237.7

3.4 10.8 1.4 4.1 5.3 11.9 2.7 10.7 12.8 8.2 2.4 6.0 8.6 6.0 2.0 8.5 14.5 2.3 5.4 6.3 5.8 7.8 4.0 4.0 5.5 2.4 9.6 12.3 5.1 22.0 15.0 14.3 1,216.0

Current account balance

% of GDP 2004 2005

–1.4 .. 15.3 –5.4 –0.6 –8.3 2.2 –16.3 24.5 –3.1 –5.1 –0.3 1.3 2.4 –1.7 –8.6 10.9 .. .. .. –3.8 –2.8 1.7 –4.0 –2.1 –1.1 –6.4 3.1 .. .. 18.1 .. ..

–1.2 –16.1 15.6 –5.2 –0.3 –21.2 1.2 –15.6 18.1 –3.9 –4.6 –4.5 1.1 2.3 –1.5 –11.4 10.8 –7.4 –9.6 –7.2 –4.8 –5.7 –11.5 –2.5 1.4 –1.2 –6.8 0.3 –3.5 5.2 17.2 –10.4 –8.0

Total reservesa

$ millions 2005

1,725 .. 86,827 1,244 73,465 603 18,226 899 .. 11,374 1,358 1,370 17,627 21,800 39,656 20,730 276,803 1,188 6,149 18,750 22,218 2,731 382 3,064 56,681 6,824 .. 16,934 3,329 2,460 31,033 350 ..

months of import coverage 2005

4.6 .. 6.6 3.3 3.1 2.1 8.1 2.8 .. 3.8 1.2 2.7 8.1 4.1 3.5 4.9 14.0 3.7 4.5 4.7 3.0 2.9 1.6 2.8 4.6 4.5 .. 4.2 5.7 6.2 8.3 0.9 ..

Note: Data for 2006 are the latest preliminary estimates and may differ from those in earlier World Bank publications. a. International reserves including gold valued at the London gold price. Source: World Bank staff estimates.

2007 World Development Indicators

189

4.1 Afghanistan Albania Algeria Angolaa Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benina Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Fasoa Burundi Cambodia Cameroon Canada Central African Republic Chad Chile Chinaa,b Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep.a Costa Rica Côte d’Ivoirea Croatia Cubaa Czech Republic Denmark Dominican Republica Ecuadora Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabona Gambia, The Georgia Germany Ghanaa Greece Guatemalaa Guinea Guinea-Bissau Haiti

190

Growth of output Gross domestic product

Agriculture

Industry

Manufacturing

average annual % growth 1990–2000 2000–05

average annual % growth 1990–2000 2000–05

average annual % growth 1990–2000 2000–05

average annual % growth 1990–2000 2000–05

.. 3.5 1.9 1.6 4.3 –1.9 4.0 2.4 –6.3 4.8 –1.7 2.1 4.8 4.0 .. 6.0 2.9 –1.8 4.0 –2.9 7.1 1.7 3.1 2.0 2.2 6.6 10.6 4.1 2.8 –4.9 1.2 5.3 3.2 0.6 4.2 1.1 2.7 6.0 1.9 4.4 4.8 5.7 0.2 3.5 2.5 1.9 2.8 3.0 –7.1 1.8 4.3 2.2 4.2 4.4 1.2 –1.5

2007 World Development Indicators

12.0 5.3 5.2 9.9 2.2 12.4 3.2 1.5 12.7 5.4 7.5 1.5 4.0 3.0 5.0 5.9 2.2 5.0 5.1 2.2 8.9 3.7 2.5 –1.4 14.5 4.3 9.6 4.3 3.5 4.4 3.9 4.2 –0.1 4.7 3.4 3.5 1.2 2.8 5.1 3.7 2.2 3.5 7.5 4.2 2.4 1.5 1.7 3.7 7.4 0.7 5.1 4.4 2.5 2.9 –0.5 –0.5

.. 4.3 3.6 –1.4 3.5 0.5 3.8 1.6 –2.1 2.9 –4.0 2.9 5.8 2.9 .. –1.2 3.3 3.0 4.2 –1.9 3.9 5.5 1.1 3.8 4.9 2.2 4.1 .. –2.2 1.4 1.0 4.1 3.5 –2.6 .. 0.0 4.6 3.9 –1.7 3.1 1.2 1.5 –3.4 2.2 1.8 2.0 –1.4 3.3 –11.0 –0.1 3.4 0.5 2.8 4.6 3.9 ..

0.4 1.4 7.3 14.1 2.5 8.4 –0.5 –0.2 6.6 2.5 6.0 –0.1 4.6 3.6 1.5 2.1 4.5 0.4 5.8 –1.5 5.7 3.9 0.9 2.6 2.2 6.0 3.9 –0.2 1.9 0.4 5.6 1.7 0.9 –0.2 .. 4.4 –0.1 2.8 4.4 3.4 1.4 0.8 –1.7 3.1 –0.2 –0.9 4.9 1.4 3.1 1.6 5.0 –2.0 2.6 4.0 4.0 ..

.. –0.5 1.8 4.4 3.8 –7.8 2.8 2.7 –0.8 7.3 –1.8 1.6 4.1 4.1 .. 5.8 2.6 –5.0 2.3 –4.3 14.3 –0.9 3.2 0.7 0.6 5.6 13.7 .. 1.8 –8.0 3.2 6.2 6.3 –2.4 .. 0.2 2.5 7.0 2.6 5.1 5.1 15.0 –3.3 4.0 3.9 1.0 2.5 1.0 –8.1 –0.1 2.6 1.0 4.3 4.7 –3.1 ..

21.1 3.4 4.4 10.5 3.8 16.8 3.0 2.1 16.7 7.3 11.1 0.4 3.8 3.2 4.9 5.7 2.3 5.7 2.7 –6.2 14.2 3.9 1.6 4.2 45.9 3.8 10.9 –3.4 4.9 9.0 1.4 3.6 –1.8 5.3 .. 3.6 –0.9 –0.6 6.2 4.3 2.1 4.1 10.5 5.8 1.7 1.4 2.8 5.9 12.6 0.6 4.6 3.3 1.5 3.1 3.6 ..

.. .. –2.1 –0.3 2.7 –4.3 2.1 2.7 –12.0 7.2 –0.7 2.5 5.8 3.8 .. 4.4 1.6 .. 1.6 –8.7 18.6 1.4 4.5 –0.2 .. 4.4 12.7 .. –2.2 –8.7 –3.0 6.8 5.5 –3.5 .. 4.3 2.2 4.9 1.5 6.4 5.2 10.6 5.9 3.8 7.6 .. 0.6 0.9 –7.0 0.2 –3.2 2.0 2.8 4.1 –2.0 ..

13.8 –0.2 2.4 13.4 3.7 9.2 1.6 1.4 9.1 6.7 11.5 0.4 2.7 3.2 5.6 0.8 1.8 8.7 2.2 .. 14.1 5.3 0.2 4.0 .. 3.7 11.1 –5.7 4.1 4.8 12.7 3.3 –3.3 4.5 .. 4.7 –2.5 0.7 4.5 2.8 2.2 6.6 11.5 2.4 1.9 1.2 .. 4.2 6.3 1.0 1.4 2.2 1.5 1.9 3.5 ..

Services

average annual % growth 1990–2000 2000–05

.. 6.9 1.8 –2.2 4.5 6.4 4.5 2.3 –2.3 4.5 –0.4 1.9 4.2 4.3 .. 7.8 3.0 –5.2 4.5 –2.8 7.1 0.2 3.0 –0.3 0.9 6.9 10.2 .. 4.2 –12.3 –0.6 4.7 2.0 1.9 .. 1.2 2.7 6.0 2.4 4.0 4.0 5.7 3.1 4.5 2.2 2.2 3.9 3.7 –0.3 2.9 5.7 2.6 4.7 3.6 –0.6 ..

21.9 8.3 5.2 6.7 0.9 12.2 3.6 1.4 8.8 5.6 5.3 1.9 3.5 2.1 5.2 5.4 1.7 5.1 12.0 10.4 8.2 7.5 3.0 –12.9 8.4 4.0 10.0 5.7 2.8 5.5 4.2 5.4 0.2 5.1 .. 3.6 1.8 4.6 4.6 3.4 2.4 3.5 6.6 3.9 2.6 1.6 0.0 5.4 7.8 1.0 5.3 4.7 2.8 1.9 0.7 ..

Honduras Hungary India Indonesiaa Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwaita Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberiaa Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysiaa Mali Mauritania Mauritius Mexico Moldova Mongolia Moroccoa Mozambique Myanmara Namibia Nepal Netherlands New Zealand Nicaragua Niger a Nigeria Norway Omana Pakistan Panama Papua New Guinea Paraguaya Peru Philippinesa Poland Portugal Puerto Ricoa

Gross domestic product

Agriculture

Industry

Manufacturing

average annual % growth 1990–2000 2000–05

average annual % growth 1990–2000 2000–05

average annual % growth 1990–2000 2000–05

average annual % growth 1990–2000 2000–05

3.2 1.6 6.0 4.2 3.1 .. 7.5 5.3 1.5 1.8 1.1 5.0 –4.1 2.2 .. 5.8 4.9 –4.1 6.5 –1.5 6.0 3.9 4.1 .. –2.7 –0.8 2.0 3.7 7.0 4.1 2.9 5.2 3.1 –9.6 2.7 2.3 5.9 6.9 4.0 4.9 2.9 3.2 3.7 2.4 2.5 4.0 4.5 3.8 4.7 4.3 2.2 4.7 3.3 4.7 2.8 4.2

3.6 4.1 7.0 4.7 5.8 –11.4 5.2 1.9 0.6 1.8 1.4 6.1 10.1 3.4 .. 4.6 7.3 4.0 6.2 7.9 4.0 2.9 –6.8 5.3 7.8 1.7 2.0 3.4 4.8 5.9 4.0 4.0 1.9 7.1 5.8 4.3 8.6 9.2 4.6 2.8 0.7 3.7 3.0 3.7 5.9 2.0 3.0 4.8 4.3 1.6 2.6 4.3 4.7 3.2 0.5 ..

2.2 –2.4 3.0 2.0 3.2 .. .. .. 2.1 –0.3 –1.6 –3.0 –8.0 1.9 .. 1.6 1.0 1.5 4.8 –5.5 2.3 2.0 .. .. –0.8 0.2 1.9 8.6 0.3 2.6 –0.2 –0.5 1.5 –11.2 3.7 –0.8 4.9 5.7 3.8 2.4 2.0 2.9 4.7 3.0 3.4 2.6 5.0 4.4 3.1 .. 3.3 5.5 1.7 0.5 –0.3 ..

3.4 7.2 2.5 3.4 5.5 –3.6 .. .. 0.2 –2.7 –0.9 12.2 4.6 2.8 .. –0.1 15.1 2.8 2.8 2.8 1.8 –1.1 .. .. 3.0 0.9 1.7 0.5 3.4 4.9 –2.4 2.0 1.9 1.4 0.1 6.9 8.3 .. 1.4 3.2 1.3 0.3 2.3 6.4 5.8 2.1 2.2 2.3 4.8 2.2 5.5 3.1 3.9 3.3 –1.8 ..

3.6 3.5 6.3 5.2 2.6 .. .. .. 0.8 –1.0 –0.3 5.2 0.6 1.2 .. 6.0 0.3 –10.3 11.1 –8.5 –0.9 5.0 .. .. 3.3 –2.3 2.4 2.0 8.6 6.4 3.4 5.5 3.8 –13.6 2.3 3.2 12.8 10.5 2.4 7.2 1.5 2.4 5.5 2.0 1.0 3.8 3.9 4.1 6.0 .. 0.6 5.4 3.5 7.1 3.1 ..

3.6 3.8 7.5 3.9 7.0 –17.0 .. .. –0.2 2.0 0.0 9.3 11.3 4.5 .. 6.3 1.9 0.6 12.1 8.4 4.5 3.7 .. .. 10.5 1.3 1.1 3.8 4.6 5.1 3.1 1.9 0.6 8.9 7.5 4.0 10.3 .. 7.3 1.1 –0.1 4.0 3.9 3.1 5.5 0.7 –0.5 6.5 1.6 –3.6 1.3 5.3 3.3 3.2 –1.1 ..

4.0 8.2 7.0 6.7 5.1 .. .. .. 1.4 –2.2 .. 5.6 2.7 1.3 .. 7.3 –0.1 –7.5 11.7 –7.6 –5.0 6.6 .. .. 5.7 –5.3 2.0 0.5 9.5 –1.4 5.8 5.3 4.3 –7.1 –9.7 2.7 10.2 7.9 2.6 8.9 .. 2.2 5.3 2.6 1.1 1.6 6.0 3.8 2.7 .. 1.4 3.8 3.0 9.9 3.6 ..

4.2 4.7 6.9 5.2 10.2 –12.8 .. .. –1.2 0.1 0.8 11.6 9.2 3.3 .. 7.0 2.5 0.9 10.4 7.4 5.1 4.1 .. .. 10.2 1.2 2.7 1.7 5.2 5.7 –4.1 0.6 0.0 7.7 5.5 3.4 14.5 .. 6.2 –0.6 .. 3.1 4.5 3.9 8.8 0.7 9.3 9.1 –1.4 –1.1 0.9 4.9 4.3 6.7 0.0 ..

4.1

ECONOMY

Growth of output

Services

average annual % growth 1990–2000 2000–05

3.8 1.2 8.0 4.0 3.8 .. .. .. 1.7 2.3 1.9 5.0 0.3 3.2 .. 5.6 3.5 –4.9 6.6 2.7 3.2 4.4 .. .. 5.5 0.5 2.3 1.6 7.3 3.0 4.9 6.4 2.9 0.7 0.2 2.8 3.6 7.2 4.5 6.4 3.3 3.5 5.2 1.9 3.3 4.0 5.0 4.4 4.5 .. 2.5 4.0 4.0 5.1 2.4 ..

2007 World Development Indicators

4.3 3.6 8.5 6.2 5.1 5.9 .. .. 0.9 1.7 1.7 4.9 10.8 3.1 .. 3.7 10.2 7.3 6.7 8.1 4.3 3.4 .. .. 7.0 2.2 1.7 3.2 5.3 6.2 6.8 5.9 2.4 6.4 7.8 3.8 7.8 .. 4.3 2.8 1.1 4.3 3.7 4.3 6.3 2.5 5.9 5.4 4.8 1.4 1.9 3.9 6.0 3.0 1.4 ..

191

4.1 Romania Russian Federation Rwandaa Saudi Arabiaa Senegala Serbia and Montenegro Sierra Leone Singapore Slovak Republica Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzaniac Thailanda Togoa Trinidad and Tobago Tunisiaa Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnama West Bank and Gazaa 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 Europe EMU

Growth of output Gross domestic product

Agriculture

Industry

Manufacturing

average annual % growth 1990–2000 2000–05

average annual % growth 1990–2000 2000–05

average annual % growth 1990–2000 2000–05

average annual % growth 1990–2000 2000–05

–0.6 –4.7 –0.3 2.1 3.2 1.4 –5.1 7.6 1.9 2.7 .. 2.1 2.7 5.3 5.4 3.3 2.1 1.0 5.1 –10.4 2.9 4.2 3.5 3.1 4.7 3.8 –4.8 7.1 –9.3 4.8 2.7 3.5 3.4 –0.2 1.6 7.9 7.3 6.0 0.5 2.1 2.9 w 4.8 3.8 5.3 2.1 3.9 8.5 –0.7 3.3 3.8 5.6 2.5 2.7 2.1

5.8 6.2 5.1 4.2 4.7 5.1 13.7 4.2 4.9 3.4 .. 3.7 3.1 4.2 6.1 2.3 2.3 0.9 3.7 9.6 6.9 5.4 2.7 8.3 4.5 5.2 .. 5.6 8.0 8.2 2.4 2.6 0.9 5.3 1.3 7.5 –0.9 3.3 4.7 –5.9 2.8 w 6.1 5.2 6.3 3.5 5.3 8.4 5.4 2.3 4.1 6.5 4.3 2.2 1.3

–1.9 –4.9 2.6 1.6 2.9 .. –13.0 –1.8 2.7 0.0 .. 1.0 3.1 1.8 9.2 1.2 –1.1 –2.0 6.0 –6.8 3.2 1.0 4.0 2.7 2.3 1.4 –5.7 3.7 –5.6 13.2 –0.2 3.7 2.8 0.5 1.2 4.3 .. 5.6 4.2 4.3 2.0 w 3.2 2.0 2.7 0.3 2.4 3.4 –1.7 2.0 2.9 3.1 3.3 1.3 1.7

8.8 4.5 4.3 1.1 1.8 –2.9 .. 0.9 4.7 0.7 .. 0.8 –0.5 0.7 .. 0.3 0.2 –5.3 3.7 10.3 5.1 1.9 2.8 –7.3 3.6 1.4 .. 4.1 3.3 2.9 0.7 0.2 5.6 6.9 4.7 3.8 .. 0.6 1.4 –8.5 2.2 w 3.0 3.6 3.8 3.1 3.4 3.7 3.5 3.3 4.5 2.4 3.8 –0.1 –0.1

–1.2 –7.1 –3.7 2.2 4.1 .. –4.5 7.7 2.4 1.3 .. 1.1 2.3 6.9 5.8 3.7 4.2 0.4 9.2 –10.8 3.1 5.7 1.8 3.2 4.6 4.1 –3.4 12.2 –12.9 3.0 1.5 3.7 1.1 –3.4 1.2 11.9 .. 8.2 –4.2 0.4 2.4 w 4.9 4.5 7.1 1.4 4.6 11.0 –2.9 3.2 4.1 6.1 1.9 1.8 0.9

5.6 6.1 5.6 3.6 6.5 1.9 .. 3.2 6.4 3.6 .. 2.7 2.6 3.3 .. 1.9 3.7 1.1 –0.7 11.6 9.7 6.9 8.1 12.7 3.1 4.6 .. 7.3 9.4 5.6 –0.1 0.7 0.7 3.9 –0.2 10.2 .. 0.0 9.6 –10.0 2.0 w 6.9 6.1 7.7 3.2 6.2 9.4 5.6 2.2 2.6 7.2 4.7 0.9 0.9

.. .. –6.0 5.6 3.1 .. 6.1 7.0 6.6 1.1 .. 1.6 .. 8.1 4.4 2.8 8.6 1.2 .. –10.0 2.7 6.9 1.8 4.9 5.5 4.9 .. 14.1 –11.2 11.9 .. .. –0.1 0.7 4.5 11.2 .. 5.7 0.8 0.4 .. w 5.9 6.9 8.5 4.4 6.8 10.8 .. 3.4 3.8 6.6 1.9 .. 2.0

.. .. 1.4 5.5 5.2 .. .. 5.2 6.7 4.2 .. 2.3 1.0 2.9 .. 1.8 2.7 0.9 29.6 10.3 8.0 7.2 7.5 6.7 3.0 5.9 .. 5.7 14.0 8.5 –0.9 1.3 2.3 1.8 0.8 11.5 .. 2.8 5.3 –12.0 2.3 w 6.9 7.2 9.4 3.1 7.2 9.8 .. 1.7 6.4 7.0 2.6 1.1 0.6

Services

average annual % growth 1990–2000 2000–05

0.9 –1.7 –1.2 2.2 3.0 .. –2.9 7.8 5.7 3.4 .. 2.7 2.7 5.7 2.7 3.6 1.9 1.2 1.5 –12.7 2.7 3.7 3.9 3.2 5.3 4.0 –5.4 8.2 –8.1 7.2 3.4 3.4 3.7 0.4 –0.1 7.5 .. 5.0 2.5 2.9 3.1 w 6.0 3.9 5.0 2.8 4.1 8.1 0.9 3.3 3.4 7.1 2.5 2.9 2.4

5.5 6.4 5.8 3.6 5.1 7.3 .. 4.8 4.1 3.8 .. 4.4 3.3 5.8 .. 3.1 1.7 0.2 5.9 7.0 6.0 4.5 –0.1 3.8 5.4 5.1 .. 7.4 7.7 9.3 3.1 2.7 –0.5 5.1 3.1 6.9 .. 6.5 4.0 –8.7 2.7 w 7.2 4.9 6.2 3.5 5.2 8.7 5.2 2.2 4.5 7.8 4.3 2.3 1.5

a. Components are at producer prices. b. China has revised its national accounts data from 1993 onwards. Data before 1993 are linked to the revised data on the basis of earlier growth rates. c. Data cover mainland Tanzania only.

192

2007 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 the current pattern of production

subsidies) not included in the valuation of output. It

indicators for calculating growth: the volume of gross

or uses of output. The new base year should repre-

is calculated without deducting for depreciation of

domestic product (GDP), real gross domestic income,

sent normal operation of the economy—that is, it

and real gross national income. The volume of GDP

should be a year without major shocks or distortions.

is the sum of value added, measured at constant

Some developing countries have not rebased their

prices, by households, government, and industries

national accounts for many years. Using an old base

operating in the economy.

year can be misleading because implicit price and

Each industry’s contribution to growth in the economy’s output is measured by growth in the industry’s

volume weights become progressively less relevant and useful.

fabricated capital assets or for depletion and degradation of natural resources. Value added is the net output of an industry after adding up all outputs and subtracting intermediate inputs. The industrial origin of value added is determined by the International Standard Industrial Classification (ISIC) revision 3.

value added. In principle, value added in constant

To obtain comparable series of constant price

• Agriculture corresponds to ISIC divisions 1–5 and

prices can be estimated by measuring the quantity

data, the World Bank rescales GDP and value added

includes forestry and fishing. • Industry covers min-

of goods and services produced in a period, valu-

by industrial origin to a common reference year. This

ing, manufacturing (also reported separately), con-

ing them at an agreed set of base year prices, and

year’s World Development Indicators continues to

struction, electricity, water, and gas (ISIC divisions

subtracting the cost of intermediate inputs, also in

use 2000 as the reference year. Because rescaling

10–45). • Manufacturing corresponds to industries

constant prices. This double-deflation method, rec-

changes the implicit weights used in forming regional

belonging to ISIC divisions 15–37. • Services cor-

ommended by the 1993 SNA and its predecessors,

and income group aggregates, aggregate growth

respond to ISIC divisions 50–99. This sector is

requires detailed information on the structure of

rates in this year’s World Development Indicators are

derived as a residual (from GDP less agriculture and

prices of inputs and outputs.

not comparable with those from earlier publications

industry) and may not properly reflect the sum of

In many industries, however, value added is

with different base years.

extrapolated from the base year using single vol-

Rescaling may result in a discrepancy between

ume indexes of outputs or, more rarely, inputs. Par-

the rescaled GDP and the sum of the rescaled com-

ticularly in the services industries, including most of

ponents. Because allocating the discrepancy would

government, value added in constant prices is often

cause distortions in the growth rates, the discrep-

imputed from labor inputs, such as real wages or

ancy is left unallocated. As a result, the weighted

number of employees. In the absence of well-defined

average of the growth rates of the components gen-

measures of output, measuring the growth of ser-

erally will not equal the GDP growth rate.

vices remains difficult. Moreover, technical progress can lead to improvements in production processes and in the quality of goods and services that, if not properly accounted for, can distort measures of value added and thus of growth. When inputs are used to estimate output, as is the case for nonmarket services, unmeasured technical progress leads to underestimates of the volume of output. Similarly, unmeasured improvements in the quality of goods and services produced lead to underestimates of the value of output and value added. The result can be underestimates of growth and productivity improvement and overestimates of

services output, including banking and financial services. For some countries it includes product taxes (minus subsidies) and may also include statistical discrepancies.

Computing growth rates Growth rates of GDP and its components are calculated using the least squares method and constant price data in the local currency. Constant price U.S. dollar series are used to calculate regional and

Data sources

income group growth rates. Local currency series are converted to constant price U.S. dollars using

National accounts data for most developing

an exchange rate in the common reference year.

countries are collected from national statistical

The growth rates in the table are average annual

organizations and central banks by visiting and

compound growth rates. Methods of computing

resident World Bank missions. Data for high-

growth rates and the alternative conversion factor

income economies come from data files of the

are described in Statistical methods.

Organisation for Economic Co-operation and Development (for information on the OECD’s

inflation. These issues are highly complex, and only

Changes in the System of National Accounts

a few high-income countries have attempted to intro-

World Development Indicators adopted the termi-

duce any GDP adjustments for these factors.

nology of the 1993 SNA in 2001. Although many

Accounts for OECD Member Countries: Data from

Informal economic activities pose a particular mea-

countries continue to compile their national accounts

1970 Onwards). The World Bank rescales constant

surement problem, especially in developing countries,

according to the SNA version 3 (referred to as the

price data to a common reference year. The com-

where much economic activity may go unrecorded.

1968 SNA), more and more are adopting the 1993

plete national accounts time series is available on

Obtaining a complete picture of the economy requires

SNA. Some low-income countries still use concepts

the World Development Indicators 2007 CD-ROM.

estimating household outputs produced for home

from the even older 1953 SNA guidelines, including

The United Nations Statistics Division publishes

use, sales in informal markets, barter exchanges,

valuations such as factor cost, in describing major

detailed national accounts for UN member coun-

and illicit or deliberately unreported activities. The

economic aggregates. Countries that use the 1993

tries in National Accounts Statistics: Main Aggre-

consistency and completeness of such estimates

SNA are identified in Primary data documentation.

gates and Detailed Tables and publishes updates

depend on the skill and methods of the compiling

national accounts series, see its Annual National

in the Monthly Bulletin of Statistics.

statisticians and the resources available to them.

2007 World Development Indicators

193

4.2

Structure of output Gross domestic product

Agriculture

$ millions

Afghanistan Albania Algeria Angolaa Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benina Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Fasoa Burundi Cambodia Cameroon Canada Central African Republic Chad Chile Chinaa,b Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep.a Costa Rica Côte d’Ivoirea Croatia Cubaa Czech Republic Denmark Dominican Republica Ecuadora Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabona Gambia, The Georgia Germany Ghanaa Greece Guatemalaa Guinea Guinea-Bissau Haiti

194

Industry

% of GDP

Manufacturing

% of GDP

Services

% of GDP

% of GDP

1990

2005

1990

2005

1990

2005

1990

2005

1990

2005

.. 2,102 62,045 10,260 141,352 2,257 319,265 164,984 8,858 30,129 17,370 202,691 1,845 4,868 .. 3,792 461,952 20,726 3,120 1,132 1,115 11,152 574,192 1,488 1,739 31,559 354,644 76,887 40,274 9,350 2,799 7,403 10,796 18,156 .. 34,880 135,838 7,074 10,356 43,130 4,801 477 5,010 12,083 138,231 1,239,256 5,952 317 7,738 1,707,383 5,886 85,929 7,650 2,818 244 2,864

7,308 8,380 102,256 32,811 183,193 4,903 732,499 306,073 12,561 60,034 29,566 370,824 4,287 9,334 9,949 10,317 796,055 26,648 5,171 800 6,187 16,875 1,113,810 1,369 5,469 115,248 2,234,297 177,703 122,309 7,103 5,091 20,021 16,344 38,506 .. 124,365 258,714 29,502 36,489 89,369 16,974 970 13,101 11,174 193,160 2,126,630 8,055 461 6,395 2,794,926 10,720 225,206 31,717 3,289 301 4,268

.. 36 11 18 8 17 4 4 29 30 24 2 36 17 .. 5 8 17 28 56 .. 25 3 48 29 9 27 0 17 31 13 12 33 11 .. 6 4 13 13 19 17 31 17 52 6 4 7 29 32 2 45 10 26 24 61 ..

36 23 9 7 9 21 3 2 10 20 10 1 32 15 10 2 8 10 31 35 34 41 .. 54 23 6 13 0 13 46 6 9 23 7 .. 3 2 12 7 15 10 23 4 48 3 2 8 33 17 1 38 5 23 25 60 28

.. 48 48 41 36 52 30 32 33 22 47 31 13 35 .. 61 39 49 20 19 .. 30 32 20 18 42 42 24 38 29 41 30 23 36 .. 49 26 31 38 29 27 12 50 12 33 27 43 13 34 38 17 26 20 33 19 ..

25 22 62 74 36 44 27 31 62 27 41 24 13 32 25 53 38 32 20 20 27 14 .. 21 51 47 48 10 34 25 46 30 26 31 .. 37 25 26 46 36 30 23 29 13 30 21 58 13 27 30 23 21 19 36 12 17

.. .. 11 5 27 33 15 21 19 13 39 .. 8 19 .. 5 .. .. 15 13 .. 15 17 11 14 20 33 17 21 11 8 23 21 29 .. .. 17 18 19 18 22 8 42 5 .. .. 6 7 24 28 10 .. 15 5 8 ..

15 12 6 4 23 21 12 20 8 17 33 17 8 14 12 4 .. 20 14 9 19 7 .. .. 5 18 34 4 15 6 6 22 19 20 .. 25 14 15 9 17 23 8 19 5 22 13 5 5 18 23 8 11 13 5 9 8

.. 16 41 41 56 31 66 64 38 48 29 67 51 49 .. 34 53 34 52 25 .. 46 65 33 53 50 31 75 45 40 47 58 44 53 .. 45 70 55 49 52 55 57 34 36 61 70 50 58 35 61 38 63 54 43 21 ..

39 56 30 19 55 35 70 68 28 53 49 75 54 53 65 44 54 59 50 45 39 45 .. 25 26 48 40 90 53 29 48 62 51 62 .. 60 74 62 48 49 60 55 67 39 68 77 35 54 56 69 39 74 58 39 28 55

2007 World Development Indicators

Gross domestic product

Agriculture

$ millions

Honduras Hungary India Indonesiaa Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait a Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberiaa Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysiaa Mali Mauritania Mauritius Mexico Moldova Mongolia Moroccoa Mozambique Myanmar a Namibia Nepal Netherlands New Zealand Nicaragua Niger a Nigeria Norway Omana Pakistan Panama Papua New Guinea Paraguaya Peru Philippinesa Poland Portugal Puerto Ricoa

Industry

% of GDP

4.2

Manufacturing

% of GDP

ECONOMY

Structure of output

Services

% of GDP

% of GDP

1990

2005

1990

2005

1990

2005

1990

2005

1990

2005

3,049 33,056 316,937 114,426 116,035 48,422 47,854 52,490 1,133,407 4,592 3,018,112 4,020 26,933 8,591 .. 263,777 18,428 2,674 866 7,447 2,838 615 384 28,905 10,507 4,472 3,081 1,881 44,024 2,421 1,020 2,383 262,710 3,593 2,093 25,821 2,463 .. 2,350 3,628 307,384 43,898 1,009 2,481 28,472 116,108 11,685 40,010 5,313 3,221 5,265 26,294 44,312 58,976 75,274 30,604

8,291 109,239 805,714 287,217 189,784 12,602 201,817 123,434 1,762,519 9,574 4,533,965 12,712 57,124 18,730 .. 787,624 80,781 2,441 2,875 15,826 21,944 1,450 548 38,756 25,625 5,766 5,040 2,072 130,326 5,305 1,850 6,290 768,438 2,917 1,880 51,621 6,636 .. 6,126 7,391 624,202 109,291 4,911 3,405 98,951 295,513 24,284 110,732 15,467 4,945 7,328 79,379 99,029 303,229 183,305 ..

22 15 31 19 19 .. 9 .. 4 .. 3 8 27 30 .. 9 1 34 61 22 .. 24 54 .. 27 9 29 45 15 46 30 13 8 36 15 18 37 57 12 51 4 7 .. 35 33 4 3 26 10 .. 28 9 22 8 9 1

14 4 18 13 10 9 3 .. 2 6 2 3 7 27 .. 3 1 34 45 4 7 17 64 .. 6 13 28 35 9 37 24 6 4 17 22 14 22 .. 10 38 2 .. 19 40 23 2 2 22 8 42 22 7 14 5 3 ..

26 39 28 39 29 .. 35 .. 32 .. 40 28 45 19 .. 42 52 36 15 46 .. 33 17 .. 31 45 13 29 42 16 29 33 28 37 41 32 18 11 38 16 28 28 .. 16 41 36 54 25 15 .. 25 27 35 50 29 42

31 31 27 46 45 70 37 .. 27 33 30 30 40 19 .. 40 51 21 30 22 22 41 15 .. 34 29 16 19 52 24 29 28 26 25 29 30 30 .. 32 21 24 .. 28 17 57 43 56 25 16 39 19 35 32 31 25 ..

16 23 17 21 12 .. .. .. 23 .. .. 15 9 12 .. 27 12 28 10 35 .. 14 .. .. 21 36 11 20 24 9 10 25 21 .. 36 18 10 8 14 6 .. 19 .. 7 6 13 3 17 10 .. 17 18 25 .. .. 40

20 23 16 28 12 2 27 .. 18 14 21 19 15 12 .. 28 2 14 21 13 14 19 12 .. 22 18 14 13 31 3 5 20 18 17 3 17 14 .. 14 8 14 .. 18 7 4 11 8 18 8 6 12 16 23 18 16 ..

51 46 41 42 52 .. 57 .. 64 .. 58 64 29 51 .. 50 47 30 24 32 .. 44 29 .. 42 47 59 26 43 39 42 54 64 27 44 50 45 32 50 34 67 65 .. 49 26 61 43 49 75 .. 47 64 44 42 62 57

55 65 54 41 45 21 60 .. 71 61 68 68 54 54 .. 56 49 45 26 74 71 41 21 .. 61 58 56 46 40 39 47 66 70 59 49 56 48 .. 58 41 74 .. 53 43 20 55 42 53 76 19 59 58 53 65 73 ..

2007 World Development Indicators

195

4.2

Structure of output Gross domestic product

Agriculture

$ millions

Romania Russian Federation Rwandaa Saudi Arabiaa Senegala Serbia and Montenegro Sierra Leone Singapore Slovak Republica Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzaniac Thailanda Togoa Trinidad and Tobago Tunisiaa Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnama West Bank and Gazaa 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 Europe EMU

Industry

% of GDP

1990

2005

38,299 516,814 2,584 116,778 5,699 .. 650 36,842 15,485 17,382 917 112,014 520,969 8,032 13,167 882 242,178 235,808 12,309 2,629 4,259 85,345 1,628 5,068 12,291 150,642 3,232 4,304 81,456 33,653 989,524 5,757,200 9,287 13,361 47,028 6,472 .. 4,828 3,288 8,784 21,784,509 t 595,576 3,253,159 1,636,824 1,615,064 3,849,735 667,503 1,101,711 1,103,860 276,910 401,938 302,890 17,935,976 5,653,415

98,565 763,720 2,153 309,778 8,238 26,215 1,193 116,764 46,412 34,354 .. 239,543 1,124,640 23,479 27,542 2,731 357,683 367,029 26,320 2,312 12,111 176,634 2,203 14,358 28,683 362,502 8,067 8,724 82,876 129,702 2,198,789 12,416,505 16,791 13,951 140,192 52,408 4,014 15,066 7,270 3,372 44,645,437 t 1,416,212 8,553,721 4,879,773 3,673,796 9,969,591 3,039,976 2,201,159 2,460,991 625,311 1,016,267 621,879 34,687,058 9,984,125

1990

24 17 33 6 20 .. 47 0 7 6 66 5 6 26 .. 13 3 3 30 33 46 13 34 3 16 18 32 57 26 2 2 2 9 33 6 39 .. 24 21 17 5w 32 16 19 11 18 25 16 9 17 31 20 3 4

Manufacturing

% of GDP 2005

10 6 42 4 18 16 46 0 4 3 .. 3 3 17 34 12 1 1 23 24 45 10 42 1 12 12 20 33 11 2 1 1 9 28 5 21 .. 13 19 18 4w 22 9 12 6 11 13 8 8 12 19 17 2 2

1990

50 48 25 49 19 .. 19 35 59 42 .. 40 34 26 .. 42 31 33 25 38 18 37 23 47 30 30 30 11 45 64 35 28 33 33 61 23 .. 27 51 33 33 w 26 39 38 39 37 40 43 36 33 27 34 32 32

Services

% of GDP 2005

35 38 21 59 19 33 24 34 29 34 .. 30 30 26 30 48 28 28 35 32 18 44 23 60 29 24 41 25 34 56 26 22 31 29 52 41 .. 41 25 23 28 w 28 38 42 32 37 46 32 34 40 27 32 26 26

1990

34 .. 18 9 13 .. 5 27 .. 34 5 24 .. 15 .. 35 .. 22 21 25 9 27 10 14 17 20 .. 6 39 8 23 19 27 22 15 12 .. 9 36 23 21 w 15 24 27 22 23 30 .. .. 14 17 17 21 ..

% of GDP 2005

24 18 8 10 11 20 4 28 19 25 .. 19 16 15 7 37 20 20 30 24 8 35 10 6 18 14 22 9 21 14 15 14 22 11 18 21 .. 5 12 13 18 w 15 23 27 19 22 32 18 12 14 16 14 17 19

1990

26 35 43 46 61 .. 34 65 34 52 .. 55 61 48 .. 45 66 64 45 29 36 50 44 50 55 52 38 32 30 35 63 70 58 34 34 39 .. 49 28 50 61 w 41 46 43 50 45 35 41 55 50 43 47 65 64

2005

55 56 37 37 63 51 30 66 67 63 .. 67 67 57 37 41 71 70 41 44 38 46 35 40 60 65 39 43 55 42 73 77 60 43 44 38 .. 45 56 59 69 w 50 53 47 62 52 41 60 59 48 54 52 72 72

a. Components are at producer prices. b. China has revised its national accounts data from 1993 onwards. Data before 1993 are not comparable with the later revised data. c. Data cover mainland Tanzania only.

196

2007 World Development Indicators

About the data

4.2

ECONOMY

Structure of output Definitions

An economy’s gross domestic product (GDP) repre-

Ideally, industrial output should be measured

• Gross domestic product (GDP) at purchaser prices

sents the sum of value added by all producers in

through regular censuses and surveys of firms. But

is the sum of gross value added by all resident pro-

that economy. Value added is the value of the gross

in most developing countries such surveys are infre-

ducers in the economy plus any product taxes (less

output of producers less the value of intermediate

quent, so earlier survey results must be extrapo-

subsidies) not included in the valuation of output. It

goods and services consumed in production, before

lated using an appropriate indicator. The choice of

is calculated without deducting for depreciation of

taking account of the consumption of fixed capital

sampling unit, which may be the enterprise (where

fabricated assets or for depletion and degradation of

in the production process. The United Nations Sys-

responses may be based on financial records) or

natural resources. Value added is the net output of

tem of National Accounts calls for estimates of value

the establishment (where production units may be

an industry after adding up all outputs and subtract-

added to be valued at either basic prices (excluding

recorded separately), also affects the quality of the

ing intermediate inputs. The industrial origin of value

net taxes on products) or producer prices (including

data. Moreover, much industrial production is orga-

added is determined by the International Standard

net taxes on products paid by producers but excluding

nized in unincorporated or owner-operated ventures

Industrial Classification (ISIC) revision 3. • Agricul-

sales or value added taxes). Both valuations exclude

that are not captured by surveys aimed at the formal

ture corresponds to ISIC divisions 1–5 and includes

transport charges that are invoiced separately by pro-

sector. Even in large industries, where regular surveys

forestry and fishing. • Industry covers mining, manu-

ducers. Total GDP shown in the table and elsewhere

are more likely, evasion of excise and other taxes and

facturing (also reported separately), construction,

in this book is measured at purchaser prices. Value

nondisclosure of income lower the estimates of value

electricity, water, and gas (ISIC divisions 10–45).

added by industry is normally measured at basic

added. Such problems become more acute as coun-

• Manufacturing corresponds to industries belong-

prices. When value added is measured at producer

tries move from state control of industry to private

ing to ISIC divisions 15–37. • Services correspond

prices, this is noted in Primary data documentation.

enterprise, because new firms enter business and

to ISIC divisions 50–99. This sector is derived as a

While GDP estimates based on the production

growing numbers of established firms fail to report.

residual (from GDP less agriculture and industry) and

approach are generally more reliable than estimates

In accordance with the System of National Accounts,

may not properly reflect the sum of services output,

compiled from the income or expenditure side, dif-

output should include all such unreported activity as

including banking and financial services. For some

ferent countries use different definitions, methods,

well as the value of illegal activities and other unre-

countries it includes product taxes (minus subsidies)

and reporting standards. World Bank staff review

corded, informal, or small-scale operations. Data on

and may also include statistical discrepancies.

the quality of national accounts data and sometimes

these activities need to be collected using techniques

make adjustments to improve consistency with

other than conventional surveys of firms.

international guidelines. Nevertheless, significant

In industries dominated by large organizations

discrepancies remain between international stan-

and enterprises, such as public utilities, data on

dards and actual practice. Many statistical offices,

output, employment, and wages are usually read-

especially those in developing countries, face severe

ily available and reasonably reliable. But in the

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

National accounts data for most developing coun-

ity in the informal or secondary economy. In develop-

pay. For further discussion of the problems of using

tries are collected from national statistical organi-

ing countries a large share of agricultural output is

national accounts data, see Srinivasan (1994) and

zations and central banks by visiting and resident

either not exchanged (because it is consumed within

Heston (1994).

World Bank missions. Data for high-income econ-

the household) or not exchanged for money. Agricultural production often must be estimated

Data sources

omies come from data files of the Organisation Dollar conversion

for Economic Co-operation and Development (for

indirectly, using a combination of methods involv-

To produce national accounts aggregates that are

information on the OECD’s national accounts

ing estimates of inputs, yields, and area under cul-

measured in the same standard monetary units,

series, see its Annual National Accounts for OECD

tivation. This approach sometimes leads to crude

the value of output must be converted to a single

Member Countries: Data from 1970 Onwards). The

approximations that can differ from the true values

common currency. The World Bank conventionally

complete national accounts time series is avail-

over time and across crops for reasons other than

uses the U.S. dollar and applies the average official

able on the World Development Indicators 2007

climatic conditions or farming techniques. Similarly,

exchange rate reported by the International Monetary

CD-ROM. 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.

2007 World Development Indicators

197

4.3 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 Chinab Hong Kong, 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

198

Structure of manufacturing Manufacturing value added

Food, beverages, and tobacco

Textiles and clothing

Machinery and transport equipment

Chemicals

Other manufacturinga

$ millions 1990 2003

% of total 1990 2003

% of total 1990 2003

% of total 1990 2003

% of total 1990 2003

% of total 1990 2003

.. .. 6,452 513 37,868 681 42,564 31,439 1,561 3,839 6,630 .. 145 826 .. 181 .. .. 460 134 58 1,581 91,671 154 239 5,613 116,573 12,357 8,034 1,029 234 1,514 2,257 4,770 .. .. 20,364 1,270 1,988 7,296 1,043 35 1,985 601 .. .. 332 18 1,773 451,915 575 .. 1,151 126 19 ..

679 610 4,458 542 29,142 541 62,976 44,672 628 7,899 4,751 48,185 301 1,036 727 323 .. 3,184 539 45 854 1,061 119,900 .. 211 13,268 539,026 5,702 11,606 302 227 3,361 2,452 4,850 .. 20,466 27,476 2,550 2,735 14,466 3,391 61 1,458 410 31,856 227,906 297 18 706 489,786 684 17,716 3,157 159 23 216

2007 World Development Indicators

.. 24 13 .. 20 .. 18 15 .. 24 .. 17 .. 28 12 51 14 .. 46 83 .. 61 15 58 .. 25 15 8 31 .. 58 47 38 22 67 .. 22 78 22 19 36 53 .. 12 13 13 45 .. .. .. .. 22 38 .. .. 51

.. .. 10 .. 30 .. 14 .. .. 39 .. 18 .. 39 .. 20 .. 19 46 16 .. 37 17 6 .. 21 13 11 21 .. .. 45 .. .. .. .. 20 109 27 .. 43 50 15 4 17 15 .. .. .. 9 22 20 32 .. .. ..

.. 33 17 .. 10 .. 6 7 .. 38 .. 7 .. 5 15 12 12 .. 3 9 .. -13 6 6 .. 8 15 36 15 .. 4 8 7 15 5 .. 4 7 10 16 14 18 .. 5 4 6 2 .. .. .. .. 20 11 .. .. 9

.. .. 19 .. 7 .. 4 .. .. 1 .. 15 .. 4 .. 5 .. 20 4 5 .. 6 9 53 .. 14 10 23 18 .. .. 5 .. .. .. .. 6 11 4 .. 26 9 13 3 12 3 .. .. .. 2 3 22 4 .. .. ..

.. .. .. .. 13 .. 20 28 .. 7 .. .. .. 1 18 .. 27 .. 1 .. .. 1 26 2 .. 5 24 21 9 .. 3 7 8 20 1 .. 24 0 5 9 4 2 .. 0 24 31 1 .. .. .. .. 12 4 .. .. ..

.. .. 3 .. 10 .. 19 .. .. 1 .. 23 .. 1 .. .. .. 19 2 .. .. 1 21 0 .. 12 32 21 5 .. .. 6 .. .. .. .. 27 1 3 .. 4 2 13 0 18 32 .. .. .. 43 1 13 3 .. .. ..

.. .. .. .. 12 .. 7 8 .. 17 .. 13 .. 3 7 .. .. .. 1 2 .. 5 10 6 .. 10 13 2 14 .. .. 9 .. 8 .. .. 12 4 8 14 24 18 .. 0 8 9 7 .. .. .. .. 10 18 .. .. ..

.. .. 4 .. 16 .. 7 .. .. 10 .. 7 .. 5 .. .. .. 10 1 6 .. 10 7 2 .. 9 11 4 11 .. .. 11 .. .. .. .. 4 5 4 .. 9 8 4 1 6 14 .. .. .. .. 4 9 4 .. .. ..

.. 44 70 .. 46 .. 49 43 .. 15 .. 62 .. 63 49 37 48 .. 49 7 .. 46 44 28 .. 52 34 33 31 .. 35 30 47 36 27 .. 39 11 56 43 23 9 .. 82 52 41 45 .. .. .. .. 36 29 .. .. 40

.. .. 64 .. 37 .. 56 .. .. 49 .. 37 .. 52 .. 75 .. 32 48 73 .. 46 46 39 .. 45 33 41 45 .. .. 34 .. .. .. .. 43 -25 61 .. 18 31 55 93 48 37 .. .. .. 47 70 36 58 .. .. ..

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

4.3

ECONOMY

Structure of manufacturing Manufacturing value added

Food, beverages, and tobacco

Textiles and clothing

Machinery and transport equipment

Chemicals

Other manufacturinga

$ millions 1990 2003

% of total 1990 2003

% of total 1990 2003

% of total 1990 2003

% of total 1990 2003

% of total 1990 2003

443 6,613 48,808 23,643 13,357 .. .. .. 240,462 853 .. 520 1,941 864 .. 64,605 2,142 706 85 2,474 .. 71 .. .. 2,164 1,411 314 313 10,665 200 94 491 49,992 .. 745 4,753 230 .. 292 209 .. 7,613 .. 163 1,562 13,450 343 6,184 502 .. 883 3,926 11,003 .. .. 12,126

1,253 15,887 84,971 68,794 15,034 263 39,819 .. 258,746 1,017 886,172 1,527 4,384 1,448 .. 141,947 1,087 255 405 1,330 2,328 189 30 .. 3,211 730 688 175 32,355 114 70 1,010 104,107 308 62 7,319 605 .. 512 441 67,292 8,097 692 179 2,268 21,702 1,782 12,387 924 215 691 8,811 18,824 33,786 21,352 27,099

45 14 12 28 12 20 27 14 8 41 9 28 .. 39 .. 11 4 .. .. .. .. .. .. 48 .. 20 39 38 13 .. .. 30 22 .. 33 22 .. .. .. 37 21 28 40 37 15 18 .. 24 51 6 56 23 39 21 15 16

57 12 12 23 10 22 37 16 9 15 8 26 .. 39 .. 32 10 .. .. 24 .. .. .. 73 23 .. 3 262 9 .. .. 78 .. 57 .. 33 75 .. .. 45 3 19 31 32 .. 3 9 35 48 19 54 .. 38 9 14 ..

10 9 15 15 20 16 4 9 13 5 5 7 .. 10 .. 12 3 .. .. .. .. .. .. 5 .. 26 36 10 7 .. .. 46 5 .. 37 17 .. .. .. 31 3 8 3 29 46 2 .. 28 8 .. 16 11 11 9 21 5

9 15 0 13 5 17 21 14 11 7 2 11 .. 7 .. 20 5 .. .. 10 .. .. .. 4 15 .. 6 9 3 .. .. 5 .. 11 .. 18 19 .. .. 19 1 7 9 .. .. 11 2 92 7 .. 15 .. 10 3 18 ..

3 26 26 12 20 4 29 32 35 .. 40 4 .. 10 .. 32 2 .. .. .. .. .. .. .. .. 14 3 1 31 .. .. 2 24 .. 1 8 .. .. .. 1 25 13 0 .. 13 25 .. 9 2 7 .. 8 13 26 13 18

1 19 4 18 28 8 13 12 27 .. 19 5 .. 17 .. 8 5 .. .. 11 .. .. .. 4 15 .. 6 6 39 .. .. 4 .. 7 .. 8 8 .. .. 2 3 12 2 .. .. 24 2 14 2 10 1 .. 8 7 17 ..

6 12 14 9 8 11 17 9 7 .. 10 15 .. 9 .. 9 3 .. .. .. .. .. .. 1 .. 9 8 18 11 .. .. 4 18 .. 1 12 .. .. .. 5 17 7 3 .. 4 9 .. 15 8 .. .. 9 12 7 6 44

4 3 5 10 13 3 6 4 8 .. 7 16 .. 8 .. 10 3 .. .. 4 .. .. .. 15 4 .. 1 50 9 .. .. 3 .. .. .. 13 4 .. .. 10 2 4 9 25 .. 8 4 15 3 4 4 .. 12 4 6 ..

36 39 34 37 40 49 24 37 37 54 37 47 .. 33 .. 36 88 .. .. .. .. .. .. 47 .. 31 14 33 39 .. .. 17 32 .. 27 41 .. .. .. 26 35 44 54 34 22 46 .. 25 31 87 29 49 26 37 45 17

2007 World Development Indicators

30 51 79 36 44 51 24 55 45 78 65 42 .. 29 .. 31 76 .. .. 51 .. .. .. 5 43 .. 84 -227 40 .. .. 9 .. 25 .. 28 -6 .. .. 23 92 58 50 43 .. 55 84 -56 39 68 27 .. 33 78 45 ..

199

4.3 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 Tanzaniac 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 Europe EMU

Structure of manufacturing Manufacturing value added

Food, beverages, and tobacco

Textiles and clothing

Machinery and transport equipment

Chemicals

Other manufacturinga

$ millions 1990 2003

% of total 1990 2003

% of total 1990 2003

% of total 1990 2003

% of total 1990 2003

% of total 1990 2003

9,152 .. 473 10,049 747 .. 28 9,562 .. 5,190 41 24,043 .. 1,077 .. 250 .. 49,484 2,508 653 361 23,217 162 681 2,075 26,882 .. 230 31,517 2,643 206,719 1,040,600 2,597 .. 6,921 793 .. 449 1,048 1,799 4,401,339 t 81,598 577,839 312,003 271,707 665,884 188,030 .. 174,072 32,128 60,477 43,592 .. ..

16,141 64,391 149 23,005 752 3,305 34 22,207 6,255 6,433 .. 29,301 134,438 2,524 1,205 432 52,365 60,215 1,737 436 685 49,735 163 747 4,480 26,753 1,045 538 9,320 11,495 238,575 1,488,100 2,078 807 14,289 8,115 .. 517 473 911 6,163,496 t 136,860 1,358,768 913,666 440,727 1,495,945 722,097 .. 281,559 56,299 108,845 50,518 4,693,358 1,390,728

19 .. .. 7 60 .. .. 4 .. 12 2 15 18 51 .. 69 10 10 35 .. 51 24 .. 31 19 16 .. 61 .. .. 13 12 31 .. 17 .. .. .. 44 28

.. .. 93 .. .. 36 .. 3 .. 10 31 16 13 37 66 37 10 .. 36 .. 39 23 .. 16 35 29 .. 10 .. .. 12 12 .. .. 25 .. .. 48 63 26

18 .. .. 1 3 .. .. 3 .. 15 3 8 8 24 .. 8 2 4 29 .. 3 30 .. 3 20 15 .. 14 .. .. 5 5 18 .. 5 .. .. .. 12 19

.. .. .. .. .. 6 .. 1 .. 8 16 13 19 33 4 2 4 .. 41 .. 23 14 7 1 12 22 .. 28 .. 2 11 8 .. .. 8 .. .. 7 8 16

14 .. .. 4 5 .. .. 53 .. 16 .. 18 25 4 .. 1 33 34 .. .. 7 19 .. 3 5 16 .. 3 .. .. 32 31 9 .. 5 .. .. .. 7 10

.. .. .. .. .. 14 .. 50 .. 17 .. 17 16 5 4 .. 24 .. 2 .. 3 4 .. 4 5 6 .. 1 .. .. 32 30 .. .. 14 .. .. 0 6 10

4 .. .. 39 9 .. .. 10 .. 9 .. 9 10 4 .. .. 9 .. .. .. 11 2 .. 19 4 10 .. 6 .. .. 11 12 10 .. 9 .. .. .. 9 6

.. .. .. .. .. 11 .. 24 .. 15 .. 9 11 6 4 .. 3 .. 2 .. 5 25 9 1 20 2 .. .. .. 3 10 10 .. .. 9 .. .. .. 12 11

45 .. .. 50 23 .. .. 29 .. 48 95 50 39 17 .. 22 47 53 36 .. 29 26 .. 44 52 43 .. 16 .. .. 38 40 32 .. 64 .. .. .. 29 38

.. .. 7 .. .. 33 .. 22 .. 50 54 45 41 20 21 60 59 .. 19 .. 29 34 84 78 29 42 .. 62 .. 95 36 39 .. .. 44 .. .. 44 12 37

a. Includes unallocated data. b. China has revised its national accounts data from 1993 onwards. Data before 1993 are not comparable with the later revised data. c. Data cover mainland Tanzania only.

200

2007 World Development Indicators

4.3

ECONOMY

Structure of manufacturing About the data

The data on the distribution of manufacturing value

Nations International Standard Industrial Classifica-

painting as well as advertising, accounting, and many

added by industry are provided by the United Nations

tion (ISIC) revision 2. First published in 1948, the

other service activities. In some cases the processes

Industrial Development Organization (UNIDO). UNIDO

ISIC has its roots in the work of the League of Nations

may be carried out by different technical units within

obtains data on manufacturing value added from a

Committee of Statistical Experts. The committee’s

the larger enterprise, but collecting data at such a

variety of national and international sources, includ-

efforts, interrupted by the Second World War, were

detailed level is not practical, nor would it be useful

ing the United Nations Statistics Division, the World

taken up by the United Nations Statistical Commis-

to record production data at the very highest level of

Bank, the Organisation for Economic Co-operation

sion, which at its first session appointed a commit-

a large, multiplant, multiproduct firm. The ISIC has

and Development, and the International Monetary

tee on industrial classification. The latest revision,

therefore adopted as the definition of an establish-

Fund. To improve comparability over time and across

ISIC revision 3, was completed in 1989, and many

ment “an enterprise or part of an enterprise which

countries, UNIDO supplements these data with infor-

countries have now switched to it. But revision 2 is

independently engages in one, or predominantly one,

mation from industrial censuses, statistics supplied

still widely used for compiling cross-country data.

kind of economic activity at or from one location . . .

by national and international organizations, unpub-

Concordances matching ISIC categories to national

for which data are available . . .” (United Nations

lished data that it collects in the field, and estimates

systems of classifi cation and to related systems

1990, p. 25). By design, this definition matches the

by the UNIDO Secretariat. Nevertheless, coverage

such as the Standard International Trade Classifica-

reporting unit required for the production accounts

may be less than complete, particularly for the infor-

tion are readily available.

of the UN System of National Accounts.

mal sector. To the extent that direct information on

In establishing a classifi cation system, compil-

inputs and outputs is not available, estimates may

ers must define both the types of activities to be

be used, which may result in errors in industry totals.

described and the organizational units whose activi-

• Manufacturing value added is the sum of gross

Moreover, countries use different reference periods

ties are to be reported. There are many possibili-

output less the value of intermediate inputs used

(calendar or fiscal year) and valuation methods (basic

ties, and the choices made affect how the resulting

in production for industries classified in ISIC major

or producer prices) to estimate value added. (See

statistics can be interpreted and how useful they

division 3. • Food, beverages, and tobacco corre-

also About the data for table 4.2.)

are in analyzing economic behavior. The ISIC empha-

spond to ISIC division 31. • Textiles and clothing

The data on manufacturing value added in U.S.

sizes commonalities in the production process and is

correspond to ISIC division 32. • Machinery and

dollars are from the World Bank’s national accounts

explicitly not intended to measure outputs (for which

transport equipment correspond to ISIC groups

files. These figures may differ from those used by

there is a newly developed Central Product Classifi -

382–84. • Chemicals correspond to ISIC groups

UNIDO to calculate the shares of value added by

cation). Nevertheless, the ISIC views an activity as

351 and 352. • Other manufacturing covers wood

industry, in part because of differences in exchange

defined by “a process resulting in a homogeneous

and related products (ISIC division 33), paper and

rates. Thus estimates of value added in a particular

set of products” (United Nations 1990 [ISIC, series

related products (ISIC division 34), petroleum and

industry calculated by applying the shares to total

M, no. 4, rev. 3], p. 9).

related products (ISIC groups 353–56), basic met-

Definitions

manufacturing value added will not match those

Firms typically use a multitude of processes to

als and mineral products (ISIC divisions 36 and 37),

from UNIDO sources. The classification of manufac-

produce a final product. For example, an automo-

fabricated metal products and professional goods

turing industries in the table accords with the United

bile manufacturer engages in forging, welding, and

(ISIC groups 381 and 385), and other industries (ISIC group 390). When data for textiles and clothing, machinery and transport equipment, or chemicals

Manufacturing continues to show strong growth in East Asia

4.3a

are shown in the table as not available, they are included in “other manufacturing.”

Value added in manufacturing (index, 1990 = 100) 500 East Asia & Pacific

450 400

Data sources Data on value added in manufacturing in U.S. dol-

350

lars are from the World Bank’s national accounts

300 South Asia

250

files. Data used to calculate shares of value added by industry are provided to the World Bank in elec-

Middle East & North Africa 200

tronic files by UNIDO. The most recent published Sub-Saharan Africa

150 100

Latin America & Caribbean

50

source is UNIDO’s International Yearbook of Industrial Statistics 2006. The ISIC system is described in the United Nations’ International Standard Indus-

1990

1995

2000

2005

trial Classification of All Economic Activities, Third

Manufacturing continues to be the dominant sector in the East Asia and Pacific region, growing by an

Revision (1990). The discussion of the ISIC draws

average of about 10 percent a year between 1990 and 2005.

on Jacob Ryten’s “Fifty Years of ISIC: Historical

Source: World Bank data files.

Origins and Future Perspectives” (1998).

2007 World Development Indicators

201

4.4

Structure of merchandise exports Merchandise exports

Food

Agricultural raw materials

Fuels

Ores and metals

Manufactures

$ millions

% of total 1990 2005

% of total 1990 2005

% of total 1990 2005

% of total 1990 2005

% of total 1990 2005

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, 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 †Data for Taiwan, China

202

235 230 12,930 3,910 12,353 .. 39,752 41,265 .. 1,671 .. 117,703a 288 926 276 1,784 31,414 5,030 152 75 86 2,002 127,629 120 188 8,372 62,091 82,390 6,766 2,326 981 1,448 3,072 4,597 5,100 12,170 36,870 2,170 2,714 3,477 582 16 .. 298 26,571 216,588 2,204 31 .. 421,100 897 8,105 1,163 671 19 160 67,245

2005

560 658 46,001 23,400 40,044 950 105,825 123,987 7,649 9,294 15,977 334,298 561 2,671 2,402 4,425 118,308 11,725 493 111 3,100 2,829 359,399 128 3,065 40,574 761,954 292,119 21,146 2,050 5,000 7,039 7,610 8,809 2,682 78,246 85,137 6,133 10,100 10,654 3,390 10 7,667 883 66,016 460,157 4,920 8 867 969,858 2,490 17,044 5,381 890 101 470 197,776

2007 World Development Indicators

.. .. 0 0 56 .. 22 3 .. 14 .. 9a 15 19 .. .. 28 .. .. .. .. 20 9 31 .. 24 13 4 33 .. .. 58 .. 13 .. .. 27 21 44 10 57 .. .. .. 2 16 .. .. .. 5 51 30 67 .. .. 14 4

.. 6 0 .. 47 12 17 6 7 8 8 8 25 21 .. 2 26 11 16 87 1 17 7 1 .. 19 3 1 18 .. .. 30 56 10 40 4 18 .. 28 10 32 .. 7 62 2 11 1 78 36 4 77 22 34 .. .. .. 1

.. .. .. 0 4 .. 10 4 .. 7 .. 2a 56 8 .. .. 3 .. .. .. .. 14 9 24 .. 9 3 1 4 .. .. 5 .. 6 .. .. 3 0 1 10 1 .. .. .. 10 2 .. .. .. 1 15 3 6 .. .. 1 2

.. 4 .. .. 1 1 3 2 1 2 3 1 61 2 .. 0 4 2 72 4 2 13 5 41 .. 7 1 1 5 .. .. 3 9 3 0 1 3 .. 4 7 1 .. 6 26 5 1 10 4 2 1 5 2 3 .. .. .. 1

.. .. 96 93 8 .. 21 1 .. 1 .. 3a 15 25 .. .. 2 .. .. .. .. 50 10 .. .. 1 8 1 37 .. .. 1 .. 9 .. .. 3 0 52 29 2 .. .. .. 1 2 .. .. .. 1 9 7 2 .. .. 0 1

.. 3 97 .. 16 2 27 5 77 0 35 7 1 49 .. 0 6 10 3 0 .. 50 20 .. .. 2 2 0 40 .. .. 0 13 14 2 3 9 .. 58 43 4 .. 7 0 4 4 76 1 3 2 3 9 6 .. .. .. 5

.. .. 0 6 2 .. 20 3 .. .. .. 4a 0 44 .. .. 14 .. .. .. .. 7 9 1 .. 55 2 1 0 .. .. 1 .. 5 .. .. 1 0 0 9 3 .. .. .. 4 3 .. .. .. 3 17 7 0 .. .. 0 1

.. 7 0 .. 3 13 20 3 1 0 1 3 1 17 .. 11 10 14 1 2 0 6 6 17 .. 56 2 1 1 .. .. 1 0 4 39 2 1 .. 0 4 3 .. 2 1 3 2 6 0 18 2 2 8 1 .. .. .. 2

.. .. 3 0 29 .. 27 88 .. 77 .. 77a 13 5 .. .. 52 .. .. .. .. 9 59 44 .. 11 72 92 25 .. .. 27 .. 68 .. .. 60 78 2 42 38 .. .. .. 83 77 .. .. .. 89 8 54 24 .. .. 85 93

.. 80 2 .. 31 71 25 80 13 90 52 79 13 11 .. 86 54 59 8 6 97 3 58 36 .. 14 92 96 36 .. .. 66 20 68 19 88 65 .. 9 31 60 .. 69 11 84 80 7 17 40 83 12 56 57 .. .. .. 91

Merchandise exports

Food

Agricultural raw materials

Fuels

Ores and metals

Manufactures

$ millions

% of total 1990 2005

% of total 1990 2005

% of total 1990 2005

% of total 1990 2005

% of total 1990 2005

1990

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

4.4

ECONOMY

Structure of merchandise exports

831 10,000 17,969 25,675 19,305 12,380 23,743 12,080 170,304 1,158 287,581 1,064 .. 1,031 1,857 65,016 7,042 .. 79 .. 494 62 868 13,225 .. 1,199 319 417 29,452 359 469 1,194 40,711 .. 661 4,265 126 325 1,085 204 131,775 9,394 330 282 13,596 34,047 5,508 5,615 340 1,177 959 3,230 8,117 14,320 16,417 ..

2005

1,695 62,109 95,096 86,226 56,252 24,096 109,853 42,659 367,200 1,500 594,905 4,302 27,849 3,293 1,338 284,419 45,011 672 510 5,161 2,337 649 200 30,110 11,813 2,041 760 520 140,949 1,109 565 2,144 213,711 1,091 1,054 10,641 1,745 2,925 2,070 850 402,407 21,729 858 502 42,277 103,780 18,692 15,917 1,010 3,192 1,688 17,206 41,255 89,288 38,133 ..

82 23 16 11 .. .. 22 8 6 19 1 10 .. 49 .. 3 1 .. .. .. .. .. .. 1 24 .. 73 91 12 36 .. 32 12 .. .. 26 .. 51 .. 13 20 45 77 .. 1 7 1 9 75 22 52 21 19 12 7 ..

55 6 9 12 4 .. 8 2 6 22 1 15 4 40 .. 1 .. 11 .. 11 16 .. .. .. 12 16 61 80 7 .. .. 28 5 53 2 21 12 .. 48 21 14 50 85 30 0 5 2 12 85 21 75 21 6 9 8 ..

4 3 4 5 .. .. 2 3 1 0 1 1 .. 6 .. 1 0 .. .. .. .. .. .. 0 6 .. 4 2 14 62 .. 1 2 .. .. 3 .. 36 .. 3 4 18 14 .. 1 2 0 10 1 9 38 3 2 3 6 ..

3 1 2 5 0 .. 0 1 1 0 1 0 1 12 .. 1 .. 8 .. 16 1 .. .. .. 3 1 6 4 3 .. .. 0 1 6 14 2 4 .. 1 1 3 10 2 4 0 0 0 1 1 3 12 2 1 1 2 ..

1 3 3 44 .. .. 1 1 2 1 0 0 .. 13 .. 1 93 .. .. .. .. .. .. 95 8 .. 1 0 18 .. .. 1 38 .. .. 4 .. 0 .. .. 10 4 0 .. 97 48 92 1 0 0 0 10 2 12 3 ..

0 3 11 28 83 .. 1 0 3 2 1 0 65 23 .. 5 .. 12 .. 9 0 .. .. .. 27 8 4 0 13 .. .. 0 15 0 5 2 15 .. 1 .. 12 2 1 2 98 68 86 4 1 22 0 11 2 5 4 ..

4 6 5 4 .. .. 1 2 1 9 1 33 .. 3 .. 1 0 .. .. .. .. .. .. .. 1 .. 8 0 2 0 .. 0 6 .. .. 15 .. 2 .. .. 3 5 1 .. 0 10 1 0 1 58 0 47 8 10 3 ..

5 2 7 8 1 .. 1 1 1 9 2 12 14 4 .. 2 .. 4 .. 4 12 .. .. .. 2 3 5 0 1 .. .. 1 2 2 58 9 58 .. 7 4 3 4 1 55 .. 6 1 0 4 49 1 49 2 4 3 ..

9 63 70 35 .. .. 70 87 88 70 96 56 .. 30 .. 94 6 .. .. .. .. .. .. 4 59 .. 14 7 54 2 .. 66 43 .. .. 52 .. 11 .. 83 59 26 8 .. 1 32 5 79 21 10 10 18 38 58 80 ..

2007 World Development Indicators

36 84 70 47 9 .. 86 83 85 66 92 72 16 21 .. 91 .. 27 .. 57 70 .. .. .. 56 72 22 16 75 .. .. 70 77 39 21 65 7 .. 41 74 68 31 11 8 2 17 6 82 9 6 13 17 89 78 75 ..

203

4.4

Structure of merchandise exports Merchandise exports

Food

Agricultural raw materials

Fuels

Ores and metals

Manufactures

$ millions

% of total 1990 2005

% of total 1990 2005

% of total 1990 2005

% of total 1990 2005

% of total 1990 2005

1990

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singaporeb 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 Europe EMU

2005

4,960 27,730 .. 243,569 110 125 44,417 181,440 761 1,641 2,539 5,065 138 158 52,730 229,649 6,355 31,956 6,681 18,633 .. .. 23,549 51,876 55,642 187,182 1,912 6,347 374 4,824 556 2,020 57,540 130,104 63,784 130,898 4,212 5,760 .. 909 331 1,481 23,068 110,110 268 569 1,960 9,035 3,526 10,494 12,959 73,414 .. 4,935 152 853 .. 34,287 23,544 115,453 185,172 382,761 393,592 904,383 1,693 3,405 .. 4,749 17,497 55,487 2,404 31,625 .. .. 692 6,380 1,309 1,720 1,726 1,820 3,474,778 t 10,433,971 t 67,127 261,853 552,565 2,795,181 272,538 1,520,827 281,789 1,274,355 620,808 3,057,040 155,928 1,185,572 .. 761,588 143,275 565,896 81,103 225,759 27,754 128,475 68,368 189,745 2,850,034 7,376,990 1,241,084 3,113,158

1 .. .. 1 53 19 .. 5 .. 7 .. 8c 15 34 60 .. 2 3 14 .. .. 29 23 5 11 22 .. .. .. 2 7 11 40 .. 2 .. .. 8 .. 44 10 w 15 17 18 16 17 15 .. 21 .. 16 .. 8 11

3 2 52 1 29 21 .. 2 4 3 .. 9 14 22 7 .. 3 3 15 .. 57 12 21 3 11 10 .. 64 12 .. 5 7 55 .. 0 23 .. 4 13 31 7w 15 9 10 8 9 6 6 15 6 11 15 6 8

3 .. .. 0 3 3 .. 3 .. 2 .. 4c 2 6 38 .. 7 1 5 .. .. 5 21 0 1 3 .. .. .. 0 1 4 21 .. 0 .. .. 1 .. 7 3w 4 4 4 5 4 6 .. 3 .. 5 .. 3 2

2 3 7 0 2 4 .. 0 2 2 .. 2 1 2 5 .. 4 0 4 .. 17 5 9 0 1 1 .. 12 1 .. 1 2 7 .. 0 2 .. 0 5 16 2w 3 2 2 2 2 2 2 2 1 2 5 2 1

18 .. .. 90 12 6 .. 18 .. 3 .. 7c 4 1 .. .. 3 0 45 .. .. 1 0 67 17 2 .. .. .. 7 8 3 0 .. 80 .. .. 74 .. 1 9w 27 21 16 28 20 13 .. 30 .. 2 .. 6 3

11 49 7 89 21 2 .. 12 7 2 .. 10 4 0 87 .. 5 0 68 .. 0 4 1 70 10 4 .. 5 10 .. 9 3 5 .. 89 21 .. 92 1 2 10 w 28 17 12 22 17 8 23 22 69 9 36 8 5

4 .. .. 1 9 10 .. 2 .. 3 .. 10 c 2 2 0 .. 3 3 1 .. .. 1 45 1 2 4 .. .. .. 78 3 3 0 .. 7 .. .. 1 .. 16 4w 5 6 5 7 6 3 .. 10 .. 4 .. 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).

204

2007 World Development Indicators

4 7 23 0 3 11 .. 1 3 4 .. 22 2 4 0 .. 3 3 1 .. 12 1 10 0 1 2 .. 2 6 .. 3 3 1 .. 2 1 .. 0 72 23 3w 3 5 4 5 5 3 5 7 2 6 10 3 2

73 .. .. 8 23 62 .. 72 .. 86 .. 29c 75 54 2 .. 83 94 36 .. .. 63 9 27 69 68 .. .. .. 12 79 75 39 .. 10 .. .. 15 .. 31 73 w 49 50 54 44 51 60 .. 36 .. 71 .. 77 80

80 19 10 9 43 61 .. 81 84 88 .. 57 77 70 0 .. 79 93 11 .. 14 77 58 26 78 82 .. 17 69 .. 77 82 32 .. 9 53 .. 4 9 28 75 w 50 65 71 58 64 81 56 54 20 72 33 78 80

4.4

ECONOMY

Structure of merchandise exports About the data

Data on merchandise trade are from customs reports

from customs storage; and (c) goods previously

reported here have not been fully reconciled with

of goods movement into or out of an economy or

included as imports for domestic consumption but

the estimates of exports of goods and services from

from reports of the financial transactions related to

subsequently exported without transformation.

the national accounts or those from the balance of

merchandise trade recorded in the balance of pay-

Under the special system exports comprise catego-

payments.

ments. Because of differences in timing and defini-

ries a and c. In some compilations categories b and

The classification of commodity groups is based

tions, estimates of trade flows from customs reports

c are classified as re-exports. Because of differences

on the Standard International Trade Classification

are likely to differ from those based on the balance of

in reporting practices, data on exports may not be

(SITC) revision 1. Most countries now report using

payments. Moreover, several international agencies

fully comparable across economies.

later revisions of the SITC or the Harmonized Sys-

process trade data, each correcting unreported or

The data on total exports of goods (merchandise)

tem. Concordance tables are used to convert data

misreported data, and this leads to other differences

in this table come from the World Trade Organization

reported in one system of nomenclature to another.

in the available data.

(WTO). The WTO uses two main sources, national

The conversion process may introduce some errors

The most detailed source of data on international

statistical offices and the IMF’s International Finan-

of classification, but conversions from later to earlier

trade in goods is the Commodity Trade (Comtrade)

cial Statistics. It supplements these with the Com-

systems are generally reliable.

database maintained by the United Nations Statistics

trade database and publications or databases of

Division. In addition, the International Monetary Fund

regional organizations, specialized agencies, eco-

(IMF) collects customs-based data on exports and

nomic groups, and private sources (such as Eurostat,

• Merchandise exports are the f.o.b. value of goods

imports of goods. The value of exports is recorded

the Food and Agriculture Organization, and country

provided to the rest of the world, valued in U.S. dol-

as the cost of the goods delivered to the frontier of

reports of the Economist Intelligence Unit). In recent

lars. • Food corresponds to the commodities in

the exporting country for shipment—the free on board

years country websites and direct contacts through

SITC sections 0 (food and live animals), 1 (bever-

(f.o.b.) value. Many countries report trade data in U.S.

email have helped to improve the collection of up-

ages and tobacco), and 4 (animal and vegetable oils

dollars. When countries report in local currency, the

to-date statistics for many countries, reducing the

and fats) and SITC division 22 (oil seeds, oil nuts,

United Nations Statistics Division applies the average

proportion of estimated figures. The WTO database

and oil kernels). • Agricultural raw materials cor-

official exchange rate for the period shown.

now covers most of the major traders in Africa, Asia,

respond to SITC section 2 (crude materials except

Countries may report trade according to the gen-

and Latin America, which together with the high-

fuels) excluding divisions 22, 27 (crude fertilizers

eral or special system of trade (see Primary data

income countries account for nearly 95 percent of

and minerals excluding coal, petroleum, and pre-

documentation). Under the general system, exports

total world trade. There has also been a remarkable

cious stones), and 28 (metalliferous ores and scrap).

comprise outward-moving goods that are (a) goods

improvement in the availability of reliable figures for

• Fuels correspond to SITC section 3 (mineral fuels).

wholly or partly produced in the country; (b) foreign

countries in Europe and Central Asia.

• Ores and metals correspond to the commodities

Definitions

goods, neither transformed nor declared for domes-

The shares of exports by major commodity group

in SITC divisions 27, 28, and 68 (nonferrous met-

tic consumption in the country, that move outward

are from Comtrade. The values of total exports

als). • Manufactures correspond to the commodities in SITC sections 5 (chemicals), 6 (basic manufactures), 7 (machinery and transport equipment), and

Developing economies’ share of world merchandise exports continues to expand 1990 East Asia & Pacific 4%

4.4a

8 (miscellaneous manufactured goods), excluding division 68.

2005 East Asia & Pacific 11%

Europe & Central Asia 4% Latin America & Caribbean 4% Middle East & North Africa 2% Sub-Saharan Africa 2% South Asia 1%

Europe & Central Asia 7%

Data sources Latin America & Caribbean 5%

High-income 83%

Middle East & North Africa 2% High-income 72%

Sub-Saharan Africa 2% South Asia 1%

The WTO publishes data on world trade in its Annual Report. The IMF publishes estimates of total exports of goods in its International Financial Statistics and Direction of 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 and imports in its Handbook of International Trade and Develop-

Developing economies’ share of world merchandise exports increased by 11 percentage points from 1990

ment Statistics. Tariff line records of exports and

to 2005. East Asia and Pacific was the biggest gainer, capturing an additional 7 percentage points.

imports are compiled in the United Nations Sta-

Source: World Bank data files.

tistics Division’s Comtrade database.

2007 World Development Indicators

205

4.5

Structure of merchandise imports Merchandise imports

Food

Agricultural raw materials

Fuels

Ores and metals

Manufactures

$ millions

% of total 1990 2005

% of total 1990 2005

% of total 1990 2005

% of total 1990 2005

% of total 1990 2005

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, 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 †Data for Taiwan, China

206

936 380 9,780 1,578 4,076 .. 41,985 49,146 .. 3,618 .. 119,702a 265 687 360 1,946 22,524 5,100 536 231 164 1,400 123,244 154 285 7,742 53,345 84,725 5,590 1,739 621 1,990 2,097 4,500 4,600 12,880 33,333 3,006 1,861 12,412 1,263 351 .. 1,081 27,001 234,436 918 188 .. 355,686 1,205 19,777 1,649 723 86 332 54,782

2005

3,200 2,614 20,357 8,150 28,692 1,768 125,280 126,179 4,200 13,839 16,699 318,658 894 2,341 7,097 3,272 77,585 18,181 1,305 267 3,700 2,885 319,686 151 770 32,542 660,003 300,160 21,204 2,175 1,415 9,798 5,350 18,547 7,125 76,707 76,018 9,614 10,309 19,819 6,766 495 10,033 4,127 58,999 497,853 1,393 235 2,491 773,804 5,005 53,965 10,493 820 119 1,454 182,569

2007 World Development Indicators

.. .. 24 .. 4 .. 5 5 .. 19 .. 10a 38 12 .. .. 9 8 .. .. .. 19 6 19 .. 4 9 8 7 .. .. 8 .. 13 12 .. 12 .. 9 32 14 .. .. .. 5 10 .. .. .. 10 11 15 10 .. .. .. 7

.. 17 22 .. 3 18 5 6 10 19 9 8 30 10 .. 14 5 5 12 6 8 18 6 17 .. 6 3 3 9 .. .. 6 22 8 22 5 11 .. 8 22 18 .. 8 21 5 8 24 38 17 7 21 11 11 .. .. .. 4

.. .. 5 .. 4 .. 2 3 .. 5 .. 2a 4 2 .. .. 3 3 .. .. .. 0 2 1 .. 2 6 2 4 .. .. 2 .. 4 3 .. 3 .. 3 7 3 .. .. .. 2 3 .. .. .. 3 1 3 2 .. .. .. 5

.. 1 2 .. 2 1 1 2 1 9 2 1 4 1 .. 1 2 1 1 1 2 2 1 27 .. 1 4 1 2 .. .. 1 1 1 1 2 2 .. 1 5 2 .. 3 1 3 1 1 1 0 1 1 1 1 .. .. .. 2

.. .. 1 .. 8 .. 6 6 .. 16 .. 8a 1 1 .. .. 27 36 .. .. .. 2 6 7 .. 16 2 2 6 .. .. 10 .. 10 32 .. 7 .. 2 3 16 .. .. .. 12 10 .. .. .. 8 17 8 17 .. .. .. 11

.. 9 1 .. 5 16 11 13 12 8 33 12 20 10 .. 4 19 5 24 8 10 26 9 17 .. 22 10 3 3 .. .. 11 17 15 22 7 7 .. 8 8 14 .. 9 12 14 13 3 16 20 11 2 18 16 .. .. .. 16

.. .. 2 .. 6 .. 1 4 .. 3 .. 6a 1 1 .. .. 5 4 .. .. .. 1 3 2 .. 1 3 2 3 .. .. 2 .. 4 1 .. 2 .. 3 2 4 .. .. .. 4 4 .. .. .. 4 0 3 2 .. .. .. 6

.. 2 1 .. 3 3 1 4 2 2 3 4 1 1 .. 1 4 7 1 1 0 1 3 2 .. 4 8 2 3 .. .. 1 1 2 1 4 2 .. 1 4 1 .. 1 2 6 3 1 1 1 4 2 3 1 .. .. .. 6

.. .. 68 .. 78 .. 85 81 .. 56 .. 68a 56 85 .. .. 56 49 .. .. .. 78 81 71 .. 75 80 85 77 .. .. 66 .. 70 46 .. 73 .. 84 56 63 .. .. .. 76 74 .. .. .. 72 70 70 69 .. .. .. 69

.. 71 74 .. 87 62 81 74 74 62 46 75 44 77 .. 75 71 65 62 82 79 53 80 37 .. 67 75 92 83 .. .. 80 48 73 54 79 76 .. 82 50 65 .. 71 64 70 75 70 43 61 68 74 66 71 .. .. .. 72

Merchandise imports

Food

Agricultural raw materials

Fuels

Ores and metals

Manufactures

$ millions

% of total 1990 2005

% of total 1990 2005

% of total 1990 2005

% of total 1990 2005

% of total 1990 2005

1990

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

ECONOMY

4.5

Structure of merchandise imports

935 10,340 23,580 21,837 20,322 7,660 20,669 16,793 181,968 1,928 235,368 2,600 .. 2,223 2,930 69,844 3,972 .. 185 .. 2,529 672 570 5,336 .. 1,206 651 575 29,258 602 388 1,618 43,548 .. 924 6,922 878 270 1,163 672 126,098 9,501 638 388 5,627 27,231 2,798 7,411 1,539 1,193 1,352 2,634 13,042 11,570 25,263 ..

2005

4,484 66,045 134,831 69,498 35,859 23,430 68,007 47,142 379,772 4,460 514,922 10,506 17,353 6,149 2,718 261,238 16,275 1,108 745 8,696 9,633 1,390 1,190 7,000 15,453 3,228 1,550 1,165 114,602 1,612 750 3,160 231,670 2,312 1,149 20,332 2,408 2,250 2,520 1,820 359,055 26,239 2,595 871 17,265 55,495 8,971 25,331 4,155 1,729 3,700 12,502 47,418 100,951 61,126 ..

10 8 3 5 .. .. 11 8 12 15 15 26 .. 9 .. 6 17 .. .. .. .. .. .. 24 12 .. 11 9 7 26 .. 12 15 .. .. 10 .. 13 .. 15 13 7 19 .. 6 6 19 17 12 18 8 24 10 8 12 ..

16 4 3 8 8 .. 8 5 9 16 10 14 7 10 .. 4 .. 15 .. 11 16 .. .. 17 8 13 14 18 5 .. .. 17 6 12 13 11 14 .. 15 17 10 8 13 34 16 7 12 11 12 16 9 11 7 6 11 ..

1 4 4 5 .. .. 2 2 6 1 7 1 .. 3 .. 8 1 .. .. .. .. .. .. 2 5 .. 1 1 1 1 .. 3 4 .. .. 6 .. 1 .. 7 2 1 1 .. 1 2 1 4 1 0 0 2 2 3 4 ..

1 1 2 3 2 .. 1 1 3 2 2 1 1 2 .. 2 .. 2 .. 3 1 .. .. 1 2 1 0 1 1 .. .. 2 1 4 0 3 1 .. 1 5 2 1 1 4 1 2 1 4 1 1 1 2 1 2 2 ..

16 14 27 9 .. .. 6 9 11 20 24 18 .. 20 .. 16 1 .. .. .. .. .. .. 0 44 .. 17 11 5 19 .. 8 4 .. .. 17 .. 5 .. 9 10 8 19 .. 0 4 4 21 16 7 14 12 15 24 11 ..

20 7 36 31 10 .. 7 15 12 23 26 23 13 24 .. 25 .. 29 .. 15 22 .. .. 1 24 19 23 11 8 .. .. 17 6 21 27 22 2 .. 10 16 15 12 18 17 16 4 4 22 18 13 16 20 14 11 14 ..

1 4 8 4 .. .. 2 3 5 1 9 1 .. 2 .. 7 2 .. .. .. .. .. .. 1 2 .. 1 1 4 1 .. 1 3 .. .. 6 .. 0 .. 2 3 3 1 .. 2 6 1 4 1 1 1 1 3 5 2 ..

1 2 5 3 2 .. 1 2 4 1 6 2 2 2 .. 7 .. 2 .. 1 2 .. .. 1 2 3 0 1 4 .. .. 1 3 1 0 3 0 .. 4 4 3 2 0 1 2 6 4 3 1 0 1 1 2 3 3 ..

71 70 51 77 .. .. 76 77 64 61 44 52 .. 66 .. 63 79 .. .. .. .. .. .. 73 35 .. 69 78 82 53 .. 76 64 .. .. 61 .. 81 .. 67 71 81 59 .. 67 82 69 54 70 73 77 61 53 60 71 ..

2007 World Development Indicators

63 77 52 55 70 .. 77 76 66 57 54 58 77 61 .. 61 .. 52 .. 66 58 .. .. 81 62 64 62 68 80 .. .. 64 83 62 59 62 49 .. 69 59 70 77 65 44 66 80 77 60 68 69 74 66 75 75 65 ..

207

4.5

Structure of merchandise imports Merchandise imports

Food

Agricultural raw materials

Fuels

Ores and metals

Manufactures

$ millions

% of total 1990 2005

% of total 1990 2005

% of total 1990 2005

% of total 1990 2005

% of total 1990 2005

1990

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 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 Europe EMU

2005

7,600 40,463 .. 125,303 288 403 24,069 59,409 1,219 3,190 3,859 11,635 149 345 60,774 200,047 6,670 35,337 6,142 20,090 .. .. 18,399 62,304 87,715 278,825 2,688 8,834 618 6,757 663 2,080 54,264 111,228 69,681 126,524 2,400 8,106 .. 1,330 1,027 2,659 33,045 118,191 581 895 1,109 5,674 5,513 13,177 22,302 116,553 .. 3,588 288 1,779 .. 36,141 11,199 80,744 222,977 510,237 516,987 1,732,348 1,343 3,879 .. 3,666 7,335 24,249 2,752 36,476 .. .. 1,571 4,260 1,220 2,750 1,847 2,330 3,549,585 t 10,684,930 t 78,024 316,559 512,919 2,552,089 273,099 1,380,029 237,015 1,172,061 592,618 2,868,603 160,502 1,061,614 163,450 747,497 120,119 520,640 80,058 181,770 39,124 188,994 57,641 168,092 2,943,620 7,816,297 1,261,194 3,018,041

12 .. .. 15 29 9 .. 6 .. 9 .. 8b 11 19 13 .. 6 6 31 .. .. 5 22 19 11 8 .. .. .. 17 10 6 7 .. 11 .. .. 27 .. 4 9w 7 10 10 10 10 8 .. 12 .. 8 .. 9 11

6 16 12 15 28 8 .. 3 6 6 .. 4 9 12 13 .. 7 6 17 .. 12 4 16 9 9 3 .. 15 7 .. 9 4 8 .. 10 6 .. 24 6 19 6w 11 6 6 6 6 4 7 6 15 5 12 7 8

4 .. .. 1 2 3 .. 2 .. 4 .. 2b 3 2 1 .. 2 2 2 .. .. 5 1 1 4 4 .. .. .. 0 3 2 4 .. 4 .. .. 1 .. 3 3w 3 4 5 3 4 5 .. 3 .. 4 .. 3 3

1 1 4 1 2 1 .. 0 1 3 .. 1 1 1 1 .. 2 1 4 .. 1 2 1 1 3 3 .. 2 1 .. 1 1 3 .. 1 3 .. 1 1 2 2w 3 2 3 1 2 3 2 1 3 2 1 1 2

38 .. .. 0 16 23 .. 16 .. 11 .. 1b 12 13 20 .. 9 5 3 .. .. 9 8 11 9 21 .. .. .. 6 6 13 18 .. 3 .. .. 40 .. 16 11 w 22 10 9 10 11 5 .. 10 .. 24 .. 11 9

14 2 16 0 23 15 .. 18 14 10 .. 14 14 13 1 .. 12 6 7 .. 10 18 29 35 10 14 .. 17 30 .. 8 17 24 .. 1 11 .. 21 12 14 14 w 22 11 15 9 13 13 11 10 10 32 14 14 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).

208

2007 World Development Indicators

6 .. .. 3 2 3 .. 2 .. 4 .. 1b 4 2 0 .. 3 3 1 .. .. 4 1 6 4 5 .. .. .. 4 4 3 2 .. 4 .. .. 1 .. 2 4w 5 3 3 4 4 3 .. 3 .. 6 .. 4 4

3 2 2 4 2 4 .. 2 3 5 .. 2 3 3 1 .. 3 4 3 .. 1 4 2 4 3 6 .. 1 4 .. 2 2 2 .. 1 3 .. 1 3 10 3w 3 4 5 3 4 6 3 3 3 5 2 3 3

39 .. .. 81 51 62 .. 73 .. 67 .. 75b 71 65 66 .. 79 84 62 .. .. 75 67 62 72 61 .. .. .. 72 75 73 69 .. 77 .. .. 31 .. 73 71 w 56 70 72 68 69 77 .. 66 .. 54 .. 71 71

76 73 67 80 45 71 .. 77 75 75 .. 70 72 69 83 .. 73 83 64 .. 76 70 53 51 76 69 .. 65 57 .. 72 72 63 .. 87 77 .. 53 78 54 72 w 61 74 71 78 73 74 72 79 66 55 67 72 71

About the data

4.5

ECONOMY

Structure of merchandise imports Definitions

Data on imports of goods are derived from the same

transformation and repair) and withdrawals for

• Merchandise imports are the c.i.f. value of goods

sources as data on exports. In principle, world

domestic consumption from bonded warehouses

purchased from the rest of the world valued in U.S.

exports and imports should be identical. Similarly,

and free trade zones. Goods transported through a

dollars. • Food corresponds to the commodities in

exports from an economy should equal the sum of

country en route to another are excluded.

SITC sections 0 (food and live animals), 1 (bever-

imports by the rest of the world from that economy.

The data on total imports of goods (merchandise)

ages and tobacco), and 4 (animal and vegetable oils

But differences in timing and definitions result in dis-

in this table come from the World Trade Organization

and fats) and SITC division 22 (oil seeds, oil nuts,

crepancies in reported values at all levels. For further

(WTO). For further discussion of the WTO’s sources

and oil kernels). • Agricultural raw materials cor-

discussion of indicators of merchandise trade, see

and methodology, see About the data for table 4.4.

respond to SITC section 2 (crude materials except

About the data for tables 4.4 and 6.2.

The shares of imports by major commodity group

fuels) excluding divisions 22, 27 (crude fertilizers

The value of imports is generally recorded as the

are from the United Nations Statistics Division’s

and minerals excluding coal, petroleum, and pre-

cost of the goods when purchased by the importer

Commodity Trade (Comtrade) database. The values

cious stones), and 28 (metalliferous ores and scrap).

plus the cost of transport and insurance to the fron-

of total imports reported here have not been fully

• Fuels correspond to SITC section 3 (mineral fuels).

tier of the importing country—the cost, insurance,

reconciled with the estimates of imports of goods

• Ores and metals correspond to the commodities

and freight (c.i.f.) value, corresponding to the landed

and services from the national accounts (shown in

in SITC divisions 27, 28, and 68 (nonferrous met-

cost at the point of entry of foreign goods into the

table 4.8) or those from the balance of payments

als). • Manufactures correspond to the commodities

country. A few countries, including Australia, Canada,

(table 4.15).

in SITC sections 5 (chemicals), 6 (basic manufac-

and the United States, collect import data on a free

The classification of commodity groups is based

tures), 7 (machinery and transport equipment), and

on board (f.o.b.) basis and adjust them for freight and

on the Standard International Trade Classification

8 (miscellaneous manufactured goods), excluding

insurance costs. Many countries collect and report

(SITC) revision 1. Most countries now report using

division 68.

trade data in U.S. dollars. When countries report in

later revisions of the SITC or the Harmonized Sys-

local currency, the United Nations Statistics Division

tem. Concordance tables are used to convert data

applies the average official exchange rate for the

reported in one system of nomenclature to another.

period shown.

The conversion process may introduce some errors

Countries may report trade according to the gen-

of classification, but conversions from later to earlier

eral or special system of trade (see Primary data

systems are generally reliable. Shares may not sum

documentation). Under the general system imports

to 100 percent because of unclassified trade.

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

Top 10 developing country exporters of merchandise in 2005

4.5a

Merchandise exports ($ billions)

1990

2005

800 700

Data sources

600

The WTO publishes data on world trade in its

500

Annual Report. The International Monetary Fund 400

publishes estimates of total imports of goods in

300

its International Financial Statistics and Direction

200

of Trade Statistics, as does the United Nations Statistics Division in its Monthly Bulletin of Statistics.

100

And the United Nations Conference on Trade and 0 China

Russian Mexico Federationa

Malaysia

Brazil

Thailand

India

Poland

Indonesia

Turkey

Development publishes data on the structure of exports and imports in its Handbook of Interna-

China continues to be the top developing country exporter. The Russian Federation has surpassed

tional Trade and Development Statistics. Tariff line

Mexico.

records of exports and imports are compiled in the United Nations Statistics Division’s Comtrade

a. Data are for 1994 and 2005. Source: World Trade Organization data files.

database.

2007 World Development Indicators

209

4.6

Structure of service exports Commercial service exports

Transport

Travel

Insurance and financial services

Computer, information, communications, and other commercial services

$ millions

% of commercial services 1990 2005

% of commercial services 1990 2005

% of commercial services 1990 2005

% of commercial services 1990 2005

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, 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

210

1 32 479 65 2,264 17 9,835 22,755 .. 296 185 26,646a 109 133 .. 183 3,706 837 34 7 50 369 18,350 17 23 1,786 5,748 .. 1,548 .. 65 583 425 2,216 .. 4,679 12,731 1,086 508 4,813 301 73 200 261 4,562 74,948 214 53 .. 50,562 79 6,514 313 91 4 43

2005

.. 1,154 .. 177 6,121 323 27,767 53,104 625 472 1,942 53,536 204 473 1,010 844 14,901 4,288 .. 7 1,052 393 52,193 .. .. 7,077 73,909 62,175 2,590 .. 223 2,579 658 9,920 .. 10,729 42,383 3,840 940 14,449 1,116 .. 3,117 789 16,895 114,955 136 80 631 148,540 1,043 34,051 1,138 31 6 112

2007 World Development Indicators

.. 20.0 41.7 48.8 51.1 .. 35.4 6.4 .. 13.0 54.1 27.5a 33.4 35.8 .. 20.4 36.4 27.5 37.1 38.7 .. 42.6 23.0 50.9 18.4 40.0 47.1 .. 31.3 .. 53.9 16.3 62.4 29.2 .. 26.5 32.5 5.6 47.6 50.1 26.2 85.7 74.7 80.7 38.4 21.7 33.4 8.8 .. 29.2 49.2 4.9 7.4 14.2 5.4 19.8

.. 10.9 .. 10.2 21.2 28.5 22.4 20.1 38.3 23.8 63.0 26.0 16.5 30.3 6.9 10.1 21.4 26.1 .. 25.6 12.0 30.5 18.4 .. .. 59.3 20.9 31.5 30.1 .. 34.3 10.9 22.0 11.0 .. 30.6 47.1 3.4 35.6 32.8 32.5 .. 40.0 59.0 14.4 23.6 59.8 19.4 49.9 25.6 14.0 50.8 8.6 21.8 22.9 ..

.. 11.1 13.4 20.6 39.9 .. 43.2 59.0 .. 6.4 13.3 14.0a 50.2 43.6 .. 64.1 37.3 38.2 34.1 51.4 .. 14.4 34.7 16.0 34.1 29.8 30.2 .. 26.2 .. 12.9 48.9 12.1 59.1 .. 33.3 26.2 66.9 37.0 22.9 25.3 1.0 13.7 2.1 25.8 27.1 1.4 87.9 .. 28.3 5.6 39.7 37.6 32.6 .. 78.9

.. 74.0 .. 49.9 45.0 43.5 53.9 29.4 12.4 14.8 13.0 18.4 58.1 50.5 56.0 66.6 25.9 56.2 .. 22.2 79.8 28.9 26.0 .. .. 17.8 39.6 16.3 47.0 .. 15.1 64.6 12.7 74.3 .. 43.1 15.6 91.4 51.7 47.4 48.6 .. 30.4 21.3 12.9 36.7 7.2 70.8 38.3 19.6 76.3 39.9 74.3 .. 16.6 98.0

.. 2.2 5.9 4.6 .. .. 4.2 2.9 .. 0.1 1.0 18.2a 6.9 10.0 .. 8.2 3.1 3.1 .. 1.6 .. 9.4 .. 18.8 0.2 4.9 4.0 .. 17.1 .. .. .. 8.3 1.4 .. 9.6 2.3 0.2 9.3 1.0 7.5 .. 0.1 0.7 0.1 14.8 5.8 0.1 .. 1.0 2.7 0.1 2.0 0.1 .. 1.3

.. 1.9 .. .. 0.1 4.7 4.7 6.6 1.3 4.8 0.2 7.9 1.5 9.0 3.5 9.0 4.3 1.1 .. 0.7 0.1 9.8 9.3 .. .. 2.6 0.9 9.0 1.2 .. 15.9 0.4 14.9 0.6 .. 4.1 .. 0.4 0.1 1.4 3.6 .. 1.8 4.0 0.9 2.7 17.1 0.5 5.0 5.6 0.8 1.1 6.3 0.4 19.5 ..

.. 66.7 39.0 26.1 9.0 .. 17.2 31.7 .. 80.6 31.6 40.3a 9.5 10.6 .. 7.3 23.2 31.2 28.9 8.3 .. 33.6 42.3 14.3 47.3 25.3 18.7 .. 25.5 .. 33.2 34.8 17.2 10.3 .. 30.6 39.0 27.3 6.1 26.1 41.1 13.3 11.5 16.6 35.6 36.4 59.4 3.3 .. 41.5 42.6 55.2 53.1 53.2 94.6 ..

.. 13.2 .. 39.9 33.8 23.3 19.1 44.0 48.0 56.6 23.7 47.8 23.9 10.2 33.6 14.4 48.4 16.6 .. 51.5 8.1 30.8 46.3 .. .. 20.4 38.6 43.2 21.7 .. 50.6 24.1 65.3 14.1 .. 22.3 37.4 4.9 12.6 18.4 15.3 .. 27.8 15.7 71.8 37.0 15.9 9.4 6.8 49.2 8.9 8.3 10.8 77.8 41.0 2.0

Commercial service exports

Transport

Travel

Insurance and financial services

Computer, information, communications, and other commercial services

$ millions

% of commercial services 1990 2005

% of commercial services 1990 2005

% of commercial services 1990 2005

% of commercial services 1990 2005

1990

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

4.6

ECONOMY

Structure of service exports

121 2,677 4,610 2,488 343 .. 3,286 4,546 48,579 976 41,384 1,430 .. 774 .. 9,155 1,054 9 11 290 .. 34 32 83 198 .. 129 37 3,769 71 14 478 7,222 .. 48 1,871 103 94 106 166 28,478 2,415 34 22 965 12,452 68 1,218 907 198 404 714 2,897 3,200 5,054 ..

2005

716 12,294 56,094b 12,570 .. .. 56,768 17,731 88,820 2,296 107,876 2,188 2,019 1,523 .. 43,927 3,790 234 .. 2,137 10,740 47 .. 419 3,075 445 142 49 19,463 227 .. 1,604 16,098 409 329 7,570 316 232 463 271 78,183 8,408 272 88 4,164 28,457 822 2,042 3,106 285 615 2,057 4,462 16,181 14,940 ..

35.1 1.6 20.8 2.8 10.5 .. 31.1 30.8 21.0 18.0 40.4 26.0 .. 32.1 .. 34.7 87.5 25.1 74.8 94.9 .. 14.1 84.6 83.8 83.6 .. 32.1 46.1 31.8 31.0 35.3 33.0 12.4 .. 41.8 9.6 61.3 10.3 .. 3.6 45.4 43.4 19.2 5.2 3.9 68.7 15.3 59.3 64.9 11.2 18.3 43.4 8.5 57.3 15.6 ..

10.2 14.9 13.3 22.6 .. .. 4.7 20.8 17.6 19.7 33.1 21.5 51.6 48.4 .. 54.4 58.4 25.9 .. 57.2 4.1 1.3 .. 27.7 51.6 30.0 28.2 32.7 20.8 13.5 .. 23.9 10.9 41.5 32.7 17.2 28.3 36.8 7.1 12.0 27.4 19.5 12.4 8.5 17.5 54.7 36.4 52.7 57.2 10.9 14.6 21.8 23.3 33.7 21.4 ..

24.0 36.8 33.8 86.5 8.2 .. 44.4 30.7 33.9 77.0 7.9 35.8 .. 60.2 .. 34.5 12.5 3.8 24.3 2.5 .. 51.2 15.4 7.7 10.9 .. 31.3 42.6 44.7 54.3 64.7 51.1 76.5 .. 10.4 68.4 .. 20.9 81.0 65.6 14.6 42.7 35.5 59.5 2.5 12.6 84.7 12.0 18.9 12.0 21.1 30.4 16.1 11.2 70.4 ..

65.9 34.8 16.8 36.0 .. .. 8.3 16.1 39.8 67.3 11.5 65.8 34.7 38.0 .. 12.9 4.4 31.3 .. 16.0 50.6 64.9 .. 59.7 30.0 18.8 43.6 67.3 45.5 61.9 .. 54.3 73.3 31.3 56.2 60.9 41.1 36.2 87.5 48.4 13.4 59.3 76.2 35.6 0.4 11.5 58.5 8.9 25.1 1.3 12.6 60.3 47.7 38.8 52.8 ..

12.9 0.2 2.7 .. 6.4 .. .. –0.3 5.5 1.4 –0.4 .. .. 0.7 .. 0.1 .. .. 0.9 .. .. .. .. .. .. .. 0.3 0.1 0.1 4.9 .. 0.1 4.6 .. 4.6 0.8 .. 0.5 5.9 .. 0.8 –0.3 .. 13.5 0.3 0.4 .. 1.4 3.8 0.5 .. 11.2 0.5 4.0 0.7 ..

2.4 2.8 3.5 3.0 .. .. 25.2 0.1 3.1 2.8 5.5 .. 1.1 0.7 .. 4.6 3.1 1.7 .. 6.5 2.3 –4.9 .. 10.3 0.5 1.9 0.1 .. 1.7 2.3 .. 1.3 9.6 0.9 1.2 1.0 0.4 .. .. 1.4 1.7 1.4 1.0 1.4 0.2 1.6 0.6 3.9 7.5 5.4 4.6 5.8 1.5 1.8 2.1 ..

28.0 61.4 42.7 10.7 74.9 .. 24.5 38.8 39.6 3.6 52.1 38.3 .. 7.1 .. 30.7 .. 71.1 .. 2.6 .. 34.7 .. 8.5 5.5 .. 36.4 11.2 23.5 9.8 .. 15.8 6.5 .. 43.2 21.2 38.7 68.3 13.1 30.8 39.2 14.2 45.4 21.8 93.3 18.3 .. 27.3 12.4 76.3 60.5 15.0 74.9 27.6 13.3 ..

2007 World Development Indicators

21.4 47.5 66.4 38.4 .. .. 61.9 63.0 39.6 10.2 49.9 12.7 12.6 12.9 .. 28.2 34.2 41.1 .. 20.3 43.0 38.7 .. 2.4 17.9 49.3 28.1 .. 32.0 22.2 .. 20.5 6.2 26.3 9.9 21.0 30.2 27.0 5.5 38.2 57.5 19.9 10.4 54.4 81.8 32.2 4.4 34.6 10.3 82.4 68.2 12.1 27.5 25.8 23.7 ..

211

4.6

Structure of service exports Commercial service exports

Transport

Travel

Insurance and financial services

Computer, information, communications, and other commercial services

$ millions

% of commercial services 1990 2005

% of commercial services 1990 2005

% of commercial services 1990 2005

% of commercial services 1990 2005

1990

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 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 Europe EMU

2005

610 5,056 .. 24,337 31 83 3,027 5,916 356 598 .. .. 45 78 12,719 51,200 1,939 3,270 1,219 3,969 .. .. 3,291 10,898 27,649 92,730 425 1,519 134 101 102 272 13,453 42,761 18,325 45,794 740 2,827 .. 103 131 1,181 6,292 20,495 114 122 322 838 1,575 3,884 7,882 25,552 .. .. .. 466 .. 8,913 .. .. 53,830 199,454 132,880 354,020 460 1,304 .. .. 1,121 1,240 .. 4,176 .. .. 82 285 95 .. 253 .. 815,799 t 2,459,852 t 13,307 84,840 97,390 412,960 46,586 217,782 51,500 195,671 110,583 495,951 22,788 137,881 41,109 142,205 25,840 72,823 .. .. 6,847 60,989 9,580 29,946 701,445 1,962,711 312,162 796,772

a. Includes Luxembourg. b. World Trade Organization estimate.

212

2007 World Development Indicators

50.5 .. 56.1 .. 19.2 .. 9.7 17.5 23.7 22.6 .. 21.6 17.2 39.7 14.1 24.5 35.8 16.3 29.8 .. 19.9 21.1 26.9 50.7 23.0 11.7 .. .. .. .. 25.2 28.1 36.9 .. 40.9 .. .. 27.2 68.9 44.3 28.4 w 23.8 29.7 32.1 26.9 29.2 32.3 .. 25.8 31.1 26.8 25.7 28.1 27.1

29.1 37.4 36.1 .. 16.1 .. 14.9 35.0 43.2 28.9 .. 14.1 16.6 44.3 3.4 10.3 20.5 9.5 8.6 54.4 17.2 22.6 38.2 35.2 29.3 15.8 .. 2.3 50.3 .. 16.5 17.9 33.8 .. 31.1 .. .. 16.1 .. .. 24.1 w 19.5 23.6 23.5 23.7 23.7 21.4 33.1 19.5 .. 22.1 16.7 24.3 22.8

17.4 .. 32.8 .. 42.8 .. 76.2 36.6 19.8 55.0 .. 55.8 67.2 30.2 15.7 29.2 21.7 40.4 43.3 .. 36.4 68.7 50.8 29.4 64.8 40.9 .. .. .. .. 29.0 37.9 51.8 .. 44.3 .. .. 48.8 13.5 25.3 34.9 w 23.3 44.9 39.5 51.4 43.0 43.5 .. 56.2 29.3 28.2 31.6 32.3 30.3

20.8 22.5 58.6 .. 35.4 .. 82.1 11.2 26.4 45.2 .. 67.3 51.4 28.3 88.7 25.5 17.2 24.2 76.9 1.5 69.7 49.3 15.7 40.8 54.7 71.0 .. 76.2 35.1 .. 15.3 28.8 45.5 .. 51.7 .. .. 63.3 .. .. 28.4 w 18.8 45.7 41.3 49.8 44.6 42.1 34.5 56.5 .. 17.4 42.1 23.5 26.5

5.6 .. 1.0 .. 0.5 .. .. 0.7 .. 1.2 .. 10.8 4.3 4.2 0.5 .. 9.1 23.7 .. .. 0.5 0.2 13.7 .. 1.5 .. .. .. .. .. 16.4 3.5 1.0 .. 0.2 .. .. .. 4.1 1.2 4.7 w 1.9 3.1 3.3 2.8 3.0 2.0 .. 4.1 3.4 2.3 5.4 5.2 5.9

2.6 2.9 0.1 .. 1.6 .. 3.0 9.4 2.3 0.9 .. 6.1 3.8 4.8 4.1 56.0 5.4 32.6 1.0 8.1 3.4 1.4 1.5 13.5 2.5 2.6 .. 4.6 0.7 .. 22.7 10.2 5.3 .. 0.1 .. .. .. .. .. 6.8 w 3.1 3.2 1.8 4.4 3.1 1.5 2.5 5.8 .. 3.7 3.8 7.8 5.1

26.6 .. 10.0 .. 37.6 .. 14.1 45.3 56.5 21.2 .. 11.9 11.3 25.9 69.7 46.3 33.5 19.6 27.0 .. 43.1 10.0 8.6 19.9 10.7 47.4 .. .. .. .. 29.4 30.5 10.3 .. 14.7 .. .. 24.0 13.4 29.2 38.5 w 51.2 22.4 25.2 19.0 24.9 22.2 .. 13.9 36.4 42.7 37.9 42.2 36.7

47.5 37.2 5.1 .. 47.0 .. 0.1 44.4 28.1 25.0 .. 12.6 28.2 22.6 3.9 8.2 56.9 33.8 13.4 36.0 9.7 26.8 44.6 10.5 13.5 10.6 .. 16.9 14.0 .. 45.4 43.1 15.4 .. 17.1 .. .. 20.6 .. .. 41.5 w 58.9 27.5 33.4 22.1 28.7 35.0 29.8 18.2 .. 56.8 37.7 45.3 45.7

About the data

4.6

ECONOMY

Structure of service exports Definitions

Balance of payments statistics, the main source of

around the clock for data processing, exploiting

• Commercial service exports are total service

information on international trade in services, have

time zone differences between their home country

exports minus exports of government services not

many weaknesses. Some large economies—such

and the host countries of their affiliates. Another

included elsewhere. International transactions in ser-

as the former Soviet Union—did not report data on

important dimension of service trade not captured

vices are defined by the IMF’s Balance of Payments

trade in services until recently. Disaggregation of

by conventional balance of payments statistics is

Manual (1993) as the economic output of intangible

important components may be limited, and it varies

establishment trade—sales in the host country by

commodities that may be produced, transferred, and

significantly across countries. There are inconsisten-

foreign affiliates. By contrast, cross-border intrafirm

consumed at the same time. Definitions may vary

cies in the methods used to report items. And the

transactions in merchandise may be reported as

among reporting economies. • Transport covers all

recording of major flows as net items is common (for

exports or imports in the balance of payments.

transport services (sea, air, land, internal waterway,

example, insurance transactions are often recorded

The data on exports of services in this table and

space, and pipeline) performed by residents of one

as premiums less claims). These factors contribute

on imports of services in table 4.7, unlike those in

economy for those of another and involving the car-

to a downward bias in the value of the service trade

editions before 2000, include only commercial ser-

riage of passengers, movement of goods (freight),

reported in the balance of payments.

vices and exclude the category “government services

rental of carriers with crew, and related support and

Efforts are being made to improve the coverage,

not included elsewhere.” The data are compiled by

auxiliary services. Excluded are freight insurance,

quality, and consistency of these data. Eurostat and

the IMF based on returns from national sources.

which is included in insurance services; goods pro-

the Organisation for Economic Co-operation and

Data on total trade in goods and services from the

cured in ports by nonresident carriers and repairs of

Development, for example, are working together

IMF’s Balance of Payments database are shown in

transport equipment, which are included in goods;

to improve the collection of statistics on trade in

table 4.15.

repairs of harbors, railway facilities, and airfield facil-

services in member countries. In addition, the Inter-

ities, which are included in construction services;

national Monetary Fund (IMF) has implemented the

and rental of carriers without crew, which is included

new classification of trade in services introduced in

in other services. • Travel covers goods and ser-

the fifth edition of its Balance of Payments Manual

vices acquired from an economy by travelers in that

(1993).

economy for their own use during visits of less than

Still, difficulties in capturing all the dimensions of

one year for business or personal purposes. Travel

international trade in services mean that the record

services include the goods and services consumed

is likely to remain incomplete. Cross-border intrafirm

by travelers, such as meals, lodging, and transport

service transactions, which are usually not captured

(within the economy visited), including car rental.

in the balance of payments, have increased in recent

• Insurance and fi nancial services cover freight

years. One example of such transactions is trans-

insurance on goods exported and other direct insur-

national corporations’ use of mainframe computers

ance such as life insurance, financial intermediation services such as commissions, foreign exchange transactions, and brokerage services; and auxil-

Top 10 developing country exporters of commercial services in 2005

4.6a

iary services such as financial market operational and regulatory services. • Computer, information,

Commercial service exports ($ billions) 80

1990

2005

communications, and other commercial services include such activities as international telecommu-

70

nications and postal and courier services; computer

60

data; news-related service transactions between

50

residents and nonresidents; construction services; royalties and license fees; miscellaneous business,

40

professional, and technical services; and personal,

30

cultural, and recreational services.

20 10 0 China

India

Turkey

Russian Thailand Malaysia Federationa

Poland

Mexico

Brazil

Egypt, Arab Rep.

Data sources

The top 10 developing country exporters of commercial services accounted for 60 percent of developing

Data on exports of commercial services are from

country commercial service exports and almost 12 percent of world commercial service exports.

the IMF. The IMF publishes balance of payments data in its International Financial Statistics and Bal-

a. Data are for 1994 and 2005. Source: International Monetary Fund and World Trade Organization data files.

ance of Payments Statistics Yearbook.

2007 World Development Indicators

213

4.7 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, 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

214

Structure of service imports Commercial service imports

Transport

Travel

Insurance and financial services

Computer, information, communications, and other commercial services

$ millions

% of commercial services 1990 2005

% of commercial services 1990 2005

% of commercial services 1990 2005

% of commercial services 1990 2005

1990

2005

97 29 1,155 1,288 2,876 40 13,388 14,104 .. 554 125 25,924a 113 291 .. 371 6,733 600 196 59 64 1,018 27,479 166 223 1,982 4,113 .. 1,683 .. 748 540 1,518 1,088 .. 3,701 10,106 435 755 3,327 296 1 123 348 7,432 59,560 984 35 .. 83,264 226 2,756 363 243 17 71

.. 1,318 .. 6,191 7,353 377 28,753 49,002 2,625 1,983 1,213 50,518 273 664 449 840 22,296 3,457 .. 104 620 1,053 64,170 .. .. 7,591 83,173 32,384 4,701 .. 1,550 1,496 1,942 3,349 .. 9,870 37,841 1,409 2,049 9,507 1,194 .. 2,132 1,178 15,061 104,897 921 45 578 200,944 1,131 14,292 1,423 195 42 431

2007 World Development Indicators

85.9 26.3 58.1 38.3 32.6 89.2 33.9 8.4 .. 71.1 34.0 23.3a 46.9 61.7 .. 57.5 44.4 40.5 64.7 62.6 24.5 45.3 21.1 49.7 45.1 47.4 78.9 .. 34.9 .. 18.4 41.2 32.1 30.5 .. 19.8 38.3 40.0 41.6 44.0 45.9 .. 76.3 76.5 26.1 29.4 23.2 65.1 .. 20.5 55.1 34.0 41.0 57.5 54.5 47.9

.. 17.2 .. 21.3 26.8 50.6 35.5 14.9 14.4 76.9 25.5 24.4 63.4 35.2 44.7 40.5 22.3 34.7 .. 21.1 57.9 31.7 22.6 .. .. 54.2 34.2 28.0 44.8 .. 19.9 42.3 51.7 18.7 .. 18.2 43.4 61.0 50.8 39.3 44.1 .. 44.4 64.9 26.8 27.3 33.5 75.4 49.0 21.7 51.3 54.2 51.3 47.3 53.5 50.4

.. .. 12.9 3.0 40.7 0.9 31.5 54.9 .. 14.1 44.6 21.1a 12.8 20.6 .. 15.0 22.4 31.5 16.6 29.0 .. 27.5 39.8 30.6 31.2 21.5 11.4 .. 27.0 .. 15.2 28.8 11.1 34.4 .. 14.2 36.5 33.1 23.2 3.9 20.6 .. 15.4 3.3 37.2 20.7 13.9 23.1 .. 46.9 5.9 39.5 27.4 12.2 19.8 52.1

.. 59.7 .. 1.2 38.3 31.1 39.2 22.4 6.3 6.6 49.8 29.3 10.6 28.1 27.3 33.6 21.2 37.4 .. 58.1 15.6 20.1 28.6 .. .. 13.9 26.2 42.8 24.0 .. 6.6 31.5 17.8 22.5 .. 24.4 21.8 25.0 19.6 17.1 29.0 .. 21.0 6.5 20.3 29.7 23.2 11.7 29.2 36.1 26.8 21.3 31.2 12.8 30.9 12.6

9.5 2.9 9.8 2.6 … 9.9 4.8 4.6 .. 6.6 12.3 14.8a 5.7 10.0 .. 5.5 2.7 4.5 5.1 6.3 .. 7.2 .. 8.9 4.4 3.3 2.3 .. 13.7 .. 1.6 6.0 4.7 3.7 .. 14.0 1.6 4.1 8.1 4.6 12.1 .. 0.3 3.4 1.1 19.2 5.3 9.0 .. 1.0 11.2 5.4 3.4 5.5 5.6 ..

.. 3.6 .. 1.9 6.1 6.8 3.9 6.7 1.9 8.1 2.0 8.0 11.3 18.5 12.8 3.7 6.5 3.6 .. 4.2 4.7 8.3 10.9 .. .. 9.6 8.9 5.7 9.2 .. .. 6.0 .. 4.1 .. 11.9 .. 7.3 7.1 10.3 9.3 .. 1.9 5.1 1.6 5.1 5.8 10.8 10.4 4.5 5.1 5.7 11.8 12.7 0.4 ..

4.6 70.8 19.2 56.1 26.7 0.0 29.8 32.1 .. 8.3 9.2 40.8a 34.6 7.6 .. 22.0 30.5 23.5 13.6 2.2 75.5 20.1 39.2 10.7 19.2 27.9 7.4 .. 24.4 .. 64.9 24.0 52.0 31.4 .. 52.0 23.6 22.9 27.2 47.5 21.5 .. 8.0 16.9 35.5 30.7 57.6 2.8 .. 31.6 27.8 21.0 28.2 24.9 20.0 ..

.. 19.5 .. 75.6 28.7 11.5 21.3 56.1 77.4 8.4 22.8 38.3 14.7 18.3 15.3 22.2 50.1 24.3 .. 16.7 21.8 40.0 37.9 .. .. 22.3 30.8 23.4 22.0 .. 73.5 20.3 30.5 54.8 .. 45.5 34.8 6.6 22.6 33.3 17.6 .. 32.7 23.5 51.3 37.9 37.5 2.2 11.4 37.7 16.8 18.9 5.7 27.2 15.1 37.0

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

4.7

ECONOMY

Structure of service imports Commercial service imports

Transport

Travel

Insurance and financial services

Computer, information, communications, and other commercial services

$ millions

% of commercial services 1990 2005

% of commercial services 1990 2005

% of commercial services 1990 2005

% of commercial services 1990 2005

1990

2005

213 2,264 5,943 5,898 3,703 .. 5,145 4,825 46,602 667 84,281 1,118 .. 598 .. 10,050 2,805 51 25 120 .. 48 74 926 177 .. 172 268 5,394 352 126 407 10,063 .. 155 940 206 73 341 159 28,995 3,251 73 209 1,901 12,247 719 1,863 666 393 361 1,070 1,721 2,847 3,772 ..

831 11,626 52,211b 23,516 .. .. 69,759 13,439 88,889 1,683 132,601 2,465 7,404 950 .. 57,746 7,571 287 .. 1,541 7,838 79 .. 2,128 1,989 483 157 222 21,750 528 .. 1,211 20,915 418 496 3,103 627 444 376 424 72,414 8,135 402 250 7,321 27,209 3,052 7,179 1,673 1,151 325 2,959 5,790 14,104 9,891 ..

45.4 8.8 57.5 47.4 47.3 .. 24.3 39.6 23.7 47.9 30.8 52.0 .. 66.2 .. 39.8 31.9 74.1 73.0 82.3 .. 67.9 60.8 41.9 90.7 .. 43.5 81.8 46.9 57.4 76.9 51.6 25.0 .. 56.2 58.3 57.7 35.4 46.9 40.8 37.7 40.6 70.7 68.3 33.6 44.6 36.6 67.0 66.6 35.6 61.6 43.5 56.9 52.4 48.5 ..

49.8 15.3 36.7 29.9 .. .. 3.5 35.1 24.8 43.1 30.5 54.4 15.8 44.0 .. 34.6 39.8 41.4 .. 32.5 17.0 65.9 .. 47.7 45.1 41.2 48.5 50.1 38.6 64.9 .. 43.1 13.0 35.2 40.2 50.9 36.6 51.2 36.1 38.0 20.7 33.8 57.9 65.3 20.7 33.7 34.4 36.2 56.3 24.2 55.6 44.4 54.0 23.5 30.8 ..

17.6 25.9 6.6 14.2 9.2 .. 22.6 29.7 22.1 17.0 27.9 30.1 .. 6.4 .. 27.5 65.5 0.8 13.3 10.9 .. 24.7 33.7 45.8 6.9 .. 23.4 5.9 26.9 15.8 18.3 23.0 54.9 .. 0.8 19.9 .. 22.6 17.9 28.5 25.4 29.5 20.1 10.4 30.3 30.0 6.5 23.1 14.8 12.8 19.8 27.6 6.5 14.9 23.0 ..

29.9 25.2 13.8 15.2 .. .. 8.7 21.5 25.2 14.8 28.3 23.7 10.2 13.0 .. 26.5 56.5 20.3 .. 37.9 36.7 34.1 .. 32.0 37.4 12.3 15.8 35.2 17.1 12.6 .. 22.7 36.3 40.4 38.8 19.7 28.1 6.5 23.3 38.5 22.3 32.7 22.5 8.9 15.2 35.9 21.1 17.8 16.2 4.8 24.2 23.0 22.1 30.8 31.1 ..

15.0 1.0 5.8 4.0 10.8 .. 1.9 4.4 10.4 6.7 2.1 5.2 .. 8.9 .. -0.1 1.2 7.6 6.4 4.8 .. 5.6 5.6 4.1 .. .. 3.5 8.7 .. 1.9 3.1 5.5 6.2 .. 6.3 6.0 4.3 2.5 6.8 3.2 0.6 2.5 7.9 4.3 3.1 1.7 4.1 1.4 10.2 4.0 11.4 10.9 3.4 1.0 5.1 ..

.. 5.4 6.5 3.7 .. .. 14.9 3.1 3.6 10.0 3.5 8.5 3.1 13.7 .. 1.4 1.9 6.8 .. 3.8 3.1 .. .. 8.5 1.7 4.3 1.0 0.0 2.9 6.5 .. 5.3 44.3 1.6 8.2 2.6 3.1 .. 5.5 6.2 2.5 4.0 3.8 3.0 .. 2.9 9.8 3.4 12.6 10.3 17.2 8.7 5.1 5.6 4.6 ..

22.0 64.4 30.1 34.5 32.8 .. 51.2 26.3 43.9 28.4 39.3 12.7 .. 18.5 .. 32.8 1.4 17.6 20.6 2.1 .. 1.7 .. 8.3 2.4 .. 29.5 3.7 26.2 24.9 1.7 19.9 14.0 .. 36.8 15.9 38.1 39.5 28.5 27.5 36.3 27.5 1.4 17.1 32.9 23.7 52.8 8.6 8.4 47.6 7.3 18.0 33.2 31.8 23.5 ..

2007 World Development Indicators

20.4 54.1 43.1 51.1 .. .. 73.0 40.3 46.5 32.1 37.8 13.3 71.0 29.2 .. 37.5 1.8 31.5 .. 25.8 43.2 .. .. 11.8 15.9 42.2 34.7 14.7 41.4 16.0 .. 28.9 6.4 22.8 12.8 26.8 32.2 42.4 35.1 17.3 54.6 29.5 15.9 22.8 64.2 27.6 34.7 42.6 14.9 60.7 3.1 24.0 18.8 40.1 33.5 ..

215

4.7

Structure of service imports Commercial service imports

Transport

Travel

Insurance and financial services

Computer, information, communications, and other commercial services

$ millions

% of commercial services 1990 2005

% of commercial services 1990 2005

% of commercial services 1990 2005

% of commercial services 1990 2005

1990

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 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 Europe EMU

2005

787 5,425 .. 38,465 94 176 12,677 14,239 368 681 .. .. 67 85 8,575 54,076 1,666 3,012 1,034 2,890 122 .. 3,594 11,863 15,197 65,159 620 2,051 202 1,801 171 431 16,959 35,023 11,093 26,089 702 2,136 .. 250 288 1,088 6,160 27,458 217 237 460 314 682 2,066 2,794 10,697 .. .. 195 783 .. 6,962 .. .. 44,713 155,861 97,950 281,168 363 857 .. .. 2,390 5,250 .. 5,282 .. .. 639 1,103 370 .. 460 .. 834,571 t 2,346,205 t 21,190 101,435 107,026 449,275 51,519 254,719 56,695 196,106 128,521 548,077 25,122 171,206 .. 132,848 33,527 88,781 18,677 43,583 9,262 64,639 18,237 50,365 701,461 1,800,743 301,701 768,538

a. Includes Luxembourg. b. World Trade Organization estimate.

216

2007 World Development Indicators

65.5 .. 69.0 18.1 60.1 .. 29.5 41.0 17.3 42.5 38.2 40.2 30.9 64.2 31.9 6.1 23.2 33.7 54.5 .. 58.0 58.1 56.9 51.7 51.4 32.2 .. 58.3 .. .. 33.2 36.3 48.2 .. 33.5 .. .. 27.6 76.8 51.8 34.9 w 55.6 48.7 61.6 35.2 49.3 65.5 30.9 34.0 49.2 60.5 45.1 31.0 26.3

36.2 13.4 76.6 29.2 55.8 .. 50.0 36.8 29.8 22.5 .. 44.9 27.1 61.8 59.9 20.4 16.3 21.3 64.6 71.3 25.4 51.0 72.7 51.7 53.6 49.8 .. 41.4 29.5 .. 23.5 31.4 48.6 .. 44.6 .. .. 47.7 .. .. 28.5 w 44.0 32.5 38.3 27.3 33.1 38.8 27.7 25.6 .. 44.9 40.8 27.3 23.8

13.1 .. 23.7 .. 12.4 .. 32.7 21.0 13.1 27.3 .. 31.5 28.0 11.9 25.4 20.6 37.1 53.0 35.5 .. 7.9 23.3 18.4 26.6 26.2 18.6 .. .. .. .. 41.0 38.9 30.7 .. 42.8 .. .. 9.9 14.6 14.4 32.5 w 13.0 25.3 15.9 35.1 24.1 15.8 19.2 40.9 16.3 10.7 22.6 34.8 31.1

17.1 46.3 20.8 .. 8.4 .. 37.8 18.2 19.0 32.9 .. 28.4 23.1 15.3 37.1 3.5 30.8 35.5 25.8 1.5 50.9 18.2 3.6 30.5 17.7 26.9 .. 17.0 40.3 .. 38.2 26.2 29.4 .. 24.4 .. .. 15.2 .. .. 28.1 w 17.6 26.8 23.6 29.8 26.4 22.1 30.9 30.2 .. 16.3 23.4 28.5 28.2

7.3 .. 0.0 2.2 8.8 .. 4.8 9.1 .. 2.5 4.2 11.6 6.3 6.8 4.9 .. 7.9 1.4 4.4 .. 6.2 5.5 9.1 9.9 7.4 .. .. 6.5 .. .. 2.4 4.5 1.5 .. 4.3 .. .. 5.4 5.3 3.4 5.0 w 5.2 4.1 4.3 3.9 4.2 2.6 6.2 5.9 6.9 5.3 7.6 5.1 7.6

5.4 5.4 .. 3.2 10.2 .. 9.7 5.6 8.7 2.2 .. 5.6 5.4 6.0 0.7 30.8 3.3 5.8 3.4 7.2 4.5 6.0 11.4 0.1 8.7 11.9 .. 6.4 5.4 .. 6.7 12.5 3.1 .. 9.3 .. .. 8.5 .. .. 8.2 w 6.0 12.6 7.3 17.5 12.4 6.6 7.1 25.7 .. 6.3 5.9 7.1 5.0

14.1 .. 7.3 79.7 18.7 .. 33.0 29.0 69.6 27.8 57.6 16.7 34.9 17.1 37.8 73.4 31.7 12.0 5.7 .. 27.9 13.3 15.6 11.9 15.0 49.2 .. 35.2 .. .. 23.4 20.4 19.6 .. 19.4 .. .. 57.1 3.3 30.4 32.3 w 26.6 22.1 18.3 26.1 22.5 16.2 44.2 19.6 27.7 23.5 25.5 34.8 35.0

41.3 35.0 2.5 67.6 25.6 .. 2.5 39.4 42.4 42.4 .. 21.1 44.4 16.9 2.3 45.3 49.6 37.4 6.3 20.0 19.2 24.8 12.4 17.7 20.1 11.4 .. 35.2 24.9 .. 31.5 30.0 18.9 .. 21.7 .. .. 28.6 .. .. 35.4 w 32.8 28.0 30.9 25.5 28.2 32.5 34.3 18.5 .. 32.5 31.0 37.3 43.0

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. International transactions in ser-

in the producing country (for example, use of a hotel

vices are defined by the IMF’s Balance of Payments

room) but are classified as imports of the traveler’s

Manual (1993) as the economic output of intangible

country. In other cases services may be supplied

commodities that may be produced, transferred, and

from a remote location; for example, insurance

consumed at the same time. Definitions may vary

services may be supplied from one location and

among reporting economies. • Transport covers all

consumed in another. For further discussion of the

transport services (sea, air, land, internal waterway,

problems of measuring trade in services, see About

space, and pipeline) performed by residents of one

the data for table 4.6.

economy for those of another and involving the car-

The data on exports of services in table 4.6 and on

riage of passengers, movement of goods (freight),

imports of services in this table, unlike those in edi-

rental of carriers with crew, and related support and

tions before 2000, include only commercial services

auxiliary services. Excluded are freight insurance,

and exclude the category “government services not

which is included in insurance services; goods pro-

included elsewhere.” The data are compiled by the

cured in ports by nonresident carriers and repairs of

International Monetary Fund (IMF) based on returns

transport equipment, which are included in goods;

from national sources.

repairs of harbors, railway facilities, and airfield facilities, which are included in construction services; and rental of carriers without crew, which is included in other services. • Travel covers goods and services acquired from an economy by travelers in that economy for their own use during visits of less than one year for business or personal purposes. Travel services include the goods and services consumed by travelers, such as meals, lodging, and transport (within the economy visited), including car rental. • Insurance and fi nancial services cover freight insurance on goods imported and other direct insurance such as life insurance, financial intermediation services such as commissions, foreign exchange transactions, and brokerage services; and auxil-

The mix of commercial service imports by developing countries is changing

iary services such as financial market operational

4.7a

1990

and regulatory services. • Computer, information, communications, and other commercial services

2005

include such activities as international telecommunications, and postal and courier services; computer data; news-related service transactions between

Other 22% Insurance and finance 4%

residents and nonresidents; construction services; Other 28%

Transport 50%

Transport 34%

Travel 24%

royalties and license fees; miscellaneous business, professional, and technical services; and personal, cultural, and recreational services.

Insurance and finance 12% Travel 26%

Data sources Data on imports of commercial services are from Between 1990 and 2005 transport was displaced by travel and insurance and other services as the

the IMF. The IMF publishes balance of payments

most important services imported by developing economies.

data in its International Financial Statistics and Bal-

Source: International Monetary Fund data files.

ance of Payments Statistics Yearbook.

2007 World Development Indicators

217

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 Chinab Hong Kong, 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

218

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 1990 2005

% of GDP 1990 2005

% of GDP 1990 2005

% of GDP 1990 2005

% of GDP 1990 2005

% of GDP 1990 2005

.. 15 23 39 10 35 16 38 44 6 46 70 14 23 .. 55 8 33 11 8 6 20 26 15 14 34 19 131 21 30 54 30 32 78 .. 45 37 34 33 20 19 11 60 6 22 21 46 60 40 25 17 18 21 31 10 18

.. 23 25 21 5 46 16 37 39 14 44 68 26 24 .. 50 7 37 24 28 13 17 26 28 28 31 16 122 15 29 46 36 27 86 .. 43 33 44 32 33 31 45 54 9 24 23 31 72 46 25 26 28 25 31 37 20

.. 21 26 9 13 .. 19 24 .. 14 29 23 10 10 .. 43 19 16 13 –5 6 16 18 0 –3 24 40 33 22 1 7 10 –4 –16 .. 28 20 22 11 21 6 13 41 12 24 21 24 5 .. 24 7 18 10 11 15 6

.. 61 57 36 77 46 59 57 51 86 47 56 87 77 .. 33 59 60 82 95 91 67 56 86 98 61 46c 57 66 79 62 73 72 75 .. 49 50 80 68 73 89 101 62 77 52 57 50 76 65 57 85 73 84 73 87 81

110 91 34 67 61 73 59 56 48 76 50 53 78 68 99 28 56 70 80 87 85 71 55 .. 58 57 37c 58 61 85 34 66 74 58 .. 49 49 74 66 71 93 82 56 82 54 57 52 96 68 59 81 67 90 86 85 92

2007 World Development Indicators

.. 19 16 35 3 18 19 19 18 4 24 20 11 12 .. 24 19 18 13 11 7 13 23 15 10 10 14 7 9 12 14 15 17 24 .. 23 25 4 11 11 10 22 16 13 22 22 13 14 10 20 9 15 7 9 10 8

9 9 12 ..a 12 11 18 18 11 6 20 23 15 14 26 25 20 19 13 28 4 10 20 .. 5 12 14 9 19 8 14 14 8 20 .. 22 26 10 11 13 10 45 18 14 23 24 7 ..a 18 19 15 16 6 5 18 8

.. 29 29 12 14 47 23 24 27 17 27 22 14 13 .. 37 20 26 18 15 8 18 21 12 7 25 36 27 19 9 16 19 7 11 .. 25 20 25 21 29 14 11 30 13 29 22 22 22 31 24 14 23 14 18 30 13

25 24 30 8 22 30 26 21 38 25 30 21 20 14 19 32 21 28 21 12 20 21 21 .. 17 23 44 21 19 14 24 26 11 31 .. 27 21 20 24 18 15 20 32 26 20 20 21 25 26 17 29 24 19 12 15 30

12 22 48 74 25 27 18 53 57 17 61 87 14 36 36 51 17 61 9 9 65 23 39 .. 59 42 38 198 22 32 82 49 50 47 .. 72 49 34 31 31 27 9 84 16 39 26 59 45 42 40 36 21 16 26 38 16

56 46 24 48 19 40 21 48 54 23 60 85 26 33 81 35 12 77 22 36 74 25 34 .. 39 34 32 185 21 39 55 54 42 56 .. 70 44 38 32 33 45 56 90 39 35 27 39 65 55 35 62 28 30 30 55 45

24 16 51 21 24 26 20 24 30 30 31 24 11 20 –2 46 22 17 7 9 14 18 23 14 21 17 51 32 18 14 29 19 13 23 .. 24 24 19 24 21 11 10 21 17 23 18 32 15 20 21 22 15 15 7 8 28

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

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 1990 2005

% of GDP 1990 2005

% of GDP 1990 2005

% of GDP 1990 2005

% of GDP 1990 2005

% of GDP 1990 2005

67 61 66 59 59 .. 58 56 57 65 53 74 52 63 .. 52 57 71 .. 53 140 139 .. 48 57 72 86 72 52 80 69 64 70 77 62 65 92 89 51 84 50 61 59 84 56 49 46 74 57 59 77 74 72 48 64 65

77 68 59 65 46 .. 44 59 59 73 57 103 53 74 .. 53 28 86 72 62 89 84 87 .. 65 78 84 95 44 79 92 67 68 93 57 58 79 .. 50 77 49 59 89 79 40 42 45 80 73 .. 75 66 80 62 66 ..

13 11 12 9 12 .. 16 30 20 13 13 25 18 19 .. 12 39 25 .. 9 25 14 .. 24 19 19 8 15 14 14 26 13 8 ..a 30 16 14 ..a 31 9 23 19 44 15 15 21 22 15 18 25 6 8 10 19 16 15

14 10 11 8 12 .. 16 28 20 15 18 15 11 17 .. 14 15 19 ..a 18 16 15 11 .. 17 19 8 17 13 10 23 14 12 16 15 23 10 .. 23 10 24 18 11 12 21 20 23 8 12 .. 10 10 10 19 21 ..

23 25 24 31 37 .. 21 25 22 26 33 32 32 24 .. 38 18 24 .. 40 18 53 .. 19 33 19 17 23 32 23 20 31 23 25 36 25 22 13 34 18 23 20 19 8 15 23 12 19 17 24 23 17 24 26 27 17

30 24 33 22 33 .. 25 19 21 32 23 24 27 17 .. 30 20 14 32 34 20 41 16 .. 25 20 22 15 20 23 45 23 22 30 36 26 20 .. 26 29 19 25 29 19 21 21 18 17 19 .. 22 19 15 19 22 ..

37 31 7 25 15 .. 57 35 19 48 11 62 74 26 .. 28 45 29 12 48 18 17 .. 40 52 26 17 24 75 17 46 64 19 48 22 27 8 3 52 11 56 27 25 15 43 40 47 16 87 41 33 16 28 29 31 ..

41 66 21 34 39 .. 83 46 26 41 13 52 54 27 .. 43 68 39 27 48 19 48 37 .. 58 45 26 27 123 26 36 57 30 53 76 36 33 .. 46 16 71 29 28 15 53 45 57 15 69 .. 47 25 47 37 29 ..

40 29 9 24 23 .. 52 45 19 52 10 93 75 31 .. 29 58 50 25 49 100 122 .. 31 61 36 28 33 72 34 61 71 20 51 49 32 36 5 67 21 52 27 46 22 29 34 28 23 79 49 40 14 33 22 38 ..

61 69 24 29 30 .. 68 51 26 61 11 93 45 35 .. 40 30 58 31 62 44 88 50 .. 65 63 40 53 100 37 95 61 32 91 84 43 42 .. 45 33 63 30 58 24 35 28 43 20 73 .. 54 19 52 37 37 ..

22 26 22 28 35 .. 19 22 21 19 34 22 .. 19 .. 37 .. 4 –4 56 22 60 .. .. .. 9 9 14 30 15 18 26 20 58 6 25 2 12 35 11 28 17 –4 –2 19 26 .. 22 24 9 20 .. 20 27 25 ..

2007 World Development Indicators

30 16 32 24 41 .. 24 .. 20 20 26 7 26 12 .. 32 .. 6 2 23 –1 27 18 .. 18 20 11 –7 36 11 –5 20 21 23 37 29 4 .. 40 31 27 17 13 10 30 37 .. 18 10 .. 16 19 31 18 13 ..

219

4.8 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 Tanzaniad 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 Europe EMU

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 1990 2005

% of GDP 1990 2005

% of GDP 1990 2005

% of GDP 1990 2005

% of GDP 1990 2005

% of GDP 1990 2005

66 49 84 47 76 .. 84 46 54 53 .. 57 60 77 .. 75 49 57 69 74 81 57 71 59 64 69 49 92 57 38 63 67 70 61 62 84 .. 74 64 63 60 w 70 59 56 63 61 50 56 67 66 69 64 60 57

78 49 85 26 77 86 90 41 56 55 .. 63 58 77 70 61 48 60 70 95 77 58 86 58 64 69 46 79 61 46 65 70 74 51 47 64 96 50 70 70 61 w 64 56 51 61 57 44 60 62 59 64 65 62 58

13 21 10 29 15 .. 8 10 22 17 .. 20 17 10 .. 18 27 11 14 9 18 9 14 12 16 11 23 8 17 16 20 17 12 25 8 12 .. 18 19 19 17 w 12 14 15 13 14 13 17 12 15 11 17 18 20

10 17 13 23 14 18 13 11 20 20 .. 20 18 9 17 28 27 12 14 9 14 12 10 9 16 13 13 14 19 11 22 16 11 16 11 6 33 16 13 27 17 w 11 15 15 14 14 13 16 15 14 10 18 18 21

30 30 15 15 14 .. 10 37 33 17 16 18 26 23 .. 19 23 31 17 25 26 41 27 13 27 24 40 13 28 21 20 18 12 32 10 13 .. 15 17 17 23 w 21 26 29 23 26 35 27 19 28 23 18 23 23

23 21 22 16 23 18 15 19 29 26 .. 18 30 26 23 19 17 20 20 14 19 32 18 20 23 25 23 21 19 24 17 19 13 23 22 35 26 27 26 14 22 w 29 27 31 22 27 38 23 21 26 31 19 20 20

17 18 6 41 25 .. 22 .. 27 91 10 24 16 29 .. 75 30 36 28 28 13 34 34 45 44 13 .. 7 28 66 24 10 24 29 40 36 .. 14 36 23 19 w 13 22 19 25 20 24 24 17 24 9 27 19 27

33 35 11 61 27 27 24 243 79 65 .. 27 25 34 18 88 49 46 37 54 17 74 34 58 48 27 65 13 54 94 26 10 30 40 41 70 14 46 16 43 26 w 25 36 34 38 34 46 41 26 37 20 33 25 37

26 18 14 32 30 .. 24 .. 36 79 38 19 19 38 .. 87 30 34 28 35 38 42 45 29 51 18 .. 19 29 41 27 11 18 48 20 45 .. 20 37 23 19 w 16 21 19 23 20 23 24 15 34 12 26 19 28

43 22 31 26 42 50 43 213 83 65 .. 29 31 46 28 95 41 39 40 73 26 75 47 46 51 34 48 27 53 76 30 15 28 30 21 75 68 38 25 53 26 w 29 33 31 35 32 41 41 23 36 25 35 25 36

22 36 11 18 6 .. 3 46 .. 24 17 20 23 17 .. 25 21 34 15 24 8 33 20 21 22 24 .. 1 36 .. 16 15 14 3 27 –2 .. 28 7 16 22 w 18 27 30 22 26 36 25 20 26 21 16 22 23

14 32 19 .. 16 10 7 .. 20 25 .. 14 22 20 17 17 23 33 14 7 9 29 10 29 21 18 34 10 22 .. 14 13 13 35 40 34 13 32 10 3 21 w 28 30 35 23 29 45 23 22 30 30 17 19 21

a. Data on general government final consumption expenditure are not available separately; they are included in household final consumption expenditure. b. China has revised its national accounts data from 1993 onwards. However, data by expenditure are not available. Data shown here are based on earlier series. c. Includes the difference between the old and the new GDP series. d. Data cover mainland Tanzania only.

220

2007 World Development Indicators

4.8

ECONOMY

Structure of demand About the data

Gross domestic product (GDP) from the expenditure

intangibles such as computer software and mineral

payments and fees to governments to obtain permits

side is made up of household final consumption

exploration outlays. Data on capital formation may

and licenses. Expenditures of nonprofit institutions

expenditure, general government final consumption

be estimated from direct surveys of enterprises and

serving households are included, even when reported

expenditure, gross capital formation (private and

administrative records or based on the commodity

separately by the country. In practice, household

public investment in fixed assets, changes in inven-

flow method using data from production, trade, and

consumption expenditure may include any statisti-

tories, and net acquisitions of valuables), and net

construction activities. The quality of data on fixed

cal discrepancy in the use of resources relative to

exports (exports minus imports) of goods and ser-

capital formation by government depends on the qual-

the supply of resources. • General government final

vices. Such expenditures are recorded in purchaser

ity of government accounting systems (which tend to

consumption expenditure includes all government

prices and include net taxes on products.

be weak in developing countries). Measures of fixed

current expenditures for purchases of goods and

Because policymakers have tended to focus on

capital formation by households and corporations—

services (including compensation of employees). It

fostering the growth of output, and because data on

particularly capital outlays by small, unincorporated

also includes most expenditures on national defense

production are easier to collect than data on spend-

enterprises—are usually unreliable.

and security but excludes government military expen-

ing, many countries generate their primary estimate

Estimates of changes in inventories are rarely

ditures that potentially have wider public use and

of GDP using the production approach. Moreover,

complete but usually include the most important

are part of government capital formation. • Gross

many countries do not estimate all the separate

activities or commodities. In some countries these

capital formation consists of outlays on additions

components of national expenditures but instead

estimates are derived as a composite residual along

to the fixed assets of the economy, net changes

derive some of the main aggregates indirectly using

with household fi nal consumption expenditure.

in the level of inventories, and net acquisitions of

GDP (based on the production approach) as the con-

According to national accounts conventions, adjust-

valuables. Fixed assets include land improvements

trol total. Household final consumption expenditure

ments should be made for appreciation of the value

(fences, ditches, drains, and so on); plant, machin-

(private consumption in the 1968 System of National

of inventory holdings due to price changes, but this

ery, and equipment purchases; and the construction

Accounts, or SNA) is often estimated as a residual, by

is not always done. In highly inflationary economies

of roads, railways, and the like, including schools,

subtracting from GDP all other known expenditures.

this element can be substantial.

offices, hospitals, private residential dwellings, and

The resulting aggregate may incorporate fairly large

Data on exports and imports are compiled from

commercial and industrial buildings. Inventories are

discrepancies. When household consumption is cal-

customs reports and balance of payments data.

stocks of goods held by firms to meet temporary

culated separately, many of the estimates are based

Although the data from the payments side provide

or unexpected fluctuations in production or sales,

on household surveys, which tend to be one-year stud-

reasonably reliable records of cross-border transac-

and “work in progress.” • Exports and imports

ies with limited coverage. Thus the estimates quickly

tions, they may not adhere strictly to the appropriate

of goods and services are the value of all goods

become outdated and must be supplemented by esti-

definitions of valuation and timing used in the bal-

and other market services provided to, or received

mates using price- and quantity-based statistical pro-

ance of payments or correspond to the change-of-

from, the rest of the world. They include the value of

cedures. Complicating the issue, in many developing

ownership criterion. This issue has assumed greater

merchandise, freight, insurance, transport, travel,

countries the distinction between cash outlays for

significance with the increasing globalization of inter-

royalties, license fees, and other services, such as

personal business and those for household use may

national business. Neither customs nor balance of

communication, construction, financial, information,

be blurred. World Development Indicators includes in

payments data usually capture the illegal transac-

business, personal, and government services. They

household consumption the expenditures of nonprofit

tions that occur in many countries. Goods carried

exclude compensation of employees and investment

institutions serving households.

by travelers across borders in legal but unreported

income (factor services in the 1968 SNA) as well as

shuttle trade may further distort trade statistics.

transfer payments. • Gross savings are calculated

General government final consumption expenditure (general government consumption in the 1968 SNA)

Gross savings represent the difference between dis-

includes expenditures on goods and services for

posable income and consumption and replace gross

individual consumption as well as those on services

domestic savings, a concept used by the World Bank

for collective consumption. Defense expenditures,

and included in World Development Indicators editions

including those on capital outlays (with certain excep-

before 2006. The change was made to conform to the

tions), are treated as current spending.

SNA concepts and definitions. For further discussion of

Gross capital formation (gross domestic invest-

the problems in compiling national accounts, see Srini-

ment in the 1968 SNA) consists of outlays on

vasan (1994), Heston (1994), and Ruggles (1994).

additions to the economy’s fixed assets plus net

For an analysis of the reliability of foreign trade and

changes in the level of inventories. It is generally

national income statistics, see Morgenstern (1963).

obtained from reports by industry of acquisition and distinguishes only the broad categories of capital

Definitions

as gross national income less total consumption, plus net transfers. Data sources National accounts indicators for most developing countries are collected from national statistical organizations and central banks by World Bank missions. Data for high-income economies come from Organisation for Economic Co-operation and Development data files (see the OECD’s Annual National Accounts for OECD Member Countries: Data from 1970 Onwards). The United Nations

formation. The 1993 SNA recognizes a third cat-

• Household final consumption expenditure is the

egory of capital formation: net acquisitions of valu-

market value of all goods and services, including

ables. Included in gross capital formation under the

durable products (such as cars, washing machines,

1993 SNA guidelines are capital outlays on defense

and home computers), purchased by households. It

establishments that may be used by the general pub-

excludes purchases of dwellings but includes imputed

lic, such as schools, airfields, and hospitals, and

rent for owner-occupied dwellings. It also includes

Statistics Division publishes detailed national accounts for UN member countries in National Accounts Statistics: Main Aggregates and Detailed Tables and updates in the Monthly Bulletin of Statistics.

2007 World Development Indicators

221

4.9

Growth of consumption investment, and trade Household final consumption expenditure

General government final consumption expenditure

Gross capital formation

Goods and services

average annual Per capita % growth average annual average annual average annual average annual Exports Imports % growth % growth % growth % growth 1990–2000 2000–05 1990–2000 2000–05 1990–2000 2000–05 1990–2000 2000–05 1990–2000 2000–05 1990–2000 2000–05

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazila Bulgaria Burkina Faso Burundi Cambodiaa Cameroon Canada Central African Republica Chada Chile Chinab Hong Kong, China Colombia Congo, Dem. Rep.a Congo, Rep.a Costa Ricaa Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republica Ecuadora Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabona Gambia, The Georgia Germany Ghana Greece Guatemalaa Guinea Guinea–Bissau Haiti

222

.. 4.3 –0.1 .. 2.7 –0.5 3.6 1.9 1.5 2.6 –0.5 1.8 2.6 3.6 .. 2.5 4.8 –3.7 4.2 –4.9 6.0 3.1 2.6 .. 1.5 7.3 8.9 3.9 2.2 –4.5 –1.7 5.1 4.3 2.8 .. 3.0 2.2 5.3 2.1 3.7 5.3 –5.0 0.6 2.8 1.7 1.6 1.2 3.6 6.1 1.9 4.4 2.2 4.2 3.7 2.6 ..

12.6 3.5 5.7 .. 0.6 8.4 4.2 1.0 12.2 4.0 11.0 1.1 2.3 2.3 –1.3 4.9 0.5 5.5 3.6 .. 8.3 4.2 3.2 .. 5.0 4.6 6.9 1.9 3.3 .. 18.5 3.0 –0.5 4.8 .. 3.3 2.1 0.7 5.8 3.0 2.3 1.8 7.0 6.7 2.9 2.2 4.5 1.8 7.1 0.3 7.6 4.1 3.7 5.2 7.3 ..

2007 World Development Indicators

.. 5.2 –1.9 .. 1.5 1.1 2.4 1.5 0.4 0.4 –0.3 1.5 –0.7 1.4 .. 0.4 3.3 –3.0 1.3 .. 3.4 0.7 1.6 .. –1.6 5.6 7.8 2.1 0.3 –7.1 –4.8 2.5 1.4 3.2 .. 3.0 1.8 3.7 0.3 1.8 3.1 –6.7 2.2 0.6 1.3 1.3 –1.7 0.1 7.6 1.6 1.9 1.4 1.9 0.5 –0.4 ..

.. 3.0 4.1 .. –0.4 8.8 2.9 0.5 11.2 2.0 11.5 0.6 –0.9 0.3 .. 4.8 –0.9 6.3 0.3 .. 6.2 2.3 2.2 .. 1.4 3.4 6.2 1.1 1.7 .. 15.0 1.1 –2.1 5.0 .. 3.3 1.8 –0.7 4.3 1.0 0.5 –2.5 7.4 4.5 2.7 1.5 2.8 –1.1 8.3 0.3 5.3 3.7 1.2 2.9 4.1 ..

.. 2.4 3.6 .. 2.2 –1.6 3.0 2.5 –1.7 4.7 –1.9 1.5 4.4 3.6 .. 7.1 –0.4 –8.4 –0.5 –2.6 7.2 0.7 0.3 .. –8.3 3.7 9.7 3.3 10.5 –17.4 –2.0 2.0 0.8 1.3 .. –0.9 2.4 5.3 –1.5 4.4 2.8 22.6 4.9 9.6 0.9 1.4 5.4 –2.2 12.0 1.8 4.8 2.1 5.1 5.0 1.9 ..

13.1 1.1 5.1 .. 0.4 8.7 3.4 1.0 4.7 11.9 0.3 2.4 8.3 2.6 7.0 .. 1.0 4.5 2.6 .. 4.0 4.6 3.0 .. 3.8 3.8 8.1 1.6 1.4 .. 9.8 1.4 3.2 0.8 .. 3.2 1.4 5.6 2.6 3.1 1.3 5.0 5.8 –0.1 2.3 1.8 0.2 4.2 1.0 0.1 –7.0 2.1 –2.5 0.3 –3.7 ..

.. 25.8 –0.6 .. 7.4 –1.9 5.7 .. 42.9 9.2 –7.5 2.6 12.2 8.5 .. 6.7 3.4 –5.0 7.0 –0.5 10.9 0.4 4.5 .. 4.0 9.3 11.5 5.6 2.0 –0.7 0.4 5.1 8.3 5.6 .. 4.8 .. 10.4 –0.7 5.8 7.1 19.1 0.2 5.9 1.3 1.8 3.8 1.9 –12.5 .. 1.3 .. 6.2 2.8 –6.5 ..

5.6 3.3 10.6 .. 5.3 22.9 9.7 .. 39.0 8.3 15.5 1.4 4.8 –2.7 3.0 –0.7 –0.1 15.1 8.2 .. 12.1 8.1 4.6 .. 5.0 8.8 13.5 0.3 13.6 .. 22.4 8.9 –9.7 14.5 .. 3.9 .. –2.9 10.1 1.3 1.3 –8.0 11.6 6.6 1.4 1.1 1.4 11.9 18.8 .. 10.7 .. 6.9 –3.6 –4.5 ..

.. 17.9 3.2 .. 8.7 –18.4 7.4 5.5 6.8 13.1 –4.8 4.7 1.8 4.5 .. 4.7 6.1 3.9 0.0 –1.2 21.7 3.2 8.7 .. 2.3 9.5 13.0 8.1 5.3 –0.5 5.1 10.9 1.5 5.9 .. 8.7 5.1 9.1 5.3 3.3 13.4 –2.5 11.2 7.1 9.9 6.9 1.5 0.1 12.2 6.0 10.1 7.6 6.2 4.6 15.4 ..

–17.6 15.6 4.4 .. 6.4 20.6 0.6 5.4 16.2 8.3 8.8 2.8 2.7 10.8 12.7 2.2 11.4 9.1 6.6 .. 17.0 1.0 0.0 .. 55.6 6.6 24.8 10.2 3.8 7.8 5.5 6.0 3.5 6.2 .. 10.1 2.8 2.7 6.6 10.5 4.1 –7.6 8.1 15.0 3.7 1.7 1.7 4.0 4.1 5.5 4.1 1.2 –1.0 0.7 4.1 ..

.. 15.8 –1.0 .. 15.6 –12.7 8.1 5.0 15.5 9.7 –8.7 4.5 2.1 6.0 .. 3.8 11.1 2.7 1.4 –1.6 14.8 5.1 7.2 .. –1.7 11.7 14.3 8.4 9.0 –2.4 2.9 9.2 6.8 4.6 .. 12.0 6.1 9.4 2.8 3.1 11.6 7.5 12.0 5.8 6.2 5.7 0.7 0.1 11.2 5.8 10.4 7.4 9.2 1.3 –0.5 ..

1.9 15.0 9.2 .. 0.5 12.5 10.4 4.3 27.3 5.6 11.7 2.8 1.8 5.4 4.5 3.3 1.1 12.4 11.3 .. 15.3 6.8 2.2 .. 16.8 10.4 20.8 8.7 9.7 25.2 26.1 5.7 3.5 8.8 .. 9.8 4.5 –3.1 10.4 5.6 3.3 –4.1 9.6 11.0 4.0 3.4 2.9 2.5 4.9 3.5 5.0 1.9 5.6 –2.4 –3.5 ..

Household final consumption expenditure

General government final consumption expenditure

Gross capital formation

4.9

ECONOMY

Growth of consumption, investment, and trade

Goods and services

average annual Per capita % growth average annual average annual average annual average annual Exports Imports % growth % growth % growth % growth 1990–2000 2000–05 1990–2000 2000–05 1990–2000 2000–05 1990–2000 2000–05 1990–2000 2000–05 1990–2000 2000–05

Hondurasa Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstana Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuaniaa Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldovaa Mongoliaa Morocco Mozambiquea Myanmar Namibia Nepal Netherlands New Zealand Nicaraguaa Niger Nigeria Norway Oman Pakistan Panamaa Papua New Guinea Paraguay Perua Philippines Polanda Portugal Puerto Rico

3.0 –0.2 4.9 6.6 3.2 .. 5.3 4.5 1.5 .. 1.5 5.9 –8.1 3.6 .. 4.9 4.5 –6.5 .. –3.9 4.2 0.5 .. .. 5.2 2.2 2.3 5.4 5.3 3.0 .. 5.1 2.4 9.9 5.4 1.6 4.7 3.9 4.8 .. 2.8 3.2 6.1 1.8 0.2 3.5 5.4 4.9 6.4 5.6 2.6 4.0 3.7 5.2 3.0 ..

5.1 6.0 5.4 4.1 7.5 .. 3.9 2.6 0.5 .. 1.2 8.4 9.6 3.0 .. 2.5 5.9 10.6 .. 8.6 4.1 3.8 .. .. 8.8 2.6 5.3 4.1 6.8 1.9 7.4 4.6 3.0 9.6 3.3 3.4 6.6 .. –0.5 .. 0.3 5.0 3.5 .. 4.0 3.3 1.3 4.6 8.0 .. 1.0 3.5 4.8 2.8 1.4 ..

0.2 0.0 3.0 5.0 1.6 .. 4.5 1.9 1.5 .. 1.3 1.9 –7.0 0.8 .. 3.9 0.6 –7.4 .. –2.7 1.9 –0.7 .. .. 6.0 1.7 –0.7 3.5 2.6 0.2 .. 3.9 0.7 10.2 4.3 0.1 1.6 .. 1.7 .. 2.2 2.0 3.9 .. .. 2.9 2.4 2.3 4.2 .. 0.2 2.2 1.5 5.1 2.7 ..

2.7 6.3 3.8 2.7 6.1 .. 2.2 0.7 –0.1 .. 1.0 5.8 9.3 0.8 .. 2.0 2.9 9.6 .. 9.3 3.0 3.7 .. .. 9.3 2.4 2.4 1.8 4.7 –1.1 4.3 3.7 1.9 9.9 2.1 1.7 4.5 .. –1.8 .. –0.2 3.6 2.6 .. .. 2.7 0.4 2.1 6.0 .. –1.0 2.0 2.9 3.0 0.7 ..

2.0 0.9 5.9 0.1 1.6 .. 4.0 3.0 –0.4 .. 2.9 1.7 –7.1 6.9 .. 4.7 –2.4 –8.8 .. 1.8 6.2 6.0 .. .. 1.9 –0.4 0.0 –4.4 4.8 3.2 .. 4.8 1.8 –12.4 11.8 3.8 3.1 .. 3.3 .. 2.0 2.5 –1.5 0.8 –1.8 2.8 2.4 0.7 1.7 2.7 2.5 5.2 3.8 3.7 2.8 ..

4.1 3.6 4.1 8.7 2.1 .. 5.6 0.8 1.8 .. 2.4 4.5 7.1 2.2 .. 4.5 6.6 1.0 .. 2.2 0.9 –2.5 .. .. 3.9 –2.0 3.6 8.5 9.9 21.1 3.1 4.6 –0.2 7.0 2.2 4.7 8.5 .. 0.9 .. 2.1 3.5 1.1 .. 3.3 2.7 6.1 5.1 3.4 .. –0.2 3.2 –0.1 3.3 2.0 ..

6.9 10.6 6.3 –0.6 –0.1 .. .. 1.5 .. .. –1.8 1.1 –18.3 6.1 .. 3.4 1.0 –3.9 .. –3.9 7.3 1.5 .. .. 11.1 3.6 3.4 –8.4 5.3 0.4 .. 4.7 4.7 –15.5 7.6 2.9 11.5 15.3 6.9 .. 3.2 6.1 11.3 4.0 5.4 6.0 4.0 1.8 10.4 0.5 0.7 7.4 4.1 10.6 5.6 ..

2.2 0.1 14.9 4.9 9.3 .. .. –3.9 .. .. –2.2 10.2 13.6 2.0 .. 3.2 13.7 –5.3 17.9 16.7 4.8 –2.3 .. .. 17.0 2.2 13.3 2.5 1.5 6.7 23.8 4.2 –0.4 8.4 4.0 5.5 5.1 .. 9.6 .. –1.5 9.3 –0.9 .. 15.0 2.8 17.0 1.6 3.2 .. 2.9 4.1 –0.6 0.3 –3.6 ..

1.6 9.9 11.0 5.9 1.2 .. 15.7 10.6 5.1 .. 4.2 2.5 –2.6 1.1 .. 16.0 –1.6 –1.6 .. 4.3 14.0 11.1 .. .. 4.9 4.2 3.9 4.0 12.0 10.0 –1.4 5.4 14.6 0.7 30.9 5.4 11.0 10.0 3.8 .. 6.8 5.2 9.3 3.1 5.0 5.6 6.2 1.7 –0.4 4.3 3.1 8.6 7.8 11.3 5.3 1.6

6.0 8.7 15.4 5.5 2.1 .. 4.7 4.1 –0.8 .. 6.2 11.5 8.0 7.4 .. 12.1 5.4 4.5 7.5 8.6 11.9 10.2 .. .. 12.1 –1.2 –2.5 2.7 6.1 6.7 –2.1 1.7 4.0 16.3 8.4 5.0 20.0 .. 7.2 .. 3.7 4.2 6.8 .. 4.4 0.8 7.0 11.6 1.0 .. 3.9 9.7 5.4 9.5 2.7 ..

3.8 11.4 12.8 5.7 –6.8 .. 14.5 7.4 3.8 .. 4.2 2.0 –11.2 9.4 .. 10.0 0.8 –8.2 .. 7.6 3.3 0.9 .. .. 7.5 7.6 4.3 –1.1 10.3 3.5 0.6 5.2 12.3 5.6 29.3 4.8 6.3 5.8 5.4 .. 6.6 6.2 12.2 –2.1 4.0 5.8 5.9 2.5 1.2 2.8 2.9 9.0 7.8 16.7 7.3 4.5

2007 World Development Indicators

7.5 8.6 18.4 7.4 14.5 .. 3.0 1.6 0.8 .. 3.3 13.0 3.5 5.2 .. 9.4 9.4 10.1 14.8 12.1 6.5 4.7 .. .. 14.8 –0.6 10.3 6.2 6.8 4.5 14.1 1.3 4.1 15.8 7.8 4.6 10.1 .. 1.4 .. 3.4 9.5 3.7 .. 11.9 3.6 12.8 7.1 3.7 .. 0.6 5.8 6.2 6.2 1.6 ..

223

4.9

Growth of consumption, investment, and trade Household final consumption expenditure

General government final consumption expenditure

Gross capital formation

Goods and services

average annual Per capita % growth average annual average annual average annual average annual Exports Imports % growth % growth % growth % growth 1990–2000 2000–05 1990–2000 2000–05 1990–2000 2000–05 1990–2000 2000–05 1990–2000 2000–05 1990–2000 2000–05

Romaniaa Russian Federation Rwandaa Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lankaa Sudan Swazilanda Sweden Switzerland Syrian Arab Republic Tajikistan Tanzaniac Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguaya 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 Europe EMU

1.4 –0.9 1.1 .. 2.4 .. –4.4 .. 4.7 3.9 .. 2.9 2.4 5.7 6.2 3.8 1.3 1.1 3.0 –4.2 2.1 3.7 5.0 0.7 4.3 3.5 .. 6.7 –6.9 7.1 2.9 3.6 5.0 .. 0.6 5.4 5.3 3.2 –3.6 0.0 3.0 w 4.2 3.9 5.1 2.6 3.9 7.5 1.1 3.5 3.1 4.6 2.8 2.8 1.9

7.1 9.3 3.5 1.9 5.4 7.8 .. 3.3 3.4 2.6 .. 4.6 3.4 .. .. 1.9 1.6 0.9 6.2 .. 1.7 5.5 0.5 13.3 5.0 4.2 .. 4.1 12.1 12.9 2.9 3.0 –1.1 .. 3.7 7.2 –1.5 4.9 3.2 –4.4 2.6 w 5.0 4.4 4.7 4.0 4.5 6.3 6.1 2.1 4.8 5.2 4.1 2.3 1.3

1.7 –0.8 –0.1 .. –0.2 .. .. .. 4.5 3.9 .. 0.7 2.0 .. 3.7 0.6 0.9 0.5 0.2 –5.5 –0.8 2.5 1.8 0.1 2.6 1.7 .. 3.4 –6.4 0.7 2.6 2.4 4.2 .. –1.5 3.8 0.9 –0.8 –5.9 –1.7 1.5 w 2.1 2.6 3.8 1.6 2.3 6.1 0.9 1.8 0.9 2.6 0.3 2.0 1.6

7.9 9.8 1.3 –0.3 2.9 14.8 .. .. 3.4 2.5 .. 3.3 1.8 .. .. 0.3 1.3 0.2 3.6 .. –0.2 4.5 –2.2 12.9 4.0 2.9 .. 0.6 13.1 5.1 2.8 2.0 –1.8 .. 1.9 5.9 –5.4 1.7 1.4 –5.0 1.4 w 3.1 3.4 3.7 3.4 3.1 5.4 6.2 0.7 2.8 3.4 1.7 1.6 0.7

0.8 –2.2 –1.7 .. 2.1 .. 10.4 .. 2.9 2.2 .. 0.3 2.7 7.5 0.2 5.5 0.6 0.8 2.0 –19.2 3.4 5.1 0.0 0.3 4.1 4.9 .. 7.1 –4.1 6.9 1.1 0.7 2.3 .. 3.7 3.2 12.8 1.7 –8.1 –2.2 1.7 w 4.4 2.9 3.9 1.6 3.0 8.1 0.1 1.4 3.2 5.3 0.5 1.5 1.4

4.0 1.8 10.4 0.6 7.5 5.7 .. 1.2 3.1 2.8 .. 5.5 4.8 .. .. –1.1 1.0 2.3 6.5 .. 19.1 3.9 1.3 4.3 4.5 –0.2 .. 5.1 5.1 0.8 3.5 3.5 –3.0 .. 6.0 6.9 1.3 8.7 7.2 –3.2 2.9 w 4.6 3.9 5.0 2.3 4.0 7.6 2.4 1.1 3.8 4.4 5.1 2.7 1.7

–5.1 –19.1 1.4 .. 7.9 .. –5.6 .. 7.9 10.9 .. 5.0 .. 6.9 11.3 2.7 1.8 .. 3.3 –17.6 –1.6 –4.0 –0.1 12.5 3.6 5.0 2.2 8.9 –18.5 5.5 4.6 7.4 6.3 –2.6 11.0 19.8 9.2 11.4 5.4 –2.5 3.2 w 5.9 2.6 4.8 –0.4 2.9 8.2 –7.2 5.1 1.9 6.0 4.1 3.3 ..

9.4 9.2 5.1 6.2 10.4 14.5 .. 0.3 5.2 5.2 .. 7.5 .. 6.7 19.5 4.3 1.2 .. 10.6 18.2 9.4 10.1 5.9 4.2 0.9 11.6 .. 8.8 5.8 5.5 2.3 1.2 –0.9 4.7 –1.0 11.5 –3.0 11.8 4.9 –10.7 2.5 w 12.4 7.8 9.8 4.1 8.4 12.0 7.6 1.6 7.3 13.2 6.8 0.6 ..

8.1 0.8 –3.8 .. 6.3 .. –11.2 .. 9.0 1.7 .. 5.6 10.5 7.5 14.2 3.8 8.6 4.0 12.0 –1.4 7.1 9.5 1.2 6.9 5.1 11.7 –6.1 14.7 –3.6 5.5 6.6 7.3 6.0 2.4 1.0 24.1 8.7 16.6 2.8 10.5 6.9 w 8.5 7.3 6.9 7.7 7.4 11.0 3.6 8.5 4.1 9.5 5.0 6.8 6.6

11.4 9.7 11.4 2.2 3.3 15.3 .. .. 11.9 7.5 .. 1.9 2.8 4.8 9.1 2.1 4.8 1.4 0.2 13.8 2.5 6.6 6.0 5.9 2.9 11.8 13.9 8.7 6.4 12.2 2.6 0.3 5.1 5.3 –0.8 16.6 –3.1 –0.3 12.7 –7.6 5.9 w 11.5 10.9 14.7 6.9 11.0 16.5 9.8 5.4 5.2 13.8 3.7 3.4 3.3

6.0 –6.1 5.0 .. 3.5 .. –0.2 .. 11.7 5.2 .. 7.1 9.4 8.6 8.8 4.5 6.3 4.2 4.4 –3.9 0.3 4.6 1.1 9.9 3.8 11.0 0.6 10.0 –6.6 6.4 6.8 9.8 9.9 –1.2 8.2 28.2 7.5 8.3 1.5 9.4 6.9 w 9.2 6.6 5.8 7.4 6.9 10.4 2.0 10.7 0.6 10.6 5.4 7.0 6.0

13.4 18.0 4.6 2.2 5.9 19.4 .. .. 10.8 7.3 .. 8.0 6.1 7.0 4.5 1.4 2.9 1.7 10.1 1.7 5.2 8.8 3.1 9.5 1.8 12.8 12.3 8.0 7.5 13.6 4.5 3.9 –0.5 4.9 4.2 19.9 –2.3 7.1 8.4 –4.2 5.2 w 13.9 10.4 12.5 8.2 10.8 15.0 11.5 4.0 8.4 15.4 7.8 3.9 3.3

a. Household final consumption expenditure includes statistical discrepancy. b. China has revised its national accounts data from 1993 onwards. However, data by expenditure are not availble. Data shown here are based on earlier series. c. Data cover mainland Tanzania only.

224

2007 World Development Indicators

4.9

ECONOMY

Growth of consumption, investment, and trade About the data

Measures of growth in consumption and capital

applying a wage (price) index or extrapolate from

expenditures on national defense and security but

formation are subject to two kinds of inaccuracy. The

the change in government employment. Neither

excludes government military expenditures that

first stems from the difficulty of measuring expendi-

technique captures improvements in productivity

potentially have wider public use and are part of

tures at current price levels, as described in About

or changes in the quality of government services.

government capital formation. • Gross capital for-

the data for table 4.8. The second arises in deflat-

Deflators for household consumption are usually cal-

mation consists of outlays on additions to the fixed

ing current price data to measure volume growth,

culated on the basis of the consumer price index.

assets of the economy, net changes in the level of

where results depend on the relevance and reliability

Many countries estimate household consumption

inventories, and net acquisitions of valuables. Fixed

of the price indexes and weights used. Measuring

as a residual that includes statistical discrepancies

assets include land improvements (fences, ditches,

price changes is more difficult for investment goods

associated with the estimation of other expenditure

drains, and so on); plant, machinery, and equipment

than for consumption goods because of the one-time

items, including changes in inventories; thus these

purchases; and the construction of roads, railways,

nature of many investments and because the rate of

estimates lack detailed breakdowns of household

and the like, including schools, offices, hospitals,

technological progress in capital goods makes cap-

consumption expenditures.

private residential dwellings, and commercial and

turing change in quality difficult. (An example is computers—prices have fallen as quality has improved.)

industrial buildings. Inventories are stocks of goods Definitions

held by firms to meet temporary or unexpected fluctu-

Several countries estimate capital formation from

• Household final consumption expenditure is the

ations in production or sales, and “work in progress.”

the supply side, identifying capital goods entering

market value of all goods and services, including

• Exports and imports of goods and services repre-

an economy directly from detailed production and

durable products (such as cars, washing machines,

sent the value of all goods and other market services

international trade statistics. This means that the

and home computers), purchased by households.

provided to, or received from, the rest of the world.

price indexes used in deflating production and inter-

It excludes purchases of dwellings but includes

They include the value of merchandise, freight, insur-

national trade, reflecting delivered or offered prices,

imputed rent for owner-occupied dwellings. It also

ance, transport, travel, royalties, license fees, and

will determine the defl ator for capital formation

includes payments and fees to governments to

other services, such as communication, construc-

expenditures on the demand side.

obtain permits and licenses. World Development Indi-

tion, financial, information, business, personal, and

Growth rates of household final consumption expen-

cators includes in household consumption expendi-

government services. They exclude compensation of

diture, household final consumption expenditure per

ture the expenditures of nonprofit institutions serving

employees and investment income (factor services in

capita, general government final consumption expen-

households, even when reported separately by the

the 1968 SNA) as well as transfer payments.

diture, gross capital formation, and exports and

country. In practice, household consumption expen-

imports of goods and services are estimated using

diture may include any statistical discrepancy in the

constant price data. (Consumption, capital forma-

use of resources relative to the supply of resources.

tion, and exports and imports of goods and services

• General government final consumption expendi-

as shares of GDP are shown in table 4.8.)

ture includes all government current expenditures

To obtain government consumption in constant

for purchases of goods and services (including

prices, countries may defl ate current values by

compensation of employees). It also includes most

Investment is rising rapidly in Asia

4.9a Data sources

Gross capital formation (2000 $ billions) 1,000

National accounts indicators for most developing

East Asia & Pacific

countries are collected from national statistical 800

organizations and central banks by visiting and resident World Bank missions. Data for high-

600

Latin America & Caribbean

income economies come from data files of the Organisation for Economic Co-operation and

400 South Asia Middle East & North Africa

200

Development (see the OECD’s Annual National Accounts for OECD Member Countries: Data from

Sub-Saharan Africa

0

1970 Onwards). The United Nations Statistics Division publishes detailed national accounts for UN

1980

1985

1990

1995

2000

2005

member countries in National Accounts Statistics:

Between 1980 and 2005 investment increased eightfold in East Asia & Pacific and fivefold in South Asia.

Main Aggregates and Detailed Tables and updates

Source: World Bank data files.

in the Monthly Bulletin of Statistics.

2007 World Development Indicators

225

4.10

Central government finances Revenuea

Afghanistanb Albaniab Algeriab 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, China Colombia Congo, Dem. Rep.b Congo, Rep. Costa Ricab Côte d’Ivoireb Croatiab Cuba Czech Republicb Denmark Dominican Republicb 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

226

Expense

% of GDP 1995 2005

% of GDP 1995 2005

.. 21.2 30.2 .. .. .. .. 38.9 18.0 .. 30.0 41.5 .. .. .. 40.5 26.9 35.5 .. 19.3 .. 13.0 20.6 .. .. .. 5.4 .. .. 5.3 .. 20.3 20.1 43.1 .. 33.2 39.1 16.0 14.1 25.9 .. .. .. .. 39.8 43.3 .. 23.7 12.2 29.9 17.0 38.6 8.4 11.2 .. ..

.. 25.6 24.2 .. .. .. .. 44.2 19.8 .. 28.7 45.6 .. .. .. 30.4 32.9 39.4 .. 23.6 .. 11.7 24.6 .. .. .. .. .. .. 8.2 .. 21.3 .. 42.5 .. 32.6 38.2 10.2 12.0 23.8 .. .. .. .. 38.5 47.3 .. .. 15.4 38.6 .. 47.9 7.6 12.1 .. ..

6.2 23.6 35.3 .. 18.1 19.3 26.0 39.9 .. 10.0 33.3 42.1 15.5 23.5 39.1 .. .. 39.0 13.2 .. 9.8 .. 20.0 8.1 .. 24.4 9.5 .. 27.6 7.9 30.9 22.8 17.1 40.4 .. 31.6 36.0 16.7 .. 19.5 16.0 .. 32.6 15.2 39.2 43.1 .. .. 18.2 28.7 23.8 41.7 10.1 .. .. ..

2007 World Development Indicators

11.9 21.9 24.1 .. 18.3 18.1 24.8 42.1 .. 8.8 29.8 42.3 24.7 26.6 36.2 .. .. 34.3 11.7 .. 7.7 .. 18.1 9.4 .. 18.7 11.1 .. 31.4 .. 19.9 22.7 16.9 40.3 .. 35.8 34.7 16.2 .. 22.6 17.7 .. 29.5 22.0 36.6 46.1 .. .. 17.4 31.2 20.9 44.2 11.0 .. .. ..

Cash surplus or deficit

% of GDP 1995 2005

.. –8.9 –1.3 .. .. .. .. –5.4 –3.1 .. –2.7 –3.8 .. .. .. 4.9 –2.7 –5.1 .. –4.7 .. 0.3 –4.4 .. .. .. .. .. .. 0.0 .. –2.1 .. –1.3 .. –0.9 1.5 0.8 0.0 –1.1 .. .. .. .. 1.9 –4.1 .. .. –4.3 –8.3 .. –10.0 –0.5 –4.3 .. ..

0.9 –3.0 1.2 .. –0.5 –1.0 1.1 –2.0 .. –0.7 0.2 –0.1 –10.3 –3.8 2.3 .. .. 3.5 –4.1 .. 0.0 .. 1.7 –0.5 .. 4.7 –2.1 .. 3.9 .. 6.4 –0.8 –1.5 –2.8 .. –3.5 1.8 –0.7 .. –5.7 –4.4 .. 2.7 –8.0 3.2 –2.8 .. .. 1.5 –2.3 –2.9 –5.1 –1.5 .. .. ..

Net incurrence of liabilities

Debt and interest payments

% of GDP Domestic Foreign 1995 2005 1995 2005

.. 7.4 –7.4 .. .. .. .. .. .. .. 2.2 .. .. .. .. 0.2 .. 7.4 .. 3.1 .. –0.3 5.0 .. .. .. 1.6 .. .. 0.0 .. .. –1.2 –2.7 .. –0.5 .. 0.0 .. .. .. .. .. .. 0.3 .. .. .. 2.2 .. .. .. .. –0.1 .. ..

0.1 1.9 1.8 .. 0.5 0.3 .. .. .. 2.3 1.3 .. 2.7 2.1 –0.1 .. .. –1.4 –3.3 .. –1.1 .. –1.0 1.2 .. –2.0 .. .. 5.2 .. .. .. –0.1 5.3 .. 1.2 .. 1.0 .. .. 3.6 .. .. 1.0 –1.0 .. .. .. –0.3 1.7 .. .. 1.5 .. .. ..

.. 2.1 8.6 .. .. .. .. .. .. .. 0.4 .. .. .. .. –0.4 .. –0.8 .. 4.0 .. 0.4 0.0 .. .. .. .. .. .. 0.2 .. –0.8 3.8 0.8 .. –0.4 .. –1.0 .. .. .. .. .. .. –1.3 .. .. .. 2.4 .. .. .. 0.4 4.5 .. ..

1.4 1.0 –1.6 .. 1.5 0.6 .. .. .. 0.9 0.3 .. 3.0 1.7 0.6 .. .. –5.8 6.4 .. 1.6 .. 0.3 0.1 .. –0.8 .. .. –1.7 .. .. .. 1.2 –1.8 .. 2.1 .. 2.3 .. .. 2.9 .. .. 7.6 2.3 .. .. .. –0.3 0.3 3.3 .. 0.7 .. .. ..

Total debt % of GDP 2005

9.4 .. 47.1 .. .. .. 21.4 66.6 .. 36.2 12.3 88.7 .. .. .. .. .. .. .. .. .. .. 48.7 .. .. .. .. .. 56.7 .. 0.2 .. 107.9 .. .. 23.1 42.2 .. .. .. 48.7 .. 9.6 .. 45.6 71.9 .. .. 35.2 44.2 .. 137.8 18.3 .. .. ..

Interest payments % of revenue 2005

0.3 15.5 8.6 .. 26.5 2.2 3.7 7.1 .. 16.4 1.1 9.5 1.5 10.0 1.5 .. .. 4.0 3.7 .. 1.8 .. 7.5 8.0 .. 3.5 4.7 .. 21.6 .. 18.1 18.0 11.3 5.4 .. 2.4 8.4 9.1 .. 29.4 13.6 .. 0.4 7.6 4.0 5.8 .. .. 5.4 5.8 14.4 11.2 11.6 .. .. ..

Revenuea

Honduras Hungary Indiab Indonesiab Iran, Islamic Rep.b Iraq Ireland Israel Italy Jamaicab Japan Jordanb Kazakhstanb Kenyab Korea, Dem. Rep. Korea, Rep.b Kuwait Kyrgyz Republicb Lao PDR Latviab Lebanon Lesothob Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysiab Mali Mauritania Mauritiusb Mexicob Moldovab Mongolia Moroccob Mozambique Myanmar Namibiab Nepalb Netherlands New Zealand Nicaraguab Niger Nigeria Norway Omanb Pakistanb Panamab Papua New Guineab Paraguay b Perub Philippinesb Poland Portugal Puerto Rico

Expense

% of GDP 1995 2005

% of GDP 1995 2005

.. .. 12.3 17.7 24.2 .. 33.6 .. 40.3 .. 20.7 28.2 14.0 21.6 .. 17.8 .. 16.7 .. 25.8 .. 49.8 .. .. .. .. .. .. 24.4 .. .. 21.6 15.3 28.4 .. .. .. 6.4 31.7 10.5 40.3 .. 15.0 .. .. .. 27.8 17.2 26.1 23.9 .. 17.4 17.7 .. 35.3 ..

.. .. 14.5 9.7 15.8 .. 37.4 .. 48.0 33.3 .. 26.1 18.7 25.9 .. 14.3 .. 25.6 .. 28.3 .. 34.4 .. .. .. .. .. .. 17.2 .. .. 19.9 15.0 38.4 .. .. .. .. 35.7 .. 49.2 .. 16.3 .. .. .. 32.4 19.1 22.0 25.8 .. 17.4 15.9 .. 37.8 ..

.. 35.9 12.5 18.5 35.6 .. 31.8 41.8 35.7 35.1 .. 28.4 21.3 19.9 .. 23.4 37.2 .. .. 26.6 21.4 47.7 .. .. 28.8 .. 60.4 .. 23.7 .. .. 21.1 .. 32.5 37.9 28.8 .. 5.0 28.1 12.8 40.2 38.0 22.4 .. .. 50.7 .. 12.9 .. 22.5 21.2 17.6 15.1 33.6 37.9 ..

.. 43.0 15.8 17.0 20.5 .. 30.3 46.7 39.4 33.8 .. 35.3 18.2 20.7 .. 21.4 26.2 .. .. 29.4 26.2 36.5 .. .. 28.3 .. 63.0 .. 20.1 .. .. 20.9 .. 30.0 30.8 31.3 .. .. 31.1 16.6 40.0 32.5 21.0 .. .. 34.1 .. 14.5 .. 22.1 16.7 17.3 18.0 36.3 42.4 ..

Cash surplus or deficit

% of GDP 1995 2005

.. .. –2.2 3.0 1.1 .. –2.1 .. –7.5 .. .. 0.9 –1.8 –5.1 .. 2.4 .. –10.8 .. –2.7 .. 5.1 .. .. .. .. .. .. 2.4 .. .. –1.3 –0.6 –6.3 .. .. .. .. –5.0 .. –8.9 .. 0.6 .. .. .. –8.9 –5.3 1.5 –0.5 .. –1.3 –0.8 .. –3.0 ..

.. –7.4 –3.6 –1.1 7.4 .. 1.0 –2.6 –3.5 –1.2 .. –4.7 2.6 –1.5 .. 0.8 8.2 .. .. –0.9 –8.4 5.1 .. .. –0.4 .. –22.5 .. –4.3 .. .. –2.1 .. 1.9 –0.5 –5.6 .. .. –6.8 –1.2 0.0 4.6 –0.7 .. .. 16.3 .. –3.2 .. –2.3 1.1 –0.8 –3.0 –2.3 –5.8 ..

Net incurrence of liabilities

4.10 Debt and interest payments

% of GDP Domestic Foreign 1995 2005 1995 2005

Total debt % of GDP 2005

.. .. 5.2 –0.6 .. .. .. .. .. .. 1.5 –2.5 0.8 3.9 .. –0.3 .. .. .. 2.4 .. 0.0 .. .. .. .. .. .. .. .. .. 3.1 .. 3.0 .. .. .. .. .. 0.6 .. .. .. .. .. .. –0.1 .. .. 1.5 .. .. –0.5 .. –3.5 ..

.. 67.2 65.4 29.0 .. .. 29.6 .. 114.4 139.6 .. 89.1 7.1 .. .. .. .. .. .. .. .. .. .. .. 21.4 .. .. .. .. .. .. 43.8 .. 33.2 119.8 63.2 .. .. .. 57.2 55.5 46.4 .. .. .. 37.0 .. .. .. 69.7 .. .. 69.9 41.5 73.7 ..

.. 1.3 3.6 0.0 –0.6 .. .. .. .. .. .. 3.1 0.9 0.7 .. –0.1 .. .. .. 0.5 –1.3 .. .. .. –0.1 .. –3.6 .. .. .. .. 2.9 .. 0.2 11.3 7.6 .. .. –20.0 0.3 .. –1.8 .. .. .. 0.0 .. .. .. 4.9 0.1 1.9 1.4 –0.3 0.3 ..

.. .. 0.0 –0.4 0.1 .. .. .. .. .. .. 6.1 2.8 –1.3 .. –0.1 .. .. .. 1.5 .. 6.2 .. .. .. .. .. .. –0.8 .. .. –0.6 5.5 2.7 .. .. .. .. .. 2.5 .. .. 3.4 .. .. .. 0.0 .. .. –0.7 .. 3.9 –0.7 .. 4.1 ..

.. 4.2 0.3 –0.4 –0.9 .. .. .. .. .. .. –3.0 –1.5 0.7 .. –0.3 .. .. .. –0.2 12.3 .. .. .. 1.0 .. 31.8 .. .. .. .. 0.3 .. –0.1 –6.8 –0.7 .. .. –0.1 0.6 .. 2.8 .. .. .. 1.1 .. .. .. –2.2 –0.7 –1.2 1.9 3.6 6.3 ..

2007 World Development Indicators

Interest payments % of revenue 2005

.. 11.3 31.9 14.8 0.7 .. 3.1 12.1 12.7 41.3 .. 6.8 1.7 10.2 .. 5.4 0.1 .. .. 1.9 56.0 3.8 .. .. 2.8 .. 14.5 .. 10.5 .. .. 12.0 .. 3.8 3.1 13.0 .. .. 9.1 7.5 5.1 4.5 7.4 .. .. 1.7 .. 31.8 .. 19.9 5.6 10.6 38.7 7.1 6.8 ..

227

ECONOMY

Central government finances

4.10

Central government finances Revenuea

Romania Russian Federation Rwandab Saudi Arabia Senegalb Serbia and Montenegrob Sierra Leoneb Singaporeb Slovak Republic Sloveniab Somalia South Africa Spain Sri Lankab Sudanb Swazilandb Sweden Switzerlandb Syrian Arab Republicb Tajikistanb Tanzania Thailand Togob Trinidad and Tobagob Tunisiab Turkey b Turkmenistan Ugandab Ukraineb United Arab Emiratesb United Kingdom United States Uruguay b Uzbekistan Venezuela, RBb 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 Europe EMU

Expense

Cash surplus or deficit

Debt and interest payments

% of GDP 1995 2005

% of GDP 1995 2005

% of GDP 1995 2005

% of GDP Domestic Foreign 1995 2005 1995 2005

Total debt % of GDP 2005

Interest payments % of revenue 2005

.. .. 10.6 .. 16.6 .. 9.4 26.7 .. 36.7 .. .. 32.0 20.4 7.2 .. 40.4 22.7 22.9 9.3 .. .. .. 27.2 30.0 17.7 .. 10.6 .. 10.1 37.2 .. 27.6 .. 16.9 .. .. 17.3 20.0 26.7 .. w 13.3 17.2 16.2 .. 16.6 8.4 .. 21.0 26.1 13.2 .. .. 34.8

.. .. 15.0 .. .. .. .. 12.4 .. 35.2 .. .. 37.1 26.0 6.8 .. 39.0 25.8 .. 11.4 .. .. .. 25.3 28.4 19.1 .. .. .. 9.3 37.1 .. 27.1 .. 18.5 .. .. 19.1 21.4 32.1 .. w 15.5 .. .. .. .. .. .. 23.1 .. 15.4 .. .. 42.3

.. .. –5.6 .. .. .. .. 19.8 .. –0.2 .. .. –5.8 –7.6 –0.4 .. 2.2 –0.6 .. –3.3 .. .. .. –0.1 –2.5 –1.8 .. .. .. 0.5 0.3 .. –1.2 .. –2.3 .. .. –3.9 –3.1 –5.4 .. w –2.7 .. .. .. .. .. .. –1.5 .. –2.7 .. .. –7.4

.. .. 2.9 .. .. .. 0.3 10.3 .. –0.4 .. .. .. 5.2 0.3 .. .. –0.5 .. 0.1 .. .. .. 2.8 0.9 .. .. .. .. .. –0.3 .. 7.9 .. 1.1 .. .. .. 28.0 –1.4 .. m .. .. .. .. .. .. .. .. .. 3.8 .. .. ..

.. 41.4 .. .. .. .. .. 108.9 37.2 .. .. .. 44.4 94.2 .. .. 54.3 28.6 .. .. .. 27.3 .. .. 59.0 .. .. .. .. .. .. 47.2 78.7 .. .. .. .. .. .. .. .. m .. .. .. .. .. .. .. .. .. 57.2 .. 48.7 69.2

8.4 3.1 .. .. .. 2.6 21.0 0.8 5.4 4.0 .. 11.1 5.5 29.1 .. 4.5 4.4 4.5 .. 5.1 .. 6.1 7.1 11.0 9.6 .. .. 6.5 2.1 .. 5.6 11.1 16.0 .. 10.4 .. .. .. .. .. 7.9 m .. 6.8 6.8 6.2 10.2 7.6 2.9 11.1 10.1 18.3 .. 5.6 5.8

25.8 30.7 .. .. .. 35.8 12.3 20.1 31.4 40.3 .. 30.2 26.9 16.1 .. 26.6 38.9 19.4 .. 13.5 .. 21.0 14.1 28.5 29.7 .. .. 12.1 36.5 .. 38.0 18.4 27.2 .. 29.3 .. .. .. .. .. 26.6 w 13.0 .. 15.0 .. .. 11.4 32.3 .. 25.6 12.5 .. 26.5 35.2

25.9 20.0 .. .. .. 39.9 23.8 15.4 34.9 41.3 .. 29.6 24.9 21.0 .. 24.4 36.1 19.2 .. 13.7 .. 16.3 15.4 24.6 29.5 .. .. 22.8 37.5 .. 41.1 21.2 27.5 .. 26.0 .. .. .. .. .. 28.2 w 15.5 .. 15.6 .. .. 12.5 29.7 .. 23.2 15.2 .. 28.4 37.2

–2.0 9.9 .. .. .. –3.0 –2.5 4.1 –3.4 –1.5 .. 0.2 1.5 –7.3 .. –2.6 2.1 0.6 .. –6.6 .. 2.5 –5.5 2.1 –3.0 .. .. –3.8 –1.4 .. –2.9 –2.9 –1.6 .. 2.3 .. .. .. .. .. –1.7 w –3.2 .. –1.7 .. .. –1.9 2.2 .. –2.8 –3.2 .. –2.0 –2.0

a. Excluding grants. b. Data were reported on a cash basis and have been adjusted to the accrual framework.

228

Net incurrence of liabilities

2007 World Development Indicators

0.4 0.3 .. .. .. .. .. 9.2 –0.6 2.0 .. 1.8 .. 5.6 .. .. –1.3 –0.6 .. .. .. –3.3 .. .. –0.3 .. .. 0.5 4.5 .. 3.6 1.7 2.4 .. 1.3 .. .. .. .. .. .. m .. 1.1 1.1 0.5 .. .. 0.3 .. .. 0.3 .. .. ..

.. .. .. .. .. .. .. 0.0 .. 0.3 .. .. .. 3.2 .. .. .. .. .. 2.3 .. .. .. 2.6 2.9 .. .. .. .. .. 0.0 .. 1.1 .. 0.1 .. .. .. 16.2 1.6 .. m .. .. .. .. .. .. .. .. .. 1.1 .. .. ..

1.7 –4.2 .. .. .. .. .. .. –4.3 –1.9 .. 0.1 .. 2.0 .. .. .. .. .. .. .. –0.9 .. .. 0.9 .. .. 4.2 0.2 .. 0.0 1.2 1.7 .. 3.4 .. .. .. .. .. .. m .. 0.6 1.1 1.7 .. .. 0.0 .. .. 1.4 .. .. ..

About the data

4.10

Definitions

Tables 4.10–4.12 present an overview of the size

borrowing for temporary periods can also be used. The

• Revenue is cash receipts from taxes, social con-

and role of central governments relative to national

definition of government excludes public corporations

tributions, and other revenues such as fines, fees,

economies. The data in these tables are based on

and quasi corporations (such as the central bank).

rent, and income from property or sales. Grants are

the concepts and recommendations of the second

Units of government meeting this definition exist

edition of the International Monetary Fund’s (IMF)

at many levels, from local administrative units to the

Government Finance Statistics Manual 2001. Before

highest level of national government, but inadequate

2005, World Development Indicators reported data

statistical coverage precludes the presentation of

derived on the basis of the 1986 manual. The 2001

subnational data. Although data for general govern-

includes compensation of employees (such as wages

manual, which is harmonized with the 1993 System of

ment are available for a few countries under the 2001

and salaries), interest and subsidies, grants, social

National Accounts, recommends an accrual account-

manual, only data for the central government are

benefits, and other expenses such as rent and divi-

ing method instead of the cash-based method of the

shown to minimize disparities. However, cross-coun-

dends. • Cash surplus or deficit is revenue (includ-

1986 manual. The new manual focuses on all eco-

try comparisons are potentially misleading due to dif-

ing grants) minus expense, minus net acquisition of

nomic events affecting assets, liabilities, revenues,

ferent accounting concepts of central government.

nonfinancial assets. In the earlier version nonfinancial

also considered as revenue but are excluded here. • Expense is cash payments for operating activities of the government in providing goods and services. It

and expenses, instead of only those represented by

Central government can refer to one of two account-

cash transactions. The new manual takes all stocks

ing concepts: consolidated or budgetary. For most

into account, so that the stock data at the end of

countries central government finance data have been

an accounting period is equal to the stock data at

consolidated into one account, but for others only

the beginning of the period plus the flows during

budgetary central government accounts are avail-

is lending minus repayments, which are brought in

the period. The 1986 manual considered only the

able. Countries reporting budgetary data are noted

below as a financing item under net acquisition of

debt stock data. Further, the new manual does not

in Primary data documentation. Because budgetary

financial assets). • Net incurrence of government

distinguish between current and capital revenue

accounts do not necessarily include all central gov-

liabilities includes domestic financing (obtained

or expenditures, unlike the 1986 manual. The new

ernment units (such as extrabudgetary accounts and

from residents) and foreign financing (obtained from

manual also introduces the concepts of nonfinancial

social security funds), the picture they provide of

and financial assets. Most countries still follow the

central government activities is usually incomplete.

previous manual, however. The IMF has reclassified

Data on government revenues and expenditures

historical Government Finance Statistics Yearbook

are collected by the IMF through questionnaires dis-

data to conform to the format of the 2001 manual.

tributed to member governments and by the Organ-

get surplus. The net incurrence of liabilities should

Because of differences in reporting, the reclassified

isation for Economic Co-operation and Development.

be offset by the net acquisition of financial assets

data understate both revenue and expense.

Despite the IMF’s efforts to systematize and stan-

(a third financing item). The difference between the

Government Finance Statistics Manual 2001

dardize the collection of public finance data, statis-

cash surplus or deficit and the three financing items

describes the economic functions of a government as

tics on public finance are often incomplete, untimely,

is the net change in the stock of cash. • Total debt

the provision of goods and services to the community

and not comparable across countries.

is the entire stock of direct government fixed-term

on a nonmarket basis for collective or individual con-

Government finance statistics are reported in local

sumption, and the redistribution of income and wealth

currency. The indicators here are shown as percent-

through transfer payments. The activities of govern-

ages of GDP. Many countries report government

ment are financed mainly by taxation and other trans-

finance data by fiscal year; see Primary data documen-

fers of income, though other forms of financing such as

tation for information on fiscal year end by country.

assets were included under revenue and expenditure in gross terms. This cash surplus or deficit is closest to the earlier overall budget balance (still missing

nonresidents), or the means by which a government provides financial resources to cover a budget deficit or allocates financial resources arising from a bud-

contractual obligations to others outstanding on a particular date. It includes domestic and foreign liabilities such as currency and money deposits, securities other than shares, and loans. It is the gross amount of government liabilities reduced by the amount of equity and financial derivatives held by the govern-

Fourteen developing economies had a cash deficit greater than 4 percent of GDP

4.10a

ment. Because debt is a stock rather than a flow, it is measured as of a given date, usually the last day of

Central government cash deficit as a share of GDP, 2005 (%) 0

the fiscal year. • Interest payments include interest payments on government debt—including long-term

–20

–25

Burkina Faso

Malaysia

El Salvador

Togo

Morocco

Taijikistan

Namibia

Sri Lanka

Hungary

Lebanon

Benin

Maldives

–15

Madagascar

–10

Jordan

bonds, long-term loans, and other debt instruments –5

—to domestic and foreign residents. Data sources Data on central government finances are from the IMF’s Government Finance Statistics Yearbook 2006 and IMF data files. Each country’s accounts are reported using the system of common defi nitions and classifications in the IMF’s Government Finance Statistics Manual 2001. See these sources for complete and authoritative explana-

Note: Data are for the most recent year available for 2003–05. Source: International Monetary Fund, Government Finance Statistics data files, and World Bank data files.

tions of concepts, definitions, and data sources.

2007 World Development Indicators

229

ECONOMY

Central government finances

4.11 Afghanistana Albaniaa Algeriaa 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, China Colombia Congo, Dem. Rep.a Congo, Rep. Costa Ricaa Côte d’Ivoirea Croatiaa Cuba Czech Republica Denmark Dominican Republica Ecuadora Egypt, Arab Rep.a El Salvador Eritrea Estonia Ethiopiaa Finland France Gabon Gambia, Thea Georgiaa Germany Ghanaa Greece Guatemalaa Guineaa Guinea-Bissau Haiti

230

Central government expenses Goods and services

Compensation of employees

Interest payments

Subsidies and other transfers

Other expense

% of expense 1995 2005

% of expense 1995 2005

% of expense 1995 2005

% of expense 1995 2005

% of expense 1995 2005

.. 18 6 .. .. .. .. 5 49 .. 39 3 .. .. .. 32 5 18 .. 20 .. 17 8 .. .. .. .. .. .. 37 .. 12 .. 35 .. 7 8 16 6 21 .. .. .. .. 10 8 .. .. 52 4 .. 10 15 17 .. ..

2007 World Development Indicators

45 12 6 .. 5 40 10 5 .. 17 11 3 72 14 23 .. .. 22 21 .. 40 .. 7 27 .. 10 .. .. 10 .. 29 11 32 8 .. 6 10 11 .. 10 18 .. 17 24 10 6 .. .. 21 5 .. 10 12 .. .. ..

.. 14 39 .. .. .. .. 13 10 .. 5 7 .. .. .. 30 8 7 .. 30 .. 40 10 .. .. .. .. .. .. 58 .. 38 .. 27 .. 9 13 41 49 26 .. .. .. .. 10 23 .. .. 11 5 .. 22 50 34 .. ..

47 30 32 .. 12 20 10 12 .. 25 13 7 25 23 28 .. .. 11 41 .. 36 .. 12 53 .. 21 .. .. 20 .. 37 41 39 26 .. 9 13 28 .. 33 39 .. 21 14 10 22 .. .. 17 5 45 25 25 .. .. ..

.. 9 13 .. .. .. .. 8 0 .. 1 18 .. .. .. 2 45 37 .. 6 .. 26 18 .. .. .. .. .. .. 1 .. 20 .. 3 .. 3 13 9 26 31 .. .. .. .. 9 6 .. .. 10 6 .. 26 12 28 .. ..

0 17 12 .. 26 2 4 7 .. 20 1 9 1 10 2 .. .. 5 6 .. 3 .. 8 9 .. 5 4 .. 19 .. 29 18 12 5 .. 2 9 10 .. 27 12 .. 0 7 4 5 .. .. 6 5 21 11 11 .. .. ..

.. 59 34 .. .. .. .. 55 41 .. 55 71 .. .. .. 36 45 38 .. 14 .. 14 64 .. .. .. .. .. .. 2 .. 26 .. 32 .. 75 64 19 .. 7 .. .. .. .. 68 51 .. .. 26 67 .. 33 18 9 .. ..

5 42 50 .. 50 36 71 52 .. 29 67 51 1 47 43 .. .. 59 32 .. 19 .. 66 .. .. 57 64 .. 42 .. 5 28 17 54 .. 68 66 40 .. 18 25 .. 45 42 71 53 .. .. 53 82 5 40 24 .. .. ..

.. 0 8 .. .. .. .. 2 0 .. 0 3 .. .. .. 2 1 2 .. 10 .. .. .. .. .. .. .. .. .. .. .. 4 .. 3 .. 5 4 6 .. .. .. .. .. .. 7 2 .. .. .. 20 .. 10 6 1 .. ..

2 0 .. .. 7 1 6 1 .. 9 8 1 .. 5 4 .. .. 3 0 .. 2 .. 6 11 .. 12 4 .. 9 .. 0 2 2 6 .. 15 4 11 .. 12 5 .. 3 14 7 2 .. .. 3 3 .. 0 28 .. .. ..

Honduras Hungary Indiaa Indonesiaa Iran, Islamic Rep.a Iraq Ireland Israel Italy Jamaicaa Japan Jordana Kazakhstana Kenyaa Korea, Dem. Rep. Korea, Rep.a Kuwait Kyrgyz Republica Lao PDR Latviaa Lebanon Lesothoa Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysiaa Mali Mauritania Mauritiusa Mexicoa Moldovaa Mongolia Moroccoa Mozambique Myanmar Namibiaa Nepala Netherlands New Zealand Nicaraguaa Niger Nigeria Norway Omana Pakistana Panamaa Papua New Guineaa Paraguaya Perua Philippinesa Poland Portugal Puerto Rico

4.11

ECONOMY

Central government expenses Goods and services

Compensation of employees

Interest payments

Subsidies and other transfers

Other expense

% of expense 1995 2005

% of expense 1995 2005

% of expense 1995 2005

% of expense 1995 2005

% of expense 1995 2005

.. .. 14 21 21 .. 5 .. 4 22 .. 7 .. 15 .. 16 .. 32 .. 20 .. 32 .. .. .. .. .. .. 23 .. .. 12 9 10 .. .. .. .. 28 .. 5 .. 16 .. .. .. 55 .. 16 19 .. 20 15 .. 7 ..

.. 8 15 8 13 .. 14 27 4 20 .. 5 18 29 .. 10 23 .. .. 13 3 31 .. .. 13 .. 14 .. 26 .. .. 13 .. 18 36 16 .. .. 28 .. 7 28 16 .. .. 11 .. 36 .. 35 11 20 18 7 6 ..

.. .. 10 20 56 .. 15 .. 14 24 .. 67 .. 28 .. 15 .. 36 .. 20 .. 45 .. .. .. .. .. .. 34 .. .. 45 19 8 .. .. .. .. 53 .. 8 .. 23 .. .. .. 30 .. 45 36 .. 19 34 .. 30 ..

.. 14 10 13 47 .. 24 24 16 32 .. 58 7 50 .. 11 31 .. .. 16 33 38 .. .. 20 .. 39 .. 30 .. .. 39 .. 14 30 43 .. .. 49 .. 8 26 30 .. .. 16 .. 4 .. 28 52 20 30 12 30 ..

.. .. 27 16 0 .. 14 .. 24 32 .. 11 3 46 .. 3 .. 5 .. 3 .. 5 .. .. .. .. .. .. 17 .. .. 12 19 11 .. .. .. .. 1 .. 9 .. 15 .. .. .. 7 28 8 20 .. 19 33 .. 10 ..

.. 10 26 16 1 .. 3 11 12 43 .. 7 2 10 .. 6 0 .. .. 2 46 5 .. .. 3 .. 23 .. 12 .. .. 12 .. 4 4 12 .. .. 8 7 5 5 9 .. .. 3 .. 29 .. 21 7 11 32 7 6 ..

.. .. 33 41 .. .. 33 .. 54 1 .. 12 58 .. .. 63 .. 27 .. 56 .. 8 .. .. .. .. .. .. 27 .. .. 28 .. 71 .. .. .. .. .. .. 42 .. 34 .. .. .. 8 2 30 26 .. 33 15 .. 43 ..

.. 61 .. 63 34 .. 36 31 46 2 .. 3 44 9 .. 52 32 .. .. 39 16 26 .. .. 58 .. 11 .. 31 .. .. 33 .. 53 31 24 .. .. 14 .. 48 39 41 .. .. 67 .. 30 .. 16 26 45 17 69 47 ..

.. .. 0 2 .. .. 1 .. 6 21 .. 4 .. 2 .. 3 .. .. .. 0 .. 3 .. .. .. .. .. .. 1 .. .. 2 .. 1 .. .. .. .. 4 .. 2 .. 13 .. .. .. 0 .. 1 1 .. 8 .. .. 11 ..

2007 World Development Indicators

.. 11 .. 0 5 .. 1 8 2 3 .. 11 28 2 .. 21 13 .. .. 31 2 .. .. .. 8 .. 13 .. 1 .. .. 3 .. 11 0 5 .. .. 2 .. 2 6 4 .. .. 6 .. .. .. .. 3 4 2 7 1 ..

231

4.11 Romania Russian Federation Rwandaa Saudi Arabia Senegala Serbia and Montenegroa Sierra Leonea Singaporea Slovak Republic Sloveniaa Somalia South Africa Spain Sri Lankaa Sudana Swazilanda Sweden Switzerlanda Syrian Arab Republica Tajikistana Tanzania Thailand Togoa 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 Europe EMU

Central government expenses Goods and services

Compensation of employees

Interest payments

Subsidies and other transfers

Other expense

% of expense 1995 2005

% of expense 1995 2005

% of expense 1995 2005

% of expense 1995 2005

% of expense 1995 2005

.. .. 52 .. .. .. .. 38 .. 19 .. .. 5 23 44 .. 11 24 .. 47 .. .. .. 20 7 8 .. .. .. 50 22 .. 13 .. 6 .. .. 8 32 16 .. m .. .. .. 14 .. .. .. 16 8 .. .. 7 5

22 15 .. .. .. 10 28 35 10 12 .. 12 4 11 .. 29 12 9 .. 29 .. 22 48 18 7 .. .. 36 12 .. 18 15 14 .. 6 .. .. .. .. .. 12 m .. 13 16 10 .. 27 13 13 8 36 .. 10 6

.. .. 36 .. .. .. .. 39 .. 21 .. .. 14 20 38 .. 9 6 .. 8 .. .. .. 36 37 28 .. .. .. 37 7 .. 17 .. 22 .. .. 67 35 34 .. m .. .. .. 29 .. .. .. 24 39 .. .. 15 14

16 19 .. .. .. 14 26 31 13 19 .. 15 9 28 .. 42 10 7 .. 9 .. 35 31 33 39 .. .. 11 13 .. 15 13 22 .. 16 .. .. .. .. .. 21 m .. 21 28 18 27 31 14 24 41 28 .. 14 14

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.

232

2007 World Development Indicators

.. .. 12 .. .. .. .. 8 .. 3 .. .. 11 22 8 .. 13 4 .. 12 .. .. .. 20 13 14 .. .. .. .. 9 .. 6 .. 27 .. .. 16 16 31 .. m .. .. .. 10 .. .. .. 19 13 27 .. 9 11

8 5 .. .. .. 2 19 1 5 4 .. 11 6 24 .. 5 5 5 .. 5 .. 8 6 13 10 .. .. 6 2 .. 5 10 16 .. 12 .. .. .. .. .. 8m .. 7 8 6 11 7 4 11 12 16 .. 5 6

.. .. 5 .. .. .. .. 15 .. 55 .. .. 42 24 10 .. 64 66 .. 33 .. .. .. 24 36 33 .. .. .. .. 54 .. 64 .. 61 .. .. 8 19 19 .. m .. .. .. .. .. .. .. .. .. 24 .. 54 54

43 53 .. .. .. 68 9 33 63 63 .. 56 51 28 .. 21 68 74 .. 27 .. 33 2 35 34 .. .. 47 68 .. 54 61 47 .. 64 .. .. .. .. .. 44 m .. 44 36 57 .. 31 56 42 .. 28 .. 52 51

.. .. .. .. .. .. .. .. .. 3 .. .. 2 10 .. .. 5 0 .. .. .. .. .. 1 7 4 .. .. .. .. 9 .. 0 .. 2 .. .. 0 0 .. .. m .. .. .. .. .. .. .. .. .. .. .. 4 3

12 9 .. .. .. 6 18 .. 9 3 .. 5 2 8 .. 2 8 5 .. 30 .. 5 13 1 10 .. .. .. 4 .. 10 2 .. .. 3 .. .. .. .. .. 5m .. 5 5 5 .. 0 8 4 .. 9 .. 3 2

About the data

4.11

ECONOMY

Central government expenses Definitions

The term expense has replaced expenditure in this

is reflected in compensation of employees, use of

• Goods and services include all government pay-

table since the 2005 edition of World Development

goods and services, and consumption of fixed capi-

ments in exchange for goods and services used for

Indicators in accordance with use in the Interna-

tal. Purchases from a third party and cash transfers

the production of market and nonmarket goods and

tional Monetary Fund’s (IMF) Government Finance

to households are shown as subsidies and other

services. Own-account capital formation is excluded.

Statistics Manual 2001. Government expenses

transfers, and other expenses. The economic clas-

• Compensation of employees consists of all pay-

include all nonrepayable payments, whether current

sification can be problematic. For example, the dis-

ments in cash, as well as in kind (such as food and

or capital, requited or unrequited. Total central gov-

tinction between current and capital expense may

housing), to employees in return for services ren-

ernment expense as presented in the IMF’s Govern-

be arbitrary, and subsidies to public corporations or

dered, and government contributions to social insur-

ment Finance Statistics Yearbook is comparable to

banks may be disguised as capital financing. Subsi-

ance schemes such as social security and pensions

the concept used in the 1993 System of National

dies may also be hidden in special contractual pric-

that provide benefits to employees. • Interest pay-

Accounts.

ing for goods and services. For further discussion of

ments are payments made to nonresidents, to resi-

government finance statistics, see About the data for

dents, and to other general government units for the

tables 4.10 and 4.12.

use of borrowed money. (Repayment of principal is

Expenses can be measured either by function (health, defense, education) or by economic type (interest payments, wages and salaries, purchases

shown as a financing item, and commission charges

of goods and services). Functional data are often

are shown as purchases of services.) • Subsidies

incomplete, and coverage varies by country because

and other transfers include all unrequited, nonrepay-

functional responsibilities stretch across levels of

able transfers on current account to private and

government for which no data are available. Defense

public enterprises; grants to foreign governments,

expenses, usually the central government’s respon-

international organizations, and other government

sibility, are shown in table 5.7. For more information

units; and social security, social assistance benefits,

on education expenses, see table 2.9; for more on

and employer social benefi ts in cash and in kind.

health expenses, see table 2.14.

• Other expense is spending on dividends, rent, and

The classification of expenses by economic type in

other miscellaneous expenses, including provision

this table shows whether the government produces

for consumption 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

Interest payments are a large part of government expenses for some developing countries

4.11a

Central government interest payments as a share of total expense (%)

1995

2005

60

50 40

30 20

Data sources

10

Data on central government expenses are from Ne vis itt s

&

Gh an a

ar sc

St

.K

iL Sr

ad ag a

M

an ka

a In di

a ge

nt in

ep Ar

ta n

ng o, R Co

is Pa k

ca ai

pp in es ili Ph

Ja m

Le ba no n

0

Interest payments accounted for more than 20 percent of total expenses in 2005 for 11 countries. Note: Data are for the most recent year for 2003–05. For Lebanon, Madagascar, and Philippines, data for 1995 refer to 2000. No data for 1995 are available for Argentina, Republic of Congo, Ghana, and St. Kitts and Nevis. Source: International Monetary Fund, Government Finance Statistics data files.

the IMF’s Government Finance Statistics Yearbook 2006 and IMF data files. Each country’s accounts are reported using the system of common defi nitions and classifications in the IMF’s Government Finance Statistics Manual 2001. See these sources for complete and authoritative explanations of concepts, definitions, and data sources.

2007 World Development Indicators

233

4.12 Afghanistana Albaniaa Algeriaa 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, China Colombia Congo, Dem. Rep.a Congo, Rep. Costa Ricaa Côte d’Ivoirea Croatiaa Cuba Czech Republica Denmark Dominican Republica Ecuadora Egypt, Arab Rep.a El Salvador Eritrea Estonia Ethiopiaa Finland France Gabon Gambia, Thea Georgiaa Germany Ghanaa Greece Guatemalaa Guineaa Guinea-Bissau Haiti

234

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 1995 2005

% of revenue 1995 2005

% of revenue 1995 2005

% of revenue 1995 2005

% of revenue 1995 2005

% of revenue 1995 2005

.. 8 65 .. .. .. .. 20 31 .. 16 36 .. .. .. 21 14 17 .. 14 .. 17 50 .. .. .. 9 .. .. 21 .. 11 15 11 .. 15 34 16 50 22 .. .. .. .. 21 17 .. 14 7 16 15 17 19 8 .. ..

2 15 66 .. 19 16 65 23 .. 12 8 37 13 9 2 .. .. 13 16 .. 7 .. 53 14 .. 30 22 .. 21 25 4 15 21 8 .. 20 38 19 .. 25 24 .. 14 15 21 24 .. .. 2 16 22 21 26 .. .. ..

2007 World Development Indicators

.. 39 10 .. .. .. .. 21 34 .. 33 23 .. .. .. 4 24 28 .. 30 .. 25 17 .. .. .. 61 .. .. 12 .. 32 14 42 .. 32 40 34 26 17 .. .. .. .. 34 25 .. 32 48 20 31 31 46 4 .. ..

3 49 9 .. 29 32 24 23 .. 29 35 25 39 41 19 .. .. 43 38 .. 37 .. 17 23 .. 41 79 .. 29 24 16 38 16 48 .. 27 44 41 .. 26 44 .. 38 12 35 24 .. .. 58 22 22 28 52 .. .. ..

.. 14 18 .. .. .. .. 0 33 .. 6 .. .. .. .. 15 2 8 .. 20 .. 28 2 .. .. .. 7 .. .. 21 .. 15 58 9 .. 4 .. 36 11 13 .. .. .. .. 0 0 .. 42 10 .. 24 0 23 62 .. ..

20 8 13 .. 16 3 2 0 .. 33 8 .. 23 3 29 .. .. 2 11 .. 21 .. 1 19 .. 2 -12 .. 3 27 7 5 41 2 .. 0 .. 28 .. 12 7 .. 0 27 .. 0 .. .. 6 .. 29 0 15 .. .. ..

.. 1 1 .. .. .. .. 5 2 .. 11 2 .. .. .. 0 4 3 .. 1 .. 3 .. .. .. .. 0 .. .. 5 .. 1 3 1 .. 1 7 1 1 13 .. .. .. .. 2 3 .. 0 .. 0 .. 3 3 2 .. ..

1 1 1 .. 14 20 0 4 .. 4 11 1 7 9 5 .. .. 0 3 .. 0 .. .. 4 .. 6 1 .. 1 1 1 2 2 1 .. 1 2 2 .. 3 3 .. 0 0 2 4 .. .. 0 .. 2 3 1 .. .. ..

.. 15 .. .. .. .. .. 41 23 .. 31 36 .. .. .. .. 31 21 .. 5 .. 2 22 .. .. .. .. .. .. 1 .. 28 5 33 .. 40 5 4 .. .. .. .. .. .. 32 47 .. 0 13 58 .. 31 2 1 .. ..

0 18 .. .. 17 14 .. 38 .. .. 35 34 .. 8 34 .. .. 26 .. .. .. .. 23 6 .. 6 .. .. 7 .. 4 32 7 34 .. 46 4 2 .. .. 13 .. 34 5 31 42 .. .. 19 58 .. 35 2 .. .. ..

.. 22 5 .. .. .. .. .. 0 .. 3 3 .. .. .. 59 26 23 .. 30 .. 25 10 .. .. .. 22 .. .. 41 .. 12 5 4 .. 8 14 9 12 35 .. .. .. .. 12 7 .. 7 22 6 9 18 6 23 .. ..

74 10 11 .. 5 14 8 .. .. 22 4 2 18 30 11 .. .. 16 33 .. 35 .. 6 34 .. 15 10 .. 39 23 69 8 13 9 .. 7 12 9 .. 33 10 .. .. 41 12 6 .. .. 17 4 26 14 4 .. .. ..

Honduras Hungary Indiaa Indonesiaa Iran, Islamic Rep.a Iraq Ireland Israel Italy Jamaicaa Japan Jordana Kazakhstana Kenyaa Korea, Dem. Rep. Korea, Rep.a Kuwait Kyrgyz Republica Lao PDR Latviaa Lebanon Lesothoa Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysiaa Mali Mauritania Mauritiusa Mexicoa Moldovaa Mongolia Moroccoa Mozambique Myanmar Namibiaa Nepala Netherlands New Zealand Nicaraguaa Niger Nigeria Norway Omana Pakistana Panamaa Papua New Guineaa Paraguaya Perua Philippinesa Poland Portugal Puerto Rico

4.12

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 1995 2005

% of revenue 1995 2005

% of revenue 1995 2005

% of revenue 1995 2005

% of revenue 1995 2005

% of revenue 1995 2005

.. .. 23 46 12 .. 37 .. 32 .. 35 10 11 35 .. 31 .. 26 .. 7 .. 15 .. .. .. .. .. .. 37 .. .. 12 27 6 .. .. .. 20 27 10 26 .. 8 .. .. .. 21 18 20 40 .. 15 33 .. 23 ..

.. 19 35 28 13 .. 37 31 33 15 .. 9 49 29 .. 29 1 .. .. 12 11 20 .. .. 23 .. 6 .. 47 .. .. 15 .. 2 16 32 .. 16 38 11 25 55 19 .. .. 34 .. 20 .. 50 10 24 40 12 20 ..

.. .. 28 33 5 .. 35 .. 21 .. 14 23 28 40 .. 32 .. 56 .. 41 .. 12 .. .. .. .. .. .. 26 .. .. 25 54 38 .. .. .. 26 32 33 24 .. 46 .. .. .. 1 27 17 8 .. 46 26 .. 32 ..

.. 36 31 32 2 .. 36 29 22 33 .. 36 38 40 .. 28 .. .. .. 39 45 17 .. .. 36 .. 16 .. 21 .. .. 45 .. 47 35 36 .. 22 20 32 28 27 42 .. .. 25 .. 34 .. 13 35 40 23 36 32 ..

.. .. 24 4 9 .. 0 .. .. .. 1 22 3 14 .. 7 .. 5 .. 3 .. 49 .. .. .. .. .. .. 12 .. .. 34 4 5 .. .. .. 12 28 26 .. .. 6 .. .. .. 3 24 11 27 .. 10 29 .. 0 ..

.. 0 14 3 6 .. 0 1 .. 10 .. 11 4 11 .. 3 2 .. .. 1 8 45 .. .. 0 .. 27 .. 6 .. .. 20 .. 6 6 11 .. 2 32 19 1 2 4 .. .. 0 .. 14 .. 26 8 6 18 0 0 ..

.. .. 0 1 1 .. 2 .. 5 .. 5 9 5 1 .. 10 .. 1 .. 0 .. 1 .. .. .. .. .. .. 5 .. .. 6 2 1 .. .. .. .. 2 4 2 .. 0 .. .. .. 2 7 3 2 .. 8 4 .. 2 ..

.. 2 0 4 1 .. 5 6 5 20 .. 14 0 0 .. 7 0 .. .. 0 12 0 .. .. 0 .. 4 .. 0 .. .. 5 .. 1 0 5 .. .. 2 4 3 0 0 .. .. 1 .. 4 .. 3 3 6 6 0 2 ..

.. .. 0 6 6 .. 17 .. 35 .. 26 .. 48 0 .. 8 .. .. .. 35 .. .. .. .. .. .. .. .. 1 .. .. 6 14 38 .. .. .. .. .. .. 40 .. 10 .. .. .. .. .. 16 0 .. 10 .. .. 29 ..

.. 35 0 3 11 .. 17 17 36 7 .. 1 .. 0 .. 16 .. .. .. 29 1 .. .. .. 30 .. .. .. .. .. .. 4 .. 24 16 .. .. .. 1 .. 34 0 16 .. .. 18 .. .. .. 0 15 9 .. 40 33 ..

.. .. 25 9 66 .. 9 .. 6 .. 18 36 6 10 .. 12 .. 11 .. 13 .. 24 .. .. .. .. .. .. 19 .. .. 16 16 2 .. .. .. 42 11 27 .. .. 29 .. .. .. 74 24 34 23 .. 11 8 .. 14 ..

2007 World Development Indicators

.. 9 19 30 67 .. 5 17 5 15 .. 28 9 20 .. 16 97 .. .. 20 24 17 .. .. 11 .. 46 .. 26 .. .. 11 .. 20 27 15 .. 60 8 34 .. 16 19 .. .. 22 .. 28 .. 8 28 15 13 12 14 ..

235

ECONOMY

Central government revenues

4.12 Romania Russian Federation Rwandaa Saudi Arabia Senegala Serbia and Montenegroa Sierra Leonea Singaporea Slovak Republic Sloveniaa Somalia South Africa Spain Sri Lankaa Sudana Swazilanda Sweden Switzerlanda Syrian Arab Republica Tajikistana Tanzania Thailand Togoa 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 Europe EMU

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 1995 2005

% of revenue 1995 2005

% of revenue 1995 2005

% of revenue 1995 2005

% of revenue 1995 2005

% of revenue 1995 2005

.. .. 11 .. 17 .. 15 26 .. 13 .. .. 28 12 17 .. 15 11 23 6 .. .. .. 50 16 32 .. 10 .. .. 39 .. 10 .. 38 .. .. 17 27 36 .. m .. 17 17 20 .. 35 .. 15 19 15 .. 25 26

9 6 .. .. .. 13 16 28 9 15 .. 50 27 13 .. 28 9 16 .. 3 .. 33 19 42 26 .. .. 12 15 .. 37 55 11 .. 21 .. .. .. .. .. 19 m .. 15 17 15 16 29 12 20 22 12 .. 30 24

.. .. 25 .. 19 .. 34 20 .. 33 .. .. 21 49 41 .. 26 21 37 63 .. .. .. 26 20 40 .. 45 .. 15 31 .. 32 .. 33 .. .. 10 22 22 .. m .. 29 31 29 .. 26 .. 31 14 31 .. 24 23

33 24 .. .. .. 39 9 24 37 33 .. 33 18 55 .. 19 34 30 .. 54 .. 40 48 21 34 .. .. 24 28 .. 31 3 49 .. 25 .. .. .. .. .. 33 m .. 36 36 37 33 32 36 41 29 33 .. 27 27

.. .. 23 .. 36 .. 39 1 .. 9 .. .. 0 17 27 .. .. 1 13 12 .. .. .. 6 28 4 .. 7 .. .. .. .. 4 .. 9 .. .. 18 36 17 .. m .. 12 12 8 .. 12 .. 10 16 24 .. .. 0

3 24 .. .. .. 7 27 0 0 0 .. 4 .. 14 .. 48 .. 1 .. 11 .. 7 22 6 7 .. .. 16 5 .. .. 1 5 .. 5 .. .. .. .. .. 6m .. 5 7 3 8 6 3 5 12 16 .. 1 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.

236

2007 World Development Indicators

.. .. 3 .. 2 .. 0 15 .. 0 .. .. 0 4 1 .. 12 2 8 0 .. .. .. 1 4 3 .. 2 .. .. 6 .. 10 .. 0 .. .. 3 0 3 .. m .. 3 3 2 .. .. .. 4 4 4 .. 4 2

1 0 .. .. .. 4 .. 11 0 4 .. 4 0 0 .. 0 11 3 .. 1 .. 1 6 16 4 .. .. 0 0 .. 6 1 3 .. 4 .. .. .. .. .. 2m .. 1 1 1 1 1 0 3 3 4 .. 4 4

.. .. 2 .. .. .. .. .. .. 42 .. .. 40 1 .. .. 35 49 0 13 .. .. .. 2 15 .. .. .. .. 1 19 .. 31 .. 4 .. .. .. 0 2 .. m .. .. .. .. .. .. .. 10 .. .. .. 32 40

42 18 .. .. .. 29 .. .. 40 38 .. 2 48 1 .. .. 37 39 .. 12 .. 5 .. 5 18 .. .. .. 35 .. 22 37 20 .. 2 .. .. .. .. .. .. m .. 16 12 30 .. .. 34 8 .. 1 .. 34 35

.. .. 36 .. 26 .. 12 38 .. 3 .. .. .. 18 14 .. 13 17 19 5 .. .. .. 15 17 21 .. 37 .. 84 5 .. 8 .. 19 .. .. 51 15 19 .. m .. 17 15 16 .. 20 .. 13 35 25 .. 12 6

13 29 .. .. .. 9 48 38 13 10 .. 7 .. 17 .. 5 10 11 .. 18 .. 14 5 11 10 .. .. 48 16 .. 4 2 12 .. 43 .. .. .. .. .. 15 m .. 14 15 11 16 25 13 15 21 31 .. 9 5

About the data

4.12

Definitions

The International Monetary Fund (IMF) classifi es

significance except with respect to the capacity to

• Taxes on income, profits, and capital gains are

government revenues as taxes, grants, and property

fi x tax rates. Direct taxes tend to be progressive,

levied on the actual or presumptive net income

income. Taxes are classified by the base on which

whereas indirect taxes are proportional.

of individuals, on the profi ts of corporations and

the tax is levied, grants by the source, and property

Social security taxes do not reflect compulsory pay-

enterprises, and on capital gains, whether real-

income by type (for example, interest, dividends,

ments made by employers to provident funds or other

ized or not, on land, securities, and other assets.

or rent). The most important source of revenue is

agencies with a like purpose. Similarly, expenditures

Intragovernmental payments are eliminated in con-

taxes. Grants are unrequited, nonrepayable, non-

from such funds are not refl ected in government

solidation. • Taxes on goods and services include

compulsory receipts from other government units

expenses (see table 4.11). For further discussion of

general sales and turnover or value added taxes,

and foreign governments or from international orga-

taxes and tax policies, see About the data for table

selective excises on goods, selective taxes on ser-

nizations. Transactions are generally recorded on an

5.6. For further discussion of government revenues

vices, taxes on the use of goods or property, taxes

accrual basis.

and expenditures, see About the data for tables 4.10

on extraction and production of minerals, and prof-

and 4.11.

its of fiscal monopolies. • Taxes on international

The IMF’s Government Finance Statistics Manual 2001 describes taxes as compulsory, unrequited

trade include import duties, export duties, profi ts

payments made to governments by individuals, busi-

of export or import monopolies, exchange profi ts,

nesses, or institutions. Taxes are classified in six

and exchange taxes. • Other taxes include employer

major groups by the base on which the tax is levied:

payroll or labor taxes, taxes on property, and taxes

income, profits, and capital gains; payroll and work-

not allocable to other categories, such as penalties

force; property; goods and services; international

for late payment or nonpayment of taxes. • Social

trade and transactions; and other taxes. However,

contributions include social security contributions by

the distinctions are not always clear. Taxes levied

employees, employers, and self-employed individu-

on the income and profits of individuals and corpora-

als, and other contributions whose source cannot

tions are classified as direct taxes, and taxes and

be determined. They also include actual or imputed

duties levied on goods and services are classified

contributions to social insurance schemes operated

as indirect taxes. This distinction may be a useful

by governments. • Grants and other revenue include

simplifi cation, but it has no particular analytical

grants from other foreign governments, international organizations, and other government units; interest; dividends; rent; requited, nonrepayable receipts

Rich countries rely more on direct taxes

4.12a

for public purposes (such as fines, administrative fees, and entrepreneurial income from government

Taxes on income and capital gains as a share of central government revenue, 2003–05 (%)

ownership of property); and voluntary, unrequited,

70

nonrepayable receipts other than grants.

60

50

40

30

Data sources 20

Data on central government revenues are from the IMF’s Government Finance Statistics Yearbook

10

2006 and IMF data files. Each country’s accounts are reported using the system of common defini-

0 100

1,000

100,000

10,000

tions and classifications in the IMF’s Government Finance Statistics Manual 2001. The IMF receives

GNI per capita ($)

additional information from the Organisation for Low-income economies

Middle-income economies

High-income economies

Economic Co-operation and Development on the

High-income economies prefer to tax income and property. Low-income economies tend to rely on indirect

tax revenues of some of its members. See the IMF

taxes on international trade and goods and services. But in all groups there are exceptions.

sources for complete and authoritative explana-

Source: International Monetary Fund, Government Finance Statistics data files, and World Bank data files.

tions of concepts, definitions, and data sources.

2007 World Development Indicators

237

ECONOMY

Central government revenues

4.13 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austriaa Azerbaijan Bangladesh Belarus Belgiuma Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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 Finlanda Francea Gabon Gambia, The Georgia Germanya Ghana Greecea Guatemala Guinea Guinea-Bissau Haiti

238

Monetary indicators Money and quasi money

Claims on private sector

Claims on governments and other public entities

annual % growth 1990 2005

Annual growth % of M2 1990 2005

Annual growth % of M2 1990 2005

.. .. 11.4 .. 1,113.3 1,076.8 12.8 .. 825.8 10.4 .. .. 28.6 52.9 .. –14.0 1,251.8 51.7 –0.5 9.6 .. –1.7 8.1 –3.7 –2.4 24.2 28.9 8.5 33.0 195.4 18.4 27.5 –2.6 .. .. .. 6.5 42.5 50.3 28.7 –17.3 .. 76.5 19.9 .. .. 3.7 8.4 .. .. 13.3 .. 22.2 –17.4 574.6 2.5

.. 14.1 10.3 60.5 21.5 27.8 7.7 .. 23.2 17.2 42.5 .. 26.3 16.8 18.7 10.6 19.6 24.5 –4.8 19.0 8.9 4.9 9.7 16.5 31.3 19.3 17.9 3.5 19.2 25.5 37.1 24.7 7.7 10.6 .. 8.4 16.1 14.3 20.9 11.5 2.7 10.7 41.9 18.6 .. .. 27.5 13.1 26.5 .. 9.3 .. 14.1 33.4 21.3 17.9

2007 World Development Indicators

.. .. 12.2 .. 1,444.7 92.0 13.8 .. 134.1 9.2 .. .. –1.3 43.7 .. 12.6 2,100.5 37.5 3.6 15.4 .. 0.9 8.4 –1.6 1.3 21.7 26.5 7.9 8.7 18.0 5.3 7.3 –3.9 .. .. .. 3.0 19.1 9.3 6.3 –30.1 .. 27.6 0.3 .. .. 1.1 7.8 .. .. 4.9 .. 19.8 13.1 90.5 –0.6

.. 10.5 5.7 20.0 10.6 16.5 17.2 .. 27.3 11.6 38.6 .. 12.5 2.9 22.4 6.1 10.8 22.0 15.8 –0.6 9.2 5.3 9.9 –1.1 8.0 21.3 6.9 3.3 14.0 11.9 1.2 21.6 0.8 14.4 .. 9.6 40.9 6.5 21.4 2.8 8.7 2.7 66.9 14.7 .. .. 7.5 4.9 49.5 .. 18.2 .. 15.2 19.8 2.5 7.9

.. .. 3.2 .. 1,573.2 583.8 –3.6 .. 150.5 –0.6 .. .. 12.4 –8.8 .. –53.1 2,400.8 43.1 –1.5 –10.1 .. –1.9 0.9 –5.0 –6.0 9.0 1.5 –1.0 –0.7 421.6 –9.4 5.0 –3.0 .. .. .. –3.1 0.6 –28.9 15.2 15.9 .. 1.7 23.1 .. .. –21.0 –35.4 .. .. 14.6 .. 13.5 3.0 460.7 2.2

.. 1.1 –25.0 –27.1 –10.5 8.0 –2.0 .. –3.0 3.4 0.3 .. 4.6 –0.3 0.1 –27.5 7.9 –0.1 0.7 6.0 –5.5 –8.5 0.8 11.9 0.8 –7.9 –0.2 –1.0 5.9 16.2 –70.2 –2.4 1.9 3.8 .. –7.5 0.4 18.1 –10.9 –1.6 –0.3 10.9 –2.9 12.8 .. .. –13.2 7.1 –8.0 .. –2.0 .. 3.7 18.1 3.4 2.2

Interest rate

Deposit 1990 2005

.. 18.5 8.0 .. 1,517.9 .. 13.5 3.4 .. 12.0 65.1 6.1 7.0 23.8 .. 6.1 9,394.3 39.5 7.0 4.0 .. 7.5 9.9 7.5 7.5 40.4 8.6 6.7 36.4 .. 7.5 21.2 7.0 658.5 .. 7.0 7.9 20.0 43.5 12.0 18.0 .. .. 3.6 7.5 4.5 7.5 11.3 .. 7.1 21.3 19.5 18.2 21.0 32.7 ..

.. 5.1 1.8 13.4 3.8 5.8 3.7 .. 8.5 8.1 9.2 1.6 3.5 4.9 3.6 9.3 17.6 3.0 3.5 .. 1.9 4.9 0.8 4.9 4.9 3.9 2.3 1.3 7.0 .. 4.9 10.1 3.5 1.7 .. 1.2 2.4 13.9 3.5 7.2 .. .. 2.1 3.5 1.0 2.1 4.9 17.3 7.6 2.7 10.2 2.2 4.3 14.4 3.5 3.4

% Lending 1990 2005

.. 20.6 .. .. .. .. 17.9 .. .. 16.0 71.6 13.0 16.0 41.8 .. 7.9 .. 52.5 16.0 12.3 .. 18.5 14.1 18.5 18.5 48.9 9.4 10.0 45.2 .. 18.5 32.6 16.0 1,157.8 .. 14.1 14.1 35.3 37.5 19.0 21.2 .. 30.5 6.0 11.6 10.6 18.5 26.5 .. 11.6 25.6 27.6 23.3 21.2 45.8 ..

.. 13.1 8.0 67.7 6.2 18.0 9.1 .. 17.0 14.0 11.4 6.7 .. 16.6 9.6 15.7 55.4 7.9 .. 19.1 17.3 17.7 4.4 17.7 17.7 6.7 5.6 7.8 14.6 66.8 17.7 24.7 .. 11.2 .. 5.8 7.1 24.1 9.3 13.1 .. .. 4.9 7.0 3.7 6.6 17.7 34.9 21.6 9.7 .. 6.8 13.0 .. .. 27.4

Real 1990

2005

.. –65.5 .. .. .. .. 14.0 .. .. 9.1 –85.1 9.9 14.2 22.0 .. 1.5 .. –53.3 14.4 6.0 .. 16.6 10.5 15.9 9.7 21.6 3.5 0.3 15.2 .. 19.7 13.2 21.5 80.9 .. –3.6 10.1 –14.5 29.9 0.5 15.7 .. –86.6 2.0 4.9 8.2 2.7 13.0 .. 8.1 –5.9 5.7 –12.3 –2.2 11.9 ..

.. 9.3 –7.0 16.9 –2.5 14.4 4.2 .. 6.1 8.5 –4.5 4.4 .. 11.5 7.8 6.4 44.9 3.8 .. 2.1 11.0 12.4 1.2 15.0 –2.0 1.8 1.6 8.0 7.9 26.4 9.8 12.2 .. 7.8 .. 4.8 4.7 19.1 2.4 7.4 .. .. –1.2 1.0 3.2 4.9 8.1 29.4 12.7 8.1 .. 3.2 4.9 .. .. 10.3

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Irelanda Israel Italya Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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 Netherlandsa New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugala Puerto Rico

Money and quasi money

Claims on private sector

Claims on governments and other public entities

annual % growth 1990 2005

Annual growth % of M2 1990 2005

Annual growth % of M2 1990 2005

21.4 29.2 15.1 44.6 18.0 .. .. 19.4 .. 21.6 6.9 8.3 .. 20.1 .. 17.2 4.8 .. 7.8 .. 55.1 9.7 21.1 19.0 .. .. 4.5 11.1 –43.7 –4.9 11.5 21.0 83.8 358.0 31.6 21.5 37.2 37.7 30.3 18.5 .. 12.5 7,677.8 –4.1 32.7 5.6 10.0 11.6 36.6 4.3 53.9 6,384.9 22.4 160.1 .. ..

20.4 13.3 15.6 16.4 22.8 .. .. 11.2 .. 10.1 0.2 21.4 26.3 10.0 .. 3.1 12.3 10.0 7.9 38.3 4.5 9.1 34.1 29.0 32.9 15.2 2.2 16.2 6.3 9.8 10.5 19.5 10.0 34.4 37.1 14.0 31.0 27.3 9.8 9.8 .. 12.2 9.8 7.0 16.2 3.4 21.3 16.5 8.3 29.5 9.8 16.8 6.4 12.2 .. ..

13.0 23.0 5.9 66.5 14.7 .. .. 18.5 .. 8.3 8.5 4.7 .. 8.0 .. 36.1 0.4 .. 3.6 .. 27.6 8.4 19.0 2.0 .. .. 23.8 15.5 –13.2 0.1 20.2 9.9 48.4 53.3 40.2 12.4 22.0 12.8 15.4 5.7 .. 4.2 4,932.9 –5.1 7.8 5.0 9.6 5.0 0.8 –1.1 37.4 2,123.7 15.6 158.7 .. ..

14.4 18.4 14.7 12.3 21.1 .. .. 10.8 .. 6.4 2.7 17.8 70.6 5.5 .. 9.4 16.8 7.1 9.0 70.5 0.5 11.0 8.7 0.9 40.4 12.9 9.2 7.7 8.1 0.1 18.7 5.0 9.3 17.8 28.0 7.9 15.9 6.8 25.3 .. .. 20.6 19.1 8.5 19.5 10.4 13.7 8.8 15.6 10.5 10.3 10.9 –1.2 6.4 .. ..

–10.9 69.4 10.5 –5.6 5.8 .. .. 4.9 .. –2.3 0.7 1.0 .. 20.5 .. –1.2 –1.6 .. –0.5 .. –35.2 –16.7 33.2 9.4 .. .. –14.8 –14.0 –28.5 –13.4 1.5 0.7 10.6 300.3 6.8 –4.9 –8.0 23.7 –7.8 6.0 .. –1.6 3,222.5 1.4 26.3 0.4 –11.2 7.5 –25.7 6.4 –5.2 2,127.1 1.8 –20.6 .. ..

–1.8 –1.3 –0.6 0.5 –4.1 .. .. –4.5 .. 2.2 0.9 4.9 –23.8 0.0 .. 2.9 –1.8 0.9 0.4 3.3 3.1 –8.3 –11.1 –128.9 1.2 –12.0 –5.6 2.3 –1.0 4.3 –15.8 1.4 –3.8 –9.1 –10.0 0.1 –9.4 23.5 3.4 2.6 .. –0.8 –1.8 –5.8 –23.6 –5.3 –12.7 4.1 –4.6 –4.6 –2.4 –2.6 –4.1 –2.7 .. ..

4.13

Interest rate

Deposit 1990 2005

8.8 24.7 .. 17.5 .. .. 6.3 14.4 6.8 23.9 3.6 8.2 .. 13.7 .. 10.0 7.4 .. 30.0 34.8 16.9 13.0 6.8 5.5 88.3 .. 20.5 12.1 5.7 7.0 5.0 12.6 30.4 .. 300.0 8.5 .. 5.9 12.8 11.9 3.3 11.7 9.5 7.0 19.8 9.7 8.3 .. 8.4 8.7 22.9 2,439.6 19.5 41.7 14.0 ..

10.9 5.2 .. 8.1 11.8 .. 0.0 3.2 0.9 7.5 0.3 2.9 .. 5.1 .. 3.7 3.5 5.8 4.8 2.8 8.1 4.0 3.4 2.1 1.2 6.6 18.8 10.9 3.0 3.5 8.0 7.3 3.5 13.2 13.0 3.5 7.8 9.5 6.2 2.3 2.3 6.7 4.0 3.5 10.5 1.8 3.3 .. 2.7 0.9 1.7 3.4 5.6 2.8 .. ..

% Lending 1990 2005

17.1 28.8 16.5 20.8 .. .. 11.3 26.4 14.9 30.5 7.0 10.3 .. 18.8 .. 10.0 8.4 .. 26.0 86.4 39.9 20.4 13.8 7.0 91.8 .. 25.8 21.0 8.8 16.0 10.0 18.0 17.7 .. 300.0 9.0 .. 8.0 23.4 14.4 11.8 16.0 22.0 16.0 25.3 14.2 9.7 .. 12.0 15.5 31.0 4,774.5 24.1 504.2 21.8 ..

18.8 8.5 10.8 14.1 16.0 .. 2.6 6.4 5.3 17.4 1.7 7.6 .. 12.9 .. 5.6 7.5 26.6 26.8 6.1 10.6 11.7 17.0 6.1 5.7 12.2 27.0 33.1 6.0 .. 23.1 21.0 9.7 19.3 23.6 11.5 19.5 15.0 10.6 8.1 2.8 11.5 12.1 .. 17.9 4.0 7.0 .. 8.7 11.5 29.9 15.0 10.2 6.8 .. ..

Real 1990

2005

–3.4 2.5 5.4 12.2 .. .. 12.1 9.1 6.0 4.3 4.4 –1.0 .. 7.3 .. –0.5 10.3 .. 11.4 21.3 21.2 10.8 10.2 0.4 –52.8 .. 12.9 9.3 4.8 10.6 7.2 6.6 7.5 .. –5.8 3.3 .. –8.9 17.9 3.2 9.3 13.2 –97.6 17.9 16.9 10.0 –12.1 .. 11.4 10.9 –3.9 –29.7 9.9 –0.4 7.6 ..

7.7 5.9 6.0 0.3 0.1 .. –0.9 5.8 3.2 7.0 3.6 3.5 .. 8.2 .. 6.1 –13.6 18.6 17.4 –2.9 10.3 8.3 7.4 –14.7 2.9 8.8 7.3 15.3 1.3 .. 3.6 15.5 4.0 11.2 10.7 9.9 12.2 –2.2 8.4 3.4 1.2 8.8 1.7 .. –7.0 –4.1 –1.4 .. 6.2 –5.0 22.7 11.2 3.7 3.9 .. ..

2007 World Development Indicators

239

ECONOMY

Monetary indicators

4.13 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spaina 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

Monetary indicators Money and quasi money

Claims on private sector

Claims on governments and other public entities

annual % growth 1990 2005

Annual growth % of M2 1990 2005

Annual growth % of M2 1990 2005

30.1 .. 5.6 4.6 –4.8 .. 74.0 20.0 .. 123.0 .. 11.9 .. 19.9 48.8 0.6 10.7 0.8 26.1 .. 41.9 25.5 9.5 6.2 7.6 53.2 .. 60.2 1,809.2 –8.2 10.5 2.7 118.5 .. 64.9 12.3 .. 11.3 47.9 15.1

20.2 36.3 18.0 13.2 8.2 .. 31.3 6.2 3.6 7.5 .. 19.9 .. 19.0 43.5 9.7 11.2 6.8 15.3 25.9 38.2 5.9 2.2 29.4 11.0 25.3 .. 16.5 53.9 30.5 13.8 7.5 0.3 .. 54.1 30.9 .. 14.4 1.9 532.7

.. .. –10.0 –4.5 –8.4 .. 4.9 13.7 .. 96.1 .. 13.7 .. 16.2 12.6 20.5 13.6 11.7 3.4 .. 22.6 36.6 1.8 2.7 5.9 43.0 .. 23.3 78.3 1.3 13.1 –0.5 56.2 .. 17.6 19.6 .. 1.4 22.8 13.5

24.3 27.3 14.5 25.1 12.7 .. 4.4 1.8 13.1 19.7 .. 21.1 .. 15.9 25.9 22.9 23.7 7.0 4.4 38.8 12.8 6.5 6.9 19.6 8.8 20.2 .. 4.3 44.1 34.4 13.2 8.1 –2.1 .. 35.7 26.8 .. 4.3 6.7 154.0

.. .. 26.8 4.2 –5.3 .. 228.6 –4.9 .. –10.4 .. 2.2 .. 4.4 27.9 –5.5 –12.2 1.0 11.4 .. 80.6 –4.4 6.9 0.9 1.8 –6.4 .. 0.8 109.3 –4.8 1.9 0.7 3.0 .. 44.3 23.7 .. 8.3 185.8 7.4

–0.9 –26.2 –13.8 –33.2 –4.1 .. –5.7 –5.7 2.8 2.9 .. –6.1 .. 3.1 –4.5 –25.1 4.6 –1.0 3.3 –8.2 16.2 –0.4 –0.8 –21.6 1.0 –0.2 .. –6.1 –16.1 –1.9 –0.1 0.4 –14.7 .. –1.4 6.9 .. –3.1 –43.2 421.5

a. As members of the European Monetary Union, these countries share a single currency, the euro.

240

2007 World Development Indicators

Interest rate

Deposit 1990 2005

.. .. 6.9 8.0 7.0 .. 40.5 4.7 8.0 682.5 .. 18.9 10.7 19.4 .. 8.7 9.9 8.3 4.0 .. 17.0 12.3 7.0 6.0 7.4 47.5 .. 31.3 148.6 .. 12.5 .. 147.5 .. 27.8 22.0 .. .. 25.7 8.8

.. 4.0 7.9 3.8 3.5 .. 11.1 0.4 2.4 3.2 .. 6.0 2.5 10.2 .. 4.0 0.8 0.8 1.0 9.7 4.7 1.9 3.5 2.2 .. 20.4 .. 8.8 8.6 .. .. .. 2.8 .. 11.6 7.1 .. 13.0 11.2 91.1

% Lending 1990 2005

.. .. 13.2 .. 16.0 .. 52.5 7.4 14.4 824.6 .. 21.0 16.0 13.0 .. 14.5 16.7 7.4 9.0 .. 31.0 14.4 16.0 12.9 4.8 .. .. 38.7 184.3 .. 14.8 10.0 163.8 .. 35.5 32.2 .. .. 35.1 11.7

.. 10.7 .. .. .. .. 24.6 5.3 6.7 7.8 .. 10.6 4.3 7.0 .. 10.6 3.3 3.1 8.0 23.3 15.1 5.8 .. 9.1 .. .. .. 19.6 16.2 .. 4.6 6.2 13.6 .. 16.8 11.0 .. 18.0 28.2 235.7

Real 1990

2005

.. .. –0.3 .. 14.6 .. –10.6 3.1 –11.0 374.3 .. 4.7 8.1 –5.9 .. –0.4 7.3 2.9 –8.7 .. 8.6 8.2 12.6 –2.3 –3.7 .. .. –4.0 –91.7 .. 6.6 5.9 27.5 .. –4.4 12.6 .. .. –34.5 –2.6

.. –7.5 .. .. .. .. 10.2 4.7 4.1 6.2 .. 5.6 –0.1 –3.1 .. 5.5 2.1 2.5 2.1 13.4 11.0 1.2 .. –0.8 .. .. .. 11.0 –3.2 .. 2.6 3.1 11.7 .. –9.5 2.4 .. 0.7 7.7 –0.6

About the data

4.13

Definitions

Money and the financial accounts that record the

during the reporting period. The valuation of finan-

• Money and quasi money comprise the sum of cur-

supply of money lie at the heart of a country’s

cial derivatives and the net liabilities of the banking

rency outside banks, demand deposits other than

financial system. There are several commonly used

system can also be difficult. The quality of commer-

those of the central government, and the time, sav-

defi nitions of the money supply. The narrowest,

cial bank reporting also may be adversely affected

ings, and foreign currency deposits of resident sec-

M1, encompasses currency held by the public and

by delays in reports from bank branches, especially

tors other than the central government. This definition

demand deposits with banks. M2 includes M1 plus

in countries where branch accounts are not com-

of the money supply, often called M2, corresponds to

time and savings deposits with banks that require

puterized. Thus the data in the balance sheets of

lines 34 and 35 in the IMF’s International Financial

a notice for withdrawal. M3 includes M2 as well as

commercial banks may be based on preliminary esti-

Statistics (IFS). The change in money supply is mea-

various money market instruments, such as cer-

mates subject to constant revision. This problem is

sured as the difference in end-of-year totals relative

tificates of deposit issued by banks, bank deposits

likely to be even more serious for nonbank financial

to M2 in the preceding year. • Claims on private sec-

denominated in foreign currency, and deposits with

intermediaries.

tor (IFS line 32d) include gross credit from the finan-

fi nancial institutions other than banks. However

Many interest rates coexist in an economy, reflect-

cial system to individuals, enterprises, nonfinancial

defined, money is a liability of the banking system,

ing competitive conditions, the terms governing loans

public entities not included under net domestic

distinguished from other bank liabilities by the spe-

and deposits, and differences in the position and

credit, and financial institutions not included else-

cial role it plays as a medium of exchange, a unit of

status of creditors and debtors. In some economies

where. • Claims on governments and other public

account, and a store of value.

interest rates are set by regulation or administra-

entities (IFS line 32an + 32b + 32bx + 32c) usually

The banking system’s assets include its net for-

tive fiat. In economies with imperfect markets, or

comprise direct credit for specific purposes, such

eign assets and net domestic credit. Net domestic

where reported nominal rates are not indicative of

as financing the government budget deficit; loans

credit includes credit extended to the private sector

effective rates, it may be difficult to obtain data on

to state enterprises; advances against future credit

and general government and credit extended to the

interest rates that reflect actual market transactions.

authorizations; and purchases of treasury bills and

nonfinancial public sector in the form of investments

Deposit and lending rates are collected by the Inter-

bonds, net of deposits by the public sector. Public

in short- and long-term government securities and

national Monetary Fund (IMF) as representative inter-

sector deposits with the banking system also include

loans to state enterprises; liabilities to the public

est rates offered by banks to resident customers.

sinking funds for the service of debt and temporary

and private sectors in the form of deposits with the

The terms and conditions attached to these rates

deposits of government revenues. • Deposit interest

banking system are netted out. Net domestic credit

differ by country, however, limiting their comparabil-

rate is the rate paid by commercial or similar banks

also includes credit to banking and nonbank financial

ity. Real interest rates are calculated by adjusting

for demand, time, or savings deposits. • Lending

institutions.

nominal rates by an estimate of the inflation rate in

interest rate is the rate charged by banks on loans to

Domestic credit is the main vehicle through which

the economy. A negative real interest rate indicates

prime customers. • Real interest rate is the lending

changes in the money supply are regulated, with cen-

a loss in the purchasing power of the principal. The

interest rate adjusted for inflation as measured by

tral bank lending to the government often playing the

real interest rates in the table are calculated as

the GDP deflator.

most important role. The central bank can regulate

(i – P) / (1 + P), where i is the nominal lending inter-

lending to the private sector in several ways—for

est rate and P is the inflation rate (as measured by

example, by adjusting the cost of the refinancing

the GDP deflator).

facilities it provides to banks, by changing market interest rates through open market operations, or by controlling the availability of credit through changes

Data sources

in the reserve requirements imposed on banks and

Monetary and financial data are published by the

ceilings on the credit provided by banks to the pri-

IMF in its monthly International Financial Statis-

vate sector.

tics and annual International Financial Statistics

Monetary accounts are derived from the balance

Yearbook. The IMF collects data on the financial

sheets of financial institutions—the central bank,

systems of its member countries. The World Bank

commercial banks, and nonbank financial interme-

receives data from the IMF in electronic files that

diaries. Although these balance sheets are usually

may contain more recent revisions than the pub-

reliable, they are subject to errors of classification,

lished sources. The discussion of monetary indi-

valuation, and timing and to differences in account-

cators draws from an IMF publication by Marcello

ing practices. For example, whether interest income

Caiola, A Manual for Country Economists (1995).

is recorded on an accrual or a cash basis can make

Also see the IMF’s Monetary and Financial Statis-

a substantial difference, as can the treatment of non-

tics Manual (2000) for guidelines for the presen-

performing assets. Valuation errors typically arise

tation of monetary and financial statistics. World

with respect to foreign exchange transactions, par-

Bank data on the GDP deflator are used to derive

ticularly in countries with flexible exchange rates or

real interest rates.

in those that have undergone a currency devaluation

2007 World Development Indicators

241

ECONOMY

Monetary indicators

4.14 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austriab Azerbaijan Bangladesh Belarus Belgiumb Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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 Finlandb Franceb Gabon Gambia, The Georgia Germany b Ghana Greeceb Guatemala Guinea Guinea-Bissau Haiti

242

Exchange rates and prices Ratio of PPP conversion factor to official exchange rate

Official exchange rate

Purchasing power parity (PPP) conversion factor

local currency units to $ 2005 2006

local currency units to international $ 1990 2005

2005

.. .. 2.1 50.3 5.0 32.3 .. 76.8 0.3 1.0 0.0 150.4 1.5 1.5 0.9 0.9 .. 0.3 9.9 12.7 .. 822.7 0.9 0.9 140.2 235.0 1.4 2.9 .. .. 1.1 2.4 .. 1.2 0.0 0.6 138.9 170.0 46.2 163.2 .. 660.6 168.8 237.3 1.3 1.3 128.0 146.1 110.9 207.0 155.8 329.4 2.1c 1.2c 6.5 5.7 126.9 852.3 .. 81.9 299.8 532.0 43.1 217.1 170.7 288.2 0.0 4.0 .. .. 5.0 14.2 8.7 8.4 2.4 12.1 0.4 0.6 0.8 1.7 0.3 0.5 1.0 3.1 0.1 7.9 0.7 1.3 1.0 0.9 1.0 0.9 319.3 441.5 2.0 4.5 .. 0.8 1.0 0.9 94.0 1,773.9 0.3 0.7 1.4 4.2 201.7 550.6 9.9 121.1 1.2 12.2

.. 0.5 0.4 0.9 0.3 0.3 1.1 1.1 0.3 0.2 0.4 1.1 0.4 0.4 .. 0.5 0.5 0.4 0.3 0.2 0.2 0.5 1.0 0.3 0.4 0.6 0.3c 0.7 0.4 0.2 1.0 0.5 0.5 0.7 .. 0.6 1.4 0.4 0.6 0.3 0.5 0.2 0.6 0.1 1.1 1.2 0.8 0.2 0.4 1.2 0.2 0.9 0.6 0.2 0.2 0.3

49.50 99.87 73.28 87.16 2.90 457.69 1.31 0.80 0.95 64.33 2,153.82 0.80 527.47 8.07 1.57 5.11 2.43 1.57 527.47 1,081.58 4,092.50 527.47 1.21 527.47 527.47 560.09 8.19 7.78 2,320.83 473.91 527.47 477.79 527.47 5.95 .. 23.96 6.00 30.41 1.00 5.78 8.75 15.37 12.58 8.67 0.80 0.80 527.47 28.58 1.81 0.80 9,072.54 0.80 7.63 3,644.33 527.47 40.45

.. 122.20a 72.65 80.37 3.05 416.04 1.33 0.80 0.89 68.93 2,140.43a 0.80 522.89 7.99a 1.56 6.05a 2.18 1.56 522.89 1,051.61a 4,122.00a 522.89 1.13 522.89 522.89 530.29 7.97 7.77 2,361.14 448.30a 522.89 511.30 522.89 5.84 .. 22.60 5.95 33.37 1.00 5.71a 8.75 15.38 12.47 8.75a 0.80 0.80 522.89 28.04a 1.78 0.80 9,225.15a 0.80 7.60 5,597.00a 522.89 39.58a

2007 World Development Indicators

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 2005 1990–2000 2000–05 1990–2000 2000–05 1990–2000 2000–05

.. .. 83.3 .. .. 97.7 125.9 105.7 .. .. .. 109.4 .. 79.8 .. .. .. 120.5 .. 71.1 88.0 109.6 119.7 122.3 .. 92.3 92.5 .. 105.3 27.8 .. 91.9 116.4 109.7 .. 125.2 108.4 104.6 147.9 .. .. .. .. .. 104.8 107.9 102.6 53.5 .. 106.9 109.5 113.7 .. .. .. ..

.. 38.0 18.5 739.4 5.2 212.5 1.5 1.7 203.0 4.0 355.1 1.8 8.7 8.6 3.4 9.7 208.0 103.3 5.0 13.4 4.4 6.1 1.5 4.5 7.1 7.9 7.9 4.0 21.7 965.0 8.9 15.9 9.2 86.0 3.0 12.8 1.6 9.4 4.3 8.7 6.2 7.9 53.6 6.3 2.1 1.4 6.2 4.2 356.7 1.7 26.7 9.2 10.4 5.6 32.5 22.7

12.2 4.0 7.3 82.2 12.4 4.2 3.4 1.6 6.0 3.8 35.8 1.9 2.9 4.8 2.5 4.2 10.1 4.0 2.7 8.3 2.8 2.1 2.4 1.9 7.6 5.3 3.2 –3.6 6.6 43.6 –0.6 9.7 2.7 3.6 2.6 2.5 2.3 20.6 11.5 5.9 2.9 15.7 3.9 4.2 0.9 2.0 2.7 16.5 6.0 1.0 22.8 3.3 7.2 13.2 0.7 18.0

.. 27.8 17.3 711.0 8.9 72.8 2.1 2.2 170.9 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 8.9 8.6 5.9 20.3 932.8 9.3 15.6 7.2 86.2 .. 7.8 2.1 8.7 37.1 8.8 8.5 .. 21.6 5.5 1.5 1.6 4.6 4.1 24.7 2.0 28.4 9.0 10.1 .. 34.0 21.9

.. 3.2 2.6 79.2 11.2 3.6 2.9 1.9 4.3 5.6 30.4 2.0 2.5 3.1 .. 2.1 9.1 5.1 2.5 7.4 2.7 1.8 2.3 2.1 2.2 2.5 1.3 –1.6 6.5 41.1 2.4 10.9 3.1 2.4 .. 2.0 2.0 20.6 10.7 5.3 3.2 .. 3.3 5.8 1.1 2.1 1.0 10.6 5.6 1.5 19.8 3.4 7.2 .. 1.0 21.3

.. .. .. .. 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 .. 83.9 .. 8.2 1.1 .. .. 6.1 .. .. 8.1 .. 1.0 .. .. .. .. 0.4 .. 3.6 .. .. .. ..

.. 5.4 3.8 .. 22.6 0.9 2.3 2.0 .. .. 35.3 1.4 .. .. .. .. 15.6 4.4 .. .. .. .. 0.7 4.4 .. 5.7 .. –0.3 6.5 .. .. 11.3 .. 1.7 .. 1.9 1.4 .. 7.5 9.8 3.1 .. 1.7 .. 0.5 1.3 .. .. .. 1.8 .. 3.3 .. .. .. ..

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Irelandb Israel Italy b Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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 Netherlandsb New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugalb Puerto Rico

Ratio of PPP conversion factor to official exchange rate

Official exchange rate

Purchasing power parity (PPP) conversion factor

local currency units to $ 2005 2006

local currency units to international $ 1990 2005

2005

1.2 21.3 5.0 652.2 168.8 .. 0.8 1.5 0.7 5.3 183.0 0.3 0.0 9.1 .. 543.8 0.3 0.0 173.9 0.0 328.5 0.6 .. .. 0.0 0.0 98.1 1.3 1.5 131.4 26.7 6.5 1.4 0.0 3.5 3.3 331.2 .. 1.0 6.4 0.9 1.6 0.0 123.7 4.2 8.3 0.3 5.9 0.6 0.5 422.6 0.1 5.8 0.2 0.5 ..

0.3 0.6 0.2 0.3 0.3 .. 1.3 0.7 1.1 0.8 1.1 0.4 0.5 0.4 .. 0.7 1.2 0.2 0.2 0.5 1.1 0.2 .. .. 0.5 0.4 0.3 0.2 0.5 0.4 0.3 0.4 0.7 0.3 0.3 0.4 0.3 .. 0.4 0.2 1.2 1.1 0.2 0.3 0.7 1.5 0.6 0.3 0.6 0.3 0.3 0.5 0.2 0.6 0.9 ..

18.83 18.90a 199.58 210.39 44.10 45.31 9,704.74 9,159.32 8,963.96 9,170.94 1,472.00 .. 0.80 0.80 4.49 4.46 0.80 0.80 62.28 66.14a 110.22 116.30 0.71 0.71 132.88 126.09 75.55 72.10 .. .. 1,024.12 954.85 0.29 0.29 41.01 40.15 10,655.17 10,184.00a 0.57 0.56 1,507.50 1,507.50 6.36 6.77 57.10 58.26a 1.31 1.31 2.77 2.75 49.28 47.47a 2,003.03 2,142.30 118.42 137.00a 3.79 3.63a 527.47 522.89 265.53 268.60a 29.50 31.71 10.90 10.90 12.60 13.17a 1,205.22 1,164.10a 8.87 8.80 23.06 25.40 5.76 5.78 6.36 6.77 71.37 72.76 0.80 0.80 1.42 1.54 16.73 17.57 527.47 522.89 131.27 127.02a 6.44 6.41 0.39 0.39 59.51 60.27 1.00 1.00 3.10 3.02a 6,177.96 5,411.40a 3.30 3.27 55.09 51.31 3.24 3.10 0.80 0.80 1.00 1.00

6.4 120.8 9.4 3,220.5 3,128.3 .. 1.0 3.1 0.8 53.3 125.1 0.3 63.8 33.3 .. 758.2 0.4 10.1 2,539.9 0.3 1,656.2 1.5 .. .. 1.4 19.4 587.6 28.5 1.8 200.4 72.5 11.5 7.6 4.2 421.1 3.3 6,224.4 .. 2.5 12.7 0.9 1.5 4.1 164.8 87.6 9.9 0.2 17.7 0.6 1.0 1,583.6 1.5 12.7 1.9 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 2005 1990–2000 2000–05 1990–2000 2000–05 1990–2000 2000–05

.. 132.6 .. .. 129.5 .. 123.5 78.0 111.0 .. 79.4 .. .. .. .. .. .. .. .. .. .. 132.8 .. .. .. 99.8 .. 75.2 95.2 .. .. .. .. 109.1 .. 91.8 .. .. .. .. 113.3 114.4 87.7 .. 123.8 111.1 .. 94.0 .. 101.0 78.0 .. 92.3 107.4 110.7 ..

18.9 19.4 8.0 15.8 27.7 .. 3.5 10.2 3.8 23.0 0.0 3.2 204.7 16.6 .. 5.7 1.5 110.6 27.0 48.0 17.9 10.0 51.8 .. 75.0 79.3 19.1 33.6 3.9 7.0 8.7 6.4 19.0 119.6 54.2 2.9 32.4 25.5 10.4 8.2 2.0 1.7 42.4 6.0 29.5 2.7 0.1 11.1 3.6 7.1 11.5 26.7 8.3 24.7 5.2 3.0

8.0 6.1 3.9 8.2 18.0 0.3 3.5 1.3 2.9 10.5 –1.5 2.1 12.0 4.4 .. 2.4 8.3 4.6 11.2 4.8 2.0 6.3 11.6 18.7 1.2 2.1 11.0 14.7 3.4 4.0 8.7 5.6 7.1 10.6 11.5 0.9 11.4 21.1 5.3 4.1 2.7 2.3 6.9 2.4 15.9 2.9 1.8 6.0 1.6 7.1 11.1 2.8 5.2 2.4 3.1 ..

18.8 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 28.2 29.2 .. 9.8 .. 5.6 32.6 10.8 18.7 33.8 3.6 5.2 6.1 6.9 19.4 19.2 36.7 3.8 31.8 25.9 .. 8.7 2.4 1.7 30.8 6.1 32.4 2.2 0.6 9.7 1.1 9.3 13.1 27.3 7.7 25.3 4.5 ..

8.2 5.8 4.0 8.9 14.4 .. 3.5 1.7 2.4 10.5 –0.4 2.4 6.8 7.9 .. 3.3 1.5 3.8 10.9 3.9 .. 8.8 .. –5.9 0.6 1.7 9.9 13.8 1.6 1.7 6.9 4.2 4.9 10.3 5.5 1.5 12.5 26.3 4.5 4.2 2.4 2.4 6.5 1.9 15.6 1.7 –0.2 4.9 0.9 8.4 9.0 2.0 5.0 2.5 3.1 ..

.. 16.8 7.4 15.4 28.4 .. 1.6 8.1 2.9 .. –1.0 .. 16.3 .. .. 3.6 1.4 35.6 .. 12.0 .. .. .. .. 24.7 8.4 .. .. 3.4 .. .. .. 18.4 .. .. 2.9 .. .. .. .. 1.3 1.4 .. .. .. 1.6 .. 10.4 1.0 .. .. 23.7 6.3 19.8 .. ..

2007 World Development Indicators

.. 2.3 4.8 7.5 10.1 .. –0.3 4.1 1.8 .. –0.5 6.0 9.4 .. .. 2.1 2.2 6.4 .. 4.3 .. .. .. .. 2.0 0.7 .. .. 3.6 .. .. .. 6.5 .. .. –0.6 .. .. .. .. 2.3 1.8 .. .. .. 3.7 .. 6.2 1.0 .. 14.5 1.9 8.4 2.9 1.9 ..

243

ECONOMY

4.14

Exchange rates and prices

4.14 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spainb 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

Exchange rates and prices Official exchange rate

Purchasing power parity (PPP) conversion factor

local currency units to $ 2005 2006

local currency units to international $ 1990 2005

2.91 28.28 557.82 3.75 527.47 72.44 2,889.59 1.66 31.02 192.71 .. 6.36 0.80 100.50 243.61 6.36 7.47 1.25 11.23 3.12 1,128.93 40.22 527.47 6.30 1.30 1.34 d .. 1,780.67 5.13 3.67 0.55 1.00 24.48 .. 2,089.75 15,858.92 .. 191.51 4,463.50 22.36

2.81 0.0 1.5 0.0 13.9 26.59a 35.6 109.8 549.94a 3.75 2.5 3.0 522.89 172.1 208.0 .. .. 59.64a 2,961.91 35.9 790.0 1.59 1.8 1.5 29.70 5.9 16.8 191.03 8.6 148.6 .. .. .. 6.77 1.1 2.9 0.80 0.6 0.8 103.91 9.8 26.2 217.15 0.7 88.9 6.77 0.9 3.2 7.38 9.1 9.1 1.25 2.0 1.7 11.23 10.2 19.2 3.30 .. 0.8 1,251.90 74.6 479.2 37.88 10.7 12.7 522.89 92.6 125.6 6.31 3.1 4.7 1.33 0.4 0.4 1.43d 1,569.1 804,128.7 .. 0.0 .. 361.9 1.81a 114.5 5.05 .. 1.3 3.67 3.3 4.1 0.54 0.5 0.6 1.00 1.0 1.0 24.48 0.6 11.9 .. 0.0 281.8 2,147.00 23.8 1,662.2 3,282.4 15,921.00a 674.1 .. .. .. 18.0 147.9 198.08a 3,603.07 17.7 2,719.3 0.9 2,844.9 250.00a

Ratio of PPP conversion factor to official exchange rate

2005

0.5 0.5 0.2 0.8 0.4 .. 0.3 0.9 0.5 0.8 .. 0.5 1.0 0.3 0.4 0.5 1.2 1.4 0.4 0.3 0.4 0.3 0.2 0.8 0.3 0.6 .. 0.2 0.3 1.1 1.1 1.0 0.5 0.3 0.8 0.2 .. 0.8 0.6 0.1

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 2005 1990–2000 2000–05 1990–2000 2000–05 1990–2000 2000–05

119.9 149.2 .. 82.3 .. .. 70.9 92.1 134.4 .. .. 108.5 112.9 .. 124.0 .. 96.9 104.0 .. .. .. .. 113.6 107.8 85.3 .. .. 88.8 106.0 .. 101.3 92.8 76.6 .. 69.0 .. .. .. 134.8 ..

98.0 161.5 14.6 1.6 5.0 55.0 32.1 1.3 11.3 28.7 .. 9.9 3.9 9.1 66.6 12.5 2.2 1.0 7.9 235.0 21.6 4.2 7.0 5.4 4.4 76.1 407.5 11.8 271.0 2.2 2.8 2.0 31.1 245.8 45.3 15.2 5.7 22.4 52.1 26.7

21.8 16.8 5.9 6.2 2.0 25.2 6.8 0.2 4.1 5.5 .. 6.6 4.2 8.7 9.6 10.6 1.5 1.0 4.2 21.2 6.3 2.3 1.2 3.5 2.3 25.5 .. 5.0 10.9 4.9 2.5 2.3 11.5 29.0 28.6 5.9 3.4 10.3 20.4 232.6

100.5 99.1 16.2 1.0 5.4 .. 29.3 1.7 8.4 12.0 .. 8.7 3.8 9.9 71.8 9.4 1.9 1.6 6.4 .. 20.9 4.9 8.5 5.7 4.4 79.9 .. 10.5 155.7 .. 2.9 2.7 33.9 .. 49.0 4.1 .. 26.3 57.0 29.0

17.7 14.4 6.8 0.2 1.3 .. 6.3 0.6 6.1 5.5 .. 5.2 3.2 9.2 7.8 6.9 1.5 0.8 .. .. 3.1 2.1 2.0 4.5 2.7 26.5 .. 4.1 7.1 .. 2.5 2.4 11.4 .. 22.0 4.5 .. 11.7 20.4 ..

93.8 99.8 .. 1.3 .. .. .. –1.0 9.5 9.1 .. 7.4 2.4 8.1 .. .. 2.4 –0.4 4.7 .. .. 3.8 .. 2.8 3.6 .. .. .. 161.6 .. 2.4 1.2 27.2 .. 44.1 .. .. .. 101.4 25.9

21.4 17.4 .. 1.3 .. .. .. 2.4 5.0 4.5 .. 5.3 2.2 9.3 .. .. 1.6 0.4 .. .. .. 4.6 .. 1.6 3.0 .. .. .. 10.8 .. 1.3 3.4 20.3 .. 32.9 .. .. .. .. ..

Note: The inconsistencies in the growth rates of the GDP deflator and the consumer and wholesale price indexes are due mainly to uneven coverage of the time period. a. Latest quarterly or monthly data available. b. As members of the European Monetary Union, these countries share a single currency, the euro. c. Based on a 1986 bilateral comparison of China and the United States (Rouen and Kai 1995), employing a different methodology than that used for other countries. This interim methodology will be revised when the next round of PPP estimates are completed in 2007. d. New liras per dollar.

244

2007 World Development Indicators

4.14

About the data

In a market-based economy the choices that house-

effective exchange rate index represents the ratio

a country, consumer price indexes are of less value

holds, producers, and governments make about the

(expressed on the base 2000 = 100) of an index of a

in making comparisons across countries. Food price

allocation of resources are influenced by relative

currency’s period-average exchange rate to a weighted

indexes, like consumer price indexes, should be inter-

prices, including the real exchange rate, real wages,

geometric average of exchange rates for currencies of

preted with caution because of the high variability

real interest rates, and a host of other prices in the

selected countries and the euro area. For most high-

across countries in the items covered.

economy. Relative prices also reflect, to a large extent,

income countries, weights are derived from trade in

Wholesale price indexes are based on the prices of

the choices of these agents. Thus relative prices con-

manufactured goods among industrial countries. The

commodities that have some significance in the output

vey vital information about the interaction of economic

data are compiled from the nominal effective exchange

or consumption of the country at the first commercial

agents in an economy and with the rest of the world.

rate index and a cost indicator of relative normalized

transaction. The prices are farm gate prices for agricul-

The exchange rate is the price of one currency

unit labor costs in manufacturing. For selected other

tural commodities and ex-factory prices for industrial

in terms of another. Offi cial exchange rates and

countries the nominal effective exchange rate index is

goods. Preference should be given to indexes that pro-

exchange rate arrangements are established by gov-

based on each country’s trade in both manufactured

vide the broadest coverage of the economy.

ernments. (Other exchange rates fully recognized by

goods and primary products with its partner or com-

The least-squares method is used to calculate the

governments include market rates, which are deter-

petitor countries. For these countries the real effective

growth rates of the GDP implicit deflator, consumer

mined largely by legal market forces, and for coun-

exchange rate index is derived from the nominal index

price index, and wholesale price index.

tries maintaining multiple exchange arrangements,

adjusted for relative changes in consumer prices. An

principal rates, secondary rates, and tertiary rates.)

increase in the real effective exchange rate represents

Also see Statistical methods for information on alter-

an appreciation of the local currency. Because of con-

• Official exchange rate is the exchange rate deter-

native conversion factors used in the World Bank

ceptual and data limitations, changes in real effective

mined by national authorities or the rate determined

Atlas method of calculating gross national income

exchange rates should be interpreted with caution.

in the legally sanctioned exchange market. It is cal-

(GNI) per capita in U.S. dollars.

Definitions

Controlling inflation is one of the primary goals of

culated as an annual average based on monthly aver-

The official or market exchange rate is often used

monetary policy and is intimately linked to the growth

ages (local currency units relative to the U.S. dollar).

to compare prices in different currencies. Since

in money supply. Inflation is measured by the rate of

• Purchasing power parity (PPP) conversion factor

exchange rates reflect at best the relative prices of

increase in a price index, but actual price change can

is the number of units of a country’s currency required

tradable goods, the volume of goods and services

be negative. Which index is used depends on which

to buy the same amount of goods and services in the

that a U.S. dollar buys in the United States may not

set of prices in the economy is being examined. The

domestic market as a U.S. dollar would buy in the

correspond to what a U.S. dollar converted to another

GDP deflator reflects changes in prices for total gross

United States. • Ratio of PPP conversion factor to

country’s currency at the official exchange rate would

domestic product. The most general measure of the

official exchange rate is the result obtained by divid-

buy in that country. Since identical volumes of goods

overall price level, it takes into account changes in

ing the PPP conversion factor by the official exchange

and services in different countries correspond to dif-

government consumption, capital formation (includ-

rate. • Real effective exchange rate is the nominal

ferent values (and vice versa) when official exchange

ing inventory appreciation), international trade, and

effective exchange rate (a measure of the value of a

rates are used, an alternative method of comparing

the main component, household final consumption

currency against a weighted average of several for-

prices across countries has been developed. In this

expenditure. The GDP defl ator is usually derived

eign currencies) divided by a price deflator or index

method national currency estimates of GNI are con-

implicitly as the ratio of current to constant price

of costs. • GDP implicit deflator measures the aver-

verted to a common unit of account by using conver-

GDP, resulting in a Paasche index. It is defective as a

age annual rate of price change in the economy as a

sion factors that reflect equivalent purchasing power.

general measure of inflation for use in policy because

whole for the periods shown. • Consumer price index

Purchasing power parity (PPP) conversion factors are

of the long lags in deriving estimates and because it

reflects changes in the cost to the average consumer

based on price and expenditure surveys conducted by

is often only an annual measure.

of acquiring a basket of goods and services that may

the International Comparison Program and represent

Consumer price indexes are produced more fre-

be fixed or may change at specified intervals, such

the conversion factors applied to equalize price levels

quently and so are more current. They are also con-

as yearly. The Laspeyres formula is generally used.

across countries. See About the data for table 1.1 for

structed explicitly, based on surveys of the cost of a

• Wholesale price index refers to a mix of agricultural

further discussion of the PPP conversion factor.

defined basket of consumer goods and services. Nev-

and industrial goods at various stages of production

The ratio of the PPP conversion factor to the official

ertheless, consumer price indexes should be inter-

and distribution, including import duties. The Laspey-

exchange rate (also referred to as the national price

preted with caution. The definition of a household, the

res formula is generally used.

level) makes it possible to compare the cost of the

basket of goods chosen, and the geographic (urban or

bundle of goods that make up gross domestic product

rural) and income group coverage of consumer price

(GDP) across countries. These national price levels

surveys can all vary widely across countries. In addi-

Data on official and real effective exchange rates

vary systematically, rising with GNI per capita. Real

tion, the weights are derived from household expen-

and consumer and wholesale price indexes are

effective exchange rates represent a nominal effective

diture surveys, which, for budgetary reasons, tend to

from the International Monetary Fund’s International

exchange rate index adjusted for relative movements

be conducted infrequently in developing countries,

Financial Statistics. PPP conversion factors and GDP

in national price or cost indicators of the home coun-

leading to poor comparability over time. Although

deflators are from the World Bank’s data files.

try, selected countries, and the euro area. A nominal

useful for measuring consumer price inflation within

Data sources

2007 World Development Indicators

245

ECONOMY

Exchange rates and prices

4.15

Balance of payments current account Goods and services

Net income

Net current transfers

Current account balance

Total reservesa

$ millions 1990 2005

$ millions 1990 2005

$ millions 1990 2005

$ millions 1990 2005

$ millions Exports 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, 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 †Data for Taiwan, China

246

261 354 13,462 3,992 14,800 .. 49,846 63,694 .. 2,064 .. 138,605b 364 977 .. 2,005 35,170 6,950 349 89 314 2,508 149,538 220 271 10,221 57,374 .. 8,679 .. 1,488 1,963 3,503 .. .. .. 48,902 1,832 3,262 9,895 973 .. 664 597 31,180 285,389 2,730 168 .. 473,672 983 13,018 1,568 829 26 318 74,172

Imports 2005

.. 1,821 .. 24,286 46,343 1,337 135,505 171,154 8,332 10,432 18,068 318,775 784 3,160 3,602 5,285 134,403 16,057 .. 92 4,017 2,894 427,955 .. .. 47,746 836,888 351,754 24,393 .. 4,964 9,716 8,289 18,876 .. 89,007 125,046 10,056 11,439 30,716 4,573 .. 10,939 1,929 82,457 555,204 4,228 181 2,171 1,127,020 3,869 51,790 4,939 811 83 593 221,604

2007 World Development Indicators

1990

727 485 10,106 3,386 6,846 .. 53,056 61,580 .. 3,960 .. 135,098 b 454 1,086 .. 1,987 28,184 8,027 758 318 507 2,475 149,118 410 488 9,166 46,706 .. 6,858 .. 1,282 2,346 3,445 .. .. .. 41,415 2,233 2,519 14,091 1,624 .. 711 1,271 33,456 283,238 1,812 192 .. 427,547 1,506 19,564 1,812 953 88 515 67,015

2005

.. 12 .. 311 .. –143 3,860 –2 174 15 1,294 –118 .. –2,268 .. 333 .. 1,420 15,144 –765 –4,031 –77 27 –236 34,916 –4,400 –6,207 998 570 4,552 1,984 .. 45 .. 409 .. 149,738 –13,176 –27,690 439 –363 –15,948 162,913 –942 –1,337 –6 –2,652 1,166 7,003 .. –1,646 .. 484 .. 14,456 –116 –798 1,613 4,691 –398 17,859 .. 56 .. 169 .. 308,430 2,316b 5,394 –2,197b –6,411 3,627b 1,129 –25 –37 97 93 –18 2,872 –249 –373 159 584 –199 8,004 .. 405 .. 1,841 .. 3,683 –106 –812 69 678 –19 97,794 –11,608 –25,967 799 3,558 –3,823 20,600 –758 310 125 1,229 –1,710 .. .. .. 332 .. –77 353 –15 –18 174 23 –69 4,559 –21 –254 120 440 –93 3,239 –558 –445 –26 116 –551 385,473 –19,388 –15,508 –796 –419 –19,764 .. –22 .. 123 .. –89 .. –21 .. 192 .. –46 38,154 –1,737 –10,624 198 1,735 –485 712,090 1,055 10,635 274 25,385 11,997 329,590 .. 312 .. –2,192 .. 24,901 –2,305 –5,563 1,026 4,089 542 .. .. .. .. .. .. 2,917 –460 –1,122 3 –22 –251 10,730 –233 –215 192 270 –424 7,174 –1,091 –662 –181 –465 –1,214 21,702 .. –1,235 .. 1,475 .. .. .. .. .. .. .. 86,461 .. –5,929 .. 888 .. 112,482 –5,708 275 –408 –4,223 1,372 11,333 –249 –1,957 371 2,734 –280 11,826 –1,210 –1,938 107 2,267 –360 34,326 –1,022 –35 7,545 5,748 2,327 7,652 –132 –571 631 2,865 –152 .. .. .. .. .. .. 11,784 –13 –700 97 100 36 4,895 –69 –5 449 1,402 –294 71,091 –3,735 –278 –952 –1,572 –6,962 577,463 –3,896 16,314 –8,199 –27,344 –9,944 2,155 –617 –965 –134 –184 168 261 –11 –32 59 69 23 3,312 .. 92 .. 359 .. 985,673 22,574 10,676 –21,954 –35,989 46,745 6,610 –111 –187 411 2,117 –223 66,626 –1,709 –7,030 4,718 3,987 –3,537 9,547 –196 –337 227 3,558 –213 964 –149 –27 70 18 –203 127 –22 –10 39 67 –45 1,756 –18 –37 193 1,254 –22 210,224 4,362 9,053 –596 –4,271 10,923

.. 638 .. –571 .. 1,440 .. 2,703 59,167 5,138 .. 3,197 5,789 6,222 28,082 –193 1 669 –42,286 19,319 43,257 4,252 17,228 11,828 167 .. 1,178 –132 660 2,825 434 .. 1,342 9,328 23,789 11,996 –288 69 657 498 511 1,795 –2,156 .. 2,531 1,469 3,331 6,309 14,199 9,200 53,799 –3,004 670 8,697 .. 305 438 –256 112 101 –356 .. 1,158 –675 37 965 26,555 23,530 33,018 .. 123 145 .. 132 231 703 6,784 16,933 160,818 34,476 831,410 20,284 24,656 124,278 –1,981 4,869 14,955 .. 261 .. 903 10 738 –959 525 2,314 –12 21 1,322 –2,585 167 8,800 .. .. .. –2,495 .. 29,554 8,616 11,226 34,028 –500 69 1,853 –59 1,009 2,148 2,103 3,620 21,857 –786 595 1,890 .. .. 28 –1,445 198 1,947 –1,568 55 1,121 9,517 10,415 11,332 –33,289 68,291 74,360 924 279 675 –44 55 98 –690 .. 479 116,035 104,547 101,676 –812 309 1,897 –17,879 4,721 2,287 –1,387 362 3,777 –162 145 97 14 18 80 54 10 134 16,162 77,653 260,272

Goods and services

4.15

Net income

Net current transfers

Current account balance

Total reservesa

$ millions 1990 2005

$ millions 1990 2005

$ millions 1990 2005

$ millions 1990 2005

$ millions Exports 1990

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

1,033 12,035 22,911 29,295 19,741 .. 26,786 17,312 219,971 2,217 323,692 2,511 .. 2,228 .. 73,297 8,268 .. 102 1,090 .. 100 .. 11,468 .. .. 471 443 32,665 420 471 1,722 48,805 .. 493 6,239 229 319 1,220 422 159,304 11,683 392 533 14,550 47,078 5,577 6,835 4,438 1,381 2,514 4,120 11,430 19,037 21,554 ..

Imports 2005

3,427 74,168 82,735 99,104 .. .. 161,366 57,874 462,709 3,994 677,782 6,584 30,548 5,126 .. 334,370 51,574 942 .. 7,526 13,037 705 .. 29,383 14,879 2,511 450 .. 161,384 1,218 .. 3,762 230,369 1,528 1,211 18,788 2,087 3,181 2,310 1,283 427,949 30,467 1,861 530 52,233 133,032 19,514 19,059 10,736 3,580 3,927 19,426 44,693 112,622 53,272 ..

1990

1,127 11,017 29,527 27,511 22,292 .. 24,576 20,228 218,573 2,390 297,306 3,569 .. 2,705 .. 76,373 7,169 .. 212 997 .. 754 .. 8,960 .. .. 809 549 31,765 830 520 1,916 51,915 .. 1,096 7,783 996 603 1,584 834 147,652 11,699 682 728 6,909 38,910 3,342 10,205 4,193 1,509 2,169 4,087 13,967 15,095 27,146 ..

2005

5,035 –237 –331 280 1,854 –51 –86 75,596 –1,427 –6,915 787 237 379 –8,106 93,918 –3,257 –4,451 2,837 22,488 –7,036 6,853 87,584 –5,190 –11,849 418 1,258 –2,988 929 .. 378 .. 2,500 .. 327 .. .. .. .. .. .. .. .. 137,081 –4,955 –30,307 2,384 691 –361 –5,331 57,525 –1,981 –2,622 5,061 6,029 163 3,756 463,295 –14,712 –17,078 –3,164 –10,061 –16,479 –27,724 5,975 –430 –676 291 1,578 –312 –1,079 607,869 22,492 103,444 –4,800 –7,573 44,078 165,783 11,859 –214 376 1,045 2,588 –227 –2,311 25,503 .. –5,357 .. –412 .. –724 6,540 –418 –108 368 1,028 –527 –495 .. .. .. .. .. .. .. 313,989 –88 –1,320 1,150 –2,502 –2,014 16,559 24,513 7,738 8,834 –4,951 –3,261 3,886 32,634 1,397 .. –81 .. 332 .. –203 .. –1 .. 56 .. –55 .. 9,936 2 –188 96 596 191 –2,002 16,222 .. 247 .. 1,057 .. –1,881 1,354 433 305 286 301 65 –44 .. .. .. .. .. .. .. 13,523 174 –281 –481 –634 2,201 14,945 16,745 .. –627 .. 662 .. –1,831 3,602 .. –55 .. 1,065 .. –81 691 –161 –27 234 80 –265 –188 .. –80 .. 99 .. –86 .. 130,609 –1,872 –6,318 102 –4,477 –870 19,980 1,625 –37 –195 225 193 –221 –409 .. –46 .. 86 .. –10 .. 4,154 –23 –8 97 61 –119 –340 243,259 –8,316 –12,242 3,975 20,484 –7,451 –4,647 2,743 .. 403 .. 570 .. –242 1,405 –44 –11 7 269 –640 63 22,739 –988 –314 2,336 5,375 –196 1,110 2,891 –97 –360 448 403 –415 –761 2,458 –192 –745 39 134 –436 112 2,495 37 151 354 669 28 634 2,711 14 48 109 1,533 –289 153 374,710 –620 6,194 –2,943 –10,497 8,089 48,936 32,921 –1,576 –7,626 138 459 –1,453 –9,622 3,292 –217 –119 202 750 –305 –800 852 –54 –13 14 104 –236 –231 24,609 –2,738 –6,732 85 3,310 4,988 24,202 81,545 –2,700 1,045 –1,476 –3,045 3,992 49,488 11,080 –254 –1,459 –874 –2,257 1,106 4,717 29,042 –1,084 –2,516 2,794 9,036 –1,661 –3,463 10,636 –255 –1,124 219 243 209 –782 2,692 –103 –538 156 291 –76 640 4,098 2 –74 43 223 390 –22 15,176 –1,733 –5,011 281 1,791 –1,419 1,030 53,635 –872 –123 714 11,403 –2,695 2,338 113,476 –3,386 –11,186 2,511 6,935 3,067 –5,105 69,078 –96 –3,932 5,507 2,731 –181 –17,007 .. .. .. .. .. .. ..

47 2,338 1,185 18,590 5,637 137,825 8,657 34,579 .. .. 8,340 12,201 5,362 869 6,598 28,059 88,595 65,954 168 2,170 87,828 846,896 1,139 5,461 .. 7,070 236 1,799 .. .. 14,916 210,552 2,929 10,165 .. 612 8 309 .. 2,360 4,210 16,618 72 519 1 25 7,225 41,880 107 3,816 .. 1,340 92 481 142 165 10,659 70,450 198 855 59 420 761 1,372 10,217 74,110 2 597 23 430 2,338 16,551 232 1,103 410 889 50 312 354 1,565 34,401 20,448 4,129 8,893 166 728 226 250 4,129 28,632 15,788 46,986 1,784 4,358 1,046 11,109 344 1,211 427 750 675 1,297 1,891 14,171 2,036 18,474 4,674 42,561 20,579 10,364 .. ..

2007 World Development Indicators

247

ECONOMY

Balance of payments current account

4.15

Balance of payments current account Goods and services

Net income

Net current transfers

Current account balance

Total reservesa

$ millions 1990 2005

$ millions 1990 2005

$ millions 1990 2005

$ millions 1990 2005

$ millions Exports 1990

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 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 Europe EMU

Imports 2005

1990

2005

6,380 32,813 9,901 42,866 .. 268,136 .. 164,718 143 257 354 659 47,381 180,551 43,880 79,274 1,453 2,180 1,840 3,194 .. .. .. .. 210 263 215 452 67,489 283,565 64,953 248,627 .. 25,241 .. 25,649 7,900 22,121 6,930 22,319 68 .. 468 .. 27,160 66,437 21,017 68,639 83,595 288,042 100,870 345,642 2,293 7,887 2,965 10,066 499 4,938 877 7,790 658 2,110 768 2,212 70,560 178,072 70,490 150,358 97,033 197,159 96,389 171,456 5,030 9,769 2,955 10,718 .. 1,254 .. 1,682 538 2,890 1,474 3,825 29,229 129,847 35,870 133,599 663 751 847 1,093 2,289 7,254 1,427 5,266 5,203 14,492 6,039 14,638 21,042 102,806 25,524 121,766 .. .. .. .. 178 1,343 686 2,584 .. 44,378 .. 43,707 .. .. .. .. 239,226 587,541 264,089 669,823 535,260 1,275,245 616,120 1,991,975 2,158 5,087 1,659 4,626 .. .. .. .. 18,806 56,821 9,451 29,371 .. 36,618 .. 38,562 .. .. .. .. 1,490 6,752 2,170 5,285 1,360 .. 1,897 .. 2,012 .. 2,001 .. 4,324,290 t 12,691,551 t 4,306,213 t 12,539,143 t 79,141 217,936 97,905 245,934 635,146 3,261,414 591,292 2,905,260 302,331 1,793,100 300,012 1,586,704 336,906 1,483,562 291,782 1,328,253 714,951 3,596,835 689,821 3,263,799 167,506 1,327,062 166,319 1,178,084 .. 926,132 .. 865,611 170,445 650,474 147,430 593,769 .. .. 105,814 222,757 34,864 114,362 48,099 131,775 78,020 228,841 72,772 209,500 3,594,026 9,151,976 3,592,287 9,318,222 1,530,521 3,731,193 1,491,055 3,590,256

a. International reserves including gold valued at London gold price. b. Includes Luxembourg.

248

2007 World Development Indicators

161 –2,900 106 4,449 –3,254 .. –19,111 .. –1,122 .. –16 –16 143 366 –85 7,968 272 –15,616 –14,418 –4,147 –129 –131 153 632 –363 .. .. .. .. .. –71 –51 7 137 –69 1,006 –541 –421 –1,184 3,122 .. –119 .. 245 .. –38 –363 46 –120 978 –84 .. 328 .. –157 –4,271 –4,929 –321 –2,011 1,552 –3,533 –21,452 2,799 –4,084 –18,009 –167 –297 541 1,828 –298 –136 –1,362 141 1,446 –372 59 20 102 128 51 –4,473 545 –1,936 –4,616 –6,339 7,878 37,132 –2,398 –8,977 6,124 –401 –863 88 751 1,762 .. –41 .. 450 .. –185 –204 562 603 –559 –853 –2,921 213 3,004 –7,281 –32 –33 132 169 –84 –397 –597 –6 56 459 –455 –1,659 828 1,501 –463 –2,508 –5,663 4,365 1,468 –2,625 .. .. .. .. .. –48 –157 293 1,139 –263 .. –985 .. 2,845 .. .. .. .. .. .. –5,154 54,814 –8,794 –21,990 –38,811 28,560 11,294 –26,660 –86,073 –78,960 –321 –585 8 121 186 .. .. .. .. .. –774 –1,984 –302 –107 8,279 .. –1,219 .. 3,380 .. .. .. .. .. .. –372 –1,657 1,790 1,406 739 –437 .. 380 .. –594 –263 .. 112 .. –140

–8,504 1,374 83,184 .. –52 44 87,131 13,437 –513 22 .. .. –103 5 33,212 27,748 –282 .. –682 112 .. 23 –9,142 2,583 –83,136 57,238 –647 447 –2,768 11 46 216 23,643 20,324 53,859 61,284 –1,061 535 –19 .. –536 193 –3,670 14,258 –206 358 1,447 513 –303 867 –23,155 7,626 .. .. –259 44 2,531 469 .. 4,891 –49,459 43,146 –791,509 173,094 –2 1,446 .. .. 25,359 12,733 217 .. .. .. 1,215 441 .. 201 .. 295

21,601 182,272 406 28,888 1,191 .. 171 115,794 15,480 8,160 .. 20,624 17,227 2,736 1,869 244 24,868 57,575 .. 189 2,049 52,076 195 4,888 4,548 52,494 .. 1,344 19,388 21,010 43,593 188,259 3,078 .. 29,803 9,051 .. 6,141 560 ..

About the data

4.15

Definitions

The balance of payments records an economy’s

by enterprises, surveys to estimate service transac-

• Exports and imports of goods and services com-

transactions with the rest of the world. Balance of

tions, and foreign exchange records. Differences in

prise all transactions between residents of an econ-

payments accounts are divided into two groups:

collection methods—such as in timing, definitions

omy and the rest of the world involving a change in

the current account, which records transactions in

of residence and ownership, and the exchange rate

ownership of general merchandise, goods sent for

goods, services, income, and current transfers, and

used to value transactions—contribute to net errors

processing and repairs, nonmonetary gold, and ser-

the capital and financial account, which records capi-

and omissions. In addition, smuggling and other ille-

vices. • Net income refers to receipts and payments

tal transfers, acquisition or disposal of nonproduced,

gal or quasi-legal transactions may be unrecorded or

of employee compensation for nonresident workers,

nonfinancial assets, and transactions in financial

misrecorded. For further discussion of issues relat-

assets and liabilities. The table presents data from

ing to the recording of data on trade in goods and

the current account with the addition of gross inter-

services, see About the data for tables 4.4–4.7.

national reserves.

The concepts and definitions underlying the data

and investment income (receipts and payments on direct investment, portfolio investment, and other investments and receipts on reserve assets). Income derived from the use of intangible assets is recorded

The balance of payments is a double-entry account-

in the table are based on the fi fth edition of the

ing system that shows all flows of goods and services

International Monetary Fund’s (IMF) Balance of Pay-

into and out of an economy; all transfers that are the

ments Manual (1993). That edition redefined as capi-

counterpart of real resources or financial claims pro-

tal transfers some transactions previously included

vided to or by the rest of the world without a quid pro

in the current account, such as debt forgiveness,

quo, such as donations and grants; and all changes

migrants’ capital transfers, and foreign aid to acquire

in residents’ claims on and liabilities to nonresidents

capital goods. Thus the current account balance now

• Current account balance is the sum of net exports

that arise from economic transactions. All transac-

reflects more accurately net current transfer receipts

of goods and services, net income, and net current

tions are recorded twice—once as a credit and once

in addition to transactions in goods, services (previ-

transfers. • Total reserves comprise holdings of

as a debit. In principle the net balance should be

ously nonfactor services), and income (previously

monetary gold, special drawing rights, reserves of

zero, but in practice the accounts often do not bal-

factor income). Many countries maintain their data

IMF members held by the IMF, and holdings of foreign

ance. In these cases a balancing item, net errors and

collection systems according to the fourth edition.

exchange under the control of monetary authorities.

omissions, is included.

Where necessary, the IMF converts such reported

The gold component of these reserves is valued at

Discrepancies may arise in the balance of pay-

data to conform to the fifth edition (see Primary data

year-end (31 December) London prices ($385.00 an

ments because there is no single source for balance

documentation). Values are in U.S. dollars converted

ounce in 1990, and $438.00 an ounce in 2004).

of payments data and therefore no way to ensure

at market exchange rates.

that the data are fully consistent. Sources include

The data in this table come from the IMF’s Balance

customs data, monetary accounts of the banking

of Payments and International Financial Statistics

system, external debt records, information provided

databases.

Top 15 economies with the largest current account surplus—and top 15 economies with the largest current account deficit in 2005

Economy

Surplus

Share of GDP

($ billions)

(%)

Economy

Japan

165.8

3.7

United States

China

160.8

7.2

Germany

116.0

4.2

Saudi Arabia

87.1

28.1

Russian Federation

83.2

10.9

Switzerland

53.9

Norway

under business services. • Net current transfers are recorded in the balance of payments whenever an economy provides or receives goods, services, income, or financial items without a quid pro quo. All transfers not considered to be capital are current.

4.15a Deficit

Share of GDP

($ billions)

(%)

–791.5

–6.4

Spain

–83.1

–7.4

United Kingdom

–49.5

–2.2

Australia

–42.3

–5.8

Data on the balance of payments are published in

France

–33.3

–1.6

the IMF’s Balance of Payments Statistics Yearbook

14.7

Italy

–27.7

–1.6

and International Financial Statistics. The World

49.5

16.7

Turkey

–23.2

–6.4

Netherlands

48.9

7.8

Grece

–17.9

–7.9

Singapore

33.2

28.4

Portugal

–17.0

–9.3

Kuwait

32.6

40.4

New Zealand

–9.6

–8.8

Canada

26.6

2.4

South Africa

–9.1

–3.8

Venezuela, RB

25.4

18.1

Romania

–8.5

–8.6

Nigeria

24.2

24.5

Hungary

–8.1

–7.4

Sweden

23.6

6.6

Ireland

–5.3

–2.6

The IMF’s International Financial Statistics and

Hong Kong, China

20.3

11.4

Poland

–5.1

–1.7

Balance of Payments databases are available on

Source: International Monetary Fund, balance of payments data files.

Data sources

Bank exchanges data with the IMF through electronic 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 the IMF’s Balance of Payments Manual, fifth edition (1993), Balance of Payments Textbook (1996), and Balance of Payments Compilation Guide (1995).

CD-ROM.

2006 World Development Indicators

249

ECONOMY

Balance of payments current account

4.16

External debt Total external debt

Long-term debt

Public and publicly guaranteed debt

$ millions 1990 2005

$ millions 1990 2005

1990

.. .. 26,688 7,603 48,676 .. .. .. .. 11,658 .. .. 1,218 3,864 .. 547 94,427 .. 748 851 1,683 5,373 .. 624 469 14,687 45,515 .. 15,784 8,994 4,187 3,367 13,223 .. .. .. .. 3,518 10,029 28,439 1,938 .. .. 8,479 .. .. 3,150 308 .. .. 2,670 .. 2,368 2,253 630 778

.. .. 26,688 7,603 46,876 .. .. .. .. 11,658 .. .. 1,218 3,687 .. 547 87,756 .. 748 851 1,683 5,144 .. 624 469 10,425 45,515 .. 14,671 8,994 4,187 3,063 10,665 .. .. .. .. 3,419 9,865 27,439 1,913 .. .. 8,479 .. .. 3,150 308 .. .. 2,637 .. 2,241 2,253 630 778

Private nonguaranteed external debt

Use of IMF credit

IBRD loans and IDA credits 1990 2005

$ millions 1990 2005

$ millions 1990 2005

.. .. 1,208 .. 2,609 .. .. .. .. 4,159 .. .. 326 587 .. 169 8,427 .. 282 .. .. 871 .. 265 .. 1,874 5,881 .. 3,874 1,161 239 412 1,920 .. .. .. .. 258 848 2,401 164 .. .. 851 .. .. 69 102 .. .. 1,423 .. 293 419 145 324

.. .. .. 0 1,800 .. .. .. .. 0 .. .. 0 177 .. 0 6,671 .. 0 0 0 230 .. 0 0 4,263 .. .. 1,113 0 0 304 2,558 .. .. .. .. 99 164 1,000 26 .. .. 0 .. .. 0 0 .. .. 33 .. 127 0 0 0

.. .. 670 0 3,083 .. .. .. .. 626 .. .. 18 257 .. 0 1,821 .. 0 43 27 121 .. 37 30 1,156 469 .. .. 521 11 11 431 .. .. .. .. 72 265 125 0 .. .. 6 .. .. 140 45 .. .. 745 .. 67 51 5 38

$ 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, 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

250

.. .. 28,149 8,592 62,233 .. .. .. .. 12,439 .. .. 1,292 4,275 .. 553 119,964 .. 832 907 1,845 6,431 .. 699 529 19,226 55,301 .. 17,222 10,259 4,934 3,756 17,251 .. .. .. .. 4,372 12,107 33,017 2,149 .. .. 8,630 .. .. 3,983 369 .. .. 3,734 .. 2,849 2,476 692 917

.. 1,839 16,879 11,755 114,335 1,861 .. .. 1,881 18,935 4,734 .. 1,855 6,390 5,564 473 187,994 16,786 2,045 1,322 3,515 7,151 .. 1,016 1,633 45,154 281,612 .. 37,656 10,600 5,936 6,223 10,735 30,169 .. .. .. 7,398 17,129 34,114 7,088 736 11,255 6,259 .. .. 3,902 672 1,911 .. 6,739 .. 5,349 3,247 693 1,323

2007 World Development Indicators

.. 1,459 16,363 9,428 85,477 1,386 .. .. 1,531 17,938 1,231 .. 1,762 5,965 4,400 438 164,001 11,922 1,920 1,228 3,155 6,114 .. 871 1,537 38,281 133,345 .. 31,480 9,412 5,161 4,118 9,854 25,848 .. .. .. 6,094 15,332 28,132 5,513 723 7,256 5,897 .. .. 3,582 626 1,626 .. 5,734 .. 3,793 2,931 671 1,276

Total 2005

.. 1,375 15,476 9,428 61,952 923 .. .. 1,344 17,938 783 .. 1,762 4,564 2,560 438 94,497 4,587 1,920 1,228 3,155 5,521 .. 871 1,537 9,096 82,853 .. 22,491 9,412 5,161 3,470 9,007 9,782 .. .. .. 6,093 10,662 24,892 4,760 723 435 5,897 .. .. 3,582 626 1,494 .. 5,734 .. 3,688 2,931 671 1,276

.. .. .. .. .. 752 .. .. 501 8,688 .. .. .. 1,673 1,403 9 .. .. .. .. .. 1,115 .. .. 899 293 20,880 .. 3,900 .. 280 60 2,185 .. .. .. .. 416 815 1,912 448 .. .. .. .. .. .. .. .. .. 4,234 .. .. .. .. ..

.. 84 887 .. 23,525 464 .. .. 187 .. 448 .. .. 1,401 1,840 .. 69,505 7,335 .. .. .. 592 .. .. .. 29,184 50,492 .. 8,989 .. .. 648 847 16,066 .. .. .. 2 4,670 3,240 754 .. 6,821 .. .. .. .. .. 132 .. 0 .. 105 .. .. ..

.. 92 0 .. 9,513 176 .. .. 164 308 0 .. 53 244 62 .. 0 660 104 58 81 272 .. 36 79 .. .. .. .. 791 26 .. 198 .. .. .. .. 400 78 .. .. .. .. 160 .. .. 68 21 232 .. 417 .. .. 87 12 21

Total external debt

Long-term debt

$ millions 1990 2005

$ millions 1990 2005

Public and publicly guaranteed debt

4.16

Private nonguaranteed external debt

Use of IMF credit

$ millions 1990 2005

$ millions 1990 2005

$ millions

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

3,718 21,201 83,628 69,872 9,020 .. .. .. .. 4,752 .. 8,333 .. 7,055 .. .. .. .. 1,768 .. 1,779 396 1,849 .. .. .. 3,689 1,558 15,328 2,468 2,113 984 104,442 .. .. 25,004 4,650 4,695 .. 1,640 .. .. 10,745 1,726 33,439 .. .. 20,663 6,493 2,594 2,105 20,044 30,580 49,364 .. ..

5,242 66,119 123,123 138,300 21,260 .. .. .. .. 6,511 .. 7,696 43,354 6,169 .. .. .. 2,032 2,690 14,283 22,373 690 2,581 .. 11,201 2,243 3,465 3,155 50,981 2,969 2,281 2,160 167,228 2,053 1,327 16,846 5,121 6,645 .. 3,285 .. .. 5,144 1,972 22,178 .. 3,472 33,675 9,765 1,849 3,120 28,653 61,527 98,821 .. ..

3,487 17,931 72,462 58,242 1,797 .. .. .. .. 4,049 .. 7,202 .. 5,639 .. .. .. .. 1,757 .. 358 378 1,116 .. .. .. 3,320 1,385 13,422 2,337 1,806 910 81,809 .. .. 23,847 4,231 4,466 .. 1,572 .. .. 8,313 1,487 31,935 .. .. 16,643 3,842 2,461 1,732 13,959 25,241 39,261 .. ..

4,660 53,725 114,335 105,993 10,574 .. .. .. .. 5,897 .. 6,878 35,334 5,520 .. .. .. 1,830 2,656 6,791 18,923 647 1,115 .. 5,876 2,084 3,178 3,040 38,805 2,843 2,043 797 160,649 1,240 1,267 16,164 4,419 5,196 .. 3,217 .. .. 4,405 1,803 20,342 .. 1,805 30,953 9,256 1,654 2,607 25,387 54,743 81,118 .. ..

Total 1990

3,420 17,931 70,974 47,982 1,797 .. .. .. .. 4,015 .. 7,202 .. 4,759 .. .. .. .. 1,757 .. 358 378 1,116 .. .. .. 3,320 1,382 11,592 2,337 1,806 762 75,974 .. .. 23,647 4,211 4,466 .. 1,572 .. .. 8,313 1,226 31,545 .. .. 16,506 3,842 1,523 1,713 13,629 24,040 39,261 .. ..

2005

4,152 21,216 80,281 72,335 10,493 .. .. .. .. 5,508 .. 6,878 2,184 5,520 .. .. .. 1,670 1,971 1,318 17,912 647 1,115 .. 1,511 1,613 3,178 3,040 22,449 2,843 2,043 731 108,786 700 1,267 13,113 3,727 5,196 .. 3,217 .. .. 4,113 1,771 20,342 .. 842 29,490 7,514 1,266 2,264 22,222 35,233 35,094 .. ..

IBRD loans and IDA credits 1990 2005

635 1,512 20,996 10,385 86 .. .. .. .. 672 .. 593 .. 2,056 .. .. .. .. 131 .. 34 .. 248 .. .. .. 797 854 1,102 498 264 195 11,030 .. .. 3,138 .. .. .. 667 .. .. 299 461 3,321 .. .. 3,922 462 349 320 1,188 4,044 55 .. ..

1,353 .. 28,919 9,132 .. .. .. .. .. .. .. 970 .. 2,663 .. .. .. .. .. .. .. 271 251 .. .. 608 .. 1,940 .. .. .. 79 .. 371 .. 2,278 1,575 .. .. .. .. .. .. .. 1,859 .. .. 9,104 .. 327 244 .. 3,082 .. .. ..

66 .. 1,488 10,261 .. .. .. .. .. 34 .. 0 .. 880 .. .. .. .. .. .. .. 0 0 .. .. .. 0 2 1,830 0 0 147 5,835 .. .. 200 19 0 .. 0 .. .. .. 261 391 .. .. 138 .. 938 19 330 1,201 .. .. ..

509 32,509 34,054 33,658 81 .. .. .. .. 390 .. .. 33,150 0 .. .. .. 161 685 5,473 1,011 .. .. .. 4,365 471 .. .. 16,356 .. .. 66 51,863 540 .. 3,051 692 .. .. .. .. .. 292 33 .. .. 963 1,463 1,742 387 343 3,165 19,510 46,024 .. ..

32 330 2,623 494 0 .. .. .. .. 357 .. 94 .. 482 .. .. .. .. 8 .. 0 15 322 .. .. .. 144 115 .. 69 70 22 6,551 .. .. 750 74 0 .. 44 .. .. 0 85 0 .. 0 835 272 61 0 755 912 509 .. ..

2007 World Development Indicators

168 0 .. 7,807 .. .. .. .. .. 0 .. 236 .. 159 .. .. .. 178 29 0 .. 35 320 .. 0 62 212 75 .. 109 69 .. 0 95 35 0 157 .. .. 20 .. .. 201 128 .. .. .. 1,492 24 0 .. 57 389 .. .. ..

251

ECONOMY

External debt

4.16

External debt Total external debt

Long-term debt

$ millions 1990 2005

$ millions 1990 2005

Public and publicly guaranteed debt

Private nonguaranteed external debt

Use of IMF credit

$ millions 1990 2005

$ millions 1990 2005

$ millions

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 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 Europe EMU

252

Total 1990

2005

IBRD loans and IDA credits 1990 2005

1,140 38,694 230 31,199 223 13,341 0 .. .. 229,042 .. 204,911 .. 75,359 .. .. 712 1,518 664 1,420 664 1,420 340 .. .. .. .. .. .. .. .. .. 3,744 3,793 3,008 3,609 2,948 3,467 835 .. .. 16,295 .. 13,186 .. 7,972 .. 2,984 1,197 1,682 940 1,420 940 1,420 92 .. .. .. .. .. .. .. .. .. .. 23,654 .. 8,493 .. 3,340 .. .. .. .. .. .. .. .. .. .. 2,370 2,750 1,926 1,882 1,926 1,882 419 .. .. 30,632 .. 20,922 .. 11,662 .. .. .. .. .. .. .. .. .. .. 5,863 11,444 5,049 10,055 4,947 9,812 946 2,095 14,762 18,455 9,651 11,659 9,155 11,163 1,048 .. 298 532 294 451 294 451 44 27 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 17,259 6,508 15,108 5,640 15,108 5,640 523 22 .. 1,022 .. 811 .. 785 .. .. 6,454 7,763 5,794 6,192 5,782 6,183 1,493 3,861 28,094 52,266 19,771 36,252 12,460 13,483 2,530 459 1,281 1,708 1,081 1,469 1,081 1,469 398 .. 2,511 2,652 2,055 1,310 1,782 1,197 41 .. 7,688 17,789 6,878 14,723 6,660 12,982 1,406 1,594 49,424 171,059 39,924 118,195 38,870 62,580 6,429 5,901 .. 1,092 .. 945 .. 912 .. .. 2,584 4,463 2,162 4,250 2,162 4,250 969 .. .. 33,297 .. 20,047 .. 10,458 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 4,415 14,551 3,114 8,286 3,045 7,866 359 .. .. 4,226 .. 4,189 .. 3,639 .. 310 33,171 44,201 28,159 33,984 24,509 29,317 974 .. 23,270 19,287 21,378 16,513 21,378 16,513 59 .. .. .. .. .. .. .. .. .. 6,352 5,363 5,160 4,717 5,160 4,717 602 .. 6,905 5,668 4,543 4,887 4,541 4,085 813 2,488 3,279 4,257 2,681 3,253 2,496 3,222 449 915 .. s .. s .. s .. s .. s .. s .. s .. s 309,885 379,239 266,712 338,595 259,250 298,209 55,118 105,286 1,020,176 2,363,139 827,982 1,808,585 775,449 1,063,425 82,201 116,307 575,126 1,146,475 474,426 834,842 441,124 548,961 54,704 80,600 445,050 1,216,664 353,557 973,742 334,325 514,464 27,497 35,708 1,330,061 2,742,378 1,094,694 2,147,179 1,034,699 1,361,634 137,319 221,593 234,079 621,223 194,620 400,185 172,984 256,316 25,306 39,829 210,841 834,484 172,395 646,633 167,474 266,975 10,429 30,447 444,637 727,628 352,724 621,868 327,705 419,555 35,877 40,379 139,541 152,724 118,031 124,308 116,613 113,334 10,074 10,102 124,396 191,479 107,527 177,441 105,800 141,681 30,717 50,329 176,568 214,841 149,398 176,743 144,122 163,773 24,916 50,507

2007 World Development Indicators

7 17,858 .. 129,552 0 .. .. .. 60 141 .. 5,214 0 .. .. .. .. 5,153 .. .. 0 .. .. 9,260 .. .. 102 243 496 496 0 .. .. .. .. .. 0 .. .. 26 12 9 7,311 22,769 0 .. 273 113 218 1,741 1,054 55,614 .. 33 0 .. .. 9,588 .. .. .. .. .. .. 69 421 .. 551 3,650 4,667 0 .. .. .. 0 .. 2 802 185 32 .. s .. s 7,462 40,385 52,533 745,160 33,302 285,881 19,232 459,278 59,996 785,545 21,635 143,869 4,921 379,658 25,018 202,313 1,418 10,974 1,727 35,760 5,276 12,970

0 .. 0 .. 314 .. 108 .. .. .. 159 .. .. 410 956 0 .. .. .. .. 140 1 87 329 176 0 .. 282 .. .. .. .. 101 .. 3,012 112 .. 0 949 7 .. s 10,671 23,981 8,452 15,529 34,652 2,085 1,305 18,298 1,815 4,537 6,612

261 0 77 .. 148 866 192 .. .. .. 160 .. .. 381 518 .. .. .. .. 127 342 .. 14 .. 0 14,646 .. 131 1,188 .. .. .. 2,304 0 .. 203 .. 292 591 111 .. s 8,322 40,857 14,021 26,836 49,179 8,545 18,810 13,122 547 2,208 5,947

About the data

4.16

Definitions

Data on the external debt of developing countries

into U.S. dollars to produce summary tables. Stock

• Total external debt is debt owed to nonresidents

are gathered by the World Bank through its Debtor

figures (amount of debt outstanding) are converted

repayable in foreign currency, goods, or services. It

Reporting System. World Bank staff calculate the

using end-of-period exchange rates, as published in

is the sum of public, publicly guaranteed, and private

indebtedness of these countries using loan-by-loan

the IMF’s International Financial Statistics (line ae).

nonguaranteed long-term debt, use of IMF credit, and

reports submitted by them on long-term public and

Flow fi gures are converted at annual average

short-term debt. Short-term debt includes all debt

publicly guaranteed borrowing, along with informa-

exchange rates (line rf). Projected debt service is

having an original maturity of one year or less and

tion on short-term debt collected by the countries

converted using end-of-period exchange rates. Debt

interest in arrears on long-term debt. • Long-term

or collected from creditors through the reporting

repayable in multiple currencies, goods, or services

debt is debt that has an original or extended maturity

systems of the Bank for International Settlements

and debt with a provision for maintenance of the

and the Organisation for Economic Co-operation and

value of the currency of repayment are shown at

Development. These data are supplemented by infor-

book value.

of more than one year. It has three components: public, publicly guaranteed, and private nonguaranteed debt. • Public and publicly guaranteed debt

mation on loans and credits from major multilateral

Because flow data are converted at annual aver-

banks, loan statements from official lending agen-

age exchange rates and stock data at end-of-period

cies in major creditor countries, and estimates by

exchange rates, year-to-year changes in debt out-

World Bank and International Monetary Fund (IMF)

standing and disbursed are sometimes not equal

staff. In addition, the table includes data on private

to net flows (disbursements less principal repay-

nonguaranteed debt for 77 countries either reported

ments); similarly, changes in debt outstanding,

to the World Bank or estimated by its staff.

including undisbursed debt, differ from commitments

repayment by a public entity. • IBRD loans and IDA

The coverage, quality, and timeliness of debt data

less repayments. Discrepancies are particularly

credits are extended by the World Bank. The Inter-

vary across countries. Coverage varies for both debt

significant when exchange rates have moved sharply

national Bank for Reconstruction and Development

instruments and borrowers. With the widening spec-

during the year. Cancellations and reschedulings of

(IBRD) lends at market rates. The International Devel-

trum of debt instruments and investors and the expan-

other liabilities into long-term public debt also con-

opment Association (IDA) provides credits at con-

sion of private nonguaranteed borrowing, comprehen-

tribute to the differences.

cessional rates. • Private nonguaranteed external

comprises the long-term external obligations of public debtors, including the national government and political subdivisions (or an agency of either) and autonomous public bodies, and the external obligations of private debtors that are guaranteed for

sive coverage of long-term external debt becomes more

Variations in reporting rescheduled debt also affect

debt consists of the long-term external obligations

complex. Reporting countries differ in their capacity to

cross-country comparability. For example, reschedul-

of private debtors that are not guaranteed for repay-

monitor debt, especially private nonguaranteed debt.

ing under the auspices of the Paris Club of official

ment by a public entity. • Use of IMF credit denotes

Even data on public and publicly guaranteed debt are

creditors may be subject to lags between the com-

repurchase obligations to the IMF for all uses of IMF

affected by coverage and accuracy in reporting—

pletion of the general rescheduling agreement and

resources (excluding those resulting from drawings

again because of monitoring capacity and sometimes

the completion of the specific bilateral agreements

on the reserve tranche). These obligations, shown for

because of an unwillingness to provide information. A

that define the terms of the rescheduled debt. Other

the end of the year specified, comprise purchases

key part often underreported is military debt.

areas of inconsistency include country treatment of

outstanding under the credit tranches (including

arrears and of nonresident national deposits denomi-

enlarged access resources) and all special facilities

Because debt data are normally reported in the currency of repayment, they have to be converted

nated in foreign currency.

(the buffer stock, compensatory financing, extended fund, and oil facilities), trust fund loans, and opera-

External debt started to decline in the Sub-Saharan African economies in 2005

4.16a

tions under the structural adjustment and enhanced structural adjustment facilities.

$ millions 700

External debt

Gross domestic product

600 500 400

Data sources 300

The main sources of external debt information 200

are reports to the World Bank through its Debtor

100

Reporting System from member countries that have received IBRD loans or IDA credits. Additional

0

information is from the files of the World Bank and 1980

1990

2000

2001

2002

2003

2004

2005

the IMF. Summary tables of the external debt of

Because GDP has risen, the ratio of external debt to gross domestic product has declined for the last

developing countries are published annually in the

five years.

World Bank’s Global Development Finance and on

Source: World Bank data files.

its Global Development Finance CD-ROM.

2007 World Development Indicators

253

ECONOMY

External debt

4.17

Debt ratios Present value of debt

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, 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

254

% of GNI 2005b

% of exports of goods, sevices, and incomea 2005b

.. 19 21 59 73 36 .. .. 18 22 20 .. 23c 38 c 52 5 34 68 22c 110 c 58 14 c .. 67c 31c 52 14 .. 43 123c 124 c 36 69 c 89 .. .. .. 37 60 36 48 57c 102 21c .. .. 63 99 c 28 .. 26c .. 20 35c 290 c 24 c

.. 51 45 72 245 100 .. .. 27 102 31 .. 112c 112c 102 8 183 105 196c 1,072c 84 61c .. 715c 51c 114 40 .. 171 383c 106c 69 131c 159 .. .. .. 61 166 99 105 213c 115 111c .. .. 92 162c 72 .. 64 c .. 77 146c 660 c 63c

2007 World Development Indicators

Total debt service

% of GNI 1990 2005

.. .. 14.7 4.0 4.6 .. .. .. .. 2.4 .. .. 2.1 8.3 .. 2.9 1.8 .. 1.1 3.8 2.7 4.8 .. 2.0 0.7 9.3 2.0 .. 10.2 4.1 22.9 7.0 13.7 .. .. .. .. 3.4 11.9 7.3 4.4 .. .. 2.0 .. .. 3.3 13.0 .. .. 6.3 .. 3.1 6.3 3.6 1.3

.. 1.0 6.1 7.8 6.0 2.8 .. .. 2.2 1.3 2.3 .. 1.6 5.9 2.6 0.5 8.1 21.5 0.9 5.0 0.5 4.9 .. 0.4 1.4 7.3 1.2 .. 8.7 3.1 3.0 3.1 3.0 13.2 .. .. .. 3.2 12.0 2.9 4.0 2.1 12.8 0.8 .. .. 1.5 6.5 2.9 .. 2.7 .. 1.5 5.0 11.3 1.4

Multilateral debt service

% of exports of goods, sevices, and incomea 1990 2005

.. .. 63.4 8.1 37.0 .. .. .. .. 25.8 .. .. 8.2 38.6 .. 4.4 22.2 .. 6.8 43.4 .. 20.4 .. 13.2 4.4 25.9 11.7 .. 40.9 .. 35.4 23.9 35.4 .. .. .. .. 10.4 32.5 20.4 15.3 .. .. 39.0 .. .. 6.4 22.2 .. .. 38.1 .. 13.6 20.1 31.1 11.1

.. 2.5 .. 9.2 20.8 7.9 .. .. 2.6 5.4 3.7 .. .. 14.8 4.9 0.9 44.8 31.5 .. 41.4 0.7 .. .. .. .. 15.4 3.1 .. 35.3 .. 2.4 5.9 5.5 24.0 .. .. .. 6.9 30.6 6.8 8.6 .. 13.7 4.1 .. .. .. 12.1 7.4 .. 7.1 .. 5.8 .. .. 3.7

% of public and publicly guaranteed debt 1990 2005

.. .. 5.0 2.2 16.2 .. .. .. .. 22.8 .. .. 95.7 67.6 .. 61.3 43.5 .. 73.1 51.1 .. 43.9 .. 50.3 73.1 35.7 7.6 .. 32.2 49.7 12.7 36.1 77.5 .. .. .. .. 50.3 34.8 18.7 60.2 .. .. 14.5 .. .. 32.6 25.3 .. .. 31.2 .. 33.4 22.1 70.4 70.5

.. 50.4 26.6 0.6 68.3 87.0 .. .. 37.0 56.2 21.8 .. 58.8 92.8 73.6 67.9 15.9 13.8 77.0 87.1 74.6 21.8 .. 99.8 74.5 20.1 21.4 .. 39.5 30.6 73.4 67.7 12.3 9.8 .. .. .. 23.1 36.5 23.8 49.7 61.6 14.6 53.6 .. .. 77.5 62.0 20.1 .. 36.5 .. 57.6 50.2 12.2 81.3

Short-term debt

% of total debt 1990 2005

% of exports of goods, sevices, and income 1990 2005

.. .. 2.8 11.5 16.8 .. .. .. .. 1.3 .. .. 4.3 3.6 .. 1.0 19.8 .. 10.1 1.5 7.4 14.6 .. 5.4 5.6 17.6 16.9 .. 8.4 7.3 14.9 10.1 20.9 .. .. .. .. 17.9 15.0 13.5 9.8 .. .. 1.7 .. .. 17.4 4.3 .. .. 8.6 .. 14.5 6.9 8.2 11.0

.. .. 5.7 24.7 62.9 .. .. .. .. 5.4 .. .. 11.9 15.5 .. 0.2 64.4 .. 16.6 13.7 .. 36.9 .. 17.1 10.9 31.6 15.4 .. 15.1 .. 49.0 18.0 101.0 .. .. .. .. 35.0 54.4 29.6 15.5 .. .. 24.0 .. .. 25.2 9.3 .. .. 33.5 .. 24.4 20.4 208.3 31.0

.. 15.7 3.1 19.8 16.9 16.0 .. .. 9.9 3.6 74.0 .. 2.1 2.8 19.8 7.4 12.8 25.1 1.0 2.7 8.0 10.7 .. 10.7 1.0 15.2 52.7 .. 16.4 3.8 12.6 33.8 6.4 14.3 .. .. .. 12.2 10.0 17.5 22.2 1.7 35.5 3.2 .. .. 6.5 3.7 2.7 .. 8.7 .. 29.1 7.1 1.4 1.9

.. 9.0 .. 9.6 38.1 17.0 .. .. 2.1 4.7 19.1 .. .. 5.1 19.9 0.6 17.1 22.7 .. 37.4 6.6 .. .. .. .. 13.8 16.8 .. 21.5 .. 15.0 20.3 8.1 21.0 .. .. .. 7.0 12.7 16.1 20.8 .. 34.4 9.4 .. .. .. 10.2 2.1 .. 14.6 .. 19.0 .. .. 1.6

Present value of debt

% of GNI 2005b

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

37c 69 16 55 13 .. .. .. .. 93 .. 65 106 28 .. .. .. 54c 63c 104 114 32 1,087c .. 52 40 37c 58 c 46 30 c 117c 37 26 70 63 34 28 c .. .. 34 c .. .. 46c 25c 34 .. 14 30 90 55 54 49 67 39 .. ..

% of exports of goods, sevices, and incomea 2005b

60 c 96 73 159 39 .. .. .. .. 141 .. 89 185 103 .. .. .. 106c 200 c 211 127 50 3,514 c .. 90 89 323c 162c 35 100 c 289 c 60 79 97 73 77 85c 148 .. 104 c .. .. 94 c 142c 53 .. 22 134 118 60 84 198 120 98 .. ..

Total debt service

% of GNI 1990 2005

13.9 13.4 2.6 9.1 0.6 .. .. .. .. 15.9 .. 16.5 .. 9.6 .. .. .. .. 1.1 .. 2.9 2.3 .. .. .. .. 7.5 7.2 10.3 2.8 13.5 6.6 4.5 .. .. 7.2 3.4 .. .. 1.9 .. .. 1.6 4.1 13.0 .. .. 4.6 6.8 17.9 6.0 1.9 8.2 1.7 .. ..

4.8 22.9 3.0 6.5 1.4 .. .. .. .. 10.8 .. 4.7 25.5 1.3 .. .. .. 5.4 6.6 19.8 16.5 3.1 0.2 .. 10.3 4.2 1.6 4.7 7.6 1.7 3.5 4.5 5.8 7.7 2.5 5.3 1.5 .. .. 1.6 .. .. 3.6 1.1 10.2 .. .. 2.3 14.5 .. 6.7 7.5 9.2 11.6 .. ..

Multilateral debt service

% of exports of goods, sevices, and incomea 1990 2005

35.3 34.3 31.9 33.3 3.2 .. .. .. .. 26.9 .. 20.4 .. 35.4 .. .. .. .. 8.7 .. .. 4.2 .. .. .. .. 45.5 29.3 12.6 12.4 29.8 8.8 20.7 .. .. 21.6 26.2 18.4 .. 15.7 .. .. 3.9 17.4 22.6 .. .. 21.3 6.2 37.2 12.4 10.8 27.0 4.9 .. ..

7.2 31.0 .. .. .. .. .. .. .. 16.3 .. 6.5 42.1 4.4 .. .. .. 10.0 .. 37.4 17.7 5.0 .. .. 16.5 8.6 17.0 .. 5.6 .. .. 7.2 17.2 10.2 .. 11.3 4.3 .. .. 4.6 .. .. 6.9 .. 15.8 .. 7.5 10.2 17.5 10.8 11.4 26.0 16.7 28.8 .. ..

% of public and publicly guaranteed debt 1990 2005

90.8 8.0 22.5 22.5 30.5 .. .. .. .. 38.6 .. 26.8 .. 44.7 .. .. .. .. 54.3 .. 27.8 44.5 99.8 .. .. .. 23.7 38.3 9.9 54.3 73.7 51.6 26.0 .. .. 39.8 30.6 43.6 .. 36.9 .. .. 21.4 70.9 15.5 .. .. 40.3 90.6 23.0 35.9 28.8 28.7 9.2 .. ..

86.0 6.8 9.1 39.2 9.1 .. .. .. .. 20.1 .. 49.0 80.4 60.7 .. .. .. 97.7 72.2 59.1 3.1 46.5 100.0 .. 19.4 41.7 66.4 63.4 3.4 77.8 63.6 18.4 12.8 70.2 38.2 38.8 63.0 2.7 .. 75.5 .. .. 45.0 98.2 5.5 .. 45.9 58.5 10.6 46.3 49.6 21.6 15.9 2.6 .. ..

4.17

Short-term debt

% of total debt 1990 2005

% of exports of goods, sevices, and income 1990 2005

5.4 13.9 10.2 15.9 80.1 .. .. .. .. 7.3 .. 12.4 .. 13.2 .. .. .. .. 0.1 .. 79.9 0.7 22.2 .. .. .. 6.1 3.8 12.4 2.5 11.2 5.3 15.4 .. .. 1.6 7.4 4.9 .. 1.5 .. .. 22.6 8.9 4.5 .. .. 15.4 36.6 2.8 17.7 26.6 14.5 19.4 .. ..

18.1 23.9 33.3 37.3 35.8 .. .. .. .. 14.1 .. 33.7 .. 41.8 .. .. .. .. 2.1 .. .. 0.5 .. .. .. .. 46.0 12.9 5.5 11.3 48.7 2.9 29.5 .. .. 4.9 115.2 69.8 .. 5.4 .. .. 602.0 27.1 10.2 .. .. 35.6 42.5 4.8 14.2 121.1 33.3 48.9 .. ..

7.9 18.8 7.1 17.7 50.3 .. .. .. .. 9.4 .. 7.6 18.5 8.0 .. .. .. 1.2 0.2 52.5 15.4 1.2 44.4 .. 47.5 4.3 2.2 1.3 23.9 0.6 7.4 63.1 3.9 35.0 1.9 4.1 10.6 21.8 .. 1.4 .. .. 10.5 2.1 8.3 .. 48.0 3.7 5.0 10.6 16.4 11.2 10.4 17.9 .. ..

2007 World Development Indicators

7.8 16.3 .. 23.7 .. .. .. .. .. 10.3 .. 6.1 25.6 9.1 .. .. .. 1.9 .. 90.3 17.3 0.7 .. .. 34.1 3.5 16.4 .. 7.3 .. .. 34.9 2.6 29.1 .. 2.8 24.9 .. .. 1.8 .. .. 21.7 .. 3.3 .. 8.4 5.1 4.1 5.4 12.0 14.9 10.8 15.0 .. ..

255

ECONOMY

Debt ratios

4.17

Debt ratios Present value of debt

% of GNI 2005b

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 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 Europe EMU

51 40 18 c .. 34 c 69 40 c .. 61 .. .. 14 .. 48 88 c 24 .. .. 27 41 22c, d 32 74 c 24 69 59 16 29c 53 .. .. .. 116 34 48 38 .. 32 29 c 85

% of exports of goods, sevices, and incomea 2005b

137 104 154 c .. 89 c 202 178 c .. 73 .. 1,137 47 .. 109 358 c 26 .. .. 69 53 95c, d 44 162c 36 125 195 23 137c 89 .. .. .. 332 88 118 56 .. 56 80 c 228

Total debt service

% of GNI 1990 2005

0.1 .. 0.8 .. 5.9 .. 3.7 .. .. .. 1.3 .. .. 4.9 0.4 4.9 .. .. 10.0 .. 4.4 d 6.3 5.4 9.6 12.1 4.9 .. 3.4 .. .. .. .. 11.0 .. 10.8 2.9 .. 3.5 6.7 5.5 .. w 3.8 4.7 4.2 .. 4.5 4.8 .. 4.2 6.4 2.9 ..

7.1 5.6 1.1 .. 2.4 4.9 2.2 .. 13.2 .. .. 2.0 .. 1.9 1.5 1.6 .. .. 0.8 3.5 1.1d 11.3 0.8 2.8 7.7 11.6 4.1 2.0 7.2 .. .. .. 13.7 5.7 4.0 1.9 .. 1.6 3.5 7.0 .. w 3.2 5.8 4.4 7.5 5.4 2.9 9.4 6.8 3.9 2.8 3.7

Multilateral debt service

% of exports of goods, sevices, and incomea 1990 2005

0.3 .. 14.2 .. 19.9 .. 10.1 .. .. .. .. .. .. 13.8 8.7 5.7 .. .. 21.8 .. 32.9d 16.9 11.9 19.3 24.5 29.4 .. 81.4 .. .. .. .. 40.8 .. 23.3 .. .. 5.6 14.7 23.2 .. w 23.9 19.6 21.6 17.5 20.1 17.6 .. 23.8 20.6 27.6 13.6

18.3 14.6 8.1 .. .. .. 9.2 .. .. .. .. 6.9 .. 4.5 6.5 1.9 .. .. 1.9 4.5 4.3d 14.6 .. .. 13.0 39.1 .. 9.2 13.0 .. .. .. 38.9 .. 9.1 2.6 .. 2.6 .. .. .. w .. 14.2 11.3 17.8 13.8 6.1 22.0 22.5 8.9 .. 8.8

Short-term debt

% of public and publicly guaranteed debt 1990 2005

% of total debt 1990 2005

% of exports of goods, sevices, and income 1990 2005

.. .. 60.6 .. 40.0 .. 26.0 .. .. .. 100.0 .. .. 13.8 100.0 73.0 .. .. 3.5 .. 52.7 22.1 40.8 4.7 26.0 23.3 .. 37.4 .. .. .. .. 16.2 .. 1.6 3.4 .. 50.9 41.0 24.0 .. w 27.8 18.3 22.3 13.7 19.5 17.7 10.2 27.6 13.1 25.3 30.0

79.8 .. 6.6 .. 11.3 .. 12.4 .. .. .. 12.0 .. .. 6.9 28.2 1.5 .. .. 12.5 .. 8.1 29.6 8.8 5.1 8.3 19.2 .. 5.4 .. .. .. .. 27.2 .. 6.0 7.7 .. 18.8 20.5 18.0 .. w 10.5 16.5 16.0 17.1 15.1 16.0 17.6 16.6 14.1 9.9 11.6

13.9 .. 31.9 .. 25.9 .. 70.5 .. .. .. .. .. .. 14.5 724.8 0.6 .. .. 39.4 .. 95.5d 26.6 15.6 5.5 10.8 37.7 .. 78.8 .. .. .. .. 49.7 .. 9.3 .. .. 39.4 103.8 29.0 .. w 37.2 29.8 31.4 27.7 30.8 20.5 36.0 39.7 23.9 30.1 ..

24.6 3.6 77.5 .. 60.2 60.2 37.5 .. 11.6 .. .. 3.7 .. 45.6 15.0 57.7 .. .. 31.2 37.1 97.0 18.5 72.6 35.3 45.8 10.6 3.2 49.7 21.3 .. .. .. 32.2 15.8 16.0 10.7 .. 57.4 40.6 30.9 .. w 15.7 17.6 26.1 11.2 17.3 20.7 9.4 22.9 25.2 16.0 13.2

18.7 10.5 1.4 .. 0.9 13.8 4.1 .. 64.1 .. 25.8 31.7 .. 8.8 34.0 15.3 .. .. 13.3 8.2 15.8 30.6 13.2 50.6 17.2 22.3 13.5 1.8 36.2 .. .. .. 27.2 0.9 23.1 13.3 .. 6.6 3.4 21.0 .. w 8.5 21.8 26.1 17.8 20.0 34.2 20.3 12.8 19.2 6.2 15.0

19.0 8.4 7.4 .. .. .. 25.6 .. .. .. .. 13.7 .. 10.2 104.7 3.6 .. .. 7.9 4.9 41.3d 12.0 .. .. 19.2 35.6 .. 4.4 26.6 .. .. .. 69.1 .. 16.7 7.0 .. 4.3 .. .. .. w 9.3 15.5 16.5 14.3 15.1 15.2 18.3 12.9 11.4 5.9 13.0

a. Includes workers’ remittances. b. The numerator refers to 2005, whereas the denominator is a three-year average of 2003–05 data. c. Data are from debt sustainability analyses undertaken as part of the Heavily Indebted Poor Countries (HIPC) Initiative. Present value estimates for these countries are for public and publicly guaranteed debt only. d. GNP and export data refer to mainland Tanzania only.

256

2007 World Development Indicators

About the data

4.17

ECONOMY

Debt ratios Definitions

The indicators in the table measure the relative bur-

using a special drawing rights reference rate, as are

• Present value of debt is the sum of short-term

den on developing countries of servicing external

obligations to the International Monetary Fund (IMF).

external debt plus the discounted sum of total debt

debt. The present value of external debt provides a

When the discount rate is greater than the interest

service payments due on public, publicly guaranteed,

measure of future debt service obligations that can

rate of the loan, the present value is less than the

and private nonguaranteed long-term external debt

be compared with the current value of such indicators

nominal sum of future debt service obligations.

over the life of existing loans. • Exports of goods,

as gross national income (GNI) and exports of goods

The ratios in the table are used to assess the

services, and income refer to international trans-

and services. The table shows the present value of

sustainability of a country’s debt service obliga-

actions involving a change in ownership of general

total debt service both as a percentage of GNI in

tions, but there are no absolute rules to determine

merchandise, goods sent for processing and repairs,

2005 and as a percentage of exports in 2005. The

what values are too high. Empirical analysis of the

nonmonetary gold, services, receipts of employee

ratios compare total debt service obligations with the

experience of developing countries and their debt

compensation for nonresident workers, investment

size of the economy and its ability to obtain foreign

service performance has shown that debt service

income, and workers’ remittances. • Total debt ser-

exchange through exports. The ratios shown here

difficulties become increasingly likely when the ratio

vice is the sum of principal repayments and interest

may differ from those published elsewhere because

of the present value of debt to exports reaches 200

actually paid on total long-term debt (public and pub-

estimates of exports and GNI have been revised to

percent. Still, what constitutes a sustainable debt

licly guaranteed and private nonguaranteed), use of

incorporate data available as of February 1, 2007.

burden varies from one country to another. Countries

IMF credit, and interest on short-term debt. • Multi-

Exports refer to exports of goods, services, income,

with fast-growing economies and exports are likely to

lateral debt service is the repayment of principal

and workers’ remittances.

be able to sustain higher debt levels.

and interest to the World Bank, regional development

The present value of external debt is calculated by

The most indebted low-income countries may be

banks, and other multilateral and intergovernmental

discounting the debt service (interest plus amortiza-

eligible for debt relief under special programs, such

agencies. • Short-term debt includes all debt having

tion) due on long-term external debt over the life of

as the Heavily Indebted Poor Countries Debt Initiative

an original maturity of one year or less and interest

existing loans. Short-term debt is included at its face

and Multilateral Debt Relief Initiative. Indebted coun-

in arrears on long-term debt.

value. The data on debt are in U.S. dollars converted

tries may also apply to the Paris and London Clubs

at official exchange rates (see About the data for

for renegotiation of obligations to public and private

table 4.16). The discount rate applied to long-term

creditors. The World Bank no longer classifies coun-

debt is determined by the currency of repayment of

tries by their level of indebtedness for the purposes

the loan and is based on reference rates for com-

of developing debt management strategies.

mercial interest established by the Organisation for Economic Co-operation and Development. Loans from the International Bank for Reconstruction and Development (IBRD) and credits from the International Development Association (IDA) are discounted The debt burden of Sub-Saharan Africa rose slightly in 2005, after falling Percent

4.17a

Total debt service (share of exports)

Total debt service (share of GNI)

20

15

Data sources The main sources of external debt information are reports to the World Bank through its Debtor

10

Reporting System from member countries that have received IBRD loans or IDA credits. Additional 5

information is from the files of the World Bank and the IMF. Data on GNI and exports of goods and services are from the World Bank’s national

0 1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

accounts files and the IMF’s Balance of Payments

The debt burden of Sub-Saharan economies rose slightly in 2005 after falling to less than half its 1995

database. Summary tables of the external debt of

level.

developing countries are published annually in the World Bank’s Global Development Finance and on

a. Includes goods, services, income, and workers’ remittances. Source: World Bank data files.

its Global Development Finance CD-ROM.

2007 World Development Indicators

257 Text figures, tables, and boxes

5

STATES & MARKETS

Introduction

C

ountry policies and governance matter for development

Governance matters for economic development. Capable governments and high-quality institutions promote growth, raise incomes, and reduce poverty. Governance indicators are tools for assessing the performance of governments and the strengths and weaknesses of public institutions. Donors and governments use them to identify weaknesses and improve the management of development programs. And by providing feedback to policymakers and citizens, governance indicators can help to improve the quality of governance over time. This section—on states and markets—includes a broad range of indicators showing how effective and accountable government, together with energetic private initiative, help to create opportunities for growth and development. The World Bank defines governance as the way public officials and public institutions acquire and exercise authority to provide public goods and services, including basic services, infrastructure, and a sound investment climate. Measuring governance and measuring corruption are not the same thing. While governance encompasses all of the state institutions and arrangements that shape the relations between the state and society, corruption is one aspect of poor governance—an outcome and a consequence of the failure of public accountability. Measuring the quality of policies, institutions, and governance—and corruption—is difficult and often subject to margins of error, whether based on objective or subjective information. The World Bank has used assessments of government performance in allocating concessional resources since the mid-1970s. Focusing at first on macroeconomic management, the assessment criteria have expanded to include trade and financial policies, business regulation, social sector policies, the effectiveness of the public sector, and transparency, accountability, and corruption. Now called the Country Performance and Institutional Assessment (CPIA), the criteria are assessed annually for all World Bank borrowers. This edition of World Development Indicators includes a new indicator table—Table 5.8, Public policies and institutions—showing the most recent CPIA data for 76 countries eligible to receive grants or credits from the International Development Association (IDA), the World Bank’s concessional lending arm. Indicator tables 5.2 and 5.3 continue to report on government policies and regulations affecting the investment climate. Improved infrastructure such as roads, ports, and rails (indicator table 5.9), power and telecommunications (indicator tables 5.10 and 5.11), and water supply and sanitation (indicator table 2.15) are crucial for citizens’ health, economic growth, and competitiveness. And effective, accountable governments are needed to complement an energetic private sector to deliver these services.

2007 World Development Indicators

259

Governance and growth

Country Policy and Institutional Assessment

The first major World Bank discussion of the role of governance was the 1991 World Bank Discussion Paper Managing Development: The Governance Dimension (World Bank 1991). A few years later World Development Report 1997: The State in a Changing World (World Bank 1997d) argued that a determining factor in development was the effectiveness of the state. The report noted that “an effective state is vital for the provision of the goods and services—and the rules and institutions—that allow markets to flourish and people to lead healthier, happier lives. . . .” The 1997 report presented systematic assessments of the reliability of governmental institutions (predictability of rulemaking, perceptions of political stability, crime against persons and property, and reliability of judicial enforcement) and of corruption from a 1996 World Bank–sponsored survey. Subsequent research suggests that the causality between growth and governance is two-way—that improvements in either income or governance can give momentum to development—but that causation is stronger from governance to growth in income. Although the links are complex, there is ample evidence of the connection between governance and long-term growth. Figure 5a shows the statistical relationship (controlling for initial income and schooling levels) between the quality of governance measured by an International Country Risk Guide (ICRG) index in 1982 and the growth of per capita income through 2002. The ICRG index comprises five elements of governance: corruption in government, rule of law, risk of expropriation, repudiation of contracts by government, and quality of the bureaucracy in 71 developing countries.

The CPIA indicators measure the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance. Country performance is assessed against 16 criteria grouped in four clusters: economic management, structural policies, policies for social inclusion and equity, and public sector management and institutions (box 5b). The overall score for each country, known as the IDA Resource Allocation Index (IRAI), is a key element of a country’s IDA country performance rating. IDA resources are allocated in per capita terms on the basis of the country performance rating and, to a limited extent, per capita gross national income. This ensures that good performers receive a higher IDA allocation, in per capita terms. The individual CPIA criteria are also used to inform the World Bank’s country policy dialogue with member governments and for other operational and research purposes. Reflecting the IDA14 funding agreement, the numerical IRAI scores and separate CPIA criteria were first publicly disclosed for IDA recipient countries in June 2006 to enhance transparency and external scrutiny of these scores (see indicator table 5.8 and figure 5c). The scores depend on actual policies and performance, rather than on promises or intentions. In some cases the passage of specific legislation can represent an important action that deserves consideration. But it is implementation of legislation that determines its impact. The average

Governance and growth go together

5a

Per capita income growth, 1982–2002 (%, residual)

Criteria for measuring economic and sector policies and governance system

Box 5b

Cluster A: Economic management Macroeconomic management Fiscal policy Debt policy

8 6 4 2 0

Cluster B: Structural policies Trade Financial sector Business regulatory environment

–2 –4 –6 –8 –20

–15

–10

–5

0

5

10

15

Quality of initial governance, 1982 (index) Source: Knack 2006.

20

25

30

Cluster C: Policies for social inclusion and equity Gender equality Equity of public resource use Building human resources Social protection and labor Policies and institutions for environmental sustainability Cluster D: Public sector management and institutions Property rights and rule-based governance Quality of budgetary and financial management Efficiency of revenue mobilization Quality of public administration Transparency, accountability, and corruption in the public sector

260

2007 World Development Indicators

5c

The IDA Resource Allocation Index is a key element of a country’s IDA performance rating IDA Resource Allocation Index (IRAI), 1 (low) to 6 (high) Zimbabwe

Bangladesh

Central African Rep.

Mongolia

Comoros

Mozambique

Côte d’Ivoire

Rwanda

Togo

Moldova

Eritrea

Lesotho

Angola

Kyrgyz Rep.

Sudan

Madagascar

Guinea-Bissau

Bosnia & Herzegovina

Haiti

Kenya

Congo, Rep

Sri Lanka

Solomon Islands

Azerbaijan

Congo, Dem. Rep.

Benin

Chad

Indonesia

Tonga

Pakistan

Burundi

Albania

São Tomé and Principe

Grenada

Lao PDR

Serbia & Montenegro

Uzbekistan

Mali

Guinea

Bolivia

Gambia, The

Nicaragua

Cambodia

Vietnam

Papua New Guinea

Senegal

Sierra Leone

Burkina Faso

Djibouti

India

Nigeria

Dominica

Vanuatu

Bhutan

Kiribati

Georgia

Mauritania

Maldives

Niger

Ghana

Cameroon

Uganda

Yemen, Rep.

Honduras

Zambia

St. Vincent & Grenadines

Nepal

Tanzania

Tajikistan

St. Lucia

Guyana

Samoa

Malawi

Cape Verde

Ethiopia

Armenia 1

2

3

4

5

6

1

2

3

4

5

6

Source: World Bank.

2007 World Development Indicators

261

Other World Bank sources of data for monitoring governance score on public sector management and institutions can be used as an aggregate indicator of the country’s governance system (focused primarily on economic governance). It is a part of the “governance factor” that is given extra weight in the IDA country performance rating for determining IDA resource allocations. (For more information see www. worldbank.org/ida.) Scores on the CPIA public sector management and institutions cluster are bunched around the mid-range, with no countries scoring in either the lowest or highest ranges, and only one country in the 4.0–4.9 range (figure 5d). Although these measures give some indication of the quality of public sector management and institutions, for some countries they do not always match the strong performance on economic management policies (macroeconomic management, fiscal policy, and debt policy). Armenia, Bangladesh, Kyrgyz Republic, Tajikistan, and Uganda score relatively well on CPIA cluster A, economic management, but not so well on cluster D, public sector management and institutions (figure 5e). These patterns reveal the complexity of the relationships between measures of the quality of public sector management and institutions and economic outcomes, requiring better diagnostics and understanding of each country’s situation to develop workable approaches to governance reform. On public sector management, countries bunch around the middle

5d

Distribution of IDA recipient scores for CPIA cluster D, public sector management and institutions, 2005 Number of IDA recipient countries 30 25 20 15 10 5 0 1.0–2.0

2.0–2.4

2.5–2.9 3.0–3.4 3.5–3.9 CPIA score, 1 (low) to 6 (high)

4.0–4.9

5.0–6.0

Source: World Bank.

Strong performance on economic management, weaker on public sector management CIPA score, 1 (low) to 6 (high)

Economic management (CPIA cluster A)

5e

Public sector management and institutions (CPIA cluster D)

6 5 4 3 2 1 Nigeria

Kyrgyz Bangla- Tajikistan Pakistan Indonesia Honduras Uganda Armenia Republic desh

Source: World Bank.

262

2007 World Development Indicators

The growing recognition of the link between good governance and successful development has stimulated efforts to monitor the performance of governments and other public institutions by private commercial rating agencies, multilateral development institutions, and nongovernmental agencies. In addition to the CPIA policy and governance measures, the World Bank has several other governance and governancerelated measurement programs and indicators that are used in monitoring governance. (See box 5g at the end of this introduction for other selected organizations’ governance measurement initiatives.) • Worldwide Governance Indicators are the most comprehensive publicly available governance indicators and among the most widely used by the media, academia, and international organizations for assessing governance. Compiled since 1996, these data measure the quality of six dimensions of governance for 213 countries, based on 31 data sources produced by 25 organizations (box 5f). The underlying data are based on hundreds of variables and reflect the perceptions and views of experts, firm survey respondents, and citizens worldwide on various dimensions of governance. The measures, also known as Kaufmann-Kraay, include the margins of error associated with each estimate, allowing users to identify a range of statistically likely ratings for each country, not just a Worldwide Governance Indicators— Six key dimensions of governance

Box 5f

The Worldwide Governance Indicators measure the quality of six dimensions of governance: • Voice and accountability, the extent to which a country’s citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and free media • Political stability and absence of violence, perceptions of the likelihood that the government will be destabilized or overthrown by unconstitutional or violent means, including political violence and terrorism • Government effectiveness, the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies • Regulatory quality, the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development • Rule of law, the extent to which agents have confi dence in and abide by the rules of society, and in particular the quality of contract enforcement, the police, and the courts, as well as the likelihood of crime and violence • Control of corruption, the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as “capture” of the state by elites and private interests.

single rating. Margins of error are present in all efforts to measure governance; some sources explicitly report them, while others do not. See www.govindicators.org. • Enterprise Surveys and the Business Environment and Enterprise Performance Surveys capture business perceptions of the biggest obstacles to enterprise growth, the relative importance of various constraints to increasing employment and productivity, and the effects of a country’s investment climate on its international competitiveness. Surveys cover almost 58,000 firms in 97 countries. Although designed to monitor the investment climate, which is a product of a number of governance-related factors, these surveys include measures, such as a business managers’ perception of corruption as a constraint to doing business, that can be directly linked to governance and are therefore useful for governance monitoring at the country level. See indicator table 5.2 and www.enterprisesurveys. org and http://info.worldbank.org/governance/beeps. • Doing Business surveys cover key indicators on the environment for doing business for 175 economies. The indicators identify regulations that enhance or constrain business investment, productivity, and growth. Some indicators, such as enforcing contracts, are useful in monitoring governance. See indicator table 5.3 and www. doingbusiness.org. • Anticorruption Diagnostic Surveys are designed to facilitate governance monitoring by providing inputs to policymakers and civil society. The World Bank Institute’s Governance Diagnostic Capacity Building program aims to strengthen the capacity of countries to conduct governance diagnostic surveys through technical assistance for the design of surveys and governance action plans, training, and partnerships between the government and civil society organizations. Agencies in several countries have undertaken governance and anticorruption diagnostic surveys. See www.worldbank.org/wbi/governance and click on Diagnostics. Other selected sources of data for monitoring governance •

• •

•

•

• HIPC (Highly Indebted Poor Countries) Public Expenditure Management Assessment and Action Plans use expenditure tracking tools developed by the World Bank and the International Monetary Fund to monitor poverty-reducing public expenditures in HIPCs. Data are collected on 15 indicators on budget formulation, execution, and reporting, and 1 indicator on government procurement. A new program to measure public expenditure and management has been developed and will be used for monitoring in HIPCs. See www.worldbank.org/hipc. • Public Expenditure and Financial Accountability Program, started by the World Bank in 2001, is now a partnership with several multilateral and bilateral development institutions that support an integrated and harmonized approach to assessment and reform in public expenditure, procurement, and financial accountability. The public expenditure and financial accountability framework includes 28 indicators on budget credibility, transparency, auditing, and procurement, and three indicators on donor practices that affect the country public financial management system. The program is being implemented in 70 countries; 8 countries have completed reviews and made them available publicly (in addition, one country has published data for the indicators). See www.pefa.org. • The Joint Venture on Procurement of the World Bank and the Organisation for Economic Co-operation and Development’s Development Assistance Committee has selected 22 pilot developing countries to test the Common Benchmarking and Assessment Tool for Public Procurement, which developing countries and donors can use to assess the quality and effectiveness of national procurement systems. See www.oecd.org and search for Joint Venture on Procurement. For an overview of the World Bank’s framework for global monitoring of governance and in-depth discussion of the uses and limitations of governance measures, see the World Bank and International Monetary Fund’s (2006a) Global Monitoring Report 2006.

Box 5g

Freedom House, a private nonprofit advocacy organization founded in 1941, was among the earliest to systematically measure and publish governance ratings. Freedom House has published Freedom in the World since 1972; it now includes ratings of political rights and civil liberties in 192 countries and territories. See www.freedomhouse.org. International Country Risk Guide is privately owned and has been assessing financial, economic, and political risks since 1980 for about 140 countries. See www.prsgroup.com. Transparency International (TI), a newer entrant, has attracted media attention since 1995 with its Corruption Perceptions Index. The index is a compilation of surveys of perceptions of resident and nonresident business people and expert assessments of the degree of corruption in a country. See www.transparency.org. Global Integrity, a Washington, D.C.–based nonprofit organization funded by private foundations and the World Bank, assesses the existence and effectiveness of anticorruption mechanisms that promote public integrity. More than 290 indicators are used to generate the Global Integrity Index for more than 40 countries. See www.globalintegrity.org. The Open Budget Initiative, sponsored by the International Budget Project, tracks 122 indicators of budget transparency for almost 60 countries. The country reports give citizens, legislators, and civil society advocates comprehensive and practical information that can be used to assess a government’s commitment to budget transparency and accountability. The initiative is funded by private foundations and bilateral aid agencies such as the U.K. Department for International Development and the Swedish International Development Cooperation Agency. See www. openbudgetindex.org.

2007 World Development Indicators

263

Tables

5.1

Private sector in the economy Domestic New busicredit to nesses private sector registered

Investment in infrastructure projects with private participationa

$ millions Telecommunications

Energy

1995–99

2000–05

1995–99

Afghanistan .. Albania .. Algeria .. Angola .. Argentina 10,498.6 Armenia 1,680.5 Australia .. Austria .. Azerbaijan 122.0 Bangladesh 438.1 Belarus 20.0 Belgium .. Benin .. Bolivia 528.0 Bosnia and Herzegovina 0.0 Botswana 97.0 Brazil 45,135.2 Bulgaria 202.5 Burkina Faso .. Burundi .. Cambodia 102.4 Cameroon 12.7 Canada .. Central African Republic 1.1 Chad 2.0 Chile 3,489.0 China 5,970.0 Hong Kong, China .. Colombia 1,384.3 Congo, Dem. Rep. 68.0 Congo, Rep. 12.2 Costa Rica .. Côte d’Ivoire 752.3 Croatia 978.0 Cuba .. Czech Republic 6,178.5 Denmark .. Dominican Republic 163.0 Ecuador 696.4 Egypt, Arab Rep. 1,914.5 El Salvador 610.5 Eritrea .. Estonia 628.2 Ethiopia .. Finland .. France .. Gabon 8.4 Gambia, The .. Georgia 61.0 Germany .. Ghana 491.1 Greece .. Guatemala 1,366.3 Guinea 120.3 Guinea-Bissau .. Haiti 1.5

284.1 443.2 3,422.5 278.7 5,925.6 243.4 .. .. 355.6 1,527.3 735.4 .. 116.9 520.5 0.0 104.0 41,018.7 2,179.1 41.9 53.6 148.1 394.4 .. .. 11.0 3,714.6 8,548.0 .. 1,570.9 453.4 61.8 .. 134.9 1,181.9 60.0 8,348.0 .. 393.0 357.8 3,360.9 821.2 40.0 287.1 .. .. .. 26.6 6.6 168.8 .. 156.5 .. 560.1 18.0 21.9 18.0

.. 0.0 .. .. 12,931.9 0.0 .. .. .. 554.9 500.0 .. .. 2,777.3 .. .. 34,196.8 .. 5.6 .. 143.0 .. .. .. .. 6,808.6 17,166.6 .. 6,964.8 .. 325.0 301.2 260.6 368.5 165.0 944.1 .. 979.0 30.0 700.0 900.2 .. 26.5 .. .. .. 294.0 .. 159.0 .. 376.0 .. 1,223.2 36.4 .. 4.7

264

2007 World Development Indicators

2000–05

Transport

Water and sanitation

% of GDP

1995–99

2000–05

1995–99

2000–05

1990

2005

.. .. 789.0 .. 962.0 .. 45.0 .. 3,830.0 6,892.1 47.0 .. .. .. .. .. 375.2 .. 501.5 0.0 .. .. .. .. .. .. 886.0 168.7 277.9 .. .. .. 28,204.0 17,195.0 2,646.0 .. .. 63.3 .. .. 88.1 120.0 91.9 90.0 .. .. .. .. 0.0 .. 1,528.3 3,104.1 6,365.7 10,852.5 .. .. 4,483.2 995.5 .. 0.0 .. .. 80.0 .. 0.0 241.3 7.1 672.2 .. .. 3,865.3 283.7 .. .. 1,306.6 .. 302.0 686.8 678.0 123.9 85.0 .. .. .. .. 1.0 300.0 .. .. .. .. .. 0.0 46.7 .. .. 13.0 .. .. .. 184.0 .. .. .. 110.0 33.8 .. .. .. .. .. ..

.. 308.0 .. 55.0 200.2 50.0 .. .. .. 0.0 .. .. .. 16.6 .. .. 4,271.3 .. .. .. 125.3 0.0 .. .. .. 4,768.6 7,948.0 .. 1,331.4 .. .. 465.2 140.0 451.0 0.0 106.7 .. 898.9 685.0 821.5 .. .. 298.4 .. .. .. 177.4 .. .. .. 10.0 .. .. .. .. ..

.. .. .. .. 3,307.1 .. .. .. .. .. .. .. .. 682.0 .. .. 2,137.0 .. .. .. .. .. .. .. .. 4,190.3 985.9 .. 233.0 .. .. .. .. .. .. 135.5 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 0.0 .. ..

.. 8.0 510.0 .. 791.6 0.0 .. .. 0.0 .. .. .. .. .. .. .. 1,587.6 152.0 .. .. .. .. .. .. .. 1,495.2 3,131.5 .. 250.0 .. .. .. .. 298.7 600.0 263.7 .. .. 550.0 .. .. .. 115.0 .. .. .. .. .. .. .. 0.0 .. .. .. .. ..

.. .. 44.4 .. 15.6 40.4 59.9 89.7 10.8 16.7 .. 35.9 20.3 24.0 .. 9.4 39.0 82.8 16.8 13.7 .. 26.7 92.2 7.2 7.3 50.6 87.7 160.6 30.8 1.8 15.7 12.2 36.5 .. .. .. 51.2 27.5 13.6 30.6 16.9 .. 20.2 13.9 85.8 94.3 12.9 11.0 .. 88.7 4.9 35.5 14.2 3.5 22.0 12.6

.. 14.9 11.8 4.8 11.7 8.0 104.6 112.9 10.0 31.5 16.2 75.1 16.6 40.7 47.9 19.0 34.8 44.5 17.3 20.8 9.4 9.4 181.4 6.7 3.1 82.3 114.4 146.2 23.9 1.9 2.9 35.8 13.8 61.2 .. 37.0 171.1 27.7 23.0 52.4 43.4 31.0 60.0 25.3 76.1 93.1 8.9 13.0 14.8 111.4 15.5 84.8 25.2 5.1 2.1 15.4

% of total businesses registered 2003

.. 6 15 .. 11 7 11 7 7 .. .. 7 .. 8 6 .. .. .. .. .. .. .. 14 .. .. .. 9 13 .. 10 .. .. .. .. .. 4 10 .. 14 .. 7 .. 10 .. 10 8 .. .. 10 23 .. 7 .. .. .. ..

Micro, small, and medium-size enterprises

Total

per 1,000 people

2000–05b 2000–05b

.. 38,331 580,000 .. .. 99,805 1,269,000 252,399 49,527 177,000 25,108 686,533 .. .. 14,986 13,137 4,903,268 216,489 .. .. .. .. 2,245,245 .. .. 700,000 8,000,000 263,959 664,000 .. .. 40,921 .. 94,088 .. .. 257,950 .. 1,043,440 .. 461,642 .. 65,194 .. 221,000 2,612,960 .. .. 33,860 3,162,111 25,679 771,000 .. .. .. ..

.. 12.3 18.8 .. .. 33.1 63.2 30.9 6.0 1.3 2.5 66.2 .. .. 3.8 7.4 27.4 27.7 .. .. .. .. 69.5 .. .. 43.4 6.3 38.4 15.0 .. .. 9.6 .. 21.2 .. .. 47.8 .. 83.6 .. 72.1 .. 48.4 .. 42.4 43.2 .. .. 7.6 38.3 1.2 69.9 .. .. .. ..

Domestic New busicredit to nesses private sector registered

Investment in infrastructure projects with private participationa

$ millions Telecommunications

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

Energy

Transport

5.1

Water and sanitation

% of GDP

1995–99

2000–05

1995–99

2000–05

1995–99

2000–05

1995–99

2000–05

1990

2005

51.3 6,430.2 7,456.8 8,852.5 28.0 .. .. .. .. 235.5 .. 39.9 1,633.5 193.0 .. .. .. 100.0 157.1 600.9 485.7 15.7 .. .. 832.7 .. 30.0 23.1 4,187.6 .. .. .. 10,757.5 84.6 21.9 1,240.0 29.0 4.0 55.2 .. .. .. 24.5 .. 69.0 .. .. 75.5 1,429.2 .. 259.3 4,774.5 5,358.3 6,403.1 .. ..

135.0 5,234.8 20,642.0 6,494.6 695.0 984.0 .. .. .. 700.3 .. 1,589.0 1,078.2 1,434.0 .. .. .. 9.1 87.8 708.9 138.1 88.4 61.3 .. 993.0 706.6 12.6 36.3 3,756.9 82.6 92.1 413.0 18,131.4 46.1 22.1 5,993.5 123.0 .. 35.2 97.3 .. .. 278.5 85.5 6,950.7 .. 1,047.0 5,572.2 183.4 .. 194.6 2,238.0 4,570.9 16,800.0 .. ..

112.1 3,812.1 7,182.7 9,942.1 .. .. .. .. .. 43.0 .. .. 1,825.0 189.0 .. .. .. .. 535.5 106.0 .. .. .. .. 10.0 .. .. .. 1,610.2 .. .. 109.3 2,095.8 60.0 .. 5,978.0 .. 394.0 4.0 98.2 .. .. 232.4 .. .. .. 183.0 4,298.3 669.2 65.0 .. 3,004.9 6,998.0 628.1 .. ..

358.8 260.6 8,882.1 1,575.6 650.0 .. .. .. .. 201.0 .. .. 300.0 .. .. .. .. .. 1,250.0 71.1 .. 0.0 .. .. 399.3 .. 0.0 0.0 6,637.6 365.9 .. 0.0 6,614.3 25.3 .. 1,049.0 1,205.8 .. 1.0 39.0 .. .. 115.0 .. 1,248.0 .. 1,364.3 598.6 445.7 .. .. 2,478.3 3,783.4 2,341.5 .. ..

10.5 135.0 1,275.1 1,530.8 .. .. .. .. .. 0.0 .. 182.0 .. 53.4 .. .. .. .. 0.0 75.0 .. .. .. .. .. .. .. 6.0 8,200.1 .. .. 42.6 4,706.1 .. .. .. 441.0 50.0 .. .. .. .. .. .. .. .. 77.5 421.3 994.6 .. 58.0 86.3 1,364.0 169.4 .. ..

120.0 3,297.5 3,941.1 3.7 .. .. .. .. .. 565.0 .. 0.0 .. .. .. .. .. .. 0.0 .. 153.0 .. .. .. .. .. 48.5 .. 4,276.4 55.4 .. .. 3,135.4 .. .. .. 334.6 .. 450.0 .. .. .. 104.0 .. 22.8 .. 473.8 71.0 51.4 .. .. 561.5 1,060.5 1,672.0 .. ..

.. 205.8 .. 955.2 .. .. .. .. .. .. .. 0.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 10.0 .. .. .. 276.5 .. .. 1,000.0 25.5 .. .. .. .. .. .. .. .. .. .. .. 25.0 71.0 .. .. 7,567.2 6.1 .. ..

220.0 0.0 2.1 36.7 .. .. .. .. .. .. .. 169.0 100.0 .. .. .. .. .. .. .. 0.0 .. .. .. .. .. .. .. 6,502.2 .. .. .. 520.7 .. .. .. 0.0 .. 0.0 .. .. .. .. 3.4 .. .. .. .. .. .. .. 128.0 0.0 64.3 .. ..

31.1 46.6 25.2 48.1 33.8 .. 47.1 57.6 54.9 31.6 197.4 72.3 .. 32.7 .. 62.8 52.1 .. 1.0 .. 79.4 15.1 30.9 31.0 .. .. 16.9 10.9 69.4 12.8 43.5 35.6 17.5 5.9 17.7 34.0 18.3 4.7 22.6 12.8 76.6 75.5 112.6 12.3 9.4 81.7 20.6 24.2 46.7 28.6 15.8 11.8 22.3 21.1 46.6 ..

42.7 51.7 40.8 26.9 40.9 .. 160.7 97.5 90.2 17.9 186.9 87.2 35.7 25.9 .. 102.1 63.1 8.0 7.0 59.9 76.3 8.4 6.6 9.0 34.9 25.9 9.9 10.5 128.3 18.4 27.0 76.7 18.2 24.2 37.5 62.2 11.2 5.6 61.4 .. 173.4 133.8 29.1 6.8 14.9 9.0 34.9 28.4 91.7 13.9 18.0 19.4 30.5 27.4 147.3 ..

Micro, small, and medium-size enterprises

% of total businesses registered

Total

per 1,000 people

2000–05b 2000–05b

2003

.. 8 .. .. .. .. 10 .. 7 4 .. .. 12 .. .. .. .. .. .. 6 .. .. .. .. 3 7 4 13 .. .. .. .. .. 5 18 46 7 .. .. .. 6 18 .. .. 8 7 5 4 .. .. .. 1 .. 7 3 ..

STATES AND MARKETS

Private sector in the economy

257,953 40.2 .. .. .. .. 41,362,315 195.3 .. .. .. .. 97,000 24.3 468,338 67.6 4,486,000 77.9 .. .. 5,712,191 44.7 141,327 26.4 .. .. 2,800,000 87.4 .. .. 2,998,223 62.4 .. .. 142,475 28.3 .. .. 32,571 13.8 .. .. .. .. .. .. .. .. 56,428 16.5 55,742 27.5 .. .. 747,396 64.9 518,996 20.5 .. .. .. .. 75,267 62.2 2,891,300 28.3 25,667 6.1 .. .. 450,000 15.8 .. .. .. .. .. .. 3,040 0.1 735,160 45.0 334,031 82.2 .. .. .. .. .. .. 316,243 68.4 7,373 2.9 2,956,704 19.0 .. .. .. .. 548,000 95.5 658,837 23.9 808,634 10.1 1,654,822 43.3 693,000 66.4 2,069 0.5

2007 World Development Indicators

265

5.1

Private sector in the economy Investment in infrastructure projects with private participationa

$ millions Telecommunications 1995–99

2000–05

Energy 1995–99

2000–05

Transport 1995–99

Romania 2,072.8 3,073.9 100.0 1,240.8 23.4 Russian Federation 5,643.1 19,583.8 2,281.3 1,714.0 406.0 Rwanda 8.0 52.3 .. 0.0 .. Saudi Arabia .. 8,537.0 .. .. 55.0 Senegal 273.9 345.1 124.0 87.0 .. Serbia and Montenegro 1,590.0 830.6 .. .. .. Sierra Leone 7.0 48.8 .. .. .. Singapore .. .. .. .. .. Slovak Republic 488.5 2,709.9 .. 4,459.6 .. Slovenia .. .. .. .. .. Somalia 0.0 13.4 .. .. .. South Africa 2,975.3 5,499.5 3.0 1,251.3 1,386.4 Spain .. .. .. .. .. Sri Lanka 601.9 679.9 192.3 254.0 240.0 Sudan 18.3 621.2 .. .. .. Swaziland 21.2 27.7 .. .. .. Sweden .. .. .. .. .. Switzerland .. .. .. .. .. Syrian Arab Republic .. 583.2 .. .. .. Tajikistan 1.2 8.5 .. 16.0 .. Tanzania 100.2 487.3 150.0 372.0 16.5 Thailand 2,735.2 5,470.7 6,550.4 4,693.3 2,001.1 Togo 5.0 0.0 0.0 67.7 0.0 Trinidad and Tobago 146.7 .. 207.0 .. .. Tunisia .. 751.0 265.0 30.0 .. Turkey 3,269.7 12,515.9 2,992.2 6,569.8 610.0 Turkmenistan .. .. .. .. .. Uganda 119.3 387.6 .. 142.1 .. Ukraine 1,094.6 3,162.8 .. 160.0 .. United Arab Emirates .. .. .. .. .. United Kingdom .. .. .. .. .. United States .. .. .. .. .. Uruguay 63.7 114.2 86.0 330.0 20.0 Uzbekistan 513.8 277.6 .. .. .. Venezuela, RB 4,877.9 3,337.0 103.0 30.0 268.0 Vietnam 256.0 430.0 435.5 2,279.0 85.0 West Bank and Gaza 265.0 279.8 .. 150.0 .. Yemen, Rep. .. 376.8 .. .. 190.0 Zambia 64.2 208.3 277.0 12.4 .. Zimbabwe 46.0 59.0 600.0 .. 85.0 .. s .. s .. s .. s .. s World Low income 11,549.1 41,659.1 15,730.4 17,689.2 3,047.9 Middle income 161,868.2 217,654.4 138,174.3 106,539.8 63,732.8 Lower middle income 88,980.5 103,357.9 101,392.0 63,499.2 35,618.9 Upper middle income 72,887.7 114,296.5 36,782.3 43,040.6 28,113.9 Low & middle income 173,417.3 259,313.5 153,904.7 124,229.0 66,780.7 East Asia & Pacific 27,681.5 29,637.6 43,840.3 26,679.7 24,203.5 Europe & Central Asia 40,629.4 81,682.2 13,812.8 25,578.5 2,375.7 Latin America & Carib. 86,847.8 80,778.2 73,997.4 51,388.2 35,219.5 Middle East & N. Africa 3,973.1 19,220.8 7,126.0 4,883.3 573.4 South Asia 8,604.5 28,856.1 12,326.4 10,275.2 1,936.4 Sub-Saharan Africa 5,681.0 19,138.6 2,801.8 5,424.1 2,472.2 High income .. .. .. .. .. Europe EMU .. .. .. .. ..

2000–05

Domestic New busicredit to nesses private sector registered

Water and sanitation 1995–99

2000–05

.. .. 1,022.0 109.4 108.0 660.5 .. .. .. 190.0 .. 52.0 55.4 20.0 .. .. .. 0.0 .. .. .. .. .. .. .. 0.0 .. .. .. .. .. .. .. 504.7 56.9 31.3 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 6.5 .. 8.5 939.0 246.3 245.6 .. .. .. .. 0.0 120.0 .. .. .. 3,943.6 942.0 .. .. .. .. .. 0.0 0.0 .. .. .. .. .. .. .. .. .. .. .. .. 196.1 .. 351.0 .. .. 0.0 34.0 29.0 15.0 30.0 38.8 174.0 .. 0.0 0.0 .. .. .. 15.6 .. .. .. .. .. .. s .. s .. s 4,856.7 155.3 188.0 44,614.8 23,098.8 20,026.7 20,239.4 13,806.6 7,688.4 24,375.4 9,292.2 12,338.3 49,471.5 23,254.1 20,214.7 14,382.9 9,874.4 10,090.0 10,236.6 1,397.4 2,684.2 17,442.2 10,879.9 6,716.2 1,498.3 1,000.0 679.0 4,012.1 .. 2.1 1,899.4 102.4 43.2 .. .. .. .. .. ..

% of GDP 1990

2005

.. 20.0 .. 25.7 6.9 13.5 54.7 53.9 26.5 23.8 .. .. 3.2 4.5 97.0 101.7 .. 36.2 34.9 53.3 .. .. 81.0 143.5 78.5 146.1 19.6 32.9 4.8 10.0 20.7 20.0 126.4 111.7 162.6 166.8 7.5 11.8 .. 17.2 13.9 10.4 83.4 93.1 22.6 16.8 44.7 38.5 55.1 65.6 16.7 26.1 .. .. 4.0 6.7 2.6 33.5 38.0 60.9 115.8 165.5 118.9 194.8 32.4 27.0 .. .. 26.2 13.6 2.5 66.0 .. .. 6.1 7.7 8.9 7.6 23.0 26.9 104.3 w 133.8 w 21.3 33.8 43.1 58.3 51.3 73.1 38.4 38.8 39.2 54.9 73.9 101.1 .. 29.7 28.6 27.8 35.0 39.9 24.2 38.7 41.0 64.8 115.4 156.3 77.9 110.5

Micro, small, and medium-size enterprises

% of total businesses registered 2003

Total

2000–05b 2000–05b

11 392,544 .. 6,891,300 .. .. .. .. .. .. 9 68,220 29 .. 13 136,363 14 70,553 7 91,066 .. .. 8 .. 10 3,168,735 8 121,426 .. 22,460 .. .. 5 898,454 7 344,000 .. .. .. 92,964 .. 2,700,000 10 842,360 6 .. .. 19,150 .. .. .. 210,134 .. .. .. 160,453 .. 343,786 .. .. 19 4,415,260 .. 5,868,737 .. 143,035 .. 212,424 .. 11,314 .. 90,935 .. 97,194 7 310,000 10 .. 0 .. 9 u 139,432,731 s 10 4,222,837 9 94,515,052 10 78,159,635 8 16,355,417 9 98,737,889 12 68,387,110 8 13,753,937 7 9,077,990 18 3,669,844 6 177,000 10 3,672,008 10 40,694,842 9 16,589,542

a. Data refer to total for the period shown. Includes projects that became privatized during financial closure years 1990–2005. b. Data are for the most recent year available.

266

2007 World Development Indicators

per 1,000 people

18.1 48.2 .. .. .. 8.4 .. 32.2 13.1 45.6 .. .. 73.0 6.3 0.7 .. 99.6 46.9 .. 14.8 74.6 13.5 .. 14.7 .. 3.1 .. 6.2 7.3 .. 73.8 20.0 42.8 8.3 0.5 1.1 27.7 16.2 .. ..

About the data

5.1

STATES AND MARKETS

Private sector in the economy Definitions

Private sector development and investment—that

by the World Bank’s Development Data Group. For

• Investment in infrastructure projects with private

is, tapping private sector initiative and investment

more information, see http://ppi.worldbank.org/.

participation refers to infrastructure projects in tele-

for socially useful purposes—are critical for poverty

Credit is an important link in the money transmis-

communications, energy (electricity and natural gas

reduction. In parallel with public sector efforts, pri-

sion process; it finances production, consumption,

transmission and distribution), transport, and water

vate investment, especially in competitive markets,

and capital formation, which in turn affect the level

and sanitation that have reached financial closure and

has tremendous potential to contribute to growth.

of economic activity. The data on domestic credit to

directly or indirectly serve the public. Incinerators,

Private markets are the engine of productivity

the private sector are taken from the banking survey

movable assets, stand-alone solid waste projects,

growth, creating productive jobs and higher incomes.

of the International Monetary Fund’s (IMF) Interna-

And with government playing a complementary role

tional Financial Statistics or, when data are unavail-

of regulation, funding, and service provision, private

able, from its monetary survey. The monetary survey

initiative and investment can help provide the basic

includes monetary authorities (the central bank),

services and conditions that empower poor people—

deposit money banks, and other banking institutions,

by improving health, education, and infrastructure.

such as finance companies, development banks, and

and small projects such as windmills are excluded. Included are operation and management contracts, operation and management contracts with major capital expenditure, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investment commitments are the sum of investments in facilities and

Investment in infrastructure projects with private

savings and loan institutions. In some cases credit to

investments in government assets. Investments in

participation has made important contributions to

the private sector may include credit to state-owned

facilities are the resources the project company com-

easing fiscal constraints, improving the efficiency of

or partially state-owned enterprises.

mits to invest during the contract period either in new

infrastructure services, and extending delivery to poor

Entrepreneurship, the effort by individuals or

facilities or in expansion and modernization of existing

people. The privatization trend in infrastructure that

groups to makes to initiate economic activity in the

facilities. Investments in government assets are the

began in the 1970s and 1980s took off in the 1990s,

formal sector under a legal form of business, lends

resources the project company spends on acquiring

peaking in 1997. Developing countries have been at

dynamism to an economy. Greater entry of new firms

government assets such as state-owned enterprises,

the head of this wave, pioneering better approaches

can foster competition and economic growth. This

rights to provide services in a specific area, or the

to providing infrastructure services and reaping the

edition of World Development Indicators introduces

use of specific radio spectrums. • Domestic credit to

benefits of greater competition and customer focus.

a new indicator measuring entrepreneurship, new

private sector refers to financial resources provided

Between 1990 and 2005 more than 3,200 projects

businesses registered as a percentage of total

to the private sector—such as through loans, pur-

in more than 139 developing countries introduced

businesses.

chases of nonequity securities, and trade credits and

private participation in at least one infrastructure sector, with $964 billion in investments.

Formal and informal micro, small, and medium-size enterprises employ more than half the working popula-

other accounts receivable—that establish a claim for repayment. For some countries these claims include credit to public enterprises. •New businesses regis-

In 2005, investments in 160 new infrastructure

tion in many market economies and account for about

projects with private participation valued at $40 bil-

90 percent of all firms. And they contribute signifi -

lion were implemented. In addition, $56 billion

cantly to innovation. If small businesses are allowed

in investment projects reached fi nancial closure

to compete on an equal playing field, the good ones

between 1990 and 2005. Telecommunications

can become larger, workers can earn higher wages,

attracted $59 billion in investment in 2005, mostly

and productivity will increase. A good investment cli-

especially in developing countries, the data on total

in standalone mobile operations. Transport also saw

mate—one that provides opportunities and incentives

registered firms may be biased upward. • Micro,

an increase, from $7 billion in 2004 to $16 billion

for firms, reduces legal and regulatory costs, lowers

small, and medium-size enterprises are business

in 2005. Energy experienced some recovery, from

the costs of financial institutions in providing financial

that may be defined by the number of employees.

$16 billion to $19 billion. Water, down from about

services, and facilitates the transfer of technology and

There is no international standard definition of firm

$4.8 billion in 2004 to about $1.5 billion in 2005,

knowledge and the upgrading of capabilities in small

size; however, many institutions that collect informa-

was the only sector in which investment in infrastruc-

and medium-size firms—is important for economic

tion use the following size categories: micro enter-

ture projects with private participation declined.

progress, better jobs, and a more inclusive society.

prises have 0–9 employees, small enterprises have

tered are the number of new firms, defined as firms registered in the current year of reporting, expressed as a percentage of total registered firms. Data are collected on firm entry and exit and total firms. Because of underreporting of firms that have closed or exited,

10–49 employees, and medium-size enterprises have

The data on investment in infrastructure projects

Data on the business registration of micro, small,

with private participation refer to all investment (pub-

and medium-size enterprises are collected by govern-

lic and private) in projects in which a private company

ments, international organizations, foundations, and

assumes operating risk during the operating period or

small business organizations. These data have been

assumes development and operating risk during the

collated by the International Finance Corporation (IFC)

contract period. Investment refers to commitments

and are available in two databases: Entrepreneurship

not disbursements. Foreign state-owned companies

Data and Micro, Small, and Medium Enterprises: A

are considered private entities for the purposes of

Collection of Published Data. This IFC initiative is a

this measure. The data are from the World Bank’s

work in progress, improved and updated as new data

Private Participation in Infrastructure (PPI) Project

become available. Because the concepts and defini-

Financial Statistics. Data on business registration

Database, which tracks more than 3,300 projects,

tions of micro, small, and medium-size enterprises

and micro, small, and medium-size enterprises

newly owned or managed by private companies,

vary by source, using these data for precise country

are from the IFC’s Micro, Small, and Medium

that reached financial closure in low- and middle-

rankings may be inappropriate. See www.ifc.org/

Enterprises database (www.ifc.org/ifcext/sme.

income economies in 1990–2005. Aggregates for

ifcext/sme.nsf/Content/Resources for additional

nsf/Content/Resources).

geographic regions and income groups are calculated

information on sources and precise firm size.

50–249 employees.

Data sources Data on investment in infrastructure projects with private participation are from the World Bank’s PPI Project database (http://ppi.worldbank. org). Data on domestic credit are from the IMF’s International

2007 World Development Indicators

267

5.2

Investment climate: enterprise surveys Survey year

Policy Corruption uncertainty

Major constraint %

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, 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

268

2005 2002 2006 2006 2005

2005 2002 2005 2004 2006 2005 2006 2003 2005 2006 2006 2003 2006

2004 2003 2006 2006 2005 2005 2005

2003 2004 2003 2002 2005 2002

2006 2005 2005 2005 2003 2006 2006

.. 18.7 38.8 1.6 16.5 14.0 .. .. 2.5 44.3 23.3 .. 61.4 30.3 33.3 0.7 75.8 27.4 .. 14.3 37.9 .. .. .. .. 15.3 32.9 .. 3.3 5.3 .. 28.3 .. 17.4 .. 21.7 .. .. 60.7 63.8 28.4 29.1 5.1 38.2 .. .. .. 2.1 44.7 5.8 .. 9.1 66.4 1.4 5.0 ..

2007 World Development Indicators

Courts

Crime

Regulation and tax administration

Lack confidence Time dealing courts Tax rates with officials uphold % of as major Major property Major Major constraint constraint management rights constraint constraint time % % % % %

.. 31.0 34.3 12.5 4.3 14.5 .. .. 19.5 57.6 6.2 .. 81.7 8.0 23.1 7.9 66.9 18.4 5.1 2.2 55.1 5.2 .. .. .. 12.9 27.3 .. 2.6 0.5 .. 39.9 .. 17.4 .. 20.2 .. .. 49.2 50.3 35.1 2.5 4.2 38.4 .. .. .. 0.6 19.6 3.8 .. 9.8 80.9 2.7 6.1 ..

.. 23.2 .. .. 2.8 9.0 .. .. 6.5 .. 2.5 .. 48.7 0.1 20.5 1.4 32.5 16.7 0.7 0.2 28.9 1.2 .. .. .. 11.9 24.9 .. 1.6 .. .. 21.9 .. 28.9 .. 24.9 .. .. 34.0 24.7 16.3 11.6 1.9 4.6 .. .. .. 2.3 11.6 2.3 .. 4.6 31.2 0.4 1.4 ..

.. 43.6 27.3 51.0 64.0 46.2 .. .. 34.7 83.0 33.4 .. 65.3 63.8 41.6 31.4 39.6 56.7 29.9 36.9 61.0 37.3 .. .. .. 22.9 17.5 .. 37.8 56.5 .. 28.1 .. 26.0 .. 53.1 .. .. 70.8 .. 46.6 29.0 29.6 18.2 .. .. .. 28.4 29.0 10.3 .. 18.2 71.3 59.0 70.3 ..

.. 8.4 .. 6.2 1.6 5.5 .. .. 4.0 39.1 2.8 .. 47.2 2.3 19.0 10.9 52.0 11.4 1.4 2.9 41.3 2.9 .. .. .. 14.7 20.0 .. 13.0 1.8 .. 28.0 .. 3.8 .. 15.5 .. .. 27.8 9.4 49.0 1.3 1.9 9.4 .. .. .. 2.3 23.6 1.9 .. 5.2 80.4 1.7 0.7 ..

.. 40.9 44.6 3.0 14.5 21.0 .. .. 24.5 35.3 20.2 .. 86.8 3.6 15.4 7.3 84.5 20.4 18.8 3.7 17.8 32.6 .. .. .. 22.8 36.8 .. 12.5 9.6 .. 38.2 .. 11.9 .. 58.9 .. .. 38.0 80.0 22.6 29.1 2.8 72.2 .. .. .. 6.5 35.7 29.4 .. 27.5 56.5 3.1 5.3 ..

.. 10.4 .. 7.1 12.3 2.3 .. .. 5.2 3.7 3.6 .. 6.5 12.8 4.3 5.0 7.2 2.8 9.5 5.7 8.6 12.8 .. .. .. 5.8 18.5 .. 14.2 6.3 .. 9.6 .. 2.7 .. 2.1 .. .. 13.4 2.1 7.2 3.8 2.3 2.1 .. .. .. 7.3 3.0 4.5 .. 3.7 12.4 2.6 2.9 ..

Average time to clear customs days

.. 1.4 8.6 16.5 4.5 5.5 .. .. 1.7 8.3 2.8 .. 6.3 10.4 2.0 1.2 7.8 1.7 3.1 4.4 6.2 4.3 .. .. .. 4.0 6.2 .. 6.5 3.6 .. 2.8 .. 2.0 .. 2.7 .. .. 5.9 4.8 1.5 3.2 1.7 4.2 .. .. .. 5.0 3.4 4.0 .. 4.9 1.9 4.1 5.6 ..

Finance

Electricity

Major Major constraint constraint % %

.. 27.1 62.3 11.6 15.7 28.0 .. .. 12.5 56.7 29.5 .. 82.7 7.3 34.9 24.3 84.0 31.1 37.0 16.0 12.2 13.4 .. .. .. 27.1 29.1 .. 7.6 14.5 .. 60.1 .. 17.9 .. 25.2 .. .. 46.8 36.7 37.6 57.0 8.8 50.0 .. .. .. 11.6 31.2 23.2 .. 23.3 47.5 8.3 19.6 ..

.. 34.5 11.4 34.5 2.5 3.0 .. .. 6.0 72.9 0.9 .. 68.5 4.5 8.2 1.7 20.3 6.4 19.6 40.7 12.6 15.1 .. .. .. 17.8 29.7 .. 4.3 45.5 .. 16.6 .. 2.1 .. 15.5 .. .. 28.3 26.5 21.5 36.7 3.3 42.5 .. .. .. 53.7 33.2 1.0 .. 4.6 26.6 61.0 41.4 ..

Labor

Major constraint % Skills Regulation

.. 10.3 25.4 1.1 6.0 2.0 .. .. 1.0 19.2 6.5 .. 25.4 2.0 3.6 9.4 39.6 10.4 .. 0.1 6.4 .. .. .. .. 23.8 30.7 .. 9.3 1.0 .. 13.4 .. 7.2 .. 12.3 .. .. 22.3 29.7 20.0 40.5 7.0 17.7 .. .. .. 1.7 14.1 6.9 .. 8.5 31.4 .. .. ..

.. 2.5 12.7 .. 15.4 1.5 .. .. 1.5 8.3 3.4 .. 35.0 3.2 3.1 1.5 56.7 7.7 .. .. 5.6 1.2 .. .. .. 25.4 20.7 .. 1.8 .. .. 24.2 .. 3.0 .. 15.5 .. .. 14.1 28.1 3.9 5.1 18.6 4.5 .. .. .. .. 7.0 9.5 .. 7.6 16.7 .. .. ..

Survey year

Policy Corruption uncertainty

Major constraint %

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

2003 2005 2006 2003

2005

2005

2005 2003 2005 2005 2005 2006 2003

2005 2005 2005 2005 2003 2006 2005 2006 2005 2004 2004

2006

2003 2005

2003 2002 2006 2006 2006 2003 2005 2005

46.7 25.5 9.2 48.2 .. .. 5.6 .. .. 41.1 .. .. 9.7 49.6 .. 40.4 .. 32.2 .. 21.8 54.1 31.1 .. .. 22.1 25.5 41.0 5.8 .. 20.8 0.7 23.9 8.5 32.2 39.2 39.2 .. .. 0.8 .. .. .. 58.2 .. .. .. 20.5 40.1 4.2 .. 11.5 17.0 29.5 39.9 21.5 ..

Courts

Crime

Regulation and tax administration

Lack confidence Time dealing courts Tax rates with officials uphold % of as major Major property Major Major constraint constraint management rights constraint constraint time % % % % %

62.7 10.3 25.0 41.5 .. .. 3.0 .. .. 45.6 .. .. 11.3 72.5 .. 8.3 .. 32.2 .. 8.9 64.9 35.1 .. .. 13.2 32.4 46.1 3.2 .. 48.7 1.5 36.1 17.8 19.6 48.5 16.9 .. .. 9.3 .. .. .. 65.7 11.2 .. .. 11.9 40.3 10.8 .. 14.8 4.3 35.2 15.0 14.3 ..

21.6 24.5 2.7 24.7 .. .. 2.8 .. .. 20.0 .. .. 14.3 .. .. 3.4 .. 16.3 .. 5.4 56.1 24.3 .. .. 14.2 28.7 33.4 1.3 .. 16.9 0.8 22.9 0.1 23.6 24.7 29.1 .. .. 0.6 .. .. .. 33.2 1.6 .. .. 14.8 20.0 1.1 .. 3.0 0.6 13.0 30.6 17.0 ..

56.1 49.7 25.3 40.8 .. .. 28.3 .. .. 26.7 .. .. 42.0 51.3 .. 37.2 .. 50.8 .. 51.3 69.8 38.2 .. .. 49.7 55.4 44.6 28.9 .. 33.1 35.6 29.4 63.6 64.7 37.1 23.5 .. .. 24.9 .. .. .. 60.4 50.0 .. .. 12.9 62.6 51.6 .. 74.1 75.2 33.8 45.4 47.7 ..

60.9 7.1 11.8 22.0 .. .. 4.8 .. .. 54.4 .. .. 6.7 69.6 .. 3.4 .. 19.3 .. 3.0 22.9 45.9 .. .. 9.3 12.2 37.2 5.1 .. 22.1 1.2 25.4 7.3 17.6 27.8 7.6 .. .. 20.6 .. .. .. 39.2 .. .. .. 8.6 21.5 7.3 .. 3.1 5.5 26.5 15.4 14.9 ..

34.4 49.7 27.5 29.5 .. .. 17.4 .. .. 60.0 .. .. 16.0 67.8 .. 14.9 .. 31.2 .. 29.2 61.2 41.9 .. .. 40.7 19.7 44.7 9.6 .. 36.4 12.8 27.8 10.6 44.2 64.9 62.6 .. .. 17.2 .. .. .. 34.7 32.8 .. .. 20.5 45.6 14.6 .. 1.4 7.7 30.4 55.4 19.6 ..

10.2 5.3 6.7 4.0 .. .. 2.3 .. .. 6.3 .. .. 3.2 11.7 .. 3.2 .. 6.1 .. 2.9 12.0 19.8 .. .. 5.1 8.2 20.8 5.8 .. 7.5 5.8 9.6 20.2 3.5 5.7 7.5 .. .. 2.9 .. .. .. 13.0 11.5 .. .. 6.9 8.7 10.2 .. 10.4 8.2 6.9 3.8 3.2 ..

Average time to clear customs days

1.6 3.3 13.6 3.4 .. .. 2.6 .. .. 4.3 .. .. 6.0 4.3 .. 6.0 .. 4.1 .. 1.7 6.4 2.3 .. .. 1.8 1.9 2.9 3.5 .. 5.8 3.9 3.3 4.5 2.6 3.5 2.0 .. .. 1.3 .. .. .. 1.5 4.0 .. .. 3.4 8.9 5.0 .. 4.4 4.1 5.4 3.1 6.6 ..

Finance

5.2

Electricity

Major Major constraint constraint % %

62.4 32.9 19.4 30.7 .. .. 13.8 .. .. 72.2 .. .. 20.0 72.5 .. 15.7 .. 32.7 .. 9.9 65.4 54.1 .. .. 15.2 41.0 68.3 27.6 .. 63.6 21.6 52.7 8.7 40.7 64.4 84.4 .. .. 11.8 .. .. .. 65.9 12.0 .. .. 38.0 47.5 6.6 .. 21.0 9.3 25.0 46.3 25.6 ..

36.4 2.3 32.0 22.3 .. .. 6.4 .. .. 45.6 .. .. 3.7 47.1 .. 8.3 .. 4.0 .. 4.5 61.2 35.1 .. .. 3.9 11.7 41.3 19.2 .. 24.0 13.0 12.7 8.2 3.5 25.8 8.9 .. .. 3.1 .. .. .. 34.7 1.6 .. .. 10.1 39.2 30.6 .. 1.9 2.2 33.4 4.0 7.8 ..

STATES AND MARKETS

Investment climate: enterprise surveys

Labor

Major constraint % Skills Regulation

26.4 13.5 7.9 18.9 .. .. 15.6 .. .. 41.1 .. .. 7.7 27.5 .. 6.8 .. 18.8 .. 17.8 38.0 29.7 .. .. 15.2 5.9 30.4 5.8 .. 20.8 3.8 42.9 3.1 12.1 28.9 21.1 .. .. 9.4 .. .. .. 17.0 .. .. .. 34.4 12.7 3.8 .. 6.2 2.4 11.9 13.8 12.1 ..

2007 World Development Indicators

14.2 9.7 8.6 25.9 .. .. 9.6 .. .. 18.9 .. .. 2.3 22.5 .. 4.1 .. 2.5 .. 3.5 38.2 17.6 .. .. 8.8 8.5 14.7 0.6 .. 3.9 0.4 27.8 1.1 9.5 10.3 16.2 .. .. 4.4 .. .. .. 6.9 0.8 .. .. 34.7 15.0 4.4 .. 5.4 4.0 24.7 16.9 17.6 ..

269

5.2

Investment climate: enterprise surveys Survey year

Policy Corruption uncertainty

Major constraint %

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 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

2005 2005 2006 2003

2005 2005 2003 2005 2004 2006

2003 2005 2006 2004

2005 2006 2005

2006 2005 2005

2002

35.3 25.8 0.9 .. 30.5 .. .. .. 12.7 11.3 .. 17.9 10.1 34.0 .. 0.6 .. .. 26.4 5.5 0.5 29.1 .. .. .. 31.1 .. 0.3 31.0 .. .. .. 5.8 10.6 .. 14.0 .. .. 56.5 ..

Courts

Crime

Regulation and tax administration

Lack confidence Time dealing courts Tax rates with officials uphold % of as major Major property Major Major constraint constraint management rights constraint constraint time % % % % %

27.6 15.4 0.8 .. 39.3 .. .. .. 10.0 3.6 .. 16.1 7.6 16.9 .. 5.2 .. .. 57.1 14.5 0.5 18.3 .. .. .. 16.7 .. 2.4 21.2 .. .. .. 2.4 6.5 .. 11.2 .. .. 45.9 ..

31.1 8.6 .. .. 13.0 .. .. .. 12.3 8.1 .. 8.8 7.8 .. .. 1.0 .. .. 8.6 4.5 .. 38.6 .. .. .. 25.5 .. 0.1 13.7 .. .. .. 1.5 5.1 .. 4.9 .. .. 38.6 ..

44.0 63.9 34.6 .. 40.5 .. .. .. 44.4 34.4 .. 20.8 16.6 31.2 .. 56.7 .. .. .. 35.9 35.4 25.8 .. .. .. 28.5 .. 35.6 48.2 .. .. .. 37.9 41.7 .. 23.1 .. .. 36.1 ..

15.1 9.0 .. .. 14.9 .. .. .. 5.0 0.9 .. 29.0 9.6 14.0 .. 18.5 .. .. 29.6 4.0 1.9 10.3 .. .. .. 18.2 .. 0.2 11.9 .. .. .. 5.3 6.8 .. 3.7 .. .. 48.8 ..

34.0 21.6 26.9 .. 50.0 .. .. .. 8.2 12.6 .. 18.6 18.7 19.1 .. 15.4 .. .. 62.3 22.0 3.9 24.4 .. .. .. 37.6 .. 11.0 45.6 .. .. .. 20.8 18.1 .. 13.6 .. .. 57.5 ..

1.5 6.3 5.9 .. 3.2 .. .. .. 3.0 3.7 .. 9.2 0.8 3.5 .. 4.4 .. .. 10.3 3.3 4.0 1.3 .. .. .. .. .. 5.2 8.1 .. .. .. 6.8 2.5 .. 5.8 .. .. 13.0 ..

Average time to clear customs days

2.6 7.2 6.7 .. 5.9 .. .. .. 5.8 2.9 .. 4.3 3.7 3.1 .. 1.9 .. .. 5.6 4.8 4.8 1.3 .. .. .. 4.5 .. 2.9 3.9 .. .. .. 1.7 4.7 .. 2.5 .. .. 1.6 ..

Finance

Electricity

Major Major constraint constraint % %

31.7 21.8 13.6 .. 71.0 .. .. .. 10.9 14.9 .. 22.6 19.7 28.0 .. 10.3 .. .. 30.0 10.0 9.3 20.6 .. .. .. 25.0 .. 6.7 40.5 .. .. .. 11.8 16.0 .. 40.5 .. .. 84.5 ..

6.4 5.1 31.8 .. 30.5 .. .. .. 2.7 2.7 .. 9.0 8.3 41.3 .. 6.8 .. .. 57.5 10.0 72.9 25.6 .. .. .. 9.2 .. 63.3 4.9 .. .. .. 5.2 7.2 .. 15.7 .. .. 39.6 ..

Labor

Major constraint % Skills Regulation

12.5 12.8 2.8 .. 18.3 .. .. .. 8.2 5.4 .. 35.5 13.8 21.3 .. 2.3 .. .. 36.1 4.5 1.4 30.0 .. .. .. 9.7 .. 0.4 19.8 .. .. .. 1.8 4.1 .. 22.2 .. .. 35.7 ..

15.1 3.0 .. .. 16.0 .. .. .. 4.5 4.5 .. 32.8 11.8 25.6 .. 0.4 .. .. 33.0 1.5 .. 11.4 .. .. .. 12.1 .. .. 6.4 .. .. .. 7.2 2.7 .. 10.8 .. .. 16.9 ..

Note: Data are based on enterprise surveys conducted by the World Bank and its partners during 2002–06. While averages are reported, there are significant variations across firms. Surveys of Eastern Europe and Central Asia were conducted under the joint World Bank–European Bank for Reconstruction and Development Business Environment and Enterprise Performance Surveys Initiative.

270

2007 World Development Indicators

About the data

5.2

STATES AND MARKETS

Investment climate: enterprise surveys Definitions

The World Bank Group’s Enterprise Surveys capture

infrastructure— telecommunications, reliable elec-

• Survey year is the year in which the underlying data

business perceptions on the biggest obstacles to

tricity supplies, and effi cient transportation—are

were collected. • Policy uncertainty measures the

enterprise growth, the relative importance of con-

more productive, and improvements in infrastruc-

percentage of senior managers who ranked economic

straints to increasing employment and productivity,

ture services also benefit households. Ill-considered

and regulatory policy uncertainty as a major or very

and the effects of a country’s investment climate on

labor regulations can discourage firms from creating

severe constraint. • Corruption measures the per-

its international competitiveness. These surveys cover

more jobs, and while some employees may benefit,

centage of senior managers who ranked corruption

almost 58,000 firms in 97 countries for 2002–06. In

the unemployed, the low skilled, and those working

addition to these surveys, data from the Doing Busi-

in the informal economy will not.

ness project, which benchmarks regulatory regimes in 175 countries, are presented in table 5.3.

Data in this table for 27 countries in the Europe and Central Asia region plus 7 comparators in Europe and

as a major or very severe constraint. • Courts measure the percentage of senior managers who ranked courts and dispute resolution systems as a major or very severe constraint. • Lack confi dence courts

Improving government policies and behaviors is key

Asia (Germany, Greece, Ireland, Republic of Korea,

to shaping the investment climate because they are

Portugal, Spain, and Vietnam) are based on the joint

influential in driving growth and poverty reduction.

European Bank for Reconstruction and Development–

Firms evaluating alternative investment options, gov-

World Bank Business Environment and Enterprise Per-

am confident that the judicial system will enforce

ernments interested in improving their investment

formance Surveys (BEEPS). All other data are from the

my contractual and property rights in business dis-

climate, and economists seeking to understand the

World Bank Group’s Enterprise Surveys. Both surveys

putes.” • Crime measures the percentage of senior

role of different factors in explaining economic per-

sample the universe of registered businesses and

managers who ranked crime, theft, and disorder as

formance have all grappled with defining and measur-

follow either a simple random sample or a stratified

a major or very severe constraint. • Tax rates as

ing the investment climate.

uphold property rights measures the percentage of managers who do not agree with the statement: “I

random sample methodology, drawing from registered

major constraint measure the percentage of senior

The indicators in the table cover eight dimensions

establishments. BEEPS use a simple random sample

managers who ranked tax rates as a major or very

of the investment climate: policy uncertainty, corrup-

methodology based on population proportions and

severe constraint. • Time dealing with officials is

tion, courts, crime, regulation and tax administration,

can be compared across countries. In the Enterprise

the percentage of management time in a given week

finance, infrastructure (electricity), and labor.

Surveys a random sample across sectors is supple-

spent on requirements imposed by government regu-

Firms in developing countries rate access to and

mented by an emphasis on firms from the same few

cost of finance as their dominant concern among

selected manufacturing industries plus the retail sec-

investment climate constraints. Another highly ranked

tor as a means of providing measures of productivity

constraint is policy uncertainty, which measures the

that can be compared across economies. Because

credibility of governments and their policies and the

the distribution of establishments in most countries is

ability to deliver on promises. Corruption—the exploi-

overwhelmingly populated by small and medium-size

tation of public office for private gain—can harm the

enterprises, Enterprise Surveys generally oversample

investment climate in several ways. It can distort

large establishments. Other differences include the

• Electricity measures the percentage of senior

policymaking, undermine government credibility, tax

question related to “problems doing business,” which

managers who ranked electricity as a major or severe

entrepreneurial activities, and divert resources from

offers a similar but slightly different response scale

constraint. • Labor skills measure the percentage

public coffers. Better courts reduce the risks firms

between the two surveys. As a result, the data are not

of senior managers who ranked skills of available

face, so that firms are willing to invest more. And the

strictly comparable. In the two countries where the

workers as a major or severe constraint. • Labor

importance of courts grows as the number of large and

two surveys overlapped (Turkey and Vietnam), BEEPS

regulations measure the percentage of senior man-

complex long-term transactions increases. Robbery,

survey data were used for Turkey and Enterprise Sur-

agers who ranked labor regulations as a major or

fraud, and other crimes against property and against

vey data were used for Vietnam (a BEEPS comparator

severe constraint.

the person undermine the investment climate.

country). Sample sizes for recent surveys range from

Most countries have room to improve regulation

lations (taxes, customs, labor regulations, licensing, and registration). • Average time to clear customs is the number of days to clear an imported good through customs. • Finance measures percentage of senior managers who ranked access to finance or cost of finance as a major or very severe constraint.

200 to 1,500 businesses.

and taxation without compromising broader social

For the 2006 Enterprise Surveys in Africa and Latin

interests. The investment climate is harmed when

America and for the 2005 surveys for Malawi and

governments impose unnecessary costs, by increas-

Niger the indicators for severity of constraints reflect

ing uncertainty and risk and by erecting unjustified

the percentage of managers who identified a particu-

barriers to competition. Improvements in the tax sys-

lar constraint as their biggest. In any given country

tem may include broadening the tax base, simplifying

some constraints may not be identified by any firms

tax structures, increasing the autonomy of tax agen-

as the biggest constraint even if they would be consid-

cies, and improving compliance through computeriza-

ered major or severe. The 2006 Enterprise Surveys

tion. When financial markets work well, they connect

in Latin America and Africa, except those for Burkina

firms to lenders and investors, which allow firms to

Faso, Cameroon, and Cape Verde, have weights for

Data on the investment climate are from the World

seize business opportunities and grow their busi-

the estimates of the aggregate indicators in order to

Bank Group’s Enterprise Surveys website (www.

nesses. But too often government distortions intro-

account for the random stratified sample design.

enterprisesurveys.org/), which compiles data

duced by state ownership or directed credit under-

For more information on the investment climate

mine financial sector development, productivity, and

and Enterprise Surveys, see www.worldbank.org/

economic growth. Firms that have access to modern

eca/econ and www.enterprisesurveys.org/.

Data sources

from surveys undertaken by the World Bank and other development partners.

2007 World Development Indicators

271

5.3

Business environment: Doing Business indicators Starting a business

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, 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

272

Registering property

Number of procedures April 2006

Time required days April 2006

Cost % of per capita income April 2006

Number of procedures April 2006

Time required days April 2006

3 11 14 13 15 9 2 9 15 8 16 4 7 15 12 11 17 9 8 11 10 12 2 10 19 9 13 5 13 13 8 11 11 10 .. 10 3 10 14 10 10 13 6 7 3 7 10 8 7 9 12 15 13 13 17 12

8 39 24 124 32 24 2 29 53 37 69 27 31 50 54 108 152 32 34 43 86 37 3 14 75 27 35 11 44 155 71 77 45 45 .. 24 5 73 65 19 26 76 35 16 14 8 60 27 16 24 81 38 30 49 233 203

67.4 22.4 21.5 486.7 12.1 5.1 1.8 5.6 9.5 87.6 26.1 5.8 173.3 140.6 37.0 10.6 9.9 7.9 120.8 222.4 236.4 152.2 0.9 209.3 226.1 9.8 9.3 3.3 19.8 481.1 214.8 23.5 134.1 12.2 .. 8.9 0.0 30.2 31.8 68.8 75.6 115.9 5.1 45.9 1.1 1.1 162.8 292.1 10.9 5.1 49.6 24.2 52.1 186.5 261.2 127.7

11 7 15 7 5 3 5 3 7 8 7 7 3 7 7 4 14 9 8 5 7 5 6 3 6 6 3 5 7 8 7 6 6 5 .. 4 6 7 10 7 6 12 3 13 3 9 8 5 6 4 7 12 5 6 9 5

252 47 51 334 44 4 5 32 61 425 231 132 50 92 331 30 47 19 107 94 56 93 10 69 44 31 32 54 23 57 137 21 32 399 .. 123 42 107 20 193 33 101 51 43 14 183 60 371 9 40 382 23 37 104 211 683

2007 World Development Indicators

Dealing with licenses

Employing workers

Enforcing contracts

Rigidity of Time employment Number of required index procedures to build a 0 (less rigid) to build a warehouse to 100 (more Number of warehouse days rigid) procedures April April April April 2006 2006 2006 2006

.. 22 25 15 23 18 17 14 28 13 18 15 16 14 16 24 19 22 32 18 28 15 15 21 16 12 29 22 12 14 15 19 22 28 .. 31 7 17 19 30 22 .. 13 12 17 10 13 17 17 11 16 17 23 29 11 12

.. 344 244 326 288 112 140 195 212 185 354 184 333 183 467 169 460 226 226 302 181 444 77 245 199 171 367 160 150 306 175 119 569 278 .. 271 70 165 149 263 144 .. 117 133 56 155 268 145 137 133 127 176 390 278 161 141

46 38 45 64 41 31 3 37 38 30 27 20 46 74 42 20 42 47 64 59 49 56 4 73 60 24 24 0 27 78 69 32 45 50 .. 28 17 42 51 53 24 20 58 34 48 56 59 27 7 44 34 58 34 41 77 24

.. 39 49 47 33 24 19 23 27 50 28 27 49 47 36 26 42 34 41 47 31 58 17 45 52 33 31 16 37 51 47 34 25 22 .. 21 15 29 41 55 41 35 25 30 27 21 32 26 24 30 29 22 36 44 40 35

Protecting investors

Closing a business

Time required days April 2006

Disclosure index 0 (less disclosure) to 10 (more disclosure) April 2006

Time to resolve insolvency years April 2006

1,642 390 397 1,011 520 185 181 342 267 1,442 225 328 720 591 595 501 616 440 446 403 401 800 346 660 743 480 292 211 1,346 685 560 615 525 561 .. 820 190 460 498 1,010 626 305 275 690 228 331 880 247 285 394 552 730 1,459 276 1,140 368

0 0 6 5 6 5 8 2 4 6 1 8 5 1 3 8 5 10 6 .. 5 8 8 4 3 8 10 10 7 3 4 2 6 2 .. 2 7 5 1 5 6 4 8 4 6 10 5 2 4 5 7 1 3 5 0 4

.. 4.0 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 1.3 4.0 3.3 4.0 4.0 .. 3.2 0.8 4.8 10.0 5.6 2.4 1.1 3.0 5.2 3.0 3.5 2.2 3.1 .. 9.2 3.0 3.5 8.0 4.2 4.0 1.7 3.0 2.4 0.9 1.9 5.0 3.0 3.3 1.2 1.9 2.0 3.0 3.8 .. 5.7

Starting a business

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

Registering property

Number of procedures April 2006

Time required days April 2006

Cost % of per capita income April 2006

Number of procedures April 2006

Time required days April 2006

13 6 11 12 8 11 4 5 9 6 8 11 7 13 .. 12 13 8 8 5 6 8 .. .. 7 10 10 10 9 13 11 6 8 10 8 6 13 .. 10 7 6 2 6 11 9 4 9 11 7 8 17 10 11 10 8 7

44 38 35 97 47 77 19 34 13 8 23 18 20 54 .. 22 35 21 163 16 46 73 .. .. 26 18 21 37 30 42 82 46 27 30 20 12 113 .. 95 31 10 12 39 24 43 13 34 24 19 56 74 72 48 31 8 7

60.6 20.9 73.7 86.7 5.4 67.6 0.3 5.1 15.2 9.4 7.5 73.0 7.0 46.3 .. 15.2 1.6 9.8 17.3 3.5 105.4 39.9 .. .. 2.8 7.4 35.0 134.7 19.7 201.9 121.6 8.0 14.2 13.3 5.1 12.7 85.7 .. 18.0 78.5 7.2 0.2 131.6 416.8 54.4 2.5 4.5 21.3 23.9 28.2 136.8 32.5 18.7 21.4 4.3 0.8

7 4 6 7 9 5 5 7 8 5 6 8 8 8 .. 7 8 7 9 8 8 6 .. .. 3 6 8 6 5 5 4 6 5 6 5 4 8 .. 9 3 2 2 8 5 16 1 2 6 7 4 6 5 8 6 5 8

36 78 62 42 36 8 38 144 27 54 14 22 52 73 .. 11 55 8 135 54 25 101 .. .. 3 98 134 118 144 33 49 210 74 48 11 46 42 .. 23 5 5 2 124 49 80 1 16 50 44 72 46 33 33 197 81 15

Dealing with licenses

Employing workers

Enforcing contracts

Rigidity of Time employment Number of required index procedures to build a 0 (less rigid) to build a warehouse to 100 (more Number of warehouse days rigid) procedures April April April April 2006 2006 2006 2006

14 25 20 19 21 14 10 21 17 14 11 16 32 11 .. 14 26 20 24 22 16 14 .. .. 14 18 19 22 25 15 19 21 12 34 18 21 13 .. 11 15 18 7 12 19 16 13 16 12 22 20 15 19 23 25 20 20

199 212 270 224 668 216 181 215 284 242 96 122 248 170 .. 52 149 218 192 152 275 265 .. .. 151 222 297 185 281 209 152 145 142 158 96 217 364 .. 105 424 184 184 192 148 465 104 242 218 121 218 273 201 197 322 327 212

36 34 41 44 49 59 33 27 54 4 29 27 23 28 .. 34 13 38 37 59 24 35 .. .. 48 54 57 21 10 51 59 30 38 54 34 63 54 .. 27 52 42 7 24 77 21 54 35 43 56 10 59 61 39 33 51 32

36 21 56 34 23 65 18 31 40 18 20 43 37 25 .. 29 52 44 53 21 39 58 .. .. 24 27 29 40 31 28 40 37 37 37 29 42 38 .. 31 28 22 28 20 33 23 14 41 55 45 22 46 35 25 41 24 43

5.3

STATES AND MARKETS

Business environment: Doing Business indicators

Protecting investors

Closing a business

Time required days April 2006

Disclosure index 0 (less disclosure) to 10 (more disclosure) April 2006

Time to resolve insolvency years April 2006

480 335 1,420 570 520 520 217 585 1,210 415 242 342 183 360 .. 230 390 140 443 240 721 695 .. .. 166 385 591 337 450 860 400 630 415 310 314 615 1,010 .. 270 590 408 109 486 360 457 277 598 880 686 440 478 300 600 980 495 620

1 2 7 8 5 4 10 7 7 4 7 5 7 4 .. 7 7 8 0 5 9 2 .. .. 6 5 5 4 10 6 0 6 8 7 5 6 7 .. 5 6 4 10 4 4 6 7 8 6 3 5 6 8 1 7 6 7

2007 World Development Indicators

3.8 2.0 10.0 5.5 4.5 .. 0.4 4.0 1.2 1.1 0.6 4.3 3.3 4.5 .. 1.5 4.2 4.0 5.0 3.0 4.0 2.6 .. .. 1.7 3.7 .. 2.6 2.3 3.6 8.0 1.7 1.8 2.8 4.0 1.8 5.0 .. 1.5 5.0 1.7 2.0 2.2 5.0 1.5 0.9 4.0 2.8 2.5 3.0 3.9 3.1 5.7 3.0 2.0 3.8

273

5.3

Business environment: Doing Business indicators Starting a business

Number of procedures April 2006

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegroa 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 Europe EMU

5 7 9 13 10 10 9 6 9 9 .. 9 10 8 10 13 3 6 12 14 13 8 13 9 10 8 .. 17 10 12 6 5 10 8 16 11 12 12 6 10 9u 10 10 10 9 10 9 9 10 10 8 11 7 8

Time required days April 2006

11 28 16 39 58 18 26 6 25 60 .. 35 47 50 39 61 16 20 43 67 30 33 53 43 11 9 .. 30 33 63 18 5 43 29 141 50 93 63 35 96 48 u 59 51 57 42 54 50 31 77 40 33 62 21 22

a. Data are for Serbia only.

274

2007 World Development Indicators

Registering property

Cost % of per capita income April 2006

4.4 2.7 188.3 58.6 112.6 10.2 1,194.5 0.8 4.8 9.4 .. 6.9 16.2 9.2 58.6 41.1 0.7 2.2 21.1 75.1 91.6 5.8 252.7 1.1 9.3 26.8 .. 114.0 9.2 36.4 0.7 0.7 44.2 14.1 25.4 44.5 324.7 228.0 29.9 35.6 69.0 u 146.3 48.6 62.9 26.0 83.5 48.8 14.7 51.0 89.5 46.6 162.9 7.7 7.8

Number of procedures April 2006

8 6 5 4 6 6 8 3 3 6 .. 6 3 8 6 11 1 4 4 6 10 2 7 8 5 8 .. 13 10 3 2 4 8 12 8 4 10 6 6 4 6u 7 6 7 6 7 5 6 7 7 7 7 5 6

Time required days April 2006

150 52 371 4 114 111 235 9 17 391 .. 23 17 63 9 46 2 16 34 37 123 2 242 162 57 9 .. 227 93 6 21 12 66 97 47 67 72 21 70 30 86 u 125 80 85 72 96 112 91 81 48 136 110 44 54

Dealing with licenses

Employing workers

Enforcing contracts

Rigidity of Time employment Number of required index procedures to build a 0 (less rigid) to build a warehouse to 100 (more Number of warehouse days rigid) procedures April April April April 2006 2006 2006 2006

17 22 17 18 15 20 48 11 13 14 .. 17 11 17 17 11 8 15 20 18 26 9 14 19 24 32 .. 19 18 21 19 18 17 19 13 14 21 13 16 21 18 u 18 18 18 18 18 18 22 15 19 16 18 16 15

242 531 252 125 185 211 236 129 272 207 .. 174 277 167 172 114 116 152 134 187 313 127 273 292 79 232 .. 156 242 125 115 69 156 287 276 133 134 107 196 481 205 u 231 210 215 201 217 152 248 200 223 227 236 155 196

51 44 49 7 61 38 63 0 39 57 .. 41 63 27 55 17 43 23 30 31 67 18 58 7 46 49 .. 7 55 20 14 0 31 34 76 37 31 33 23 34 37 u 44 35 35 35 38 24 40 32 42 35 47 30 46

43 31 27 44 33 33 58 29 27 25 .. 26 23 20 67 31 19 22 47 46 21 26 37 37 21 34 .. 19 28 34 19 17 39 35 41 37 26 37 21 33 35 u 39 35 35 36 37 33 31 39 42 39 38 26 25

Time required days April 2006

335 178 310 360 780 635 515 120 565 1,350 .. 600 515 837 770 972 208 215 872 257 393 425 535 1,340 481 420 .. 484 183 607 229 300 655 195 435 295 700 360 404 410 540 u 572 575 576 573 574 507 357 655 643 969 581 398 473

Protecting investors

Closing a business

Disclosure index 0 (less disclosure) to 10 (more disclosure) April 2006

Time to resolve insolvency years April 2006

9 7 2 8 4 7 3 10 2 3 .. 8 5 4 0 1 6 0 6 0 3 10 4 4 0 8 .. 7 1 4 10 7 3 4 3 4 7 6 3 8 5u 4 5 4 5 5 5 5 4 6 4 4 6 6

4.6 3.8 .. 2.8 3.0 2.7 2.6 0.8 4.0 2.0 .. 2.0 1.0 2.2 .. 2.0 2.0 3.0 4.1 3.0 3.0 2.7 3.0 .. 1.3 5.9 .. 2.2 2.9 5.1 1.0 1.5 2.1 4.0 4.0 5.0 .. 3.0 3.1 3.3 2.9 u 3.1 3.2 3.2 3.2 3.2 2.6 3.7 3.3 2.8 3.6 3.0 1.8 1.3

About the data

The table presents key indicators on the environment for doing business. The indicators identify regulations that enhance or constrain business investment, productivity, and growth. The data are from the World Bank’s Doing Business database, which now includes data on 175 economies. A vibrant private sector is central to promoting growth and expanding opportunities for poor people. But encouraging firms to invest, improve productivity, and create jobs requires a legal and regulatory environment that fosters access to credit, protects property rights, and supports efficient judicial, taxation, and customs systems. The indicators in the table point to the administrative and regulatory reforms and institutions needed to create a favorable environment for doing business. When entrepreneurs start a business, the first obstacles they face are the administrative and legal procedures required to register the new firm. Countries differ widely in how they regulate the entry of new businesses. In some countries the process is straightforward and affordable. But in others the procedures are so burdensome that entrepreneurs may opt to run their business informally. The data on starting a business cover the number of start-up procedures, the time required, and cost to complete them. Property registries were first developed to help raise tax revenue, but they have benefited entrepreneurs as well. Securing rights to land and buildings, a major source of wealth in most countries, strengthens incentives to invest and facilitates trade. More complex procedures to register property are associated with less perceived security of property rights, more informality, and more corruption. The data cover the number procedures required and time required to secure rights to property. Lack of access to credit is one of the biggest barriers entrepreneurs face in starting and operating a business. Indicators covering access to credit and financial information are presented in table 5.5. There are many types of business licenses required, and striking the right balance between the ease of doing business and consumer safety requires continuous reform. Since construction is a large sector in most economies, the procedures required for a business in the construction industry to build a standardized warehouse are recorded. These include obtaining all necessary licenses and permits, completing all required notifications and inspections, and submitting the relevant documents to the authorities. The data cover the number of procedures and time needed by the construction firm to complete all procedures. Every economy has a complex system of laws and institutions to protect the interests of workers and guarantee a minimum standard of living for its population. The rigidity of employment index focuses on the regulation of employment, specifically employing workers and the rigidity of working hours. This index is the average of three subindexes: a difficulty of hiring index, a rigidity of hours index, and a difficulty of firing index. All subindexes have several components

5.3

STATES AND MARKETS

Business environment: Doing Business indicators Definitions

and take values between 0 and 100, with higher values indicating more rigid regulation. Contract enforcement is critical to enable businesses to engage with new borrowers or customers. Without good contract enforcement, trade and credit will be restricted to a small community of people who have developed relationships through repeated dealings or the security of assets. The institution that enforces contracts between debtors and creditors, and suppliers and customers, is the court. The effi ciency of contract enforcement is refl ected in two indicators: the number of judicial procedures to resolve a dispute and the time it takes to enforce a commercial contract. What companies disclose to the public has a large impact on investor protection. Both investors and entrepreneurs benefit greatly from such legal protection. The disclosure index is based on several measures that cover ownership disclosure measures that reduce expropriation, and disclosures to help investors. Unviable businesses prevent assets and human capital from being allocated to more productive uses in new companies or in viable companies that are financially distressed. The time to close a business (resolve an insolvency) captures the average time to complete a procedure, as estimated by insolvency lawyers. Information is collected on the sequence of bankruptcy procedures, and on whether any procedures can be carried out simultaneously. Delays due to legal derailment tactics that parties to the insolvency may use, in particular extension of response periods or appeals, are taken into account. To ensure cross-country comparability, several standard characteristics of a company are defined in all surveys, such as size, ownership, location, legal status, and type of activities undertaken. For example, for the starting a business data, these standard characteristics include that the business is a limited liability company; operates in the country’s most populous city; is 100 percent domestically owned and has five owners, none of whom is a legal entity; has start-up capital of 10 times income per capita at the end of 2005, has paid in cash; performs general industrial or commercial activities, such as production or sale of products or services to the public; does not perform foreign trade activities or handle products subject to a special tax regime; does not use heavily polluting production processes; leases the commercial plant and offices and is not a proprietor of real estate; does not qualify for investment incentives or any special benefits; has up to 50 employees within one month of commencement of operations, all of them nationals; has turnover at least 100 times income per capita; and has a company deed at least 10 pages long. The data were collected through a study of laws and regulations in each country, surveys of regulators or private sector professionals on each topic, and cooperative arrangements with private consulting firms and business and law associations. For more information on the methodology, see www.doingbusiness.org/.

• Number of procedures for starting a business is the number of procedures required to start a business, including interactions required to obtain necessary permits and licenses and to complete all inscriptions, verifications, and notifications to start operations. Data are for businesses with specific characteristics of ownership, size, and type of production. • Time required for starting a business is the number of calendar days needed to complete the required procedures for legally operating a business. If a procedure can be speeded up at additional cost, the fastest procedure, independent of cost, is chosen. • Cost for starting a business is normalized by presenting it as a percentage of gross national income (GNI) per capita. • Number of procedures for registering property is the number of procedures required for a business to secure rights to property • Time required for registering property is the number of calendar days needed for a business to secure rights to property • Number of procedures to build a warehouse is the number of interactions of a company’s employees or managers with external parties, including government agency staff, public inspectors, notaries, land registry and cadastre staff, and technical experts apart from architects and engineers • Time required to build a warehouse is the number of calendar days needed to complete the required procedures for building a warehouse. If a procedure can be speeded up at additional cost, the fastest procedure, independent of cost, is chosen. • Rigidity of employment index measures the regulation of employment, specifically the employing of workers and the rigidity of working hours. This index is the average of three subindexes: a difficulty of hiring index, a rigidity of hours index, and a difficulty of firing index. The index ranges from 0 and 100, with higher values indicating more rigid regulations. • Number of procedures for enforcing contracts is the number of independent actions, mandated by law or court regulation, that demand interaction between the parties to a contract or between them and the judge or court officer. • Time required for enforcing contracts is the number of calendar days from the time of the filing of the lawsuit in court to the final determination and payment. • Disclosure index measures the degree to which investors are protected through disclosure of ownership and financial information. The index ranges from 0 to 10, with higher values indicating more disclosure. • Time to resolve insolvency is the number of years from the time of filing for insolvency in court until resolution of distressed assets.

Data sources Data on the business environment are from the World Bank’s Doing Business project (www.doingbusiness.org).

2007 World Development Indicators

275

5.4

Stock markets Market capitalization

$ 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, 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

276

% of GDP

2000

2006

.. .. .. .. 166,068 2 372,794 29,935 4 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 .. 11,002 107,666 141 704 28,741 2,041 .. 1,846 .. 293,635 1,446,634 .. .. 24 1,270,243 502 110,839 240 .. .. ..

.. .. .. .. 79,730 43 804,074 126,324 .. 3,610 .. 327,065 .. 2,200 .. 3,947 711,100 10,325 .. .. .. .. 1,480,891 .. .. 174,556 2,426,326 1,006,228 56,204 .. .. 1,478 4,155 29,006 .. 48,604 178,038 .. 4,040 93,477 3,623 .. 5,963 .. 209,504 1,710,029 .. .. 355 1,221,250 1,729 145,013 .. .. .. ..

2007 World Development Indicators

Market liquidity

Turnover ratio

Value traded % of GDP

Value of shares traded % of market capitalization

Listed domestic companies

number

S&P/EMDB indexes

% change

2000

2005

2000

2005

2000

2006

2000

2006

2005

2006

.. .. .. .. 58.4 0.1 93.3 15.4 0.1 2.5 .. 78.7 .. 20.7 .. 15.8 37.6 4.9 .. .. .. .. 117.8 .. .. 80.3 48.5 369.4 11.4 .. .. 18.3 11.4 14.9 .. 19.4 67.3 0.8 4.4 28.8 15.5 .. 33.7 .. 243.6 108.9 .. .. 0.8 66.8 10.1 96.7 1.2 .. .. ..

.. .. .. .. 33.6 0.9 109.8 41.3 .. 5.1 .. 88.2 .. 23.6 .. 23.6 59.6 19.1 .. .. .. .. 133.0 .. .. 118.4 34.9 566.2 37.6 .. .. 7.4 14.2 33.5 .. 30.8 68.8 .. 8.8 89.1 21.3 .. 26.7 .. 108.5 80.4 .. .. 5.5 43.7 12.8 64.4 .. .. .. ..

.. .. .. .. 2.1 0.0 56.6 4.8 .. 1.6 .. 16.4 .. 0.8 .. 0.8 16.8 0.5 .. .. .. .. 88.8 .. .. 8.1 60.2 223.9 0.5 .. .. 0.7 0.3 1.0 .. 11.6 57.2 .. 0.1 11.1 0.2 .. 6.0 .. 171.4 81.6 .. .. 0.1 56.3 0.2 83.0 0.0 .. .. ..

.. .. .. .. 9.0 0.0 84.1 15.0 .. 1.7 .. 30.7 .. 0.0 .. 0.4 19.4 5.2 .. .. .. .. 75.9 .. .. 16.4 26.2 258.9 5.2 .. .. 0.8 0.2 2.1 .. 33.0 58.8 .. 0.4 28.4 0.4 .. 18.9 .. 141.6 69.4 .. .. 0.6 63.1 0.4 29.0 .. .. .. ..

.. .. .. .. 4.8 4.6 56.5 29.8 .. 74.4 .. 20.7 .. 0.1 .. 4.8 43.5 9.2 .. .. .. .. 77.3 .. .. 9.4 158.3 61.3 3.8 .. .. 12.0 2.6 7.4 .. 60.3 86.0 .. 5.5 34.7 1.3 .. 18.9 .. 64.3 74.1 .. .. .. 79.1 1.5 63.7 0.0 .. .. ..

.. .. .. .. 6.6 3.7 78.0 43.3 .. 31.7 .. 20.8 .. 0.1 .. 2.4 42.5 25.0 .. .. .. .. 63.6 .. .. 19.2 136.4 49.3 22.3 .. .. 7.7 3.7 9.8 .. 77.5 92.3 .. 9.0 55.2 2.3 .. 27.5 .. 139.1 82.7 .. .. 13.6 146.0 3.4 48.3 .. .. .. ..

.. .. .. .. 127 105 1,330 97 2 221 .. 174 .. 26 .. 16 459 503 .. .. .. .. 1,418 .. .. 258 1,086 779 126 .. .. 21 41 64 .. 131 225 6 30 1,076 40 .. 23 .. 154 808 .. .. 269 1,022 22 329 44 .. .. ..

.. .. .. .. 103 198 1,643 92 .. 269 .. 145 .. 36 .. 18 392 347 .. .. .. .. 3,721 .. .. 244 1,440 1,126 114 .. .. 19 40 183 .. 29 168 .. 34 603 35 .. 16 .. 134 664 .. .. 257 648 32 307 .. .. .. ..

.. .. .. .. 45.4 a .. .. .. .. –27.7b .. .. .. .. .. –3.2b 47.6a 16.4b .. .. .. .. .. .. .. 14.7a 13.3a .. 108.1b .. .. .. 16.9 b 7.4b .. 43.5a .. .. 26.0 b 158.0 a .. .. 22.8 b .. .. .. .. .. .. .. –33.9 b .. .. .. .. ..

.. .. .. .. 57.6a .. .. .. .. 12.9 b .. .. .. .. .. 53.0 b 43.1a 31.4b .. .. .. .. .. .. .. 28.6a 80.7a .. 12.7b .. .. .. 35.6b 85.2b .. 30.9a .. .. 32.0 b 10.2a .. .. 30.3b .. .. .. .. .. .. .. 9.7b .. .. .. .. ..

Market capitalization

$ millions

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

% of GDP

2000

2006

458 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 .. 126 116,935 .. 1,090 1,331 125,204 392 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 ..

.. 41,935 818,879 138,886 38,724 .. 114,134 173,306 798,167 12,277 4,736,513 29,729 10,521 11,378 .. 835,188 128,940 42 .. 2,705 8,279 .. .. .. 10,191 646 .. .. 235,356 .. .. 3,598 348,345 574 46 49,360 .. .. 542 963 727,515 40,620 .. .. 32,819 190,952 16,158 45,518 5,074 4,863 234 59,658 68,382 149,054 66,981 ..

Market liquidity

Turnover ratio

Listed domestic companies

Value traded % of GDP

Value of shares traded % of market capitalization

number

5.4

STATES AND MARKETS

Stock markets

S&P/EMDB indexes

% change

2000

2005

2000

2005

2000

2006

2000

2006

2005

2006

8.8 25.6 32.2 16.3 7.3 .. 85.1 55.5 70.0 44.6 67.9 58.4 7.3 10.1 .. 33.5 55.1 0.3 .. 7.2 9.4 .. .. .. 13.9 0.2 .. 7.2 129.5 .. 97.2 29.8 21.5 30.4 3.9 32.7 .. .. 9.1 14.4 165.7 35.8 .. .. 9.2 39.0 17.4 9.0 24.0 49.6 3.5 19.8 34.4 18.3 53.9 ..

.. 29.8 68.6 28.4 20.4 .. 56.6 97.3 45.3 136.1 104.5 296.1 18.4 34.1 .. 91.2 161.0 1.7 .. 16.0 22.5 .. .. .. 31.9 11.2 .. .. 139.1 .. .. 41.6 31.1 22.1 2.4 52.7 .. .. 6.8 13.0 116.6 37.2 .. .. 19.6 64.6 26.0 41.5 32.8 98.3 3.2 45.3 40.5 31.0 36.5 ..

.. 25.8 110.8 8.7 1.1 .. 15.0 20.3 70.9 0.9 57.9 4.9 0.5 0.4 .. 208.7 11.2 1.7 .. 2.9 0.7 .. .. .. 1.8 3.3 .. 0.5 64.8 .. .. 1.7 7.8 1.9 0.8 3.3 .. .. 0.6 0.6 175.2 20.5 .. .. 0.6 36.0 2.8 45.0 1.3 0.0 0.1 2.9 10.9 8.5 48.3 ..

.. 21.9 55.0 14.6 4.3 .. 32.1 48.5 63.3 4.5 110.2 187.3 1.9 2.7 .. 152.7 116.4 0.5 .. 0.6 4.2 .. .. .. 2.9 1.7 .. .. 38.3 .. .. 2.4 6.9 0.6 0.1 8.0 .. .. 0.1 0.3 121.3 15.9 .. .. 2.0 65.9 7.4 127.3 0.5 0.3 0.0 2.5 7.0 9.9 21.2 ..

.. 90.7 133.6 32.9 12.7 .. 19.2 36.3 104.0 2.5 69.9 7.7 25.1 3.6 .. 233.2 21.3 .. .. 48.6 6.7 .. .. .. 14.8 6.6 .. 13.8 44.6 .. .. 5.0 32.3 5.8 7.3 9.2 .. .. 4.5 6.9 101.4 45.9 .. .. 7.3 93.4 14.2 475.5 1.7 .. 3.5 12.6 15.8 49.9 85.5 ..

.. 88.2 96.4 45.5 19.1 .. 56.7 60.7 140.5 3.0 118.8 59.0 14.9 15.8 .. 173.7 45.1 34.1 .. 4.9 38.1 .. .. .. 23.5 18.3 .. .. 33.2 .. .. 6.0 29.7 5.9 6.1 32.9 .. .. 4.6 2.4 112.2 41.3 .. .. 13.8 117.2 22.1 251.4 1.8 0.4 0.7 9.2 22.1 46.8 55.4 ..

46 60 5,937 290 304 .. 76 654 291 46 2,561 163 23 57 .. 1,308 77 80 .. 64 12 .. .. .. 54 1 .. 7 795 .. 40 40 179 36 410 53 .. .. 13 110 234 142 .. .. 195 191 131 762 29 7 56 230 228 225 109 ..

.. 41 4,796 344 420 .. 53 612 275 41 3,279 227 83 51 .. 1,694 163 8 .. 40 11 .. .. .. 44 57 .. .. 1,027 .. .. 41 131 23 392 65 .. .. 9 105 170 154 .. .. 202 202 124 652 24 9 54 193 238 267 52 ..

.. 16.1a 33.6a 9.1a .. .. .. 24.1a .. –14.1b 21.7c 117.8 b .. 60.0 b .. 58.8a .. .. .. 32.8b 111.8b .. .. .. 6.2b .. .. .. –2.9a .. .. 10.5b 43.9a .. .. 8.4a .. .. –1.1b .. .. .. .. .. 20.7b .. 38.0 b 58.5b .. .. .. 29.8 a 21.3a 20.8a .. ..

.. 31.4 a 46.7a 67.9a .. .. .. –6.3a .. –1.5b 5.9 c –36.0 b .. 60.3b .. 13.3a –4.6b .. .. 1.5b –9.2b .. .. .. 9.7b .. .. .. 34.6a .. .. 44.3b 41.1a .. .. 78.5a .. .. 12.8 b .. .. .. .. .. 34.0 b .. 7.9 b 1.3b .. .. .. 82.5a 50.3a 38.1a .. ..

2007 World Development Indicators

277

5.4

Stock markets Market capitalization

$ millions

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 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 Europe EMU

% of GDP

Market liquidity

Turnover ratio

Value traded % of GDP

Value of shares traded % of market capitalization

2000

2006

2000

2005

2000

2005

1,069 38,922 .. 67,171 .. 734 .. 152,827 1,217 2,547 .. 204,952 504,219 1,074 .. 73 328,339 792,316 .. .. 233 29,489 .. 4,330 2,828 69,659 .. 35 1,881 5,727 2,576,992 15,104,037 161 32 8,128 .. 765 .. 236 2,432 32,187,882 s 166,928 1,852,197 979,347 872,850 2,019,125 780,487 176,208 626,283 60,573 157,695 217,880 30,168,757 5,425,933

32,784 1,321,833 .. 326,869 .. 5,409 .. 208,300 5,574 15,182 .. 715,025 960,024 7,769 .. 197 403,948 938,624 .. .. 588 139,564 .. 15,571 4,446 162,399 .. 103 42,870 138,531 3,058,182 16,997,982 354 37 8,251 .. 4,461 .. 989 26,557 43,642,048 s 944,645 7,273,817 3,854,955 3,418,862 8,218,463 3,008,514 1,863,241 1,469,731 201,450 875,775 799,751 38,980,586 6,465,158

2.9 15.0 .. 35.6 .. 4.7 .. 164.8 6.0 13.2 .. 154.2 86.8 6.6 .. 5.3 135.7 322.0 .. .. 2.6 24.0 .. 53.1 14.5 35.0 .. 0.6 6.0 8.1 178.6 154.7 0.8 0.2 6.9 .. 18.6 .. 7.3 32.9 103.0 w 23.9 37.4 35.9 39.3 35.8 47.2 19.2 32.6 20.0 26.2 89.3 117.9 87.5

20.9 71.8 .. 208.6 .. 20.6 .. 178.4 9.5 23.0 .. 236.0 85.4 24.4 .. 7.2 112.9 255.7 .. .. 4.9 69.9 .. 118.2 10.0 44.6 .. 1.2 30.1 173.9 139.1 136.9 2.1 0.3 3.6 .. 111.1 .. 13.6 71.2 99.6 w 54.2 49.5 41.1 60.1 50.1 41.3 45.8 44.6 49.1 60.4 137.0 112.9 64.8

0.6 7.8 .. 9.2 .. 0.1 .. 98.7 4.4 2.4 .. 58.3 169.8 0.9 .. 0.0 161.2 247.6 .. .. 0.4 19.0 .. 1.7 3.2 89.9 .. 0.0 0.9 0.2 127.2 326.3 0.0 0.1 0.6 .. 4.6 .. 0.2 3.8 153.4 w 78.0 26.8 32.4 19.7 33.1 50.0 25.4 8.6 5.0 90.3 32.1 179.7 80.8

3.4 20.9 .. 356.2 .. 2.5 .. 102.6 0.1 2.3 .. 83.8 138.5 4.8 .. 0.0 129.7 240.7 .. .. 0.1 50.6 .. 4.4 1.6 55.5 .. 0.0 0.8 110.4 189.5 173.2 0.0 0.3 0.2 .. 61.7 .. 0.2 9.8 108.1 w 49.7 21.8 21.3 22.4 25.3 26.4 21.9 10.7 16.4 58.2 46.2 130.3 72.8

2000

2006

Listed domestic companies

number 2000

23.1 16.9 5,555 36.9 65.7 249 .. .. .. 27.1 269.1 75 .. .. .. 0.0 15.3 6 .. .. .. 52.1 63.1 418 129.8 1.9 493 20.7 10.3 38 .. .. .. 33.9 49.5 616 210.7 163.9 1,019 11.0 14.8 239 .. .. .. 9.8 0.0 6 111.2 118.9 292 82.0 100.1 252 .. .. .. .. .. .. 2.4 2.3 4 53.2 67.6 381 .. .. .. 3.1 3.0 27 23.3 15.2 44 206.2 143.0 315 .. .. .. .. 3.1 2 19.6 4.6 139 3.9 65.9 54 66.6 141.9 1,904 200.8 129.1 7,524 0.5 0.0 16 .. 184.7 5 8.9 9.4 85 .. .. .. 10.0 89.1 24 .. .. .. 20.8 2.0 9 10.8 7.9 69 122.4 w 78.2 w 47,884 s 151.6 96.6 7,929 71.5 75.3 15,533 92.3 95.2 5,931 47.0 50.4 9,602 81.3 78.2 23,462 125.2 123.1 3,190 81.9 68.5 8,295 26.9 29.2 1,806 12.6 27.0 1,807 167.6 108.7 7,269 22.2 32.6 1,095 131.0 122.2 24,422 90.6 116.8 4,405

2006

S&P/EMDB indexes

% change 2005

2,478 58.7b 309 64.9a .. .. 86 111.0 b .. .. 864 .. .. .. 557 .. 173 16.6b 100 –6.9 b .. .. 401 24.8a 3,300 .. 237 29.3b .. .. 6 .. 252 .. 263 .. .. .. .. .. 6 .. 476 3.8 a .. .. 37 –1.3b 48 11.1b 314 49.5a .. .. 5 .. 249 52.8 b 81 .. 2,759 4.4 d 5,143 3.0 e 11 .. 114 .. 53 –22.0 b .. .. 28 .. .. .. 12 .. 80 36.6b 49,946 s .. 6,122 .. 11,141 .. 5,057 .. 6,084 .. 17,263 .. 3,525 .. 4,490 .. 1,342 .. 1,078 .. 5,954 .. 874 .. 28,733 .. 5,995 ..

2006

54.2b 62.0a .. –48.9 b .. .. .. .. 24.0 b 74.3b .. 17.2a .. 45.3b .. .. .. .. .. .. .. 6.2a .. –6.5b 47.9 b –4.0a .. .. 48.6b –44.6b 26.2d 13.6e .. .. 79.0 b .. .. .. .. 912.3b .. .. .. .. .. .. .. .. .. .. .. .. .. ..

a. Data refer to the S&P/IFC Investable index. b. Data refer to the S&P/IFC Global index. c. Data refer to the Nikkei 225 index. d. Data refer to the FT 100 index. e. Data refer to the S&P 500 index.

278

2007 World Development Indicators

About the data

5.4

STATES AND MARKETS

Stock markets Definitions

The development of an economy’s financial markets

companies is another measure of market size. Mar-

• Market capitalization (also known as market

is closely related to its overall development. Well

ket size is positively correlated with the ability to

value) is the share price times the number of shares

functioning financial systems provide good and eas-

mobilize capital and diversify risk.

outstanding. • Market liquidity is the total value

ily accessible information. That lowers transaction

Market liquidity, the ability to easily buy and

traded divided by GDP. Value traded is the total

costs, which in turn improves resource allocation

sell securities, is measured by dividing the total

value of shares traded during the period. This indi-

and boosts economic growth. Both banking systems

value traded by GDP. The turnover ratio—the

cator complements the market capitalization ratio by

and stock markets enhance growth, the main fac-

value of shares traded as a percentage of market

showing whether market size is matched by trading.

tor in poverty reduction. At low levels of economic

capitalization—is also a measure of liquidity as well

• Turnover ratio is the total value of shares traded

development commercial banks tend to dominate

as of transaction costs. (High turnover indicates low

during the period divided by the average market capi-

the financial system, while at higher levels domestic

transaction costs.) The turnover ratio complements

talization for the period. Average market capitaliza-

stock markets tend to become more active and effi -

the ratio of value traded to GDP, because the turn-

tion is calculated as the average of the end-of-period

cient relative to domestic banks.

over ratio is related to the size of the market and

values for the current period and the previous period.

Open economies with sound macroeconomic poli-

the value traded ratio to the size of the economy. A

• Listed domestic companies are the domestically

cies, good legal systems, and shareholder protection

small, liquid market will have a high turnover ratio

incorporated companies listed on the country’s stock

attract capital and therefore have larger financial mar-

but a low value traded ratio. Liquidity is an impor-

exchanges at the end of the year. This indicator does

kets. Recent research on stock market development

tant attribute of stock markets because, in theory,

not include investment companies, mutual funds, or

shows that new communications technology and

liquid markets improve the allocation of capital and

other collective investment vehicles. • S&P/EMDB

increased financial integration have resulted in more

enhance prospects for long-term economic growth.

indexes measure the U.S. dollar price change in the

cross-border capital flows, a stronger presence of

A more comprehensive measure of liquidity would

stock markets covered by the S&P/IFCI country index

financial firms around the world, and the migration of

include trading costs and the time and uncertainty

and S&P/IFCG indexes.

stock exchange activities to international exchanges.

in finding a counterpart in settling trades.

Many firms in emerging markets now cross-list on

The S&P/EMDB, the source for all the data in

international exchanges, which provides them with

the table, provides regular updates on 56 emerg-

lower cost capital and more liquidity-traded shares.

ing stock markets encompassing more than 2,200

However, this also means that exchanges in emerg-

stocks. Standard & Poor’s maintains a series of

ing markets may not have enough financial activity

indexes for investors interested in investing in stock

to sustain them, putting pressure on them to rethink

markets in developing countries. At the core of the

their operations.

S&P/EMDB indexes, the Global (S&P/IFCG) index

The stock market indicators in the table include

is intended to represent the most active stocks in

measures of size (market capitalization, number

the markets it covers and to be the broadest pos-

of listed domestic companies) and liquidity (value

sible indicator of market movements. The Investable

traded as a percentage of gross domestic product,

(S&P/IFCI) index, which applies the same calculation

value of shares traded as a percentage of market

methodology as the S&P/IFCG index, is designed

capitalization). The comparability of such indicators

to measure the returns that foreign portfolio inves-

between countries may be limited by conceptual

tors might receive from investing in emerging market

and statistical weaknesses, such as inaccurate

stocks that are legally and practically open to foreign

reporting and differences in accounting standards.

portfolio investment. These indexes are widely used

The percentage change in stock market prices in

benchmarks for international portfolio management.

U.S. dollars, from the Standard & Poor’s Emerg-

See Standard & Poor’s (2000) for further information

ing Markets Data Base (S&P/EMDB) indexes, is an

on the indexes.

important measure of overall performance. Regula-

Because markets included in Standard & Poor’s

tory and institutional factors that can affect investor

emerging markets category vary widely in level of

confidence, such as entry and exit restrictions, the

development, it is best to look at the entire category

Data on stock markets are from Standard & Poor’s

existence of a securities and exchange commission,

to identify the most significant market trends. And

Global Stock Markets Factbook 2006, which draws

and the quality of laws to protect investors, may influ-

it is useful to remember that stock market trends

on the Emerging Markets Data Base, supple-

ence the functioning of stock markets but are not

may be distorted by currency conversions, espe-

mented by other data from Standard & Poor’s.

included in the table.

cially when a currency has registered a significant

The firm collects data through an annual survey

devaluation.

of the world’s stock exchanges, supplemented by

Stock market size can be measured in a number

Data sources

of ways, and each may produce a different ranking

About the data is based on Demirgüç-Kunt and

information provided by its network of correspon-

of countries. Market capitalization shows the overall

Levine (1996), Beck and Levine (2001), and Claes-

dents and by Reuters. Data on GDP are from the

size of the stock market in U.S. dollars and as a

sens, Klingebiel, and Schmukler (2002).

World Bank’s national accounts data files.

percentage of GDP. The number of listed domestic

2007 World Development Indicators

279

5.5

Financial access, stability, and efficiency Getting credit

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, 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

280

Legal rights index 0 (weaker) to 10 (stronger)

Credit information index 0 (less) to 10 (more)

April 2006

April 2006

0 9 3 3 3 5 9 5 7 7 2 5 4 3 8 7 2 6 4 2 0 3 7 3 4 4 2 10 3 3 3 4 3 5 .. 6 8 4 3 1 4 3 4 5 6 5 4 4 6 8 5 3 4 4 3 3

2007 World Development Indicators

0 0 2 4 6 3 5 6 4 2 3 4 1 5 5 5 5 4 1 1 0 2 6 2 1 6 4 5 4 0 2 6 1 0 .. 5 4 6 5 2 6 0 5 2 5 4 2 0 3 6 0 4 5 1 1 2

Bank capital to asset ratio

% of adults Private Public credit bureau credit registry coverage coverage April 2006

0.0 0.0 0.2 2.9 25.4 1.5 0.0 1.2 1.1 0.6 0.0 56.2 10.3 11.5 0.0 0.0 9.2 20.7 2.4 0.1 0.0 3.4 0.0 1.1 0.2 31.3 10.2 0.0 0.0 0.0 1.4 2.5 3.1 0.0 .. 3.5 0.0 11.9 15.2 1.5 30.5 0.0 0.0 0.1 0.0 12.3 2.6 0.0 0.0 0.5 0.0 0.0 16.1 0.0 1.0 0.7

April 2006

0.0 0.0 0.0 0.0 100.0 0.0 100.0 39.9 0.0 0.0 0.0 0.0 0.0 32.3 22.9 43.2 43.0 .. 0.0 0.0 0.0 0.0 100.0 0.0 0.0 19.3 0.0 64.5 28.3 0.0 0.0 39.2 0.0 0.0 .. 51.0 11.5 57.1 43.7 0.0 79.6 0.0 18.2 0.0 14.9 0.0 0.0 0.0 0.0 93.9 0.0 37.5 9.2 0.0 0.0 0.0

Bank nonperforming loans to total gross loans

Domestic credit provided by banking sector

Interest rate spread

Risk premium on lending

Prime lending rate minus treasury bill rate percentage points

%

%

% of GDP

Lending rate minus deposit rate percentage points

2005

2005

2005

2005

2005

.. .. .. 11.3 13.0 21.5 5.9 7.4 14.2 3.8 19.8 2.7 .. 11.3 15.0 9.7 9.2 10.5 .. .. .. .. 4.5 .. .. 6.8 3.8 12.2 12.3 .. .. 12.2 .. 8.7 .. 5.8 5.7 9.4 9.6 .. 7.6 .. 8.6 .. 8.8 4.4 .. .. 18.8 4.4 12.0 5.0 .. .. .. ..

.. .. .. 13.3 5.2 6.9 0.2 2.2 7.2 15.3 1.9 2.0 .. 11.2 5.3 2.8 4.4 1.7 .. .. .. .. 0.5 .. .. 0.9 10.5 1.5 2.7 .. .. 1.5 .. 4.0 .. 4.3 0.7 5.9 4.9 25.0 12.0 .. 0.2 .. 0.3 3.5 15.8 .. 3.8 4.8 13.9 5.5 .. .. .. ..

.. 48.6 11.1 2.1 38.3 8.8 109.8 127.6 11.8 43.9 22.2 105.2 13.2 50.1 47.7 –5.3 82.5 43.6 16.2 35.1 7.6 12.4 206.1 17.5 7.5 85.8 135.7 142.8 35.1 2.7 1.5 43.4 18.2 74.1 .. 43.6 177.5 40.4 16.8 105.5 47.5 141.2 71.3 54.9 78.5 109.5 10.4 23.8 21.7 135.8 29.7 110.9 31.4 15.8 9.1 29.3

.. 8.0 6.3 54.3 2.4 12.2 5.4 .. 8.5 5.9 2.1 5.2 .. 11.7 6.0 6.5 37.8 4.8 .. .. 15.4 12.8 3.6 12.8 12.8 2.7 3.3 6.5 7.5 .. 12.8 14.5 .. 9.5 .. 4.6 .. 10.2 5.8 5.9 .. .. 2.8 3.5 2.7 4.4 12.8 17.6 14.1 .. .. 4.3 8.7 .. .. 24.0

.. 7.6 6.7 .. .. 13.9 .. .. 9.5 .. .. 4.7 .. 11.7 .. .. 36.6 6.1 .. .. .. .. 1.7 .. .. .. .. 4.1 .. .. .. .. .. .. .. 3.8 .. .. .. 4.6 .. .. .. 6.8 .. 4.3 .. .. 12.1 .. .. 4.4 .. .. .. 19.1

Getting credit

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

Legal rights index 0 (weaker) to 10 (stronger)

Credit information index 0 (less) to 10 (more)

April 2006

April 2006

6 6 5 5 5 4 8 8 3 6 6 5 5 8 .. 6 4 5 2 8 4 5 .. .. 4 6 2 8 8 3 5 6 2 6 5 3 4 .. 5 4 7 9 4 3 7 6 3 4 6 6 3 4 3 4 4 6

5 5 3 2 3 0 5 5 5 0 6 2 4 2 .. 5 3 3 0 4 5 0 .. .. 6 3 1 0 6 1 1 1 6 0 3 1 3 .. 5 2 5 5 5 1 0 4 1 4 6 0 6 6 3 4 4 5

Bank capital to asset ratio

% of adults Private Public credit bureau credit registry coverage coverage April 2006

8.3 0.0 0.0 8.4 13.7 0.0 0.0 0.0 7.0 0.0 0.0 0.7 0.0 0.0 .. 0.0 0.0 0.0 0.0 1.9 4.3 0.0 .. .. 4.2 2.1 0.3 0.0 42.2 2.9 0.2 10.2 0.0 0.0 10.2 2.3 0.7 .. 0.0 0.0 0.0 0.0 12.5 1.2 0.0 0.0 17.5 0.3 0.0 0.0 10.6 19.2 0.0 0.0 72.0 0.0

April 2006

18.7 5.9 6.1 0.2 0.0 0.0 100.0 100.0 67.8 0.0 .. 0.0 5.5 0.1 .. 76.6 16.1 0.4 0.0 0.0 0.0 0.0 .. .. 7.2 0.0 0.0 0.0 .. 0.0 0.0 0.0 69.5 0.0 0.0 0.0 0.0 .. 35.2 0.1 68.9 100.0 3.4 0.0 0.0 100.0 0.0 1.1 59.8 0.0 52.2 28.6 4.8 38.1 9.1 63.6

Bank nonperforming loans to total gross loans

Domestic credit provided by banking sector

5.5

STATES AND MARKETS

Financial access, stability, and efficiency

Interest rate spread

Risk premium on lending

Prime lending rate minus treasury bill rate percentage points

%

%

% of GDP

Lending rate minus deposit rate percentage points

2005

2005

2005

2005

2005

8.4 9.1 6.3 10.5 .. .. 4.7 6.7 7.3 8.7 4.2 7.2 8.7 .. .. 5.8 12.6 .. .. 7.6 .. .. .. .. 7.3 .. 6.2 .. 7.9 .. .. .. 12.0 17.0 .. 7.7 6.5 .. 7.8 .. 4.0 .. 8.8 .. 9.9 5.1 .. 7.7 12.8 .. 11.0 7.7 12.3 7.8 5.2 ..

6.6 2.1 5.2 15.6 .. .. 0.7 10.3 6.3 2.9 1.8 13.6 9.6 5.2 .. 1.2 4.5 .. .. 0.7 15.8 .. .. .. 2.5 .. 10.1 .. 9.9 .. .. .. 1.8 4.3 .. 15.7 4.6 .. 2.0 .. 1.2 .. 8.0 .. 21.9 0.7 .. 10.6 1.8 .. 3.2 2.1 20.0 7.7 1.6 ..

34.2 62.9 60.4 47.0 46.2 .. 160.2 84.8 108.9 53.2 318.7 111.6 24.7 38.4 .. 106.6 71.7 9.5 8.8 72.8 184.0 –1.1 188.9 –50.7 42.3 20.9 12.9 22.1 143.7 17.5 –6.1 108.8 35.3 32.3 37.1 88.0 8.8 28.1 65.9 .. 184.8 132.8 79.4 10.7 9.0 10.0 34.9 43.6 88.2 21.9 20.1 17.6 50.9 32.6 150.7 ..

7.9 3.4 .. 6.0 4.2 .. 2.6 3.2 4.9 9.9 1.4 4.7 .. 7.8 .. 1.9 4.0 20.8 22.1 3.3 2.5 7.8 13.6 4.0 4.5 5.6 8.3 22.2 3.0 .. 15.1 13.8 6.2 6.0 10.6 7.9 11.7 5.5 4.4 5.9 0.4 4.9 8.1 .. 7.4 2.2 3.7 .. 6.0 10.6 28.2 11.5 4.6 4.0 .. ..

.. 1.6 .. .. .. .. .. 2.1 3.1 4.0 .. .. .. 4.5 .. .. .. 22.2 8.2 2.1 5.4 4.5 .. 0.6 3.2 .. 8.2 8.7 3.5 .. 11.2 .. 0.5 15.6 .. .. 10.4 .. 3.5 5.9 .. 5.0 .. .. 10.3 .. .. .. .. 7.7 .. .. 4.1 .. .. ..

2007 World Development Indicators

281

5.5

Financial access, stability, and efficiency Getting credit

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 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 European Monetary Union

Legal rights index 0 (weaker) to 10 (stronger)

Credit information index 0 (less) to 10 (more)

April 2006

April 2006

4 3 1 3 3 5a 5 9 9 6 .. 5 5 3 4 6 6 6 5 4 5 5 3 6 3 3 .. 3 8 3 10 7 4 3 4 4 5 3 7 6 4.8 w 3.9 4.7 4.6 5.0 4.5 4.7 5.4 4.4 3.7 3.8 4.2 6.2 5.4

a. Data are for Serbia only.

282

2007 World Development Indicators

5 0 2 5 1 5a 0 4 3 3 .. 5 6 3 0 5 4 5 0 0 0 5 1 3 3 5 .. 0 0 2 6 6 6 0 0 3 3 2 0 0 2.6 w 1.0 2.8 2.7 3.1 2.2 1.5 3.0 3.4 1.9 1.8 1.3 4.6 4.9

Bank capital to asset ratio

Bank nonperforming loans to total gross loans

Domestic credit provided by banking sector

Interest rate spread

Risk premium on lending

Prime lending rate minus treasury bill rate percentage points

%

%

% of GDP

Lending rate minus deposit rate percentage points

April 2006

April 2006

2005

2005

2005

2005

2005

2.6 0.0 0.2 0.2 4.7 0.1a 0.0 0.0 1.0 2.9 .. 0.0 44.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.6 0.0 11.6 6.7 .. 0.0 0.0 1.7 0.0 0.0 13.2 0.0 0.0 2.7 0.7 0.1 0.0 0.0 3.8 m 0.9 4.6 4.4 4.9 3.3 3.7 1.8 7.5 4.1 0.1 1.5 6.0 17.6

5.5 0.0 0.0 12.5 0.0 43.4 a 0.0 38.6 45.3 0.0 .. 53.0 7.4 3.1 0.0 39.0 100.0 24.5 0.0 0.0 0.0 21.7 0.0 31.5 0.0 .. .. 0.0 0.0 0.0 86.1 100.0 85.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 17.4 m 0.2 14.2 10.4 20.4 9.1 3.2 8.7 27.6 0.0 1.3 3.8 52.9 39.9

8.8 13.5 .. 8.8 8.4 17.2 11.6 10.5 7.6 7.4 .. 8.3 4.9 6.7 .. .. 5.8 5.1 .. .. .. 9.8 .. .. 7.7 13.5 .. 10.3 11.5 8.3 8.5 10.3 8.6 .. 11.1 .. .. .. .. 12.1 8.6 m .. 9.3 10.2 8.7 9.7 .. 10.5 9.5 .. 6.3 .. 5.8 5.0

8.3 3.2 34.1 3.0 14.2 19.8 14.8 3.8 2.0 4.9 .. 1.5 0.6 9.6 .. .. 1.1 0.5 .. .. .. 11.1 .. .. 20.9 4.8 .. 2.2 19.6 8.3 1.0 0.7 2.7 .. 1.2 .. .. .. 10.8 23.2 4.4 m .. 4.6 7.2 2.5 7.3 .. 4.2 3.1 .. 10.1 .. 1.5 2.0

.. 6.7 .. .. .. .. 13.5 4.9 4.2 4.6 .. 4.6 .. –3.2 .. 6.6 2.5 2.4 7.0 13.5 10.4 3.9 .. 6.9 .. .. .. 10.9 7.6 .. .. .. 10.8 .. 5.2 3.9 .. 5.0 17.0 144.6 6.5 m 11.7 6.5 7.5 6.0 7.4 5.5 6.0 7.8 4.8 5.9 12.2 4.3 4.3

.. 7.6 .. .. .. .. 1.6 3.3 .. 4.1 .. 3.7 .. –2.0 .. 3.6 1.6 2.4 .. .. 4.4 .. .. 4.2 .. .. .. 11.1 .. .. 0.1 3.0 9.5 .. .. 4.9 .. 3.1 11.9 50.6 .. .. .. .. .. .. .. .. .. .. .. .. .. ..

% of adults Private Public credit bureau credit registry coverage coverage

20.8 20.7 9.7 46.9 23.1 .. 24.9 70.8 49.6 64.8 .. 184.6 159.7 44.3 12.7 16.3 120.7 179.9 31.3 16.4 14.0 111.1 17.2 25.7 71.5 56.6 .. 9.9 34.6 59.5 168.0 224.3 40.5 .. 13.1 69.6 .. 4.7 22.0 94.0 164.6 w 48.1 76.0 95.5 50.6 72.2 121.4 35.5 52.0 53.6 57.2 81.8 191.0 128.6

5.5

STATES AND MARKETS

Financial access, stability, and efficiency About the data

This year’s table has been revised to include data

comprise tier 2 and tier 3 capital). Total assets

ability of more credit information, from either a pub-

on getting credit from the World Bank Group’s Doing

include all nonfinancial and financial assets. Data

lic registry or a private bureau, to facilitate lending

Business database.

are from internally consistent financial statements

decisions. • Public credit registry coverage reports

to enhance the quality and analytical usefulness of

the number of individuals and firms listed in a pub-

the indicators.

lic credit registry with current information on repay-

Financial sector development has positive impacts on economic growth and poverty. The size of the sector determines the amount of resources mobilized for

The ratio of bank nonperforming loans to total gross

ment history, unpaid debts, or credit outstanding.

investment. Access to finance can expand opportuni-

loans is a measure of bank health and efficiency. It

The number is expressed as a percentage of the

ties for all—not just the rich and well connected—

helps to identify problems with asset quality in the

adult population • Private credit bureau coverage

with higher levels of access and use of banking

loan portfolio. A high ratio may signal deterioration

reports the number of individuals or firms listed by

services associated with lower financing obstacles

in the quality of the credit portfolio. International

a private credit bureau with current information on

for people and businesses. A stable financial sys-

guidelines recommend that loans be classified as

repayment history, unpaid debts, or credit outstand-

tem that promotes efficient savings and investment

nonperforming when payments of principal and inter-

ing. The number is expressed as a percentage of the

is also crucial for a thriving democracy and market

est are past due by 90 days or more or when future

adult population. • Bank capital to asset ratio is the

economy. The banking system is the largest sector

payments are not expected to be received in full.

ratio of bank capital and reserves to total assets.

in the financial system in most countries, so most

See the International Monetary Fund’s (IMF) Global

Capital and reserves include funds contributed by

indicators in the table cover the banking system.

Financial Stability Report for detailed background

owners, retained earnings, general and special

information.

reserves, provisions, and valuation adjustments.

There are several aspects of access to financial services: availability, cost, and quality of services.

Domestic credit provided by the banking sector as

• Bank nonperforming loans to total gross loans

The development and growth of credit markets

a share of GDP is a measure of banking sector depth

are the value of nonperforming loans divided by the

depend on access to timely, reliable, and accurate

and financial sector development in terms of size.

total value of the loan portfolio (including nonper-

data on borrowers’ credit experiences. For secured

In a few countries governments may hold interna-

forming loans before the deduction of specific loan

transactions, such as mortgages or vehicle loans,

tional reserves as deposits in the banking system

loss provisions). The loan amount recorded as non-

having rapid access to information in property reg-

rather than in the central bank. Since the claims on

performing should be the gross value of the loan as

istries is also vital, and for small business loans,

the central government are a net item (claims on

recorded on the balance sheet, not just the amount

corporate registry data are needed. An effective way

the central government minus central government

that is overdue. • Domestic credit provided by bank-

to improve access to credit is to increase informa-

deposits), this net figure may be negative, resulting

ing sector includes all credit to various sectors on a

tion about potential borrowers’ creditworthiness

in a negative figure of domestic credit provided by

gross basis, except credit to the central government,

and make it easy to create and enforce collateral

the banking sector.

which is net. The banking sector includes monetary

agreements. Lenders look at the borrower’s credit

The interest rate spread—the margin between

authorities, deposit money banks, and other banking

history and collateral when extending loans. Where

the cost of mobilizing liabilities and the earnings on

institutions for which data are available (including

credit registries and effective collateral laws are

assets—is a measure of the efficiency by which the

institutions that do not accept transferable depos-

absent—as in many developing countries—banks

financial sector intermediates funds. A narrow inter-

its but do incur such liabilities as time and savings

make fewer loans. Indicators that cover financial

est rate spread means low transaction costs, which

deposits). • Interest rate spread is the interest

access, or getting credit, include legal rights index,

lowers the overall cost of funds for investment, cru-

rate charged by banks on loans to prime customers

credit information index, public registry coverage,

cial to economic growth. The risk premium on lending

minus the interest rate paid by commercial or similar

and private bureau coverage. Other measures of

is the spread between the lending rate to the private

banks for demand, time, or savings deposits. • Risk

access and use, such as number of bank branches

sector and the “risk-free” government rate. A small

premium on lending is the interest rate charged by

per capita and number of bank deposits per capita

spread indicates that the market considers its best

banks on loans to prime private sector customers

are not presented in the table this year since they

corporate customers to be low risk. Interest rate

minus the “risk free” treasury bill interest rate at

are not collected or updated regularly.

spreads are expressed as annual averages. In some

which short-term government securities are issued

The size and mobility of international capital flows

countries this spread may be negative, indicating

or traded in the market.

have made it increasingly important to monitor the

that the market considers its best corporate clients

strength of financial systems. Robust financial sys-

to be lower risk than the government.

tems help to increase economic activity and welfare, but instability in the financial system can disrupt

Definitions

financial activity and impose huge and widespread costs on the economy. The ratio of bank capital to

• Legal rights index measures the degree to which

assets, a measure of bank solvency and resiliency,

collateral and bankruptcy laws protect the rights of

provides a measure of the extent to which banks

borrowers and lenders and thus facilitate lending.

can deal with unexpected losses. Capital includes

The index ranges from 0 to 10, with higher scores

tier 1 capital (paid-up shares and common stock),

indicating that these laws are better designed to

Doing Business project (www.doingbusiness.org).

which is a common feature in all countries’ banking

expand access to credit. • Credit information index

Data on bank capital and nonperforming loans are

systems, and total regulatory capital, which includes

measures rules affecting the scope, accessibility,

from the IMF’s Global Financial Stability Report.

several specified types of subordinated debt instru-

and quality of credit information available through

Data on credit and interest rates are from the

ments that need not be repaid if the funds are

public or private credit registries. The index ranges

IMF’s International Financial Statistics.

required to maintain minimum capital levels (these

from 0 to 6, with higher values indicating the avail-

Data sources Data on getting credit are from the World Bank’s

2007 World Development Indicators

283

5.6

Tax policies Tax revenue collected by central government

% of GDP

Afghanistanb Albaniab Algeriab 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, China Colombia Congo, Dem. Rep.b Congo, Rep. Costa Ricab Côte d’Ivoireb Croatiab Cuba Czech Republicb Denmark Dominican Republicb 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

284

2000

2005

.. 16.1 36.9 .. 9.8 .. 22.1 19.6 12.7 7.6 16.6 27.4 .. 13.2 .. .. 12.2 18.3 .. 13.6 8.2 12.3 15.3 .. .. 16.6 6.8 .. .. 0.0 9.2 12.1 14.6 26.2 .. 15.4 31.0 14.7 .. 14.6 10.7 .. 16.8 10.8 24.9 23.4 .. .. 7.7 11.9 17.2 25.5 10.1 11.1 .. ..

3.9 17.3 .. .. 14.2 14.3 23.9 20.0 .. 8.1 20.6 26.5 14.6 16.6 21.8 .. .. 23.4 12.1 .. 8.0 .. 14.4 6.0 .. 19.2 8.8 c .. 15.1 .. 8.5 13.7 14.5 23.3 .. 15.6 30.6 15.1 .. .. 12.6 .. 17.0 12.7c 22.9 22.7 .. .. 12.1 11.0 22.4 21.8 9.6 .. .. ..

2007 World Development Indicators

Taxes payable by businesses

Highest marginal tax ratea

Number of payments Fiscal year 2006

Time to prepare, file, and pay taxes hours Fiscal year 2006

Total tax rate % of profi t Fiscal year 2006

2 42 61 42 34 50 11 20 36 17 125 10 72 41 73 24 23 27 45 40 27 39 10 54 65 10 48 4 68 34 94 41 71 39 .. 14 18 87 8 41 66 18 11 20 19 33 27 47 35 32 35 33 50 55 47 53

275 240 504 272 615 1,120 107 272 1,000 400 1,188 160 270 1,080 100 140 2,600 616 270 140 121 1,300 119 504 122 432 872 80 456 312 576 402 270 196 .. 930 135 178 600 536 224 216 104 212 264 128 272 376 423 105 304 204 294 416 208 160

36.3 55.8 76.4 64.4 116.8 42.5 52.2 56.1 44.9 40.3 186.1 70.1 68.5 80.3 50.4 53.3 71.7 40.7 51.1 286.7 22.3 46.2 43.0 209.5 68.2 26.3 77.1 28.8 82.8 235.4 57.3 83.0 45.7 37.1 .. 49.0 31.5 67.9 34.9 50.4 27.4 86.3 50.2 32.8 47.9 68.2 48.3 291.4 37.8 57.1 32.3 60.2 40.9 49.4 47.5 40.5

% 2006

.. 20 .. .. 35 .. 47 50 35 .. .. 50 35c .. 15 25 28 24 .. .. 20 .. 29 .. .. 40 45 20 22 50 .. 25 10 45 .. 32 59 30 25 20 .. .. 23 35c 33 48 .. .. 12 42 25 40 31 .. .. ..

Individual On income over $ 2006

.. 2,003 .. .. 41,379 .. 72,519 63,750 12,632 .. .. 39,625 .. .. .. 19,569 11,486 4,586 .. .. 36,652 .. 97,756 .. .. 6,127 8,637 11,568 43,154 4,920 .. 19,414 4,550 3,765 .. 13,823 53,117 29,596 61,440 6,920 .. .. 1,908 .. 72,750 60,673 .. .. .. 65,190 10,581 28,750 38,663 .. .. ..

Corporate % 2006

.. 20 .. .. 35 .. 30 25 22 .. .. 34 38 c 25 30 15 15 15 .. .. 20 .. 22 .. .. 17 .. 18 39 40 .. 30 35 20 .. 24 28 30 25 .. .. .. 23 30 c 26 33 .. .. 20 25 25 29 31 .. .. ..

Tax revenue collected by central government

% of GDP

Honduras Hungary Indiab Indonesiab Iran, Islamic Rep.b Iraq Ireland Israel Italy Jamaicab Japanb Jordanb Kazakhstanb Kenyab Korea, Dem. Rep. Korea, Rep.b Kuwait Kyrgyz Republicb Lao PDR Latviab Lebanon Lesothob Liberia Libya Lithuania Macedonia, FYRb Madagascar Malawi Malaysiab Mali Mauritania Mauritiusb Mexicob Moldovab Mongolia Morocco Mozambique Myanmar b Namibiab Nepalb Netherlands New Zealand Nicaraguab Niger Nigeria Norway Omanb Pakistanb Panamab Papua New Guineab Paraguay b Perub Philippinesb Poland Portugal Puerto Rico

2000

2005

.. 22.5 9.0 11.3 6.3 .. 26.1 31.0 23.2 24.7 .. 19.0 10.2 16.8 .. 16.1 1.0 11.7 .. 14.2 12.2 32.4 .. .. 14.5 .. 56.6 .. 14.3 .. .. 18.2 11.7 14.7 .. 22.3 .. 3.0 30.0 8.7 22.2 29.4 13.8 .. .. 27.7 7.2 10.2 10.2 19.0 .. 12.2 13.7 16.0 21.5 ..

.. 20.5 10.2 12.5 7.9 .. 25.0 29.3 21.3 27.3 .. 24.2 20.6 16.9 .. 15.8 1.0 .. .. 15.3 16.1 41.7 .. .. 17.5 .. 54.4 .. 17.6 .. .. 18.1 .. 18.9 22.6 22.6 .. .. 25.9 10.1 23.2 31.8 16.6 .. .. 30.4 .. 9.5 .. .. 12.1 13.5 13.0 16.5 21.6 ..

Taxes payable by businesses

Number of payments Fiscal year 2006

48 24 59 52 28 13 8 33 15 72 15 26 34 17 .. 27 14 89 31 8 21 21 .. .. 13 54 25 29 35 60 61 7 49 44 42 28 36 .. 34 35 22 9 64 44 35 3 14 47 59 44 33 53 59 43 7 17

STATES AND MARKETS

5.6

Tax policies

Highest marginal tax ratea

Time to prepare, file, and pay taxes hours Fiscal year 2006

Total tax rate % of profi t Fiscal year 2006

2006

2006

2006

424 304 264 576 292 312 76 225 360 414 350 101 156 432 .. 290 118 204 180 320 208 352 .. .. 162 96 304 878 190 270 696 158 552 250 204 468 230 .. .. 408 250 70 240 270 1,120 87 52 560 560 198 328 424 94 175 328 140

51.4 59.3 81.1 37.2 46.4 38.7 25.8 39.1 76.0 52.3 52.8 31.9 45.0 74.2 .. 30.9 55.7 67.4 32.5 42.6 37.3 25.6 .. .. 48.4 43.5 43.2 32.6 35.2 50.0 104.3 24.8 37.1 48.8 32.2 52.7 39.2 .. 25.6 32.8 48.1 36.5 66.4 46.0 31.4 46.1 20.2 43.4 52.4 44.3 43.2 40.8 53.0 38.4 47.0 40.9

25 36 30 35 35 .. 42 49 43 25 37 .. 20 30 .. 35 0 .. .. 25 .. .. .. .. 33 24 .. .. 28 .. .. 30 29 20 .. .. 32 .. 35 .. 52 39 30 .. .. .. 0 35 30 .. 10 30 32 40 42 33

26,553 7,766 5,669 20,608 114,101 .. 40,000 94,530 125,000 1,993 163,310 .. 55,810 5,841 .. 78,116 .. .. .. .. .. .. .. .. .. 14,610 .. .. 65,963 .. .. 16,949 9,470 1,667 .. .. 43,710 .. 31,447 .. 65,285 42,254 29,886 .. .. .. .. 11,763 200,000 .. .. .. 9,076 22,854 75,000 50,000

25 16 34 30 25 .. 13 31 33 33 30 .. 30 30 .. 25 0 .. .. 15 .. .. .. .. 15 15 .. .. 28 .. .. 25 29 15 .. .. 32 .. 35 .. 30 33 30 .. .. 28 12 37 30 .. .. 30 35 19 25 20

%

Individual On income over $

2007 World Development Indicators

Corporate %

285

5.6

Tax policies Tax revenue collected by central government

% of GDP

Romania Russian Federation Rwandab Saudi Arabia Senegalb Serbia and Montenegrob Sierra Leoneb Singaporeb Slovak Republic Sloveniab Somalia South Africa Spain Sri Lankab Sudanb Swazilandb Sweden Switzerlandb Syrian Arab Republicb Tajikistanb Tanzania Thailand Togob Trinidad and Tobagob Tunisiab Turkey b Turkmenistan Ugandab Ukraineb United Arab Emiratesb United Kingdom United States Uruguay b Uzbekistan Venezuela, RBb Vietnamb 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 Europe EMU

2000

2005

11.7 13.6 .. .. 17.3 23.0 10.2 15.4 .. 21.2 .. 24.0 16.2 14.5 6.4 .. 19.7 11.3 17.4 7.7 .. .. .. 22.1 21.3 22.1 .. 10.9 14.1 1.7 29.0 12.7 16.7 .. 13.3 .. .. 9.4 18.4 .. 15.8 w 9.8 12.5 9.7 .. 12.0 7.7 15.5 11.8 15.7 9.3 .. 16.6 19.2

.. 16.6 .. .. .. .. 11.0 12.4 15.1 21.4 .. 27.5 12.6 14.3 7.0 c 26.0 20.8 .. .. 9.8 .. 17.1 13.3 24.0 21.3 .. .. 11.9 17.8 .. 28.3 11.2 18.5 .. 16.1 .. .. .. .. .. 16.1 w 10.6 12.8 10.7 .. 12.4 9.8 17.3 .. .. 10.1 .. 16.0 18.1

Taxes payable by businesses

Number of payments Fiscal year 2006

89 70 43 14 59 41d 20 16 30 34 .. 23 7 61 66 34 5 13 21 55 48 46 51 28 45 18 .. 31 98 15 7 10 41 130 68 32 50 32 37 59 35 u 43 38 43 30 40 32 48 42 33 30 41 17 19

Highest marginal tax ratea

Time to prepare, file, and pay taxes hours Fiscal year 2006

Total tax rate % of profi t Fiscal year 2006

198 256 168 75 696 168d 399 30 344 272 .. 350 602 256 180 104 122 68 336 224 248 104 270 114 268 254 .. 237 2,185 12 105 325 300 152 864 1,050 154 248 131 216 334 u 331 378 442 280 361 273 448 437 276 305 336 220 250

48.9 54.2 41.1 14.9 47.7 38.9d 277 28.8 48.9 39.4 .. 38.3 59.1 74.9 37.1 39.5 57.0 24.9 35.5 87.0 45.0 40.2 48.3 37.2 58.8 46.3 .. 32.2 60.3 15.0 35.4 46.0 27.6 122.3 51.9 41.6 31.5 48.0 22.2 37.0 54.0 u 70.5 48.6 50.0 46.5 56.4 43.9 58.2 49.4 43.8 45.1 71.2 43.8 56.0

%

Individual On income over $

Corporate %

2006

2006

2006

16 13 .. 0 0 10 .. 21 19 50 .. 40 29 35 .. 33 25 .. .. .. 30 37 .. 25 .. 35 .. 30 13 0 40 35 0 29 34 40 .. .. 30 45

.. .. .. .. .. .. .. 192,771 .. .. .. 47,170 58,524 4,975 .. 11,792 61,673 .. .. .. 5,740 99,453 .. .. .. .. .. 2,763 .. .. 60,545 326,450 .. 960 93,767 5,044 .. .. 368 26,249

16 24 .. 0 .. 10 .. 20 19 25 .. 29 35 35 .. 30 28 9 .. .. 30 30 .. 25 .. 30 .. 30 25 .. 30 35 30 12 34 28 .. .. 35 30

a. These data are from PriceWaterhouseCoopers’ Worldwide Tax Summaries online b. Data on central government taxes were reported on a cash basis and have been adjusted to the accrual framework of the Government Finance Statistics Manual 2001. c. World Bank staff estimate. d. Data are for Serbia only.

286

2007 World Development Indicators

About the data

5.6

STATES AND MARKETS

Tax policies Definitions

Taxes are the main source of revenue for most

activities, and they have a certain level of start-up

• Tax revenue collected by central government

governments. The sources of tax revenue and their

capital, employees, and turnover. For details about

refers to compulsory transfers to the central gov-

relative contributions are determined by government

the assumptions, see Doing Business 2007.

ernment for public purposes. Certain compulsory

policy choices about where and how to impose taxes

A potentially important influence on both domestic

transfers such as fines, penalties, and most social

and by changes in the structure of the economy. Tax

and international investors is a tax system’s progres-

security contributions are excluded. Refunds and

policy may refl ect concerns about distributional

sivity, as reflected in the highest marginal tax rate

corrections of erroneously collected tax revenue are

effects, economic efficiency (including corrections

levied at the national level on individual and corpo-

treated as negative revenue. The analytic framework

for externalities), and the practical problems of

rate income. Figures for individual marginal tax rates

of the International Monetary Fund’s (IMF) Govern-

administering a tax system. There is no ideal level

generally refer to employment income. In some coun-

ment Finance Statistics Manual 2001 (GFSM 2001)

of taxation. But taxes influence incentives and thus

tries the highest marginal tax rate is also the basic

is based on accrual accounting and balance sheets.

the behavior of economic actors and the economy’s

or flat rate, and other surtaxes, deductions, and the

For countries still reporting government finance data

competitiveness.

like may apply. And in many countries several differ-

on a cash basis, the IMF adjusts reported data to the

Taxes are compulsory transfers to governments

ent corporate tax rates may be levied, depending on

GFSM 2001 accrual framework. These countries are

from individuals, businesses, or institutions. Certain

the type of business (mining, banking, insurance,

footnoted in the table. • Number of tax payments

compulsory transfers, such as fines, penalties, and

agriculture, manufacturing), ownership (domestic

by businesses is the total number of taxes paid by

most social security contributions are excluded from

or foreign), volume of sales, or whether surtaxes or

businesses during one year. When electronic filing is

tax revenue.

exemptions are included. The corporate tax rates in

available, the tax is counted as paid once a year even

The level of taxation is typically measured by tax

the table are mainly general rates applied to domes-

if payments are more frequent. • Time to prepare,

revenue as a share of gross domestic product (GDP).

tic companies. For more detailed information, see

file, and pay taxes is the time, in hours per year, it

Comparing levels of taxation across countries pro-

the country’s laws, regulations, and tax treaties.

takes to prepare, file, and pay (or withhold) three

vides a quick overview of the fiscal obligations and

major types of taxes: the corporate income tax, the

incentives facing the private sector. The table shows

value-added or sales tax, and labor taxes, includ-

only central government data, which may significantly

ing payroll taxes and social security contributions.

understate the total tax burden, particularly in coun-

• Total tax rate is the total amount of taxes payable

tries where provincial and municipal governments are

by businesses (except for labor taxes) after account-

large or have considerable tax authority.

ing for deductions and exemptions as a percentage

Low ratios of tax revenue to GDP may reflect weak

of profit. For further details on the method used for

administration and large-scale tax avoidance or eva-

assessing the total tax payable, see Doing Business

sion. Low ratios may also reflect a sizable parallel

2007. • Highest marginal tax rate is the highest rate

economy with unrecorded and undisclosed incomes.

shown on the national level schedule of tax rates

Tax revenue ratios tend to rise with income, with

applied to the annual taxable income of individuals

higher income countries relying on taxes to finance

and corporations. Also presented are the income lev-

a much broader range of social services and social

els for individuals above which the highest marginal

security than lower income countries are able to.

tax rates levied at the national level apply.

The new indicators covering taxes payable by businesses go beyond the usual measures of tax rates, which capture only part of the taxpayer burden. In some countries tax systems are so complex that businesses must make more than 100 payments and spend up to 2,600 hours a year to prepare and pay taxes. Taxes are measured at all levels of government and include corporate income tax, personal income tax

Data sources

withheld by businesses, value-added or sales taxes,

Data on central government tax revenues are from

property transfer taxes, financial transactions taxes,

print and electronic editions of the IMF’s Govern-

dividend taxes, waste collection taxes, and vehicle

ment Finance Statistics Yearbook. Data on taxes

and road taxes. To make the data comparable across

payable by businesses are from Doing Business

countries, several assumptions are made about

2007 (www.doingbusiness.org). Data on individ-

the business. The main assumptions are that they

ual and corporate tax rates are from Pricewater-

are limited liability companies, they operate in the

houseCoopers’s Worldwide Tax Summaries online

country’s most populous city, they are domestically

(www.pwc.com).

owned, they perform general industrial or commercial

2007 World Development Indicators

287

5.7

Defense expenditures and arms transfers Military expenditures

% 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 CentralAfrican Republic Chad Chile China Hong Kong, 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

288

Armed forces personnel

% of central government expenditure

thousands

% of labor force

1995

2005

1995

2005

1995

2005

1995

.. 2.1 3.0 8.1 1.6 4.1 1.9 0.9 2.3 1.4 1.6 1.6 .. 1.9 .. 3.5 2.1 2.6 1.5 4.2 5.4 1.4 1.6 1.2 1.4 3.1 1.7a .. 2.6 1.5 .. .. 0.8 9.4 .. 1.7 1.7 0.6 2.4 3.5 1.0 20.8 1.0 1.6 1.5 3.0 .. 0.8 2.2 1.6 0.8 4.2 1.0 1.4 0.9 0.1

.. 1.4 2.8 5.0 1.0 2.7 1.8 0.7 2.1 1.1 1.2 1.2 .. 1.9 1.8 2.5 1.6 2.4 1.5 0.0 1.8 1.3 1.1 1.1 0.9 3.8 2.0a .. 3.7 2.1 1.4 .. 1.6 1.6 .. 1.8 1.4 0.6 2.4 2.8 0.6 19.3 1.6 3.1 1.2 2.5 1.4 0.3 3.1 1.4 0.7 4.5 0.4 .. .. ..

.. 8.2 12.2 .. .. .. .. 2.0 11.7 .. 5.5 3.4 .. .. .. 11.4 4.8 6.6 .. 17.8 .. 11.8 6.4 .. .. .. ..a .. .. 13.5 .. .. .. 22.2 .. 5.2 .. 5.6 9.2 14.7 .. .. .. .. .. 6.2 .. .. 8.2 4.2 .. 8.8 13.1 .. .. ..

.. 5.7 .. .. 5.9 15.0 7.2 1.7 .. 13.6 4.2 2.9 .. 7.1 4.8 .. .. 7.0 12.6 .. 23.1 .. 6.3 12.3 .. 20.2 18.2a .. 11.9 .. 6.9 .. 8.9 4.0 .. 5.0 4.2 3.3 .. .. 3.5 .. 6.2 .. 3.3 5.4 .. .. 18.1 4.3 3.8 10.1 3.8 .. .. ..

383 87 163 122 99 61 57 56 127 171 106 47 7 64 92 9 681 136 10 15 309 24 76 5 35 130 4,130 .. 233 65 17 16 15 150 124 92 33 40 57 610 39 55 6 120 35 502 10 1 14 365 13 202 57 19 9 7

27 23 319 118 102 49 53 40 82 252 183 37 8 70 12 11 673 85 11 82 191 23 71 3 35 116 3,755 .. 336 65 12 0 19 31 76 28 21 40 47 799 16 202 8 183 31 359 7 1 23 285 7 168 48 13 9 0

5.5 6.0 1.8 2.3 0.7 4.2 0.6 1.4 3.8 0.3 2.1 1.1 0.3 2.2 5.2 1.4 0.9 3.5 0.2 0.5 6.2 0.5 0.5 0.3 1.3 2.3 0.6 .. 1.4 0.4 1.4 1.2 0.3 7.2 2.5 1.8 1.2 1.3 1.3 3.5 1.8 4.4 0.8 0.5 1.4 2.0 2.0 0.2 0.5 0.9 0.2 4.5 1.8 0.5 1.9 0.2

2007 World Development Indicators

Arms transfers

2005

0.3 1.7 2.4 1.7 0.6 3.8 0.5 1.0 2.0 0.4 3.8 0.8 0.2 1.7 0.6 1.8 0.7 2.7 0.2 2.1 2.8 0.4 0.4 0.2 1.0 1.8 0.5 .. 1.5 0.3 0.8 0.0 0.3 1.6 1.4 0.5 0.7 1.0 0.7 3.5 0.6 11.3 1.2 0.6 1.2 1.3 1.2 0.1 1.0 0.7 0.1 3.3 1.2 0.3 1.4 0.0

$ millions 1990 prices Exports

Imports

1995

2005

1995

2005

0 .. .. 0 3 .. 28 0 .. .. 8 299 .. .. 0 .. 28 2 .. .. 0 .. 369 .. 0 0 962 .. .. .. .. .. .. 0 .. 122 8 .. .. 7 0 0 0 0 20 681 .. .. 0 1,430 .. 0 .. .. .. ..

.. .. .. 0 0 .. 50 3 .. .. 0 173 .. .. 0 .. 62 0 .. .. 0 .. 365 .. 0 0 129 .. .. .. .. .. .. 0 .. 10 2 .. .. 0 .. 0 0 0 22 2,399 .. .. 0 1,855 .. 0 .. .. .. ..

0 24 346 1 70 49 147 23 0 121 0 16 0 1 0 7 237 0 0 0 0 0 339 0 1 468 523 .. 37 0 0 0 2 22 0 0 127 0 10 1,700 3 3 18 0 159 43 0 0 0 252 0 870 3 0 0 ..

22 31 149 22 67 0 396 21 0 27 0 0 0 9 0 0 142 158 19 0 0 0 112 0 0 456 2,697 .. 11 14 0 0 0 0 0 630 78 0 33 596 0 276 10 0 77 3 0 0 0 216 0 1,114 0 0 0 ..

Military expenditures

% of GDP 1995

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

.. 1.6 2.7 1.6 2.4 .. 1.0 9.0 1.7 0.6 1.0 12.4 1.1 1.6 .. 2.8 13.6 1.6 2.9 0.9 6.4 3.7 31.2 4.1 0.5 3.0 0.9 0.8 2.8 2.2 2.0 0.4 0.6 0.9 1.7 4.6 1.5 3.7 1.9 0.9 1.9 1.4 1.1 1.0 0.7 2.4 14.6 6.0 1.2 1.0 1.4 1.9 1.4 2.0 2.4 ..

Armed forces personnel

% of central government expenditure

thousands

2005

1995

2005

1995

2005

0.6 1.3 2.9 0.9 4.5 .. 0.6 7.9 1.8 0.7 1.0 7.7 1.1 1.5 .. 2.6 5.7 2.8 .. 1.7 3.8 2.4 .. 1.9 1.8 2.2 .. 0.7 1.9 1.9 1.0 0.2 0.4 0.3 1.7 4.3 1.4 .. 3.0 2.0 1.6 1.0 0.7 1.1 0.9 1.6 12.2 3.4 .. 0.5 0.8 1.2 0.8 1.8 2.1 ..

.. .. 18.4 16.2 15.2 .. 2.7 .. 3.6 1.7 .. 47.5 5.7 6.4 .. 19.4 .. 6.1 .. 3.1 .. 10.7 .. .. .. .. .. .. 16.0 .. .. 1.8 3.8 2.4 .. .. .. .. .. .. 3.8 .. 6.8 .. .. .. 45.2 31.4 5.6 3.9 .. 10.7 8.5 .. 5.7 ..

.. 3.1 18.6 6.5 21.7 .. 1.9 17.0 4.5 2.1 .. 21.7 5.8 7.6 .. 12.1 21.9 .. .. 5.8 14.4 6.8 .. .. 6.4 .. .. .. 13.8 .. .. 1.0 .. 1.0 6.2 13.7 .. .. 8.5 11.8 4.0 3.1 3.3 .. .. 4.8 .. 23.1 .. .. 4.5 7.2 4.5 4.9 5.1 ..

24 73 2,150 461 763 407 13 178 585 4 252 129 75 29 1,243 641 22 7 137 11 63 2 21 81 9 18 29 10 140 15 21 2 189 15 31 238 12 371 8 63 78 10 12 11 89 31 48 846 12 4 28 178 149 302 104 ..

20 44 3,047 582 585 227 10 176 445 3 272 111 101 29 1,295 693 23 18 129 5 85 2 15 76 29 19 22 7 135 12 21 2 204 10 16 251 11 483 15 131 60 9 14 10 161 47 46 921 12 3 25 157 147 162 93 ..

Arms transfers

% of labor force 1995

1.2 1.7 0.6 0.5 4.4 7.0 0.9 8.5 2.6 0.3 0.4 10.2 1.0 0.2 12.4 3.0 2.5 0.4 7.7 0.9 5.5 0.3 2.7 5.2 0.5 2.2 0.5 0.2 1.7 0.4 2.3 0.4 0.5 0.8 3.3 2.7 0.2 1.7 1.5 0.8 1.0 0.6 0.8 0.3 0.2 1.4 6.2 2.2 1.1 0.2 1.4 1.8 0.5 1.7 2.1 ..

5.7

STATES AND MARKETS

Defense expenditures and arms transfers

2005

0.6 1.0 0.7 0.5 2.1 2.7 0.5 6.4 1.8 0.3 0.4 6.0 1.2 0.2 12.1 2.8 1.7 0.8 5.5 0.5 6.0 0.3 1.3 3.3 1.8 2.2 0.3 0.1 1.2 0.2 1.7 0.4 0.5 0.5 1.3 2.3 0.1 1.8 2.3 1.2 0.7 0.4 0.7 0.2 0.3 1.9 4.8 1.6 0.8 0.1 0.9 1.2 0.4 0.9 1.7 ..

$ millions 1990 prices Exports 1995

.. 6 2 25 1 0 0 110 340 .. 16 0 24 .. 52 21 0 61 .. 0 0 .. .. 0 0 0 .. 0 0 .. .. .. .. 0 .. .. .. .. .. .. 383 0 5 .. 0 22 0 0 .. .. .. 0 .. 176 0 ..

Imports

2005

.. 70 0 8 0 0 .. 160 827 .. 0 15 0 .. 0 38 0 0 .. 0 0 .. .. 0 0 29 .. 0 0 .. .. .. .. 4 .. .. .. .. .. .. 840 0 0 .. 0 13 0 9 .. .. .. 0 .. 124 0 ..

1995

2005

0 24 943 339 373 0 0 265 315 0 877 19 99 0 68 1,674 631 0 0 16 34 0 0 0 4 0 0 0 898 0 1 0 45 6 0 30 0 216 4 1 46 7 0 0 2 83 157 .. 0 0 0 32 36 125 18 ..

0 12 1,471 19 403 290 4 1,422 224 0 250 23 68 25 2 544 55 3 0 7 1 0 0 0 9 0 0 0 467 0 0 0 35 0 0 32 0 20 0 0 129 8 0 0 0 9 98 .. 0 0 1 368 38 96 406 ..

2007 World Development Indicators

289

5.7

Defense expenditures and arms transfers Military expenditures

% of central government expenditure

% of GDP 1995

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 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 Europe EMU

2.8 4.4 4.4 9.3 1.8 5.3 2.9 4.4 3.2 1.6 .. 2.2 1.4 5.3 2.7 2.4 2.3 1.3 7.1 1.0 1.5 2.3 2.4 0.5 1.9 3.9 2.3 2.2 2.8 5.2 3.0 3.8 2.1 1.1 1.6 2.6 .. 6.4 2.2 3.6 2.5 w 2.7 2.3 2.1 2.7 2.4 1.8 3.4 1.7 4.1 3.0 2.1 2.5 2.0

Armed forces personnel

2005

2.1 3.7 2.2 8.2 1.5 2.7 1.1 4.7 1.8 1.7 .. 1.4 1.0 2.7 2.3 .. 1.6 1.0 6.2 2.2 1.1 1.1 1.5 .. 1.5 3.2 .. 2.5 2.4 1.9 2.6 4.1 1.4 0.5 1.1 .. .. 5.6 .. 3.4 2.5 w 2.6 2.0 2.0 2.0 2.1 1.8 2.7 1.3 3.7 2.8 1.6 2.6 1.7

1995

2005

.. .. .. .. .. .. .. 35.1 .. 4.7 .. .. 3.9 20.3 .. .. .. 5.2 .. .. .. .. .. 1.8 6.7 20.4 .. .. .. 49.2 .. .. 7.9 .. 8.7 .. .. 33.4 .. 11.2 .. w 19.6 .. .. .. .. .. .. 5.1 18.1 20.4 .. .. 3.9

.. 18.8 .. .. .. .. 5.1 30.5 5.1 4.0 .. 4.8 4.2 12.7 .. .. 4.3 .. .. 15.8 .. 7.0 9.8 .. 5.1 .. .. 11.1 6.5 .. 6.3 19.3 5.0 .. 4.4 .. .. .. .. .. 11.1 w 18.5 13.2 15.4 .. 13.8 16.5 11.2 .. .. 19.0 .. 10.6 4.5

thousands

Arms transfers

% of labor force

1995

2005

1995

297 1,800 47 178 17 165 7 66 51 13 225 277 282 236 134 3 100 31 531 18 36 421 8 7 59 690 11 52 519 71 233 1,636 27 42 80 622 .. 70 23 68 30,182 s 7,698 16,128 11,456 4,672 23,826 8,021 4,971 2,112 3,172 3,852 1,698 6,356 2,270

177 1,452 53 216 19 110 13 167 20 12 0 56 220 200 123 .. 29 109 416 13 28 421 10 3 47 617 26 47 273 51 217 1,546 25 91 82 495 56 138 16 51 30,898 s 8,536 16,527 12,989 3,538 25,063 10,125 3,581 2,076 3,169 4,578 1,534 5,836 1,750

2.4 2.5 2.0 3.0 0.5 3.5 0.4 3.7 2.1 1.3 8.3 1.7 1.7 3.3 1.6 1.1 2.2 0.8 11.2 0.9 0.2 1.3 0.4 1.3 2.1 3.0 0.7 0.6 2.0 5.5 0.8 1.2 1.8 0.5 0.9 1.8 .. 1.8 0.6 1.4 1.2 w 1.0 1.2 1.0 1.9 1.1 0.9 2.3 1.1 4.2 0.8 0.7 1.3 1.7

2005

1.7 2.0 1.3 2.7 0.4 2.8 0.6 7.5 0.7 1.2 0.0 0.3 1.1 2.4 1.2 .. 0.6 2.6 5.5 0.6 0.1 1.2 0.4 0.5 1.2 2.3 1.2 0.4 1.2 1.9 0.7 1.0 1.4 0.8 0.6 1.1 7.3 2.3 0.3 0.9 0.9 w 0.9 0.9 0.8 1.3 0.9 0.7 1.7 0.8 2.9 0.8 0.5 1.1 1.2

$ millions 1990 prices Exports 1995

2005

Imports 1995

2005

6 17 0 579 3,273 5,771 40 0 .. .. 0 0 0 36 975 470 .. .. 2 0 0 0 18 0 .. .. 15 0 0 3 237 423 91 0 220 0 .. .. 19 2 .. .. 0 0 15 39 38 606 82 113 363 281 .. .. 49 8 .. .. 3 0 .. .. 0 0 184 592 95 104 38 74 93 144 0 0 43 0 .. .. 0 0 .. .. 0 0 0 0 558 98 .. .. 3 0 .. .. 0 0 .. .. 42 156 0 28 1,562 746 .. .. 0 0 .. .. 38 0 242 188 0 29 27 10 426 2,381 1,206 791 633 94 10,689 7,101 415 387 0 0 8 18 0 0 0 0 0 0 0 7 .. .. 270 291 .. .. 1 0 .. .. 124 289 0 0 0 0 .. .. 0 0 21,064 s 21,941 s 20,951 s 21,804 s 115 9 1,813 2,459 4,996 6,465 8,354 9,196 1,304 406 4,598 5,352 3,692 6,059 3,756 3,844 5,111 6,474 10,167 11,655 1,039 137 2,920 3,632 4,011 6,212 2,227 2,349 36 62 914 1,147 8 15 2,872 2,037 2 9 1,114 1,528 15 39 120 962 15,953 15,467 10,584 10,149 3,235 6,232 2,105 2,475

Note: For some countries data are partial or uncertain or based on rough estimates; see SIPRI (2006). a. Estimates differ from official statistics of the govenment of China, which has published the following estimates: military expenditure as 1.1 percent of GDP in 1995 and 1.6 percent in 2004 and 9.3 percent of central government expenditure in 1995 and 7.7 percent in 2004 (see National Bureau of Statistics of China, www.stats.gov.cn).

290

2007 World Development Indicators

About the data

Although national defense is an important function of government and security from external threats contributes to economic development, high levels of defense spending burden the economy and may impede growth. Data on military expenditures as a share of gross domestic product (GDP) are a rough indicator of the portion of national resources used for military activities and of the burden on the national economy. Comparisons of defense spending between countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic. As an “input” measure, military spending is not directly related to the “output” of military activities, capabilities, or military security. Data on defense spending reported by governments are not compiled using standard definitions. They are often incomplete and unreliable. Even in countries where the parliament vigilantly reviews budgets and spending, defense spending and arms transfers rarely receive close scrutiny and full, public disclosure (see Ball 1984 and Happe and Wakeman-Linn 1994). The data on military expenditures as a share of GDP and a share of central government expenditure are estimated by the Stockholm International Peace Research Institute (SIPRI). Central government expenditures are from the International Monetary Fund (IMF). Therefore the data shown in the table may differ from comparable data published by national governments. SIPRI’s primary source of military expenditure data is official data provided by national governments. These data are derived from national budget documents, defense white papers, and other public documents from official government agencies, including governments’ responses to questionnaires sent by SIPRI, the United Nations, or the Organization for Security and Co-operation in Europe. Secondary sources include international statistics, such as those of the North Atlantic Treaty Organization (NATO) and the IMF’s Government Finance Statistics Yearbook. Other secondary sources include country reports of the Economist Intelligence Unit, country reports by IMF staff, and specialist journals and newspapers. Lack of sufficiently detailed data makes it difficult to apply a common definition of military expenditure globally, so SIPRI has adopted a definition (derived from the NATO definition) as a guideline (see Definitions). This definition cannot be applied for all countries, however, since that would require much more detailed information than is available about what is included in military budgets and off-budget military expenditure items. In the many cases where SIPRI cannot make independent estimates, it uses the national data provided. Because of the differences in definitions and the difficulty in verifying the accuracy and completeness of data, the data on military spending are not strictly comparable across countries. The data on armed forces are from the International Institute for Strategic Studies’ The Military

5.7

STATES AND MARKETS

Defense expenditures and arms transfers Definitions

Balance 2007. These data refer to military personnel on active duty, including paramilitary forces. Reserve forces, which are units that are not fully staffed or operational in peace time, are not included. These data also exclude civilians in the defense establishment and so are not consistent with the data on military spending on personnel. Moreover, because data exclude personnel not on active duty, they underestimate the share of the labor force working for the defense establishment. Because governments rarely report the size of their armed forces, such data typically come from intelligence sources. The data on arms transfers are from SIPRI’s Arms Transfers Project, which reports on international flows of conventional weapons. Data are collected from open sources, and since publicly available information is inadequate for tracking all weapons and other military equipment, SIPRI covers only what it terms major conventional weapons. SIPRI’s data on arms transfers cover sales of weapons, manufacturing licenses, aid, and gifts; therefore the term arms transfers rather than arms trade is used. The transferred weapons must be transferred voluntarily by the supplier, must have a military purpose, and must be destined for the armed forces, paramilitary forces, or intelligence agencies of another country. SIPRI data also cover weapons supplied to or from rebel forces in an armed conflict as well as arms deliveries for which neither the supplier nor the recipient can be identified with an acceptable degree of certainty; these data are available in SIPRI’s database. SIPRI’s estimates of arms transfers, presented in 1990 constant price U.S. dollars, are designed as a trend-measuring device in which similar weapons have similar values, reflecting both the value and quality of weapons transferred. The trends presented in the tables are based on actual deliveries only. SIPRI cautions that these estimated values do not reflect financial value (payments for weapons transferred) for three reasons: reliable data on the value of the transfer are not available; even when the value of a transfer is known, it usually includes more than the actual conventional weapons such as spares, support systems, and training; and even when the value of the transfer is known, details of the financial arrangements such as credit and loan conditions and discounts are usually not known. Given these measurement issues, SIPRI’s method of estimating the transfer of military resources includes an evaluation of the technical parameters of the weapons. Weapons for which a price is not known are compared with the same weapons for which actual acquisition prices are available (“core weapons”) or for the closest match. These weapons are assigned a value in an index that reflects their military resource value in relation to the core weapons. These matches are based on such characteristics as size, performance, and type of electronics, and adjustments are made for second-hand weapons. More information on SIPRI’s arms transfers project is available at www.sipri.org/contents/armstrad/.

• Military expenditures data from SIPRI are derived from the NATO definition, which includes all current and capital expenditures on the armed forces, including peacekeeping forces; defense ministries and other government agencies engaged in defense projects; paramilitary forces, if these are judged to be trained and equipped for military operations; and military space activities. Such expenditures include military and civil personnel, including retirement pensions of military personnel and social services for personnel; operation and maintenance; procurement; military research and development; and military aid (in the military expenditures of the donor country). Excluded are civil defense and current expenditures for previous military activities, such as for veterans’ benefits, demobilization, conversion, and destruction of weapons. This definition cannot be applied for all countries, however, since that would require much more detailed information than is available about what is included in military budgets and off-budget military expenditure items. (For example, military budgets might or might not cover civil defense, reserves and auxiliary forces, police and paramilitary forces, dual-purpose forces such as military and civilian police, military grants in kind, pensions for military personnel, and social security contributions paid by one part of government to another.) • Armed forces personnel are active duty military personnel, including paramilitary forces if the training, organization, equipment, and control suggest they may be used to support or replace regular military forces. • Arms transfers cover the supply of military weapons through sales, aid, gifts, and those made through manufacturing licenses. Data cover major conventional weapons such as aircraft, armored vehicles, artillery, radar systems, missiles, and ships designed for military use. Excluded are transfers of other military equipment such as small arms and light weapons, trucks, small artillery, ammunition, support equipment, technology transfers, and other services. See About the data for more detail.

Data sources Data on military expenditures and arms transfers are from SIPRI’s Yearbook 2006: Armaments, Disarmament, and International Security. Data on armed forces personnel are from the International Institute for Strategic Studies’ The Military Balance 2007.

2007 World Development Indicators

291

5.8

Public policies and institutions IDA Resource Allocation Index

Economic management

Structural policies

1 (low) to 6 (high)

1 (low) to 6 (high)

1 (low) to 6 (high)

Albania 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 Indonesia Kenya Kiribati Kyrgyz Republic

Macroeconomic management

Fiscal policy

Debt policy

Average

2005

2005

2005

2005

2005

3.7 2.6 4.3 3.7 3.4 3.7 3.8 3.7 3.6 3.8 3.0 3.1 3.3 4.1 2.4 2.9 2.4 2.8 2.8 2.5 3.1 3.8 2.5 3.4 3.1 3.8 3.9 3.7 3.0 2.7 3.4 2.8 3.9 3.8 3.7 3.6 3.2 3.5

4.5 3.0 5.5 4.5 4.0 4.5 4.0 4.0 4.0 4.5 3.5 4.0 4.0 4.5 3.0 4.0 3.0 3.5 3.5 2.5 3.5 4.0 2.0 3.5 3.5 4.5 4.0 4.0 2.5 2.5 3.5 3.5 4.5 4.5 4.5 4.5 2.5 4.5

3.5 2.5 5.0 4.5 3.5 4.0 4.0 4.0 3.5 4.5 3.5 3.5 3.5 4.0 3.0 3.0 2.5 3.5 3.0 2.0 3.0 4.0 2.0 4.0 3.0 4.0 4.5 3.0 3.0 2.5 3.5 3.0 4.5 3.0 4.0 4.0 2.5 3.5

4.0 2.0 5.5 4.5 4.5 3.5 4.0 4.0 4.0 4.5 3.0 3.5 2.5 4.0 1.5 3.0 1.5 2.5 2.5 1.5 3.0 3.0 2.5 3.5 2.5 4.0 4.0 2.5 2.5 2.0 3.5 2.5 4.0 4.5 4.5 4.0 5.0 4.0

4.0 2.5 5.3 4.5 4.0 4.0 4.0 4.0 3.8 4.5 3.3 3.7 3.3 4.2 2.5 3.3 2.3 3.2 3.0 2.0 3.2 3.7 2.2 3.7 3.0 4.2 4.2 3.2 2.7 2.3 3.5 3.0 4.3 4.0 4.3 4.2 3.3 4.0

Trade

Financial sector

Business regulatory environment

Average

2005

2005

2005

2005

4.5 4.0 4.5 4.0 3.0 4.5 3.0 5.0 4.0 4.0 3.0 3.5 3.5 4.0 3.5 3.0 2.0 4.0 3.0 3.5 4.0 4.0 1.5 3.0 4.0 3.5 4.0 4.0 4.5 3.5 4.0 4.0 4.5 3.5 4.5 4.0 3.0 4.5

4.0 2.5 3.5 3.0 3.0 3.5 3.0 3.5 4.0 3.0 3.0 2.0 3.0 4.0 2.5 3.0 2.5 2.0 2.5 3.0 3.5 4.0 2.0 3.0 3.0 3.5 3.5 3.5 3.0 2.5 3.5 3.0 3.5 4.0 3.5 3.5 3.0 3.5

3.5 2.0 4.0 3.5 3.5 4.0 3.5 3.0 3.5 3.0 2.5 3.5 3.5 4.0 2.0 3.0 2.5 3.0 2.5 3.0 3.0 4.5 2.0 3.5 3.0 4.0 4.0 4.5 3.0 3.0 3.0 2.5 4.0 3.5 3.0 4.0 3.0 3.5

4.0 2.8 4.0 3.5 3.2 4.0 3.2 3.8 3.8 3.3 2.8 3.0 3.3 4.0 2.7 3.0 2.3 3.0 2.7 3.2 3.5 4.2 1.8 3.2 3.3 3.7 3.8 4.0 3.5 3.0 3.5 3.2 4.0 3.7 3.7 3.8 3.0 3.8

About the data The International Development Association (IDA)

mid-1970s. Over time the criteria have been revised

The CPIA exercise is intended to capture the qual-

is the part of the World Bank Group that helps the

from a largely macroeconomic focus to include gov-

ity of a country’s policies and institutional arrange-

poorest countries reduce poverty by providing con-

ernance aspects and a broader coverage of social

ments, focusing on key elements that are within the

cessional loans and grants for programs aimed at

and structural dimensions. Country performance is

country’s control, rather than on outcomes (such

boosting economic growth and improving living con-

assessed against a set of 16 criteria grouped into four

as economic growth rates) that are influenced by

ditions. IDA funding helps these countries deal with

clusters: economic management, structural policies,

events beyond the country’s control. More spe-

the complex challenges they face in striving to meet

policies for social inclusion and equity, and public sec-

cifi cally, the CPIA measures the extent to which a

the Millennium Development Goals.

tor management and institutions. IDA resources are

country’s policy and institutional framework sup-

The World Bank’s IDA Resource Allocation Index

allocated to a county on per capita terms on the basis

ports sustainable growth and poverty reduction

(IRAI), which is presented in the table, is based on the

of its IDA country performance rating and, to a limited

and, consequently, the effective use of develop-

results of the annual Country Policy and Institutional

extent, on the basis of its per capita gross national

ment assistance.

Assessment (CPIA) exercise, which covers the IDA-

income. This ensures that good performers receive a

All criteria within each cluster receive equal weight,

eligible countries. Country assessments have been

higher IDA allocation in per capita terms. The IRAI is a

and each cluster has a 25 percent weight in the

carried out annually by World Bank staff since the

key element in the country performance rating.

overall score, which is obtained by averaging the

292

2007 World Development Indicators

IDA Resource Allocation Index

Economic management

Structural policies

1 (low) to 6 (high)

1 (low) to 6 (high)

STATES AND MARKETS

5.8

Public policies and institutions 1 (low) to 6 (high)

Lao PDR Lesotho Madagascar Malawi Maldives Mali Mauritania Moldova Mongolia Mozambique Nepal Nicaragua Niger Nigeria Pakistan Papua New Guinea Rwanda Samoa São Tomé and Principe Senegal Serbia and Montenegro Sierra Leone Solomon Islands Sri Lanka St. Lucia St. Vincent & Grenadines Sudan Tajikistan Tanzania Togo Tonga Uganda Uzbekistan Vanuatu Vietnam Yemen, Rep. Zambia Zimbabwe

Macroeconomic management

Fiscal policy

Debt policy

Average

2005

2005

2005

2005

2005

3.0 3.5 3.5 3.4 3.8 3.7 3.2 3.5 3.4 3.5 3.3 3.7 3.3 3.1 3.7 3.1 3.5 4.0 3.0 3.8 3.7 3.1 2.8 3.6 4.0 3.9 2.6 3.3 3.9 2.5 2.9 3.9 3.0 3.1 3.7 3.3 3.3 1.8

4.0 4.0 3.5 3.0 3.5 4.5 2.0 3.5 4.0 4.0 4.5 3.5 3.5 4.0 4.5 4.0 4.0 4.0 3.0 4.5 3.5 4.0 3.5 3.5 4.5 4.0 3.5 4.5 5.0 2.5 3.0 4.5 3.0 3.0 5.0 4.0 3.5 1.0

3.5 4.0 3.0 3.0 3.5 4.0 2.5 3.5 3.5 4.0 3.5 4.0 3.0 4.0 3.5 3.0 3.5 3.5 3.0 4.0 4.5 3.5 3.5 3.0 3.5 4.0 3.5 4.0 4.5 2.0 2.0 4.5 3.5 3.0 4.0 3.0 3.5 1.0

3.5 4.0 3.5 3.0 4.5 4.5 4.0 3.0 3.0 4.5 3.5 4.5 3.5 3.5 4.5 3.5 3.0 4.0 2.5 4.0 3.5 3.5 2.5 3.5 4.0 4.0 1.5 4.0 4.0 1.5 3.5 4.5 4.0 4.0 4.0 4.5 3.0 1.0

3.7 4.0 3.3 3.0 3.8 4.3 2.8 3.3 3.5 4.2 3.8 4.0 3.3 3.8 4.2 3.5 3.5 3.8 2.8 4.2 3.8 3.7 3.2 3.3 4.0 4.0 2.8 4.2 4.5 2.0 2.8 4.5 3.5 3.3 4.3 3.8 3.3 1.0

Trade

Financial sector

Business regulatory environment

Average

2005

2005

2005

2005

3.5 3.5 4.0 4.0 4.0 4.0 4.5 3.5 4.5 4.0 4.0 4.5 4.0 2.5 4.0 4.0 3.5 4.5 4.0 4.5 4.5 3.5 3.0 3.5 4.0 4.0 3.0 4.0 4.0 4.0 3.0 4.0 2.5 4.0 3.5 4.5 4.0 2.0

1.5 3.5 3.5 3.0 4.0 3.0 2.5 3.5 3.0 2.5 3.0 3.0 3.0 3.0 4.5 3.0 3.5 4.0 2.5 3.5 3.0 3.0 3.0 4.0 4.0 4.0 2.5 3.0 3.5 2.5 3.0 3.5 2.5 3.0 3.0 2.5 3.0 2.5

3.0 3.0 4.0 3.5 4.0 3.5 3.5 4.0 3.5 3.0 3.0 3.5 3.5 3.0 4.0 3.0 3.5 4.0 3.0 3.5 3.5 2.5 2.5 4.0 4.5 4.5 3.0 3.5 3.5 3.0 3.0 4.0 2.5 3.0 3.5 3.0 3.0 2.0

2.7 3.3 3.8 3.5 4.0 3.5 3.5 3.7 3.7 3.2 3.3 3.7 3.5 2.8 4.2 3.3 3.5 4.2 3.2 3.8 3.7 3.0 2.8 3.8 4.2 4.2 2.8 3.5 3.7 3.2 3.0 3.8 2.5 3.3 3.3 3.3 3.3 2.2

average scores of the four clusters. For each of the

developmentally neutral manner. Accordingly, higher

process involves two key phases. In the bench-

16 criteria countries are rated on a scale of 1 (low)

scores can be attained by a country that, given its

marking phase a small representative sample of

to 6 (high). The scores depend on the level of perfor-

stage of development, has a policy and institutional

countries drawn from all regions is rated. Country

mance in a given year assessed against the criteria,

framework that more strongly fosters growth and

teams prepare proposals that are reviewed first at

rather than on changes in performance compared

poverty reduction.

the regional level and then in a Bankwide review pro-

with the previous year. All 16 CPIA criteria contain a

The country teams that prepare the ratings are

cess. A similar process is then followed to assess

detailed description of each rating level. In assessing

very familiar with the country, and their assess-

the performance of the remaining countries, using

country performance World Bank staff evaluate the

ments are based on country diagnostic studies

the benchmark countries’ scores as guideposts.

country’s actual performance on each of the criteria

prepared by the World Bank or other development

The final ratings are determined following a Bank-

and assign a rating. The ratings reflect a variety of

organizations and on their own professional judg-

wide review. The numerical IRAI overall score and

indicators, observations, and judgments based on

ment. An early consultation is conducted with coun-

the separate criteria scores were first publicly dis-

country knowledge and on relevant publicly available

try authorities to make sure that the assessments

closed in June 2006.

indicators. In interpreting the assessment scores, it

are informed by up-to-date information. To ensure

should be noted that the criteria are designed in a

that scores are consistent across countries, the

See IDA’s website at www.worldbank.org/ida for more information.

2007 World Development Indicators

293

5.8 Albania 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 Indonesia Kenya Kiribati Kyrgyz Republic

Public policies and institutions Policies for social inclusion and equity

Public sector management and institutions

1 (low) to 6 (high)

1 (low) to 6 (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

2005

2005

2005

2005

2005

2005

2005

2005

2005

2005

4.0 3.0 4.5 4.0 4.0 3.0 4.5 3.5 4.0 3.5 3.5 3.5 3.5 4.5 2.5 2.5 3.0 3.0 3.0 2.5 3.0 4.5 3.5 3.0 3.5 4.5 4.0 4.5 4.0 3.0 3.5 3.0 4.0 3.5 3.5 3.0 3.0 4.0

3.5 2.5 4.5 3.5 3.5 3.0 4.0 4.0 3.0 4.0 3.0 3.0 3.0 4.5 2.0 3.0 3.0 3.0 3.0 1.5 3.0 3.5 3.0 4.5 3.0 4.0 4.0 3.5 3.0 3.0 3.5 2.5 4.0 4.0 4.0 3.0 3.5 3.5

3.0 2.5 4.0 3.0 4.0 3.5 4.5 4.0 3.5 3.5 3.0 3.5 3.5 4.0 2.0 3.0 3.0 3.0 3.0 2.0 3.5 4.0 3.5 3.5 3.5 4.0 3.5 4.0 3.0 2.5 3.5 2.5 4.0 4.0 3.5 3.5 2.5 3.5

3.5 2.5 4.5 3.5 3.5 3.0 3.5 3.5 3.5 3.5 3.0 3.0 3.0 4.5 2.0 3.0 2.5 3.0 2.5 2.5 3.0 3.5 3.0 3.5 2.5 3.5 3.5 3.5 3.5 2.5 3.0 2.5 4.0 3.5 3.5 3.0 3.0 3.5

3.0 2.5 3.5 3.0 3.0 3.5 4.5 3.5 3.0 3.5 2.5 2.5 4.0 4.0 2.5 2.5 2.0 2.5 3.0 3.0 3.0 3.0 3.0 3.5 3.0 3.5 3.5 4.0 2.5 3.0 3.0 2.5 3.0 3.5 2.5 3.0 3.0 3.0

3.4 2.6 4.2 3.4 3.6 3.2 4.2 3.7 3.4 3.6 3.0 3.1 3.4 4.3 2.2 2.8 2.7 2.9 2.9 2.3 3.1 3.7 3.2 3.6 3.1 3.9 3.7 3.9 3.2 2.8 3.3 2.6 3.8 3.7 3.4 3.1 3.0 3.5

3.0 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 2.0 2.0 2.5 4.0 2.5 2.5 3.5 3.5 3.5 4.0 2.0 2.5 3.0 2.0 3.5 3.5 2.5 3.0 3.5 2.5

4.0 2.5 4.0 4.0 3.0 4.0 3.5 3.5 3.5 4.0 2.5 2.5 3.5 3.5 2.0 3.0 2.0 2.5 3.0 2.5 3.0 3.0 2.5 3.5 2.5 3.5 3.5 3.5 3.0 2.5 3.5 2.5 4.0 4.0 3.5 3.5 3.5 3.0

3.5 2.5 4.0 3.5 3.0 3.5 4.0 4.0 4.0 3.5 3.0 3.0 4.0 3.5 2.5 2.5 2.5 2.5 3.0 4.0 3.5 3.5 3.5 4.0 3.5 4.0 4.5 3.5 3.0 3.0 3.5 2.5 4.0 4.0 3.5 4.0 3.0 3.0

3.0 2.5 4.0 3.0 3.0 3.0 4.0 3.5 3.0 3.5 2.5 2.5 3.0 4.0 2.0 2.5 2.0 2.5 2.5 2.0 2.5 3.5 3.0 3.0 3.0 3.5 3.5 3.5 3.0 2.5 2.5 2.5 3.0 3.5 3.5 3.0 3.0 2.5

2005

2005

3.0 2.5 3.5 2.5 2.5 3.5 4.0 3.0 3.0 3.5 3.0 2.5 2.5 4.5 2.5 2.0 2.5 2.0 2.5 2.0 2.5 4.0 2.5 2.5 2.0 3.5 3.5 4.0 2.5 2.5 3.0 2.0 3.0 3.5 3.0 3.0 3.5 2.5

3.3 2.4 3.8 3.2 2.9 3.4 3.8 3.3 3.3 3.6 2.7 2.6 3.1 3.9 2.2 2.4 2.3 2.3 2.6 2.5 2.8 3.6 2.8 3.1 2.9 3.6 3.7 3.7 2.7 2.6 3.1 2.3 3.5 3.7 3.2 3.3 3.3 2.7

Definitions • IDA Resource Allocation Index is obtained by

sustainability. • Structural policies cluster: Trade

• Equity of public resource use assesses the extent

calculating the average score for each cluster and

assesses how the policy framework fosters trade in

to which the pattern of public expenditures and rev-

then by averaging those scores. For each of 16 cri-

goods. • Financial sector assesses the structure of

enue collection affects the poor and is consistent

teria countries are rated on a scale of 1 (low) to

the financial sector and the policies and regulations

with national poverty reduction priorities. • Build-

6 (high) • Economic management cluster: Macro-

that affect it. • Business regulatory environment

ing human resources assesses the national policies

economic management assesses the monetary,

assesses the extent to which the legal, regulatory,

and public and private sector service delivery that

exchange rate, and aggregate demand policy frame-

and policy environments help or hinder private busi-

affect the access to and quality of health and edu-

work. • Fiscal policy assesses the short- and

nesses in investing, creating jobs, and becoming

cation services, including prevention and treatment

medium-term sustainability of fiscal policy (taking

more productive. • Policies for social inclusion

of HIV/AIDS, tuberculosis, and malaria. • Social

into account monetary and exchange rate policy

and equity cluster: Gender equality assesses the

protection and labor assess government policies in

and the sustainability of the public debt) and its

extent to which the country has installed institutions

social protection and labor market regulations that

impact on growth. • Debt policy assesses whether

and programs to enforce laws and policies that pro-

reduce the risk of becoming poor, assist those who

the debt management strategy is conducive to mini-

mote equal access for men and women in educa-

are poor to better manage further risks, and ensure

mizing budgetary risks and ensuring long-term debt

tion, health, the economy, and protection under law.

a minimal level of welfare to all people. • Policies

294

2007 World Development Indicators

Lao PDR Lesotho Madagascar Malawi Maldives Mali Mauritania Moldova Mongolia Mozambique Nepal Nicaragua Niger Nigeria Pakistan Papua New Guinea Rwanda Samoa São Tomé and Principe Senegal Serbia and Montenegro Sierra Leone Solomon Islands Sri Lanka St. Lucia St. Vincent & Grenadines Sudan Tajikistan Tanzania Togo Tonga Uganda Uzbekistan Vanuatu Vietnam Yemen, Rep. Zambia Zimbabwe

5.8

Policies for social inclusion and equity

Public sector management and institutions

1 (low) to 6 (high)

1 (low) to 6 (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

2005

2005

2005

2005

2005

2005

2005

2005

2005

2005

3.5 4.0 3.5 3.5 4.0 3.5 3.5 4.5 3.5 3.5 3.0 4.0 2.5 3.0 2.0 2.5 3.5 4.0 3.0 3.5 4.5 3.0 3.0 4.0 4.5 4.5 2.0 3.5 4.0 3.0 2.5 3.5 3.5 3.0 4.5 2.5 3.5 2.5

3.5 3.0 3.5 3.5 4.0 3.5 3.0 3.5 3.5 3.5 3.5 4.0 3.5 3.5 3.5 3.0 4.0 4.0 3.5 3.5 4.0 3.0 3.0 3.5 3.5 3.5 2.5 3.0 4.0 2.0 3.5 4.5 3.5 3.5 4.0 3.5 3.5 1.5

3.0 3.5 3.5 3.5 4.0 3.5 3.5 4.0 3.5 3.5 3.5 3.5 3.0 3.0 3.5 2.5 4.0 4.0 2.5 3.5 3.5 3.0 3.0 4.5 4.0 4.0 2.5 3.0 4.0 3.0 4.0 4.0 4.0 2.5 4.0 3.0 3.5 2.0

2.0 3.0 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.0 3.0 3.5 3.0 3.0 3.0 3.0 3.5 3.5 2.5 3.0 4.0 3.0 2.5 3.5 3.5 3.5 2.0 3.5 3.5 2.5 3.0 3.5 3.5 2.0 3.0 3.5 3.0 1.5

3.5 3.0 4.0 3.5 4.0 3.0 3.5 3.5 2.5 3.0 3.0 3.5 3.0 3.0 3.5 1.5 3.0 4.0 2.5 3.5 3.5 2.5 2.0 3.5 3.5 3.5 2.5 2.5 3.5 2.5 3.0 4.0 3.5 3.0 3.5 3.0 3.5 2.5

3.1 3.3 3.6 3.5 3.9 3.4 3.4 3.8 3.3 3.3 3.2 3.7 3.0 3.1 3.1 2.5 3.6 3.9 2.8 3.4 3.9 2.9 2.7 3.8 3.8 3.8 2.3 3.1 3.8 2.6 3.2 3.9 3.6 2.8 3.8 3.1 3.4 2.0

3.0 3.5 3.5 3.5 4.0 3.5 3.0 3.5 3.0 3.0 2.5 3.0 3.0 2.5 3.0 2.5 3.0 4.0 2.5 3.5 3.0 2.5 2.5 3.5 4.0 4.0 2.0 2.5 3.5 2.5 3.5 3.5 2.0 3.0 3.5 2.5 3.0 1.0

2.5 3.0 3.0 3.0 3.0 4.0 2.0 3.5 4.0 3.5 3.5 3.5 3.5 3.0 3.5 3.5 3.5 4.0 3.0 3.5 3.5 3.5 3.0 4.0 4.0 3.5 2.5 3.0 4.5 2.0 2.5 4.0 3.0 3.5 4.0 3.0 3.0 2.5

2.5 4.0 3.5 4.0 4.0 4.0 4.0 3.0 3.5 3.5 3.5 4.0 3.5 3.0 3.5 3.5 3.5 4.0 3.5 4.5 3.5 3.0 2.5 3.5 3.5 3.5 3.0 3.0 4.0 2.5 3.0 3.0 3.0 3.5 3.5 3.0 4.0 3.5

2.5 3.0 3.5 3.5 4.0 3.0 3.0 3.0 3.5 3.0 3.0 3.5 3.0 2.5 3.5 3.0 3.5 4.0 3.0 3.5 4.0 3.0 2.0 3.0 3.5 3.5 2.5 2.5 3.5 2.0 2.5 3.0 2.5 2.5 3.5 3.0 3.0 2.0

2005

2005

2.0 3.5 3.5 3.0 3.0 3.5 2.5 3.0 2.5 3.0 2.5 3.5 3.0 3.0 2.5 3.0 3.0 4.0 3.5 3.0 3.0 2.5 3.0 3.5 4.5 4.0 2.0 2.0 3.5 2.0 2.0 3.0 1.5 3.0 3.0 3.0 3.0 1.5

2.5 3.4 3.4 3.4 3.6 3.6 2.9 3.2 3.3 3.2 3.0 3.5 3.2 2.8 3.2 3.1 3.3 4.0 3.1 3.6 3.4 2.9 2.6 3.5 3.9 3.7 2.4 2.6 3.8 2.2 2.7 3.3 2.4 3.1 3.5 2.9 3.2 2.1

and institutions for environmental sustainability

accurate accounting and fiscal reporting, including

extent to which public employees within the executive

assess the extent to which environmental policies

timely and audited public accounts. • Effi ciency

are required to account for administrative decisions,

foster the protection and sustainable use of natural

of revenue mobilization assesses the overall pat-

use of resources, and results obtained. The three

resources and the management of pollution. • Public

tern of revenue mobilization—not only the de facto

main dimensions assessed here are the account-

sector management and institutions cluster: Prop-

tax structure, but also revenue from all sources as

ability of the executive to oversight institutions and

erty rights and rule-based governance assess the

actually collected. • Quality of public administration

of public employees for their performance, access

extent to which private economic activity is facili-

assesses the extent to which civilian central govern-

of civil society to information on public affairs, and

tated by an effective legal system and rule-based

ment staff is structured to design and implement

state capture by narrow vested interests.

governance structure in which property and contract

government policy and deliver services effectively.

rights are reliably respected and enforced. • Quality

• Transparency, accountability, and corruption in

of budgetary and financial management assesses

the public sector assess the extent to which the

Data on public policies and institutions are from

the extent to which there is a comprehensive and

executive can be held accountable for its use of

the World Bank Group’s CPIA database available

credible budget linked to policy priorities, effective

funds and for the results of its actions by the elec-

at www.worldbank.org/ida.

fi nancial management systems, and timely and

torate and by the legislature and judiciary, and the

Data sources

2007 World Development Indicators

295

5.9

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, 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

296

Railways

Passengers carried million Rail lines total route- passengerkm km

Total road network km

Paved roads %

Passengers carried million passengerkm

2000–04a

2000–04a

2000–04a

2000–04a

2000–05a

.. 197 .. 166,045 .. 1,867 .. 69,000 10,279 .. 9,382 126,680 .. .. .. .. .. 14,401 .. .. .. .. .. .. .. .. 769,560 .. .. .. .. .. .. 3,716 .. 90,055 61,258 .. 10,641 .. .. .. 2,465 219,113 69,400 781,000 .. 16 5,200 1,062,700 .. .. .. .. .. ..

.. .. .. 4,709 .. 280 152,777 26,411 6,965 .. 13,969 54,856 .. .. .. .. .. 6,840 .. .. .. .. 184,774 .. .. .. 784,090 .. .. .. .. .. .. 4,373 .. 475 17,766 .. 5,453 .. .. .. 6,722 2,456 28,100 197,000 .. .. 570 232,296 .. 18,360 .. .. .. ..

.. 447 3,572 2,761 35,753 732 9,528 5,781 2,122 2,855 5,498 3,542 578 3,698 1,000 888 29,314 4,163 622 .. 650 974 57,671 .. .. 2,030 62,200 .. 2,137 3,641 795 950 639 2,726 4,382 9,513 2,212 1,743 966 5,150 283 306 959 .. 5,732 29,286 810 .. 1,336 34,228 977 2,576 886 1,115 .. ..

34,782 18,000 108,302 51,429 400,000 7,633 810,200 133,928 59,141 239,226 93,310 150,567 19,000 62,479 .. 24,455 1,751,868 44,033 15,272 12,322 38,257 50,000 1,408,900 .. .. 79,604 1,870,661 1,943 .. 153,497 17,289 35,330 80,000 28,344 .. 127,672 71,847 .. 43,197 92,370 .. .. 56,856 36,469 78,158 951,220 9,170 3,742 20,247 .. 47,787 114,931 .. 44,348 3,455 ..

23.7 39.0 70.2 10.4 30.0 100.0 .. 100.0 49.4 9.5 87.0 78.0 9.5 7.0 .. 36.5 5.5 99.0 31.2 10.4 6.3 10.0 .. .. .. 20.2 81.0 100.0 .. 1.8 5.0 24.4 8.1 84.7 .. 100.0 100.0 .. 15.0 81.0 .. .. 23.5 19.1 64.7 100.0 10.2 19.3 39.4 100.0 17.9 .. .. 9.8 27.9 ..

2007 World Development Indicators

Goods hauled million ton-km

2000–05a

.. 73 929 .. 6,979 30 1,290 8,586 789 4,340 13,568 9,150 66 286 53 171 .. 2,389 .. .. 45 324 2,790 .. .. 1,571 583,320 .. .. 140 135 .. 10 1,266 .. 6,631 5,459 .. .. 40,837 .. .. 248 .. 3,478 77,219 95 .. 720 72,568 85 1,854 .. .. .. ..

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

2000–05a

2005

2005

2005

2005

.. 196 3,037 240 6,938 556 44,657 8,038 1,134 1,634 282 3,341 .. 1,892 73 230 37,662 654 66 .. 169 384 45,230 .. .. 5,939 136,722 20,230 9,984 .. 52 953 .. 1,361 813 4,706 10,340 .. 2,011 4,888 2,541 .. 578 1,667 7,075 52,477 465 .. 249 90,789 96 9,452 .. .. .. ..

.. 0 32 68 133 7 2,445 537 12 183 1 705 .. 25 1 0 1,531 3 .. .. 1 24 1,527 .. .. 1,054 7,579 7,764 1,092 .. .. 10 .. 2 31 39 190 .. 5 287 21 .. 1 133 354 5,802 66 .. 3 7,722 7 64 .. .. .. ..

.. 26 1,471 .. .. 678 46,164 17,060 7,551 896 43,559 8,130 86 1,057 1,173 842 221,600 5,166 .. .. 92 1,119 338,661 .. .. 3,848 1,934,612 .. 7,751 444 231 .. 675 2,835 .. 14,385 1,888 .. .. 3,917 .. .. 10,311 .. 9,706 41,263 2,219 .. 6,127 88,022 242 613 .. .. .. ..

.. .. .. .. 1,196 .. 4,830 .. .. 901 .. 7,890 .. .. .. .. 5,598 .. .. .. .. .. 4,163 .. .. 1,813 88,549b .. 1,165 .. .. 779 710 .. .. .. 1,321 537 633 3,691 .. .. .. .. 1,313 3,840 .. .. .. 13,507 .. 1,779 776 .. .. ..

.. 4 46 5 81 6 343 142 12 7 5 152 .. 26 5 8 515 10 1 .. 3 11 1,018 .. .. 93 1,349 123 162 .. 5 36 .. 22 11 75 160 .. 30 45 24 .. 8 31 107 728 9 .. 5 1,024 1 131 .. .. .. ..

Roads

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

Railways

Passengers carried million Rail lines total route- passengerkm km

Total road network km

Paved roads %

Passengers carried million passengerkm

2000–04a

2000–04a

2000–04a

2000–04a

2000–05a

.. 13,300 .. .. .. .. .. .. .. .. 947,562 .. 85,240 .. .. 9,169 .. 5,624 .. 2,779 .. .. .. .. 23,184 .. .. .. .. .. .. .. 410,000 1,640 381 .. .. .. 47 .. .. .. .. .. .. 56,573 .. 209,959 .. .. .. .. .. 29,996 .. ..

.. 12,505 .. .. .. .. 6,500 .. 184,756 .. 327,632 .. 43,910 22 .. 518 .. 847 .. 2,330 .. .. .. .. 12,279 .. .. .. .. .. .. .. 199,800 1,577 1,889 1,251 .. .. 591 .. 45,700 .. .. .. .. 13,614 .. .. .. .. .. .. .. 85,989 20,470 10

699 7,950 63,465 .. 7,131 1,963 1,919 899 16,751 272 20,052 293 14,204 1,917 5,214 3,392 .. 424 .. 2,375 401 .. 490 2,757 1,772 699 732 710 1,667 733 717 .. 26,662 1,075 1,810 1,907 3,070 .. .. 59 2,813 3,898 6 .. 3,528 4,087 .. 7,791 355 .. 441 2,177 .. 19,599 2,839 96

.. 159,568 3,383,344 368,360 179,388 .. 96,602 17,446 484,688 20,996 1,177,278 7,500 90,018 63,265 .. 100,279 5,749 18,840 31,210 69,532 .. .. .. .. 79,331 .. .. 15,451 98,721 18,709 .. 2,015 337,192 12,733 49,250 57,493 .. .. 42,237 17,380 126,100 92,931 18,669 14,565 193,200 91,916 34,965 258,340 11,643 .. .. 78,829 200,037 423,997 78,470 25,645

.. 43.9 47.4 58.0 67.4 .. 100.0 100.0 100.0 73.3 77.7 100.0 93.4 14.1 .. 86.8 85.0 .. 14.4 100.0 .. .. .. .. 91.3 .. .. 45.0 81.3 18.0 .. 100.0 49.5 86.2 3.5 56.9 .. .. 12.8 30.3 .. 64.3 11.4 25.0 15.0 77.5 27.7 64.7 .. .. .. 14.4 21.6 69.7 .. 95.0

Goods hauled million ton-km

2000–05a

.. 7,135 575,702 25,535 11,149 570 1,781 1,618 47,368 .. 145,957 .. 12,129 226 .. 31,004 .. 50 .. 894 .. .. .. .. 428 94 10 25 1,181 196 .. .. 74 355 1,128 2,987 172 .. .. .. 14,730 .. .. .. 174 2,440 .. 23,045 .. .. .. 119 .. 16,742 3,412 ..

Ports

5.9

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

2000–05a

2005

2005

2005

2005

.. 9,005 407,398 4,698 19,127 1,682 303 1,149 21,045 .. 22,632 1,024 171,855 1,399 .. 10,108 .. 561 .. 17,921 .. .. .. .. 12,457 530 12 88 1,178 189 .. .. .. 2,980 8,857 5,919 768 .. .. .. 4,026 3,853 .. .. 77 9,568 .. 4,796 .. .. .. 1,159 .. 46,060 2,422 ..

553 .. 4,938 5,503 1,326 .. 980 1,525 9,856 1,671 16,777 .. .. .. .. 15,113 .. .. .. .. .. .. .. .. .. .. .. .. 12,027 .. .. 382 2,145 .. .. 561 .. .. .. .. 9,521 1,614 .. .. 513 .. 2,727 1,391 3,068 .. .. 992 3,634 428 905 1,727

.. 2,735 27,528 26,836 12,708 .. 42,873 4,392 36,116 1,574 102,279 1,737 1,160 2,424 101 33,888 2,433 226 293 1,032 1,076 .. .. 918 505 192 575 132 20,369 .. 139 1,146 21,858 232 295 3,493 347 1,504 306 480 26,133 11,952 .. .. 584 11,568 3,369 5,364 1,796 819 446 4,332 8,057 3,554 10,140 ..

.. 21 773 440 98 .. 107 1,213 1,365 16 8,549 224 16 253 2 7,433 242 2 2 2 87 .. .. 0 1 0 15 1 2,578 .. 0 212 390 1 6 61 5 3 60 7 4,894 781 .. .. 10 182 237 408 37 21 .. 139 323 71 235 ..

.. 47 327 321 121 .. 304 34 446 22 652 20 17 28 2 221 19 5 9 23 12 .. .. 8 12 2 18 5 176 .. 2 15 331 4 5 49 10 26 6 6 241 209 .. .. 8 234 31 49 30 20 10 61 59 79 149 ..

2007 World Development Indicators

297

5.9

Transport services Roads

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 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 Europe EMU

Total road network km

Paved roads %

Passengers carried million passengerkm

2000–04a

2000–04a

2000–04a

198,817 537,289 14,008 152,044 13,576 45,290 11,300 3,188 43,000 38,451 .. 364,131 666,292 97,286 .. 3,594 424,947 71,214 94,890 27,767 78,891 57,403 .. .. 19,232 426,906 .. 70,746 169,447 .. 387,674 6,433,272 60,000 .. .. 222,179 4,996 .. 91,440 97,267

Railways

Goods hauled million ton-km 2000–04a

Passengers carried million Rail lines total route- passengerkm km 2000–05a

2000–05a

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

2000–05a

2005

2005

2005

2005

50.7 5,283 267 10,781 7,960 16,032 771 .. 164 5,702 85,542 164,262 1,801,601 1,803 19.0 .. .. .. .. .. .. 29.9 .. .. 1,020 393 1,192 897 29.3 .. .. 906 138 371 .. 62.0 .. 452 3,809 .. .. .. 8.0 .. .. .. .. .. .. 100.0 .. .. .. .. .. 23,192 87.3 32,214 18,517 3,659 2,166 9,326 .. 100.0 980 9,007 1,228 777 3,245 .. .. .. .. .. .. .. .. 17.3 .. .. 20,047 991 108,513 2,868 99.0 397,117 132,868 14,484 21,047 11,586 9,170 81.0 21,067 .. .. 4,682 138 2,455 .. .. .. 5,478 40 766 .. .. .. .. 301 .. 11,394 .. .. 106,868 37,677 9,867 5,673 13,120 1,217 100.0 96,845 15,000 3,252 14,277 9,313 .. 20.1 .. .. 2,702 571 2,075 .. .. .. .. 616 50 1,117 .. 628 c 1,196c .. 8.6 .. .. 2,600 c 98.5 .. .. 4,044 9,195 4,037 5,115 .. .. .. 568 .. .. .. .. .. .. .. .. .. 440 65.8 .. 16,611 1,909 1,294 2,082 .. 41.6 174,312 156,853 8,697 5,036 8,939 3,170 .. .. .. 2,529 1,286 8,670 .. 23.0 .. .. 259 .. 218 .. 97.2 58,308 28,847 22,001 52,655 223,980 580 .. .. .. .. .. .. 9,846 100.0 736,000 160,000 16,208 44,036 22,110 8,599 38,519 64.5 7,780,158 2,034,915 228,999 8,869 2,717,513d .. .. .. 2,993 .. .. .. .. .. .. 4,014 2,012 18,007 .. .. .. .. 682 .. 32 1,121 .. .. .. 2,671 4,558 2,928 2,694 100.0 .. .. .. .. .. .. .. .. .. .. .. .. .. 22.0 .. .. 1,273 186 554 .. 19.0 .. .. .. .. .. .. .. m .. m .. m .. s 2,278 m 5,543 m 369,847 s .. .. .. .. .. .. 9,772 .. .. .. .. 1,290 5,919 155,727 .. .. .. .. 1,286 3,977 122,240 50.5 .. .. .. 1,571 10,311 33,487 .. .. .. .. .. .. 166,361 .. .. .. .. 4,558 4,037 117,522 .. 9,815 6,602 207,077 1,649 9,005 5,530 .. .. .. .. .. .. 21,509 .. .. .. .. 1,265 2,082 .. 30.3 .. .. .. 13,864 2,846 9,685 .. .. .. .. .. .. .. 100.0 .. 29,960 .. 8,586 10,847 203,486 100.0 126,680 51,147 119,711 8,868 9,706 58,759

39 1,708 5 391 26,522 1,541 .. .. .. 116 15,933 1,021 6 450 .. 25 1,414 6 0 17 8 77 17,744 7,571 14 712 0 18 758 3 .. .. .. 148 11,845 923 586 49,855 1,022 20 2,818 310 9 511 43 .. .. .. 146 9,019 264 135 9,663 1,110 17 1,240 22 7 479 6 7 263 2 124 18,903 2,002 .. .. .. 14 1,055 48 21 1,997 18 146 16,944 383 14 1,654 10 0 49 29 42 2,513 39 96 16,210 4,417 1,018 93,603 5,998 9,970 e 720,548e 37,358e 9 586 4 22 1,639 72 136 5,043 2 54 5,454 230 .. .. .. 17 1,083 67 6 54 0 4 243 22 24,878 s 2,020,604 s 142,571 s 714 54,774 2,307 5,297 440,245 22,500 3,283 295,803 14,626 2,014 144,442 7,875 6,011 495,019 24,807 2,220 220,887 13,285 1,014 71,522 2,240 1,596 105,739 4,566 387 35,547 1,132 417 37,955 1,682 379 23,368 1,903 18,866 1,525,585 117,763 4,070 337,896 27,960

a. Data are for the latest year available in the period shown. b. Includes Hong Kong, China. c. Excludes Tazara railway. d. Refers to Class 1 railways only. e. Data cover only carriers desIgnated by the U.S. Department of Transportation as major and national air carriers.

298

2007 World Development Indicators

About the data

5.9

STATES AND MARKETS

Transport services Definitions

Transport infrastructure—highways, railways, ports

either air transport for passengers and freight or

• Total road network covers motorways, highways,

and waterways, and airports and air traffic control

sea transport for freight tend to be more competi-

main or national roads, secondary or regional roads,

systems—and the services that flow from it are cru-

tive. The railways indicators in the table focus on

and all other roads in a country. • Paved roads are

cial to the activities of households, producers, and

scale and output measures: total route-kilometers,

roads surfaced with crushed stone (macadam) and

governments. Because performance indicators vary

passenger-kilometers, and goods (freight) hauled in

hydrocarbon binder or bituminized agents, with con-

significantly by transport mode and focus (whether

ton-kilometers.

crete, or with cobblestones. • Passengers carried

physical infrastructure or the services flowing from

Measures of port container traffi c, much of it

by road are the number of passengers transported

that infrastructure), highly specialized and carefully

commodities of medium to high value added, give

by road times kilometers traveled. • Goods hauled

specified indicators are required. The table provides

some indication of economic growth in a country.

by road are the volume of goods transported by road

selected indicators of the size, extent, and produc-

But when traffic is merely transshipment, much of

vehicles, measured in millions of metric tons times

tivity of roads, railways, and air transport systems

the economic benefit goes to the terminal operator

kilometers traveled. • Rail lines are the length of rail-

and of the volume of traffic in these modes as well

and ancillary services for ships and containers rather

way route available for train service, irrespective of

as in ports.

than to the country more broadly. In transshipment

the number of parallel tracks. • Passengers carried

centers empty containers may account for as much

by railway are the number of passengers transported

as 40 percent of traffic.

by rail times kilometers traveled. • Goods hauled

Data for transport sectors are not always internationally comparable. Unlike for demographic statistics, national income accounts, and international

The air transport data represent the total (inter-

by railway are the volume of goods transported by

trade data, the collection of infrastructure data has

national and domestic) scheduled traffic carried by

railway, measured in metric tons times kilometers

not been “internationalized.” But data on roads are

the air carriers registered in a country. Countries

traveled. • Port container traffic measures the flow

collected by the International Road Federation (IRF),

submit air transport data to ICAO on the basis of

of containers from land to sea transport modes and

and data on air transport by the International Civil

standard instructions and definitions issued by ICAO.

vice versa in twenty-foot-equivalent units (TEUs), a

Aviation Organization (ICAO).

In many cases, however, the data include estimates

standard-size container. Data cover coastal shipping

National road associations are the primary source

by ICAO for nonreporting carriers. Where possible,

as well as international journeys. Transshipment

of IRF data. In countries where such an association

these estimates are based on previous submissions

traffic is counted as two lifts at the intermediate

is lacking or does not respond, other agencies are

supplemented by information published by the air

port (once to off-load and again as an outbound

contacted, such as road directorates, ministries

carriers, such as flight schedules.

lift) and includes empty units. • Registered car-

of transport or public works, or central statistical

The data cover the air traffic carried on scheduled

rier departures worldwide are domestic takeoffs

offices. As a result, due to differing definitions and

services, but changes in air transport regulations

and takeoffs abroad of air carriers registered in the

data collections methods and quality, the compiled

in Europe have made it more diffi cult to classify

country.• Passengers carried by air include both

data are of uneven quality. Moreover, the quality of

traffic as scheduled or nonscheduled. Thus recent

domestic and international passengers of air car-

transport service (reliability, transit time, and condi-

increases shown for some European countries may

riers registered in the country. • Air freight is the

tion of goods delivered) is rarely measured, though

be due to changes in the classification of air traffic

volume of freight, express, and diplomatic bags car-

it may be as important as quantity in assessing an

rather than actual growth. For countries with few air

ried on each flight stage (operation of an aircraft from

economy’s transport system. Several new initiatives

carriers or only one, the addition or discontinuation

takeoff to its next landing), measured in metric tons

are under way to improve data availability and con-

of a home-based air carrier may cause significant

times kilometers traveled.

sistency. The IRF is collaborating with national and

changes in air traffic.

international development agencies to improve the quality and coverage of road statistics. To improve measures of progress and performance, the World Bank is also working on better measures of access, affordability, efficiency, quality, and fiscal and institutional aspects of infrastructure.

Data sources Data on roads are from the IRF’s World Road

Unlike the road sector, where numerous qualified

Statistics, supplemented by World Bank staff

motor vehicle operators can operate anywhere on

estimates. Data on railways are from a database

the road network, railways are a restricted transport

maintained by the World Bank’s Transport and

system with vehicles confined to a fixed guideway.

Urban Development Department, Transport Divi-

Considering their cost and service characteristics,

sion, based on data from the International Union

railways generally are best suited to carry—and can

of Railways. Data on port container traffic are from

effectively compete for—bulk commodities and con-

Containerisation International’s Containerisation

tainerized freight for distances of 500–5,000 kilo-

International Yearbook. Data on air transport are

meters, and passengers for distances of 50–1,000

from the ICAO’s Civil Aviation Statistics of the World

kilometers. Below these limits road transport tends

and ICAO staff estimates.

to be more competitive, while above these limits

2007 World Development Indicators

299

5.10

Power and communications Electric power

Telephones Access

Quality

Affordability and efficiency

Population Transmission $ per month covered International and Price Cost of Total teleper 1,000 people by mobile voice traffic Faults basket for call to U.S. communications Consumption distribution telephonya minutes per per 100 residential Price basket losses $ per revenuea per capita Mobile Fixed % of output mainlinesa subscribersa persona mainlinesa fi xed lineb for mobilea 3 minutesa kWh % % of GDP 2004

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, 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

300

.. 1,200 812 124 2,301 1,428 11,193 7,850 2,437 140 3,144 8,576 67 435 2,180 1,325 1,955 3,939 .. .. .. 207 17,156 .. .. 3,084 1,585 5,699 866 93 131 1,667 176 3,316 1,177 6,224 6,631 1,071 687 1,215 629 .. 5,484 33 16,780 7,900 928 .. 1,577 7,029 247 5,148 514 .. .. 30

2004

2005

.. 36 16 14 15 16 6 5 13 9 11 5 .. 12 16 10 17 12 .. .. .. 19 7 .. .. 8 6 13 19 3 74 11 16 17 15 6 4 32 42 12 13 .. 11 10 3 6 18 .. 16 6 15 9 4 .. .. 53

3 88 78 6 227 192 564 450 130 8 336 461 9 70 248 75 230 321 7 4 3 6 566 2 1 211 269 546 168 0 4 321 14 425 75 314 619 101 129 140 141 9 328 9 404 586 28 29 151 667 15 568 99 3 7 17

2007 World Development Indicators

2005

40 405 416 69 570 106 906 991 267 63 419 903 89 264 408 466 462 807 43 20 75 138 514 25 22 649 302 1,252 479 48 123 254 121 672 12 1,151 1,010 407 472 184 350 9 1,074 6 997 789 470 163 326 960 129 904 358 20 42 48

2005

.. 90 75 .. .. 88 96 99 99 80 88 99 43 .. 95 99 88 100 72 .. 88 73 95 .. .. 100 .. 100 80 .. 80 .. 55 98 61 100 .. .. .. 98 95 0 99 .. 99 99 78 .. 95 99 69 100 .. .. .. ..

2005

.. .. 52 .. .. 29 .. 293 33 5 64 .. 7 49 190 76 .. 37 7 .. 2 .. .. .. 2 48 5 1,049 52 .. .. 82 17 170 29 73 338 .. .. 23 397 10 109 2 .. 177 61 .. 57 .. 15 .. 129 .. .. ..

2005

25.0 .. 0.8 .. .. 52.9 11.2 5.0 45.2 .. 23.1 5.9 5.8 .. .. .. 1.6 4.2 18.4 6.0 .. .. .. 56.0 .. .. .. 1.1 30.6 .. .. 4.0 81.0 14.0 7.6 6.5 9.0 .. .. 0.1 1.7 54.3 .. 100.0 .. .. 45.0 .. .. .. 5.6 13.8 .. .. .. ..

2005

11.3 5.1 6.3 .. 6.8 2.4 30.5 29.0 8.5 6.9 2.4 33.1 16.1 8.5 4.9 10.4 15.6 8.9 16.9 4.5 5.2 9.3 .. .. 16.9 9.7 .. 12.6 8.0 .. .. 6.0 28.2 13.1 13.1 24.1 30.7 23.3 9.0 4.0 12.8 6.2 15.6 2.9 28.7 29.0 32.4 .. 4.7 26.5 14.8 21.1 15.4 .. .. ..

2005

10.8 22.7 7.5 11.9 7.8 8.3 18.3 23.7 15.1 2.5 11.8 18.9 13.2 5.6 6.5 8.6 26.5 16.6 13.1 12.4 5.1 16.5 6.9 12.6 13.3 11.4 2.9 2.2 10.2 11.0 11.0 1.9 22.2 14.9 22.6 17.7 6.1 8.6 18.9 5.8 8.5 .. 8.8 3.0 6.8 30.0 14.7 .. 44.1 17.3 6.9 23.6 6.1 7.7 21.9 4.6

2005

0.39 1.34 2.08 3.23 .. 2.42 .. 0.71 4.18 2.02 1.90 0.75 4.80 .. 3.62 2.88 0.71 0.57 1.14 2.45 2.94 .. .. 1.99 .. .. 2.90 0.77 .. .. 5.39 .. 2.25 .. 7.49 1.06 0.89 0.22 .. 1.45 2.40 3.59 0.90 4.01 1.80 0.84 2.77 1.81 .. 0.43 0.39 1.09 1.21 .. .. 2.15

2005

5.1 6.0 3.4 .. 3.1 3.0 5.7 2.4 1.7 1.5 1.3 2.1 1.6 5.8 7.1 3.0 3.7 6.7 3.1 .. 0.4 5.3 2.7 1.1 .. .. 3.2 3.8 5.2 6.6 2.9 2.5 4.3 2.6 2.6 4.1 2.6 0.5 2.2 3.5 36.1 2.6 5.8 1.7 3.0 2.4 1.8 .. 5.8 3.0 .. 4.4 .. .. .. ..

Total telephone subscribers per employeea 2005

1,576 414 302 .. 969 146 506 592 139 .. 203 586 364 680 337 665 .. 364 383 234 539 420 456 134 127 .. 928 623 .. 513 .. 388 678 407 .. 699 439 .. 667 312 1,182 71 486 81 420 585 244 .. .. 559 557 612 .. .. .. 92

Electric power

5.10

Telephones Access

Quality

Affordability and efficiency

Population Transmission $ per month covered International and Price Cost of Total teleper 1,000 people by mobile voice traffic Faults basket for call to U.S. communications Consumption distribution telephonya minutes per per 100 residential Price basket losses $ per revenuea per capita Mobile Fixed % of output mainlinesa subscribersa persona mainlinesa fi xed lineb for mobilea 3 minutesa kWh % % of GDP 2004

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

586 3,680 457 478 2,036 1,126 6,169 6,803 5,640 2,455 8,072 1,602 3,621 140 827 7,391 14,955 1,421 .. 2,549 2,499 .. .. 2,519 3,145 3,183 .. .. 3,166 .. .. .. 1,838 1,228 .. 595 367 104 1,389 69 6,920 8,937 417 .. 104 24,645 3,836 425 1,466 .. 816 794 597 3,418 4,526 ..

2004

2005

23 12 26 13 17 6 8 3 7 10 5 13 16 17 16 3 11 30 .. 19 15 .. .. 28 7 21 .. .. 5 .. .. .. 16 38 .. 16 10 20 18 19 4 13 24 .. 34 8 15 25 17 .. 4 10 13 9 9 ..

69 333 45 58 278 37 489 424 427 129 460 119 167 8 44 492 201 85 13 318 277 27 .. 133 235 262 4 8 172 6 13 289 189 221 61 44 4 9 64 17 466 422 43 2 9 460 103 34 136 11 54 80 41 309 401 285

2005

178 924 82 213 106 20 1,012 1,120 1,232 1,017 742 304 327 135 .. 794 939 105 108 814 277 137 49 41 1,275 620 27 33 771 64 243 574 460 259 218 411 62 4 244 9 970 861 217 21 141 1,028 519 82 418 4 320 200 419 764 1,085 689

2005

2005

.. 99 .. 90 90 .. 99 99 100 95 99 99 .. .. .. 99 100 90 .. 98 100 80 16 .. 100 99 30 .. .. .. .. 100 100 97 .. 98 95 .. 88 .. 100 98 60 15 58 .. .. .. 89 .. .. .. 92 99 100 100

82 45 .. 5 8 .. .. .. 236 233 43 128 .. 5 .. 81 .. 17 3 52 .. .. .. .. 37 .. 1 .. .. .. 20 92 119 92 4 55 17 3 .. 6 .. 363 65 .. .. 260 185 .. .. .. 28 64 29 61 137 ..

2005

.. 8.2 .. .. .. .. 5.6 .. .. 31.0 .. 10.0 .. 130.4 .. 1.0 4.0 .. .. 20.3 .. 75.0 .. .. 3.8 .. 59.6 .. 7.3 .. .. .. 1.8 5.1 20.6 25.0 66.0 125.0 40.4 68.0 .. .. 4.8 .. 20.6 .. 89.7 .. 13.9 .. 8.2 .. .. .. 9.7 ..

2005

5.9 28.5 3.3 5.8 2.4 .. 39.5 10.5 26.6 9.1 26.1 10.0 .. 13.9 .. 8.3 10.5 5.9 5.6 13.3 15.0 18.4 .. .. 17.7 11.4 18.5 5.8 8.7 16.1 11.6 7.9 16.1 4.5 2.4 23.0 17.6 .. 12.3 3.1 .. 28.6 9.2 10.5 .. 37.9 12.1 5.1 10.3 7.3 6.4 19.6 11.6 14.3 31.8 33.5

2005

10.8 11.7 2.4 4.3 2.6 2.6 19.7 9.3 14.4 7.5 20.5 6.7 11.5 16.5 .. 14.2 75.2 6.4 3.8 9.5 20.1 14.2 .. 6.3 9.1 14.8 7.9 10.2 5.0 13.8 .. 4.2 14.0 17.1 5.5 16.3 10.3 .. 14.0 2.0 23.4 19.1 15.1 16.9 10.6 20.2 5.5 2.4 16.7 14.8 3.3 22.9 5.3 7.8 23.6 ..

2005

2.52 1.01 1.19 2.79 0.55 .. 0.71 0.59 0.79 0.87 1.63 1.44 .. 3.00 .. 0.76 1.51 5.40 1.11 1.63 2.19 3.28 .. .. 1.55 .. 0.59 .. 0.71 .. .. 1.59 0.83 1.46 .. 1.69 1.17 0.17 .. 2.04 0.32 1.30 3.15 .. 1.49 .. 1.87 1.03 .. .. 0.90 1.80 1.20 1.35 1.04 ..

2005

5.9 4.7 1.9 2.2 1.3 .. 2.5 4.5 3.0 4.1 3.9 8.3 .. 4.1 .. 4.8 3.4 4.5 1.7 1.5 7.2 .. .. .. 3.5 5.7 12.8 4.5 4.8 7.6 19.5 3.2 2.7 9.9 4.4 5.4 1.8 0.6 4.8 1.2 .. 3.9 3.7 2.2 3.5 3.3 2.3 2.5 3.9 .. 4.2 0.8 3.9 3.8 5.1 ..

2007 World Development Indicators

Total telephone subscribers per employeea 2005

186 670 .. 1,084 407 .. 401 692 948 686 1,283 444 108 141 .. 567 338 91 130 587 .. .. .. .. .. .. 148 .. 770 .. 533 451 617 220 116 821 392 66 .. 110 .. 962 334 .. 256 445 583 213 273 .. .. 472 1,555 184 988 ..

301

STATES AND MARKETS

Power and communications

5.10

Power and communications Electric power

Telephones Access

Quality

Affordability and efficiency

Population Transmission $ per month covered International and Price Cost of Total teleper 1,000 people by mobile voice traffic Faults basket for call to U.S. communications Consumption distribution telephonya minutes per per 100 residential Price basket losses $ per revenuea per capita Mobile Fixed % of output mainlinesa subscribersa persona mainlinesa fi xed lineb for mobilea 3 minutesa kWh % % of GDP 2004

Romania 2,271 Russian Federation 5,642 Rwanda .. Saudi Arabia 6,571 Senegal 176 Serbia and Montenegro 4,029 Sierra Leone .. Singapore 8,170 Slovak Republic 5,088 Slovenia 6,835 Somalia .. South Africa 4,885 Spain 5,924 Sri Lanka 344 Sudan 92 Swaziland .. Sweden 15,424 Switzerland 8,204 Syrian Arab Republic 1,317 Tajikistan 2,240 Tanzania 53 Thailand 1,865 Togo 87 Trinidad and Tobago 4,658 Tunisia 1,157 Turkey 1,782 Turkmenistan 1,740 Uganda .. Ukraine 3,152 United Arab Emirates 11,331 United Kingdom 6,206 United States 13,351 Uruguay 1,867 Uzbekistan 1,796 Venezuela, RB 2,760 Vietnam 501 West Bank and Gaza .. Yemen, Rep. 165 Zambia 692 Zimbabwe 795 World 2,606 w Low income 375 Middle income 1,840 Lower middle income 1,448 Upper middle income 3,454 Low & middle income 1,243 East Asia & Pacific 1,343 Europe & Central Asia 3,637 Latin America & Carib. 1,674 Middle East & N. Africa 1,289 South Asia 414 Sub-Saharan Africa 550 High income 9,609 European Monetary Union 6,869

2004

11 12 .. 7 15 16 .. 6 4 6 .. 6 9 17 16 .. 7 6 24 15 23 8 34 6 12 15 13 .. 15 7 8 6 31 9 27 10 .. 23 4 15 9w 23 11 10 12 13 7 12 17 16 26 9 6 6

2005

2005

203 617 280 838 3 32 164 575 23 148 332 585 .. 22 425 1,010 222 843 408 879 12 61 101 724 422 952 63 171 18 50 31 177 717 935 689 921 152 155 39 41 4 52 110 430 10 72 248 613 125 566 263 605 80 11 3 53 256 366 273 1,000 528 1,088 606 680 290 333 67 28 136 470 191 115 96 302 39 95 8 81 25 54 180 w 342 w 37 77 211 379 205 306 230 671 135 247 214 282 273 624 177 439 160 229 39 79 17 125 503 835 531 980

2005

98 .. 75 .. 85 95 .. 100 99 99 .. 96 99 85 .. .. 99 100 99 .. 25 .. 85 .. 98 96 .. 85 96 100 99 99 100 .. .. .. 95 68 51 .. .. w .. .. .. .. .. .. .. 90 90 .. .. 99 99

2005

2005

.. 10.4 .. .. .. .. .. .. 55 .. 113 25.0 .. .. .. 0.3 88 9.5 .. 13.6 .. .. .. .. 117 14.2 .. 8.1 11 .. 47 70.0 .. .. .. .. 44 50.0 10 144.0 .. .. 12 2.5 21 .. 381 .. 84 30.0 31 30.4 .. .. 2 .. 36 .. .. 0.3 .. .. 279 13.2 .. .. .. .. .. .. .. .. 66 69.4 .. .. 7 108.0 24 7.7 .. w .. m .. .. 22 .. 14 25.0 .. .. .. .. 6 .. .. 15.7 .. .. 30 25.0 .. .. .. .. 171 5.8 .. 7.8

2005

10.1 .. 6.6 11.7 15.4 2.7 .. 6.7 19.8 17.6 .. 22.7 25.8 8.2 6.3 8.3 26.7 29.5 2.7 0.8 14.0 8.3 15.4 7.0 3.7 14.7 .. 16.8 .. 17.4 31.3 25.0 10.7 1.0 .. 3.7 7.5 2.8 6.0 4.3 11.7 m 8.7 9.7 8.5 12.1 10.1 5.9 9.5 10.0 7.3 5.1 14.0 27.6 29.0

2005

2005

10.5 6.0 12.3 9.7 9.6 6.4 71.9 6.1 12.3 10.3 5.1 13.3 22.1 1.2 4.0 13.3 6.2 28.4 9.9 23.3 9.5 4.4 12.3 6.7 5.4 12.6 17.2 9.3 9.3 4.1 14.0 5.2 16.1 1.8 1.2 6.2 9.8 4.3 14.2 3.4 10.5 m 9.6 10.1 10.2 9.5 9.9 5.0 11.8 9.4 6.3 2.4 12.3 17.8 20.9

0.82 2.03 2.43 .. 1.02 2.27 .. 0.69 1.06 0.65 .. 0.79 0.60 2.11 .. 2.97 0.41 0.32 .. 7.84 3.17 0.67 3.98 2.19 .. 2.40 .. 3.21 1.65 1.73 0.77 .. 0.52 .. 0.84 1.95 1.17 2.39 1.41 .. 1.44 m 1.99 1.65 2.08 1.06 1.81 1.16 1.51 1.80 1.66 2.02 2.43 0.76 0.73

Total telephone subscribers per employeea

2005

3.8 2.9 2.7 3.2 6.9 2.9 .. 3.4 3.8 3.2 .. 5.7 3.0 2.2 3.8 2.0 2.8 3.5 2.8 0.6 .. 3.2 5.7 2.7 4.4 3.6 .. 4.2 5.9 2.7 3.1 2.7 .. .. 3.5 0.0 0.7 1.3 2.7 4.4 3.6 w 0.7 3.6 1.9 3.6 3.6 2.7 3.6 4.3 1.3 2.0 3.3 4.5 3.0

2005

263 334 .. .. 408 .. .. .. 508 1,228 .. 725 643 313 651 279 858 525 221 57 .. 1,271 363 .. 740 883 .. 750 .. 485 .. 346 .. .. .. 79 871 .. 175 243 479 m 141 497 444 583 279 1,006 364 390 501 125 248 586 592

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. Calculated by the World Bank based on ITU data.

302

2007 World Development Indicators

About the data

5.10

Definitions

The quality of an economy’s infrastructure, includ-

Operators are the main source of telecommunica-

• Electric power consumption measures the produc-

ing power and communications, is an important ele-

tions data, so information on subscribers is widely

tion of power plants and combined heat and power

ment in investment decisions for both domestic and

available for most countries. This gives a general

plants less transmission, distribution, and trans-

foreign investors. Government effort alone is not

idea of access, but a more precise measure is the

formation losses and own use by heat and power

enough to meet the need for investments in modern

penetration rate—the share of households with

plants. • Electric power transmission and distri-

infrastructure; public-private partnerships, especially

access to telecommunications. Also important are

bution losses are losses in transmission between

those involving local providers and financiers, are

data on actual use of the telecommunications equip-

sources of supply and points of distribution and in

critical for lowering costs and delivering value for

ment. Ideally, statistics on telecommunications (and

distribution to consumers, including pilferage.• Fixed

money. In telecommunications, competition in the

other information and communications technologies)

telephone mainlines are telephone lines connecting

marketplace, along with sound regulation, is lower-

should be compiled for all three measures: subscrip-

a subscriber to the telephone exchange equipment.

ing costs and improving the quality of and access to

tion and possession, access, and use. The quality

• Mobile telephone subscribers are subscribers to

services around the globe.

of data varies among reporting countries as a result

a public mobile telephone service using cellular tech-

of differences in regulations covering the provision

nology. • Population covered by mobile telephony

of data.

is the percentage of people within range of a mobile

An economy’s production and consumption of electricity is a basic indicator of its size and level of development. Although a few countries export

Globally, there have been huge improvements in

cellular signal regardless of whether they are sub-

electric power, most production is for domestic

access to telecommunications, driven mainly by

scribers. • International voice traffic is the sum of

consumption. Expanding the supply of electricity

mobile telephony. By 2002 access to mobiles out-

international incoming and outgoing telephone traffic

to meet the growing demand of increasingly urban-

paced access to fixed-line telephones in developing

(in minutes) divided by total population. • Telephone

ized and industrialized economies without incurring

countries, and rural areas are catching up with urban

mainline faults are the number of reported faults

unacceptable social, economic, and environmental

areas (although gaps are still large). By 2004 approxi-

for the year divided by the number of telephone

costs is one of the great challenges facing develop-

mately 98 percent of the population in high income

mainlines and multiplied by 100. • Price basket

ing countries.

countries and about 64 percent of the population in

for residential fixed line is calculated as one-fifth

developing countries were covered by mobile tele-

of the installation charge, the monthly subscription

phony (within range of a mobile cellular signal).

charge, and the cost of local calls (15 peak and 15

Data on electric power production and consumption are collected from national energy agencies by the International Energy Agency (IEA) and adjusted

Telephone mainline faults are a measure of tele-

off-peak calls of three minutes each). • Price bas-

by the IEA to meet international definitions (for data

communications quality. The definition varies among

ket for mobile is calculated as the pre-paid price

on electricity production, see table 3.9). Electricity

countries: some operators define faults as includ-

for 25 calls per month spread over the same mobile

consumption is equivalent to production less power

ing malfunctioning customer equipment while others

network, other mobile networks, and mobile to fixed

plants’ own use and transmission, distribution, and

include only technical faults.

calls and during peak, off-peak, and weekend times.

transformation losses less exports plus imports. It

Although access is the key to delivering telecom-

It also includes 30 text messages per month. • Cost

includes consumption by auxiliary stations, losses

munications services to people, if that service is

of call to U.S. is the cost of a three-minute, peak

in transformers that are considered integral parts of

not affordable to most people, then goals of univer-

rate, fixed-line call from the country to the United

those stations, and electricity produced by pumping

sal usage will not be met. Three indicators of tele-

States. • Total telecommunications revenue is the

installations. Where data are available, it covers elec-

communications affordability are presented in the

revenue from the provision of telecommunications

tricity generated by primary sources of energy—coal,

table (price basket for fixed-line telephone service,

services such as fixed-line, mobile, and data divided

oil, gas, nuclear, hydro, geo-thermal, wind, tide and

price basket for mobile service, and the cost of an

by GDP. • Total telephone subscribers per employee

wave, and combustible renewables. Neither produc-

international call). Telecommunications efficiency is

are telephone subscribers (fi xed-line plus mobile)

tion nor consumption data capture the reliability of

measured by total telecommunications revenue as

divided by the total number of telecommunications

supplies, including breakdowns, load factors, and

percent of GDP and by total telephone subscribers

employees.

frequency of outages.

per employee.

Over the past decade new financing and technology, along with privatization and liberalization, have

Data sources

spurred dramatic growth in telecommunications

Data on electricity consumption and losses are

in many countries. With the rapid development of

from the IEA’s Energy Statistics and Balances of

mobile telephony and the global expansion of the

Non-OECD Countries 2003–2004, the IEA’s Energy

Internet, information and communication technolo-

Statistics of OECD Countries 2003–2004, and the

gies are increasingly recognized as essential tools of

United Nations Statistics Division’s Energy Statis-

development, contributing to global integration and

tics Yearbook. Data on telecommunications are

enhancing public sector effectiveness, effi ciency,

from the International Telecommunication Union’s

and transparency. The table presents telecommu-

World Telecommunication Development Report

nications indicators covering access, quality, and

database and World Bank estimates.

affordability and efficiency.

2007 World Development Indicators

303

STATES AND MARKETS

Power and communications

5.11

The information age Daily Households newspapers with televisiona

Personal computers and the Internet

Access per 1,000 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, 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

304

per 1,000 people

%

2000

2005

.. .. 27 11 40 .. 161 309 10 .. .. 153 5 99 .. 25 46 173 1 2 .. 6 168 2 0 .. 59 218 26 3 6 70 16 134 54 .. 283 28 98 31 29 .. 192 0 445 142 29 2 5 291 14 .. .. .. 5 ..

2007 World Development Indicators

.. 90 88 9 .. 91 96 95 .. 23 97 98 20 .. 87 .. 91 97 7 14 43 18 99 2 2 87 89 99 92 2 6 93 35 94 .. .. 97 76 89 89 .. 14 93 2 94 95 54 12 89 95 26 100 .. 9 26 27

Personal Internet computersa usersa 2005

2005

.. .. 11 .. 83 66 683 607 23 12 .. 348 4 23 .. 45 105 59 2 5 3 10 700 3 2 141 41 601 41 .. 4 219 15 190 33 240 656 .. 39 38 51 8 483 3 481 575 33 16 42 545 5 89 19 5 .. ..

1 60 58 11 177 53 698 486 81 3 347 458 50 52 206 34 195 206 5 5 3 15 520 3 4 172 85 508 104 2 13 254 11 327 17 269 527 169 47 68 93 16 513 2 534 430 48 33 39 455 18 180 79 5 20 70

Information and communications technology expenditures

Quality Broadband Schools connected to subscribers the Internet per 1,000 peoplea % 2005

.. .. 53 .. .. .. 97 .. .. .. .. .. .. .. .. 4 50 60 .. .. .. .. 98 .. .. 62 .. 100 .. .. .. 15 .. 100 .. 95 100 .. .. 66 .. .. 75 1 .. 94 .. 13 .. .. 1 .. .. .. .. ..

2005

0.0 0.0 5.9 .. 21.7 0.3 103.4 142.8 0.3 0.0 0.2 191.3 0.0 1.2 3.5 .. 17.7 0.2 0.0 0.0 0.0 .. 207.6 0.0 .. 43.5 28.7 238.9 7.0 0.0 0.0 6.6 0.0 20.2 0.0 43.7 249.3 7.4 2.0 1.5 6.1 0.0 133.1 0.0 223.8 155.5 1.1 0.0 0.3 129.7 0.1 14.4 2.2 0.0 .. ..

Application Affordability Secure Internet servers Price basket International Internet bandwidth per million people for Internet Per capita $ per montha % of GDP bits per capitaa $ October 2005 2006 2005 2005 2005

0 4 .. .. 316 12 5,903 6,681 29 0 48 11,279 5 44 40 .. 149 318 6 .. 1 .. 6,800 0 0 788 104 9,451 488 0 0 241 3 1,074 8 .. 34,891 .. 48 49 23 2 3,566 .. 4,326 3,286 145 6 .. 6,860 8 589 57 .. .. ..

0 2 0 0 12 3 581 284 0 0 1 146 0 3 4 1 16 11 0 0 0 0 645 0 .. 22 0 191 6 0 0 67 0 48 0 64 614 6 5 1 6 .. 163 0 380 96 5 1 5 349 0 40 6 .. .. 1

.. 16.3 9.4 34.3 14.4 52.5 22.8 15.5 10.0 24.0 10.5 37.2 20.7 12.3 7.8 21.3 26.0 7.3 90.6 52.0 33.1 44.6 8.9 147.8 86.3 25.6 9.8 3.9 7.8 93.2 84.5 28.1 67.1 16.1 30.0 18.8 23.2 18.8 37.0 5.0 22.6 28.6 10.8 23.3 22.2 12.4 40.1 17.8 9.9 7.4 23.6 16.4 54.3 24.7 75.0 71.0

.. .. 2.4 .. 7.1 .. 6.2 5.5 .. 2.4 .. 5.8 .. 5.5 .. .. 7.8 3.8 .. .. .. 5.0 5.9 .. .. 6.1 5.3 8.9 8.5 .. .. 7.7 .. .. .. 7.1 6.0 .. 3.2 1.5 .. .. .. .. 6.9 6.3 .. .. .. 6.1 .. 4.1 .. .. .. ..

.. .. 76 .. 337 .. 2,247 2,059 .. 10 .. 2,061 .. 56 .. .. 333 130 .. .. .. 52 2,034 .. .. 430 90 2,280 227 .. .. 358 .. .. .. 866 2,849 .. 87 18 .. .. .. .. 2,527 2,213 .. .. .. 2,059 .. 822 .. .. .. ..

Daily Households newspapers with televisiona

Personal computers and the Internet

Access per 1,000 people

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

per 1,000 people

%

2000

2005

.. 162 60 23 .. .. 148 .. 109 .. 566 74 .. 8 .. .. .. .. .. 138 63 9 14 14 31 54 5 2 95 1 .. 116 94 153 18 29 3 9 17 .. 279 202 .. 0 25 569 .. 39 .. .. .. 23 .. 102 102 ..

58 96 32 65 77 .. 95 93 96 70 99 96 .. 17 .. .. 95 .. 30 98 93 12 .. .. 98 95 8 3 89 15 21 93 93 .. 30 76 6 3 39 .. 99 98 .. 5 26 100 79 47 77 9 76 69 63 91 99 97

Personal Internet computersa usersa 2005

2005

16 146 16 14 109 .. 494 740 367 63 542 56 .. 9 .. 545 237 19 17 217 114 .. .. .. 155 222 5 2 197 3 14 162 136 27 133 25 6 8 109 4 682 474 40 1 7 573 47 .. 46 64 75 100 45 193 133 ..

36 297 55 73 103 1 276 470 478 404 668 118 27 32 .. 684 276 54 4 448 196 24 .. 36 358 79 5 4 435 4 7 146 181 96 105 152 7 2 37 4 739 672 26 2 38 735 111 67 64 23 32 164 54 262 279 221

5.11 Information and communications technology expenditures

Quality Broadband Schools connected to subscribers the Internet per 1,000 peoplea % 2005

.. 85 .. .. .. .. .. 95 .. .. 99 18 .. .. .. 100 .. .. .. 97 20 .. .. .. 56 100 .. 1 .. .. .. .. 60 50 19 .. 0 .. 13 .. .. 100 .. .. .. .. .. .. .. .. .. .. .. 90 .. ..

2005

0.0 64.6 1.2 .. 0.3 .. 65.1 177.6 115.7 .. 175.0 1.9 0.1 0.0 .. 252.4 8.1 0.5 0.0 113.4 36.3 0.0 0.0 .. 68.6 6.1 0.0 0.0 19.4 0.0 0.1 2.2 22.4 2.5 0.7 8.3 .. 0.0 0.0 .. 251.2 80.8 1.9 0.0 0.0 214.4 3.3 0.3 5.4 .. 0.9 12.5 0.7 32.6 114.9 ..

Application Affordability Secure Internet servers Price basket International Internet bandwidth per million people for Internet Per capita $ per montha % of GDP bits per capitaa $ October 2005 2006 2005 2005 2005

.. 991 18 7 15 .. 6,043 2,499 2,080 .. 1,038 58 .. 3 .. 1,030 348 15 3 972 81 4 .. .. 1,460 17 2 2 128 2 15 50 110 97 16 235 1 2 4 2 20,549 1,126 1 2 1 9,368 194 5 292 .. 42 358 39 560 833 ..

4 36 1 1 0 0 416 182 53 17 331 4 1 0 .. 22 35 1 0 46 11 .. .. 0 26 2 0 0 17 0 1 18 10 4 4 1 0 0 8 1 413 594 3 0 0 389 3 0 57 1 1 6 3 38 65 33

33.4 11.0 6.8 17.3 2.3 .. 31.1 22.0 24.8 34.3 13.8 11.1 15.8 75.9 .. 32.6 22.2 12.0 27.6 12.5 10.0 38.6 .. 22.0 7.2 25.3 45.9 41.9 7.4 28.4 54.3 17.5 20.0 24.1 10.7 26.8 32.9 48.9 48.7 8.1 12.4 11.9 28.1 101.8 50.4 29.8 14.5 9.5 38.4 25.0 11.7 23.6 1.8 11.3 37.8 ..

4.6 5.8 5.8 3.4 2.5 .. 4.4 8.3 4.3 10.6 7.5 8.4 .. 2.8 .. 6.9 1.4 .. .. .. .. .. .. .. .. .. .. .. 7.0 .. .. .. 3.3 .. .. 6.3 .. .. .. .. 6.3 9.8 .. .. 3.5 5.1 .. 6.9 8.4 .. .. 6.6 7.0 4.2 4.4 ..

2007 World Development Indicators

53 632 42 44 69 .. 2,127 1,475 1,308 381 2,678 195 .. 15 .. 1,127 437 .. .. .. .. .. .. .. .. .. .. .. 360 .. .. .. 246 .. .. 108 .. .. .. .. 2,402 2,611 .. .. 27 3,252 .. 49 403 .. .. 187 83 331 758 ..

305

STATES AND MARKETS

The information age

5.11

The information age Daily Households newspapers with televisiona

Personal computers and the Internet

Access per 1,000 people

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 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 European Monetary Union

per 1,000 people

%

2000

2005

.. .. 0 .. .. .. .. 273 131 168 .. 25 98 29 .. .. 410 372 .. .. .. 197 2 .. 19 .. 7 3 175 .. 326 196 .. .. .. 6 .. .. 22 .. 90 w 45 55 61 .. 49 60 .. 61 .. 59 12 263 188

94 98 2 99 29 92 7 99 99 96 8 59 99 32 49 18 94 100 80 .. 6 92 51 88 92 92 .. 5 97 86 .. 98 93 .. 83 83 93 43 26 26 79 m 15 88 84 91 48 36 92 87 84 32 14 97 96

Personal Internet computersa usersa 2005

2005

113 122 .. 354 21 48 .. .. 358 404 6 85 277 27 90 32 763 865 42 .. 7 58 30 79 57 52 .. 9 38 197 600 762 125 .. 82 13 48 15 10 92 130 w 11 58 45 113 40 38 98 88 48 16 15 579 421

208 152 6 66 46 148 2 571 464 545 11 109 348 14 77 32 764 498 58 1 9 110 49 123 95 222 8 17 97 308 473 630 193 34 125 129 67 9 20 77 137 w 44 115 95 196 84 89 190 156 89 49 29 527 439

Information and communications technology expenditures

Quality Broadband Schools connected to subscribers the Internet per 1,000 peoplea % 2005

2005

57 43 .. .. .. 70 .. 100 65 96 .. .. .. .. .. .. .. .. .. .. .. .. .. 15 25 40 .. 1 .. .. 99 100 50 .. .. .. .. .. .. .. .. m .. .. .. 60 .. .. .. .. .. .. .. .. ..

34.7 11.1 .. 0.8 1.6 .. .. 153.3 25.7 85.0 0.0 3.5 115.1 0.7 0.0 0.0 214.0 232.0 0.0 0.0 0.0 0.7 0.0 8.3 1.6 22.1 .. .. .. 28.3 163.8 166.6 17.7 0.1 13.4 2.5 2.1 .. 0.0 0.8 41.6 w 0.9 22.6 23.1 21.0 13.4 25.9 20.9 16.4 0.5 1.0 .. 163.2 134.7

Application Affordability Secure Internet servers Price basket International Internet bandwidth per million people for Internet Per capita $ per montha % of GDP bits per capitaa $ October 2005 2006 2005 2005 2005

623 100 .. 31 66 87 .. 5,826 2,636 1,258 0 19 2,822 25 6 .. 17,531 9,671 .. 0 .. 106 7 375 75 405 .. 3 17 923 13,062 3,306 462 1 51 43 66 .. 2 4 816 w 15 92 116 218 59 97 211 161 .. 18 .. 4,530 5,784

7 3 .. 4 0 2 0 291 28 95 .. 23 100 2 .. 4 405 577 0 .. 0 6 0 28 2 25 .. 0 2 54 560 870 29 0 5 0 1 0 0 0 74 w 0 5 2 17 3 1 13 12 1 1 2 443 184

17.0 12.7 30.1 21.3 25.6 13.2 10.6 20.5 18.9 18.6 .. 63.2 31.7 4.6 65.5 51.7 19.2 7.9 14.0 12.3 93.6 6.9 44.7 13.4 12.4 11.6 69.5 99.6 7.7 13.1 27.3 15.0 23.9 5.7 42.6 10.7 15.6 10.9 68.4 24.6 22.0 m 30.1 17.0 16.8 17.0 23.4 10.7 12.2 25.8 11.8 8.1 45.3 19.9 20.4

3.6 3.6 .. 2.3 8.3 .. .. 9.4 5.6 3.1 .. 9.9 3.7 5.5 .. .. 7.4 7.5 .. .. .. 4.1 .. .. 5.8 7.9 .. .. 8.0 3.6 7.3 8.8 7.9 .. 3.9 15.1 .. .. .. 7.7 6.8 w 5.9 5.4 5.5 5.2 5.4 5.3 5.1 5.9 3.1 5.7 7.4 7.2 5.4

164 191 .. 285 59 .. .. 2,537 486 532 .. 504 959 66 .. .. 2,941 3,691 .. .. .. 112 .. .. 167 396 .. .. 141 1,027 2,683 3,690 385 .. 205 95 .. .. .. 33 538 w 41 149 108 312 109 89 274 278 66 40 .. 2,466 1,726

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.

306

2007 World Development Indicators

About the data

5.11

Definitions

The digital and information revolution has changed

The number of secure Internet servers, from the

• Daily newspapers refer to those published at

the way the world learns, communicates, does busi-

Netcraft Secure Server Survey, gives an indication

least four times a week and calculated as average

ness, and treats illnesses. New information and com-

of how many companies are conducting encrypted

circulation (or copies printed) per 1,000 people.

munications technologies offer vast opportunities for

transactions over the Internet.

• Households with television are the percentage of

progress in all walks of life in all countries—opportu-

The data on information and communications

households with a television set. Some countries

nities for economic growth, improved health, better

technology expenditures cover the world’s 75 larg-

report only the number of households with a color

service delivery, learning through distance educa-

est buyers of such technology among countries and

television set, and therefore the true number may

tion, and social and cultural advances. The table

regions.

be higher than reported. • Personal computers are

presents indicators of the penetration of the infor-

Ensuring universal access to information and com-

self-contained computers designed for use by a

mation economy (newspapers, televisions, personal

munication technology is a goal of many countries,

single individual. • Internet users are people with

computers, and Internet use), quality (broadband

but not all countries regularly track accessibility.

access to the worldwide network. • Schools con-

subscribers, international Internet bandwidth, and

There is no common set of information and com-

nected to the Internet are the share of primary and

secure Internet servers), and some of the econom-

munications technology indicators and definitions,

secondary schools in the country that have access

ics of the information age (Internet access charges

and data are often drawn from administrative records

to the Internet. • Broadband subscribers are the

and spending on information and communications

rather than from specific surveys. Access needs to

number of broadband subscribers with a digital

technology).

be accurately measured in three major areas: indi-

subscriber line, cable modem, or other high-speed

vidual, household, and community access.

technologies. Reporting countries may have differ-

The data on the number of daily newspapers in circulation are from surveys by the United Nations

ent definitions of broadband, so data are not strictly

Educational, Scientifi c, and Cultural Organization

comparable across countries. • International Inter-

(UNESCO) Institute for Statistics. In some countries

net bandwidth is the contracted capacity of inter-

definitions, classifications, and methods of enumera-

national connections between countries for trans-

tion do not entirely conform to UNESCO standards.

mitting Internet traffic. • Secure Internet servers

For example, newspaper circulation data should refer

are servers using encryption technology in Internet

to the number of copies distributed, but in some

transactions.• Price basket for Internet is calculated

cases the figures reported are the number of copies

based on the cheapest available tariff for accessing

printed. The data for other electronic communica-

the Internet 20 hours a month (10 hours peak and

tions and information technology are from the Inter-

10 hours off-peak). The basket does not include the

national Telecommunication Union (ITU), the Internet

telephone line rental but does include telephone

Software Consortium, Netcraft, the World Informa-

use charges if applicable. Data are compiled in the

tion Technology and Services Alliance (WITSA),

national currency and converted to U.S. dollars using

Global Insights, and World Bank staff estimates.

the annual average exchange rate. • Information and

Estimates of households with television are derived

communications technology expenditures include

from household surveys; data presented in the table

computer hardware (computers, storage devices,

are from the ITU and World Bank staff estimates.

printers, and other peripherals); computer software

The estimates of personal computers are derived

(operating systems, programming tools, utilities,

from an annual ITU questionnaire, supplemented by

applications, and internal software development);

other sources. In many countries mainframe comput-

computer services (information technology consult-

ers are used extensively. Since thousands of users

ing, computer and network systems integration, Web

can be connected to a single mainframe computer,

hosting, data processing services, and other ser-

the number of personal computers understates the

vices); and communications services (voice and data

total use of computers.

communications services) and wired and wireless

The data on Internet users and related Internet

communications equipment.

indicators are based on nationally reported data. Some countries derive these data from Internet

Data sources

surveys, but since survey questions and definitions

Data on newspapers are compiled by the UNESCO

differ across countries, the estimates may not be

Institute for Statistics. Data on televisions, per-

strictly comparable. For example, questions on the

sonal computers, Internet users, broadband sub-

age of Internet users and frequency of use vary by

scribers, international Internet bandwidth, and

country. Countries that do not have surveys generally

price basket for Internet are from the ITU’s World

derive their estimates from reported Internet service

Telecommunication Development Report data-

provider subscriber counts, calculated by multiplying

base. Data on schools connected to the Internet

the number of subscribers by a selected multiplier.

are World Bank staff estimates. Data on secure

This method may undercount the actual number of

Internet servers are from Netcraft (www.netcraft.

people using the Internet, particularly in developing

com/). Data on information and communications

countries, where many commercial subscribers rent

technology expenditures are from WITSA’s Digital

out computers connected to the Internet or prepaid

Planet 2006: The Global Information Economy and

cards are used to access the Internet.

from Global Insight, Inc.

2007 World Development Indicators

307

STATES AND MARKETS

The information age

5.12

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, 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

308

per million people

per million people

2000–04c

2000–04c

.. .. .. .. 720 .. 3,759 2,968 .. .. .. 3,065 .. 120 .. .. 344 1,263 .. .. .. .. 3,597 .. .. 444 708 1,564 109 .. 30 .. .. 1,296 .. 1,594 5,016 .. 50 .. 47 .. 2,523 .. 7,832 3,213 .. .. .. 3,261 .. 1,413 .. .. .. ..

.. .. .. .. 316 .. .. 1,254 .. .. .. 1,473 .. 6 .. .. 332 477 .. .. .. .. 770 .. .. 303 .. 225 77 .. 32 .. .. 455 .. 923 2,713 .. .. .. .. .. 427 .. .. .. .. .. .. 1,089 .. 895 .. .. .. ..

2007 World Development Indicators

High-technology exports

% of GDP

$ millions

2003

2000–04c

2005

.. .. 285 .. 3,086 175 15,809 4,906 107 204 532 6,604 .. .. .. 76 8,684 829 .. .. .. 123 24,803 .. .. 1,500 29,186 .. 337 .. .. 84 .. 845 264 2,950 5,291 .. .. 1,720 .. .. 368 99 5,202 31,971 .. .. 117 44,305 83 3,770 .. .. .. ..

.. .. .. .. 0.41 0.25 1.70 2.26 0.30 0.62 0.62 1.90 .. 0.28 .. .. 0.98 0.51 .. .. .. .. 1.93 .. .. 0.61 1.44 0.60 0.17 .. .. 0.39 .. 1.14 0.65 1.28 2.63 .. 0.07 0.19 .. .. 0.91 .. 3.51 2.16 .. .. 0.29 2.49 .. 0.58 .. .. .. ..

.. 5 7 .. 809 5 3,276 11,623 5 3 216 22,809 0 28 .. .. 8,007 326 3 0 4 2 29,777 0 .. 195 214,246 94,808 362 .. .. 1,775 93 689 .. 7,662 11,733 .. 64 15 37 .. 931 0 13,835 69,673 28 0 76 137,547 19 994 98 .. .. ..

Royalty and license fees

% of manufactured Receipts exports $ millions

Patent applications fileda

Trademark applications filedb

Payments $ millions

Residents

Nonresidents

Residents

Nonresidents

2004

2004

2005

2005

2005

2004

2004

.. 1 1 .. 7 1 13 13 1 0 3 9 0 9 .. .. 13 5 10 6 0 2 14 0 .. 5 31 34 5 .. .. 38 8 12 .. 13 22 .. 8 1 4 .. 18 0 25 20 15 6 23 17 9 10 3 .. .. ..

.. 1 .. 49 54 .. 508 177 0 0 3 1,107 0 2 .. 0 102 5 .. 0 0 0 3,471 .. .. 54 157 218 10 .. .. 0 0 73 .. 63 .. 0 0 136 2 .. 5 0 1,207 5,924 .. .. 9 6,828 0 60 0 0 .. 0

.. 4 .. 3 635 .. 1,645 1,334 0 3 20 1,107 2 11 .. 12 1,404 79 .. 0 7 2 6,649 .. .. 322 5,321 1,111 118 .. .. 57 22 193 .. 216 .. 31 42 182 30 .. 25 1 1,123 3,230 .. .. 5 6,589 0 442 0 0 0 0

.. 0 58 .. 786 151 8,555 1,965 .. .. 1,065 519 .. .. 47 0 3,892 263 .. .. .. .. 3,929 .. .. .. 65,586 127 52 .. .. .. .. 383 .. 619 1,843 .. 13 157 .. .. 27 .. 2,004 14,230 .. 0 248 48,329 .. 487 .. .. .. ..

.. .. 334 .. 3,816 6 21,651 549 .. .. 382 188 .. .. 349 .. 14,800 133 .. .. .. .. 33,298 .. .. .. 64,798 9,878 198 .. .. .. .. 841 .. 633 172 .. 489 537 .. .. 97 .. 216 3,060 .. .. 210 10,905 .. 27 .. .. .. ..

.. .. 1,488 .. 61,953 1,598 37,202 7,336 144 .. 2,410 21,010d .. .. 267 .. 81,036 5,978 .. 20 409 .. 17,719 .. .. .. 527,591 7,374 .. .. .. .. .. 1,283 379 9,365 4,185 .. 5,571 .. .. .. 1,241 .. 2,598 57,784 .. .. 148 62,576 .. 5,290 .. .. .. ..

.. .. 871 .. 19,139 256 16,007 1,049 339 .. 1,027 10,695d .. .. 700 .. 13,218 1,086 .. 132 1,638 .. 22,169 .. .. .. 52,788 11,208 .. .. .. .. .. 767 1,937 1,042 944 .. 4,822 .. .. .. 583 .. 722 2,935 .. .. 132 3,342 .. 1,143 .. .. .. ..

Researchers Technicians Scientific Expenditures in R&D in R&D and for R&D technical journal articles

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

per million people

per million people

2000–04c

2000–04c

.. 1,472 .. 207 1,279 .. 2,674 .. 1,213 .. 5,287 .. 629 .. .. 3,187 .. .. .. 1,434 .. .. .. 361 2,136 504 15 .. 299 .. .. .. 268 .. .. .. .. 17 .. 59 2,482 3,945 .. .. .. 4,587 .. .. 97 .. 79 .. 48 1,581 1,949 ..

.. 466 .. .. .. .. 621 .. .. .. 528 .. 92 .. .. 567 .. .. .. 318 .. 0 .. 493 427 69 45 .. 58 .. .. .. 96 .. .. .. .. 133 .. 137 1,725 833 .. .. .. 1,754 .. .. 387 .. 113 .. 8 282 307 ..

High-technology exports

% of GDP

$ millions

2003

2000–04c

2005

.. 2,503 12,774 178 1,806 .. 1,758 6,941 24,696 .. 60,067 263 128 258 .. 13,746 244 .. .. 153 223 .. .. .. 322 74 .. .. 520 .. .. .. 3,747 78 .. 428 .. .. .. .. 13,475 3,034 .. .. 384 3,339 114 368 .. .. .. 129 179 6,770 2,625 ..

0.05 0.88 0.85 0.05 0.67 .. 1.21 4.46 1.14 0.07 3.15 .. 0.22 .. .. 2.64 0.20 0.20 .. 0.42 .. 0.01 .. .. 0.76 0.26 0.12 .. 0.69 .. .. 0.35 0.40 .. 0.28 0.62 0.59 0.07 .. 0.66 1.85 1.16 0.05 .. .. 1.75 .. 0.22 0.34 .. 0.10 0.10 0.11 0.58 0.78 ..

6 13,045 2,840 6,571 98 .. .. 4,937 24,616 .. 122,680 160 72 18 .. 83,527 .. 4 .. 159 26 .. .. .. 410 16 1 6 57,376 .. .. 298 32,262 11 0 702 9 .. 15 1 65,758 943 4 1 9 3,010 25 211 1 47 14 64 26,077 2,688 2,639 ..

Royalty and license fees

% of manufactured Receipts exports $ millions 2005

2 25 5 16 3 .. .. 14 8 .. 22 5 2 3 .. 32 .. 2 .. 5 2 .. .. .. 6 1 1 7 55 .. .. 21 20 3 0 10 8 .. 3 0 30 14 5 3 2 17 2 2 1 39 7 3 71 4 9 ..

Patent applications fileda

5.12

STATES AND MARKETS

Science and technology

Trademark applications filedb

Payments $ millions

Residents

Nonresidents

Residents

Nonresidents

2005

2005

2004

2004

2004

2004

0 834 25 263 .. .. 589 610 1,131 13 17,655 .. 0 18 .. 1,827 0 2 .. 10 0 18 .. 0 2 3 1 .. 27 0 .. 0 70 2 .. 13 2 0 0 .. 3,866 101 0 .. .. 364 .. 15 0 .. 196 2 6 61 60 ..

22 1,068 421 961 .. .. 19,426 537 1,942 11 14,653 .. 31 37 .. 4,398 0 2 .. 14 0 .. .. 0 21 10 3 .. 1,370 1 .. 5 111 2 .. 45 5 0 3 .. 3,692 555 0 0 45 546 .. 110 42 .. 1 69 265 1,036 328 ..

161 1,919 10,671 3,441 .. .. 58 8,929 .. 54 60,739 .. 102 .. .. 35,088 .. 1 .. 44 .. .. .. .. 45 415 22 .. .. .. .. .. 12,667 9 86 .. .. .. .. .. 556 4,952 .. .. .. .. .. 1,081 .. .. 173 785 2,539 5,359 66 ..

1,149 4,293 .. .. .. .. 1,285 215 .. 663 110,270 .. 2,908 .. .. 91,935 .. 133 25 1,290 .. .. .. .. 1,929 478 411 138 .. .. .. .. 41,813 1,574 423 .. .. .. .. .. .. 8,426 .. .. .. .. .. 8,319 .. .. .. 8,227 6,861 13,776 8,123 ..

3,388 1,047 .. .. .. .. 1,142 5,022 .. 1,433 18,573 .. 1,070 .. .. 16,529 .. 345 656 595 .. .. .. .. 570 515 321 440 .. .. .. .. 20,775 372 1,321 .. .. .. .. .. .. 7,864 .. .. .. 6,981 .. 4,455 .. .. .. 5,661 5,253 1,153 1,012 ..

7 738 6,795 226 .. .. 787 1,329 .. 15 362,342 .. 1,696 .. .. 105,027 .. 179 .. 107 .. .. .. .. 69 37 16 .. .. .. .. .. 531 297 143 .. .. .. .. .. 2,187 1,579 .. .. .. .. 0 .. .. .. 12 38 157 2,381 121 ..

2007 World Development Indicators

309

5.12

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 and Montenegro 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 Europe EMU

per million people

per million people

2000–04c

2000–04c

2003

249 557 .. .. .. .. .. 381 445 1,600 .. 73 861 .. .. .. .. 2,319 .. .. .. 208 .. .. 34 37 .. .. .. .. .. .. 50 .. .. .. .. .. .. .. .. w .. .. .. 308 .. .. 384 .. .. .. .. .. ..

988 15,782 .. 573 79 600 .. 3,122 943 969 .. 2,364 16,826 141 .. .. 10,237 8,542 67 .. 86 1,072 .. .. 452 6,224 .. 90 2,089 193 48,288 211,233 194 192 563 216 .. .. .. 96 697,397 s 14,929 100,288 49,969 50,319 115,217 31,351 42,695 18,588 5,358 13,487 3,738 582,180 156,184

976 3,319 .. .. .. .. .. 4,999 1,984 2,543 .. 307 2,195 128 .. .. 5,416 3,601 .. .. .. 287 .. .. 1,013 341 .. .. .. .. .. 4,605 366 .. .. 115 .. .. .. .. .. w .. 725 504 1,453 .. 708 1,993 256 .. .. .. 3,781 2,607

High-technology exports

% of GDP

$ millions

2000–04c

2005

0.40 758 1.17 3,690 .. 1 .. 215 .. 75 1.17 .. .. .. 2.25 105,078 0.53 1,960 1.61 786 .. .. 0.76 1,739 1.11 10,409 0.14 60 0.34 0 .. .. 3.74 17,070 2.57 25,544 .. 6 .. .. .. 1 0.26 22,480 .. 0 0.12 34 0.63 370 0.66 906 .. .. 0.81 18 1.16 869 .. .. 1.89 82,841 2.68 233,079 0.26 22 .. .. 0.28 118 0.19 594 .. .. .. 11 .. 2 .. 5 2.28 w 1,243,114 s 0.73 .. 0.85 332,483 1.12 .. 0.67 120,551 0.83 270,664 1.44 .. 0.94 26,767 0.56 40,879 .. 1,405 0.73 .. .. .. 2.45 1,156,714 1.92 358,491

Royalty and license fees

% of manufactured Receipts exports $ millions 2005

2005

Patent applications fileda

Trademark applications filedb

Payments $ millions

Residents

Nonresidents

Residents

Nonresidents

2005

2004

2004

2004

2004

3 48 173 937 163 10,298 1,193 8 260 1,593 22,944 7,246 23,571 7,088 25 0 0 .. .. .. .. 1 0 0 61 552 .. .. 12 0 7 .. .. .. .. .. .. .. 381 .. 1,085 647 .. 1 0 .. .. .. .. 57 544 8,647 509 8,076 4,839 18,409 7 50 91 214 239 2,912 1,148 5 16 113 327 42 1,615 550 .. .. .. .. .. .. .. 7 45 1,071 .. .. .. .. 7 561 2,639 2,864 320 52,718 2,059 1 .. .. 95 189 3,989 1,773 0 0 0 .. .. .. .. .. 0 88 .. .. .. .. 17 3,324 1,498 2,752 478 .. .. 22 .. .. 1,827 466 .. .. 1 .. 12 .. .. .. .. .. 1 0 32 2 65 253 1 0 0 .. .. .. .. 27 17 1,674 681 4,329 22,612 9,241 0 0 2 .. .. .. .. 1 .. .. 2 205 340 1,317 5 14 8 .. .. .. .. 2 0 439 465 383 30,136 2,101 .. .. .. .. .. .. .. 14 7 1 .. .. .. .. 4 22 421 4,086 1,692 11,516 2,434 .. .. .. .. .. .. .. 28 13,303 9,069 18,816 11,138 23,186 4,653 32 57,410 24,501 185,008 171,935 213,495 26,988 2 0 4 37 514 4,589 6,732 .. .. .. 273 205 438 494 3 0 239 .. .. .. .. 6 .. .. .. .. .. .. .. .. .. .. .. .. .. 5 .. 9 .. .. .. .. 1 .. .. .. .. .. .. 1 .. .. .. .. .. .. 22 w 123,690 s 134,689 s 872,278 s 473,770 s 1,337,033 s 267,528 s 4 47 224 7,259 12,067 10,198 8,827 21 2,693 19,573 105,144 120,688 678,572 107,953 26 1,083 10,892 76,157 90,921 566,801 71,992 17 1,610 8,681 28,987 29,767 111,771 35,961 21 2,740 19,797 112,403 132,755 688,770 116,780 34 471 9,599 66,112 70,866 535,284 61,115 8 1,405 5,353 34,767 19,989 96,993 23,355 15 542 3,204 4,498 29,255 47,763 27,534 3 163 256 215 871 1,488 871 4 18 113 6,795 11,752 8,319 4,455 4 141 1,272 16 22 411 321 22 120,950 114,892 759,875 341,015 648,263 150,748 16 21,814 42,096 72,974 15,757 133,351 8,184

Note: The original information on patent and trademark application 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. a. Excludes applications filed under the auspices of the European Patent Office (32,178 by residents, 91,523 by nonresidents) and the Eurasian Patent Organization (1,630 by nonresidents). b. Excludes applications filed under the auspices of the EU Office for Harmonization in the Internal Market (40,305 by residents, 18,540 by nonresidents). c. Data are for the most recent year available. d. Includes Luxembourg and the Netherlands.

310

2007 World Development Indicators

About the data Technological innovation, often fueled by government-led research and development (R&D), has been the driving force for industrial growth around the world. The best opportunities to improve living standards, including new ways of reducing poverty, will come from science and technology. Science, advancing rapidly in virtually all fields—particularly in biotechnology—is playing a growing economic role: countries able to access, generate, and apply relevant scientific knowledge will have a competitive edge over those that cannot. And there is greater appreciation of the need for high-quality scientific input into public policy issues such as regional and global environmental concerns. Science and technology cover a range of issues too complex and too broad to be quantified by any single set of indicators, but those in the table shed light on countries’ “technology base”—the availability of skilled human resources, the number of scientific and technical articles published, the competitive edge countries enjoy in high-technology exports, sales and purchases of technology through royalties and licenses, and the number of patent and trademark applications filed. The United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics (UIS) collects data on researchers, technicians and expenditure in R&D from countries and territories around the World, through questionnaires and special surveys as well as from other international sources. Data on researchers and technicians are normally calculated in terms of full-time equivalents. R&D expenditures include all expenditures for R&D performed within a country, including both capital expenditures and current costs (annual wages and salaries and all associated costs of researchers, technicians and supporting staff, and other current costs, such as noncapital purchases of materials, supplies and R&D equipment such as utilities, books, journals, reference materials, subscriptions to libraries and scientific societies, and materials for laboratories). The information does not reflect the quality of training and education, which varies widely. Similarly, R&D expenditures are no guarantee of progress; governments need to pay close attention to the practices that make R&D expenditures effective. Article counts are from a set of journals classified and covered by the Institute for Scientific Information’s Science Citation Index (SCI) and Social Sciences Citation Index (SSCI). Article counts are based on fractional assignments; for example, an article with two authors from different countries is counted as onehalf of an article for each country (see Definitions for the fields covered). The SCI and SSCI databases cover the core set of scientific journals but may exclude some of regional or local importance. They may also reflect some bias toward English-language journals. The method used for determining a country’s high technology exports was developed by the Organisation for Economic Co-operation and Development in collaboration with Eurostat. Termed the “product approach” to distinguish it from a “sectoral approach,” the method is based on the calculation of R&D intensity (R&D expenditure divided by total

5.12

STATES AND MARKETS

Science and technology Definitions sales) for groups of products from six countries (Germany, Italy, Japan, the Netherlands, Sweden, and the United States). Because industrial sectors characterized by a few high-technology products may also produce many low-technology products, the product approach is more appropriate for analyzing international trade than is the sectoral approach. To construct a list of high-technology manufactured products (services are excluded), the R&D intensity was calculated for products classified at the three-digit level of the Standard International Trade Classification revision 3. The final list was determined at the four- and five-digit levels. At these levels, since no R&D data were available, final selection was based on patent data and expert opinion. This method takes only R&D intensity into account. Other characteristics of high technology are also important, such as know-how, scientific and technical personnel, and technology embodied in patents; considering these characteristics would result in a different list. (See Hatzichronoglou 1997 for further details.) Moreover, the R&D for high-technology exports may not have occurred in the reporting country. Most countries have adopted systems that protect patentable inventions. The Patent Cooperation Treaty provides an international system for filing patent applications. The procedure consists of an international phase followed by a national or regional phase. In the international phase an applicant files an international application and designates the countries in which patent protection is eventually to be sought (since 2004 all eligible countries are automatically designated in every application under the treaty). The application is searched, published, and, optionally, an international preliminary examination is conducted. In the national (or regional) phase the applicant requests national processing of the application, pays additional fees, and initiates the national search, examination, and granting procedure. International applications under the treaty provide only for a national patent grant— there is no international patent. The national phase filing represents an action on the part of the applicant to actively seek patent protection for a given territory, whereas international filings and designations, while they represent a legal right, do not accurately reflect where patent protection is eventually sought. Resident filings are those from applicants who are a resident of the country or region concerned. Nonresident filings are from applicants outside the relevant country or region. In the case of regional offices such as the European Patent Office, an application from a resident of any member state of the regional patent convention is considered a resident filing. Some offices (notably the U.S. Patent and Trademark Office) use the residence of the inventor rather than the applicant to classify resident and nonresident filings. A trademark provides protection to its owner by ensuring the exclusive right to use it to identify goods or services or to authorize another to use it in return for payment. The period of protection varies, but a trademark can be renewed indefinitely by paying additional fees. The trademark system helps consumers identify and purchase a product or service whose nature and quality, indicated by its unique trademark, meet their needs.

• Researchers in R&D are professionals engaged in conceiving of or creating new knowledge, products, processes, methods, and systems, and also in managing the projects concerned. Postgraduate students at the PhD level (International Standard Classification of Education 1997 level 6) engaged in R&D are considered researchers. • Technicians in R&D and equivalent staff are people whose main tasks require technical knowledge and experience in fields of engineering, physical and life sciences (technicians), and social sciences and humanities (equivalent staff). They participate in R&D by performing scientific and technical tasks involving the application of concepts and operational methods, normally under the supervision of researchers. • Scientific and technical journal articles refer to published scientific and engineering articles in physics, biology, chemistry, mathematics, clinical medicine, biomedical research, engineering and technology, and earth and space sciences. • Expenditures for R&D are current and capital expenditures on creative work undertaken systematically to increase the stock of knowledge, including knowledge of humanity, culture, and society, and the use of this knowledge to devise new applications. The term R&D covers basic research, applied research, and experimental development. • High-technology exports are products with high R&D intensity, such as in aerospace, computers, pharmaceuticals, scientific instruments, and electrical machinery. • Royalty and license fees are payments and receipts between residents and nonresidents for the authorized use of intangible, nonproduced, nonfinancial assets, and proprietary rights (such as patents, copyrights, trademarks, franchises, and industrial processes) and for the use, through licensing agreements, of produced originals of prototypes (such as films and manuscripts). • Patent applications filed are worldwide patent applications filed through the Patent Cooperation Treaty procedure or with a national patent office. A patent is an exclusive right to an invention (a product or process that provides a new way of doing something or offers a new technical solution to a problem). It must be of practical use and display a new characteristic unknown in the body of existing knowledge in its technical field. A patent grants protection to the owner of the patent for a specified period, generally 20 years. • Trademark applications filed are applications to register a trademark with a national or regional trademark office. Trademarks are distinctive signs identifying goods or services as produced or provided by a specific person or enterprise. Trademarks protect owners of the mark by ensuring exclusive right to use it to identify goods or services or to authorize its use in return for payment. Data sources Data on R&D are provided by UIS. Data on scientifi c and technical journal articles are from the U.S. National Science Foundation’s Science and Engineering Indicators 2006. Data on hightechnology exports are from the United Nations Statistics Division’s Commodity Trade (Comtrade) database. Data on royalty and license fees are from the International Monetary Fund’s Balance of Payments Statistics Yearbook. Data on patents and trademarks are from the World Intellectual Property Organization’s WIPO Patent Report: Statistics on Worldwide Patent Activity (2006 edition).

2007 World Development Indicators

311 Text figures, tables, and boxes

6

GLOBAL LINKS

Introduction

G

lobalization and global links

Globalization—the integration of the world economy—has been a persistent theme of the past quarter century. The growth of cross-border economic activity has changed the structure of economies and the political and social organization of countries. Not all effects of globalization can be measured directly. But the scope and pace of change can be monitored along four important channels: trade in goods and services, financial flows, the movement of people, and the diffusion of technology and knowledge. • Exports and imports of goods and services exceeded $26 trillion in 2005, or 58 percent of total global output, up from 44 percent in 1980. Developing economies still account for less than one-third of global trade, but their share has been increasing steadily. • Gross private capital flows across national borders exceeded 32 percent of global output in 2005, up from 9 percent in 1980. Foreign direct investment and cross-border portfolio investment flows to developing economies have soared despite occasional setbacks. • People have become more mobile. More than 800 million people traveled to foreign destinations in 2005, nearly triple the number in 1980. Some 190 million people are estimated to reside outside their land of birth, nearly double the 1980 level. • Technology and knowledge are diffusing at unprecedented speed across countries. International phone traffic, measured in minutes, increased more than fourfold between 1995 and 2005 (see section 5). Many factors have accelerated the pace of globalization. Barriers to international trade and investment are coming down. Technological progress has dramatically cut transportation and communications costs, enabling production processes and distribution networks to move from local to global. Some previously nontradable services can now be traded easily around the world. Efficiency gains due to resource allocation at global scale have made globalization an increasingly powerful source of growth. Globalization has created opportunities and challenges for developing countries. While the experiences of China, India, Indonesia, Thailand, and some other countries have demonstrated that integration into the global economy is necessary for long-term growth and poverty reduction, concerns have emerged over equality of opportunity and the unequal distribution of benefits. Many poor countries and poor people in many countries have not been able to take full advantage of the opportunities brought by globalization or to participate in its benefits. Removing the obstacles to full participation by poor countries and poor people is essential to making globalization more inclusive. For example, subsidies to domestic farmers in highincome economies have created formidable barriers for developing economies trying to reach global markets for agricultural products. But there is much that developing countries need to do to make their economies more competitive. Scaling up and increasing the flexibility of official development assistance could assist low-income countries’ efforts to attract investment and improve their trade-facilitating infrastructure, whose limitations now constrict poor countries’ capacity to take advantage of growing global opportunities.

2007 World Development Indicators

313

Expanding trade

Expanding flows of private financial resources

International trade is the hallmark of an integrated global

International private financial flows have increased rapidly in

economy. Between 1990 and 2005 growth in trade outpaced

both gross and net terms. Between 1990 and 2005 total

growth in the overall economy across the board (figure 6a).

gross capital flows recorded in the balance of payments tri-

Low- and middle-income economies gained market share in

pled as a share of world GDP, and high-income economies still

world merchandise exports—from about 16 percent in 1990

account for the lion’s share (indicator table 6.1). All types of

to almost 30 percent in 2005 (indicator table 6.3)—but the

external financial flows to developing economies have soared

Sub-Saharan share lagged at around 1.5 percent.

during this period, but foreign direct investment (FDI) remains

Trade between developing economies has expanded

the largest (figure 6c). From a low initial level of less than

considerably and now makes up about 8 percent of world

$25 billion in 1990, net inflows of FDI to developing countries

merchandise exports. Between 1990 and 2005 merchandise

increased tenfold by 2005 (indicator table 6.8).

exports between developing economies grew at an impres-

Large differences in external financial inflows exist among

sive average annual rate of 13 percent, compared with less

developing economies. The top 10 receivers of FDI net inflows

than 6 percent for exports between high-income economies

accounted for about two-thirds of total FDI inflows among

(figure 6b). But tariff barriers affecting exports to developing

developing economies in 2005. FDI inflows are dominant in

economies are still much higher than those affecting exports

Latin America and Caribbean and East Asia and Pacific; port-

to high-income economies. The simple mean tariff rate aver-

folio investments are more important in South Asia (figure

ages 9 percent in developing economies but less than 4 per-

6d). Meanwhile, some developing economies are increasingly

cent in high-income economies (indicator table 6.7).

investing overseas to expand their global operations.

Trade growth outpaces GDP growth

6a

Average annual growth, 1990–2005 (%) 10

Trade

GDP

Foreign direct investment leads resource flows to developing economies

6c

$ billions 300 Foreign direct investment

250

8

200

6

150

Remittances

4

Aid

100 Equity

2

50

High-income

Middle-income

Low-income

Sub-Saharan Africa

Source: World Bank staff estimates.

1990

1995

2000

2005

Source: World Bank staff estimates.

Exports from developing countries have grown fast

6b

Developing economies differ greatly in external resource flows Foreign direct investment

Merchandise exports ($ trillions) 10

6d Equity

Bond

Lending

East Asia & Pacific

Developing-country to developing-country

8

Europe & Central Asia

Developing-country to high-income

Latin America & Caribbean

6 High-income to developing-country

Middle East & North Africa

4

South Asia

2

Sub-Saharan Africa

High-income to high-income

0 1990 Source: World Bank staff estimates.

314

Bond

0

0

2007 World Development Indicators

2005

–2

–1

0

1

Source: World Bank staff estimates.

2 3 4 Share of GDP (%)

5

6

7

8

Expanding aid and increasing emphasis on effective aid

Expanding movements of people

Developed economies have committed to providing more and

The flow of people across national borders is another mark

better aid, especially to the poorest economies that commit

of integration. Important for many developing economies, in-

themselves to poverty reduction and good governance. After

ternational tourism has increased rapidly since its downturn

a period of decline and stagnation, aid flows began to rise,

in 2001. In 2005 international tourist arrivals worldwide ex-

particularly after the Financing for Development conference

ceeded 800 million, nearly double the 1990 level. Receipts

in Monterrey, Mexico, in 2002. Total official development as-

reached $680 billion (excluding air tickets), accounting for

sistance (ODA) rose to a record high of $106.8 billion in 2005

6.5 percent of global exports of goods and services (indicator

(indicator table 6.9). However, many donor economies still

table 6.15). Developing economies, accounting for a third of

need to scale up aid significantly to fulfill commitments made

international tourist arrivals, are attracting new tourists at a

at the Monterrey conference and at the Gleneagles Group of

faster rate than the world as whole (figure 6g).

Eight summit in 2005 (figure 6e).

International migration is a major global development

A large amount of aid is earmarked for special pur-

issue, posing opportunities and challenges to both devel-

poses. In 2005 more than half of ODA was used for special

oped and developing economies. In 2005 recorded remit-

purposes such as debt relief, technical cooperation and

tance flows repatriated by developing economy migrants were

administrative costs, and emergency relief and food aid

$188 billion, close to 2 percent of GDP (indicator table 6.14).

(indicator table 6.10). Excluding these “special purpose”

While high-income economies are the most popular destina-

items and setting aside aid to Afghanistan and Iraq, only

tions, migration between developing economies accounts for

41 percent of ODA in 2005 was available to finance devel-

nearly half the migrants from developing economies (figure

opment projects and budget support for general financing

6h). Migration between developing economies occurs primar-

needs (fi gure 6f).

ily between neighbors, particularly in Europe and Central Asia and Sub-Saharan Africa.

Aid flows are rising

6e

Official development assistance as share of GNI (%)

Fast growth in tourism, especially for low-income countries

6g

Average annual growth in arrivals, 1990–2005 (%) 10

0.35 Total

0.30

8

0.25

Excluding special-purpose grants

6

0.20 0.15

4

To Sub-Saharan Africa

0.10

2

0.05 0.00

0 1990

1995

2000

2005

Source: Organisation for Economic Co-operation and Development, Development Assistance Committee.

Only 41 percent of aid finances development projects and general budget support

6f

2004 $ billions

50 least Other low-income Upper developed and lower middle-income countries middle-income Source: World Tourism Organization.

Debt relief

100 Emergency aid and food aid

80 60

Technical cooperation and administrative costs

40

High-income

Migration to developing economies accounts for almost half of all migrants

6h Within own group To high-income Rest

Share of migrants from developing economies (%)

120

East Asia & Pacific Europe & Central Asia Latin America & Caribbean Middle East & North Africa South Asia

Aid to Afghanistan and Iraq

20 Aid excluding special purpose grants and Afghanistan and Iraq

0 1999

World

2000

2001

2002

2003

2004

Sub-Saharan Africa All developing countries

2005 0

Source: Organisation for Economic Co-operation and Development, Development Assistance Committee.

20

40

60

80

100

Source: Ratha and Shaw 2007.

2007 World Development Indicators

315

Tables

6.1

Integration with the global economy Merchandise trade

Trade in services

Growth in real trade less growth in real GDP

Gross private capital flows

% of GDP

% of GDP

percentage points

% of GDP

Foreign direct investment

% of GDP 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, 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

316

.. 29.0 36.6 53.5 11.6 .. 25.6 54.8 .. 17.6 .. 110.2a 30.0 33.1 .. 98.4 11.7 48.9 22.0 27.0 22.4 30.5 43.7 18.4 27.2 51.1 32.5 217.4 30.7 43.5 57.2 46.4 47.9 88.8 .. 83.6 51.7 73.2 44.2 36.8 38.4 77.0 .. 11.4 38.8 36.4 52.5 69.1 .. 45.5 35.7 32.4 36.8 49.5 43.0 17.2

2005

51.4 39.0 64.9 96.2 37.5 55.4 31.6 81.7 94.3 38.5 110.5 176.1 33.9 53.7 95.5 74.6 24.6 112.2 34.8 47.2 109.9 33.9 61.0 20.4 70.1 63.4 63.6 333.3 34.6 59.5 126.0 84.1 79.3 71.0 .. 124.6 62.3 53.4 55.9 34.1 59.8 52.1 135.1 44.8 64.7 45.0 78.4 52.7 52.5 62.4 69.9 31.5 50.0 52.0 73.1 45.1

2007 World Development Indicators

1990

.. 2.9 2.9 18.7 3.9 .. 7.5 22.7 .. 3.6 .. 25.5a 13.9 9.4 .. 15.4 2.4 6.9 9.1 12.9 5.7 12.8 8.3 16.0 15.5 12.4 2.9 .. 8.3 .. 31.0 15.7 20.5 .. .. .. 17.0 21.7 13.0 22.6 13.4 .. 9.1 5.5 8.9 11.1 21.0 34.5 .. 8.7 6.6 11.1 9.7 18.6 11.0 4.3

2005

.. 30.4 .. 21.2 7.6 14.7 7.9 33.7 26.6 5.7 10.9 28.9 12.4 12.5 14.8 16.6 5.1 29.2 .. 18.4 28.1 11.3 10.6 .. .. 13.0 7.1 53.2 6.1 .. 35.3 20.6 17.6 34.6 .. 16.7 31.0 18.2 8.6 28.1 13.9 .. 40.5 19.7 16.7 10.4 15.2 27.1 20.7 12.8 21.7 21.7 8.6 8.9 19.3 13.6

1990–2005

.. 12.5 0.1 .. 4.0 –6.9 3.1 3.4 13.8 4.2 –1.5 2.2 –2.4 1.7 –2.7 –2.3 4.4 5.9 –1.2 .. 9.7 1.8 3.1 .. 3.3 3.5 6.3 3.6 2.7 9.6 3.8 3.2 0.5 4.1 .. 8.4 3.2 0.2 2.1 –1.1 5.8 –2.6 7.2 4.1 4.3 3.8 –1.3 –3.2 8.9 4.4 2.9 3.3 2.7 –1.6 4.1 ..

Net inflows

1990

2005

1990

.. 18.0 2.6 10.1 8.2 .. 9.0 9.6 .. 0.9 .. 78.8a 10.7 3.1 .. 9.0 1.9 39.2 1.0 3.7 3.2 15.5 8.1 2.2 5.6 14.4 2.5 .. 3.1 .. 6.6 5.4 3.5 .. .. .. 14.8 5.0 11.0 6.8 2.0 53.0 3.9 1.1 17.2 20.2 18.0 0.9 .. 10.2 2.9 3.8 2.9 3.9 23.0 1.1

.. 7.3 .. 29.5 9.1 10.5 32.5 51.9 87.0 2.9 5.4 382.1a 8.1 14.1 28.0 12.3 5.9 34.9 .. 3.0 13.6 14.4 14.3 .. .. 20.4 10.9 78.4 16.3 .. 34.2 16.9 6.8 23.1 .. 22.0 27.9 6.6 14.3 16.4 11.7 .. 93.9 5.4 39.2 32.9 14.6 15.5 12.2 30.7 5.2 38.0 9.1 1.5 9.1 2.6

.. 2.8 0.0 –3.3 1.3 81.4 2.5 0.4 0.0 0.0 0.0 3.7a 3.4 0.6 .. 2.5 0.2 0.0 0.0 0.1 1.7 –1.0 1.3 0.0 0.5 2.1 1.0 .. 1.2 0.2 –0.5 2.2 0.4 .. .. 0.0 0.8 1.9 1.2 1.7 0.0 .. 2.1 0.1 0.6 1.1 1.2 4.5 .. 0.2 0.3 1.2 0.6 0.6 0.8 0.3

2005

.. 3.1 1.1 –4.0 2.6 5.3 –4.7 3.0 13.4 1.3 1.0 8.6 0.5 –3.0 3.0 2.7 1.9 9.8 0.4 0.1 6.1 0.1 3.1 0.4 12.9 5.8 3.5 20.2 8.5 5.7 14.2 4.3 1.6 4.6 .. 4.1 2.0 3.5 4.5 6.0 3.0 1.2 22.9 2.4 2.1 3.3 3.7 11.3 7.0 1.1 1.0 0.3 0.7 3.1 3.3 0.2

Net outflows 1990

.. 0.0 0.0 0.0 0.0 .. 0.3 1.0 .. 0.0 .. 2.9a 0.0 0.0 .. 0.2 0.1 0.0 0.0 0.0 .. 0.1 0.9 0.3 0.0 0.0 0.2 .. 0.0 .. 0.0 0.0 0.0 .. .. .. 1.1 0.0 0.0 0.0 0.0 .. 0.0 0.0 2.0 2.8 0.5 0.0 .. 1.4 0.0 0.0 0.0 .. 0.0 –0.3

2005

.. 0.0 .. 0.2 0.2 0.1 2.7 2.5 13.9 0.0 0.0 5.1 0.1 0.0 0.0 2.5 1.6 –0.9 .. 0.0 0.2 .. 4.8 .. .. 1.0 0.1 24.0 0.1 .. 0.0 0.3 0.0 1.0 .. 0.5 –4.1 0.0 0.0 0.2 0.0 .. 2.4 .. –0.8 2.3 0.3 .. 0.2 –0.3 0.0 0.3 0.0 0.0 0.2 0.0

Merchandise trade

Trade in services

Growth in real trade less growth in real GDP

Gross private capital flows

% of GDP

% of GDP

percentage points

% of GDP

GLOBAL LINKS

6.1

Integration with the global economy

Foreign direct investment

% of GDP 1990

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

57.9 61.5 13.1 41.5 34.2 55.4 92.8 55.0 31.1 67.2 17.3 91.1 .. 37.9 .. 51.1 59.8 .. 30.5 .. 106.5 119.3 374.1 64.2 .. 103.8 31.5 52.7 133.4 39.7 84.1 118.0 32.1 .. 75.7 43.3 40.8 .. 95.6 24.1 83.9 43.0 95.9 27.0 67.5 52.8 71.1 32.6 35.4 73.6 43.9 22.3 47.8 43.9 55.4 ..

2005

74.5 117.3 28.5 54.2 48.5 155.9 88.1 72.8 42.4 62.3 24.5 116.5 79.1 50.4 .. 69.3 75.9 72.9 43.6 87.6 54.5 140.6 253.5 95.8 106.4 91.4 45.8 81.3 196.1 51.3 71.1 84.3 58.0 116.7 117.1 60.0 62.6 .. 74.9 36.1 122.0 43.9 70.3 40.3 60.2 53.9 91.2 37.3 33.4 99.5 73.5 37.4 89.5 62.7 54.1 ..

1990

11.7 16.0 3.4 7.5 3.8 .. 18.0 18.1 8.5 37.5 4.2 67.5 .. 21.4 .. 7.5 25.2 .. 5.8 9.2 .. 19.8 .. 5.2 .. .. 12.8 16.2 21.2 19.0 16.0 38.0 7.0 .. 9.7 13.4 12.5 .. 20.7 10.2 19.2 13.3 17.0 10.9 10.3 21.6 6.7 8.8 33.5 18.9 16.2 7.5 11.3 10.3 12.1 ..

2005

19.6 22.0 8.2 12.8 .. .. 63.0 25.5 10.2 42.4 5.4 38.0 17.1 16.1 .. 13.2 16.8 22.4 .. 23.5 84.8 10.3 .. 7.4 20.1 16.9 7.5 .. 31.9 15.9 .. 45.1 4.9 29.9 52.2 23.1 14.9 .. 15.2 11.0 24.6 15.3 15.0 11.6 11.6 18.9 14.3 10.1 31.5 29.7 13.7 6.6 10.4 10.1 13.7 ..

1990–2005

0.3 8.4 4.4 0.6 –2.9 .. 6.1 1.2 1.7 .. 2.9 –0.7 –2.8 2.2 .. 6.4 –3.0 –1.2 .. 4.8 0.4 –0.2 .. .. 8.0 4.1 2.3 –1.7 2.9 1.8 –1.0 –0.5 7.7 11.2 15.7 2.1 2.8 .. 0.0 .. 3.5 2.3 5.2 .. 1.7 1.0 2.5 –0.5 –3.5 .. –1.4 3.3 2.4 7.3 2.8 –0.5

1990

7.2 4.6 0.8 4.1 2.7 .. 21.9 6.5 10.3 8.4 5.4 6.3 .. 3.5 .. 5.3 19.3 .. 3.7 2.3 .. 9.6 .. 7.3 .. .. 1.8 3.2 10.3 2.0 48.8 8.0 9.2 .. 26.0 5.5 0.4 .. 16.5 3.5 28.6 17.7 9.0 2.8 5.9 11.9 3.5 4.2 106.6 5.7 5.4 3.2 4.4 11.0 10.8 ..

2005

7.5 26.2 5.9 7.2 .. .. 355.6 23.1 28.3 50.3 15.9 20.7 39.7 6.3 .. 7.1 24.0 9.4 .. 36.3 .. 6.7 .. 7.9 29.1 11.8 0.8 .. 24.1 8.2 .. 13.0 7.7 13.8 26.1 8.4 6.6 .. 23.5 4.2 94.0 6.0 7.8 3.7 25.4 40.1 11.1 4.3 56.8 20.4 7.6 10.4 17.8 13.6 50.9 ..

Net inflows 1990

1.4 1.9 0.1 1.0 –0.3 .. 1.3 0.3 0.6 3.0 0.1 0.9 0.4 0.7 .. 0.3 0.0 .. 0.7 0.6 0.2 2.8 58.6 .. 0.1 0.0 0.7 1.2 5.3 0.2 0.7 1.7 1.0 0.7 0.6 0.6 0.4 .. .. 0.2 3.5 4.0 0.1 1.6 2.1 0.9 1.2 0.6 2.6 4.8 1.5 0.2 1.2 0.2 3.5 ..

2005

5.6 5.9 0.8 1.8 0.0 .. –14.7 4.5 1.1 7.1 0.1 12.1 3.5 0.1 .. 0.6 0.3 1.7 1.0 4.6 11.7 6.3 35.4 .. 4.0 1.7 0.6 0.1 3.0 3.0 6.2 0.6 2.4 6.8 9.7 3.0 1.6 .. .. 0.0 6.5 1.8 4.9 0.4 2.0 1.1 0.8 2.0 6.6 0.7 0.9 3.2 1.1 3.2 1.7 ..

Net outflows 1990

0.0 0.0 0.0 0.0 0.0 .. 0.8 0.4 0.7 0.0 1.7 –0.8 .. 0.0 .. 0.4 1.3 .. 0.0 0.0 .. 0.0 .. 0.4 .. .. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 .. 0.1 0.0 4.5 3.6 0.0 0.0 0.0 1.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 ..

2007 World Development Indicators

2005

0.0 1.1 0.2 .. .. .. 8.6 2.7 1.1 0.7 0.7 0.0 –3.0 0.0 .. 0.7 3.2 2.0 .. 0.7 .. 0.0 .. –0.7 1.2 0.0 0.0 .. 1.3 0.0 .. 0.5 0.5 0.1 0.0 0.0 0.0 .. –0.4 .. 2.9 –0.8 0.0 0.0 .. 0.8 0.0 0.1 0.0 .. 0.1 0.0 0.5 0.3 3.4 ..

317

6.1

Integration with the global economy Merchandise trade

Trade in services

Growth in real trade less growth in real GDP

Gross private capital flows

% of GDP

% of GDP

percentage points

% of GDP

Foreign direct investment

% of GDP

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 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 Europe EMU

1990

2005

1990

2005

32.8 .. 15.4 58.6 34.7 .. 44.2 308.1 110.8 102.4 .. 37.4 27.5 57.3 7.5 138.2 46.2 56.6 53.7 .. 31.9 65.7 52.1 60.6 73.5 23.4 .. 10.2 .. 103.2 41.2 15.8 32.7 .. 52.8 79.7 .. 46.9 76.9 40.7 32.3 w 23.6 34.5 31.6 38.3 32.5 47.1 49.7 23.2 43.5 16.5 41.9 32.3 44.0

69.2 48.3 24.5 77.7 58.6 63.7 42.2 368.0 145.0 112.7 .. 47.7 41.4 64.7 42.0 150.2 67.5 70.1 52.7 96.8 34.2 129.3 66.5 102.4 82.5 52.4 105.7 30.2 85.0 151.3 40.6 21.2 43.4 60.3 56.9 129.9 .. 70.6 61.5 123.1 47.3 w 41.1 62.1 58.9 66.4 59.2 74.6 68.6 44.2 57.6 31.2 57.8 43.9 61.4

3.6 .. 6.6 21.8 20.9 .. 20.9 58.2 .. 18.0 11.2 6.4 8.4 13.4 3.0 32.4 12.7 12.8 14.3 .. 9.8 14.9 24.1 15.9 20.6 7.4 .. 4.5 .. .. 10.6 4.6 9.2 .. 7.9 .. .. 16.3 15.0 8.6 7.8 w 6.2 7.1 6.4 8.0 7.0 7.3 7.1 5.7 9.2 4.2 10.8 7.9 11.0

10.8 8.4 20.1 10.9 17.9 .. 14.1 90.4 19.4 20.1 .. 9.7 14.1 15.5 7.1 27.1 21.9 20.0 20.8 17.2 19.7 27.3 18.9 10.0 21.6 10.4 .. 14.6 20.4 .. 16.5 5.6 13.3 .. 4.8 18.0 .. 10.2 .. .. 11.0 w 9.8 10.5 10.0 11.1 10.6 10.3 12.6 6.8 .. 8.2 13.1 11.1 15.9

a. includes Luxembourg.

318

2007 World Development Indicators

1990–2005

8.5 3.5 0.5 .. 1.6 .. .. .. 6.8 2.0 .. 2.6 5.5 2.5 4.7 1.0 4.1 2.9 2.7 3.3 –1.2 2.9 –1.1 3.2 –0.4 6.9 12.1 2.8 3.2 1.8 3.2 3.9 2.3 –0.9 0.6 14.2 –1.2 2.4 1.6 5.5 .. .. .. .. .. .. .. .. .. .. .. .. .. ..

1990

2.9 .. 2.8 8.8 4.8 .. 11.0 54.3 .. 3.4 21.3 2.4 11.1 13.1 0.3 10.7 33.6 28.1 18.0 .. 0.2 13.5 9.6 11.4 9.5 4.3 .. 1.1 .. .. 35.3 5.6 12.7 .. 51.6 .. .. 16.2 64.7 1.7 10.3 w 2.4 6.6 4.4 7.9 5.9 5.0 5.3 7.9 5.0 1.4 5.1 11.0 13.5

2005

16.7 19.6 1.6 31.6 6.2 .. 5.7 95.5 15.5 33.3 .. 10.6 46.0 5.9 14.6 7.4 39.0 83.9 2.6 9.2 5.7 12.6 17.7 17.1 3.2 14.8 .. 5.2 31.4 .. 122.8 14.4 26.5 .. 17.9 8.3 .. 2.1 .. .. 32.4 w 6.7 13.3 11.5 15.7 13.1 11.4 20.3 9.8 .. 5.4 14.2 37.2 58.7

Net inflows

Net outflows

1990

2005

1990

2005

0.0 0.3 0.3 .. 1.0 .. 5.0 15.1 0.6 0.9 0.6 –0.1 2.7 0.5 –0.2 3.4 0.8 2.4 0.6 0.5 0.0 2.9 1.1 2.2 0.6 0.5 .. –0.1 0.3 .. 3.4 0.8 0.4 0.1 1.0 2.8 .. –2.7 6.2 –0.1 1.0 w 0.4 0.9 0.8 1.2 0.8 1.6 1.0 0.8 0.3 0.1 0.4 1.0 1.1

6.7 2.0 0.4 .. 0.7 5.6 4.9 17.2 4.1 1.6 .. 2.6 2.0 1.2 8.4 –0.6 3.0 4.2 1.6 2.4 3.9 2.6 0.1 7.7 2.5 2.7 0.8 2.9 9.4 .. 7.2 0.9 4.2 0.3 2.1 3.7 .. –1.8 3.6 3.0 2.2 w 1.5 3.1 3.1 3.1 2.9 3.2 3.5 2.9 2.4 1.0 2.7 2.1 3.2

0.0 .. 0.0 0.0 –0.2 .. 0.0 5.5 .. 0.0 0.0 0.0 0.7 0.0 0.0 0.9 6.0 2.3 0.0 .. 0.0 0.2 0.0 0.0 0.0 0.0 .. 0.0 .. .. 2.0 0.6 0.0 .. 0.8 .. .. .. 0.0 0.0 1.2 w 0.0 0.1 0.1 0.3 0.1 0.2 0.0 0.1 0.0 0.0 0.0 1.4 1.7

0.1 1.8 0.0 0.0 0.0 .. 0.0 9.9 0.1 1.7 .. 0.7 4.8 0.0 0.0 0.1 4.4 7.2 0.0 0.0 .. 0.1 –0.4 –2.1 0.0 0.3 .. 0.0 0.0 .. 3.8 2.2 0.1 .. –0.3 .. .. 0.0 .. .. 2.1 w 0.2 0.5 0.3 0.7 0.5 0.1 0.8 0.7 .. 0.2 0.3 2.4 2.7

About the data

6.1

GLOBAL LINKS

Integration with the global economy Definitions

The growing integration of societies and economies

abundant labor to gain a competitive advantage in

• Merchandise trade is the sum of merchandise

has helped reduce poverty in many countries. One

labor-intensive manufactures and services.

exports and imports divided by the value of GDP,

indication of increasing global economic integration

This year the table includes net inflows and out-

is the growing importance of trade in the world econ-

flows of foreign direct investment based on balance

sum of services exports and imports divided by the

omy. Another is the increasing size and importance

of payments data reported by the International Mon-

value of GDP, all in current U.S. dollars. • Growth

of private capital flows to developing countries that

etary Fund (IMF), supplemented by staff estimates

in real trade less growth in real GDP is the differ-

have liberalized their financial markets.

using data reported by the United Nations Confer-

ence between annual growth in trade of goods and

ence on Trade and Development and official national

services and annual growth in GDP. Growth rates are

sources.

calculated using constant price series taken from

The table presents standardized measures of the size of trade and capital flows relative to gross

all in current U.S. dollars. • Trade in services is the

domestic product (GDP). The numerators on trade

The internationally accepted definition of foreign

national accounts and are expressed as a percent-

and private capital flows are based on gross flows

direct investment is provided in the fifth edition of

age. • Gross private capital flows are the sum of the

that capture the two-way flow of goods, services,

the IMF’s Balance of Payments Manual (1993). For

absolute values of direct, portfolio, and other invest-

and capital. In conventional balance of payments

a more detailed explanation of foreign direct invest-

ment inflows and outflows recorded in the balance

accounting exports are recorded as a credit and

ment, see About the data for table 6.8.

of payments financial account, excluding changes

imports as a debit. And in the financial account

Foreign direct investment may be understated in

in the assets and liabilities of monetary authorities

inward investment is a credit and outward investment

many developing countries. Some countries fail to

and general government. The indicator is calculated

a debit. Thus net flows, the sum of credits and deb-

report reinvested earnings, and the definition of long-

as a ratio to GDP in U.S. dollars. • Foreign direct

its, represent a balance in which many transactions

term loans differs among countries. Underreporting

investment net inflows are the net inflows of invest-

are canceled out. Gross flows are a better measure

of FDI outflows is more pervasive, particularly when

ment to acquire a lasting management interest in an

of integration because they show the total value of

investors are attempting to avoid controls on capital

enterprise operating in an economy other than that

financial transactions during a given period.

and foreign exchange or high taxes on investment

of the investor. It is the sum of equity capital, rein-

Merchandise trade and trade in services (exports

income. Some countries do not identify FDI outflows

vestment of earnings, and other short- and long-term

and imports) are shown relative to total GDP. Merchan-

in their balance of payments statistics. However, the

capital, as shown in the balance of payments. This

dise trade is an important part of global trade. Trade in

quality and coverage of the data are improving as

series shows net inflows in the reporting economy

services (such as transport, travel, finance, insurance,

a result of continuous efforts by international and

and is divided by the value of GDP. • Foreign direct

royalties, construction, communications, and cultural

national statistics agencies.

investment net outfl ows are the net outflows of

services) is an increasingly important element of

Trade and capital flows are converted to U.S. dollars

global integration. The difference between the growth

at the IMF’s average official exchange rate for the year

of real trade in goods and services and the growth of

shown. An alternative conversion factor is applied if

GDP helps to identify economies that have integrated

the official exchange rate diverges by an exceptionally

with the global economy by liberalizing trade, lowering

large margin from the rate effectively applied to trans-

barriers to foreign investment, and harnessing their

actions in foreign currencies and traded products.

investment from the reporting economy to the rest of the world.

Data sources Private capital flows are rising, but they remain below the peak of 2000 Gross private capital flows (GPCF) as a share of GDP (%) 60

GPCF, low- and middle-income countries

Data on merchandise trade are from the World

6.1a GPCF, high-income countries

Foreign direct investment (FDI) net inflows as a share of GDP (%) 6

World Bank’s national accounts files, converted from national currencies to U.S. dollars using the official exchange rate, supplemented by an alternative conversion factor if the official exchange

FDI, high-income countries 50

5

40

4

rate is judged to diverge by an exceptionally large margin from the rate effectively applied to transactions in foreign currencies and traded

FDI, low- and middle-income countries

30

Trade Organization. Data on GDP are from the

3

20

2

10

1

0

0

products. Data on trade in services are from the IMF’s Balance of Payments database. Data on real trade and GDP growth are from the World Bank’s national accounts files. Gross private capital flows and foreign direct investment are reported

1990

Source: World Bank staff estimates.

1995

2000

2005

in the World Bank Debtor Reporting System and are calculated using mainly the IMF’s Balance of Payments database.

2007 World Development Indicators

319

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

1980–90

Afghanistan Albania Algeria Angola Argentinaa Armenia Australiaa Austriaa Azerbaijan Bangladesh Belarus Belgiuma Benin Bolivia Bosnia and Herzegovina Botswana Brazila Bulgaria Burkina Faso Burundi Cambodia Cameroon Canadaa Central African Republic Chad Chile China† Hong Kong, Chinaa Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmarka Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finlanda Francea Gabon Gambia, The Georgia Germanya, b Ghana Greecea Guatemala Guinea Guinea-Bissau Haiti †Data for Taiwan, China

320

1990–2005

.. .. 4.7 9.0 .. .. 6.3 .. .. 7.1 .. .. 11.8 3.1 .. 14.8 4.6 .. –0.3 3.5 .. 7.0 6.4 0.0 8.6 9.2 13.6 15.4 7.9 9.7 7.4 3.8 2.6 .. .. .. 4.1 –0.9 7.1 13.4 –4.6 .. .. –1.0 .. 3.6 2.5 2.2 .. .. –17.2 5.0 –1.1 .. –2.0 –0.4 26.1

2007 World Development Indicators

.. .. 2.7 5.8 7.8 .. 5.6 .. .. 10.5 .. 5.8 3.0 4.9 .. 3.8 5.9 .. 12.1 8.0 .. 3.3 6.6 14.7 7.7 9.4 15.6 7.9 4.4 4.4 4.5 10.1 4.6 .. .. .. 4.9 3.5 4.7 3.4 3.2 –2.8 .. 9.7 .. 3.3 5.8 –9.3 .. .. 4.7 8.9 6.8 3.2 14.0 11.6 2.8

1980–90

.. .. –8.0 –1.9 .. .. 6.0 .. .. 1.8 .. .. –9.9 –1.3 .. 11.5 –1.2 .. 3.8 1.0 .. 4.8 7.4 4.2 11.0 –3.0 11.9 13.7 –2.1 12.2 3.3 5.2 –2.1 .. .. .. 3.1 2.9 –1.8 8.1 4.6 .. .. 4.0 .. 3.7 –3.5 –6.0 .. .. –19.3 6.4 0.1 .. –0.3 –4.6 30.3

Net barter terms of trade index

2000 = 100

1990–2005

1980–90

1990–2005

1980–90

1990–2005

1990

2005

.. .. 1.7 8.2 6.8 .. 7.8 .. .. 4.1 .. 5.4 5.9 5.2 .. 2.3 2.9 .. 5.0 6.3 .. 7.2 7.0 1.6 13.0 6.8 15.1 7.7 6.2 11.0 6.7 11.8 0.7 .. .. .. 5.0 8.3 7.6 –0.5 6.6 8.1 .. 9.6 .. 3.5 1.3 –0.8 .. .. 6.5 9.3 9.8 2.5 –4.1 10.8 3.3

.. .. –3.1 6.4 .. .. 12.1 .. .. 7.8 .. .. 18.8 –1.9 .. 18.8 4.9 .. 7.9 2.5 .. 1.4 7.7 3.5 9.4 8.1 12.8 21.7 7.7 2.7 2.1 4.6 1.7 .. .. .. 7.3 –2.1 –0.4 7.3 –4.6 .. .. –1.1 .. .. –3.9 6.6 .. .. –2.7 21.4 –2.2 4.0 4.2 –1.2 14.9

.. .. 5.8 9.6 7.9 .. 7.4 .. .. 12.4 .. 8.0 3.6 5.6 .. 3.4 7.5 .. 10.5 –6.3 .. 2.7 9.1 1.7 7.8 8.2 15.5 7.1 5.9 –3.3 8.6 11.3 6.0 .. .. .. 5.9 4.2 6.2 4.2 6.9 –4.2 .. 7.6 .. 2.9 2.5 –9.1 .. .. 6.6 16.4 6.7 0.2 11.7 11.2 5.8

.. .. –2.7 0.7 .. .. 12.5 .. .. 3.6 .. .. –4.9 –0.3 .. 11.1 –1.9 .. 4.3 2.2 .. 0.1 9.3 7.9 12.6 2.8 13.5 19.8 0.0 3.1 5.3 4.4 –1.5 .. .. .. 5.0 5.4 –1.3 12.6 2.4 .. .. 4.3 .. .. 1.1 2.5 .. .. 0.6 26.0 0.6 9.7 5.2 –2.9 12.4

.. .. 1.2 9.3 5.6 .. 8.9 .. .. 8.2 .. 8.2 6.3 5.8 .. 1.3 8.0 .. 5.2 –3.9 .. 4.6 8.6 –1.3 13.6 7.1 15.4 7.0 6.6 1.5 6.1 11.0 3.0 .. .. .. 6.1 8.6 8.8 2.0 9.4 7.2 .. 10.2 .. 2.7 1.1 –1.3 .. .. 6.6 16.4 10.7 –0.5 –3.1 12.4 6.4

.. .. 74 94 85 .. 116 98 .. 117 .. 106 107 102 .. 98 138 .. 119 128 .. 81 97 238 112 114 102 100 81 86 63 75 143 .. .. .. 102 96 114 101 84 99 .. 121 111 103 157 100 .. 100 100 97 115 122 146 132 97

.. .. 126 121 107 .. 131 103 .. 88 .. 99 93 108 .. 92 101 .. 98 84 .. 112 111 99 101 116 92 98 93 94 121 102 121 .. .. .. 104 96 109 107 91 93 .. 91 86 111 125 115 .. 101 123 95 93 107 94 87 92

Honduras Hungarya India Indonesia Iran, Islamic Rep. Iraq Irelanda Israela Italya Jamaicaa Japana Jordana Kazakhstan Kenyaa Korea, Dem. Rep. Korea, Rep.a Kuwait Kyrgyz Republic Lao PDR Latviaa Lebanon Lesotho Liberia Libyaa Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritiusa Mexico Moldova Mongolia Moroccoa Mozambique Myanmar Namibia Nepal Netherlandsa New Zealanda Nicaragua Niger Nigeria Norwaya Oman Pakistana Panama Papua New Guinea Paraguay Peru Philippinesa Polanda Portugala Puerto Rico

Export volume

Import volume

Export value

Import value

average annual % growth

average annual % growth

average annual % growth

average annual % growth

1980–90

1990–2005

4.1 3.4 4.2 7.6 .. .. 9.3 6.9 4.3 –1.0 5.0 10.2 .. .. .. 12.4 .. .. .. .. .. 7.2 .. 2.8 .. .. –2.5 2.4 4.8 4.4 3.9 .. 15.3 .. .. 6.0 –9.5 –8.4 .. .. 4.4 3.5 –4.8 –5.2 –4.4 4.2 11.2 9.1 –0.5 –0.6 12.8 2.7 .. 4.8 11.9 ..

2.7 11.8 11.5 5.9 .. .. 12.1 8.4 2.9 1.5 2.8 7.4 .. .. .. 14.8 .. .. .. 7.5 .. 16.0 .. –3.3 .. .. 4.6 3.0 11.4 10.9 1.2 3.6 12.4 .. .. 3.4 21.5 18.5 1.5 .. 5.9 4.2 8.6 1.4 1.9 4.9 2.4 3.4 5.1 –6.8 1.9 10.0 2.3 11.5 0.1 ..

1980–90

1.6 1.3 4.7 0.3 .. .. 4.8 5.8 5.3 .. 6.6 1.5 .. 0.2 .. 11.9 .. .. .. .. .. 3.9 .. –1.8 .. .. –6.2 –0.1 8.5 3.0 –3.1 13.6 0.9 .. .. 3.4 –2.7 –18.1 .. .. 4.3 4.4 –3.5 –5.2 –20.8 3.5 .. 2.9 –6.7 .. 10.4 –2.0 .. 1.5 15.1 ..

1990–2005

1980–90

1990–2005

10.5 11.8 10.8 3.3 .. .. 8.7 6.3 3.4 .. 4.8 4.7 .. 5.9 .. 8.6 .. .. .. .. .. 2.8 .. –1.3 .. .. 4.3 –0.3 8.5 6.2 3.9 3.3 11.1 .. .. 5.2 2.3 8.6 4.7 .. 5.4 6.1 7.2 –0.9 5.2 6.5 .. 4.4 4.2 .. 1.6 5.4 –1.9 14.5 –0.2 ..

1.6 8.3 7.3 –1.3 .. .. 13.5 9.3 10.5 15.4 1.8 14.9 .. .. .. 17.1 .. .. .. .. .. 3.7 .. –5.8 .. .. –1.2 2.0 8.6 6.0 8.0 .. 5.9 .. .. 12.4 –9.6 –7.6 .. .. 3.7 10.7 –5.8 –5.4 –8.4 4.2 3.3 18.3 –0.5 4.9 11.6 –1.5 .. 56.1 22.7 ..

4.9 23.4 9.7 6.5 .. .. 13.2 8.8 7.4 13.7 0.8 9.3 .. .. .. 15.4 .. .. .. 10.7 .. 14.7 .. 5.8 .. .. 7.9 0.8 9.3 8.6 –3.0 3.5 12.9 .. .. 4.3 16.3 16.7 0.3 .. 6.6 4.9 7.7 0.8 5.7 8.6 7.4 11.6 7.0 1.8 3.6 8.9 3.9 23.1 0.1 ..

1980–90

0.6 7.0 4.2 2.6 .. .. 7.8 7.1 8.7 .. –1.3 4.2 .. 11.2 .. 14.8 .. .. .. .. .. 3.5 .. –1.1 .. .. –4.3 3.3 7.7 2.7 –2.1 12.7 6.4 .. .. 10.4 0.1 –4.7 .. .. 3.2 9.8 –3.1 –3.5 –15.0 7.1 0.7 11.8 –3.6 0.7 4.2 1.3 .. 40.3 21.8 ..

6.2

GLOBAL LINKS

Growth of merchandise trade

Net barter terms of trade index

2000 = 100

1990–2005

1990

2005

11.6 23.8 10.3 3.2 .. .. 10.1 6.4 7.5 .. 4.3 7.7 .. 18.2 .. 13.1 .. .. .. .. .. 1.6 .. 3.9 .. .. 5.8 1.0 7.2 5.7 0.3 4.2 11.6 .. .. 5.5 3.1 14.8 1.7 .. 6.3 6.4 9.3 1.4 5.8 5.8 6.5 14.9 5.3 –0.7 2.7 5.9 –1.5 25.2 0.0 ..

78 111 86 95 .. .. 106 90 94 .. 105 93 .. 85 .. 126 .. .. .. .. .. 100 .. 89 .. .. 81 148 103 135 97 97 102 .. .. 97 175 252 93 .. 101 105 155 165 89 67 .. 109 69 .. 103 114 88 96 104 ..

90 97 76 104 .. .. 99 95 101 .. 83 89 .. .. .. 77 .. .. .. .. .. 91 .. 186 .. .. 82 82 99 113 95 85 98 .. .. 100 94 102 97 .. 100 112 91 131 122 122 .. 75 94 .. 112 110 89 107 102 ..

2007 World Development Indicators

321

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

1980–90

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singaporea Slovak Republic Slovenia Somalia South Africaa Spaina Sri Lankaa Sudan Swaziland Swedena Switzerland Syrian Arab Republic Tajikistan Tanzania Thailanda Togo Trinidad and Tobago Tunisia Turkeya Turkmenistan Uganda Ukraine United Arab Emirates United Kingdoma United Statesa Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe

1990–2005

1980–90

1990–2005

.. .. –2.6 1.1 10.5 .. .. 10.3 .. .. .. 4.5 9.2 5.5 .. 4.0 7.6 .. .. .. 8.6 9.2 12.6 .. 6.6 11.4 .. 14.4 .. .. 5.0 4.7 3.4 .. 2.4 .. .. .. 6.5 7.7

.. .. 1.8 .. 0.4 .. .. 8.6 .. .. .. –0.2 8.8 1.6 .. 2.4 5.0 .. .. .. .. 11.2 0.7 .. 1.6 .. .. –6.8 .. .. 7.1 7.2 –0.5 .. –4.1 .. .. –7.2 2.0 3.4

.. .. –0.2 .. 6.1 .. .. 7.2 .. .. .. 5.7 8.8 6.9 .. 2.0 5.4 .. .. .. 2.0 2.6 0.3 .. 5.6 10.6 .. 13.2 .. .. 6.4 7.9 3.4 .. 1.6 .. .. 6.3 6.6 7.2

.. .. 2.6 –8.3 1.2 .. .. 12.1 .. .. .. 2.2 2.5 4.2 .. 7.6 4.4 .. .. .. .. 13.8 –1.2 .. 3.0 .. .. –13.5 .. .. 4.5 3.6 4.4 .. 3.4 .. .. .. –0.5 3.6

1980–90

.. .. –0.9 –12.7 3.5 .. .. 8.6 .. .. .. 15.3 9.0 13.5 .. 4.7 10.7 .. .. .. –5.1 16.5 1.1 .. 3.5 .. .. –4.0 .. .. 7.7 5.7 4.5 .. –4.4 .. .. –3.2 0.9 2.5

Net barter terms of trade index

2000 = 100

1990–2005

1980–90

1990–2005

1990

2005

.. .. –0.5 5.6 4.7 .. .. 8.7 .. .. .. 13.8 11.4 15.1 .. 4.6 9.3 .. .. .. 8.7 15.7 7.1 .. 6.5 10.6 .. 9.8 .. .. 5.3 5.2 2.9 .. 5.7 .. .. 17.0 –0.1 2.6

.. .. 2.7 –6.1 1.4 .. .. 6.7 .. .. .. 12.6 10.8 10.7 .. –0.5 9.1 .. .. .. –0.5 15.1 2.0 .. 2.7 .. .. 4.5 .. .. 11.3 8.2 –1.2 .. –3.2 .. .. –5.0 0.0 –0.5

.. .. –0.9 2.3 5.8 .. .. 6.7 .. .. .. 15.5 10.9 15.2 .. 3.1 8.3 .. .. .. 2.7 10.6 6.3 .. 5.5 10.8 .. 12.7 .. .. 6.6 8.4 3.5 .. 2.5 .. .. 2.9 4.5 1.9

.. .. 40 .. 172 .. .. 116 .. .. .. 112 100 75 .. 100 108 .. .. .. 107 118 133 .. 109 109 .. 146 .. .. 101 101 116 .. 90 .. .. .. 207 98

.. .. 89 .. 96 .. 79 87 .. .. .. 109 102 101 121 94 90 .. .. .. 100 93 30 .. 99 101 .. 88 .. .. 105 97 108 .. 108 .. .. .. 119 105

a. Data are from the International Monetary Fund’s International Financial Statistics database. b. Data prior to 1990 refer to the Federal Republic of Germany before unification.

322

2007 World Development Indicators

About the data

6.2

GLOBAL LINKS

Growth of merchandise trade Definitions

Data on international trade in goods are available

unions may need to collect data through direct inquiry

• Export and import volumes are average annual

from each country’s balance of payments and

of companies. Economic or political concerns may lead

growth rates calculated for low- and middle-income

customs records. While the balance of payments

some national authorities to suppress or misrepresent

economies from UNCTAD’s quantum index series

focuses on the financial transactions that accom-

data on certain trade flows, such as oil, military equip-

and for high-income economies from export and

pany trade, customs data record the direction of

ment, or the exports of a dominant producer. In other

import data deflated by the IMF’s trade price defla-

trade and the physical quantities and value of goods

cases reported trade data may be distorted by deliber-

tors. • Export and import values are average annual

entering or leaving the customs area. Customs data

ate under- or over-invoicing to effect capital transfers or

growth rates calculated from UNCTAD’s value indexes

may differ from data recorded in the balance of pay-

avoid taxes. And in some regions smuggling and black

or from current values of merchandise exports and

ments because of differences in valuation and time

market trading result in unreported trade flows.

imports. • Net barter terms of trade index is cal-

of recording. The 1993 System of National Accounts

By international agreement customs data are

culated as the ratio of the export price index to the

and the fifth edition of the International Monetary

reported to the United Nations Statistics Division,

corresponding import price index measured relative

Fund’s (IMF) Balance of Payments Manual (1993)

which maintains the Commodity Trade (Comtrade)

to the base year 2000.

attempted to reconcile defi nitions and reporting

database. The United Nations Conference on Trade

standards for international trade statistics, but dif-

and Development (UNCTAD) compiles international

ferences in sources, timing, and national practices

trade statistics, including price and volume indexes,

limit comparability. Real growth rates derived from

based on Comtrade data. The IMF also compiles data

trade volume indexes and terms of trade based on

on trade prices and volumes. The growth rates and

unit price indexes may therefore differ from those

terms of trade for most low- and middle-income econ-

derived from national accounts aggregates.

omies shown in the table were calculated from index

Trade in goods, or merchandise trade, includes all

numbers compiled by UNCTAD. The growth rates and

goods that add to or subtract from an economy’s

terms of trade for high-income and selected develop-

material resources. Trade data are collected on the

ing countries were calculated from index numbers

basis of a country’s customs area, which in most

compiled in the IMF’s International Financial Statis-

cases is the same as its geographic area. Goods

tics. In some cases price and volume indexes from

provided as part of foreign aid are included, but

different sources may vary significantly as a result

goods destined for extraterritorial agencies (such

of differences in estimation procedures. All indexes

as embassies) are not.

are rescaled to a 2000 base year.

Collecting and tabulating trade statistics are difficult.

The terms of trade measures the relative prices of

Some developing countries lack the capacity to report

a country’s exports and imports. There are several

timely data, especially countries that are landlocked

ways to calculate it. The most common is the net

and those whose territorial boundaries are porous.

barter (or commodity) terms of trade index, or the

Their trade has to be estimated from the data reported

ratio of the export price index to the import price

by their partners. (For further discussion of the use

index. When a country’s net barter terms of trade

of partner country reports, see About the data for

index increases, its exports become more valuable

table 6.3.) Countries that belong to common customs

or its imports cheaper.

Terms of trade are deteriorating for non-oil-exporting developing countries

6.2a

Index (2000 = 100) 200 180

Oil-exporting developing countries

160 140 120

Other developing countries

Data sources

100 80

The main source of trade indexes data for developHigh-income countries

ing countries is UNCTAD’s annual Handbook of International Trade and Development Statistics. The

60

IMF’s International Financial Statistics provides

40 1980

1985

1990

1995

2000

Source: United Nations Conference on Trade and Development and International Monetary Fund.

2005

these data for high-income and selected developing economies.

2007 World Development Indicators

323

6.3

Direction and growth of merchandise trade

Direction of trade High-income importers

% of world trade, 2005

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

All highincome

28.9 22.4 0.8 1.8 3.8 8.0 1.9 1.3 3.8 1.1 0.7 0.2 0.9 0.2 0.3 0.2 0.5 0.2 36.9

2.8 0.5 .. 0.6 1.7 1.8 1.4 0.8 0.1 0.0 0.1 0.0 0.1 0.0 0.0 0.0 0.1 0.1 4.6

9.7 3.1 1.4 .. 5.3 6.5 2.3 1.6 0.2 0.1 3.0 0.2 0.2 0.1 0.2 0.2 0.5 0.0 16.2

12.7 3.7 1.9 3.6 3.5 5.1 3.6 2.4 0.4 0.2 0.4 0.1 0.3 0.0 0.3 0.3 0.2 0.1 17.8

54.0 29.7 4.0 6.0 14.3 21.5 9.2 6.2 4.6 1.4 4.1 0.6 1.5 0.4 0.9 0.7 1.2 0.3 75.5

Low- and middle-income importers

% of world trade, 2005

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

324

East Asia & Pacifi c

Europe & Central Asia

Latin America & Caribbean

Middle East & N. Africa

South Asia

Sub-Saharan Africa

All low& middleincome

7.3 1.0 1.4 0.7 4.3 2.2 1.2 0.4 0.2 0.2 0.2 0.1 0.2 0.0 0.1 0.1 0.2 0.0 9.6

4.3 3.6 0.1 0.2 0.4 2.8 0.4 0.4 2.1 0.8 0.1 0.1 0.1 0.0 0.0 0.0 0.0 0.0 7.1

1.6 0.5 0.2 0.7 0.3 1.2 0.2 0.2 0.0 0.0 0.8 0.2 0.0 0.0 0.0 0.0 0.1 0.0 2.8

1.3 0.8 0.1 0.1 0.3 0.7 0.2 0.1 0.2 0.1 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.0 1.9

1.0 0.3 0.1 0.1 0.5 0.6 0.3 0.2 0.1 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.0 0.0 1.5

0.9 0.5 0.1 0.1 0.2 0.6 0.2 0.1 0.0 0.0 0.1 0.0 0.0 0.0 0.1 0.1 0.2 0.1 1.5

16.4 6.7 1.8 1.8 6.0 8.1 2.5 1.4 2.8 1.0 1.3 0.5 0.6 0.1 0.4 0.3 0.5 0.1 24.5

2007 World Development Indicators

World

70.4 36.4 5.9 7.8 20.3 29.6 11.7 7.5 7.3 2.4 5.4 1.1 2.1 0.4 1.3 1.0 1.7 0.5 100.0

6.3

GLOBAL LINKS

Direction and growth of merchandise trade Nominal growth of trade High-income importers

annual % growth, 1995–2005

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

All highincome

6.5 6.9 1.5 3.9 6.8 12.6 14.1 21.3 15.1 14.9 5.8 6.2 10.6 14.4 8.7 9.9 10.5 5.1 7.5

3.2 2.7 .. –1.5 5.6 7.7 8.2 11.4 3.8 1.7 2.4 1.1 9.6 –5.1 –0.8 1.1 20.1 5.1 4.7

5.9 9.0 1.1 .. 6.0 12.7 13.9 20.8 10.4 3.9 11.3 10.0 22.2 20.2 10.6 11.9 16.7 2.5 8.1

5.0 5.5 3.1 4.3 6.7 11.9 11.7 15.5 12.2 10.1 10.1 12.0 12.6 22.0 13.0 15.1 17.7 4.6 6.6

5.8 6.8 2.0 3.5 6.3 12.0 12.0 17.0 14.3 12.5 9.7 8.0 12.0 16.2 9.9 11.6 13.9 4.5 7.2

Low- and middle-income importers

annual % growth, 1995–2005

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

East Asia & Pacifi c

Europe & Central Asia

Latin America & Caribbean

Middle East & N. Africa

South Asia

Sub-Saharan Africa

All low& middleincome

World

8.7 6.7 6.0 7.0 10.6 16.7 16.0 18.3 12.3 11.7 16.8 13.4 21.5 57.3 17.7 19.6 29.3 3.6 10.1

11.9 12.4 15.2 5.8 10.6 12.2 21.4 25.9 11.1 11.1 15.5 15.7 10.3 11.4 6.4 6.7 15.6 4.8 12.0

2.4 1.5 0.5 3.4 3.1 8.8 15.2 19.5 6.0 8.0 6.9 9.1 16.6 26.7 18.6 22.7 22.6 4.7 4.7

8.2 7.8 5.7 3.4 12.1 13.8 16.1 23.1 13.6 17.0 9.2 10.0 14.3 16.2 15.1 20.4 10.8 8.8 9.8

9.1 9.0 2.5 7.5 10.9 14.4 16.5 20.3 12.0 9.9 15.4 10.6 9.2 8.1 14.9 13.0 8.6 10.7 10.7

6.2 6.3 0.6 6.7 8.3 15.8 18.7 22.4 17.7 34.4 13.9 16.4 10.5 9.7 15.2 15.5 15.6 3.5 9.0

8.4 8.9 5.4 5.2 10.1 13.1 16.9 21.1 11.4 11.4 9.2 11.0 14.6 19.3 14.7 15.9 18.9 4.4 9.7

6.4 7.2 3.0 3.8 7.3 12.3 12.9 17.6 13.1 12.0 9.6 9.2 12.7 16.6 11.1 12.8 15.2 4.5 7.8

2007 World Development Indicators

325

6.3

Direction and growth of merchandise trade

About the data

Definitions

The table provides estimates of the flow of trade in goods between groups of economies. The data are from the International Monetary Fund’s (IMF) Direc-

• Merchandise trade includes all trade in goods; Three regions account from more than 75 percent of exports to other developing regions, 2005

trade in services is excluded. • High-income econo-

6.3a

South Asia 7%

oping countries report trade on a timely basis, covering about 80 percent of trade for recent years. Trade by less timely reporters and by countries that do not report is estimated using reports of trading partner

of the missing trade flows can be estimated from partner reports. Partner country data may introduce discrepancies due to smuggling, confidentiality, dif-

as all high-income EU members: Austria, Belgium,

Sub-Saharan Africa 7%

Cyprus, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Malta, the Netherlands,

Middle East & North Africa 8%

Europe & Central Asia 35%

countries. Because the largest exporting and importing countries are reliable reporters, a large portion

mies are those classified as such by the World Bank (see inside front cover). • European Union is defined

tion of Trade database. All developed and 23 devel-

Latin America & Caribbean 15%

Portugal, Slovenia, Spain, Sweden, and the United Kingdom. • Other high-income economies include all high-income economies (OECD and non-OECD) except the European Union, Japan, and the United

East Asia & Pacific 28%

ferent exchange rates, overreporting of transit trade,

States. • Low- and middle-income regional groupings are based on World Bank classifications and may differ from those used by other organizations.

inclusion or exclusion of freight rates, and different points of valuation and times of recording.

Source: IMF Direction of Trade database.

In addition, estimates of trade within the European Union (EU) have been significantly affected by changes in reporting methods following the creation of a customs union. The current system for collecting data on trade between EU members—Intrastat, introduced in 1993—has less exhaustive coverage 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. Most countries report their trade data in national currencies, which are converted into U.S. dollars using the IMF’s published period average exchange rates (series rf or rh, monthly averages of the market or official rates) for the reporting country or, if those are not available, monthly average rates in New York. Because imports are reported at cost, insurance, and freight (c.i.f.) valuations, and exports at free on board (f.o.b.) valuations, the IMF adjusts country reports of import values by dividing them by 1.10 to estimate equivalent export values. This approximation is more or less accurate, depending on the set of partners and the items traded. Other factors affecting the accuracy of trade data include lags in reporting, recording differences across countries, and whether the country reports trade according to the general or special system of trade. (For further discussion of the measurement of exports and imports, see About the data for tables 4.4 and 4.5.)

Data sources

The regional trade flows shown in the table were

Data on the direction and growth of merchandise

calculated from current price values. The growth rates

trade were calculated using the IMF’s Direction of

are presented in nominal terms; that is, they include

Trade database.

the effects of changes in both volumes and prices.

326

2007 World Development Indicators

6.4

GLOBAL LINKS

High-income economy trade with low- and middle-income economies Exports to low-income economies High-income countries

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

1995

2005

1995

2005

1995

2005

1995

2005

67.6

134.9

33.1

58.3

9.3

12.5

7.9

16.8

8.6 3.1 2.7 2.7 4.3 0.0 3.5 79.5 12.4 3.6 45.0 0.2 4.4 0.2 13.6 2.3

6.0 1.9 1.8 3.3 7.6 0.6 5.1 77.4 11.0 3.4 41.0 0.2 3.5 0.1 18.1 3.9

8.5 2.6 1.3 1.5 2.2 0.0 2.2 84.4 12.2 4.3 44.2 0.3 1.7 0.2 21.4 2.0

6.4 1.5 1.4 3.5 4.3 0.0 4.2 81.4 11.7 3.5 40.2 0.3 1.3 0.1 24.2 2.9

0.5 0.2 1.7 0.7 0.9 0.0 0.8 95.1 6.6 6.6 69.2 0.1 3.0 0.0 9.5 1.0

0.4 0.1 1.4 1.1 0.9 0.0 0.8 92.9 7.2 9.3 62.2 0.2 3.6 0.0 10.4 3.3

20.3 14.4 6.7 3.1 1.5 0.0 1.4 64.9 14.4 1.6 38.3 0.1 1.5 0.1 9.0 3.5

12.1 7.4 4.3 2.5 2.2 0.0 1.3 71.7 10.2 1.7 44.3 0.2 2.0 0.1 13.1 7.4

68.0

178.4

34.2

66.3

8.4

13.0

16.0

66.7

20.3 0.6 6.1 5.9 19.4 17.9 1.3 47.9 2.7 1.3 2.8 0.2 26.0 1.3 13.6 0.4

10.8 0.6 2.0 3.7 29.8 24.6 4.0 53.2 3.6 1.7 5.4 1.5 23.1 3.1 14.8 0.6

24.8 0.3 7.6 4.9 11.4 10.8 0.5 51.2 2.7 0.7 2.8 0.2 26.5 2.1 16.1 0.2

15.2 0.4 3.5 4.9 16.2 12.0 2.0 59.6 4.1 1.9 6.1 1.6 27.4 5.8 12.6 0.7

25.4 0.2 7.1 16.7 15.2 12.0 2.3 35.1 1.0 2.4 0.6 0.5 16.9 0.3 13.4 0.5

16.5 0.3 1.8 8.7 35.6 28.6 5.1 36.9 2.9 1.2 10.3 1.7 9.6 2.3 8.9 0.5

8.3 0.2 1.7 2.0 33.0 31.6 1.5 54.3 2.3 1.3 2.2 0.2 29.8 0.6 18.0 0.6

5.3 0.1 0.8 0.9 43.6 38.7 4.2 48.7 2.5 1.4 3.6 1.8 26.1 1.4 11.9 0.7

from low-income economies (%) 9.7 7.1 11.6 16.5 11.7 65.6 2.0 2.4 0.3 1.8 1.6 0.5 4.7 1.7 0.1 7.6 1.1 0.0 5.3 1.9 0.3 5.4 3.4 1.6 3.0 2.4 1.2 3.3 2.1 0.5 2.4 1.7 0.4 4.1 2.6 0.2 9.7 6.0 4.2 10.3 6.7 4.5 6.6 4.0 2.2 10.4 1.1 0.6 4.9 3.5 2.8

6.8 20.5 0.2 0.5 0.1 0.0 0.2 1.1 1.8 0.3 0.4 0.0 2.5 2.5 1.3 0.4 1.7

12.9 14.4 2.8 2.7 1.3 0.0 1.7 4.3 1.1 0.3 0.0 0.2 6.7 11.0 5.3 0.0 2.8

12.8 42.7 1.2 0.6 1.0 0.5 1.7 2.2 0.7 0.2 0.0 0.0 4.8 9.1 3.0 0.0 2.4

3.8 5.2 0.6 0.4 2.5 0.0 3.1 6.7 1.8 3.6 0.9 5.6 13.2 19.0 8.3 1.4 6.0

3.4 2.4 0.4 0.4 0.7 0.1 1.1 4.8 0.9 0.2 0.4 1.4 10.3 10.7 6.1 0.2 4.4

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 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 Average

2007 World Development Indicators

327

6.4

High-income economy trade with low- and middle-income economies

Exports to middle-income economies High-income countries

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

1995

2005

1995

2005

1995

2005

1995

2005

681.0

1,501.9

263.4

609.5

103.4

177.6

147.0

285.3

7.5 2.0 2.3 2.1 2.3 0.2 1.7 83.3 11.0 3.0 47.4 0.5 6.4 0.3 14.7 2.5

4.6 0.9 1.8 2.9 3.4 0.2 2.7 84.3 12.1 3.2 50.5 0.5 4.2 0.2 13.7 3.0

9.3 1.5 1.4 1.6 1.7 0.3 1.2 82.7 11.8 2.8 45.7 0.9 5.7 0.4 15.4 3.2

5.0 0.7 1.4 2.0 2.1 0.0 1.9 86.0 13.3 3.6 48.3 0.8 4.5 0.3 15.2 3.4

0.3 0.0 1.0 1.3 0.5 0.0 0.5 95.7 6.6 6.5 68.1 0.1 2.7 0.0 11.6 1.2

0.4 0.0 0.9 2.6 1.0 0.0 0.9 91.6 9.0 6.8 61.3 0.2 2.1 0.0 12.2 3.6

10.9 4.8 3.9 1.9 2.3 0.0 1.5 77.4 11.4 1.2 46.1 0.6 4.5 0.1 13.5 3.7

8.1 2.2 3.4 3.1 4.0 0.0 3.4 77.6 11.7 1.1 47.9 0.4 3.5 0.0 13.0 3.8

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

746.9

2,194.9

257.5

801.5

99.4

210.6

221.1

722.1

11.5 0.3 3.7 6.0 13.2 8.7 2.2 63.8 3.8 3.0 22.3 1.4 13.9 2.9 16.5 1.8

6.4 0.3 1.5 4.1 17.9 11.9 3.4 68.6 3.3 2.5 32.9 2.3 9.5 1.7 16.4 1.5

14.5 0.2 4.9 7.7 16.8 10.8 2.9 53.4 5.2 3.4 14.1 1.6 13.9 1.6 13.6 2.7

7.8 0.3 2.0 4.5 21.8 14.7 4.1 62.1 3.3 2.9 28.0 2.1 9.5 1.4 14.8 1.8

16.7 0.2 5.9 11.0 17.4 9.5 1.2 47.9 2.8 2.5 11.8 1.6 14.6 1.5 13.0 1.2

9.2 0.3 2.1 8.4 16.6 7.8 1.6 62.5 3.6 1.6 27.6 1.6 10.9 1.2 15.9 1.2

8.2 0.2 1.9 3.5 11.9 9.1 2.5 72.3 2.7 2.3 31.5 1.6 12.4 4.1 17.7 2.1

4.7 0.1 1.0 2.3 18.7 13.9 3.6 71.4 2.8 1.9 35.1 3.3 8.8 2.2 17.3 2.0

Simple applied tariff rates on 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 Average

from low-income economies (%) 15.4 9.5 24.7 21.6 15.3 63.3 2.0 1.9 1.0 1.7 1.1 1.8 4.1 1.5 0.7 13.8 1.2 0.0 4.5 1.9 1.1 5.6 3.2 4.1 3.6 2.2 3.6 3.1 1.3 1.7 3.2 1.8 2.0 5.0 3.1 1.8 11.0 6.6 8.9 12.1 7.7 9.6 6.8 3.9 5.0 7.6 1.1 2.0 5.5 3.5 5.8

11.7 28.8 0.3 0.7 0.1 0.0 0.2 1.1 1.8 0.2 0.4 0.0 2.9 3.0 1.4 0.4 2.1

14.4 22.8 1.5 0.5 0.6 0.0 1.1 1.8 1.4 0.6 0.0 0.0 4.7 15.0 2.5 0.0 2.5

13.2 34.2 1.6 0.1 0.8 0.5 1.6 2.2 0.8 0.2 0.0 0.0 6.7 17.7 3.2 0.0 2.6

3.1 2.7 0.6 0.5 1.1 0.0 1.7 3.9 1.4 3.5 0.6 0.5 11.0 14.7 5.3 1.0 3.6

3.4 2.0 0.4 0.5 0.6 0.2 1.0 3.0 1.0 0.2 0.3 0.3 8.8 9.2 4.2 0.4 2.9

328

2007 World Development Indicators

About the data

6.4

GLOBAL LINKS

High-income economy trade with low- and middle-income economies Definitions

Developing countries are becoming increasingly

countries has grown. Moreover, trade between devel-

The product groups in the table are defined in accor-

important in the global trading system. Since the

oping countries has grown substantially over the past

dance with the SITC revision 1: food (0, 1, 22, and

early 1990s trade between high-income countries

decade. This growth has resulted from many factors,

4) and cereals (04); agricultural raw materials

and low- and middle-income economies has grown

including developing countries’ increasing share of

(2 excluding 22, 27, and 28); ores and nonferrous

faster than trade among high-income economies. The

world output and the liberalization of their trade.

metals (27, 28, and 68); fuels (3), crude petroleum

increased trade benefits consumers and producers.

Yet trade barriers remain high. The table includes

But as the World Trade Organization’s (WTO) Minis-

information about tariff rates by selected product

terial Conferences in Doha, Qatar, in October 2001,

groups. Applied tariff rates are the tariffs in effect

Cancun, Mexico, in September 2003, and Hong

for partners in preferential trade agreements such

and steel (67), machinery and transport equipment

Kong, China, in December 2005 showed, achieving a

as the North American Free Trade Agreement. When

(7), furniture (82), textiles (65 and 84), footwear

more pro-development outcome from trade remains

these are unavailable, most favored nation rates are

(85), and other manufactured goods (6 and 8 exclud-

a challenge. Meeting it will require strengthening

used. The difference between most favored nation

ing 65, 67, 68, 82, 84, and 85); and miscellaneous

international consultation. Negotiations after the

and applied rates can be substantial. Simple aver-

goods (9). • Exports are all merchandise exports

Doha meetings were launched on services, agricul-

ages of applied rates are shown because they are

by high-income countries to low-income and middle-

ture, manufactures, WTO rules, the environment,

generally a better indicator of tariff protection.

income economies as recorded in the United Nations

(331), and petroleum products (332); manufactured goods (5–8 excluding 68), chemical products (5), iron

dispute settlement, intellectual property rights pro-

The data are from the United Nations Conference

tection, and disciplines on regional integration. At

on Trade and Development (UNCTAD). Partner country

the most recent negotiations in Hong Kong, China,

reports by high-income countries were used for both

trade ministers agreed to eliminate subsidies of agri-

exports and imports. Exports are recorded free on

cultural exports by 2013; to abolish cotton export

board (f.o.b.); imports include insurance and freight

recorded in the United Nations Statistics Division’s

subsidies in 2006 and grant unlimited export access

charges (c.i.f.). Because of differences in sources

Comtrade database. • High-, middle-, and low-

to selected cotton-growing countries in Sub-Saha-

of data, timing, and treatment of missing data, the

income economies are those classified as such by

ran Africa; to cut more domestic farm supports in

numbers in the table may not be fully comparable

the World Bank (see inside front cover). • European

the European Union, Japan, and the United States;

with those used to calculate the direction of trade

Union is defined as all high-income EU members:

and to offer more aid to developing countries to help

statistics in table 6.3 or the aggregate flows in tables

Austria, Belgium, Cyprus, Denmark, Finland, France,

them compete in global trade.

4.4, 4.5, and 6.2. Data are classified using the Har-

Germany, Greece, Ireland, Italy, Luxembourg, Malta,

Trade flows between high-income countries and

monized System of trade at the six- or eight-digit

low- and middle-income economies reflect the chang-

level. Tariff line data were matched to Standard Inter-

ing mix of exports to and imports from developing

national Trade Classification (SITC) revision 1 codes

economies. While food and primary commodities

to define commodity groups. For further discussion

have continued to fall as a share of high-income

of merchandise trade statistics, see About the data

countries’ imports, the share of manufactures in

for tables 4.4, 4.5, 6.2, and 6.3, and for information

goods imports from both low- and middle-income

about tariff barriers, see table 6.7.

Imports from low- and middle-income economies to high-income economies vary considerably Share of total imports, 2005 (%) 80

Statistics Division’s Comtrade database. • Imports are all merchandise imports by high-income countries from low-income and middle-income economies as

the Netherlands, Portugal, Slovenia, Spain, Sweden, and the United Kingdom.

6.4a

Imports from low-income

Imports from middle-income

70 60 50 40 30 20 10

Data sources 0 Food

Fuels

Manufacturing

Machinery and transport equipment

Textiles

Trade values are from United Nations Statistics Division’s Comtrade database. Tariff data are

The major manufactured imports of high-income economies from developing countries are manufac-

from UNCTAD’s Trade Analysis and Information

tured textiles from low-income economies and machinery and transport equipment from middle-income

System database and are calculated by World

economies.

Bank staff using the World Integrated Trade Solu-

Source: United Nations Statistics Division’s Comtrade database.

tion system.

2007 World Development Indicators

329

6.5

Primary commodity prices

1970

World Bank commodity price index (1990 = 100) Nonenergy commodities Agriculture Beverages Food Raw materials Fertilizers Metals and minerals Petroleum Steel productsa MUV G-5 index

Commodity prices (1990 prices) Agricultural raw materials Cotton (cents/kg) Logs, Cameroon ($/cu. m)a Logs, Malaysian ($/cu. m) Rubber (cents/kg) Sawnwood, Malaysian ($/cu. m) Tobacco ($/mt) Beverages (cents/kg) Cocoa Coffee, robustas Coffee, Arabica Tea, avg., 3 auctions Energy Coal, Australian ($/mt) Coal, U.S. ($/mt) Natural gas, Europe ($/mmbtu) Natural gas, U.S. ($/mmbtu) Petroleum ($/bbl)

1980

1990

1995

2000

2001

2002

2003

2004

2005

2006

156 163 203 166 130 108 144 19 111 28

159 175 230 177 133 164 120 204 100 79

100 100 100 100 100 100 100 100 100 100

104 112 129 100 116 88 87 64 91 117

89 90 91 87 93 109 85 127 79 97

84 84 76 91 81 105 80 113 71 94

89 93 91 97 89 108 78 117 73 93

91 95 87 96 98 106 82 126 79 100

100 98 88 103 99 118 105 154 114 107

114 106 109 103 107 126 133 218 129 107

138 115 111 109 124 130 195 254 122 110

225 153 154 145 625 3,836

260 319 248 181 503 2,887

182 344 177 86 533 3,392

182 290 218 135 632 2,258

134 283 195 69 612 3,063

112 282 169 61 510 3,185

109 .. 175 82 565 2,947

140 .. 187 108 550 2,643

128 .. 184 122 543 2,560

114 .. 190 140 616 2,606

115 .. 217 191 678 2,672

240 330 409 298

330 411 440 211

127 118 197 206

122 237 285 127

93 94 198 193

113 64 146 169

191 71 146 162

175 81 141 151

145 74 166 157

144 104 237 154

144 135 228 170

.. .. .. 1 4

51 55 4 2 47

40 42 3 2 23

34 33 2 1 15

27 34 4 4 29

34 48 4 4 26

27 43 3 4 27

26 .. 4 5 29

49 .. 4 6 35

44 .. 6 8 50

44 .. 8 6 58

About the data

Primary commodities—raw or partially processed

the prices paid by importers are used. Annual price

indexes are compiled for petroleum and steel prod-

materials that will be transformed into fi nished

series are generally simple averages based on higher

ucts, which are not included in the nonenergy com-

goods—are often the most signifi cant exports of

frequency data. The constant price series in the

modity price index.

developing countries, and revenues obtained from

table is deflated using the manufactures unit value

The MUV index is a composite index of prices

them have an important effect on living standards.

(MUV) index for the Group of Five (G-5) countries

for manufactured exports from the fi ve major

Price data for primary commodities are collected from

(see below).

(G-5) industrial countries (France, Germany, Japan,

a variety of sources, including trade journals, inter-

The commodity price indexes are calculated as

the United Kingdom, and the United States) to low-

national study groups, government market surveys,

Laspeyres index numbers, in which the fixed weights

and middle-income economies, valued in U.S. dol-

newspaper and wire service reports, and commodity

are the 1987–89 export values for low- and middle-

lars. The index covers products in groups 5–8 of the

exchange spot and near-term forward prices.

income economies, rebased to 1990. Each index rep-

Standard International Trade Classification revision

The table is based on frequently updated price

resents a fixed basket of primary commodity exports.

1. To construct the MUV G-5 index, unit value indexes

reports. When possible, the prices received by

The nonenergy commodity price index contains 37

for each country are combined using weights deter-

exporters are used; if export prices are unavailable,

price series for 31 nonenergy commodities. Separate

mined by each country’s export share.

330

2007 World Development Indicators

1970

Commodity prices (continued) (1990 prices) Fertilizers ($/mt) Phosphate rock TSP Food Fats and oils ($/mt) Coconut oil Groundnut oil Palm oil Soybeans Soybean meal Soybean oil Grains ($/mt) Sorghum Maize Rice Wheat Other food Bananas ($/mt) Beef (cents/kg) Oranges ($/mt) Sugar, EU domestic (cents/kg) Sugar, U.S. domestic (cents/kg) Sugar, world (cents/kg) Metals and minerals Aluminum ($/mt) Copper ($/mt) Iron ore (cents/dmtu) Lead (cents/kg) Nickel ($/mt) Tin (cents/kg) Zinc (cents/kg)

1980

1990

1995

2000

2001

2002

2003

6.5

2004

2005

GLOBAL LINKS

Primary commodity prices

2006

39 152

59 229

41 132

30 128

45 142

44 135

43 143

38 149

38 174

39 188

40 186

1,417 1,350 927 417 367 1,021

855 1,090 740 376 332 758

337 964 290 247 200 447

572 846 536 221 168 534

463 734 319 218 195 348

337 721 303 208 192 375

452 738 419 228 188 488

467 1,242 443 264 211 553

617 1,085 440 286 225 576

576 991 394 257 200 509

549 878 433 243 189 542

185 208 450 196

164 159 521 219

104 109 271 136

102 105 274 151

91 91 208 117

101 95 183 134

109 107 206 159

106 105 197 146

103 104 222 147

90 92 267 142

111 110 276 174

590 465 599 40 59 29

481 350 496 62 84 80

541 256 531 58 51 28

380 163 454 59 43 25

436 199 374 57 44 19

618 226 631 56 50 20

568 226 606 59 50 16

374 198 680 60 47 16

490 235 803 63 42 15

563 245 817 62 44 20

613 231 751 58 44 30

1,982 5,038 35 108 10,148 1,310 105

1,847 2,768 36 115 8,270 2,128 97

1,639 2,662 33 81 8,864 609 151

1,542 2,508 24 54 7,028 531 88

1,594 1,866 30 47 8,888 559 116

1,531 1,673 32 50 6,303 475 94

1,449 1,674 31 49 7,271 436 84

1,430 1,777 32 51 9,617 489 83

1,603 2,678 35 83 12,915 795 98

1,774 3,437 61 91 13,776 690 129

2,327 6,086 70 117 21,960 795 297

a. Series not included in the nonenergy index.

Definitions

• Nonenergy commodity price index covers the

iron ore, lead, nickel, tin, and zinc. • Petroleum

Price Data” (also known as the “Pink Sheet”) at the

31 nonenergy primary commodities that make up

price index refers to the average spot price of Brent,

Global Prospects Web site (www.worldbank.org/

the agriculture, fertilizer, and metals and minerals

Dubai, and West Texas Intermediate crude oils,

prospects, click on Products).

indexes. • Agriculture includes beverages, food,

equally weighted. • Steel products price index is the

and agricultural raw materials. • Beverages include

composite price index for eight steel products based

cocoa, coffee, and tea. • Food includes rice, wheat,

on quotations free on board (f.o.b.) Japan excluding

maize, sorghum, soybeans, soybean oil, soybean

shipments to China and the United States, weighted

meal, palm oil, coconut oil, groundnut oil, bananas,

by product shares of apparent combined consump-

Data on commodity prices and the MUV G-5 index

beef, oranges, and sugar. • Agricultural raw mate-

tion (volume of deliveries) for Germany, Japan, and

are compiled by the World Bank’s Development

rials include cotton, timber (logs and sawnwood),

the United States. • MUV G-5 index is the manu-

Prospects Group. Monthly updates of commodity

natural rubber, and tobacco. • Fertilizers include

factures unit value index for G-5 country exports to

prices are available on the Web at www.worldbank.

phosphate rock and triple superphosphate (TSP).

low- and middle-income economies. • Commodity

org/prospects.

• Metals and minerals include aluminum, copper,

prices—for definitions and sources, see “Commodity

Data sources

2007 World Development Indicators

331

6.6

Regional trade blocs

Merchandise exports within bloc

Year of creation High-income and lowand middle-income economies APECa CEFTA CIS EMFTA European Union FTAA NAFTA Latin America and the Caribbean ACS Andean Group CACM CARICOM Central American Group of Four Group of Three LAIA (ALADI) Mercosur OECS Middle East and Asia Arab Common Market ASEAN Bangkok Agreement EAEC ECO GAFTA GCC SAARC UMA Sub-Saharan Africa CEMAC CEPGL COMESA Cross Border Initiative EAC ECCAS ECOWAS Indian Ocean Commission MRU SADC UDEAC UEMOA

1989 1992 1991 1995 1957 1994 1994

$ millions 1990

1995

1999

2000

2001

2002

2003

2004

2005

901,560 1,688,708 1,896,213 2,261,791 2,070,973 2,168,700 2,420,739 2,905,271 3,286,979 4,235 12,118 13,226 15,123 17,054 19,180 25,309 37,541 48,726 .. 29,943 20,842 27,043 22,262 28,029 36,540 40,446 55,521 1,089,631 1,488,243 1,700,902 1,744,696 1,737,269 1,857,562 2,253,496 2,706,304 2,883,467 1,011,019 1,385,805 1,579,070 1,608,174 1,612,634 1,721,082 2,087,311 2,482,418 2,642,578 300,700 525,346 734,848 855,659 810,360 787,232 826,281 967,653 1,110,730 226,273 394,472 581,161 676,141 639,419 626,020 651,060 737,591 824,550

1994 1969 1961 1973 1993 1995 1980 1991 1981

5,398 1,312 667 448 399 1,046 12,331 4,127 29

11,049 4,812 1,594 867 1,026 3,460 35,299 14,199 39

11,199 3,929 2,175 1,136 1,369 2,912 34,785 15,313 37

16,267 5,300 2,586 1,050 1,765 3,721 42,901 17,829 38

15,699 5,609 2,739 1,420 1,886 4,178 40,780 15,156 37

15,769 5,065 2,763 1,184 1,906 3,839 36,054 10,228 40

1964 1967 1975 1990 1985 1997 1981 1985 1989

911 27,365 4,476 281,067 1,243 13,313 6,906 863 958

1,368 79,544 12,066 634,606 4,746 13,129 6,832 2,024 1,109

951 77,889 14,463 612,415 3,903 13,752 7,306 2,180 919

1,312 98,060 16,844 772,423 4,518 16,238 7,958 2,593 1,094

1,728 86,331 16,733 698,552 4,498 17,528 8,103 2,827 1,137

1,998 91,684 17,957 779,384 5,014 19,195 8,899 3,402 1,202

1994 1976 1994 1992 1996 1983 1975 1984 1973 1992 1964 1994

139 7 963 613 230 163 1,557 73 0 1,630 139 621

120 8 1,386 1,002 530 163 1,936 127 1 3,373 120 560

127 9 1,348 964 438 179 2,364 91 4 4,224 126 805

97 10 1,653 1,166 595 191 2,835 106 5 4,282 96 741

118 11 1,819 1,070 664 203 2,371 134 4 3,771 117 775

136 13 2,031 1,373 685 199 3,229 105 5 4,316 134 857

15,138 5,036 3,156 1,410 2,036 3,167 39,863 12,732 48

20,058 7,261 3,574 1,734 2,315 5,669 55,826 17,354 60

25,071 9,453 4,064 2,078 2,631 7,437 70,430 21,118 68

1,797 6,303 7,138 101,054 122,914 142,955 21,808 24,925 29,506 940,950 1,177,286 1,335,003 7,468 9,978 13,993 21,511 35,554 44,777 9,580 12,532 16,507 4,873 5,706 7,062 1,338 1,375 1,926 148 15 2,436 1,536 706 198 3,140 179 5 5,377 146 1,076

176 19 2,849 1,705 750 238 4,499 155 6 6,384 174 1,233

201 22 3,330 1,913 857 272 5,673 159 6 6,384 198 1,390

Note: Regional bloc memberships are as follows: Asia Pacific Economic Cooperation (APEC), Australia, Brunei Darussalam, Canada, Chile, China, Hong Kong (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; Central European Free Trade Area (CEFTA), Bulgaria, the Czech Republic, Hungary, Poland, Romania, the Slovak Republic, and Slovenia; Commonwealth of Independent States (CIS), Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, the Kyrgyz Republic, Moldova, the Russian Federation, Tajikistan, Ukraine, and Uzbekistan; Euro-Mediterranean Free Trade Area (EMFTA), European Union, Algeria, Cyprus, Egypt, Israel, Jordan, Lebanon, Malta, Morocco, Syrian Arab Republic, Tunisia, Turkey, and West Bank and Gaza; European Union (EU; formerly European Economic Community and European Community), Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal, Spain, Sweden, and the United Kingdom; Free Trade Areas of the Americas (FTAA), Antigua and Barbuda, Argentina, Bahamas, Barbados, Belize, Bolivia, Brazil, Canada, Chile, Colombia, Costa Rica, Dominica, Dominican Republic, Ecuador, El Salvador, Grenada, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Republica Bolivariana de Venezuela, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Suriname, Trinidad and Tobago, the United States, and Uruguay; North American Free Trade Agreement (NAFTA), Canada, Mexico, and the United States; Economic and Monetary Community of Central Africa (CEMAC), Cameroon, the Central African Republic, Chad, the Republic of Congo, Equatorial Guinea, Gabon, and São Tomé and Principe; Economic Community of the Countries of the Great Lakes (CEPGL), Burundi, the Democratic Republic of Congo, and Rwanda; Common Market for Eastern and Southern Africa (COMESA), Angola, Burundi, Comoros, the Democratic Republic of Congo, Djibouti, the Arab Republic of Egypt, Eritrea, Ethiopia, Kenya, Madagascar, Malawi, Mauritius, Namibia, Rwanda, Seychelles, Sudan, Swaziland, Uganda, Tanzania, Zambia, and Zimbabwe; Cross Border Initiative, Burundi, Comoros, Kenya, Madagascar, Malawi, Mauritius, Namibia, Rwanda, Seychelles, Swaziland, Tanzania, Uganda, Zambia, and Zimbabwe; East African Community (EAC), Kenya, Tanzania, and Uganda; 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, Rwanda, and São Tomé and Principe; Economic Community of West African States (ECOWAS), Benin, Burkina Faso, Cape Verde, Côte d’Ivoire, the Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, and Togo; Indian Ocean Commission, Comoros, Madagascar, Mauritius, Réunion, and Seychelles; Mano River Union (MRU), Guinea, Liberia, and Sierra Leone; Southern African Development Community a. No preferential trade agreement.

332

2007 World Development Indicators

6.6

GLOBAL LINKS

Regional trade blocs Merchandise exports within bloc

High-income and lowand middle-income economies APECa CEFTA CIS EMFTA FTAA European Union NAFTA Latin America and the Caribbean ACS Andean Group CACM CARICOM Central American Group of Four Group of Three LAIA (ALADI) Mercosur OECS Middle East and Asia Arab Common Market ASEAN Bangkok Agreement EAEC ECO GAFTA GCC SAARC UMA Sub-Saharan Africa CEMAC CEPGL COMESA Cross Border Initiative EAC ECCAS ECOWAS Indian Ocean Commission MRU SADC UDEAC UEMOA

% of total bloc exports

Year of creation

1990

1995

1999

2000

2001

2002

2003

2004

2005

1989 1992 1991 1995 1994 1957 1994

68.3 16.3 .. 69.6 46.6 66.8 41.4

71.7 14.8 27.6 68.7 52.5 66.1 46.2

71.8 13.8 20.7 70.4 59.7 67.8 54.6

73.1 9.9 19.2 69.3 60.7 66.8 55.7

72.6 8.0 18.2 68.3 60.5 66.2 55.5

73.3 7.3 18.8 68.5 60.8 66.3 56.6

72.6 14.6 19.6 69.4 60.0 67.2 56.1

72.0 14.2 16.6 69.2 60.0 66.8 55.9

70.7 14.6 17.1 68.3 60.3 66.0 55.8

1994 1969 1961 1973 1993 1995 1980 1991 1981

8.4 4.1 15.3 8.1 13.7 2.0 10.8 8.9 8.1

8.5 12.0 21.8 12.0 22.2 3.2 17.1 20.3 12.6

5.6 8.8 13.6 16.9 14.6 1.7 12.7 20.6 13.1

6.7 8.7 19.1 14.7 23.0 1.7 12.8 20.0 10.0

6.9 10.5 22.8 16.5 27.0 2.1 12.8 17.1 6.0

6.9 9.5 19.5 13.7 21.4 1.9 11.2 11.5 4.0

6.3 8.9 20.2 12.3 21.4 1.5 11.4 11.9 7.6

6.9 8.6 20.9 12.5 21.4 2.3 12.6 12.7 11.7

7.2 8.2 18.9 11.8 18.2 2.5 13.2 12.9 11.3

1964 1967 1975 1985 1997 1981 1985 1989

2.7 18.9 3.7 39.7 3.2 10.3 8.0 3.2 2.9

6.7 24.5 4.9 47.9 7.9 9.9 6.8 4.4 3.8

3.3 21.7 5.1 43.8 5.8 8.9 6.7 4.0 2.5

2.9 23.0 5.1 46.6 5.6 7.2 4.8 4.1 2.3

4.4 22.4 5.5 46.6 5.5 8.4 5.2 4.3 2.6

5.1 22.7 5.5 48.1 5.9 9.3 5.9 4.8 2.8

4.1 22.1 5.7 49.4 6.6 8.5 5.1 5.7 2.4

7.9 22.3 5.2 49.8 6.7 10.0 5.0 5.6 1.9

8.6 22.7 5.4 49.2 7.6 9.8 4.8 5.5 2.0

1994 1976 1994 1992 1996 1983 1975 1984 1973 1992 1964 1994

2.3 0.5 6.6 10.3 13.4 1.4 7.9 4.1 0.0 17.0 2.3 13.0

2.1 0.5 7.7 11.9 17.4 1.5 9.0 6.0 0.1 31.6 2.1 10.3

1.7 0.8 7.4 12.1 14.4 1.3 10.4 4.8 0.4 11.9 1.7 13.1

1.1 0.8 6.1 11.8 20.5 1.1 7.9 4.4 0.4 9.3 1.0 13.1

1.4 0.8 7.9 11.5 21.4 1.3 8.5 5.6 0.3 8.6 1.4 12.7

1.5 0.9 7.4 14.5 19.3 1.1 10.9 4.3 0.2 9.5 1.4 12.2

1.4 1.2 7.4 13.0 18.2 1.0 8.6 6.2 0.3 9.8 1.4 13.3

1.3 1.2 6.8 13.8 16.6 0.9 9.4 4.3 0.3 9.5 1.2 12.9

0.9 1.3 5.9 14.0 15.0 0.6 9.5 4.6 0.3 7.7 0.9 13.4

(SADC; formerly Southern African Development Coordination Conference), Angola, Botswana, the Democratic Republic of Congo, Lesotho, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Swaziland, Tanzania, Zambia, and Zimbabwe; Central African Customs and Economic Union (UDEAC; formerly Union Douanière et Economique de l’Afrique Centrale), Cameroon, the Central African Republic, Chad, the Republic of Congo, Equatorial Guinea, and Gabon; West African Economic and Monetary Union (UEMOA), Benin, Burkina Faso, Côte d’Ivoire, Guinea-Bissau, Mali, Niger, Senegal, and Togo; Association of Caribbean States (ACS), Antigua and Barbuda, the Bahamas, Barbados, Belize, Colombia, Costa Rica, Cuba, Dominica, the Dominican Republic, El Salvador, Grenada, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Suriname, Trinidad and Tobago, and República Bolivariana de Venezuela; Andean Group, Bolivia, Colombia, Ecuador, Peru, and República Bolivariana de Venezuela; Central American Common Market (CACM), Costa Rica, El Salvador, Guatemala, Honduras, and Nicaragua; Caribbean Community and Common Market (CARICOM), Antigua and Barbuda, the Bahamas (part of the Caribbean Community but not of the Common Market), Barbados, Belize, Dominica, Grenada, Guyana, Jamaica, Montserrat, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Suriname, and Trinidad and Tobago; Central American Group of Four, El Salvador, Guatemala, Honduras, and Nicaragua; Group of Three, Colombia, Mexico, and República Bolivariana de Venezuela; Latin American Integration Association (LAIA; formerly Latin American Free Trade Area), Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Mexico, Paraguay, Peru, Uruguay, and República Bolivariana de Venezuela; Common Market of the South (Mercosur), Argentina, Brazil, Paraguay, and Uruguay; Organization of Eastern Caribbean States (OECS), Antigua and Barbuda, Dominica, Grenada, Montserrat, St. Kitts and Nevis, St. Lucia, and St. Vincent and the Grenadines; Arab Common Market, the Arab Republic of Egypt, Iraq, Jordan, Libya, Mauritania, the Syrian Arab Republic, and the Republic of Yemen; Association of South-East Asian Nations (ASEAN), Brunei, Cambodia, Indonesia, the Lao People’s Democratic Republic, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam; Bangkok Agreement, Bangladesh, India, the Republic of Korea, the Lao People’s Democratic Republic, the Philippines, Sri Lanka, and Thailand; East Asia Economic Caucus (EAEC; formerly East Asia Economic Group), Brunei, China, Hong Kong (China), Indonesia, Japan, the Republic of Korea, Malaysia, the Philippines, Singapore, Taiwan (China), and Thailand; Economic Cooperation Organization (ECO), Afghanistan, Azerbaijan, the Islamic Republic of Iran, Kazakhstan, the Kyrgyz Republic, Pakistan, Tajikistan, Turkey, Turkmenistan, and Uzbekistan; Gulf Cooperation Council (GCC), Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates; South Asian Association for Regional Cooperation (SAARC), Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka; and Arab Maghreb Union (UMA), Algeria, Libya, Mauritania, Morocco, and Tunisia.

2007 World Development Indicators

333

6.6

Regional trade blocs

Total merchandise exports by bloc

High-income and lowand middle-income economies APECa CEFTA CIS EMFTA FTAA European Union NAFTA Latin America and the Caribbean ACS Andean Group CACM CARICOM Central American Group of Four Group of Three LAIA (ALADI) Mercosur OECS Middle East and Asia Arab Common Market ASEAN Bangkok Agreement EAEC ECO GAFTA GCC SAARC UMA Sub-Saharan Africa CEMAC CEPGL COMESA Cross Border Initiative EAC ECCAS ECOWAS Indian Ocean Commission MRU SADC UDEAC UEMOA

334

% of world exports

Year of creation

1990

1995

1999

2000

2001

2002

2003

2004

2005

1989 1992 1991 1995 1994 1957 1994

39.0 1.3 .. 46.3 19.1 44.8 16.2

46.3 1.6 2.1 42.7 19.7 41.3 16.8

46.6 1.9 1.8 42.7 21.7 41.1 18.8

48.5 1.9 2.2 39.4 22.1 37.7 19.0

46.5 2.2 2.0 41.4 21.8 39.7 18.7

46.0 2.4 2.3 42.2 20.2 40.4 17.2

44.5 2.7 2.5 43.3 18.4 41.5 15.5

44.2 2.9 2.7 42.9 17.7 40.8 14.5

45.0 3.1 3.1 40.8 17.8 38.7 14.3

1994 1969 1961 1973 1993 1995 1980 1991 1981

1.9 0.9 0.1 0.2 0.1 1.5 3.4 1.4 0.0

2.6 0.8 0.1 0.1 0.1 2.1 4.1 1.4 0.0

3.5 0.8 0.3 0.1 0.2 3.0 4.8 1.3 0.0

3.8 0.9 0.2 0.1 0.1 3.3 5.3 1.4 0.0

3.7 0.9 0.2 0.1 0.1 3.2 5.2 1.4 0.0

3.6 0.8 0.2 0.1 0.1 3.1 5.0 1.4 0.0

3.2 0.8 0.2 0.2 0.1 2.7 4.7 1.4 0.0

3.2 0.9 0.2 0.2 0.1 2.7 4.8 1.5 0.0

3.4 1.1 0.2 0.2 0.1 2.9 5.1 1.6 0.0

1964 1967 1975 1990 1985 1997 1981 1985 1989

1.0 4.3 3.6 20.9 1.1 3.8 2.6 0.8 1.0

0.4 6.4 4.8 26.1 1.2 2.6 2.0 0.9 0.6

0.5 6.3 5.0 24.7 1.2 2.7 1.9 1.0 0.6

0.7 6.7 5.2 26.0 1.3 3.5 2.6 1.0 0.8

0.6 6.3 4.9 24.4 1.3 3.4 2.5 1.1 0.7

0.6 6.3 5.1 25.2 1.3 3.2 2.3 1.1 0.7

0.6 6.1 5.1 25.4 1.5 3.4 2.5 1.1 0.7

0.9 6.0 5.3 25.9 1.6 3.9 2.7 1.1 0.8

0.8 6.1 5.3 26.2 1.8 4.4 3.3 1.3 0.9

1994 1976 1994 1992 1996 1983 1975 1984 1973 1992 1964 1994

0.2 0.0 0.4 0.2 0.1 0.3 0.6 0.1 0.1 0.3 0.2 0.1

0.1 0.0 0.4 0.2 0.1 0.2 0.4 0.0 0.0 0.2 0.1 0.1

0.1 0.0 0.3 0.1 0.1 0.2 0.4 0.0 0.0 0.6 0.1 0.1

0.1 0.0 0.4 0.2 0.0 0.3 0.6 0.0 0.0 0.7 0.1 0.1

0.1 0.0 0.4 0.2 0.1 0.3 0.5 0.0 0.0 0.7 0.1 0.1

0.1 0.0 0.4 0.1 0.1 0.3 0.5 0.0 0.0 0.7 0.1 0.1

0.1 0.0 0.4 0.2 0.1 0.3 0.5 0.0 0.0 0.7 0.1 0.1

0.2 0.0 0.5 0.1 0.0 0.3 0.5 0.0 0.0 0.7 0.2 0.1

0.2 0.0 0.5 0.1 0.1 0.4 0.6 0.0 0.0 0.8 0.2 0.1

2007 World Development Indicators

6.6

GLOBAL LINKS

Regional trade blocs About the data

Trade blocs are groups of countries that have estab-

partners. But making it work effectively is challenging

international trade unit. The date of each trade bloc’s

lished special preferential arrangements governing

for any government. All sectors of an economy may

creation is also included. Although bloc exports have

trade between members. Although in some cases

be affected, and some sectors may expand while oth-

been calculated back to 1990 on the basis of current

the preferences—such as lower tariff duties or

ers contract, so it is important to weigh the potential

membership, several of the blocs came into exis-

exemptions from quantitative restrictions—may be

costs and benefits that membership may bring.

tence in later years and their membership may have

no greater than those available to other trading part-

The table shows the value of merchandise intra-

changed over time. For this reason, and because

ners, such arrangements are intended to encourage

trade for important regional trade blocs (service

systems of preferences also change over time, intra-

exports by bloc members to one another—some-

exports are excluded) as well as the size of intra-

trade in earlier years may not have been affected by

times called intratrade.

trade relative to each bloc’s total exports of goods

the same preferences as in recent years. In addi-

Most countries are members of a regional trade

and the share of the bloc’s total exports in world

tion, some countries belong to more than one trade

bloc, and more than a third of the world’s trade takes

exports. Although the Asia Pacific Economic Coopera-

bloc, so shares of world exports exceed 100 percent.

place within such arrangements. While trade blocs

tion (APEC) has no preferential arrangements, it is

Exports of blocs include all commodity trade, which

vary widely in structure, they all have the same main

included in the table because of the volume of trade

may include items not specified in trade bloc agree-

objective: to reduce trade barriers between member

between its members.

ments. Differences from previously published esti-

countries. But effective integration requires more than

The data on country exports are drawn from the

reducing tariffs and quotas. Economic gains from

International Monetary Fund’s (IMF) Direction of

competition and scale may not be achieved unless

Trade database and should be broadly consistent

other barriers that divide markets and impede the free

with those from other sources, such as the United

flow of goods, services, and investments are lifted.

Nations Statistics Division’s Commodity Trade (Com-

• Merchandise exports within bloc are the sum of

For example, many regional trade blocs retain con-

trade) database. However, trade flows between many

merchandise exports by members of a trade bloc

tingent protections or restrictions on intrabloc trade.

developing countries, particularly in Sub-Saharan

to other members of the bloc. They are shown both

These include antidumping, countervailing duties, and

Africa, are not well recorded. Thus the value of intra-

in U.S. dollars and as a percentage of total mer-

“emergency protection” to address balance of pay-

trade for certain groups may be understated. Data on

chandise exports by the bloc. • Total merchandise

ments problems or to protect an industry from surges

trade between developing and high-income countries

exports by bloc as a share of world exports are the

in imports. Other barriers include differing product

are generally complete.

ratio of the bloc’s total merchandise exports (within

standards, discrimination in public procurement, and cumbersome and costly border formalities.

Membership in the trade blocs shown is based on the most recent information available, from the World

Membership in a regional trade bloc may reduce

Bank Policy Research Report Trade Blocs (2000a),

the frictional costs of trade, increase the credibility

from the World Bank’s Global Economic Prospects

of reform initiatives, and strengthen security among

2005, and from consultation with the World Bank’s

Preferential regional trade agreements have a mixed impact on trade

mates may be due to changes in bloc membership or to revisions in the underlying data. Definitions

the bloc and to the rest of the world) to total merchandise exports by all economies in the world.

6.6a

Merchandise exports within bloc as share of total bloc exports (%)

1995

2000

2005

80 70

Data sources

60

Data on merchandise trade flows are published in

50

the IMF’s Direction of Trade Statistics Yearbook and 40

Direction of Trade Statistics Quarterly; the data in the table were calculated using the IMF’s Direction

30

of Trade database. The United Nations Conference

20

on Trade and Development (UNCTAD) publishes 10

data on intratrade in its Handbook of International

0

Trade and Development Statistics. The information Asia Pacific Economic Cooperation

European Union

North American Free Trade Agreement

Latin American Integration Association

Mercosur

Association of Southeast Asian Nations

on trade bloc membership is from the World Bank Policy Research Report Trade Blocs (2000a), the

Regional trade agreements do not necessarily create net trade gains among bloc members.

World Bank’s Global Economic Prospects 2005,

Source: International Monetary Fund’s Direction of Trade database.

and the World Bank’s international trade unit.

2007 World Development Indicators

335

6.7

Tariff barriers All products

Primary products

Manufactured products

%

Afghanistan Albania Algeria Angola Argentina Armenia Australia Azerbaijan Bangladesh Belarus Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Estonia Ethiopia European Unionb Gabon Gambia, The Georgia Ghana Guatemala Guinea Honduras Hong Kong, China Hungary India Indonesia Iran, Islamic Rep. Iraq Israel Jamaica Japan Jordan Kazakhstan Kenya

336

Most recent year

.. 2005a 2005 2005 2005a 2001 2005a 2005 2005 2002 2005 2005a 2001a 2005a 2005a 2005a 2005 2005 2003a 2005 2005a 2005 2005 2005a 2005a 2005a 2003 2005 2005 2005 2005a 2005a 2003 2005a 2005a 2005a 2005a 2003 2002 2005a 2005 2003 2004 2004 2005a 2005 2005a 2005 2002 2005a 2005a 2004 .. 2005a 2003 2005a 2005a 2004 2005

Binding coverage

Simple mean bound rate

Simple mean tariff

Weighted mean tariff

Share of lines with international peaks

Share of lines with specific rates

Simple mean tariff

Weighted mean tariff

Simple mean tariff

Weighted mean tariff

.. 100.0 .. 100.0 100.0 100.0 97.1 .. 14.9 0.0 39.1 100.0 .. 96.3 100.0 100.0 39.3 20.9 .. 12.6 99.7 .. .. 100.0 100.0 100.0 .. .. 100.0 33.2 100.0 31.0 100.0 100.0 99.9 99.1 100.0 100.0 .. 100.0 100.0 13.0 100.0 13.5 100.0 39.0 100.0 45.7 96.2 73.8 96.6 .. .. 76.3 100.0 99.7 100.0 .. 14.0

.. 7.0 .. 59.2 31.9 8.5 10.0 .. 162.1 11.3 28.6 40.0 .. 19.0 31.4 24.7 41.9 67.5 .. 79.9 5.1 .. .. 25.1 10.0 42.8 .. .. 42.9 11.2 5.9 21.3 5.0 34.9 21.8 36.6 36.6 8.7 .. 4.2 21.4 101.8 7.2 92.1 42.2 20.1 32.5 0.0 9.8 49.6 37.1 .. .. 20.9 49.6 3.0 16.3 .. 95.1

.. 6.3 15.8 7.6 10.6 3.3 4.3 10.4 16.8 8.9 14.4 7.2 5.3 9.9 12.3 10.7 13.1 19.6 16.0 18.4 4.5 17.9 17.2 4.9 9.2 11.9 13.1 19.1 7.0 12.6 2.4 10.5 5.0 9.0 11.8 18.9 6.4 1.0 19.7 2.7 19.9

.. 7.1 10.6 6.0 5.2 2.5 3.1 5.8 55.8 16.4 12.4 5.5 5.1 11.2 7.1 9.1 11.7 19.9 16.4 16.5 1.5 16.8 12.5 3.9 4.9 9.6 13.0 17.7 4.1 10.3 1.2 9.6 4.4 8.5 8.7 12.0 6.7 0.9 13.5 2.0 16.8

.. 0.0 38.6 10.3 22.6 0.0 6.0 0.0 36.0 0.0 57.6 0.0 0.0 23.6 27.7 26.8 48.6 46.5 25.9 52.6 7.7 58.0 48.7 0.0 19.1 21.5 42.7 56.4 0.5 44.3 3.0 11.0 4.8 27.2 23.8 21.8 2.5 5.4 52.9 6.7 60.6

.. 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 11.1 0.0 0.0 0.0 0.2 0.0 1.9 0.0 0.0 0.0 0.0 3.5 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 9.0 0.0

.. 7.3 15.3 12.0 8.0 6.6 1.6 12.0 21.8 7.1 15.4 7.5 3.8 5.1 7.9 15.9 13.6 26.1 17.4 20.9 6.4 21.8 22.1 4.4 8.8 11.5 14.7 22.9 10.4 14.9 4.9 10.8 5.6 12.5 11.0 85.8 10.4 8.1 22.1 7.9 22.9

.. 6.5 9.0 13.1 1.8 3.4 0.7 5.4 19.4 11.3 12.0 5.2 5.3 1.0 1.5 10.0 10.1 25.5 15.6 19.5 3.4 24.8 25.0 2.8 3.4 9.5 12.4 22.1 6.1 11.2 2.3 8.6 4.1 7.6 6.6 16.4 8.3 4.0 6.7 2.3 19.4

.. 6.1 15.7 6.8 10.8 2.8 4.6 10.1 16.0 10.4 14.2 7.2 5.5 10.1 12.6 10.0 13.0 18.5 15.8 18.0 4.1 17.4 16.5 4.9 9.2 11.9 12.8 18.5 6.6 12.2 2.1 10.4 4.9 8.5 11.8 11.6 5.8 0.0 19.4 1.7 19.3

.. 7.2 11.0 4.4 5.7 1.5 3.7 5.9 76.7 12.8 5.6 5.0 12.9 9.2 8.8 12.6 18.7 16.6 15.5 1.0 13.2 10.3 4.4 5.3 9.5 13.3 16.2 3.6 9.9 0.7 10.1 4.3 8.8 9.1 10.5 5.8 0.0 15.7 1.8 15.8

7.5 13.2 6.7 14.2 6.7 0.0 8.9 17.0 6.5 18.7 .. 2.7 9.4 3.3 12.4 2.3 12.1

9.5 11.0 5.8 12.7 6.0 0.0 7.9 14.5 6.0 13.8 .. 1.7 9.8 2.5 7.6 1.9 7.5

5.4 45.3 1.0 58.6 0.2 0.0 10.9 15.5 8.7 43.4 .. 1.5 36.5 8.1 34.5 0.0 36.4

0.7 0.0 0.0 0.0 0.0 0.0 0.0 3.5 0.0 0.0 .. 1.1 0.0 2.7 0.0 0.0 0.0

11.8 17.4 8.8 16.3 9.7 0.0 17.9 24.4 7.2 14.9 .. 6.9 15.7 8.4 15.6 3.3 15.9

13.2 17.1 5.5 14.3 7.2 0.0 6.7 16.5 3.5 11.2 .. 3.9 11.0 3.8 4.0 3.1 8.6

6.8 12.3 6.4 13.9 6.3 0.0 7.7 15.9 6.4 18.9 .. 2.1 8.4 2.3 11.9 2.2 11.6

7.1 8.8 5.9 11.2 5.3 0.0 8.0 12.8 6.7 14.5 .. 0.9 9.3 1.4 9.9 1.6 6.7

2007 World Development Indicators

%

%

All products

Primary products

6.7

GLOBAL LINKS

Tariff barriers

Manufactured products

%

Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia South Africa Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago

Most recent year

2004 a 2005 2003 2005a 2001 2005a 2005a 2002 2003a 2005 2005 2001a 2005a 2005 2001 2005 2005a 2001 2005 2005 2005a 2005a 2005a 2005a 2005a 2005a 2005 2005 2003a 2005a 2005a 2005a 2005 2005a 2005a 2005a 2003a 2005a 2005 2005a 2005a 2005 2005 2004 2005a 2002 2003a 2005a 2005a 2002 2005a 1989 2005a 2002 2002 2005a 2005a 2005 2003

Binding coverage

Simple mean bound rate

Simple mean tariff

Weighted mean tariff

Share of lines with international peaks

Share of lines with specific rates

Simple mean tariff

Weighted mean tariff

Simple mean tariff

Weighted mean tariff

94.5 .. 99.9 .. 100.0 .. .. .. 100.0 100.0 29.7 30.2 83.7 40.7 39.4 18.0 100.0 .. 100.0 100.0 .. 16.5 96.3 .. 100.0 100.0 96.8 18.2 100.0 100.0 44.8 99.9 100.0 100.0 100.0 67.0 96.2 100.0 0.0 100.0 .. 100.0 .. 100.0 69.3 100.0 100.0 96.3 36.8 .. 96.3 .. 99.8 .. .. 13.4 74.8 13.2 100.0

15.7 .. 7.4 .. 12.8 .. .. .. 9.2 6.9 27.4 74.9 14.5 28.8 19.6 94.0 35.0 .. 17.5 41.3 .. 83.3 19.4 .. 10.3 41.7 44.3 117.8 3.0 13.8 52.2 23.4 31.7 33.6 30.1 25.6 11.9 39.9 11.4 89.5 .. 30.0 .. 47.4 6.9 5.0 23.7 19.4 29.6 .. 19.4 .. 0.0 .. .. 120.0 25.8 80.0 55.8

9.0 4.7 4.3 9.2 3.3 7.2 9.9 20.2 1.3 4.1 11.6 13.5 7.5 12.4 12.8 8.5 9.2 4.8 4.2 19.4 13.1 4.5 5.6 14.7 5.0 6.8 12.7 11.6 2.6 3.8 14.6 7.4 6.1 8.3 9.2 5.4 5.5 6.6 9.6 17.2 4.1 14.0 8.2

9.3 4.5 4.3 14.0 2.6 6.3 16.8 25.2 0.7 3.3 5.2 10.2 4.4 10.7 9.9 4.7 3.0 2.9 4.3 13.7 8.6 4.1 1.3 14.3 4.6 5.4 12.8 10.8 1.9 3.2 12.4 6.9 2.2 5.8 8.3 3.1 3.4 3.1 17.9 9.7 4.1 9.2 7.9

5.6 0.0 0.1 23.5 3.0 12.2 24.1 46.6 3.3 12.2 37.1 42.4 22.4 43.7 51.1 19.7 12.4 0.2 0.0 57.0 38.2 5.3 15.2 21.6 10.0 0.5 47.6 41.0 4.2 0.1 42.6 1.8 25.3 16.8 11.1 4.8 9.9 21.0 16.0 47.0 0.0 53.8 20.0

0.0 0.0 2.2 0.0 0.0 0.0 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.0 0.0 0.0 0.0 0.0 0.0 4.8 0.0 0.0 0.0 5.7 0.0 0.0 0.0 0.3 0.0 0.0 0.0 3.3 0.0 10.7 0.0 0.0 0.0 0.0

20.3 3.9 6.7 18.5 8.1 14.2 7.4 19.2 3.5 10.0 16.9 13.4 3.4 14.4 12.6 9.0 8.5 8.4 5.0 23.3 16.4 7.6 3.7 13.9 9.9 10.6 14.9 14.9 17.8 4.1 13.8 11.2 14.9 6.3 10.7 6.9 27.8 13.3 12.2 12.7 3.2 14.9 13.2

17.7 3.0 6.2 29.7 5.4 6.1 3.3 15.1 1.3 6.4 4.1 9.0 2.3 11.7 10.0 5.3 2.2 2.7 5.1 12.1 9.1 4.8 0.5 9.3 8.7 5.4 14.7 14.9 8.6 2.9 8.6 7.9 3.1 1.5 9.8 5.1 12.6 7.2 11.5 5.5 2.7 8.2 10.7

7.2 4.8 3.8 8.5 2.5 6.1 10.0 20.1 1.0 3.5 11.0 13.4 8.2 12.2 12.8 8.3 9.2 4.1 4.1 18.8 12.6 4.2 5.9 14.7 4.3 6.3 12.4 11.3 0.5 3.8 14.6 7.0 4.8 8.5 9.1 5.1 2.5 5.7 8.9 17.7 4.3 13.8 7.4

4.5 4.8 2.9 10.3 1.5 6.3 17.6 28.5 0.4 1.7 5.9 10.7 4.8 10.4 9.9 4.2 3.1 3.0 3.7 14.3 8.5 3.8 1.6 16.4 3.4 5.4 12.1 9.3 0.2 3.3 14.5 6.4 1.7 7.2 7.6 2.7 1.2 1.8 12.2 4.4 10.4 7.0

0.1 22.1 4.4 8.5 11.3 21.1 10.8 5.4 2.7 14.7 7.6 12.9 10.6 14.6 9.8

0.0 21.2 1.8 5.4 7.7 19.6 10.5 4.3 1.4 15.5 6.1 8.4 4.9 10.4 5.5

0.1 50.8 11.4 21.3 23.4 43.8 26.6 3.6 8.7 23.3 6.8 38.0 22.1 55.3 36.6

0.1 0.0 0.0 1.0 0.4 0.0 0.0 0.0 35.1 0.0 1.7 0.0 0.9 0.0 0.0

0.3 19.2 7.0 5.4 18.2 28.2 10.3 1.4 14.0 14.4 9.6 18.7 13.1 15.4 15.5

0.1 12.8 3.9 1.7 9.5 24.0 4.3 1.0 8.1 11.7 5.7 10.6 2.3 9.7 4.8

0.0 22.3 3.9 8.8 10.3 20.5 10.8 6.0 0.6 14.5 7.3 12.2 10.0 14.4 8.8

0.0 23.5 1.2 6.5 6.8 18.9 10.8 5.0 0.1 16.6 6.6 7.7 5.7 11.0 5.9

%

%

2007 World Development Indicators

337

6.7

Tariff barriers All products

Primary products

Manufactured products

%

Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United States Uruguay Uzbekistan Venezuela, RB Vietnam 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 & the Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High-income OECD Non-OECD

Most recent year

2005a 2005a 2002 2005a 2002 2005a 2005a 2005a 2001 2005a 2005a 2000 2005a 2003 .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

Binding coverage

Simple mean bound rate

Simple mean tariff

Weighted mean tariff

Share of lines with international peaks

57.9 47.7 .. 14.9 .. .. 100.0 100.0 .. 99.9 .. .. 15.9 20.8 77.4 48.2 88.5 86.6 90.8 76.2 79.0 85.6 97.1 93.4 61.1 48.7 83.0 98.6 67.3

57.7 29.6 .. 73.5 .. .. 3.6 31.6 .. 36.8 .. .. 105.7 90.7 30.8 47.1 31.0 31.5 30.4 34.8 32.4 11.9 42.8 34.8 42.6 43.0 13.3 7.4 21.3

13.4 2.4 5.4 12.4 7.6 4.8 3.2 9.9 10.4 12.8 13.2 12.8 14.6 16.7 7.7 13.0 8.8 9.6 7.9 9.4 9.0 6.3 9.6 10.6 15.2 12.2 3.4 3.1 4.1

9.1 1.6 2.9 9.0 3.9 4.8 1.6 3.5 5.8 12.7 13.6 11.8 9.4 17.3 3.3 14.2 5.3 5.8 4.7 6.1 5.0 4.9 5.3 8.9 16.1 8.1 1.9 2.0 1.2

31.0 4.8 14.8 38.3 11.2 0.2 6.1 26.1 26.7 23.7 34.1 11.2 34.5 38.8 13.8 30.1 16.5 18.1 15.0 17.8 18.4 10.2 17.4 23.7 31.4 33.7 4.1 3.7 5.0

Share of lines with specific rates

0.0 0.0 2.8 0.0 0.0 0.0 5.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 0.8 0.7 1.3 0.8 0.9 0.1 1.5 0.0 0.0 1.6 0.0 0.2 0.0 0.8

%

%

Simple mean tariff

Weighted mean tariff

Simple mean tariff

Weighted mean tariff

27.4 12.6 14.8 16.7 6.9 4.9 2.8 6.3 10.5 12.2 17.7 13.6 14.9 19.5 9.9 15.9 11.8 12.5 11.2 12.3 9.6 9.9 11.9 15.8 18.4 14.3 4.6 3.7 6.3

13.8 2.6 12.6 10.1 1.5 4.7 0.8 1.2 4.2 11.3 14.9 10.8 9.3 19.8 3.3 14.1 4.6 4.4 5.0 5.9 3.5 6.0 3.9 8.9 15.1 8.1 2.1 2.1 1.9

12.0 1.4 3.8 11.9 7.6 4.8 3.3 10.1 10.5 12.8 12.3 12.7 14.5 16.2 7.4 12.6 8.3 9.2 7.4 9.0 8.8 5.8 9.3 9.8 14.6 11.9 3.2 3.0 3.7

7.5 1.2 1.1 8.4 6.4 4.8 1.8 4.8 6.2 12.9 12.8 12.4 9.4 14.7 3.2 14.2 5.5 6.2 4.6 6.1 5.4 4.5 5.6 8.8 16.8 8.1 1.8 1.9 1.1

Note: Tariff rates include ad valorem equivalents of specific rates unavailable in previous years. a. Rates are either partially or fully recorded applied rates. All other simple and weighted tariff rates are most favored nation rates. b. Data refer to all 25 member states of the European Union.

338

2007 World Development Indicators

6.7

GLOBAL LINKS

Tariff barriers About the data

Poor people in developing countries work primar-

Free Trade Agreement. The difference between

and Development (UNCTAD) and the World Trade

ily in agriculture and labor-intensive manufactures,

most favored nation and applied rates can be sub-

Organization (WTO). Data are classifi ed using the

sectors that confront the greatest trade barriers.

stantial. As more countries report their free trade

Harmonized System of trade at the six- or eight-digit

Removing barriers to merchandise trade could

agreements, suspensions of tariffs, or other spe-

level. Tariff line data were matched to Standard

increase growth by about 0.8 percent a year in these

cial preferences, World Development Indicators will

International Trade Classifi cation (SITC) revision

countries—even more if trade in services (retailing,

include their effectively applied rates. All estimates

2 codes to defi ne commodity groups and import

business, fi nancial, and telecommunications ser-

are calculated using the most up-to-date information,

weights. Import weights were calculated using the

vices) were also liberalized.

which is not necessarily updated every year. As a

United Nations Statistics Division’s Commodity

result, data for the same year may differ from data

Trade (Comtrade) database. Data are shown only for

in last year’s publication.

the last year for which complete data are available.

In general, tariffs in high-income countries on imports from developing countries, though low, are twice the size of those collected from other high-

Three measures of average tariffs are shown: sim-

income countries. But protection is also an issue

ple bound rates and the simple and the weighted

for developing countries, which maintain high tariffs

mean tariffs. The most favored nation or applied

on agricultural commodities, labor-intensive manu-

rates are calculated using all traded items, while

factures, and other products and services. In some

bound rates are based on all products in a country’s

• Binding coverage is the percentage of product

developing regions new trade policies could make

tariff schedule. Weighted mean tariffs are weighted

lines with an agreed bound rate. • Simple mean

the difference between achieving important Millen-

by the value of the country’s trade with each trading

bound rate is the unweighted average of all the lines

nium Development Goals—reducing poverty, lower-

partner. Simple averages are often a better indicator

in the tariff schedule in which bound rates have been

ing maternal and child mortality rates, improving

of tariff protection than weighted averages, which are

set. • Simple mean tariff is the unweighted average

educational attainment—and falling far short.

biased downward because higher tariffs discourage

of effectively applied rates or most favored nation

Countries use a combination of tariff and nontariff

trade and reduce the weights applied to these tariffs.

rates for all products subject to tariffs calculated

measures to regulate imports. The most common

Bound rates have resulted from trade negotiations

for all traded goods. • Weighted mean tariff is the

form of tariff is an ad valorem duty, based on the

that are incorporated into a country’s schedule of

average of effectively applied rates or most favored

value of the import, but tariffs may also be levied

concessions and are thus enforceable. If a contract-

nation rates weighted by the product import shares

on a specific, or per unit, basis or may combine ad

ing party raises a tariff to a higher level than its

corresponding to each partner country. • Share of

valorem and specific rates. Tariffs may be used to

bound rate, beneficiaries of the earlier binding have

lines with international peaks is the share of lines

raise fiscal revenues or to protect domestic indus-

a right to receive compensation, usually as reduced

in the tariff schedule with tariff rates that exceed 15

tries from foreign competition—or both. Nontariff

tariffs on other products they export to the country.

percent. • Share of lines with specific rates is the

barriers, which limit the quantity of imports of a par-

If the beneficiaries are not compensated, they may

share of lines in the tariff schedule that are set on

ticular good, include quotas, prohibitions, licensing

retaliate by raising their own tariffs against an equiv-

a per unit basis or that combine ad valorem and per

schemes, export restraint arrangements, and health

alent value of the original country’s exports.

unit rates. • Primary products are commodities clas-

and quarantine measures.

To conserve space, data for the European Union are shown instead of data for individual members. Definitions

Some countries set fairly uniform tariff rates across

sified in SITC revision 2 sections 0–4 plus division

Nontariff barriers are generally considered less

all imports. Others are more selective, setting high tar-

68 (nonferrous metals). • Manufactured products

desirable than tariffs because changes in an export-

iffs to protect favored domestic industries. The share

are commodities classified in SITC revision 2 sec-

ing country’s efficiency and costs no longer result in

of tariff lines with international peaks (those for which

tions 5–8 excluding division 68.

changes in market share in the importing country.

ad valorem tariff rates exceed 15 percent) provides an

Further, the quotas or licenses that regulate trade

indication of how selectively tariffs are applied. The

become very valuable, and resources are often wasted

effective rate of protection—the degree to which the

in attempts to acquire these assets. A high percent-

value added in an industry is protected—may exceed

age of products subject to nontariff barriers suggests

the nominal rate if the tariff system systematically

a protectionist trade regime, but the frequency of

differentiates among imports of raw materials, inter-

nontariff barriers does not measure how much they

mediate products, and finished goods.

restrict trade. Moreover, a wide range of domestic

The share of tariff lines with specific rates shows

policies and regulations (such as health regulations)

the extent to which countries use tariffs based on

may act as nontariff barriers. Based on the difficulty of

physical quantities or other, non–ad valorem mea-

combining nontariff barriers into an aggregate indica-

sures. Some countries apply only specific duties.

tor, they are not included in this table.

Data sources

Specific duties are not included in the table, except

All indicators in the table were calculated by World

The tariff rates used in calculating the indicators in

for Switzerland. Work is under way to complete the

Bank staff using the World Integrated Trade Solution

the table are most favored nation rates unless they

estimations for ad valorem equivalents of specific

system. Data on tariffs were provided by UNCTAD and

are specified as applied rates. Effectively applied

duties for all countries.

the WTO. Data on global imports are from the United

rates are those in effect for partners in preferen-

The indicators were calculated from data sup-

tial trade agreements such as the North American

plied by the United Nations Conference on Trade

Nations Statistics Division’s Comtrade database.

2007 World Development Indicators

339

6.8

Global private financial flows Foreign direct investment

Portfolio investment flows

Bank and traderelated lending

$ millions $ 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, 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

340

Bonds

Equity

1990

2005

1990

2005

.. .. 0 –335 1,836 1,836 8,111 653 4 3 .. 8,047a 62 27 .. 96 989 4 0 1 .. –113 7,581 1 9 661 3,487 .. 500 23 –14 163 48 .. .. 0 1,132 133 126 734 2 .. .. 12 812 13,183 73 14 .. 3,004 15 1,005 48 18 2 8

.. 262 1,081 –1,304 4,730 258 –34,420 9,057 1,680 802 305 31,959 21 –277 299 279 15,193 2,614 19 1 379 18 34,146 6 705 6,667 79,127 35,897 10,375 402 724 861 266 1,761 .. .. 5,238 1,023 1,646 5,376 517 11 2,997 265 3,978 70,686 300 52 450 32,034 107 640 208 102 10 10

.. .. –15 0 –857 .. .. .. .. 0 .. .. 0 0 .. 0 129 .. 0 0 0 0 .. 0 0 –7 –48 .. –4 0 0 –42 –1 .. .. .. .. 0 0 –1 0 .. .. 0 .. .. 0 0 .. .. 0 .. –11 0 0 0

.. 0 0 0 1,872 0 .. .. 0 0 0 .. 0 0 0 0 3,580 –1,257 0 0 0 0 .. 0 0 584 2,702 .. 496 –1 0 –32 0 –785 .. –201 .. –20 650 1,554 375 0 0 0 .. .. 0 0 0 .. 0 .. 0 0 0 0

2007 World Development Indicators

1990

.. 0 0 0 0 0 .. .. 0 0 0 .. 0 0 .. 0 103 0 0 0 .. 0 .. 0 0 367 0 .. 0 .. 0 0 0 .. .. 0 .. 0 0 0 0 .. .. 0 .. .. 0 0 .. .. 0 .. 0 .. 0 0

$ millions 2005

1990

2005

.. .. .. .. –48 1 .. .. 0 1 1 .. .. .. .. 62 6,451 92 .. 0 .. .. .. .. .. 1,635 20,346 .. 86 .. .. 0 35 113 .. .. .. 0 2 729 .. .. –1,349 0 .. .. .. .. 3 .. 0 .. .. .. .. 0

.. .. –409 570 –1,195 .. .. .. .. 55 .. .. .. –24 .. –18 –555 .. .. –6 .. –14 .. –1 –1 1,194 4,668 .. –151 –12 –100 –99 10 .. .. 669 .. –3 58 –65 5 .. .. –57 .. .. 29 –7 .. .. –23 .. 1 –19 .. ..

.. 34 –821 1,550 –824 83 .. .. 9 –9 42 .. –4 314 282 –2 –1,708 2,421 .. –5 .. –44 .. .. –1 2,593 2,442 .. –768 –2 0 287 –163 2,429 .. –4,524 .. 195 –80 2,936 78 .. 425 116 .. .. 6 .. 46 .. 13 .. –15 .. .. ..

Foreign direct investment

Portfolio investment flows

GLOBAL LINKS

6.8

Global private financial flows

Bank and traderelated lending

$ millions $ millions

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

Bonds

1990

2005

44 623 237 1,093 –362 .. 627 151 6,411 138 1,777 38 .. 57 .. 789 0 .. 6 .. 6 17 225 .. .. .. 22 23 2,332 6 7 41 2,549 .. .. 165 9 163 .. 6 10,676 1,735 1 41 588 1,003 142 245 136 155 77 41 530 89 2,610 ..

464 6,436 6,598 5,260 30 .. –29,730 5,585 19,585 682 3,214 1,532 1,975 21 .. 4,339 250 43 28 730 2,573 92 194 .. 1,032 100 29 3 3,966 159 115 39 18,772 199 182 1,552 108 300 .. 2 40,416 1,979 241 12 2,013 3,285 715 2,183 1,027 34 64 2,519 1,132 9,602 3,200 ..

1990

0 921 147 26 0 .. .. .. .. 0 .. 0 .. 0 .. .. .. .. 0 .. 0 0 0 .. .. .. 0 0 –1,239 0 0 0 661 .. .. 0 0 0 .. 0 .. .. 0 0 0 .. 0 0 –2 0 0 0 395 0 .. ..

Equity

$ millions

2005

1990

2005

1990

2005

0 2,978 –3,959 3,791 0 .. .. .. .. 919 .. 134 3,050 0 .. .. .. 0 0 125 1,070 0 0 .. –405 187 0 0 492 0 0 0 –839 –6 0 –41 0 0 .. 0 .. .. 0 0 0 .. 0 1,092 529 0 0 2,640 1,081 11,384 .. ..

0 0 0 0 0 .. .. .. .. 0 .. 0 .. 0 .. .. .. .. 0 .. .. 0 .. .. .. .. 0 1 0 0 0 0 1,995 .. 0 0 0 0 .. 0 .. .. 0 .. 0 .. 0 0 –1 0 0 0 0 0 .. ..

0 –16 11,968 –165 .. .. .. .. .. .. .. 60 170 3 .. .. .. 0 .. 27 1,436 .. .. .. 130 52 .. .. –1,200 9 .. 36 3,353 1 .. 64 .. .. .. .. .. .. 0 .. .. .. 10 451 0 .. .. 766 1,461 1,341 .. ..

32 –1,379 1,458 1,804 –30 .. .. .. .. –46 .. 214 .. 65 .. .. .. .. .. .. 6 0 .. .. .. .. –15 2 –617 –1 –1 44 4,396 .. .. 318 26 –8 .. –14 .. .. 20 10 –121 .. .. –63 –4 49 –9 18 –286 –18 .. ..

57 2,124 4,338 –2,306 644 .. .. .. .. 22 .. 11 3,557 –8 .. .. .. .. 228 2,352 –37 –8 0 .. 374 –79 –1 –3 –1,396 3 14 –36 1,705 90 0 115 –21 –26 .. .. .. .. 17 –7 –171 .. –524 –158 –148 –164 2 –981 66 2,717 .. ..

2007 World Development Indicators

341

6.8

Global private financial flows Foreign direct investment

Portfolio investment flows

Bank and traderelated lending

$ millions $ millions

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 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 Europe EMU

1990

2005

0 .. 8 .. 57 .. 32 5,575 93 .. 6 –76 13,984 43 –31 30 1,982 5,545 71 .. 0 2,444 18 109 76 684 .. –6 .. .. 33,504 48,490 42 .. 451 180 .. –131 203 –12 203,236 s 2,343 22,237 11,999 10,238 24,580 10,512 3,333 8,242 741 542 1,210 178,656 61,012

6,630 15,151 8 .. 54 1,481 59 20,071 1,908 541 24 6,257 22,789 272 2,305 –16 10,679 15,420 427 54 473 4,527 3 1,100 723 9,805 62 257 7,808 .. 158,801 109,754 711 45 2,957 1,954 .. –266 259 103 974,283 s 20,522 260,273 150,874 109,399 280,795 96,898 73,687 70,017 13,765 9,869 16,559 693,488 315,043

a. Includes Luxembourg.

342

Bonds

2007 World Development Indicators

1990

0 .. 0 .. 0 .. 0 .. .. .. 0 .. .. 0 0 0 .. .. 0 .. 0 –87 0 –52 –60 597 .. 0 .. .. .. .. –16 .. 345 0 .. 0 0 –30 .. s 116 966 388 577 1,082 –952 1,893 101 –76 147 –31 .. ..

Equity 2005

249 10,033 .. .. 0 0 0 .. –934 .. 0 406 .. 0 0 0 .. .. 0 0 0 1,156 0 –150 –136 3,212 .. 0 576 .. .. .. 573 0 5,365 724 .. 0 0 0 .. s –2,144 57,254 21,431 35,823 55,110 9,947 28,406 16,640 2,581 –2,868 405 .. ..

1990

0 .. 0 .. 1 .. 0 .. .. .. .. 389 .. 0 0 –2 .. .. 0 .. 0 440 4 0 5 89 .. 0 .. .. .. .. 0 .. 0 .. .. .. 0 0 .. s 7 3,383 545 2,838 3,390 440 89 2,464 5 1 393 .. ..

$ millions 2005

229 –215 0 .. .. .. .. .. .. .. .. 7,230 .. –216 0 0 .. .. .. 0 3 5,665 .. .. 12 5,669 .. 2 82 .. .. .. 20 .. 91 .. .. .. .. .. .. s 12,471 54,209 35,662 18,547 66,680 26,108 6,328 12,351 2,311 12,204 7,379 .. ..

1990

4 .. –2 .. –15 .. 4 .. .. .. .. .. .. 10 .. –2 .. .. –9 .. 5 1,574 0 –126 –137 466 .. 16 .. .. .. .. –176 .. –922 .. .. 161 –9 127 .. s 1,623 13,172 6,437 6,735 14,795 7,180 3,612 2,430 –350 1,446 477 .. ..

2005

7,066 33,290 .. .. 18 2,071 .. .. –1,380 .. .. 587 .. –89 64 11 .. .. –3 –3 3 –1,565 .. .. 4 14,588 –85 3 3,284 .. .. .. –234 –240 –512 –43 .. 24 127 –16 .. s 3,902 77,231 11,838 65,393 81,134 –2,772 75,498 –75 2,350 4,086 2,046 .. ..

About the data

6.8

GLOBAL LINKS

Global private financial flows Definitions

The data on foreign direct investment (FDI) are based

and thus omit nonequity crossborder transactions

• Foreign direct investment is net inflows of invest-

on balance of payments data reported by the Interna-

such as intrafirm flows of goods and services. For

ment to acquire a lasting management interest

tional Monetary Fund (IMF), supplemented by staff

a detailed discussion of the data issues, see the

in an enterprise operating in an economy other than

estimates using data reported by the United Nations

World Bank’s World Debt Tables 1993–94 (vol. 1,

that of the investor. It is the sum of equity capital,

Conference on Trade and Development and official

chap. 3).

reinvestment of earnings, other long-term capital,

national sources.

Portfolio fl ow data are compiled from several

and short-term capital, as shown in the balance

The internationally accepted definition of FDI is

market and official sources, including Euromoney

of payments. • Portfolio investment flows are net

provided in the fi fth edition of the IMF’s Balance

databases and publications; Micropal; Lipper Ana-

and include portfolio debt flows (public and publicly

of Payments Manual (1993). Under this definition

lytical Services; published reports of private invest-

guaranteed and private nonguaranteed bond issues

FDI has three components: equity investment,

ment houses, central banks, national securities

purchased by foreign investors) and non-debt-creat-

reinvested earnings, and short- and long-term inter-

and exchange commissions, and national stock

ing portfolio equity flows (the sum of country funds,

company loans between parent firms and foreign

exchanges; and the World Bank’s Debtor Reporting

depository receipts, and direct purchases of shares

affiliates. Distinguished from other kinds of interna-

System.

by foreign investors). • Bank and trade-related

tional investment, FDI is made to establish a lasting

Gross statistics on international bond and equity

lending covers commercial bank lending (public and

interest in or effective management control over an

issues are produced by aggregating individual trans-

publicly guaranteed and private nonguaranteed) and

enterprise in another country. As a guideline, the

actions reported by market sources. Transactions of

other private credits.

IMF suggests that investments should account for at

public and publicly guaranteed bonds are reported

least 10 percent of voting stock to be counted as FDI.

through the Debtor Reporting System by World Bank

In practice, many countries set a higher threshold.

member economies that have received either loans

Also, many countries fail to report reinvested earn-

from the International Bank for Reconstruction and

ings, and the definition of long-term loans differs

Development or credits from the International Devel-

among countries.

opment Association. Information on private nonguar-

FDI data do not give a complete picture of inter-

anteed bonds is collected from market sources,

national investment in an economy. Balance of

because official national sources reporting to the

payments data on FDI do not include capital raised

Debtor Reporting System are not asked to report the

locally, which has become an important source of

breakdown between private nonguaranteed bonds

financing for investment projects in some developing

and private nonguaranteed loans. Information on

countries. In addition, FDI data capture only cross-

transactions by nonresidents in local equity markets

border investment flows involving equity participation

is gathered from national authorities, investment positions of mutual funds, and market sources. The volume of portfolio investment reported by the

Private capital flows to developing countries are rising

6.8a

Bank Portfolio bond Portfolio equity Foreign direct investment

Share of GDP (%) 5

World Bank generally differs from that reported by other sources because of differences in sources, classifi cation of economies, and method used to adjust and disaggregate reported information. Differences in reporting arise particularly for foreign investments in local equity markets because clarity, adequate disaggregation, and comprehensive

4

and periodic reporting are lacking in many develop3

ing economies. By contrast, capital flows through international debt and equity instruments are well

2

recorded, and for these the differences in reporting lie primarily in classification of economies, exchange

1

rates used, whether particular installments of the transactions are included, and treatment of certain

0

offshore issuances. Net private capital flows—calculated as the sum of

–1 1995

2000

2005

Economic integration over the past decade has favored foreign direct investment infl ows to

foreign direct investment, portfolio investment flows, and bank and trade-related lending—are no longer included in the table because they are likely to be

Data sources Data are compiled from a variety of public and private sources, including the World Bank’s Debtor

overestimated. The source of overestimation is the

Reporting System, the IMF’s International Finan-

possible double counting of intercompany lending,

cial Statistics and Balance of Payments data-

has improved markedly. Other private capital

which is a debt liability but may also be included in

bases, and other sources mentioned in About the

flows have also surged.

foreign direct investment flows. There is currently

data. These data are also published in the World

no practical way to know when double counting has

Bank’s Global Development Finance 2007.

developing countries whose investment climate

Source: World Bank Debtor Reporting System.

occurred and therefore to adjust for it.

2007 World Development Indicators

343

6.9

Financial flows from Development Assistance Committee members

Net disbursements Other official flowsa

Official development assistancea

$ millions Australia Austria Belgium Canada Denmark Finland France Germany Greece Ireland Italy Japan Luxembourg Netherlands New Zealand Norway Portugal Spain Sweden Switzerland United Kingdom United States Total

Total 2005

Bilateral grants 2005

Bilateral loans 2005

Contributions to multilateral institutions 2005

2005

1,680 1,573 1,963 3,756 2,109 902 10,026 10,082 384 719 5,091 13,147 256 5,115 274 2,786 377 3,018 3,362 1,767 10,767 27,622 106,777

1,449 1,244 1,328 2,853 1,384 591 7,707 8,248 207 482 2,213 9,195 187 3,696 224 1,968 201 2,020 2,247 1,380 8,244 26,042 83,109

.. –12 –20 –20 –27 6 –468 –801 .. .. 57 1,212 .. –13 .. 64 17 –157 9 20 –80 –762 –976

231 341 655 923 751 305 2,787 2,635 178 237 2,821 2,740 69 1,432 50 754 159 1,155 1,106 367 2,603 2,343 24,644

74 310 391 –534 –8 .. –1,390 7,055 .. .. –1,125 –2,421 .. 152 7 5 –3 67 –4 .. –99 –1,048 1,430

Private flowsa

Total 2005

2,786 2,192 539 9,178 33 723 7,107 11,399 325 4,271 44 12,278 .. 17,091 26 .. 728 3,716 159 5,375 34,924 69,206 182,100

Net Total net grants by flowsa NGOsa

Bilateral Multilateral Foreign portfolio portfolio direct investment investment investment 2005 2005 2005

1,588 2,090 1,422 6,647 33 149 6,856 12,986 325 .. 951 14,472 .. 2,348 26 .. 556 4,158 430 6,827 29,865 18,965 110,695

1,066 0 .. 1,744 .. 736 1,163 –1,504 .. 4,271 –2,358 1,158 .. 4,604 .. .. 0 0 0 0 5,683 50,091 66,652

.. .. .. .. .. .. .. 47 .. .. .. 81 .. –474 .. .. .. .. .. –722 .. 255 –814

Private export credits 2005

2005

2005

132 102 –884 787 .. –161 –911 –131 .. .. 1,451 –3,433 .. 10,614 .. .. 172 –442 –271 –729 –625 –104 5,567

825 139 249 973 81 16 .. 1,523 1 308 94 255 8 422 94 .. 6 .. 29 332 726 8,629 14,712

5,366 4,215 3,142 13,373 2,215 1,642 15,744 30,059 709 5,298 4,103 23,259 265 22,781 401 2,791 1,109 6,801 3,545 7,474 46,318 104,410 305,019

Official development assistance Net disbursementsb

total $ millions 2000 2005

Australia Austria Belgium Canada Denmark Finland France Germany Greece Ireland Italy Japan Luxembourg Netherlands New Zealand Norway Portugal Spain Sweden Switzerland United Kingdom United States Total

1,424 1,557 632 1,539 1,191 1,924 2,165 3,410 2,441 2,076 522 883 5,931 9,893 7,089 10,013 348 372 365 703 2,073 4,958 12,786 13,534 179 248 4,774 5,036 184 251 1,766 2,494 417 371 1,895 2,911 2,407 3,377 1,258 1,757 6,031 10,640 10,861 26,888 66,740 104,835

per capita $ 2000 2005

74 78 116 70 457 101 101 86 32 96 36 101 406 300 48 393 41 47 271 175 103 39 79

77 187 184 105 382 168 163 121 34 176 85 106 552 308 61 538 36 67 373 236 177 91 119

Note: Components may not sum to totals because of gaps in reporting. a. At current prices and exchange rates. b. At 2004 prices and exchange rates.

344

2007 World Development Indicators

Gross disbursementsb

Commitmentsb

total $ millions 2000 2005

total $ millions 2000 2005

1,424 635 1,223 2,195 2,467 532 7,223 8,182 348 365 2,409 15,429 179 4,914 184 1,775 642 2,202 2,408 1,261 6,099 11,851 73,945

1,557 1,553 1,975 3,429 2,140 888 11,377 11,515 372 703 5,127 19,190 248 5,120 251 2,494 376 3,393 3,377 1,764 11,030 27,682 115,561

1,653 823 1,223 2,477 2,313 497 6,774 8,061 348 365 2,435 16,199 179 5,240 195 1,572 642 2,202 1,925 1,281 6,099 13,757 76,258

1,907 1,586 2,062 3,395 2,446 1,117 11,970 12,435 372 703 5,489 19,934 248 4,367 340 2,535 376 3,393 3,749 1,707 11,030 27,926 119,086

Net disbursementsa

% of GNI 2000 2005

% of general government disbursement 2000 2005

0.27 0.23 0.36 0.25 1.06 0.31 0.30 0.27 0.20 0.29 0.13 0.28 0.71 0.84 0.25 0.76 0.26 0.22 0.80 0.34 0.32 0.10 0.22

0.73 0.44 0.72 0.59 1.93 0.63 0.60 0.59 0.38 0.77 0.27 0.74 1.61 1.86 0.54 1.77 0.56 0.53 1.31 1.07 0.83 0.30 0.57

0.25 0.52 0.53 0.34 0.81 0.46 0.47 0.36 0.17 0.42 0.29 0.28 0.82 0.82 0.27 0.94 0.21 0.27 0.94 0.44 0.47 0.22 0.33

0.68 1.04 1.06 0.85 1.54 0.92 0.88 0.77 0.37 1.05 0.60 0.78 1.63 1.79 0.62 2.20 0.43 0.70 1.67 1.32 1.09 0.60 0.80

6.9

GLOBAL LINKS

Financial flows from Development Assistance Committee members About the data

The flows of official and private financial resources

Union, and certain advanced developing countries and

than 25 percent. • Private flows consist of flows at

from the members of the Development Assistance

territories. This distinction has been dropped. ODA

market terms financed from private sector resources

Committee (DAC) of the Organisation for Economic

recipients now comprise all low- and middle-income

in donor countries. They include changes in hold-

Co-operation and Development (OECD) to developing

countries, except those that are members of the Group

ings of private long-term assets by residents of the

economies are compiled by DAC, based principally on

of Eight or the European Union (including countries with

reporting country. • Foreign direct investment is

reporting by DAC members using standard question-

a firm date for EU accession).The content and structure

investment by residents of DAC member countries

naires issued by the DAC Secretariat.

of tables 6.9 through 6.12 have been revised to reflect

to acquire a lasting management interest (at least

DAC exists to help its members coordinate their

this change. Because official aid flows are quite small

10 percent of voting stock) in an enterprise operating

development assistance and to encourage the expan-

relative to ODA, the net effect of these changes is

in the recipient country. The data reflect changes in

sion and improve the effectiveness of the aggregate

believed to be minor.

the net worth of subsidiaries in recipient countries

resources flowing to recipient economies. In this capac-

Flows are transfers of resources, either in cash or

whose parent company is in the DAC source country.

ity DAC monitors the flow of all financial resources, but

in the form of commodities or services measured on

• Bilateral portfolio investment covers bank lend-

its main concern is official development assistance

a cash basis. Short-term capital transactions (with

ing and the purchase of bonds, shares, and real

(ODA). Grants or loans to countries and territories on

one year or less maturity) are not counted. Repay-

estate by residents of DAC member countries in

the DAC list of aid recipients have to meet three criteria

ments of the principal (but not interest) of ODA loans

recipient countries. • Multilateral portfolio invest-

to be counted as ODA. They are undertaken by the offi -

are recorded as negative flows. Proceeds from offi -

ment records the transactions of private banks and

cial sector. They promote economic development and

cial equity investments in a developing country are

nonbanks in DAC member countries in the securities

welfare as the main objective. And they are provided

reported as ODA, while proceeds from their later sale

issued by multilateral institutions. • Private export

at concessional financial terms (if a loan they have a

are recorded as negative flows.

credits are loans extended to recipient countries

grant element of at least 25 percent, calculated at

Because the table is based on donor country

by the private sector in DAC member countries to

a discount rate of 10 percent). The DAC Statistical

reports, it does not provide a complete picture of the

promote trade; they may be supported by an offi -

Reporting Directives provide the most detailed expla-

resources received by developing economies for two

cial guarantee. • Net grants by nongovernmental

nation of this definition and all ODA-related rules.

reasons. First, flows from DAC members are only part

organizations (NGOs) are private grants by nongov-

This definition excludes nonconcessional flows

of the aggregate resource flows to these economies.

ernmental organizations, net of subsidies from the

from official creditors, which are classified as “other

Second, the data that record contributions to mul-

official sector. • Total net fl ows comprise ODA or

official flows,” and aid for military purposes. Transfer

tilateral institutions measure the flow of resources

official aid flows, other official flows, private flows,

payments to private individuals, such as pensions,

made available to those institutions by DAC mem-

and net grants by nongovernmental organizations.

reparations, and insurance payouts, are in general

bers, not the flow of resources from those institu-

• Net disbursements are gross disbursements of

not counted. In addition to financial flows, technical

tions to developing and transition economies.

grants and loans minus repayments of principal on

cooperation is included in ODA. Most expenditures for

Aid as a share of gross national income (GNI), aid

earlier loans. • Gross disbursements are the actual

peacekeeping under UN mandates and assistance to

per capita, and ODA as a share of the general gov-

international transfer of fi nancial resources and

refugees are counted in ODA. Also included are contri-

ernment disbursements of the donor are calculated

goods and services (valued at the cost to the donor).

butions to multilateral institutions, such as the United

by the OECD. The denominators used in calculating

• Commitments are firm obligations, expressed in

Nations and its specialized agencies, and concessional

these ratios may differ from corresponding values

writing and backed by the necessary funds, under-

funding to multilateral development banks. In 1999,

elsewhere in this book because of differences in tim-

taken by an official donor to provide specified assis-

to avoid double counting of extrabudgetary expendi-

ing or definitions.

tance to a recipient country or a multilateral organi-

tures reported by DAC countries and flows reported by the United Nations, all UN agencies revised their

zation. • Aid as a percentage of GNI is the donor’s Definitions

contribution of ODA as a share of its gross national

data to include only regular budgetary expenditures

• Official development assistance comprises flows

income. • Aid as a percentage of general govern-

since 1990 (except for the World Food Programme and

that meet the DAC definition of ODA and are made to

ment disbursements is the donor’s contribution of

the United Nations High Commissioner for Refugees,

countries and territories on the DAC list of aid recipi-

ODA as a share of public spending.

which revised their data from 1996 onward).

ents. • Bilateral grants are transfers of money or in

DAC has revised the list of countries and territories

kind for which no repayment is required. • Bilateral

that are counted as aid recipients. These revisions will

loans are loans extended by governments or official

govern aid reporting for three years, starting with 2005

agencies that have a grant element of at least 25 per-

Data on financial flows are compiled by OECD-DAC

flows. In the past DAC distinguished aid going to Part I

cent (calculated at a rate of discount of 10 percent).

and published in its annual statistical report, Geo-

and Part II countries. Part I countries, the recipients of

• Contributions to multilateral institutions are con-

graphical Distribution of Financial Flows to Aid Recipi-

ODA, comprised many of the countries classified by the

cessional funding received by multilateral institutions

ents and its annual Development Cooperation Report.

World Bank as low- and middle-income economies. Part

from DAC members in the form of grants or capital

Data are available electronically on the OECD’s

II countries, whose assistance was designated official

subscriptions. • Other official flows are transactions

International Development Statistics CD-ROM and

aid, included the more advanced countries of Central

by the official sector whose main objective is other

at www.oecd.org/dac/stats/idsonline.

and Eastern Europe, countries of the former Soviet

than development or whose grant element is less

Data sources

2007 World Development Indicators

345

6.10

Allocation of bilateral aid from Development Assistance Committee members

Aid by purpose and tying status Total

Share of bilateral ODA commitment %

$

Australia Austria Belgium Canada Denmark Finland France Germany Greece Ireland Italy Japan Luxembourg Netherlands New Zealand Norway Portugal Spain Sweden Switzerland United Kingdom United States Total

millionsa

Investment projects, program aid, and other resource provisions

Debt-related aid

Emergency assistance and developmental food aid

Technical cooperation and administrative charges

Untied aidb

2000

2005

2000

2005

2000

2005

2000

2005

2000

2005

2000

2005

758 378 498 1,412 940 200 3,412 2,968 99 154 729 13,854 93 2,834 85 795 320 913 1,093 630 2,759 10,030 44,954

1,449 1,260 1,360 2,816 1,739 693 8,862 9,236 207 482 2,686 17,265 187 3,529 224 2,033 224 2,362 2,256 1,407 8,509 25,836 94,623

14.9 18.8 14.5 10.9 67.1 15.8 27.0 26.1 40.5 66.3 42.1 87.1 80.1 32.2 90.4 44.0 1.5 17.9 58.9 52.4 42.5 24.3 47.7

7.3 4.2 11.5 36.3 50.0 33.1 19.3 28.6 12.5 67.2 21.7 46.4 68.7 55.8 48.9 48.0 26.3 21.6 58.3 41.0 16.8 24.4 30.8

1.1 32.9 10.6 0.9 0.0 .. 23.4 2.9 .. .. 29.7 10.4 .. 6.7 .. 0.9 53.6 6.6 3.3 0.9 5.6 1.3 7.8

1.4 69.4 35.1 16.7 3.8 0.2 42.4 42.7 0.0 0.1 62.6 32.9 0.0 2.5 0.0 0.1 1.5 38.7 2.3 15.9 41.5 16.3 27.5

14.2 8.5 8.1 19.9 10.8 16.8 5.2 6.6 7.7 12.7 15.0 1.0 12.6 13.9 3.4 25.6 1.1 4.6 20.2 23.1 12.5 21.0 10.5

22.4 8.0 9.1 14.1 2.2 21.6 8.2 4.1 13.5 17.6 3.1 3.9 12.8 16.3 29.3 20.3 5.7 6.1 17.9 23.4 7.4 18.1 11.0

60.0 26.7 69.0 26.1 13.3 37.5 43.5 61.4 22.6 5.1 6.6 24.9 3.6 18.5 8.8 18.1 29.5 17.1 13.9 16.7 31.4 56.0 35.5

56.3 15.7 40.2 33.2 9.0 9.5 30.2 24.1 51.7 9.1 5.5 15.0 8.0 22.0 25.0 22.5 58.1 26.1 11.8 13.1 14.9 41.9 26.5

77.4 59.2 85.7 24.9 80.5 89.5 68.0 93.2 23.5 .. 38.2 86.4 96.7 95.3 .. 97.7 98.2 47.2 85.4 93.6 91.5 .. 81.1

71.9 88.7 95.7 59.4 86.5 95.1 94.7 93.0 73.6 100.0 92.1 89.6 99.1 96.2 92.3 99.6 60.7 86.6 98.3 97.4 100.0 .. 91.8

Aid by sector Social infrastructure and services

Share of bilateral ODA commitment (%) Australia Austria Belgium Canada Denmark Finland France Germany Greece Ireland Italy Japan Luxembourg Netherlands New Zealand Norway Portugal Spain Sweden Switzerland United Kingdom United States Average

Total 2005

Education 2005

Health 2005

45.2 15.2 32.9 39.8 41.3 36.5 25.2 18.2 55.5 54.0 10.5 20.0 51.8 37.6 34.7 43.0 55.8 26.8 36.5 19.6 25.3 42.8 30.5

5.6 7.6 8.9 8.8 7.4 7.1 16.5 4.4 18.4 12.0 2.0 4.9 14.9 14.1 14.9 9.5 28.6 9.2 4.9 2.9 3.9 2.7 6.1

6.1 2.2 6.2 10.3 5.6 4.0 3.1 1.3 15.3 20.0 3.8 1.2 18.4 3.4 5.0 7.7 4.4 4.9 4.8 2.6 3.3 4.9 3.8

Population 2005

2.9 0.1 1.4 1.5 0.9 1.0 0.1 0.8 0.2 2.0 0.4 0.0 4.6 3.3 2.3 2.1 0.0 1.2 4.1 0.3 3.6 5.2 2.3

Economic infrastructure and services

Government Water and civil supply and society sanitation 2005 2005

2.4 1.3 2.8 1.5 10.3 6.2 1.3 4.1 0.3 3.5 2.6 12.3 6.6 5.4 1.0 2.1 1.1 2.5 3.0 2.5 0.5 3.9 4.8

21.4 3.3 7.5 15.6 13.9 16.1 1.2 5.3 18.6 14.9 1.4 0.6 2.5 8.8 9.9 16.1 11.1 4.5 16.2 10.2 12.8 18.3 9.7

a. At current prices and exchange rates. b. Excludes technical cooperation and administrative charges.

346

2007 World Development Indicators

Production sectors

MultiTotal sector or sector crossallocable cutting

Total 2005

Transport and communications 2005

Total 2005

Agriculture 2005

2005

2005

3.8 0.7 6.2 4.3 14.9 9.2 9.4 12.0 8.9 1.5 10.9 23.4 2.9 8.8 1.2 7.9 13.1 8.5 5.9 6.2 2.7 7.8 10.6

3.0 0.1 2.7 0.8 10.5 1.4 6.4 1.7 8.5 1.1 0.3 17.1 0.6 1.2 0.5 0.6 12.2 3.2 2.6 2.2 1.7 4.7 5.2

6.6 1.9 4.8 5.5 18.3 7.4 2.2 3.1 1.2 3.8 1.3 7.7 5.4 4.7 4.0 5.5 2.7 4.4 4.9 7.7 3.2 5.4 5.2

5.3 0.8 3.8 4.4 13.2 6.2 1.4 2.3 0.4 3.4 0.7 5.8 3.8 3.9 2.2 4.0 1.3 3.0 2.9 4.4 1.9 2.5 3.3

14.1 1.6 3.3 6.0 10.2 16.6 4.9 16.2 5.3 4.2 4.4 3.1 10.5 14.3 3.5 10.5 8.4 8.2 7.2 13.4 3.9 4.4 6.5

69.8 19.4 47.1 55.6 84.6 69.6 41.7 49.4 70.9 63.6 27.1 54.3 70.6 65.4 43.3 66.9 80.0 47.9 54.5 46.8 35.1 60.4 52.8

6.10

About the data

Aid can be used in many ways. The sectoral destina-

food aid aim to provide humanitarian relief to lessen

equipment). Contributions mainly take the form of

tion to which aid goes, the form that aid takes, and the

the adverse impact of sudden disasters and to sup-

the supply of human resources from donors or action

procurement restrictions attached to aid are among

port food programs in nonemergency situations.

directed to human resources (such as training or

important factors that influence aid effectiveness. The

These types of aid do not generally contribute to

advice). Assistance provided specifically to facilitate

data on allocation of official development assistance

financing long-term development.

implementation of a capital project is not included.

(ODA) presented in this table are based principally on

The proportion of untied aid is reported here

• Administrative charges include the total current

reporting by members of the Organisation for Economic

because tying arrangements may prevent recipi-

budget outlays of institutions responsible for the

Co-operation and Development (OECD) Development

ents from obtaining the best value for their money

formulation and implementation of donor’s aid pro-

Assistance Committee (DAC). For more detailed expla-

and so reduces the value of the aid received. Tying

grams as well as other administrative costs incurred

nation of ODA, see About the data for table 6.9.

arrangements require recipients to purchase goods

by donors in the process of aid delivery. • Untied aid

The sector of destination for an ODA contribution

and services from the donor country or from a speci-

is the share of ODA that is not subject to restrictions

is defined as the specific area of the recipient coun-

fied group of countries. Such arrangements may be

by donors on procurement sources. • Social infra-

try’s economic or social structure that the transfer

justified on the grounds that they prevent a recipient

structure and services refer to efforts to develop the

is intended to foster. The DAC sector classification

from misappropriating or mismanaging aid receipts,

human resources potential of aid recipients. • Educa-

comprises a hierarchy of three levels. The top level is

but they may also be motivated by a desire to benefit

tion includes general teaching and instruction at all

grouped by themes, including social infrastructure and

suppliers in the donor country. The same volume

levels, as well as construction specifically to improve

services, economic infrastructure and services, pro-

of aid may have different purchasing power depend-

or adapt educational establishments. Training in a

duction sectors, and multisector cross-cutting areas.

ing on the relative costs of suppliers in countries to

particular fi eld, such as agriculture, is reported

The second level includes six sectors under social

which the aid is tied and the degree to which each

against the sector concerned. • Health covers

infrastructure and services (for example, education

recipient’s aid basket is untied.

assistance to hospitals, clinics, other medical and

and health), five sectors under economic infrastruc-

Reporting on the sectoral destination and the form

dental services, public health administration, and

ture and services (for example, transport and stor-

of aid by donors may not be complete. Furthermore,

medical insurance programs. • Population covers

age), and three production sectors (for example, agri-

measures of aid allocation may differ from the per-

all activities related to family planning and research

culture). The third level comprises subsectors, such

spectives of donors and recipients because of dif-

into population problems. • Water supply and sani-

as basic education and basic health. Some contribu-

ference in classification, availability of information,

tation cover assistance for water supply and use,

tions are not susceptible to allocation by sectors and

and time of recording.

sanitation, and water resources development (includ-

are reported as nonsector allocable aid. Examples include aid for general development purposes, bal-

Definitions

ing rivers). • Government and civil society include assistance to strengthen the administrative appa-

ance of payment support, aid relating to debt, emer-

• Bilateral (ODA) commitments are firm obligations,

ratus and government planning, and activities pro-

gency assistance, administrative costs of donors, and

expressed in writing and backed by the necessary

moting good governance and strengthening civil soci-

support to nongovernmental organizations.

funds, undertaken by official bilateral donors to pro-

ety. • Economic infrastructure and services group

The form in which an ODA contribution reaches the

vide specified assistance to a recipient country or

assistance for networks, utilities, and services that

benefiting sector or the economy in general is also

a multilateral organization. Bilateral commitments

facilitate economic activity. • Transport and commu-

important. A distinction is made between technical

are recorded in the full amount of expected transfer,

nications cover road, rail, water, and air transport and

cooperation and resource provision. Aid in the form

irrespective of the time required for completing dis-

post and telecommunications, radio, television, and

of technical cooperation includes grants to nationals

bursements. • Investment projects, program aid,

print media. • Production sectors refer to contribu-

of aid recipient countries receiving education or train-

and other resource provisions are aid contributions

tions to all directly productive sectors. • Agriculture

ing at home or abroad, and payments to consultants,

in the form of cash transfers, aid in kind, and the

includes agricultural sector policy, agricultural devel-

advisers, and similar personnel as well as teachers

financing of capital projects. Their aim is to increase

opment and inputs, crop and livestock production,

and administrators serving in recipient countries

or improve the recipient’s stock of physical capital

and agricultural credit, cooperatives, and research.

(including the cost of associated equipment). Because

and to support recipient’s development plans and

• Multisector or cross-cutting includes support for

technical cooperation is spent mostly in the donor

other activities with finance and commodity supply.

projects that straddle several sectors. • Total sector

economy, it is combined with the administrative costs

• Debt-related aid groups all actions relating to debt,

allocable is the sum of aid that can be assigned to a

of donor aid programs. Resource provision involves

including forgiveness, swaps, buybacks, reschedul-

specific sector or multisector.

mainly cash or in-kind transfers and financing of capi-

ing, and refinancing. • Emergency assistance and

tal projects, with deliverables being financial support

developmental food aid comprise emergency and

and the provision of commodities and supplies.

Data sources

distress relief (including aid to refugees and assis-

Data on aid flows are published by OECD-DAC in its

Two other types of aid are presented because they

tance for disaster preparedness) as well as all food

annual statistical report, Geographical Distribution

serve distinctive purposes. Debt-related aid aims to

aid–related costs. • Technical cooperation refers to

of Financial Flows to Aid Recipients, and its annual

provide debt relief on liabilities that recipient coun-

the provision of resources whose main aim is to aug-

Development Cooperation Report. Data are avail-

tries have difficulty servicing. Thus, this type of aid

ment the stock of human intellectual capital, such

able electronically on the OECD’s International

may not provide a full value of new resource flows for

as the level of knowledge, skills, technical know-how,

Development Statistics CD-ROM and at www.oecd.

development, in particular for heavily indebted poor

and productive aptitude of the population in the aid

org/dac/stats/idsonline.

countries. Emergency assistance and development

recipient country (including the cost of associated

2007 World Development Indicators

347

GLOBAL LINKS

Allocation of bilateral aid from Development Assistance Committee members

6.11

Aid dependency Net official development assistance

$ millions 2000 2005

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

348

Aid per capita

Aid dependency ratios

$ 2000

2005

Aid as % of GNI 2000 2005

Aid as % of gross capital formation 2000 2005

Aid as % of imports of goods and services 2000 2005

Aid as % of central government expenditure 2000 2005

136 317 201 302 53 216

2,775 319 371 442 100 193

.. 104 7 22 1 70

.. 102 11 28 3 64

.. 8.4 0.4 4.1 0.0 11.0

37.8 3.7 0.4 1.5 0.1 3.9

.. 32.5 1.5 22.0 0.1 60.6

151.6 16.1 1.2 17.9 0.3 13.3

.. 21.0 .. 4.1 0.1 21.2

.. 8.1 .. 2.3 0.2 8.4

.. .. 1.8 .. .. ..

316.9 .. .. .. .. 21.7

139 1,168 40

223 1,321 54

17 9 4

27 9 5

2.8 2.4 0.3

2.0 2.1 0.2

12.8 10.8 1.2

4.7 9.0 0.6

5.8 11.7 0.5

2.5 8.6 0.3

.. .. 1.5

.. .. 0.6

238 472 737 31 232 311 335 93 396 379

349 583 546 71 192 .. 660 365 538 414

33 57 192 17 1 39 30 14 31 26

41 63 140 40 1 .. 50 48 38 25

10.6 5.8 13.1 0.5 0.0 2.5 12.9 12.8 11.2 4.0

8.2 6.5 5.2 0.7 0.0 .. 12.8 46.8 9.1 2.5

55.9 31.0 68.8 1.4 0.2 13.5 56.8 212.5 64.2 22.5

41.6 45.4 28.6 2.2 0.1 .. 61.8 378.2 44.2 11.6

31.7 19.3 17.4 1.0 0.2 3.7 48.5 55.8 16.1 12.8

.. 17.3 6.7 1.4 0.2 .. .. 97.7 11.0 ..

.. .. .. .. .. 7.6 .. .. .. ..

32.9 23.5 15.2 .. .. .. 108.9 .. 112.6 ..

75 130 49 1,728 4 187 177 33 11 351 66 44 438

95 380 152 1,757 .. 511 1,828 1,449 30 119 125 88 ..

20 16 3 1 1 4 4 10 3 21 15 4 43

24 39 9 1 .. 11 32 362 7 7 28 8 ..

8.0 9.5 0.1 0.1 0.0 0.2 4.5 1.5 0.1 3.6 0.4 .. 0.8

7.0 8.6 0.1 0.1 .. 0.4 26.9 36.8 0.2 0.8 0.3 .. ..

72.8 40.4 0.3 0.4 0.0 1.6 118.7 4.9 0.4 31.2 1.8 .. 2.6

.. 39.8 0.6 0.2 .. 2.2 181.2 118.1 0.6 6.8 1.0 .. ..

.. .. 0.2 0.6 0.0 1.1 .. 1.6 0.1 7.9 0.6 .. 1.1

.. .. 0.3 0.2 .. 1.6 .. 35.7 0.3 1.5 0.5 .. ..

.. .. .. .. .. .. 15.2 .. 0.3 .. 0.8 .. 2.3

.. .. 0.7 .. .. 1.3 .. .. 0.7 4.3 0.8 .. ..

56 146 1,328 180 176 64 686

77 210 926 199 355 .. 1,937

7 12 20 29 49 47 11

9 16 13 29 81 .. 27

0.3 1.0 1.3 1.4 27.7 1.2 8.8

0.3 0.6 1.0 1.2 36.9 .. 17.4

1.2 4.6 6.8 8.1 86.9 4.2 42.7

1.3 2.4 5.7 7.6 182.2 .. 66.0

0.5 2.3 5.6 3.0 34.5 1.2 41.0

0.6 1.5 2.6 2.4 .. .. 39.2

2.1 .. 6.6 .. .. .. ..

.. .. .. 58.0 .. .. ..

12 49 169

54 58 310

9 37 36

39 38 69

0.3 12.2 5.3

0.7 13.0 4.8

0.9 66.9 25.7

3.3 50.4 18.4

0.5 .. 13.6

.. 19.6 8.9

.. .. 47.9

.. .. 27.9

600

1,120

30

51

12.4

10.6

50.5

36.0

17.3

16.4

..

..

263 153 80 208

254 182 79 515

24 18 59 26

20 19 50 60

1.4 5.0 39.5 5.5

0.8 5.6 27.4 12.1

7.7 22.4 329.8 20.8

4.2 46.1 180.0 ..

4.4 15.7 .. 15.1

2.5 .. .. 28.7

12.5 .. .. ..

7.3 .. .. ..

2007 World Development Indicators

Net official development assistance

Aid per capita

$ millions 2000 2005

Honduras Hungarya India Indonesia Iran, Islamic Rep. Iraq Ireland Israela Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep.a Kuwait a Kyrgyz Republic Lao PDR Latviaa Lebanon Lesotho Liberia Libya Lithuaniaa 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 Polanda Portugal Puerto Rico

GLOBAL LINKS

6.11

Aid dependency Aid dependency ratios

$ 2000

2005

Aid as % of GNI 2000 2005

Aid as % of gross capital formation 2000 2005

Aid as % of imports of goods and services 2000 2005

Aid as % of central government expenditure 2000 2005

449 252 1,463 1,654 130 100

681 .. 1,724 2,524 104 21,654

70 25 1 8 2 ..

94 .. 2 11 2 ..

7.7 0.6 0.3 1.1 0.1 ..

8.6 .. 0.2 0.9 0.1 ..

24.5 1.8 1.3 4.5 0.4 ..

27.5 .. 0.6 4.0 0.2 ..

12.8 0.6 1.8 2.5 0.7 ..

12.4 .. .. 2.5 .. ..

.. 1.3 2.0 .. 0.2 ..

.. .. .. .. 0.3 ..

800

..

127

..

0.7

..

3.2

..

1.4

..

1.5

..

10

36

4

13

0.1

0.4

0.5

1.2

0.2

0.5

0.4

1.1

552 189 510 73 –198 3 215 282 91 199 37 67 14 99 251 322 446 45 359 211 20 –56 123 217 419 876 106 152 387

622 229 768 81 .. .. 268 296 .. 243 69 236 24 .. 230 929 575 32 691 190 32 189 192 212 652 1,286 145 123 428

114 13 17 3 –4 1 44 53 38 59 21 22 3 28 125 20 39 2 31 80 17 –1 29 91 15 49 2 80 16

114 15 22 4 .. .. 52 50 .. 68 38 72 4 .. 113 50 45 1 51 62 26 2 46 83 22 65 3 61 16

6.4 1.1 4.1 .. 0.0 0.0 16.7 17.0 1.2 1.2 3.4 17.4 .. 0.9 7.1 8.4 26.1 0.1 15.0 19.4 0.5 0.0 9.4 23.1 1.3 24.7 .. 4.4 7.0

4.8 0.4 4.1 .. .. .. 11.4 11.4 .. 1.1 3.9 54.1 0.1 .. 4.0 18.7 28.4 0.0 13.6 9.9 0.5 0.0 5.9 11.6 1.3 20.7 .. 2.0 5.8

30.9 5.7 23.0 .. –0.1 0.1 78.3 77.7 4.9 5.9 10.1 .. 0.3 4.4 31.5 55.1 188.7 0.2 60.4 101.0 1.8 0.0 39.7 63.5 5.3 69.1 .. 22.8 29.0

20.7 1.5 24.4 .. .. .. 76.5 32.2 .. 5.5 11.7 270.9 .. .. 20.0 82.4 191.1 0.1 57.5 23.1 2.2 0.1 22.1 31.8 4.9 95.2 .. 7.9 20.0

8.7 1.8 12.9 .. –0.1 0.0 28.5 44.1 2.3 .. 4.4 .. 0.2 1.6 10.6 20.3 65.7 0.0 34.4 .. 0.7 0.0 11.3 27.5 3.1 49.7 4.0 8.2 21.2

5.1 0.7 11.4 .. .. .. 18.0 .. .. 1.3 4.8 .. 0.2 .. 6.1 127.9 .. 0.0 .. .. 0.7 0.1 6.7 .. 2.7 38.4 .. .. 15.3

24.1 7.5 23.9 .. –0.2 .. 99.2 .. 4.1 3.8 .. .. .. 3.2 .. 15.6 .. 0.3 .. .. 2.2 –0.1 32.9 .. .. .. .. 14.1 ..

13.9 2.2 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 2.5 .. 21.9 .. 4.0 .. .. .. 34.4

561 208 174

740 515 6,437

114 18 1

144 37 49

15.0 11.7 0.4

15.4 15.2 7.4

47.2 101.4 1.9

51.3 81.8 31.2

23.6 43.0 1.1

21.6 .. 20.1

74.2 .. ..

71.7 .. ..

45 692 16 275 82 398 575 1,396

31 1,666 20 266 51 398 562 ..

18 5 5 52 15 15 8 36

12 11 6 45 9 14 7 ..

0.2 1.0 0.1 8.2 1.2 0.8 0.7 0.8

.. 1.5 0.1 .. 0.7 0.5 0.5 ..

1.9 5.4 0.6 .. 6.1 3.7 3.6 3.3

.. 8.9 0.6 .. 3.2 2.7 3.7 ..

0.6 4.8 0.2 13.7 2.3 3.4 1.1 2.3

0.2 5.2 0.2 8.2 1.2 1.9 1.0 ..

0.9 5.6 0.6 26.2 .. 4.2 4.3 ..

.. 10.4 .. .. 4.2 2.9 3.2 ..

2007 World Development Indicators

349

6.11

Aid dependency Net official development assistance

$ millions 2000 2005

Romaniaa Russian Federationa Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singaporea Slovak Republica Sloveniaa 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 Emiratesa 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 Europe EMU

Aid per capita

Aid dependency ratios

$ 2000

2005

Aid as % of GNI 2000 2005

Aid as % of gross capital formation 2000 2005

Aid as % of imports of goods and services 2000 2005

Aid as % of central government expenditure 2000 2005

432 1,561 321 22 423 1,134 181 1 113 61 101 487

.. .. 576 26 689 1,132 343 .. .. .. 236 700

19 11 40 1 41 139 40 0 21 31 14 11

.. .. 64 1 59 140 62 .. .. .. 29 15

1.2 0.6 17.9 0.0 9.9 13.2 29.4 0.0 0.6 0.3 .. 0.4

.. .. 27.1 0.0 8.5 4.4 29.6 .. .. .. .. 0.3

6.0 3.2 101.3 0.1 46.2 92.9 356.3 0.0 2.1 1.2 .. 2.3

.. .. 119.5 0.1 35.8 23.5 191.6 .. .. .. .. 1.6

2.9 2.2 71.2 0.0 21.9 .. 68.8 0.0 0.7 0.5 .. 1.3

.. .. 82.0 0.0 .. .. 67.5 .. .. .. .. 0.9

.. .. .. .. 70.9 .. 98.8 0.0 .. 0.8 .. 1.3

.. .. .. .. .. .. .. .. .. .. .. 1.0

276 220 13

1,189 1,829 46

14 7 13

61 50 41

1.8 2.1 0.9

5.1 7.1 1.7

6.0 9.7 4.8

19.3 28.5 9.1

3.2 8.5 0.9

11.4 19.9 2.0

7.3 .. ..

24.1 .. ..

158 124 1,019 698 70 –2 222 327 31 817 541 3

78 241 1,505 –171 87 –2 376 464 28 1,198 410 ..

9 20 29 11 13 –1 23 5 7 34 11 1

4 37 39 –3 14 –2 38 6 6 42 9 ..

0.9 13.1 11.4 0.6 5.4 0.0 1.2 0.2 1.2 14.1 1.8 0.0

0.3 10.9 12.5 –0.1 4.0 0.0 1.4 0.1 0.4 14.0 0.5 ..

5.0 109.9 63.7 2.5 29.4 –0.1 4.2 0.7 3.1 69.1 8.5 0.0

1.5 73.0 65.8 –0.3 22.4 .. 5.6 0.5 1.5 64.8 2.6 ..

2.4 .. 45.7 0.9 10.5 0.0 2.1 0.5 .. 51.9 2.8 ..

0.7 13.9 36.6 –0.1 .. .. 2.3 0.4 .. 42.9 0.9 ..

.. 160.3 .. .. .. .. 4.1 0.5 .. 92.4 6.4 ..

.. .. .. –0.6 25.5 .. 4.5 .. .. .. 1.3 ..

5 8 3 21 215 15 74 14 10 w 9 8 7 11 11 5 23 9 16 3 20

4 7 2 23 304 16 81 28 17 w 17 15 17 5 20 5 11 11 88 6 44

0.1 1.4 0.1 5.5 13.3 3.0 25.8 2.5 0.2 w 2.3 0.5 0.6 0.3 0.9 0.5 1.1 0.3 1.0 0.7 4.1

0.1 1.2 0.0 3.7 25.0 2.5 13.9 11.4 0.2 w 2.9 0.6 0.9 0.1 1.1 0.3 0.2 0.3 3.9 0.9 5.5

0.6 8.3 0.3 18.2 47.4 14.3 131.4 17.5 0.8 w 9.8 1.9 2.2 1.2 3.7 1.6 5.0 1.2 4.1 2.9 21.6

0.3 .. 0.3 9.3 .. 6.2 53.6 .. 0.6 w 9.2 1.5 2.0 0.7 2.9 1.4 2.6 0.9 3.3 3.6 10.9

0.3 .. 0.1 4.7 .. 4.7 .. .. 0.7 w .. 1.5 2.5 0.2 2.9 0.8 0.5 0.9 11.2 .. 13.4

17 15 186 172 76 49 1,681 1,905 637 1,102 263 336 795 945 176 368 57,760 s 106,372 s 18,718 40,353 24,895 46,913 17,560 43,146 6,176 2,776 55,970 106,338 8,589 9,497 11,203 5,731 4,841 6,309 4,534 26,946 4,194 9,260 13,194 32,620

0.7 5.4 0.2 10.3 106.7 8.3 50.3 78.6 .. w 9.9 2.0 2.7 0.3 3.8 0.8 1.0 1.2 15.1 3.0 27.3

Note: Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. a. Starting with 2005 flows, official development assistance will not be reported for these countries.

350

2007 World Development Indicators

0.3 .. 0.3 .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. ..

0.3 .. 0.1 .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. ..

6.11

About the data

Ratios of aid to gross national income (GNI), gross

The table does not distinguish among different

capital formation, imports, and government spend-

types of aid (program, project, or food aid; emer-

the balance of payments. Moreover, DAC statistics

ing provide a measure of the recipient country’s

gency assistance; postconflict peacekeeping assis-

The nominal values used here may overstate the

dependency on aid. But care must be taken in draw-

tance; or technical cooperation), each of which may

real value of aid to the recipient. Changes in interna-

ing policy conclusions. For foreign policy reasons,

have very different effects on the economy. Expendi-

tional prices and in exchange rates can reduce the

some countries have traditionally received large

tures on technical cooperation do not always directly

purchasing power of aid. The practice of tying aid,

amounts of aid. Thus aid dependency ratios may

benefit the economy to the extent that they defray

still prevalent though declining in importance, also

reveal as much about a donor’s interest as they do

costs incurred outside the country on the salaries

tends to reduce its purchasing power (see About the

about a recipient’s needs. Ratios in Sub-Saharan

and benefits of technical experts and the overhead

data for table 6.10).

Africa are generally much higher than those in other

costs of firms supplying technical services.

exclude purely military aid.

The values for population, GNI, gross capital for-

regions, and they increased in the 1980s. These

In 1999, to avoid double counting extrabudgetary

mation, imports of goods and services, and central

high ratios are due only in part to aid flows. Many

expenditures reported by DAC countries and flows

government expenditure used in computing the ratios

African countries saw severe erosion in their terms

reported by the United Nations, all UN agencies

are taken from World Bank and International Mon-

of trade in the 1980s, which, along with weak poli-

revised their data since 1990 to include only regular

etary Fund (IMF) databases. The aggregates also

cies, contributed to falling incomes, imports, and

budgetary expenditures (except for the World Food

refer to World Bank definitions. Therefore the ratios

investment. Thus the increase in aid dependency

Programme and the United Nations Office of the High

shown may differ somewhat from those computed

ratios reflects events affecting both the numerator

Commissioner for Refugees, which revised their data

and published by the Organisation for Economic Co-

and the denominator.

from 1996 onward). These revisions have affected

operation and Development (OECD). Aid not allocated

net ODA and official aid and, as a result, aid per

by country or region—including administrative costs,

capita and aid dependency ratios.

research on development issues, and aid to nongov-

As defined here, aid includes official development assistance (ODA; see About the data for table 6.9). The data cover loans and grants from Development

Because the table relies on information from

ernmental organizations—is included in the world

Assistance Committee (DAC) member countries, mul-

donors, it is not necessarily consistent with infor-

total. Thus regional and income group totals do not

tilateral organizations, and non-DAC donors. They do

mation recorded by recipients in the balance of pay-

sum to the world total.

not reflect aid given by recipient countries to other

ments, which often excludes all or some technical

developing countries. As a result, some countries

assistance—particularly payments to expatriates

that are net donors (such as Saudi Arabia) are shown

made directly by the donor. Similarly, grant commod-

• Net offi cial development assistance comprises

in the table as aid recipients (see table 6.10a).

ity aid may not always be recorded in trade data or in

flows (net of repayment of principal) that meet the

Definitions

DAC definition of ODA and are made to countries and territories on the DAC list of aid recipients. See

Official development assistance from non-DAC donors, 2001–05

6.11a

About the data for table 6.9. • Aid per capita is ODA divided by population. • Aid dependency ratios are

Net disbursements ($ millions)

calculated using values in U.S. dollars converted at 2001

2002

2003

2004

2005

26

45

91

108

135

and central government expenditure, see Definitions

Hungary

..

..

21

70

100

for tables 1.1, 4.8, and 4.11.

Iceland

10

13

18

21

27

265

279

366

423

752

36

14

27

118

205

OECD members (non-DAC) Czech Republic

Korea, Rep. Poland Slovak Republic

official exchange rates. For definitions of GNI, gross capital formation, imports of goods and services,

8

7

15

28

56

64

73

67

339

601

73

20

138

209

547

Data on fi nancial fl ows are compiled by DAC

Saudi Arabia

490

2,478

2,391

1,734

..

and published in its annual statistical report,

United Arab Emirates

127

156

188

181

141

Geographical Distribution of Financial Flows to

93

131

112

84

85

Cooperation Report. Data are available in elec-

2

3

4

22

87

tronic format on the OECD’s International Devel-

1,194

3,218

3,436

3,759

3,231

opment Statistics CD-ROM and at www.oecd.org/

Turkey Arab countries Kuwait

Data sources

Other donors Israela Other donors Total

Aid Recipients, and in its annual Development

dac/stats/idsonline. Data on population, GNI, Note: China also provides aid, but does not disclose the amount. a. Includes $50.1 million in 2001, $87.8 million in 2002, $68.8 million in 2003, $47.9 million in 2004, and $49.2 million in 2005 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. Source: Organisation for Economic Co-operation and Development.

gross capital formation, imports of goods and services, and central government expenditure are from World Bank and IMF databases.

2007 World Development Indicators

351

GLOBAL LINKS

Aid dependency

6.12

Distribution of net aid by Development Assistance Committee members Ten major DAC donors

$ millions Total $ millions 2005

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, 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

352

United States 2005

Japan 2005

United Kingdom 2005

Germany 2005

France 2005

Netherlands 2005

Italy 2005

Canada 2005

Sweden 2005

Spain 2005

Other DAC donors $ millions 2005

2,191.7 190.0 289.7 258.2 77.9 148.1

1,341.8 42.6 1.4 64.1 1.5 53.6

71.1 17.6 1.9 26.3 11.0 5.4

219.9 3.8 .. 14.1 .. 6.2

99.2 30.5 2.6 12.2 13.0 30.0

19.5 12.6 255.0 23.6 12.3 25.2

79.1 9.1 0.1 12.8 0.3 11.6

27.4 8.6 9.5 11.6 21.6 1.2

89.5 0.6 1.9 4.0 3.5 1.0

44.2 8.6 2.2 11.2 0.4 1.6

19.0 7.8 –4.0 16.1 12.3 0.4

181.1 48.1 19.2 62.3 2.0 11.9

109.7 562.9 33.7

44.1 49.7 1.7

8.3 –1.0 0.4

0.0 203.3 0.1

19.1 46.1 13.9

15.1 12.2 3.8

5.5 60.7 0.2

1.2 1.9 0.1

1.4 50.8 0.2

1.2 23.9 5.7

0.1 0.2 1.8

13.7 115.3 5.8

206.9 388.3 287.6 51.9 170.9

23.6 90.6 46.1 39.8 –29.6

17.9 40.6 16.7 –0.9 30.8

.. –24.3 6.6 0.3 6.5

27.6 51.4 26.1 3.5 77.0

42.9 16.5 28.5 1.4 28.7

22.7 46.7 21.1 1.0 15.4

0.0 4.6 2.7 .. 1.5

10.8 14.9 7.7 1.9 8.6

1.4 20.8 46.9 0.3 2.4

0.5 66.7 6.1 0.2 10.2

59.6 59.9 79.2 4.5 19.4

338.5 180.7 344.4 336.0

20.0 54.7 67.5 13.6

18.9 0.5 100.6 19.3

2.6 14.8 21.5 4.6

29.7 11.4 24.8 183.0

79.6 14.5 30.1 21.5

53.8 22.9 8.0 17.5

1.5 3.4 1.9 0.8

16.5 5.1 8.5 34.9

15.2 5.3 14.8 8.7

2.9 0.7 0.7 –5.6

97.9 47.6 66.0 37.7

62.2 166.6 75.6 1,689.4

17.2 61.8 –0.1 19.6

0.1 2.1 10.6 1,064.3

.. –0.7 1.1 55.5

3.0 24.0 35.2 255.1

35.0 44.9 14.4 153.6

0.4 1.6 0.8 27.8

0.4 0.1 –1.3 –12.8

1.6 6.2 3.8 30.0

1.3 2.5 2.4 10.1

0.6 1.6 4.1 8.4

2.7 22.7 4.7 78.0

457.9 1,034.3 1,359.5 25.0 151.0 61.3 68.7

334.3 141.4 15.1 –12.1 32.7 21.2 10.1

–2.2 376.3 0.2 –1.4 1.4 0.5 5.8

1.3 77.6 0.6 5.9 3.1 1.8 9.0

21.5 51.1 63.7 5.4 13.2 7.1 3.5

–2.0 88.0 1,014.3 4.9 67.9 3.5 3.5

29.9 46.2 6.1 3.3 2.4 0.3 1.3

–6.0 1.0 61.2 –0.4 1.2 –1.5 0.1

9.1 24.8 22.3 3.0 6.5 0.4 8.0

14.6 23.7 2.2 1.0 3.6 5.6 0.9

31.0 9.2 134.2 2.3 3.6 0.3 15.2

26.7 195.1 39.6 13.2 15.4 22.0 11.3

56.6 174.8 658.8 162.4 226.4

18.9 53.2 397.4 46.8 141.5

3.0 6.2 –36.1 22.7 7.2

0.5 0.3 6.2 0.0 3.1

14.7 17.0 109.2 8.9 4.9

–5.9 2.6 80.4 3.4 1.7

1.3 13.2 7.8 6.2 5.8

–4.4 0.1 –2.8 0.2 25.0

2.5 4.5 15.7 4.4 4.2

0.2 1.1 1.6 6.0 3.1

21.4 48.2 28.5 42.6 0.3

4.3 28.5 51.0 21.3 29.4

1,201.7

625.2

34.2

75.5

49.9

15.9

58.7

86.9

64.9

68.4

4.5

117.7

29.8 15.0 198.4

1.8 2.0 73.3

6.1 4.4 7.3

.. 1.5 3.3

1.9 1.4 51.1

16.8 0.6 17.5

0.0 0.3 12.0

0.1 0.3 1.3

2.6 2.5 3.5

.. 0.7 4.2

0.2 0.2 0.1

0.2 1.2 24.8

602.7

66.8

44.2

119.7

66.4

39.2

70.5

3.5

51.7

26.7

38.9

75.1

218.5 127.8 39.4 354.4

37.8 42.8 1.4 154.0

32.8 12.0 0.0 0.9

0.1 1.5 .. 1.4

18.1 19.3 0.7 3.7

3.4 32.4 15.6 82.0

26.4 1.0 2.6 3.3

–1.6 .. 0.2 ..

8.1 11.5 2.0 81.7

15.2 1.3 .. 1.6

38.9 0.6 2.3 10.3

39.3 5.6 14.7 15.4

2007 World Development Indicators

6.12

Ten major DAC donors

$ millions Total $ millions 2005

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

United States 2005

Japan 2005

United Kingdom 2005

Germany 2005

France 2005

Netherlands 2005

Italy 2005

Canada 2005

Sweden 2005

Spain 2005

Other DAC donors $ millions 2005

456.1

88.1

103.5

30.2

24.0

4.5

16.3

23.5

28.6

20.3

95.0

22.0

846.3 2,247.2 78.2 21,426.6

53.3 160.8 3.8 10,829.7

71.5 1,223.1 –2.5 3,502.9

579.2 24.1 0.7 1,317.5

–68.8 164.7 40.6 2,019.7

–8.0 29.3 14.8 635.8

72.8 176.0 6.8 120.5

3.5 3.4 0.6 953.7

34.0 95.9 .. 385.5

16.5 21.6 0.2 11.3

11.2 33.7 0.4 191.8

81.1 314.7 13.0 1,458.2

11.2

17.5

–17.9

23.1

–13.8

–1.3

–4.0

–3.4

7.6

0.3

0.2

2.9

440.8 153.3 494.6 39.4

353.9 57.1 137.8 7.9

23.6 66.2 60.9 ..

6.1 1.7 86.3 ..

21.9 14.1 49.6 5.2

1.1 4.1 8.1 –0.4

0.8 2.4 28.3 0.7

14.4 .. –10.5 0.8

7.9 1.2 21.6 1.6

0.6 0.8 42.1 5.5

3.2 1.1 1.5 ..

7.4 4.7 69.1 18.2

126.4 159.0

41.4 7.4

21.0 54.1

9.4 0.2

27.6 15.0

1.7 22.6

3.1 2.9

.. ..

0.7 3.7

2.5 15.0

0.1 ..

19.0 38.2

129.8 39.1 148.6 16.8

38.4 1.9 90.0 0.1

1.0 6.7 .. 0.3

0.6 7.6 7.5 ..

12.9 5.0 1.3 3.7

57.9 –1.3 1.6 2.4

0.2 0.1 7.2 0.2

0.5 .. 0.0 9.3

3.4 3.7 2.9 ..

0.4 0.0 14.8 ..

2.5 .. 1.5 0.1

12.2 15.3 21.7 0.7

167.1 500.5 322.1 20.1 378.2 124.5 22.2 160.6 106.1 131.9 289.3 770.8 77.8 98.8 348.7

45.3 80.4 53.1 3.4 58.0 21.5 1.2 128.6 30.5 18.1 –13.2 96.0 4.1 39.5 54.7

11.3 39.6 19.7 –2.1 23.2 14.7 16.6 11.8 3.7 56.5 –54.2 14.8 25.5 0.4 63.4

2.8 13.5 102.0 1.3 1.3 .. –0.8 –9.7 3.0 0.3 .. 80.8 10.6 1.3 61.6

28.9 11.0 25.3 7.9 29.0 12.5 –0.9 25.3 7.8 28.2 61.8 42.6 4.4 21.4 63.1

3.0 91.2 2.4 –4.1 90.0 47.5 3.6 19.4 25.8 6.8 197.6 13.7 1.6 3.4 –1.7

29.7 1.0 19.4 0.3 65.8 0.6 0.0 0.2 8.3 7.5 1.6 64.5 0.7 3.2 12.0

2.6 51.0 0.0 .. .. 1.9 .. 0.1 .. 0.1 39.4 21.6 0.3 0.0 0.0

0.3 2.2 17.0 1.5 35.5 3.4 1.6 5.8 0.6 1.5 4.5 56.2 0.5 1.5 10.2

11.2 0.0 19.3 0.7 21.7 0.6 0.0 0.3 8.5 2.5 0.6 79.3 4.5 5.4 1.2

0.9 135.4 1.2 0.5 5.3 15.7 .. –24.5 0.1 .. 29.0 29.4 .. 7.6 0.1

31.2 75.2 62.6 10.7 48.5 6.1 1.0 3.1 17.8 10.5 22.2 272.1 25.7 15.0 84.1

509.5 255.7 5,966.3

102.4 30.6 120.5

49.2 23.7 69.2

6.1 8.0 2,200.9

24.5 24.8 1,180.9

1.9 70.2 1,436.1

33.9 7.6 202.0

81.0 0.8 529.6

9.0 17.0 19.2

40.9 1.6 0.6

60.1 16.2 1.9

100.4 55.3 205.5

3.9 832.2 17.3 245.3 55.3 310.2 526.4

–1.2 362.4 7.5 0.0 9.4 76.4 98.4

3.7 73.8 2.1 –5.2 27.5 43.5 276.4

.. 63.1 0.1 .. –0.2 3.3 6.4

0.2 34.1 1.1 2.4 2.5 39.0 49.4

0.9 26.0 0.3 0.1 0.4 6.8 –8.5

.. 43.1 0.1 2.5 1.9 13.5 22.3

0.0 –0.8 .. .. 0.1 1.2 –8.6

.. 51.1 1.1 0.4 3.2 15.4 19.4

.. 9.1 .. 0.1 1.9 3.6 2.5

0.0 4.6 4.5 .. 7.1 65.5 10.4

0.2 165.6 0.4 244.9 1.6 42.1 58.2

2007 World Development Indicators

353

GLOBAL LINKS

Distribution of net aid by Development Assistance Committee members

6.12

Distribution of net aid by Development Assistance Committee members Ten major DAC donors

$ millions

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 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 Europe EMU

Total $ millions 2005

United States 2005

Japan 2005

292.0 13.8 440.1 808.2 130.4

63.3 1.2 39.8 181.5 21.0

2.9 5.2 28.0 121.6 2.1

82.0 .. 6.9 93.0 60.6

18.5 1.2 34.3 67.8 6.4

14.0 6.1 158.2 57.5 4.2

28.4 0.1 20.5 10.8 7.2

146.1 486.0

36.9 136.6

.. 16.1

10.7 70.3

5.1 37.0

1.7 28.3

857.1 1,472.0 20.2

58.9 771.5 0.9

312.9 2.1 25.9

13.7 196.5 –9.3

75.2 44.9 –1.2

5.9 105.9 871.0 –219.9 59.4 6.1 269.1 51.8 11.8 704.3 252.1

0.4 57.6 108.9 15.0 3.0 0.5 –15.2 –13.9 9.6 242.3 113.4

–45.3 9.9 36.1 –313.9 0.8 2.0 51.1 –62.3 0.1 14.4 2.5

0.2 4.4 215.9 0.3 0.9 0.1 21.2 –1.1 0.1 55.6 10.8

.. 0.6 0.2 96.6 23.5 20.3 165.7 45.5 8,164.0 s 4,932.4 1,851.5 1,695.4 138.6 8,164.0 225.4 150.3 154.6 1,398.7 1,142.7 3,745.8

2.8 124.1 20.8 1,252.1 569.4 134.7 835.9 178.8 82,133.3 s 26,746.4 39,752.9 37,437.8 1,646.6 82,112.6 7,665.1 2,973.8 4,589.8 24,468.5 5,735.4 23,066.3

–1.5 2.2 37.5 54.4 9.0 4.3 27.1 602.7 180.2 5.8 17.6 8.4 124.2 131.9 33.4 4.1 25,279.5 s 10,406.1 s 5,685.2 2,280.5 14,537.6 6,862.5 13,962.1 6,799.7 376.5 43.5 25,278.5 10,395.5 773.3 3,222.5 842.7 284.9 1,344.8 409.5 11,801.1 3,468.4 1,921.9 632.6 4,192.2 1,133.1

United Kingdom 2005

Germany 2005

France 2005

Netherlands 2005

Canada 2005

Sweden 2005

0.2 .. 11.7 16.1 0.7

10.0 .. 23.5 9.2 6.9

23.4 .. 0.6 35.5 2.1

0.9 .. 82.5 16.3 2.4

48.6 0.1 34.3 199.0 16.9

14.2 55.5

11.1 3.2

6.0 14.6

12.9 22.9

0.1 0.4

47.3 101.3

40.7 23.0 0.2

56.2 154.8 0.1

20.8 16.8 0.3

45.7 21.8 3.8

51.7 45.5 ..

3.5 9.7 ..

177.7 185.6 –0.5

12.9 8.3 49.9 9.2 8.4 0.4 29.0 –33.6 1.2 51.4 53.2

26.3 0.7 4.5 2.1 30.5 1.2 182.3 114.6 0.7 7.7 15.5

2.2 0.9 90.2 7.9 5.3 0.0 –1.9 4.5 0.0 80.1 0.6

0.4 .. 4.7 1.5 0.0 .. –9.2 –5.0 .. 3.9 0.0

1.9 6.5 33.0 7.8 3.3 1.8 1.0 –2.4 0.1 12.8 18.6

0.2 4.6 91.8 6.5 0.4 .. 0.5 2.5 .. 47.9 10.6

1.1 0.0 4.1 0.8 2.0 0.1 5.6 12.4 .. –0.6 0.5

5.8 13.0 232.0 43.0 5.0 0.0 4.7 36.0 0.2 188.5 26.5

0.7 17.0 2.0 82.9 39.8 41.8 118.2 13.5 7,446.8 s 2,471.1 4,011.0 3,811.0 122.8 7,445.6 667.8 370.0 433.5 2,418.8 251.0 2,444.2

3.7 3.8 6.7 96.8 30.6 6.3 15.8 3.8 7,239.2 s 2,822.0 3,657.2 3,092.9 504.1 7,233.1 439.8 338.3 250.1 1,528.4 89.1 3,892.2

0.0 0.5 0.1 56.1 29.9 31.9 55.9 13.6 3,682.7 s 1,555.5 989.5 852.0 67.2 3,682.7 324.5 120.5 272.5 218.7 332.3 1,400.2

Italy 2005

–2.8 2.1 0.4 .. 0.9 1.0 0.2 1.8 0.1 –3.2 28.4 41.9 15.9 15.9 36.9 2.9 1.1 0.6 0.2 49.7 34.2 1.1 13.5 15.1 2,269.5 s 2,832.8 s 2,255.9 s 803.2 941.5 799.2 1,268.7 989.2 497.1 1,232.9 891.1 436.6 24.8 68.5 37.1 2,269.5 2,831.3 2,255.9 –16.7 205.9 147.2 30.6 50.3 151.0 121.9 368.9 170.8 1,037.7 445.8 66.3 52.8 288.0 146.8 872.7 977.1 793.2

Note: Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region.

354

2007 World Development Indicators

Spain 2005

Other DAC donors $ millions 2005

2.3 –4.3 0.1 8.4 –5.4 2.0 9.1 213.7 39.4 151.5 0.1 3.8 0.2 139.8 0.8 34.4 1,863.0 s 10,693.7 s 433.1 4,022.8 1,110.2 3,978.5 1,043.8 3,620.4 36.6 227.0 1,863.0 10,693.6 83.0 1,592.5 48.0 587.3 584.2 479.1 319.2 1,765.5 38.7 839.6 563.8 3,051.8

6.12

About the data

The table shows net bilateral aid to low- and middle-

been assigned to regional totals based on the World

In 1999 all UN agencies revised their data since

income economies from members of the Develop-

Bank’s regional classification system. Aid to unspeci-

1990 to include only regular budgetary expenditures

ment Assistance Committee (DAC) of the Organi-

fied economies has been included in regional totals

(except for the World Food Programme and the United

sation for Economic Co-operation and Development

and, when possible, in income group totals. Aid not

Nations Office of the High Commissioner for Refu-

(OECD). The DAC compilation of the data includes

allocated by country or region—including adminis-

gees, which revised their data from 1996 onward).

aid to some countries and territories not shown in

trative costs, research on development issues, and

They did so to avoid double counting extrabudgetary

the table and aid to unspecified economies that is

aid to nongovernmental organizations—is included

expenditures reported by DAC countries and flows

recorded only at the regional or global level. Aid to

in the world total. Thus regional and income group

reported by the United Nations.

countries and territories not shown in the table has

totals do not sum to the world total.

The table is based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to

The flow of bilateral aid from DAC members reflects global events and priorities

6.12a

information on such aid expenditures as development-oriented research, stipends and tuition costs

Total bilateral aid, 2005

for aid-financed students in donor countries, and

All DAC members

United States

payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipi-

Iraq 26% Others 45%

Others 59%

Iraq 43%

Nigeria 7%

ent countries, and flows from countries that are not members of DAC. The expenditures that countries report as official development assistance (ODA) have changed. For

Indonesia 3% Afghanistan 3% China 2%

Japan

example, some DAC members have reported as Egypt, Arab Rep. 2% Ethiopia 2%

Afghanistan 5% Sudan 3%

United Kingdom

ODA the aid provided to refugees during the first 12 months of their stay within the donor’s borders. Some of the aid recipients shown in the table are also aid donors. See table 6.10a for a summary of ODA from non-DAC countries.

Iraq 34%

Others 34%

Nigeria 27% Others 44%

Definitions • Net aid comprises net bilateral official development assistance to part I recipients and net bilateral

Congo, Dem. Rep. 4%

China 10%

Iraq 16%

Indonesia 12%

table 6.9). • Other DAC donors are Australia, Aus-

India 7%

Vietnam 6%

official aid to part II recipients (see About the data for tria, Belgium, Denmark, Finland, Greece, Ireland,

Tanzania 3% Afghanistan 3%

Luxembourg, New Zealand, Norway, Portugal, and Switzerland.

Germany

France

Nigeria 20%

Iraq 27% Others 50%

Others 50% Nigeria 16%

Congo, Rep. 14% Iraq 9%

Data sources Data on financial flows are compiled by DAC and published in its annual statistical report, Geograph-

Indonesia 2%

China 3% Cameroon 2%

Morocco 3%

Algeria 4%

ical Distribution of Financial Flows to Aid Recipients, and its annual Development Cooperation Report.

The figure shows the distribution of aid from all DAC members and the top five donors to the top five

Data are available electronically on the OECD’s

recipients in 2005.

International Development Statistics CD-ROM and

Source: Organisation for Economic Co-operation and Development, Development Assistance Committee.

at www.oecd.org/dac/stats/idsonline.

2007 World Development Indicators

355

GLOBAL LINKS

Distribution of net aid by Development Assistance Committee members

6.13

Net financial flows from multilateral institutions International financial institutions

United Nations

Total

$ millions

IMF

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, 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

356

World Bank IDA IBRD 2005 2005

Concessional

Nonconcessional

2005

2005

.. 29.6 0.0 25.3 0.0 31.4

.. 0.0 –125.3 0.0 –566.4 –0.6

.. 2.7 0.0 0.0 0.0 –22.6

.. 0.0 –611.6 0.0 –3,571.2 –2.1

45.6 394.5 0.0

0.0 0.0 –8.9

–5.2 97.4 0.0

42.8 62.2 55.6 –0.5 0.0 0.0 102.0 18.9 33.7 19.0

Regional development banks ConcesNonsional concessional

$ millions

2005

Others 2005

UNDP 2005

UNFPA 2005

UNICEF 2005

WFP 2005

Others 2005

$ millions 2005

.. 0.0 0.0 0.4 0.0 0.0

.. 11.5 –211.6 –1.6 60.9 –7.9

.. 21.4 –493.3 1.8 0.0 –0.4

7.1 1.5 1.0 5.9 0.6 1.3

4.9 0.4 0.7 1.9 0.5 0.6

17.1 1.1 1.1 7.8 0.6 0.8

.. 1.3 3.7 9.1 .. 1.0

26.3 1.9 5.6 15.6 3.2 2.7

55.3 71.3 –1,429.7 66.2 –4,071.8 4.1

–23.9 0.0 –8.6

0.5 57.3 0.0

–11.8 86.1 –10.9

5.0 –13.8 –14.9

2.9 16.0 0.6

0.7 5.4 0.3

1.2 11.6 0.8

1.9 18.2 ..

3.6 10.2 0.8

20.5 682.7 –41.0

0.0 0.0 –23.6 –1.1 –255.4 114.8 0.0 0.0 0.0 –31.5

–6.2 0.0 –22.5 –3.5 0.0 –39.4 0.0 0.0 0.0 –23,809.8 0.0 –443.4 11.5 0.0 21.1 0.0 –8.7 0.0 –34.7 0.0

34.0 98.1 0.0 –1.8 0.0 0.0 55.6 5.9 82.2 20.4

–0.3 –60.8 –3.8 17.6 722.8 1.0 0.0 –2.7 0.0 –17.6

–13.0 76.5 61.8 –13.8 87.7 –9.1 15.7 –2.2 8.6 –14.8

3.2 0.9 0.9 0.6 0.7 .. 5.9 6.5 4.5 4.5

2.7 1.5 0.3 0.9 1.1 .. 2.7 1.1 2.0 2.8

3.1 1.8 0.9 1.0 2.7 .. 6.8 5.1 4.8 3.2

2.7 3.2 .. .. .. .. 2.1 1.7 1.2 1.2

0.0 59.6 –0.7 –146.7 .. –0.7 225.9 29.7 –0.2 0.0 0.0 .. 0.0

0.0 –1.8 –151.2 273.7 .. 486.9 0.0 0.0 –12.1 0.0 11.0 .. –19.2

–4.9 –7.2 0.0 0.0 .. 0.0 39.4 7.5 0.0 –91.1 0.0 .. 0.0

0.0 0.0 0.0 0.0 .. 0.0 0.0 –7.8 0.0 0.0 0.0 .. 0.0

0.0 19.6 –1.4 0.0 .. –20.7 22.5 9.9 –11.2 0.1 0.0 .. 0.0

0.0 0.0 –29.8 688.3 .. –1,145.9 0.0 –30.5 –156.0 –0.3 51.9 .. 0.0

–0.4 9.7 0.0 –0.3 .. 75.7 –10.9 –2.3 –25.5 –14.5 57.2 .. –48.9

2.6 5.2 0.7 9.4 .. 1.3 14.9 1.4 0.4 3.5 0.6 1.0 ..

2.3 2.1 0.2 4.7 .. 1.1 7.0 0.6 0.6 1.6 .. 0.7 ..

2.7 8.4 0.5 14.4 .. 1.4 21.6 1.1 0.7 4.4 0.2 0.7 ..

3.1 12.8 .. 8.0 .. 2.9 0.5 2.0 .. 2.9 .. 3.9 ..

5.2 3.7 1.9 12.4 .. 2.8 12.9 6.5 2.5 9.0 4.4 2.0 ..

10.7 112.2 –179.8 863.8 .. –595.3 333.8 18.1 –200.7 –84.5 125.3 8.3 –68.0

–0.7 –1.1 27.8 –0.8 56.7 0.0 161.8

26.4 –35.2 19.8 88.4 0.0 –5.1 0.0

0.0 0.0 0.0 0.0 0.0 0.0 –4.0

219.9 –195.0 0.0 0.0 0.0 0.0 0.0

–21.0 –26.6 –0.4 –23.1 12.3 0.0 127.0

86.1 –80.4 –91.4 16.7 0.0 0.0 –7.6

0.1 54.5 125.5 28.4 –5.5 –3.8 33.1

0.7 1.3 1.2 0.7 3.3 .. 12.1

0.8 0.9 1.8 0.8 2.0 .. 4.2

1.1 1.1 2.7 1.1 2.5 .. 24.1

0.0 0.2 4.1 0.7 4.4 .. 14.1

2.0 2.9 5.1 1.5 8.6 .. 13.1

315.4 –277.5 96.2 114.3 84.3 –8.9 377.8

0.0 15.6 52.1

–7.0 0.0 0.0

0.0 –2.0 –9.5

–24.7 0.0 –3.4

–0.2 7.2 0.0

–35.5 0.0 –2.3

17.4 21.0 0.6

0.6 2.2 1.9

0.1 0.6 0.5

0.6 1.1 0.9

.. 1.7 0.8

5.2 2.9 4.1

–43.4 50.2 45.7

290.8

–2.2

7.4

0.0

57.7

–3.4

19.7

4.2

3.7

4.5

3.3

8.9

394.5

0.0 22.1 10.6 –5.1

1.4 0.0 0.0 0.0

0.0 –25.7 –2.9 –4.5

0.0 0.0 –0.3 15.3

–17.6 4.3 3.5 53.6

–7.0 –7.4 0.0 0.0

8.9 –24.5 2.7 –1.6

1.0 2.3 2.7 4.8

1.5 1.4 1.0 4.2

1.0 3.7 1.6 3.0

3.6 3.0 2.1 1.8

2.5 13.6 2.0 1.8

–4.7 –7.2 23.1 73.3

2007 World Development Indicators

2005

4.5 73.4 2.2 159.5 9.0 61.7 4.3 7.1 5.4 –23,245.0 .. –336.8 3.5 205.8 2.7 58.1 3.3 131.6 5.8 –42.0

International financial institutions

United Nations

GLOBAL LINKS

6.13

Net financial flows from multilateral institutions

Total

$ millions

IMF World Bank IDA IBRD 2005 2005

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

Concessional

Nonconcessional

2005

2005

Regional development banks ConcesNonsional concessional 2005

$ millions

2005

Others 2005

UNDP 2005

UNFPA 2005

UNICEF 2005

WFP 2005

Others 2005

$ millions 2005

142.9 0.0 571.9 40.1 0.0 ..

–69.7 –39.5 715.8 –805.2 102.1 ..

–0.4 0.0 0.0 0.0 0.0 ..

0.0 0.0 0.0 –1,144.7 0.0 ..

52.1 0.0 0.0 48.2 0.0 ..

–21.1 –6.7 419.5 465.4 0.0 ..

31.4 –121.7 –12.8 –38.3 1.0 ..

1.1 .. 15.4 8.2 0.6 ..

2.7 .. 13.7 15.7 1.5 4.7

1.3 .. 34.7 6.4 2.1 1.9

1.1 .. 9.7 7.6 0.4 ..

1.2 .. 14.5 11.5 9.8 2.1

142.7 –167.9 1,782.3 –1,385.0 117.5 8.7

..

..

..

..

..

..

..

..

..

..

..

..

..

0.0

–22.0

0.0

–0.9

–5.3

–35.0

11.0

0.7

..

1.1

..

1.7

–48.7

–2.6 0.0 –20.1 .. .. .. 29.5 26.1 0.0 0.0 8.1 0.0 .. 0.0 5.6 209.2 32.6 0.0 102.0 40.1 –0.6 0.0 23.6 12.1 –1.4 221.6 0.0 0.0 2.5

–25.0 –621.0 –1.1 .. .. .. 0.0 0.0 –61.8 –6.2 –3.5 0.0 .. –97.4 43.2 0.0 –0.2 –92.3 0.0 0.0 –6.3 –381.8 –13.4 0.0 –46.3 0.0 0.0 0.0 0.0

0.0 0.0 66.5 .. .. .. –13.0 –6.1 0.0 0.0 0.0 0.0 .. 0.0 –8.1 6.5 –3.3 0.0 –14.8 –9.6 0.0 0.0 0.0 –5.9 0.0 –10.5 0.0 0.0 0.0

–77.1 0.0 0.0 .. .. .. 0.0 0.0 0.0 0.0 0.0 –0.1 .. –24.8 12.8 0.0 –3.2 0.0 0.0 0.0 0.0 0.0 –21.5 0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.1 19.0 .. .. .. 29.6 57.4 0.0 0.0 –2.1 0.0 .. 0.0 0.0 18.4 25.9 0.0 53.0 7.1 –0.1 0.0 0.0 26.3 0.9 76.6 0.0 0.0 17.6

0.0 –181.3 –5.7 .. .. .. –8.8 11.1 –5.9 0.0 –1.7 0.0 .. –2.6 6.4 0.0 –1.9 –39.4 0.0 –1.4 –8.2 346.4 –13.5 0.0 292.8 12.7 0.0 0.0 0.0

19.3 –30.4 –14.8 .. .. .. 3.8 –2.7 9.3 –8.3 –3.1 0.0 .. –7.1 –0.6 –2.0 1.7 13.2 21.3 39.3 14.5 0.0 –7.4 4.4 196.8 31.9 –1.9 0.0 5.1

0.6 0.9 5.7 2.6 .. .. 2.4 4.3 .. 0.7 0.9 4.1 .. .. 1.2 6.0 7.7 0.6 4.3 2.8 0.2 1.0 1.8 1.4 1.0 7.4 11.5 0.7 6.3

0.3 0.6 3.8 1.0 .. .. 0.8 1.2 .. 0.6 0.1 0.8 .. .. 0.0 1.5 3.7 0.5 1.7 2.2 0.0 2.2 0.4 1.1 2.8 5.9 4.0 0.7 6.6

1.2 1.4 4.9 2.6 .. .. 1.1 2.2 .. 0.8 1.3 3.8 .. .. 1.1 5.9 6.1 0.4 6.9 1.8 .. 1.2 1.1 1.1 1.6 8.7 8.6 1.1 4.9

0.3 .. 11.9 8.4 .. .. .. 2.9 .. .. 4.9 .. 1.1 .. .. 3.7 5.5 .. 5.7 6.4 .. .. .. .. 0.0 6.4 1.1 0.6 5.5

104.6 2.2 26.9 3.0 .. .. 2.8 2.2 .. 68.6 2.1 16.6 1.8 .. 3.1 2.6 4.4 3.8 3.1 2.5 1.7 4.0 1.2 3.3 3.0 5.3 6.8 5.0 9.5

21.7 –827.5 97.0 17.6 .. .. 48.2 98.6 –58.3 56.3 7.0 25.2 3.0 –132.0 64.8 251.8 78.9 –113.3 183.2 91.2 1.0 –27.1 –27.8 43.7 451.2 366.0 30.2 8.1 57.9

63.1 64.6 245.9

0.0 0.0 –223.6

–8.9 12.1 0.0

0.0 0.0 0.0

120.9 14.7 9.1

–8.0 –2.5 –82.7

17.7 41.7 0.0

3.3 6.5 8.0

2.5 3.3 7.9

1.2 8.2 23.7

1.8 15.8 ..

1.5 3.0 8.0

195.0 167.4 –3.7

0.0 513.0 0.0 –3.6 –1.5 0.0 –6.8 0.0

0.0 –94.4 –30.5 –2.9 –8.2 –17.3 –246.6 78.9

0.0 –76.8 0.0 0.0 0.0 0.0 0.0 0.0

0.0 –160.5 –9.8 –61.1 0.0 –39.5 –317.4 0.0

0.0 158.0 –8.9 –1.3 –15.2 –9.5 –18.4 0.0

0.0 187.5 12.8 3.5 3.6 268.3 5.0 0.0

–182.8 –10.8 –8.4 –2.6 –2.9 –1.0 –4.3 0.0

.. 11.6 0.7 2.2 0.5 0.7 2.2 ..

0.2 9.5 0.5 0.7 0.8 22.0 5.7 ..

0.0 14.0 0.5 1.7 1.0 1.7 2.9 ..

.. 10.7 .. .. .. 3.5 .. ..

1.1 22.6 1.7 3.6 1.0 2.2 3.0 ..

–181.5 584.5 –41.5 –59.8 –21.0 231.0 –574.8 78.9

2007 World Development Indicators

357

6.13

Net financial flows from multilateral institutions International financial institutions

United Nations

Total

$ millions

IMF World Bank IDA IBRD 2005 2005

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 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 Europe EMU

Concessional

Nonconcessional

2005

2005

Regional development banks ConcesNonsional concessional 2005

$ millions

2005

Others 2005

UNDP 2005

UNFPA 2005

UNICEF 2005

WFP 2005

Others 2005

$ millions 2005

0.0 0.0 46.7 .. 170.5 88.4 18.6 .. 0.0 .. 0.0 0.0

76.6 –528.3 0.0 .. 0.0 –19.6 0.0 .. –55.5 .. 0.0 8.3

0.0 0.0 –2.0 .. –28.4 0.0 18.2 .. 0.0 .. 0.0 0.0

–151.9 –3,388.7 0.0 .. 0.0 –21.7 0.0 .. 0.0 .. 0.0 0.0

18.8 0.0 35.1 .. 19.6 0.0 19.0 .. 0.0 .. 0.0 0.0

–43.9 86.9 0.0 .. –0.2 49.8 0.0 .. –1.6 .. 0.0 52.2

461.2 0.0 –3.2 .. 31.4 137.2 11.9 .. 8.2 .. 0.0 0.0

.. .. 4.0 .. 3.9 .. 5.0 .. .. .. 6.3 2.3

.. .. 1.9 .. 2.4 .. 1.7 .. .. .. 0.3 0.9

.. .. 4.3 0.2 3.6 1.1 4.6 .. .. .. 7.9 1.4

.. .. 4.2 .. 3.2 .. 5.4 .. .. .. 5.2 0.0

.. .. 5.9 1.8 4.4 20.7 14.3 .. .. .. 5.2 6.1

360.8 –3,830.0 96.7 2.0 210.4 255.8 98.7 .. –48.8 .. 24.9 71.2

73.6 –1.3 –0.3

–1.0 0.0 0.0

0.0 0.0 0.0

114.5 –28.3 0.0

129.1 0.0 –1.0

37.1 –2.2 22.3

24.9 100.4 42.4

2.6 11.6 0.5

2.9 8.1 0.5

0.7 13.0 0.8

6.7 43.8 0.6

6.0 6.8 1.9

397.0 151.8 67.9

–1.5 34.8 260.5 –3.4 0.0 0.0 –2.1 –5.9 0.0 111.7 0.0 ..

0.0 0.0 0.0 –97.5 0.0 –13.0 –54.5 –294.0 –1.8 0.0 316.0 ..

0.0 15.1 –38.1 0.0 –11.2 0.0 0.0 0.0 0.0 –30.2 0.0 ..

0.0 0.0 0.0 0.0 0.0 0.0 0.0 –5,319.9 0.0 0.0 –299.6 ..

0.0 26.1 122.8 –2.4 0.0 –0.1 0.0 0.0 0.0 63.7 0.0 ..

0.0 0.5 0.0 –368.3 –1.4 –9.6 –8.5 0.0 0.0 –1.6 22.0 ..

–40.8 0.4 17.2 –12.7 9.2 –7.5 250.0 286.9 –2.9 17.8 –65.8 ..

1.5 3.7 7.8 2.2 1.7 0.7 0.7 0.7 1.0 6.1 2.8 ..

2.0 0.8 5.1 2.1 0.6 .. 0.4 1.1 0.5 3.8 0.7 ..

1.0 1.9 10.9 1.3 1.7 .. 0.7 1.8 0.9 9.6 1.4 ..

1.8 1.7 5.7 .. 0.3 .. .. .. .. .. .. ..

38.4 1.6 5.0 12.1 2.1 0.9 2.2 7.5 1.2 9.6 3.6 ..

2.4 86.5 396.8 –466.6 2.9 –28.5 189.0 –5,321.9 –1.0 190.6 –18.9 ..

0.0 7.1 0.0 377.7 .. 102.2 75.8 0.0 .. s 4,692.4 699.2 698.4 0.8 5,391.6 340.1 397.4 267.6 125.8 1,566.8 2,693.9

30.3 4.3 –92.9 0.0 .. 0.0 0.0 –1.7 .. s 392.1 –3,302.8 –1,062.8 –2,240.0 –2,910.6 –973.1 –1,125.8 –1,024.1 –135.5 620.4 -272.6

0.6 3.1 0.5 6.5 .. 5.8 5.3 3.1 398.9 s 291.5 96.6 81.6 13.0 398.9 61.0 29.2 25.6 13.8 62.2 196.4

0.5 0.9 0.8 7.6 .. 3.6 1.8 4.4 386.4 s 165.3 114.8 96.2 11.1 386.4 49.5 10.4 50.8 21.1 46.8 115.2

0.5 2.3 0.9 5.1 1.9 5.3 4.5 2.0 710.8 s 348.1 105.0 91.3 12.8 710.5 57.1 19.9 30.6 21.9 84.7 245.1

0.0 –171.7 –2.4 0.0 –18.4 0.0 0.0 0.0 0.0 –53.5 0.0 217.6 .. .. .. –44.3 –11.4 0.0 –62.8 0.0 17.8 0.0 –164.7 0.0 .. s .. s .. s –271.9 –432.7 1,589.8 –75.6 –39,380.9 364.9 –79.1 –26,717.8 373.0 3.5 –12,663.2 –8.1 –347.5 –39,813.6 1,594.6 –74.2 –1,523.2 410.5 –40.5 –9,754.7 75.2 –13.1 –27,566.7 206.3 –45.1 –700.1 1.1 20.6 –39.9 377.5 –195.1 –229.0 884.1

25.4 –0.1 64.3 0.0 –182.3 –75.5 –2.2 –2.5 .. .. 0.0 56.2 –9.2 4.9 0.0 3.4 .. s .. s 643.7 374.6 512.0 999.6 385.7 576.4 126.2 423.2 1,155.7 1,374.2 774.3 –40.1 –6.6 788.9 –202.5 292.5 –18.7 –59.3 730.1 –2.3 –120.9 394.5

.. 0.8 –116.2 .. 2.6 66.0 .. 2.3 –346.2 .. 5.6 561.8 .. 307.4 309.3 7.1 6.8 131.4 7.4 10.1 55.5 125.4 4.3 –23.7 554.5 s 1,410.2 s .. s 380.8 366.1 8,539.7 83.0 849.5 –38,934.7 81.9 658.7 –24,816.4 1.1 126.4 –14,193.1 554.5 1,408.3 –29,737.0 29.3 117.4 –771.6 6.6 100.4 –9,499.7 23.5 84.6 –27,824.9 20.4 597.7 –156.9 54.9 93.0 3,614.9 342.4 346.2 4,400.2

Note: The aggregates for the regional development banks, United Nations, and total net financial flows include amounts for economies not specified elsewhere.

358

2007 World Development Indicators

About the data

6.13

Definitions

The table shows concessional and nonconcessional financial flows from the major multilateral institutions— the World Bank, the International Monetary Fund (IMF), regional development banks, UN agencies, and regional groups such as the Commission of the European Communities. Much of the data comes from the World Bank’s Debtor Reporting System. The multilateral development banks fund their nonconcessional lending operations primarily by selling low-interest, highly rated bonds (the World Bank, for example, has a AAA rating) backed by prudent lending and financial policies and the strong financial support of their members. These funds are then on-lent at slightly higher interest rates and with relatively long maturities (15–20 years) to developing countries. Lending terms vary with market conditions and the policies of the banks. Concessional fl ows from bilateral donors are defined by the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) as financial flows containing a grant element of at least 25 percent. The grant element of loans is evaluated assuming a nominal market interest rate of 10 percent. The grant element is nil for a loan carrying a 10 percent interest rate, and it is 100 percent for a grant, which requires no repayment. Concessional flows from multilateral development agencies are credits provided through their concessional lending facilities. The cost of these loans is reduced through subsidies provided by donors or drawn from other resources available to the agencies. Grants provided by multilateral agencies are not included in the net flows. All concessional lending by the World Bank is carried out by the International Development Association

(IDA). Eligibility for IDA resources is based on gross national income (GNI) per capita; countries must also meet performance standards assessed by World

• Net financial flows are disbursements of public

Bank staff. Since July 1, 2005, the GNI per capita cutoff has been set at $825, measured in 2003 using the World Bank Atlas method (see Users guide). In exceptional circumstances IDA extends eligibility temporarily to countries that are above the cutoff and are undertaking major adjustment efforts but are not creditworthy for lending by the International Bank for Reconstruction and Development (IBRD). An exception has also been made for small island economies. Lending by the International Finance Corporation is not included in this table. The IMF makes concessional funds available through its Poverty Reduction and Growth Facility, which replaced the Enhanced Structural Adjustment Facility in 1999, and through the IMF Trust Fund. Eligibility is based principally on a country’s per capita income and eligibility under IDA, the World Bank’s concessional window. Regional development banks also maintain concessional windows for funds. Loans from the major regional development banks—the African Development Bank, Asian Development Bank, and InterAmerican Development Bank—are recorded in the table according to each institution’s classification. In 1999 all UN agencies revised their data since 1990 to include only regular budgetary expenditures (except for the World Food Programme and the United Nations Office of the High Commissioner for Refugees, which revised their data from 1996 onward). They did so to avoid double counting extrabudgetary expenditures reported by DAC countries and flows reported by the United Nations.

opment Association, the concessional loan window

or publicly guaranteed loans and credits, less repayments of principal. • IDA is the International Develof the World Bank. • IBRD is the International Bank for Reconstruction and Development, the founding and largest member of the World Bank Group. • IMF is the International Monetary Fund. Its nonconcessional lending consists of the credit it provides to its members, mainly to meet their balance of payments needs. It provides concessional assistance through the Poverty Reduction and Growth Facility and the IMF Trust Fund. • Regional development banks include the African Development Bank, in Tunis, Tunisia, which lends to all of Africa, including North Africa; the Asian Development Bank, in Manila, Philippines, which serves countries in South and Central Asia and East Asia and Pacific; the European Bank for Reconstruction and Development, in London, United Kingdom, which serves countries in Europe and Central Asia; the European Development Fund, in Brussels, Belgium, which serves countries in Africa, the Caribbean, and the Pacific; and the Inter-American Development Bank, in Washington, D.C., which is the principal development bank of the Americas. Concessional financial flows cover disbursements made through concessional lending facilities. Nonconcessional financial flows cover all other disbursements. • Others is a residual category in the World Bank’s Debtor Reporting System. It includes such institutions as the Caribbean Development Bank and the European Investment Bank. • United Nations includes the United Nations Development Programme

Maintaining financial flows from multilateral institutions to developing countries

6.13a

(UNDP), United Nations Population Fund (UNFPA), United Nations Children’s Fund (UNICEF), World Food

World Bank

$ billions

Regional development banks

International Monetary Fund

Other international financial institutions

Programme (WFP), and other UN agencies, such as the Office of the High Commissioner for Refugees,

40

United Nations Relief and Works Agency for Palestine

30

Refugees in the Near East, and United Nations Regu-

20

lar Programme for Technical Assistance.

10 0

Data sources

–10

Data on net fi nancial fl ows from international

–20

financial institutions are from the World Bank’s

–30

Debtor Reporting System. These data are pub-

–40

lished in the World Bank’s Global Development Finance 2007 and electronically as GDF Online.

–50 1970

1975

1980

1985

1990

1995

2000

2005

Data on aid from UN agencies are from the DAC annual Development Cooperation Report. Data are

As developing countries pay off loans from international financial institutions, net disbursements from

available in electronic format on the OECD’s Inter-

these institutions have fallen greatly in recent years.

national Development Statistics CD-ROM and at

Source: World Bank Debtor Reporting System.

www.oecd.org/dac/stats/idsonline.

2007 World Development Indicators

359

GLOBAL LINKS

Net financial flows from multilateral institutions

6.14 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, 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

360

Movement of people Net migration

Migration stock

thousands 1990–95 2000–05

thousands 1990 2005

3,313 –423 –58 143 50 –500 390 262 –116 –260 15 85 105 –100 –1,000 –7 –184 –309 –128 –250 194 –5 643 37 20 90 –1,281 300 –200 1,208 14 62 200 153 –100 38 58 –220 –50 –600 –57 –359 –117 888 43 424 20 45 –560 2,688 40 470 –360 350 20 –105

29 66 274 34 1,650 659 3,984 473 361 882 1,271 899 76 60 56 27 804 22 345 333 38 171 4,319 63 74 108 380 2,218 102 919 130 418 1,953 475 100 424 220 103 78 176 47 12 382 1,155 61 5,907 128 118 338 5,936 717 412 264 402 14 19

2,140 –100 –100 145 –100 –100 500 100 –100 –350 –10 67 99 –100 40 –6 –130 –50 100 192 –10 13 1,050 –45 271 30 –1,950 300 –200 –322 –14 84 –371 100 –160 50 61 –140 –250 –450 –38 280 –10 –150 41 300 –15 31 –248 1,100 12 179 –300 –299 1 –105

2007 World Development Indicators

43 83 242 56 1,500 235 4,097 1,234 182 1,032 1,191 719 175 116 41 80 641 104 773 100 304 137 6,106 76 437 231 596 2,999 123 539 288 441 2,371 661 74 453 389 156 114 166 24 15 202 555 156 6,471 245 232 191 10,144 1,669 974 53 406 19 30

Refugees

By country of origin 1996 2005

2,674.2 5.8 2.2 249.7 .. 203.2 .. .. 236.1 58.0 0.5 .. .. .. 993.9 .. .. .. .. 428.7 62.2 2.1 .. 0.2 58.4 12.8 105.8 .. 2.2 158.8 0.2 .. 0.3 310.1 25.5 1.0 .. .. .. 1.2 19.6 332.2 .. 96.3 .. .. .. .. 48.5 .. 15.1 .. 40.3 0.5 0.9 15.1

2,166.1 12.7 12.0 215.8 .. 14.0 .. .. 233.7 7.3 8.9 .. .. .. 109.9 .. .. .. .. 438.7 17.8 9.1 .. 42.9 48.4 0.9 124.1 .. 60.5 430.9 24.4 .. 18.3 119.1 19.0 3.6 .. .. .. 6.3 4.3 144.1 .. 65.5 .. .. .. .. 7.3 .. 18.4 .. 3.4 5.8 1.1 13.5

Workers’ remittances and compensation of employees

thousands By country of asylum 1996 2005

18.8 4.9 190.3 9.4 10.4 219.0 67.3 89.1 233.7 30.7 30.5 36.1 6.0 0.7 40.0 0.2 2.2 1.4 28.4 20.7 0.0 46.4 138.4 36.6 0.1 0.3 290.1 1.3 0.2 676.0 20.5 23.2 327.7 165.4 1.7 2.3 66.4 0.6 0.2 6.0 0.2 2.1 .. 390.5 11.4 151.3 0.8 6.9 0.1 1,266.0 35.6 5.8 1.6 663.9 15.4 ..

0.0 0.1 94.1 14.0 3.1 219.6 65.0 21.2 3.0 21.1 0.7 15.3 30.3 0.5 10.6 3.1 3.5 4.4 0.5 20.7 0.1 52.0 147.2 24.6 275.4 0.8 301.0 1.9 0.2 204.3 66.1 11.3 41.6 2.9 0.7 1.8 44.4 .. 10.1 88.9 0.0 4.4 0.0 100.8 11.8 137.3 8.5 7.3 2.5 700.0 53.5 2.4 0.4 63.5 7.6 ..

$ millions Received Paid 1990 2005 1990 2005

.. 0 352 .. 15 .. 2,370 635 .. 779 .. 3,583 101 5 .. 86 573 .. 140 .. 9 23 .. .. 1 1 210 .. 495 .. 4 12 44 .. .. .. 464 315 51 4,284 366 .. .. 5 63 4,035 .. 10 .. 4,876 6 1,817 119 27 1 61

.. 1,290 1,950 .. 413 940 a 2,858 2,941 693 4,251 370 7,155 63a 338 1,844 125 3,540 2,130 50a .. 200 11a .. .. .. 3 22,492a 240 3,346 .. 11 421 160 1,222 .. 1,017 1,075a 2,717 2,038 5,017 2,842 .. 265 174 695 12,742 6a 58 346 6,542 99 1,220 3,033 42a 28a 985

.. .. 31 150 21 .. 674 320 .. .. .. 2,310 21 8 .. 119 12 .. 81 6 14 111 .. 36 39 7 5 .. 44 .. 55 .. 471 .. .. .. 160 .. 2 27 3 .. .. 1 16 6,949 147 .. .. 6,856 4 122 14 20 12 63

.. 7 .. 215 279 146 1,358 2,543 268 6 94 2,758 7a 66 40 123 498 13 44 a 1a 144 63a .. .. .. 6 2,602 335 56 .. 45 209 592 62 .. 2,135 1,226a 26 38 57 24 .. 50 16 249 4,867 110a 1a 29 12,519 6a 809 33 48a 5a 59

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordanb Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanonb 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

Net migration

Migration stock

thousands 1990–95 2000–05

thousands 1990 2005

By country of origin 1996 2005

–40 101 –1,407 –725 –1,512 170 –1 484 573 –100 248 495 –1,509 222 0 –115 –626 –273 –10 –174 178 –84 –283 10 –100 –27 –6 –835 230 –260 –15 –7 –1,800 –121 –60 –300 650 –126 3 –101 190 79 –110 5 –96 42 25 –2,611 8 0 –25 –450 –900 –77 –7 –4

270 348 7,493 466 3,809 84 230 1,633 1,346 21 877 1,146 3,619 146 34 572 1,551 623 23 805 520 7 81 457 349 95 58 1,157 1,014 60 94 9 702 579 7 85 122 101 119 413 1,192 529 41 115 447 185 452 6,556 62 33 183 56 164 1,127 436 322

.. .. 7.6 11.4 104.1 714.7 .. .. .. .. .. .. 40.2 9.4 .. .. .. 17.1 46.9 .. 10.9 .. 784.0 .. .. 13.0 .. .. .. 55.2 83.2 .. .. 5.8 .. .. 34.7 143.0 .. .. .. .. 22.8 10.4 4.8 .. .. 7.5 .. .. .. 6.7 0.6 12.9 .. ..

–30 50 –1,400 –1,000 –1,379 240 194 158 600 –100 270 100 –600 –212 0 –80 240 –75 –7 –12 –35 –36 –245 10 –20 –10 0 –20 150 –134 30 0 –2,000 –40 –50 –400 –20 70 –6 –100 150 78 –100 –10 –170 58 –160 –1,810 8 0 –25 –300 –900 –80 250 –3

26 316 5,700 160 1,959 28 585 2,661 2,519 18 2,048 2,225 2,502 345 37 551 1,669 288 25 449 657 6 50 618 165 121 63 279 1,639 46 66 21 644 440 9 132 406 117 143 819 1,638 642 28 124 971 344 628 3,254 102 25 168 42 374 703 764 418

Refugees

.. .. 16.3 34.4 99.4 262.3 .. .. .. .. .. .. 4.3 4.6 .. .. .. 3.1 24.4 .. 18.3 .. 231.1 .. .. 8.6 .. .. .. 0.5 31.7 .. .. 12.1 .. .. 0.1 164.9 .. .. .. .. 1.5 0.7 22.1 .. .. 29.9 .. .. .. 4.9 0.5 19.6 .. ..

Workers’ remittances and compensation of employees

thousands By country of asylum 1996 2005

0.1 7.5 233.4 0.1 2,030.4 113.0 0.1 0.0 64.7 0.0 5.3 0.9 15.6 223.6 .. 0.0 3.8 16.7 0.0 0.0 2.4 .. 120.1 7.7 0.0 5.1 .. 1.3 5.3 18.2 15.9 .. 34.6 .. .. 0.1 0.2 .. 2.2 126.8 102.6 3.8 0.6 25.8 8.5 48.4 .. 1,202.7 0.9 10.2 0.1 0.7 0.7 0.6 0.3 ..

6.14

GLOBAL LINKS

Movement of people

0.0 8.0 139.3 0.1 974.3 50.2 7.1 0.6 20.7 .. 1.9 1.0 7.3 251.3 .. 0.1 1.5 2.6 .. 0.0 1.1 .. 10.2 12.2 0.5 1.3 .. 4.2 33.7 11.2 0.6 .. 3.2 0.1 .. 0.2 2.0 .. 5.3 126.4 118.2 5.3 0.2 0.3 9.0 43.0 0.0 1,084.7 1.7 10.0 0.1 0.8 0.1 4.6 0.4 ..

$ millions Received Paid 1990 2005 1990 2005

63 .. 2,384 166 1,200 .. 286 812 5,075 229 508 499 .. 139 .. 1,037 .. .. 11 .. 1,818 428 .. .. .. .. 8 .. 325 107 14 .. 3,098 .. .. 2,006 70 6 13 0 709 762 0 14 10 158 39 2,006 110 5 34 87 1,465 .. 4,479 ..

1,796 300 23,725a 1,883 1,032a .. 651 851 2,398 1,783 1,080 2,500 178 524 .. 808 .. 322 1a 381 4,924 327 .. 15 534 226 3 1a 1,281a 155a 2a 215a 21,772 920 202a 4,589 57 117a 16a 1,211 2,227 739 600 60a 3,329 429 39 4,280 126 13 268 1,440 13,566 3,549 3,017 ..

.. .. 106 .. .. .. 165 850 3,764 27 290 71 .. 7 .. 364 770 .. .. .. .. .. .. 446 .. .. 18 .. 230 45 31 1 .. .. .. 16 25 .. 30 .. 1,393 367 .. 66 9 295 856 1 22 43 .. 75 5 .. 77 ..

2007 World Development Indicators

1 155 1,008a 1,178 .. .. 1,128 2,349 5,815 394 1,281 349 1,670 56 .. 3,336 2,648 122 1a 20 4,233a 17 .. 914 47 16 8 1a 5,679 64 a .. 11a .. 68 49a 40 21 25a 17a 65 5,678 936 .. 25a 18 953 2,257 3 91 135 .. 164 15 598 1,151 ..

361

6.14 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 Republicb 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 Gazab 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 Europe EMU

Movement of people Net migration

Migration stock

thousands 1990–95 2000–05

thousands 1990 2005

–529 –150 143 133 1,858 400 11,525 12,080 –1,714 45 73 121 –325 250 4,743 6,361 –100 –100 293 326 200 –100 130 512 –380 438 112 119 250 200 727 1,843 9 5 41 124 38 10 178 167 –1,083 170 633 282 1,125 50 1,225 1,106 500 2,025 766 4,790 –182 –160 461 368 –158 –519 1,273 639 –38 –6 73 45 151 157 781 1,117 80 40 1,376 1,660 –30 –30 711 985 –313 –345 426 306 591 –345 574 792 –88 –50 391 1,050 –122 –4 163 183 –24 –20 51 38 –22 –20 38 38 71 –250 1,150 1,328 50 –10 307 224 135 –15 550 518 598 –700 7,097 6,833 340 960 1,330 3,212 381 686 3,753 5,408 5,200 5,800 23,251 38,355 –20 –10 98 84 –340 –300 1,653 1,268 40 40 1,024 1,010 –270 –200 28 21 –5 –40 911 1,680 650 –100 107 265 –7 –65 280 275 –182 –50 804 511 ..c 154,688 s 190,206 s ..c –3,286 –4,000 31,745 27,120 –9,673 –11,987 51,290 50,804 –10,872 –10,086 26,469 24,999 1,200 –1,901 24,821 25,804 –12,958 –15,987 83,035 77,923 –3,072 –3,939 2,748 4,432 –3,398 –2,665 34,071 31,137 –3,776 –4,012 6,343 5,777 –1,030 –2,374 8,828 9,642 –1,368 –1,679 15,845 11,229 –314 –1,318 15,200 15,706 12,929 15,970 71,653 112,282 5,285 5,036 17,950 30,335

Refugees

By country of origin 1996 2005

Workers’ remittances and compensation of employees

thousands By country of asylum 1996 2005

11.9 11.5 0.3 173.7 103.0 246.7 469.1 100.3 25.3 .. .. 9.9 17.6 8.7 65.0 104.0 190.0 563.2 375.1 40.5 13.5 .. .. 0.0 .. .. 1.4 .. .. 10.0 637.0 395.6 0.7 .. .. 22.6 .. .. 5.7 109.6 108.1 0.0 475.3 693.6 393.9 .. .. 0.6 .. .. 191.2 .. .. 84.4 8.6 16.4 27.8 107.5 54.8 1.2 .. .. 498.7 .. .. 108.0 25.6 51.1 12.6 .. .. .. .. .. 0.2 50.4 170.6 8.2 .. .. 15.6 28.3 34.2 264.3 6.1 84.2 3.6 .. .. 0.5 .. .. 98.6 .. .. 607.0 .. .. 0.1 69.7 8.3 2.9 .. .. 1.6 518.3 358.3 34.4 80.2 349.7 .. .. .. 53.5 .. .. 131.1 0.0 11.3 0.6 11,701.6d s 8,300.6d s 13,357.1d, e s 7,935.2 5,811.7 5,762.5 3,766.4 2,488.9 4,523.9 3,182.8 2,042.2 3,972.8 583.6 446.7 551.1 11,701.6 8,300.6 10,286.4 888.3 724.6 450.7 2,411.3 1,179.3 1,545.8 145.1 107.9 88.4 940.1 765.0 2,457.3 2,963.6 2,434.3 1,612.4 4,353.1 3,089.5 4,131.9 .. .. 3,070.7 .. .. 1,743.7

2.1 1.5 45.2 240.7 20.7 148.3 60.0 0.0 0.4 0.3 0.5 29.7 5.4 0.1 147.3 0.8 74.9 48.0 26.1 1.0 548.8 117.1 9.3 .. 0.1 2.4 12.0 257.3 2.3 0.1 303.2 379.3 0.1 44.0 0.4 2.4 .. 81.9 155.7 13.9 8,662.0d, e s 3,895.6 2,364.2 2,230.4 133.8 6,259.8 464.5 483.8 37.7 1,340.5 1,371.6 2,561.6 2,402.2 1,041.8

$ millions Received Paid 1990 2005 1990 2005

.. 4,733 .. 3,117 3 21 .. .. 142 633a .. 4,650a .. 2 .. .. .. 424 a 38 264 .. .. 136 658 2,186 7,927 401 2,088 62 1,016 113 81 153 630 924 1,910 385 823 .. 466 .. 16 973 1,187 27 148a 3 87a 551 1,393 3,246 851 .. .. .. 476 .. 595 .. .. 2,099 6,722 1,170 2,924 .. 78 .. .. 1 148 .. 4,000a .. 436a 1,498 1,283 .. .. 1 .. 68,584 s 262,489 s 7,664 48,188 23,474 144,716 14,370 97,779 9,104 46,937 31,138 192,904 3,263 45,053 3,246 31,363 5,763 48,201 11,432 24,001 5,572 35,558 1,862 8,728 37,446 69,585 27,744 48,981

.. .. 21 11,221 79 .. .. .. .. 2 .. 1,199 254 .. 2 4 654 8,168 .. .. .. 199 13 22 13 .. .. .. .. .. 2,034 11,850 .. .. 701 .. .. 106 17 16 66,295 s 1,305 4,770 959 3,811 6,075 527 .. 996 1,566 115 2,871 60,220 22,226

34 7,651 35 14,318 77a .. 2 .. 16a 95 .. 1,055 7,733 257 2 11 611 13,200 40 145 41 .. 34 a .. 15 .. .. 374 34 .. 3,087 41,072 2 .. 211 .. .. 109 24 a .. 178,677 s 3,379 34,784 8,759 26,025 38,163 9,918 13,420 2,288 8,014 1,338 3,185 140,514 51,944

a. World Bank staff estimates. b. Palestinian refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East are not included in statistics from the United Nations Office of the High Commissioner for Refugees (UNHCR). c. 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. d. World totals include refugees without a specified country or region, which are classified by UNHCR in the category “various.” e. World totals come from UNHCR. Thus regional and income group totals do not add up to the world total.

362

2007 World Development Indicators

6.14

About the data

Movement of people, most often through migration, is a significant part of integration. Migrants contribute to the economies of both their host country and their country of origin. Yet reliable statistics on migration are difficult to collect and are often incomplete, making international comparisons a challenge. The United Nations Population Division provides data on net migration and migration stock. To derive estimates of net migration, the organization takes into account the past migration history of a country or area, the migration policy of a country, and the influx of refugees in recent periods. The data to calculate these official estimates come from a variety of sources, including border statistics, administrative records, surveys, and censuses. When no official estimates can be made due to insufficient data, net migration is derived through the balance equation, which is the difference between overall population growth and the natural increase during the 1990– 2000 intercensal period. The data used to estimate the international migrant stock at a particular point in time are obtained mainly from population censuses. The estimates are derived from the data on foreign-born population—those who have residence in one country but who were born in another country. When data on the foreign-born population are not available, data on foreign population are used as estimates. After the breakup of the Soviet Union in 1991, people living in one of the newly independent countries who were born in another of the countries were classified as international migrants. Estimates of migration stock in the newly independent states from 1990 on are based on the 1989 census of the Soviet Union. For countries with information on the international migration stock for at least two points in time,

interpolation or extrapolation was used to estimate the international migrant stock on July 1 of the reference years. For countries with only one observation, estimates for the reference years were derived using rates of change in the migrant stock in the years preceding or following the single observation available. A model was used to estimate migration for countries that had no data. Registration, together with other sources— including estimates and surveys—are the main sources of refugee data. Yet there are difficulties in collecting accurate statistics. Although refugees are often registered individually, the accuracy of registrations varies greatly. Many refugees may not be aware of the need to register or may choose not to do so. And administrative records tend to overestimate the number of refugees because it is easier to register than to de-register. Palestinian refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East are not included in the statistics of the United Nations Office of the High Commissioner for Refugees (UNHCR). Workers’ remittances and compensation of employees are World Bank staff estimates based on data from the International Monetary Fund’s (IMF) Balance of Payments Yearbook. The IMF data are supplemented by World Bank staff estimates for missing data for countries where workers’ remittances are important. The data reported here are the sum of three items defined in the IMF Balance of Payments Manual (fifth edition). These are workers’ remittances, compensation of employees, and migrants’ transfers. The distinction between these three items is not always consistent in the data reported by countries to the IMF. In some cases, countries compile data on the basis of the citizenship of migrant workers rather than

High-skill workers in developing countries are increasingly emigrating to high-income countries

6.14a 1990

Emigration rate as share of adult labor force (%)

2000

15

12

9

their residency status. Some countries also report remittances entirely as workers’ remittances or compensation of employees. Following the fifth edition of the Balance of Payments Manual in 1993, migrants’ transfers are considered a capital transaction but in previous editions they were regarded as current transfers. For these reasons the figures presented in the table take all three items into account. Definitions • Net migration is the net total of migrants during the period, that is, the total number of immigrants less the total number of emigrants, including both citizens and noncitizens. Data are five-year estimates. • Migration stock is the number of people born in a country other than that in which they live. It includes refugees. • Refugees are people who are recognized as refugees under the 1951 Convention Relating to the Status of Refugees or its 1967 Protocol, the 1969 Organization of African Unity Convention Governing the Specific Aspects of Refugee Problems in Africa, people recognized as refugees in accordance with the UNHCR statute, people granted a refugee-like humanitarian status, and people provided with temporary protection. Asylum seekers are people who have applied for asylum or refugee status and who have not yet received a decision or who are otherwise registered as asylum seekers. • Country of origin generally refers to the nationality or country of citizenship of a claimant. • Country of asylum is the country where an asylum claim was filed. • Workers’ remittances and compensation of employees, received and paid comprise current transfers by migrant workers and wages and salaries earned by nonresident workers. Workers’ remittances are classified as current private transfers from migrant workers who are residents of the host country to recipients in their country of origin. They include only transfers made by workers who have been living in the host country for more than a year, irrespective of their immigration status. Compensation of employees is the income of migrants who have lived in the host country for less than a year. Migrants’ transfers are defined as the net worth of migrants who are expected to remain in the host country for more than one year that is transferred from one country to another at the time of migration. Data sources

6

Data on net migration come from the United Nations Population Division’s World Population

3

Prospects: The 2006 Revision. Data on migration stock come from the United Nations Population

0

Division’s Trends in Total Migrant Stock: The 2005 China

India

Turkey

Bulgaria

Morocco

Ecuador

Poland

Mexico

Romania Philippines

Revision. Data on refugees are from the United

The increase in migration among high-skill workers is due partly to selective immigration policies in Organi-

Nations Office of the High Commissioner for Refu-

sation for Economic Co-operation and Development countries and partly to rising skill premiums in these

gees’ Statistical Yearbook 2005. Data on remit-

labor markets.

tances are World Bank staff estimates based on

Source: Docquier and Marfouk 2004.

IMF balance of payments data.

2007 World Development Indicators

363

GLOBAL LINKS

Movement of people

6.15

Travel and tourism International tourists

Tourism expenditure in the country

thousands Inbound Outbound 1995 2005 1995 2005

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, 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

364

.. 40 520 9 2,289 12 3,726 17,173 93 156 161 5,560 138 284 115 521 1,991 3,466 124 34 220 100 16,932 26 19 1,540 20,034 10,200 1,433 35 37 785 188 1,485 742 3,381 2,124 1,776 440 2,871 235 315 530 103 1,779 60,033 125 45 85 14,847 286 10,130 563 12 .. 145

.. 46 1,443 210 3,895 319 5,497 19,952 1,177 208 91 6,747 174 504 190 1,523 5,358 4,837 222 148 1,422 190 18,770 8 29 2,027 46,809 23,359 933 61 .. 1,453 .. 8,467 2,261 6,336 3,358 3,691 861 8,244 1,154 83 1,900 227 2,080 76,001 222 111 560 21,500 584 14,276 1,316 45 5 96

2007 World Development Indicators

.. 12 1,090 3 3,815 .. 2,519 3,713 432 830 626 5,645 .. 249 .. .. 2,600 3,524 .. 36 31 .. 18,206 .. .. 1,070 4,520 3,023 1,057 .. .. 273 .. .. 72 44,873 5,035 168 271 2,683 348 .. 1,764 120 5,147 18,686 .. .. 228 55,800 .. .. 333 .. .. ..

.. 2,097 1,513 .. 4,002 269 4,754 6,564 1,830 1,767 572 9,318 .. 312 .. .. 4,696 4,235 .. .. 239 .. 21,101 7 .. 2,343 31,026 4,957 1,553 .. .. 425 .. .. 115 36,650 4,630 419 661 3,644 1,397 .. 2,075 .. 6,035 22,270 236 387 .. 77,400 .. .. 982 .. .. ..

$ millions 1995 2005

.. 70 32 27 2,550 14 11,900 14,529 88 .. 28 .. 79 92 .. 176 1,085 662 .. 2 71 75 9,176 4 43 1,186 12,626 .. 887 .. 15 763 103 .. 963 .. .. .. 315 2,954 152 58 452 177 2,384 31,295 94 67 75 24,052 30 4,182 216 1 .. ..

.. 880 178 82 3,241 161 20,637 19,310 100 78 346 10,879 108 346 604 550 4,169 3,026 .. 2 927 162 15,830 4 .. 1,779 31,842 13,586 1,570 .. 23 1,804 76 7,625 1,920 5,580 .. .. 488 7,206 838 66 1,207 533 3,055 .. 74 57 288 38,381 827 13,697 883 32 2 ..

% of exports 1995 2005

.. 23.2 .. 0.7 10.2 4.7 17.1 16.2 11.2 .. 0.5 .. 12.1 7.5 .. 7.3 2.1 9.8 .. 1.9 7.3 3.7 4.2 .. .. 6.1 6.1 .. 7.2 .. 1.1 17.1 2.4 .. .. .. .. .. 6.1 22.3 7.5 .. 17.6 23.1 5.0 8.6 3.2 30.5 13.1 4.0 1.9 26.9 7.7 0.1 .. ..

.. 48.3 .. 0.6 7.0 12.0 15.2 11.3 1.2 0.7 1.9 3.4 15.1 11.0 16.8 12.4 3.1 18.8 .. 2.1 23.1 5.6 3.7 .. .. 3.7 3.8 3.9 6.4 .. 0.6 18.6 1.2 40.4 .. 6.3 .. .. 4.3 23.5 18.3 .. 11.0 27.6 3.7 .. 1.8 31.6 13.3 3.4 21.4 26.4 17.9 4.3 2.6 ..

Tourism expenditure in other countries

$ millions 1995 2005

1 19 186 113 4,013 12 7,272 11,686 165 251 101 .. 48 72 .. 153 3,982 312 .. .. 22 140 12,658 43 38 934 9,220 .. 1,162 .. 69 336 312 .. .. .. .. 267 331 1,371 99 .. 121 30 2,853 20,699 183 16 171 66,527 74 1,495 167 29 6 ..

.. 808 394 86 3,572 146 15,076 12,755 188 371 672 16,636 53 258 160 280 5,905 1,836 .. 62 138 294 23,061 32 .. 1,381 24,715 .. 1,562 .. 176 556 551 786 .. 2,605 .. 494 616 1,932 430 .. 538 59 3,529 .. 275 7 238 80,276 472 3,046 500 29 22 173

% of imports 1995 2005

.. 2.3 .. 3.2 15.4 1.7 9.7 12.7 12.8 3.4 1.8 .. 5.4 4.6 .. 7.5 6.3 4.8 .. .. 1.6 8.7 6.3 .. .. 5.1 5.6 .. 7.3 .. 5.1 7.1 8.2 .. .. .. .. 4.4 5.8 8.0 2.7 .. 4.2 2.1 7.6 6.2 10.6 6.9 12.1 11.2 3.5 6.0 4.5 2.9 6.7 ..

.. 20.9 .. 0.8 10.2 7.4 10.1 7.8 2.7 2.6 3.8 5.4 4.9 9.0 2.0 7.7 6.0 8.9 .. 17.6 3.0 9.1 6.0 .. .. 3.6 3.5 .. 6.3 .. 8.9 5.2 11.0 3.6 .. 3.0 .. 4.4 5.2 5.6 5.6 .. 4.6 1.6 5.0 .. 12.8 2.7 7.2 8.1 7.1 4.6 5.2 3.0 17.3 9.9

International tourists

Tourism expenditure in the country

thousands Inbound Outbound 1995 2005 1995 2005

Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. 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

271 2,878 2,124 4,324 489 61 4,818 2,215 31,052 1,147 3,345 1,075 .. 896 .. 3,753 72 36 60 539 450 87 .. 56 650 147 75 192 7,469 42 .. 422 20,241 32 108 2,602 .. 117 272 363 6,574 1,409 281 35 656 2,880 279 378 345 42 438 479 1,760 19,215 9,511 3,131

749 3,446 3,915 5,002 1,659 .. 7,333 1,903 36,513 1,479 6,728 2,987 3,073 1,199 .. 6,022 91 315 672 1,116 1,140 .. .. 149 2,000 197 229 471 16,431 143 .. 761 21,915 23 301 5,843 470 232 695 375 10,012 2,365 712 55 962 3,859 1,116 798 702 69 341 1,486 2,623 15,200 11,617 3,686

149 13,083 3,056 1,782 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

301 18,622 6,200 4,106 .. .. 6,113 3,687 23,349 .. 17,404 1,523 3,915 .. .. 10,078 1,928 239 .. 2,959 .. .. .. .. 3,502 .. 67 .. 30,761 .. .. 183 13,305 57 .. 1,746 .. .. .. 373 17,086 1,872 740 .. .. 3,122 .. .. 285 .. 188 1,841 2,144 40,841 .. 1,410

$ millions 1995 2005

85 2,938 .. .. 205 .. 2,698 3,491 30,426 1,199 4,894 973 155 590 .. 6,670 309 .. 52 37 710 29 .. 4 102 35 106 22 5,044 26 .. 616 6,847 71 33 1,469 .. 169 .. 232 10,611 .. 51 26 47 2,730 .. 582 372 .. 162 521 1,141 6,927 5,646 1,828

476 4,581 4,128 5,092 1,324 .. 6,722 3,414 38,264 1,783 15,555 1,759 793 969 .. 8,148 414 94 .. 446 5,869 .. .. 261 975 92 265 36 10,389 142 .. 1,189 12,801 163 205 5,426 138 98 426 160 .. .. 211 29 49 3,884 679 827 1,108 .. 96 1,371 2,620 7,127 9,222 3,239

% of exports 1995 2005

5.2 14.9 .. .. 1.1 .. 5.5 12.7 10.3 35.3 1.0 28.0 2.6 20.0 .. 4.5 2.2 .. 12.8 1.8 .. 14.6 .. 0.1 3.2 2.7 14.2 4.7 6.1 4.9 .. 26.2 7.7 8.0 6.5 16.2 .. 12.9 .. 22.5 4.4 .. 7.7 7.1 0.4 4.9 .. 5.7 4.9 .. 3.4 7.9 4.3 19.4 17.5 ..

13.9 6.2 5.0 5.1 .. .. 4.2 5.9 8.3 44.6 2.3 26.7 2.6 18.9 .. 2.4 1.2 10.0 .. 5.9 45.0 .. .. 1.5 6.6 3.7 51.5 .. 6.4 11.7 .. 31.6 5.6 10.7 16.9 28.9 6.6 3.1 18.4 12.5 .. .. 11.3 7.0 0.1 2.9 3.5 4.3 10.3 .. 2.4 7.1 5.9 6.3 17.3 ..

6.15

Tourism expenditure in other countries

$ millions 1995 2005

99 1,501 .. .. 247 .. .. 2,626 17,219 173 46,966 719 296 183 .. 6,947 2,513 7 34 62 .. 17 .. 98 107 30 79 53 2,722 74 30 184 3,587 73 22 356 .. 38 .. 167 13,151 .. 56 26 939 4,481 .. 654 181 .. 173 428 551 5,865 2,540 1,155

315 3,037 4,758 4,741 4,353 .. 6,168 3,780 26,459 291 48,102 653 854 .. .. 16,831 4,150 71 .. 655 3,535 36 .. 789 757 94 184 58 4,339 126 .. 295 8,951 197 207 999 187 32 .. 221 .. .. 161 39 1,469 8,788 838 1,748 388 .. 129 900 1,547 4,686 3,763 1,663

% of imports 1995 2005

5.3 7.5 .. .. 1.6 .. .. 7.4 6.9 4.6 11.2 14.7 4.9 5.2 .. 4.5 19.9 0.7 4.5 2.8 .. 1.6 .. 1.7 2.7 1.7 8.0 8.0 3.1 7.5 5.9 7.5 4.4 7.3 4.2 3.2 .. 1.5 .. 10.3 6.1 .. 4.9 5.7 7.3 9.6 .. 4.6 2.3 .. 3.3 4.5 1.7 17.3 6.4 ..

2007 World Development Indicators

6.3 4.0 5.1 5.4 .. .. 4.5 6.6 5.7 4.9 7.9 5.5 3.3 .. .. 5.4 21.6 5.1 .. 6.6 21.8 2.7 .. 7.4 4.5 2.6 24.0 .. 3.3 7.8 .. 7.1 3.7 7.2 14.7 4.4 6.5 1.3 .. 8.2 .. .. 4.9 5.7 7.0 12.2 7.6 6.0 3.6 .. 3.1 5.9 2.9 4.1 5.4 ..

365

GLOBAL LINKS

Travel and tourism

6.15

Travel and tourism International tourists

Tourism expenditure in the country

thousands Inbound Outbound 1995 2005 1995 2005

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 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 Europe EMU

366

2,757 10,290 .. 3,325 280 228 38 6,070 903 732 .. 4,488 34,920 403 29 300 2,310 6,946 815 .. 285 6,952 53 260 4,120 7,083 218 160 3,716 2,315 23,537 43,490 2,022 92 700 1,351 220 61 163 1,363 524,060 t 10,879 159,782 66,091 93,691 170,661 44,254 58,037 39,667 13,420 3,744 12,119 353,399 197,165

.. 22,201 .. 9,100 387 725 40 7,080 1,515 1,555 .. 7,369 55,577 549 61 839 .. 7,229 .. .. 566 11,567 81 463 6,378 20,273 12 468 15,629 5,871 29,971 49,209 1,808 262 706 3,468 88 336 515 .. 736,109 t 17,998 265,628 123,098 142,530 279,291 91,295 90,756 54,142 27,605 6,254 17,247 456,818 250,904

2007 World Development Indicators

5,737 21,329 .. .. .. .. 6 2,867 218 .. .. 2,520 3,648 504 195 .. 10,127 11,148 1,746 .. 157 1,820 .. 261 1,778 3,981 21 148 6,552 .. 41,345 51,285 562 246 534 .. .. .. .. 256 427,305 t .. 208,088 38,567 169,521 213,011 36,006 142,185 21,025 11,226 4,522 .. 214,294 124,665

7,140 28,416 .. 3,811 .. .. 63 5,165 486 2,800 .. .. 5,121 727 .. 1,082 12,598 .. 4,564 .. .. 3,047 .. .. 2,241 8,246 33 189 15,488 .. 66,494 63,502 658 455 1,067 .. .. .. .. .. 568,830 t 9,317 273,023 88,101 184,922 282,340 81,084 161,107 32,407 14,092 8,792 .. 365,507 174,788

$ millions 1995 2005

689 .. 4 .. 168 .. .. .. 630 1,128 .. 2,655 27,369 367 .. 54 4,390 11,354 .. .. 344 9,257 .. 232 1,838 .. 13 .. 448 632 27,577 93,700 725 15 995 .. .. .. 47 145 497,633 t .. 93,536 49,702 50,435 101,738 35,094 .. 20,620 11,096 .. 6,385 393,204 175,291

1,310 7,402 .. 6,111 269 .. .. .. 932 1,894 .. 8,448 52,960 729 .. 109 8,584 12,961 2,283 10 836 12,629 25 661 2,782 .. .. 357 3,542 2,200 39,573 122,944 690 57 713 1,880 .. .. 161 99 787,293 t 12,490 187,387 104,938 83,641 205,221 66,121 .. 38,687 25,288 6,343 17,893 580,977 253,742

% of exports 1995 2005

7.3 .. 5.4 .. 11.2 .. .. .. 5.7 10.9 .. 7.7 20.4 7.9 .. 5.3 4.6 9.2 .. .. 28.4 13.2 .. 8.3 23.0 .. 0.7 .. 2.2 .. 8.6 11.8 20.7 .. 4.8 .. .. .. 6.1 .. 8.0 w .. 8.2 7.6 8.9 8.1 7.1 .. 7.1 12.3 .. 6.8 7.9 8.2

Tourism expenditure in other countries

$ millions 1995 2005

4.0 749 2.8 .. .. 13 3.4 .. 14.7 154 .. .. .. 51 .. .. 3.5 338 8.6 606 .. .. 12.7 2,414 18.4 5,826 9.2 279 .. .. 4.9 45 4.8 6,816 6.6 9,478 23.4 .. 0.8 .. 28.9 424 9.7 4,791 3.3 41 7.8 91 19.2 294 .. .. .. 74 26.6 .. 8.0 405 .. .. 6.7 30,749 9.6 60,924 13.6 332 .. .. 1.3 1,852 5.1 .. .. .. .. .. .. 83 .. 106 6.5 w 383,191 t 5.9 .. 6.4 43,377 6.1 31,176 6.6 26,008 6.4 46,653 5.0 20,679 6.1 .. 5.7 18,505 23.0 3,287 4.6 .. 9.2 5,739 6.5 336,538 7.1 141,996

1,022 18,795 .. 3,763 129 .. 34 .. 903 1,019 .. 4,813 18,440 553 .. 54 11,847 11,060 593 .. 577 5,790 38 288 443 .. .. 137 3,078 5,300 73,786 99,624 328 .. 1,837 .. .. 224 .. .. 621,415 t 11,060 126,571 62,312 64,259 131,051 39,432 41,223 29,372 9,217 6,951 8,670 490,364 171,072

% of imports 1995 2005

6.6 .. 3.5 .. 8.5 .. 19.4 .. 3.2 5.6 .. 7.2 4.3 4.7 .. 3.5 8.4 8.7 .. .. 21.6 5.8 6.1 4.3 3.3 .. 4.1 .. 1.9 .. 9.4 6.8 9.3 .. 11.0 .. .. .. 6.2 .. 7.9 w .. 5.6 5.4 6.9 5.7 4.9 .. 6.5 4.3 .. 7.0 8.2 8.2

2.4 11.4 .. 4.7 4.9 .. 7.4 .. 2.6 4.6 .. 7.0 5.3 5.5 .. 2.4 7.9 6.5 5.5 .. 15.1 4.3 3.5 2.7 3.0 .. .. 5.3 7.0 .. 11.0 5.0 7.1 .. 6.3 .. .. 4.2 .. .. 6.3 w 6.2 5.1 4.3 6.0 5.1 3.7 6.8 5.2 7.1 5.1 6.7 6.8 6.6

About the data

6.15

GLOBAL LINKS

Travel and tourism Definitions

Tourism is defined as the activities of people trav-

outbound tourists refer to the number of arrivals and

• International inbound tourists (overnight visitors)

eling to and staying in places outside their usual

departures of visitors within the reference period, not

are the number of tourists who travel to a country

environment for no more than one year for leisure,

to the number of people traveling. Thus a person who

other than that in which they have their usual resi-

business, and other purposes not related to an activ-

makes several trips to a country during a given period

dence, but outside their usual environment, for a

ity remunerated from within the place visited. The

is counted each time as a new arrival. International

period not exceeding 12 months and whose main

social and economic phenomenon of tourism has

visitors include tourists (overnight visitors), same-day

purpose in visiting is other than an activity remuner-

grown substantially over the past quarter century.

visitors, cruise passengers, and crew members.

ated from within the country visited. • International

Past descriptions of tourism focused on the char-

The World Tourism Organization is improving its

outbound tourists are the number of departures that

acteristics of visitors, such as the purpose of their

coverage of tourism expenditure data. It is now

people make from their country of usual residence

visit and the conditions in which they traveled and

using balance of payments data from the Interna-

to any other country for any purpose other than a

stayed. Now, there is a growing awareness of the

tional Monetary Fund (IMF), supplemented by data

remunerated activity in the country visited. • Tour-

direct, indirect, and induced effects of tourism on

received from individual countries. The new data,

ism expenditure in the country is expenditures by

employment, value added, personal income, govern-

shown in the table, now include travel and passenger

international inbound visitors, including payments to

ment income, and the like.

transport items as defined in the IMF’s Balance of

national carriers for international transport. These

Payments Manual.

receipts include any other prepayment made for

Statistical information on tourism is based mainly on data on arrivals and overnight stays along with

Aggregates are based on the World Bank’s classifi -

goods or services received in the destination coun-

balance of payments information. But these data

cation of countries and differ from those in the World

try. They also may include receipts from same-day

do not completely capture the economic phenom-

Tourism Organization’s publications. Countries not

visitors, except in cases where these are important

enon of tourism or give governments, businesses,

shown in the table but for which data are available

enough to justify separate classification. Their share

and citizens the information needed for effective

are included in the regional and income group totals.

in exports is calculated as a ratio to exports of goods

public policies and efficient business operations.

The aggregates in the table are calculated using the

and services (for definition of exports of goods and

Credible data are needed on the scale and signifi -

World Bank’s weighted aggregation methodology

services see Definitions for table 4.8). • Tourism

cance of tourism. Information on the role tourism

(see Statistical methods) and differ from aggregates

expenditure in other countries is expenditures of

plays in national economies throughout the world

provided by the World Tourism Organization.

international outbound visitors in other countries,

is particularly deficient. Although the World Tourism

including payments to foreign carriers for interna-

Organization reports that progress has been made

tional transport. These expenditures may include

in harmonizing definitions and measurement units,

those by residents traveling abroad as same-day

differences in national practices still prevent full

visitors, except in cases where these are important

international comparability.

enough to justify separate classification. Their share

The data in the table are from the World Tourism

in imports is calculated as a ratio to imports of goods

Organization, a specialized agency of the United

and services (for definition of imports of goods and

Nations. The data on international inbound and

services see Definitions for table 4.8).

International tourism generated more than $2 billion a day in 2005

6.15a

$ billions 700 600

Middle East

500 Europe

400 300

Data sources 200

Asia and Pacific

100

America Africa

0

Data on visitors and tourism expenditure are available in the World Tourism Organization’s Yearbook of Tourism Statistics and Compendium of Tourism

1980

1985

1990

1995

2000

2005

Statistics 2007. Data in the table are updated

International tourism has become an important pillar of some economies and accounted for 40 percent

from electronic files provided by the World Tour-

of global services trade in recent years. In 2005 Africa recorded the highest growth rate in international

ism Organization. Data on exports and imports

tourism receipts (8.5 percent), followed by Asia and Pacific (4.3 percent).

are from the IMF’s International Financial Statistics

Source: World Tourism Organization.

and World Bank staff estimates.

2007 World Development Indicators

367 Text figures, tables, and boxes

PRIMARY DATA DOCUMENTATION The World Bank is not a primary data collection agency for most areas other than business and investment climate surveys, living standards surveys, and external debt. As a major user of socioeconomic data, however, the World Bank recognizes the importance of data documentation to inform users of differences in the methods and conventions used by the 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 Country Statistical Information Database at www. worldbank.org/data. 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 is central to implementing poverty reduction strategies and lies at the heart of the Millennium Development Goals and the new 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.

2007 World Development Indicators

369

PRIMARY DATA DOCUMENTATION Currency

National accounts

SNA System of price Reference National Accounts valuation year

Base year

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria

Afghan afghani Albanian lek Algerian dinar Angolan kwanza Argentine peso Armenian dram Australian dollar Euro New Azeri manat Bangladesh taka Belarusian rubel Euro CFA franc Boliviano Konvertible mark Botswana pula Brazilian real Bulgarian lev

Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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

CFA franc Burundi franc Cambodian riel CFA franc Canadian dollar CFA franc CFA franc Chilean peso Chinese yuan Hong Kong dollar Colombian peso Congo franc CFA Franc Costa Rican colon CFA franc Croatian kuna Cuban peso Czech koruna Danish krone Dominican peso U.S. dollar Egyptian pound U.S. dollar Eritrean nakfa Estonian kroon Ethiopian birr Euro Euro CFA franc Gambian dalasi Georgian lari Euro Ghanaian cedi Euro Guatemalan quetzal Guinean franc CFA franc Haitian gourde

370

2007 World Development Indicators

2002/03 a

1996

1980 1997 1993 a a

b

1996 2000 2003 2000

1990 1980 2000 2000 2000 1987 1995 1996 2000 2000 1994 1987 1978 1991 1996 a

b

b

1996

b b

1993/94 1990 a

b

b

2000 1985 1990 a

b

b

1995/96 a

b

b

2000 a

b

b

2002

b

b b

b

1990

b b b

b

1997

1984 2000 1995 2000 1990 2000 1991/92 1990 1992 2000 1999/2000 2000 a 2000 1991 1987 a 1994 2000 1975 a 2000 1958 1996 1994 1986 1975/76

b

b b

b

b b b b

b b

VAB VAB VAB VAP VAB VAB VAB VAB VAB VAB VAB VAB VAP VAB VAB VAB VAB VAB VAP VAB VAB VAB VAB VAB VAB VAB VAP VAB VAB VAB VAP VAB VAP VAB VAP VAB VAB VAP VAB VAB VAB VAB VAB VAB VAB VAB VAP VAB VAB VAB VAP VAB VAP VAB VAB VAB

Balance of payments and trade

Alternative conversion factor

PPP survey year

1996 1991–96 1971–84 1990–95

1992–95 1990–95 1992 1960–85

1978–89, 1991–92 1992–93

1996 2000 2002 2002 2000 1996 2000 2002 1996 1996 1996 1996 2002

1996 2002

1978–93

1996 1986 1996

1992–94 1999–2001 1996 1996 2002 2002 2002 1996 1996 1982–90 1991–95

2002

1993

2002 2002 1996

1990–95

2000 2002

1973–87 2002 1980 1996 1991

Balance of Payments Manual in use

External debt

System of trade

BPM5 BPM5 BPM4 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5

Actual Actual Preliminary Actual Actual

G S S S S G S G G G S S S

BPM4 BPM5 BPM5 BPM5 BPM5 BPM4 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5

Actual Actual Actual Preliminary

BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM4 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5

Actual Actual Actual Preliminary Actual Actual Actual Actual Actual

Preliminary Actual Actual Preliminary Actual Preliminary Preliminary Actual Estimate Actual

Actual Preliminary Actual Actual Actual Actual Actual

Preliminary Actual Actual Actual Actual Estimate Estimate Actual

G S G G S G S G S S S S G S S S S S G G G G G S S S G G G S S G G S G S S S G G

Government IMF data finance dissemination standard

Accounting concept

B C B C C C C C C C C B C C B C C

G G S S S S G G S S G G G S S

B C C B C B C C B C C C C C C C

G G G S G G S G S S G G S G S

C C C B B C

S S G S S S

C C C C B B C C B C B B

S G S S G G G S G S G G G

PRIMARY DATA DOCUMENTATION Latest population census

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria

1979 2001 1998 1970 2001 2001 2001 2001 1999 2001 1999 2001 2002 2001 1991 2001 2000 2001

Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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

1996 1990 1998 1987 2001 2003 1993 2002 2000 2006 2005 1984 1996 2000 1998 2001 2002 2001 2001 2002 2001 1996 1992 1984 2000 1994 2000 1999 2003 2003 2002 2004 2000 2001 2002 1996 1991 2003

Latest demographic, education, or health household survey

MICS, 2003 RHS, 2002 MICS, 2000 MICS, 2001 DHS, 2000

RHS, 2001 DHS, 2004

DHS, 2001 DHS, 2003 MICS, 2000 MICS, 2000 DHS, 1996

DHS, 2003 MICS, 2000 DHS, 2005 DHS, 2004 MICS, 2000 DHS, 2004 Intercensal survey 1995

Source of most recent income and expenditure data

Vital Latest Latest registration agricultural industrial complete census data

LSMS, 2004 HLSS, 1995

Yes

EPH, 2003 ILCS, 2003 SIHC, 1994 Microcensus 2000 HBS, 2003 HES, 2000 IES, 2002 ECHP, 2000 CWIQ, 2003 MECOVI, 2002 LSMS, 2001 HIES, 1993–94 PNAD, 2004 HBS, 2003

Yes Yes Yes Yes Yes

EVCBM, 2003 Priority survey, 1998 SES, 2004 Priority survey, 2001 EBC, 2001 SLID, 2000 EPI, 1993 CASEN, 2003 HHS (Rural/Urban), 2004

Yes Yes

1998 2001 1964–65 2002

1990 2004

2001 1999–2000

2004 2004

1996 1994 1999–2000c 1992 1984–88

2004

Yes 1993 1996 Yes

Yes

Yes

MICS, 2000 RHS, 1993 DHS, 2002 RHS, 2004 DHS, 2005 RHS, 2002/03 DHS, 2002 DHS, 2005

DHS, 2000 MICS, 2000 MICS, 1999; RHS, 1999 SPA, 2002; DHS, 2003 RHS, 2002 DHS, 2005 MICS, 2000 DHS, 2000

ECV, 2003

EHPM, 2003 LSMS, 2002 HBS, 2001 Microcensus 1996/97 Income Tax Register 1997 ENFT, 2004 LSMS, 1998 HECS, 2000 EHPM, 2002 HBS, 2003 ICES, 2000 IDS, 2000 HBS, 1994/95 HHS, 1998 SGH, 2003 GSOEP, 2000 LSMS, 1998/99 ECHP, 2000 ENEI-2, 2002 LSMS, 1994 IES, 1993 ECVH, 2001

Yes Yes Yes Yes Yes

Yes Yes Yes Yes Yes

Yes Yes Yes Yes

2004 1999 2001 1991 2003 1995 2003

1993

2004 2004

1984 1996/2001 1985

2002 2004 2004 1975 2004 2003 2002 2004

1996–97 1997

Yes DHS, 2005 MICS, 2001 DHS, 2005 RHS, 1993 MICS, 2000; AIS, 2005

2001

2001 1990 1985–86 1973 2001 2003 2000 1999–2000 1971 1999–2000 1999–2000 1970–71 2001 2001–02 1990–2000 1999–2000 1974–75 2001–02 1999–2000 1984 1999–2000 2003 2000 1988 1971

1988 2004 1997 1992 1989 1998 2004 2004 2004 2004 2003 2003 2002 2004 2004 1982 2003 2004 2004 2004

1996

Latest trade data

Latest water withdrawal data

1977 2005 2004 1991 2005 2005 2005 2005 2005 2004 2005 2005 2005 2005 2005 2003 2005 2005

1987 1995 1995 1987 1995 1994 1985 1991 1995 1990 1990

2004 2005 2004 2005 2005 2005 1995 2005 2005 2005 2005 1986 1995 2005 2005 2005 2004 2005 2005 2001 2005 2004 2004 2003 2005 2003 2005 2005 2004 2005 2005 2005 2004 2005 2005 2002 1995 1997

2007 World Development Indicators

1994 1987 1995 1992 1992 1988 1992 1987 1987 1987 1991 1987 1987 1987 1993 1996 1990 1987 1997 1987 1996 1995 1991 1990 1994 1997 1996 1992 1995 1987 1991 1999 1987 1982 1990 1991 1997 1980 1992 1987 1991 1991

371

PRIMARY DATA DOCUMENTATION Currency

National accounts

SNA System of price Reference National Accounts valuation year

Base year

Honduras Hungary India

Honduran lempira Hungarian forint Indian rupee

Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep.

Indonesian rupiah 2000 Iranian rial 1997/98 Iraqi dinar 1997 Euro 2000 Israeli new shekel 2000 Euro 2000 Jamaica dollar 1996 Japanese yen 2000 Jordan dinar 1994 a Kazakh tenge Kenya shilling 2001 Democratic Republic of Korea won Korean won 2000 Kuwaiti dinar 1995 a Kyrgyz som Lao kip 1990 Latvian lat 2000 Lebanese pound 2003 Lesotho loti 1995 Liberian dollar 1992 Libyan dinar 1975 Lithuanian litas 2000 Macedonian denar 1997 Malagasy ariary 1984 Malawi kwacha 1994 Malaysian ringgit 1987 CFA franc 1987 Mauritanian ouguiya 1985 Mauritian rupee 1997/98 Mexican new peso 1993 a Moldovan leu Mongolian tugrik 2000 Moroccan dirham 1980 Mozambican metical 1995 Myanmar kyat 1985/86 Namibia dollar 1995/96 Nepalese rupee 1994/95 a Euro New Zealand dollar 2000/01 Nicaraguan gold cordoba 1994 CFA franc 1987 Nigerian naira 1987 a Norwegian krone Rial Omani 1988 Pakistan rupee 1999/2000 Panamanian balboa 1996 Papua New Guinea kina 1983 Paraguayan guarani 1994 Peruvian new sol 1994 Philippine peso 1985 a Polish zloty Euro 2000 U.S. dollar 1954

Korea, Rep. 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

372

2007 World Development Indicators

1978 a

2000

b b

1999/2000

b b b

1995

b b

b

1995

b

b

b

b

1995

b

b

1996

b b

b

2000

b

b

2000

b

b b

b

2002

b b

VAB VAB VAB VAP VAB VAB VAB VAP VAB VAB VAB VAB VAB VAB

VAB VAP VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAP VAB VAB VAB VAB VAB VAB VAP VAB VAP VAB VAB VAB VAB VAB VAP VAB VAB VAP VAB VAB VAB VAP VAB VAP VAB VAB VAP

Balance of payments and trade

Alternative conversion factor

Balance of Payments Manual in use

External debt

System of trade

Accounting concept

2002

BPM5 BPM5 BPM5

Actual Actual Actual

S S G

C C

1996 1996

BPM5 BPM5

Preliminary Actual

2002 2002 2002 1996 2002 1996 2000 1996

BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5

S G S G S S G G G G G

2002

BPM5 BPM5 BPM5 BPM5 BPM5 BPM4 BPM5

PPP survey year

1988–89

1980–90

1987–95

1990–95 1991–95

1986 1990–95

1987–95

2000 1993 2002 1996

2002 2002 1996 1996 1993 1996 1996 2002 2000 2000 1996

1992–95

1996 2002 2002 1965–93 1993 1971–98

1989 1982–88 1985–91

Government IMF data finance dissemination standard

1996 2002 1996 1996 1996

1996 1996 2002 2002

Preliminary Actual Actual Preliminary

Actual Preliminary Actual Actual Actual Estimate

BPM5 BPM5 Actual BPM5 Actual BPM5 Actual BPM5 Actual BPM5 Estimate BPM4 Actual BPM4 Actual BPM5 Actual BPM5 Actual BPM5 Actual BPM5 Actual BPM5 Actual BPM5 Actual BPM5 Estimate BPM5 BPM5 Actual BPM5 BPM5 BPM5 Actual BPM5 Preliminary BPM5 Preliminary BPM5 BPM5 Actual BPM5 Actual BPM5 Actual BPM5 Actual BPM5 Actual BPM5 Actual BPM5 Actual BPM5 Actual BPM5

S S G G S G G G G G S G G G G G G G S S S G S S G S S G G G G S G S S G S S G

G S S

C C

S

C C C C C B C B

S S S G S G S G

C C B

S G S

C B C

S G G G

C

S G G G S G G G S S G S G

C B C

C C C C C C B C C C C

C B C C B B C B C C

G G S G G G S G G G G S S S S

PRIMARY DATA DOCUMENTATION Latest population census

Honduras Hungary India

2001 2001 2001

Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep.

2000 1996 1997 2006 1995 2001 2001 2005 2004 1999 1999 1993

Korea, Rep. 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

2000 1995 1999 2005 2000 1970 1996 1984 1995 2001 2002 1993 1998 2000 1998 2000 2000 2000 2004 2000 2004 1997 1983 2001 2001 2001 2006 2005 2001 2006 2001 2003 1998 2000 2000 2002 2005 2000 2002 2001 2000

Latest demographic, education, or health household survey

DHS, 2005 MICS, 2000 DHS, 2002 DHS, 2000 MICS, 2000

RHS, 2002/03

Source of most recent income and expenditure data

EPHPM, 2003 FBS, 2002 NSS, 2004/05 SUSENAS, 2002 SECH, 1998 ECHP, 2000 HES, 2001 SHIW, 2000 LSMS, 2004

Vital Latest Latest registration agricultural industrial complete census data

Yes

Yes Yes Yes Yes Yes

DHS, 2002 DHS, 1999 DHS, 2003; SPA, 2004 MICS, 2000

HIES, 1997 HBS, 2003 WMS II, 1997

NSFIE, 1998/99 FHS, 1996 DHS, 1997 MICS, 2000 MICS, 2000 DHS, 2004 MICS, 1995 MICS, 2000

DHS, 2003/04 DHS, 2004 DHS, 2001 DHS, 2000/01

HBS, 2003 ECS I, 2002 HBS, 2003

Yes

HBS, 1995

HBS, 2003 HBS, 2003 Priority survey, 2001 HHS, 2004/05 HIBAS, 1997 EMCES, 2001 LSMS, 2000

Yes Yes

2004 2004 2004

2005 2005 2005

1992 1991 1990

2003 2004 2004 2004 2004 2003 2004 2004 2004

1977–79

2004

2005 2005 1976 2005 2005 2005 2004 2005 2005 2005 2004

1990 1993 1990 1980 1997 1998 1993 1992 1993 1993 1990 1987

2000 1970 2002 1998–99 2001 1998–99 1999–2000

2004 2001

2001 1994 1994 1984–85 1993

2004 2003 1996 2003 2004 2002

2005 2001 2005 1974 2005 2004 2002 1984 2004 2005 2005 2004 2005 2005 2001 1996 2005 2005 2005 2005 2005 2005 1993 2003 2003 2005 2005 2005 2005 2003 2005 2005 2005 2005 2003 2004 2005 2005 2005 2005

1994 1994 1994 1987 1994 1996 1987 1987 1999 1995 1996 1984 1994 1995 1987 1985

Yes 1984 1984–85 Yes

ENPF, 1995 DHS, 2005 MICS, 2000 DHS, 2003/04 DHS, 2003 MICS, 2000 DHS, 2000 DHS, 2001

DHS, 2001 MICS, 2000 DHS, 2003 FHS, 1995 RHS, 2000/01 LSMS, 2003 DHS, 1996 RHS, 2004 DHS, 2004 DHS, 2003

RHS, 1995/96

ENIGH, 2004 HBS, 2003 LSMS/Integrated Survey, 2002 LSMS, 1998/99 NHS, 2002/03 NHIES, 1993 LSMS, 2003/04 ECHP, 1999

1991 Yes Yes

Yes Yes

LSMS, 2001 LSMS, 2003 IF 2000 PIHS, 2002 EH, 2003 HGS, 1996 EIH, 2003 ENAHO, 2003 FIES, 2003 HBS, 2002

Yes

Yes Yes Yes Yes

Latest water withdrawal data

1993 2000 1995–96/ 2000–01 2003 2003 1981 2000 1981 2000 1978–79 2000 1997

Yes

Yes Yes Yes

Latest trade data

1996 1999–2000 2003 1996–97 2002 1999–2000c 2002 2001 1980 1960 1999 1978–79 2000 2001 1991 1994 2002 1996/2002 1999 1997/2002

2003 1985

1978 2004 2000 2003 1995 2001 2004 1994 2002 2004 2004 2004 2002 2004 2003 2004 2004 2004 2004 1996 2004 2004 2004 2002

2007 World Development Indicators

1998 1992 1993 1998 1992 1987 1991 1994 1991 1991 1998 1988 1987 1985 1991 1991 1990 1987 1987 1992 1995 1991 1990

373

PRIMARY DATA DOCUMENTATION Currency

National accounts

SNA System of price Reference National Accounts valuation year

Base year a

Romania

New Romanian leu

Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro

Russian ruble Rwanda franc Saudi Arabian riyal CFA franc Yugoslav new dinar

2000 1995 1999 1999 1998

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

2001 1995 2000

Tunisia Turkey Turkmenistan

Sierra Leonean leone Singapore dollar Slovak koruna Slovenian tolar Somali shilling South African rand Euro Sri Lankan rupee Sudanese dinar Lilangeni Swedish krona Swiss franc Syrian pound Tajik somoni Tanzania shilling Thai baht CFA franc Trinidad and Tobago dollar Tunisian dinar Turkish lira Turkmen manat

Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan

Uganda shilling Ukrainian hryvnia U.A.E. dirham Pound sterling U.S. dollar Uruguayan peso Uzbek sum

Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe

Venezuelan bolivar Vietnamese dong Israeli new shekel Yemen rial Zambian kwacha Zimbabwe dollar

374

2007 World Development Indicators

a

1985 2000 2000 1996 1981/82d 1985 a

1999

b

VAB

b

VAB VAP VAP VAP VAB

1987

b

1990

b b

1995 2000

b b

b b

1982 2000

2000 2000 a

1997

1992 1988 1978 2000

b

b

1990 1987 a

1987

b

2003

b

1997/98 a

1995 2000 a

b

2000

1983 a

1984 1994 1997 1990 1994 1990

1997

b

b

VAB VAB VAP VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAP VAP VAB VAP VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAP VAB VAP VAB VAB

Balance of payments and trade

Government IMF data finance dissemination standard

Alternative conversion factor

PPP survey year

Balance of Payments Manual in use

External debt

System of trade

Accounting concept

1987–89, 1992 1987–95

2002

BPM5

Actual

S

C

S

2002

Preliminary Preliminary

G G G S

C C

S G

1996

BPM5 BPM5 BPM4 BPM5

B C

G

1996 1996 2002 2002

BPM5 BPM5 BPM5 BPM5

Actual

G G G S

B C C C

G S S S

2002 1996

BPM5 BPM5 BPM5 BPM5

S S G G

C C B B B C C C C

S S G G G S S

1977–90

1970–2005 1990–95

1987–95, 1997–2005

1996 2002 2002 1996 2000 1996 1996

Preliminary Actual

Actual Estimate Preliminary Actual Actual Actual

1996

BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5

Estimate Preliminary Estimate Preliminary Actual Actual

G S S G S G S S

1996 2002 2000

BPM5 BPM5 BPM5

Actual Actual Actual Actual Actual

C B C

G G S G G

G S G

C B

S S

B C C C C C

G S

Actual Actual

G G G G G S G

C C B B B C

G G G G G G

1990–95

2000

1990–95

2002 2002 1996 2000

BPM5 BPM5 BPM4 BPM5 BPM5 BPM5 BPM5

1991

1996 1996

BPM5 BPM4

Actual Actual

G G

1991–96 1990–92 1991, 1998

1996 1996 1996

BPM5 BPM5 BPM5

Actual Actual Actual

G G G

S S S

PRIMARY DATA DOCUMENTATION Latest population census

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 Togo Trinidad and Tobago

2002

Latest demographic, education, or health household survey

RHS, 1999

2002 RHS, 1996 2002 DHS, 2005 2004 Demographic survey, 1999 2002 DHS, 2005 Serbia 2002, MICS, 2000 Montenegro 2003 2004 MICS, 2000 2000 General household, 2005 2001 2002 1987 MICS, 1999 2001 DHS, 1998 2001 2001 DHS, 1987 1993 MICS, 2000 1997 MICS, 2000 2005 2000 1994 MICS, 2000 2000 MICS, 2000 2002 DHS, 2004 2000 DHS, 1987 1981 MICS, 2000 2000 MICS, 2000

Source of most recent income and expenditure data

Latest water withdrawal data

2005

1994

Yes

2002

LMS, Round 9, 2002 LSMS, 1999/2000

Yes

1994–95 1984 1999 1998–99

2000 2004 1989 1997 2002

2005 2003 2005 2005 2004

1994 1993 1992 1987

1984–85

1993 2004 1999 2003 2003 2003 2004 2001 2001 2004 2004 1997 2004

2002 2005 2005 2005 1982 2005 2005 2005 2005 2002 2005 2005 2004 2000 2005 2005 2005 2005

1987 1975 1991 1996 1987 1990 1997 1990 1995 1991 1991 1995 1994 1994 1990 1987 1997

ESASM 1995 Yes

SHEHEA, 1989–90 Microcensus, 1996 HBS, 1998

Yes Yes Yes

2001 2000

IES, 2000 ECHP, 2000 HIEs, 2002

Yes Yes

1999 2002

SHIES, 2000/01 HINK, 2000 EVE, 2000

Yes Yes

LSMS, 2003 HIES, 2000/01 SES, 2002

Yes

LSMS, 1992

Yes

2004 2000 1995

MICS, 2000 DHS, 1998 DHS,2000

Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan

2002 2001 2005 2001 2000 1996 1989

DHS, 2000/01; AIS, 2004 NIHS III, 2002 MICS, 2000 HBS, 2003

Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe

2001 1999 1997 2004 2000 2002

MICS, 2000; DHS special, 2002 MICS, 2000 DHS 2002; AIS 2005 Health Survey, 2000 DHS, 1997 DHS, 2001/02; SPA, 2005 DHS, 1999

Latest trade data

LSMS, 2003

Tunisia Turkey Turkmenistan

CPS (monthly)

Vital Latest Latest registration agricultural industrial complete census data

LSMS, 2000 LSMS, 2002 LSMS, 1998

2004 2002 2004 2004

2004 2001

2004 2004

2005 2005 2000

1996 1997 1994

1991

2004 2004 2004 2004 1997

2005 2005 2001 2005 2005 2005

1970 1992 1995 1991 1990 1965 1994

2003 2000

2005 2003

1970 1990

2003 2004 2004

2005 2005 2004

1990 1994 1987

Yes

Yes

FRS, 1999 CPS, 2000 ECH, 2003 FBS, 2003

Yes Yes Yes Yes

EHM, 2003 LSMS, 2004

Yes

HBS, 1998 LCMS II, 2004 LCMS III, 1995

2000 1999–2000 2000 1981 1994 2003 2003 1996 2004

1998 1999–2000c 1997/2002 2000

1997 2001 1971 2002 1990 1960

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. Conducted annually. d. Reporting period switch from fiscal year to calendar year from 1996. Pre-1996 data converted to calendar year.

2007 World Development Indicators

375

Primary data documentation notes

• Base year is the year used as the base or pricing

IMF’s Balance of Payments Manual (1977), and BPM5

reliable economic, financial, and sociodemographic

period for constant price calculations in the country’s

to the 5th edition (1993). • External debt shows debt

statistics. IMF member countries voluntarily elect to

national accounts. Price indexes derived from national

reporting status for 2005 data. Actual indicates that

participate in either the SDDS or the GDDS. Both the

accounts aggregates, such as the implicit deflator for

data are as reported; preliminary indicates that data

SDDS and the GDDS are expected to enhance the

gross domestic product (GDP), express the price level

are preliminary and include an element of staff esti-

availability of timely and comprehensive data and

relative to prices in the base year. • Reference year

mation; and estimate indicates that data are World

therefore contribute to the pursuit of sound macro-

is the year in which the local currency, constant price

Bank staff estimates. • System of trade refers to the

economic policies. The SDDS is also expected to

series of a country is valued. In most cases the refer-

United Nations general trade system (G) or the spe-

improve the functioning of financial markets. • Latest

ence year is same as the base year used to report

cial trade system (S). For imports under the general

population census shows the most recent year in

the constant price series. However, when the con-

trade system both goods entering directly for domes-

which a census was conducted and in which at least

stant price data are chain linked, the base year is

tic consumption and goods entered into customs

preliminary results have been released. It includes

changed annually, so the data are rescaled to a spe-

storage are recorded as imports at the time of arrival;

registration-based censuses. Some countries with

cifi c reference year to provide a consistent time

under the special trade system goods are recorded

complete population registration systems produce

series. In a few other cases, when the country has

as imports when they are declared for domestic con-

similar tables every 5 or 10 years instead of conduct-

not rescaled following a change in base year, World

sumption whether at the time of entry or on with-

ing regular censuses. • Latest demographic, educa-

Bank staff rescale the data to maintain a longer his-

drawal from customs storage. Exports under the

tion, or health household survey gives information

torical series. To allow for cross-country comparison

general system comprise outward-moving goods:

on the household surveys used in compiling the

and aggregation of the data, constant price data

(a) national goods wholly or partly produced in the

demographic, education, and health data in sec-

reported in World Development Indicators are rescaled

country; (b) foreign goods, neither transformed nor

tion 2. AIS is the AIDS indicator Survey, CPS is Cur-

to a common reference year (2000) and currency

declared for domestic consumption in the country,

rent Population Survey, DHS is Demographic and

(U.S. dollars). • System of National Accounts identi-

that move outward from customs storage; and

Health Survey, ENPF is National Family Planning Sur-

fies countries that use the 1993 System of National

(c) nationalized goods that have been declared from

vey (Encuesta Nacional de Planifi cacion Familiar),

Accounts (1993 SNA), the terminology applied in

domestic consumption and move outward without

FHS is Family Health Survey, MICS is Multiple Indica-

World Development Indicators since 2001, to compile

having been transformed. Under the special system

tor Cluster Survey, RHS is Reproductive Health Sur-

their national accounts. Although more and more

of trade exports comprise categories (a) and (c). In

vey; and SPA is Service Provision Assessments.

countries are adopting the 1993 SNA, many countries

some compilations categories (b) and (c) are classi-

Detailed information for AIS, DHS, and SPA are avail-

continue to follow the 1968 SNA, and some low-

fied as re-exports. Direct transit trade, consisting of

able at www.measuredhs.com/aboutsurveys; for

income countries still use concepts from the 1953

goods entering or leaving for transport purposes only,

MICS at www.childinfo.org; and for RHS at www.cdc.

SNA. • SNA price valuation shows whether value

is excluded from both import and export statistics.

gov/reproductivehealth/surveys. • Source of most

added in the national accounts is reported at basic

See About the data for tables 4.4, 4.5, and 6.2 for

recent income and expenditure data shows house-

prices (VAB) or at producer prices (VAP). Producer

further discussion. • Government finance account-

hold surveys that collect income and expenditure

prices include the value of taxes paid by producers

ing concept describes the accounting basis for

data. HBS is Household Budget Survey; ICES is

and thus tend to overstate the actual value added in

reporting central government financial data. For most

Income, Consumption, and Expenditure Survey; IES

production. However, the VAB prices can be higher

countries government finance data have been con-

is Income and Expenditure Survey; LSMS is Living

than VAP prices in countries that have high agricul-

solidated (C) into one set of accounts capturing all

Standards Measurement Study; and SES is Socio-

tural subsidies. See About the data for tables 4.1 and

the central government’s fiscal activities. Budgetary

Economic Survey. • Vital registration complete iden-

4.2 for further discussion of national accounts valu-

central government accounts (B) exclude some cen-

tifies countries judged to have at least 90 percent

ation. • Alternative conversion factor identifies the

tral government units. See About the data for tables

complete registries of vital (birth and death) statistics

countries and years for which a World Bank–esti-

4.10, 4.11, and 4.12 for further details. • IMF data

by the United Nations Department of Economic and

mated conversion factor has been used in place of

dissemination standard shows the countries that

Social Affairs Statistics Division and reported in

the official exchange rate (line rf in the International

subscribe to the IMF’s Special Data Dissemination

Population and Vital Statistics Reports. Countries with

Monetary Fund’s [IMF] International Financial Statis-

Standard (SDDS) or General Data Dissemination Sys-

complete vital statistics registries may have more

tics). See Statistical methods for further discussion

tem (GDDS). S refers to countries that subscribe to

accurate and more timely demographic indicators

of the use of alternative conversion factors . • Pur-

the SDDS and have posted data on the Dissemination

than other countries. • Latest agricultural census

chasing power parity (PPP) survey year refers to the

Standards Bulletin Board web site (posted data are

shows the most recent year in which an agricultural

latest available survey year for the International Com-

at http://dsbb.imf.org). G refers to countries that

census was conducted and reported to the Food and

parison Program’s estimates of PPPs. For a more

subscribe to the GDDS. The SDDS was established

Agriculture Organization of the United Nations. • Lat-

detailed description of PPPs see About the data for

by the IMF for member countries that have or that

est industrial data refer to the most recent year for

table 1.1. • Balance of Payments Manual in use

might seek access to international capital markets

which manufacturing value added data at the three-

refers to the classification system used for compiling

to guide them in providing their economic and finan-

digit level of the International Standard Industrial

and reporting data on balance of payments items in

cial data to the public. The GDDS helps countries

Classification (ISIC, revision 2 or revision 3) are avail-

table 4.15. BPM4 refers to the 4th edition of the

disseminate comprehensive, timely, accessible, and

able in the United Nations Industrial Development

376

2007 World Development Indicators

Primary data documentation notes

Organization database. • Latest trade data show the

Fiscal year end

Reporting period for national accounts data FY

most recent year for which structure of merchandise trade data from the United Nations Statistical Division’s Commodity Trade (Comtrade) database are available. • Latest water withdrawal data show the

Afghanistan

Mar. 20

most recent year for which data on freshwater with-

Australia

Jun. 30

FY

drawals have been compiled from a variety of sources.

Bangladesh

Jun. 30

FY

See About the data for table 3.5 for more informa-

Botswana

Jun. 30

FY

tion.

Canada

Mar. 31

CY

Egypt, Arab Rep.

Jun. 30

FY

Ethiopia

Jul. 7

FY

Gambia, The

Jun. 30

CY

Exceptional reporting periods In most economies the fi scal year is concurrent with the calendar year. The exceptions are shown in this table. The fiscal year ending date reported here refers to the fiscal year of the central government. Fiscal years for other levels of government and the reporting years for statistical surveys may differ. Further, some countries that follow a fiscal year report

Haiti

Sep. 30

FY

India

Mar. 31

FY

Indonesia

Mar. 31

CY

Iran, Islamic Rep.

Mar. 20

FY

Japan

Mar. 31

CY

their national accounts data on a calendar year basis

Kenya

Jun. 30

CY

as shown in the reporting period column.

Kuwait

Jun. 30

CY

Lesotho

Mar. 31

CY

is designated as either calendar year basis (CY) or

Malawi

Mar. 31

CY

fiscal year basis (FY). Most economies report their

Mauritius

Jun. 30

FY

national accounts and balance of payments data

Myanmar

Mar. 31

FY

using calendar years, but some use fiscal years

Namibia

Mar. 31

CY

that straddle two calendar years. In World Devel-

Nepal

Jul. 14

FY

opment Indicators fiscal year data are assigned to

New Zealand

Mar. 31

FY

Pakistan

Jun. 30

FY

Puerto Rico

Jun. 30

FY

Sierra Leone

Jun. 30

CY

The reporting period for national accounts data

the calendar year that contains the larger share of the fiscal year. If a country’s fiscal year ends before June 30, the data are shown in the first year of the fiscal period; if the fiscal year ends on or after June 30, the data are shown in the second year of the period. Balance of payments data are reported in

Singapore

Mar. 31

CY

South Africa

Mar. 31

CY

Swaziland

Mar. 31

CY

so are not comparable to the national accounts data

Sweden

Jun. 30

CY

of the countries that report their national accounts

Thailand

Sep. 30

CY

on a fiscal year basis.

Uganda

Jun. 30

FY

United States

Sep. 30

CY

Zimbabwe

Jun. 30

CY

World Development Indicators by calendar year and

2007 World Development Indicators

377

STATISTICAL METHODS This section describes some of the statistical procedures used in preparing the

of the ratios (using the value of the denominator or, in some cases, another

World Development Indicators. It covers the methods employed for calculating

indicator as a weight) and denoted by a u when calculated as unweighted

regional and income group aggregates and for calculating growth rates, and it

averages. The aggregate ratios are based on available data, including data

describes the World Bank Atlas method for deriving the conversion factor used

for economies not shown in the main tables. Missing values are assumed

to estimate gross national income (GNI) and GNI per capita in U.S. dollars. Other

to have the same average value as the available data. No aggregate is cal-

statistical procedures and calculations are described in the About the data sec-

culated if missing data account for more than a third of the value of weights

tions following each table.

in the benchmark year. In a few cases the aggregate ratio may be computed as the ratio of group totals after imputing values for missing data according to the above rules for computing totals.

Aggregation rules Aggregates based on the World Bank’s regional and income classifications of

•

Aggregate growth rates are denoted by a w when calculated as a weighted

economies appear at the end of most tables. The countries included in these

average of growth rates. In a few cases growth rates may be computed from

classifications are shown on the flaps on the front and back covers of the book.

time series of group totals. Growth rates are not calculated if more than half

Most tables also include the aggregate Europe EMU. This aggregate includes the

the observations in a period are missing. For further discussion of methods

member states of the Economic and Monetary Union (EMU) of the European Union that have adopted the euro as their currency: Austria, Belgium, Finland, France,

of computing growth rates see below. •

Aggregates denoted by an m are medians of the values shown in the table.

Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Slovenia,

No value is shown if more than half the observations for countries with a

and Spain. Other classifications, such as the European Union and regional trade

population of more than 1 million are missing.

blocs, are documented in About the data for the tables in which they appear.

Exceptions to the rules occur throughout the book. Depending on the judgment

Because of missing data, aggregates for groups of economies should be

of World Bank analysts, the aggregates may be based on as little as 50 percent of

treated as approximations of unknown totals or average values. Regional and

the available data. In other cases, where missing or excluded values are judged to be

income group aggregates are based on the largest available set of data, including

small or irrelevant, aggregates are based only on the data shown in the tables.

values for the 152 economies shown in the main tables, other economies shown in table 1.6, and Taiwan, China. The aggregation rules are intended to yield esti-

Growth rates

mates for a consistent set of economies from one period to the next and for all

Growth rates are calculated as annual averages and represented as percentages.

indicators. Small differences between sums of subgroup aggregates and overall

Except where noted, growth rates of values are computed from constant price

totals and averages may occur because of the approximations used. In addition,

series. Three principal methods are used to calculate growth rates: least squares,

compilation errors and data reporting practices may cause discrepancies in theo-

exponential endpoint, and geometric endpoint. Rates of change from one period

retically identical aggregates such as world exports and world imports.

to the next are calculated as proportional changes from the earlier period.

Five methods of aggregation are used in World Development Indicators: •

For group and world totals denoted in the tables by a t, missing data are

Least-squares growth rate. Least-squares growth rates are used wherever

imputed based on the relationship of the sum of available data to the total

there is a sufficiently long time series to permit a reliable calculation. No growth

in the year of the previous estimate. The imputation process works forward

rate is calculated if more than half the observations in a period are missing.

and backward from 2000. Missing values in 2000 are imputed using one of

The least-squares growth rate, r, is estimated by fi tting a linear regression trend

several proxy variables for which complete data are available in that year. The

line to the logarithmic annual values of the variable in the relevant period. The

imputed value is calculated so that it (or its proxy) bears the same relation-

regression equation takes the form

ship to the total of available data. Imputed values are usually not calculated ln Xt = a + bt

if missing data account for more than a third of the total in the benchmark 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

which is equivalent to the logarithmic transformation of the compound growth

in agriculture, industry, manufacturing, and services in U.S. dollars.

equation,

Aggregates marked by an s are sums of available data. Missing values are

Xt = Xo (1 + r ) t.

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.

In this equation X is the variable, t is time, and a = ln Xo and b = ln (1 + r) are

Aggregates of ratios are denoted by a w when calculated as weighted averages

parameters to be estimated. If b* is the least-squares estimate of b, then the

378

2007 World Development Indicators

average annual growth rate, r, is obtained as [exp(b*) – 1] and is multiplied by 100

The inflation rate for Japan, the United Kingdom, the United States, and the

for expression as a percentage. The calculated growth rate is an average rate that

Euro Zone, representing international inflation, is measured by the change in the

is representative of the available observations over the entire period. It does not

SDR deflator. (Special drawing rights, or SDRs, are the International Monetary

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 p 0 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 Yt$ = (Yt/Nt)/et*

growth rate over n periods is calculated as r = exp[ln(pn/p 0)/n] – 1.

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 Atlas conversion factor for any year is the average of a country’s exchange

the rate effectively applied to domestic transactions of foreign currencies and

rate (or alternative conversion factor) for that year and its exchange rates for the

traded products. This applies to only a small number of countries, as shown

two preceding years, adjusted for the difference between the rate of inflation in

in Primary data documentation. Alternative conversion factors are used in the

the country and that in Japan, the United Kingdom, the United States, and the Euro

Atlas methodology and elsewhere in World Development Indicators as single-year

Zone. A country’s inflation rate is measured by the change in its GDP deflator.

conversion factors.

2007 World Development Indicators

379

CREDITS Credits

contributions were made by Edward Gillin and Carola Fabi of the Food and Agri-

World Development Indicators draws on a wide range of World Bank reports and

culture Organization of the United Nations; Ricardo Quercioli of the International

numerous external sources, listed in the bibliography following this section. Many

Energy Agency; Amay Cassara, Christian Layke, Daniel Prager, and Robin White

people inside and outside the World Bank helped in writing and producing this book.

of the World Resources Institute; Laura Battlebury of the World Conservation

The team would like to particularly acknowledge the help and encouragement of

Monitoring Centre; and Gerhard Metchies of German Technical Cooperation (GTZ).

François Bourguignon, Senior Vice President and Chief Economist of the World Bank,

The World Bank’s Environment Department devoted substantial staff resources

and Shaida Badiee, Director, Development Data Group. The team is also grateful to

to the book, for which the team is very grateful. M.H. Saeed Ordoubadi wrote

the people who provided valuable comments on the entire book. This note identi-

the introduction with valuable comments from Sarwar Lateef, Jeffrey Lewis, and

fies many of those who made specific contributions. Numerous others, too many to

Eric Swanson. Other contributions were made by Kiran Pandey (biodiversity);

acknowledge here, helped in many ways for which the team is extremely grateful.

Susmita Dasgupta, Craig Meisner, Kiran Pandey, and David Wheeler (air and water pollution); Solly Angel, Augusto Clavijo, Maria Emilia Ferire, Mahyar Eshragh-

1. World view

Tabary, Christine Kessides, and Micah Perlin (urban housing conditions); and Kirk

The introduction to section 1 was prepared by Sebastien Dessus and Eric

Hamilton, Beat Hintermann, and Giovanni Ruta (adjusted savings).

Swanson. Alan Gelb, Sarwar Lateef, and Jeffrey Lewis provided valuable suggestions. Changqing Sun and Raymond Muhula provided the decomposition of

4. Economy

poverty rates. K.M. Vijayalakshmi prepared tables 1.1 and 1.6. Changqing Sun

Section 4 was prepared by K.M. Vijayalakshmi in close collaboration with the

prepared the estimates of gross national income in purchasing power parity terms

Macroeconomic Data Team of the World Bank’s Development Data Group, led

and table 1.4. Tables 1.2, 1.3, and 1.5 were prepared by Masako Hiraga. Dorte

by Soong Sup Lee. K.M. Vijayalakshmi and Eric Swanson wrote the introduction

Domeland-Narvaez of the World Bank’s Economic Policy and Debt Department

with valuable suggestions from Sarwar Lateef and Sebastien Dessus. Contribu-

provided the estimates of debt relief for the Heavily Indebted Poor Countries Debt

tions to the section were provided by Azita Amjadi (trade) and Ibrahim Levent

Initiative and Multilateral Debt Relief Initiative. The team is grateful to Yasmin

(external debt). The national accounts data for low- and middle-income economies

Ahmad and Aimee Nichols at the Organisation for Economic Co-operation and

were gathered by the World Bank’s regional staff through the annual Unified

Development for data and advice on official development assistance flows and

Survey. Maja Bresslauer, Mahyar Eshragh-Tabary, Victor Gabor, and Soong Sup

agricultural support estimates.

Lee worked on updating, estimating, and validating the databases for national accounts. The team is grateful to the International Monetary Fund, World Trade

2. People

Organization, United Nations Industrial Development Organization, and the

Section 2 was prepared by Masako Hiraga and Sulekha Patel in partnership with

Organisation for Economic Co-operation and Development for access to the

the World Bank’s Human Development Network and the Development Research

databases.

Group in the Development Economics Vice Presidency. Mehdi Akhlaghi and William Prince provided invaluable assistance in data and table preparation, and

5. States and markets

Kiyomi Horiuchi prepared the demographic estimates and projections under the

Section 5 was prepared by David Cieslikowski and Raymond Muhula, in partner-

guidance of Eduard Bos. Sulekha Patel wrote the introduction with valuable com-

ship with the World Bank’s Financial and Private Sector Development Network,

ments from Davidson Gwatkin, Sarwar Lateef, Jeffrey Lewis, and Eric Swanson.

Sustainable Development Network, Poverty Reduction and Economic Manage-

The poverty estimates were prepared by Shaohua Chen and and Prem Sangraula

ment Network, the International Finance Corporation, and external partners.

of the World Bank’s Poverty Monitoring Group with help from Changquin Sun.

Brian Pascual assisted in data and table preparation. David Cieslikowski wrote

The data for table 2.19 on health gaps by income and gender were based on

the introduction to the section with valuable comments from Rui Coutinho,

data prepared by Darcy Gallucio and Davidson Gwatkin of the Human Develop-

Steve Knack, Aart Kraay, Sarwar Lateef, Raymond Muhula, and Eric Swanson.

ment Network. Other contributions were provided by Eduard Bos and Emi Suzuki

Other contributors include Ada Karina Izaguirre (privatization and infrastructure

(population, health, and nutrition); Montserrat Pallares-Miralles (vulnerability and

projects); Michael Ingram (micro, small, and medium-size enterprises); David

security); Raymond Muhula, Juan Cruz Perusia, and Lianqin Wang of the United

Stewart (investment climate); Caralee McLeish (business environment); Alka

Nations Educational, Scientific, and Cultural Organization Institute for Statistics

Banerjee and Isilay Cabuk (Standard & Poor’s global stock market indexes);

(education); and Lucia Fort and Juan Carlos Guzman Roa (gender).

Himmat Kalsi (financial); Rui Coutinho (public policies and institutions); Nigel Adderley of the International Institute for Strategic Studies (military person-

3. Environment

nel); Bjorn Hagelin and Petter Stålenheim of the Stockholm International

Section 3 was prepared by Mehdi Akhlaghi and M. H. Saeed Ordoubadi in part-

Peace Research Institute (military expenditures and arms transfers); Henrich

nership with the World Bank’s Sustainable Development Network. Important

Bofi nger, Tsukasa Hattori, and Peter Roberts (transport); Jane Degerlund of

380

2007 World Development Indicators

Containerisation International (ports); Vanessa Grey and Esperanza Magpantay

Amy Ditzel, Laura Peterson Nussbaum, and Zachary Schauf provided copyedit-

of the International Telecommunication Union, and Mark Williams (communica-

ing, proofreading, and production assistance. Communications Development’s

tions and information); Ernesto Fernandez Polcuch of the United Nations Educa-

London partner, Peter Grundy of Peter Grundy Art & Design, provided art direc-

tional, Scientific, and Cultural Organization Institute for Statistics (research and

tion and design. Staff from External Affairs oversaw printing and dissemination

development, researchers, and technicians); and Anders Halvorsen of the World

of the book.

Information Technology and Services Alliance (information and communication technology expenditures).

Client services The Development Data Group’s Client Services Team (Azita Amjadi, Uranbileg

6. Global links

Batjargal, Richard Fix, and William Prince) contributed to the design and planning

Section 6 was prepared by Changqing Sun and Azita Amjadi in partnership with

of World Development Indicators and helped coordinate work with the Office of

the World Bank’s Development Research Group (trade), Prospects Group (com-

the Publisher.

modity prices), and external partners. Many thanks to Amy Heyman, Sarwar Lateef, Ibrahim Levent, and Eric Swanson for initial comments and feedback

Administrative assistance and office technology support

about possible revisions to the section. Substantial input for the data came

Estela Zamora and Awatif Abuzeid provided administrative assistance. Jean-

from Azita Amjadi, Jerzy Rozanski (tariffs), Gloria Moreno, and Ibrahim Levent

Pierre Djomalieu, Gytis Kanchas, Nacer Megherbi, and Shahin Outadi provided

(fi nancial data). Other contributors include David Cristallo and Henri Laurencin

information technology support.

of the United Nations Conference on Trade and Development, Francis Ng, and Dominique van der Mensbrugghe (trade); Betty Dow (commodity prices); Dilek

Publishing and dissemination

Aykut (foreign direct investment fl ows); Brian Hammond, Aimee Nichols, and

The Office of the Publisher, under the direction of Dirk Koehler, provided valuable

Yasmin Ahmad of the Organisation for Economic Co-operation and Develop-

assistance throughout the production process. Stephen McGroarty, Randi Park,

ment (aid); Khassoum Diallo and Henrik Pilgaard of the United Nations Offi ce

and Nora Ridolfi coordinated printing and supervised marketing and distribution.

of the High Commissioner for Refugees; Bela Hovy and Francois Pelletier of

Merrell Tuck-Primdahl of the Development Economics Vice President’s Office

the United Nations Population Division (migration); K.M. Vijayalatshmi (remit-

managed the communications strategy.

tances); and John Kester and Teresa Ciller of the World Tourism Organization (tourism). Mehdi Akhlaghi and William Prince provided valuable technical

World Development Indicators CD-ROM

assistance.

Programming and testing were carried out by Reza Farivari and his team: Azita Amjadi, Uranbileg Batjargal, Ying Chi, Ramgopal Erabelly, Nacer Megherbi, Shahin

Other parts of the book

Outadi, and William Prince. Masako Hiraga produced the social indicators tables.

Jeff Lecksell of the World Bank’s Map Design Unit coordinated preparation of

William Prince coordinated user interface design and overall production and

the maps on the inside covers. David Cieslikowski prepared the Users guide. Eric

provided quality assurance. Photo credits: Curt Carnemark, Julio Etchart, Alan

Swanson wrote Statistical methods. K.M. Vijayalakshmi coordinated preparation

Gignoux, John Isaac, and Bill Lyons (World Bank).

of Primary data documentation, and Uranbileg Batjargal assisted in updating the Primary data documentation table. Richard Fix prepared Partners and Index of

The interactive World Development Indicators 2007 was designed and programmed for this CD-ROM by Dohatec New Media.

indicators.

WDI Online Database management

Design, programming, and testing were carried out by Reza Farivari and his

Mehdi Akhlaghi coordinated management of the integrated World Development

team: Mehdi Akhlaghi, Azita Amjadi, Uranbileg Batjargal, Saurabh Gupta, Nacer

Indicators database with assistance from William Prince. Operation of the data-

Megherbi, Gonca Okur, and Shahin Outadi. William Prince coordinated production

base management system was made possible by the Systems Upgrade team

and provided quality assurance. Valentina Kalk and Triinu Tombak of the Office

under the leadership of Reza Farivari.

of the Publisher were responsible for implementation of WDI Online and management of the subscription service.

Design, production, and editing Richard Fix and Azita Amjadi coordinated all stages of production with Commu-

Client feedback

nications Development Incorporated, which provided overall design direction,

The team is grateful to the many people who have taken the time to provide com-

editing, and layout, led by Meta de Coquereaumont, Bruce Ross-Larson, and

ment on its publications. Their feedback and suggestions have helped improve

Christopher Trott. Elaine Wilson created the graphics and typeset the book.

this year’s edition.

2007 World Development Indicators

381

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2007 World Development Indicators

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INDEX OF INDICATORS References are to table numbers.

A

livestock

3.3

value added

Agriculture agricultural raw materials exports as share of total total

total tariff rates applied by high-income countries

4.1

as share of GDP

4.2

per worker

3.3

4.4, 6.4 6.4

Aid

imports as share of total

annual growth

by recipient 4.5, 6.4

aid dependency ratios

6.11

6.4

per capita

6.11

6.4

total

6.11

cereal

net concessional flows

area under production

3.2

from international financial institutions

6.13

exports, as share of total exports

6.4

from UN agencies

6.13

exports, total

6.4

imports, as share of total imports

6.4

as share of general government disbursements

imports, total

6.4

as share of GNI of donor country

tariff rates applied by high-income countries

6.4

average annual change in volume

yield

3.3

by type

3.2

for basic social services, as share of sector-allocable ODA

employment, as share of total

net official development assistance by DAC members

fertilizer

commitments

1.4

6.5

from major donors, by recipient

consumption, per hectare of arable land

3.2

per capita of donor country

food exports, as share of total exports exports, total imports, as share of total imports

total 6.5

6.12 6.10 6.9, 6.10, 6.12

untied aid

6.10

4.4, 6.4 6.4

AIDS—see HIV, prevalence

4.5, 6.4

imports, total

6.4

tariff rates applied by high-income countries

6.4

freshwater withdrawals for, as share of total

3.5

labor force, male and female as share of total

2.3

Air pollution—see Pollution Air transport

land agricultural, as share of land area

3.2

arable, as share of land area

3.1

arable, per 100 people

3.1

area under cereal production

3.2

irrigated, as share of cropland

3.2

permanent cropland, as share of land area

3.1

machinery tractors per 100 square kilometers of arable land

6.10 6.9

commodity prices

commodity prices

6.10 1.4, 6.10

3.2

air freight

5.9

passengers carried

5.9

registered carrier departures

5.9

Asylum seekers—see Migration Average years of schooling

2.13

B Balance of payments

production indexes crop

3.3

current account balance

4.15

food

3.3

exports and imports of goods and services

4.15

2007 World Development Indicators

389

INDEX OF INDICATORS net current transfers

4.15

net income

4.15

total reserves

4.15

C Carbon dioxide damage

See also Exports; Imports; Investment; Private capital flows; Trade

3.15

emissions Bank and trade-related lending

per capita

6.8

per 2000 PPP dollar of GDP total

Biodiversity GEF benefits index

1.3, 3.8 3.8 1.6, 3.8

3.4 Cause of death communicable diseases, maternal, perinatal, and nutritional conditions 2.18

Biological diversity assessment, date prepared, by country benefits index treaty

3.14 3.4

noncommunicable diseases

2.18

injury

2.18

3.14 Child labor by economic activity

Birds species

3.4

threatened species

3.4

Birth rate, crude Births attended by skilled health staff

2.1 1.2, 2.16, 2.19

male and female study and work

Breastfeeding, exclusive

2.17 2.17, 2.19

2.4

work only

2.4

Cities

in largest city

3.10

in selected cities

3.13

in urban agglomerations of more than one million

3.10 3.10

See also Urban environment

closing a business time to resolve insolvency

3.13

population

urban population

Business environment

2.4 2.4

total

air pollution Birthweight, low

2.4

5.3 Closing a business—see Business environment

dealing with licenses number of procedures to build a warehouse

5.3

time required to build a warehouse

5.3

Commodity prices and price indexes

rigidity of employment index

5.3

Communications—see Internet, users; Newspapers; Telephones; Television

protecting investors disclosure, index

5.3

6.5

employing workers

enforcing contracts procedures to enforce a contract

5.3

time to enforce a contract

5.3

new businesses registered

4.11

Computers per 1,000 people

5.11

5.1 Consumption

registering property number of procedures

5.3

distribution—see Income, distribution

time to register

5.3

fixed capital

3.15

government, general

starting a business

390

Compensation of government employees

cost to start a business

5.3

annual growth

4.9

number of start-up procedures

5.3

as share of GDP

4.8

time to start a business

5.3

2007 World Development Indicators

household

average annual growth per capita as share of GDP

4.9

total IMF credit, use of

4.16

4.8

long-term

4.16

See also Purchasing power parity (PPP) Corruption, major constraint, in investment climate Contraceptive prevalence rate

4.17

4.9

5.2

Contract enforcement 5.3

time required for

5.3

4.17

private nonguaranteed

4.16

public and publicly guaranteed

2.16, 2.19

number of procedures

present value

IBRD loans and IDA credits

4.16

Total

4.16

short-term

4.17

total

4.16

Defense armed forces personnel

Country Policy and Institutional Assessment (CPIA)—see Economic

as share of labor force

5.7

management; Policies for social inclusion and equity; Public sector

total

5.7

management and institutions; Structural policies

arms transfers

Courts lack confidence in courts to uphold property rights

5.2

major constraint, in investment climate

5.2

exports

5.7

imports

5.7

military expenditure as share of central government expenditure

5.7

as share of GDP

5.7

Credit getting credit

Deforestation

credit information index

5.5

legal rights index

5.5

private credit registry coverage

5.5

public credit registry coverage

5.5

provided by banking sector

5.5

to private sector

5.1

average annual

3.4

Density—see Population, density Dependency ratio—See Population Development assistance—see Aid

Crime, major constraint, in investment climate

5.2 Disease—see Health risks

Current account balance

4.15

See also Balance of payments Customs, average time to clear

Distribution of income or consumption—see Income, distribution 5.2

D

E Economic management (Country Policy and Institutional Assessment)

DAC (Development Assistance Committee)—see Aid Death rate, crude

2.1

debt policy

5.8

economic management cluster average

5.8

fiscal policy

5.8

macroeconomic management

5.8

See also Mortality rate Education Debt, external

attainment

debt service multilateral

share of cohort reaching grade 5, male and female 4.17

2.11

enrollment ratio

2007 World Development Indicators

391

INDEX OF INDICATORS female to male enrollment in primary and secondary schools gross, by level

1.2

net, by level

2.11, 2.13

out of school children, male and female

2.10, 2.13

male and female

3.8

per capita

2.10

gross intake rate, grade 1 primary completion rate

GDP per unit

2.10

average annual growth

3.7

total

3.7

total

1.2, 2.12, 2.13

3.7

See also Electricity; Fuels

2.12, 2.13

public expenditure on

Enforcing contracts—see Business environment

as share of GDP

2.9

as share of total government expenditure

2.9

per student, as share of GDP per capita, by level

2.9

pupil-teacher ratio, primary level repeaters, primary level teachers, primary, trained transition to secondary school unemployment by level of educational attainment

2.9

Entry regulations for business—see Business environment Environmental strategy, year adopted

3.14

2.11 2.9

Exchange rates

2.12 2.5

Electricity

official, local currency units to U.S. dollar

4.14

ratio of PPP conversion factor to official exchange rate

4.14

real effective

4.14

See also Purchasing power parity (PPP)

consumption major constraint, in investment climate

5.10 5.2

Exports

production share of total sources transmissions and distribution losses

arms 3.9

goods and services

3.9

as share of GDP

5.10

average annual growth total

Employment

5.7 4.8 4.9 4.15

high-technology

in agriculture, as share of total employment

3.2

share of manufactured exports

5.12

in agriculture, male and female

2.3

total

5.12

in industry, male and female

2.3

merchandise

in informal sector, urban

annual growth

6.3

2.8

by high-income OECD countries, by product

6.4

in services, male and female

2.3

by regional trade blocs

6.6

rigidity index

5.3

direction of trade

6.3

structure

4.4

male and female

Employing workers rigidity of employment index

5.3

Endangered species—see Biological diversity; Birds; Mammals; Plants

volume, average annual growth

6.2

structure

4.6

total

4.6

travel

imports, net

3.7

production

3.7

use average annual growth

3.7

combustible renewables and waste

3.7

2007 World Development Indicators

6.2

transport

3.15

emissions—see Pollution

392

4.4

value, average annual growth services

Energy depletion, as share of GNI

total

4.6 4.6, 6.15

See also Trade

F Female-headed households

2.8

Fertility rate

imports

adolescent total

2.16

as share of total imports

2.16, 2.19

4.5, 6.4

total

6.4

prices Finance, major constraint, in investment climate

5.2

Financial access, stability, and efficiency bank capital to asset ratio

5.5

bank nonperforming loans

5.5

tariff rates applied by high-income countries

6.4

G GEF benefits index for biodiversity

3.4

Gender differences

Financial flows, net from DAC members

3.12

6.9

in child employment

2.4

in education

from multilateral institutions

enrollment, primary and secondary

international financial institutions

6.13

total

6.13

in employment

United Nations

6.13

in HIV prevalence

1.2 2.3 2.18

in labor force participation

2.2

grants from NGOs

6.9

in life expectancy at birth

1.5

other official flows

6.9

in literacy

official development assistance and official aid

private

6.9

adult

2.12

total

6.9

youth

2.12

in mortality

See also Aid Food—see Agriculture, production indexes; Commodity prices and price indexes Foreign direct investment, net—see Investment; Private capital flows

deforestation, average annual net depletion

2.20

child

2.20

in smoking

2.18

in survival to age 65

2.20

in youth employment

2.8

unpaid family workers

1.5

3.1

women in parliaments

1.5

3.4

women in nonagricultural sector

1.5

Forest area, as share of total land area

adult

3.15 Gini index

2.7

Freshwater Government, central

annual withdrawals as share of internal resources

3.5

cash surplus or deficit

for agriculture

3.5

debt

for domestic use

3.5

as share of GDP

for industry

3.5

interest, as share of revenue

4.10

interest, as share of total expenses

4.11

renewable internal resources flows

3.5

per capita

3.5

See also Water, access to improved source of

4.10 4.10

expense as share of GDP

4.10

by economic type

4.11

military

5.7

net incurrence of liabilities, as share of GDP

Fuels exports as share of total exports total

4.4, 6.4 6.4

domestic

4.10

foreign

4.10

revenues, current

2007 World Development Indicators

393

INDEX OF INDICATORS as share of GDP

4.10

pregnant women receiving prenatal care

grants and other

4.12

pregnant women receiving tetanus vaccinations

social contributions

4.12

reproductive

tax, as share of GDP tax, by source

5.6 4.12

births attended by skilled health staff

1.5, 2.16, 2.19 2.16 1.2, 2.16, 2.19

contraceptive prevalence rate

2.16, 2.19

fertility rate Gross capital formation

adolescent

annual growth

4.9

as share of GDP

4.8

2.16

total

2.16, 2.19

low-birthweight babies

2.17

maternal mortality ratio Gross domestic product (GDP) annual growth

1.1, 1.6, 4.1

implicit deflator—see Prices per capita, annual growth total

1.2, 2.16

tetanus vaccinations, share of pregnant women receiving

2.16

unmet need for contraception

2.16

tuberculosis 1.1, 1.6 4.2

DOTS detection rate incidence treatment success rate

2.15 1.3, 2.18 2.15

Gross foreign direct investment—see Investment Health expenditure Gross national income (GNI) per capita PPP dollars rank

1.1, 1.6 1.1

U.S. dollars

1.1, 1.6

rank PPP dollars

1.1

U.S. dollars

1.1

total

as share of GDP

2.14

external resources

2.14

out of pocket

2.14

per capita

2.14

public

2.14

total

2.14

Health risks child malnutrition, prevalence

PPP dollars

1.1, 1.6

diabetes, prevalence

U.S. dollars

1.1, 1.6

HIV, prevalence

Gross savings, as share of GNI

3.15

Gross savings, as share of GDP

4.8

2.18 1.3, 2.18

overweight children, prevalence

2.17

road traffic injury, mortality caused by

2.18

smoking prevalence tuberculosis, incidence undernourishment, prevalence

H

1.2, 2.17, 2.19

2.18 1.3, 2.18 2.17

Heavily indebted poor countries (HIPCs)

Health care child children sleeping under treated bednets

2.15

children with acute respiratory infection taken to health provider 2.15

assistance

1.4

completion point

1.4

decision point

1.4

Multilateral Debt Relief Initiative (MDRI) assistance

4.1

children with diarrhea who received oral rehydration and continued feeding

2.15

HIV, prevalence

children with fever receiving antimalarial drugs

2.15

female

health worker density index hospital beds per 1,000 people immunization physicians per 1,000 people

394

2007 World Development Indicators

2.14 2.14

Hospital beds—see Health care

2.15, 2.19 2.14

Housing conditions, national and urban

1.3, 2.18 2.18

durable dwelling units

3.11

as share of GDP

4.2

home ownership

3.11

labor force, male and female as share of total

2.3

household size

3.11

multiunit dwellings

3.11

overcrowding

3.11

vacancy rate

3.11

I IDA Resource Allocation Index (IRAI)

5.8

Immunization rate

Inflation—see Prices Information and communications technology expenditures as share of GDP

5.11

per capita

5.11

Integration, global economic, indicators

6.1

Interest payments—see Government, central, debt

child DPT, share of children ages 12–23 months

2.15, 2.19

measles, share of children ages 12–23 months

2.15, 2.19

deposit

4.13

2.16

lending

4.13

real

4.13

tetanus, share of pregnant women receiving Imports arms

5.7

energy, as share of total energy use

3.7

goods and services

Interest rates

risk premium on lending

5.5

spread

5.5

International Bank for Reconstruction and Development (IBRD)

as share of GDP

4.8

IBRD loans and IDA credits

4.16

average annual growth

4.9

net financial flows from

6.13

total

4.15

merchandise

International Development Association (IDA)

annual growth

6.3

IBRD loans and IDA credits

4.16

by high-income OECD countries, by product

6.4

net concessional flows from

6.13

direction of trade

6.3

structure tariffs

4.5 6.4, 6.7

total

4.5

value, average annual growth

6.2

volume, average annual growth

6.2

services

International Monetary Fund (IMF) net financial flows from

6.13

use of IMF credit

4.16

Internet broadband subscribers

5.11

structure

4.7

price basket

5.11

total

4.7

secure servers

5.11

4.7

users

5.11

transport travel

4.7, 6.15

See also Trade Income

international bandwidth

5.11

schools connected

5.11

Investment

distribution Gini index percentage of

climate 2.7 1.2, 2.7

Industry annual growth

5.2

foreign direct, net inflows as share of GDP

6.1

total

6.8

foreign direct, net outflows 4.1

as share of GDP

6.1

2007 World Development Indicators

395

INDEX OF INDICATORS infrastructure, private participation in

Literacy

energy

5.1

adult, male and female

1.6, 2.12

telecommunications

5.1

youth, male and female

1.6, 2.12

transport

5.1

water and sanitation

5.1

portfolio bonds

6.8

equity

6.8

Malnutrition, in children under age 5

1.2, 2.19

Malaria

See also Gross capital formation; Private capital flows Iodized salt, consumption of

M

2.17

L

children sleeping under treated bednets

2.15

children with fever receiving antimalarial drugs

2.15

Mammals

Labor force annual growth

species

3.4

threatened species

3.4

2.2

armed forces

5.7

child labor

2.4

female

2.2

Manufacturing

in agriculture, male and female as share of total

2.3

exports

4.4, 6.4

in industry, male and female as share of total

2.3

imports

4.5, 6.4

in services, male and female as share of total

2.3

structure

male

2.2

value added

participation of population ages 15–64

2.2

annual growth

4.1

regulation, major constraint, in investment climate

5.2

as share of GDP

4.2

total

4.3

skills, major constraint, in investment climate

5.2

total

2.2

Management time dealing with officials

5.2

4.3

See also Merchandise

See also Employment; Migration; Unemployment Market access to high-income countries Land area

goods admitted free of tariffs

1.4

arable—see Agriculture, land, land use

support to agriculture

1.4

See also Protected areas; Surface area

tariffs on exports from low- and middle-income countries

Land use

agricultural products

1.4

textiles and clothing

1.4

arable land, as share of total land

3.1

area under cereal production

3.2

by type

3.1

forest area, as share of total land

3.1

agricultural raw materials

irrigated land

3.2

cereals

6.4

permanent cropland, as share of total land

3.1

chemicals

6.4

total area

3.1

food

Merchandise exports

footwear Life expectancy at birth male and female total

396

1.5 1.6, 2.19

2007 World Development Indicators

4.4, 6.4

4.4, 6.4 6.4

fuels

4.4

furniture

6.4

iron and steel

6.4

machinery and transport equipment

6.4

manufactures

4.4

as share of GNI of donor country

ores and metals

4.4

as share of total ODA commitments

textiles

6.4

access to improved water source

total

4.4

access to improved sanitation facilities

1.3, 2.15

value, average annual growth

6.2

births attended by skilled health staff

1.2, 2.16

volume, average annual growth

6.2

carbon dioxide emissions per capita

imports agricultural raw materials

1.4 1.4 1.3, 2.15, 3.5

1.3, 3.8

children sleeping under treated bednets

2.15

4.5

consumption, national share of poorest quintile

cereals

6.4

female to male enrollments, primary and secondary

chemicals

6.4

heavily indebted poor countries (HIPCs)

food

4.5

assistance

1.4

footwear

6.4

completion point

1.4

fuels

4.5

decision point

1.4

furniture

6.4

Multilateral Debt Relief Initiative (MDRI) assistance

1.4

iron and steel

6.4

malnutrition, prevalence

1.2, 2.17, 2.19

machinery and transport equipment

6.4

maternal mortality ratio

1.2, 2.16

manufactures

4.5

primary enrollment ratio, net

ores and metals

4.5

poverty gap

2.6

textiles

6.4

poverty, population below a $1 a day

2.6

total

4.5

telephone lines, fixed-line and mobile

1.3, 5.10

value, average annual growth

6.2

tuberculosis, incidence

1.3, 2.18

volume, average annual growth

6.2

under-five mortality rate

1.2, 2.20

trade as share of GDP

1.2, 2.7 1.2

2.10

undernourishment, prevalence 6.1

direction

6.3

growth

6.3

regional trading blocs

6.6

2.17

youth unemployment

1.3, 2.8

Minerals, depletion of

3.15

Monetary indicators Methane emissions percentage change

3.8

total

3.8

Micro, small, and medium-size enterprises

claims on governments and other public entities

4.13

claims on private sector

4.13

Money and quasi money, annual growth

4.13

Mortality rate

employment per 1,000 people

5.1

adult, male and female

number of firms

5.1

caused by road traffic injury

2.18

child, male and female

2.20

Migration

2.20

children under age 5

net

6.14

infant

stock

6.14

maternal

1.2, 2.20 2.20 1.2, 2.16

See also Refugees; Remittances Motor vehicles Military—see Defense Millennium Development Goals, indicators for aid

passenger cars

3.12

per kilometer of road

3.12

per 1,000 people

3.12

See also Roads; Traffic

2007 World Development Indicators

397

INDEX OF INDICATORS N

Plants, higher

Nationally protected areas—see Protected areas

species

3.4

threatened species

3.4

Net national savings

3.15

Policy uncertainty, major constraint, in investment climate

Newspapers, daily

5.11

Pollution carbon dioxide damage, as share of GNI

Nitrous oxide

5.2

3.15

carbon dioxide emissions

emissions

per capita

3.8

percentage change

3.8

per 2000 PPP dollar of GDP

3.8

total

3.8

total

3.8

methane Nutrition

emissions

breastfeeding iodized salt consumption malnutrition, child

2.17, 2.19

percentage change

2.17 1.2, 2.17, 2.19

overweight children, prevalence

2.17

undernourishment, prevalence

2.17

vitamin A supplementation

2.17

O

total nitrogen dioxide, selected cities

3.8 3.8 3.13

nitrous oxide emissions percentage change

3.8

total

3.8

organic water pollutants, emissions

Official development assistance—see Aid

by industry

3.6

per day

3.6

per worker Official flows, other

6.9

P Passenger cars per 1,000 people

3.12

Particulate matter emission damage

particulate matter, selected cities

3.6 3.13

sulfur dioxide, selected cities

3.13

urban-population-weighted PM10

3.12

Population age dependency ratio

2.1

annual growth

2.1

3.15

by age group

selected cities

3.13

0–14

2.1

urban-population-weighted PM10

3.12

15–64

2.1

65 and older Patent applications filed

5.12

density female, as share of total

Pension

2.1 1.1, 1.6 1.5

rural

average, as share of per capita income

2.8

annual growth

3.1

contributors, as share of labor force

2.8

as share of total

3.1

contributors, as share of working-age population

2.8

public expenditure on as share of GDP Physicians—see Health care

398

2007 World Development Indicators

total

1.1, 1.6, 2.1

urban 2.8

as share of total

3.10

average annual growth

3.10

in largest city

3.10

in selected cities

3.13

in urban agglomerations

3.10

total

3.10

Protected areas marine

See also Migration Portfolio investment flows

as share of total surface area

3.4

total

3.4

national

bonds

6.8

as share of total land area

3.4

equity

6.8

total

3.4

Ports, container traffic in

5.9

Poverty

Protecting investors disclosure, index

5.3

Public sector management and institutions (Country Policy and Institutional

international poverty line

Assessment)

population below $1 a day

2.6

efficiency of revenue mobilization

population below $2 a day

2.6

property rights and rule-based governance

5.8

poverty gap at $1 a day

2.6

public sector management and institutions cluster average

5.8

poverty gap at $2 a day

2.6

quality of budgetary and financial management

5.8

quality of public administration

5.8

transparency, accountability, and corruption in the public sector

5.8

national poverty line population below

2.6

national

2.6

rural

2.6

survey year

2.6

conversion factor

urban

2.6

gross national income

1.5

Prices commodity prices and price indexes

Purchasing power parity (PPP) 4.14 1.1, 1.6

R

Power—see Electricity, production Prenatal care, pregnant women receiving

5.8

6.5

Railways goods hauled by

5.9

lines, total

5.9

passengers carried

5.9

consumer, annual growth

4.14

GDP implicit deflator, annual growth

4.14

Regulation and tax administration

6.2

average days to clear customs

5.2

management time dealing with officials

5.2

tax rates, major constraint, in investment climate

5.2

terms of trade wholesale, annual growth

4.14

Private capital flows Refugees

bank and trade-related lending

6.8

foreign direct investment, net inflows

6.8

country of asylum

6.14

from DAC members

6.9

country of origin

6.14

gross, as share of GDP

6.1

portfolio investment

6.8

Regional development banks, net financial flows from

6.13

See also Investment Registering property Productivity in agriculture value added per worker water productivity, total

number of procedures

5.3

time to register

5.3

3.3 3.5

Relative prices (PPP)—see Purchasing power parity (PPP)

2007 World Development Indicators

399

INDEX OF INDICATORS Remittances

See also Research and development

workers’ remittances and compensation of employees, paid

6.14

workers’ remittances and compensation of employees, received

6.14

Services exports

Research and development

structure

4.6

5.12

total

4.6

researchers

5.12

imports

technicians

5.12

expenditures

structure

4.7

total Reserves, gross international—see Balance of payments Roads

4.7

labor force by economic activity, male and female as share of total

2.3

trade, as share of GDP

6.1

value added

goods hauled by

5.9

annual growth

4.1

passengers carried

5.9

as share of GDP

4.2

paved, as share of total

5.9

total network traffic

5.9

Smoking, prevalence, male and female

2.18

3.12 Social inclusion and equity policies (Country Policy and Institutional Assessment)

Royalty and license fees payments

5.11

building human resources

receipts

5.11

equity of public resource use

5.8

gender equity

5.8

policy and institutions for environmental sustainability

5.8

Rural environment access to improved sanitation facilities

3.10

population annual growth

3.1

as share of total

3.1

S

5.8

social inclusion and equity cluster average

5.8

social protection and labor

5.8

Starting a business—see Business environment Stock markets listed domestic companies

S&P/EMDB Indices

5.4

Sanitation access to improved facilities, population with rural

3.10

total

1.3, 2.15

urban

5.4

market capitalization as share of GDP

5.4

total

5.4

market liquidity

5.4

S&P/EMDB Indices

5.4

turnover ratio

5.4

3.10 Structural policies (Country Policy and Institutional Assessment)

Savings

business regulating environment

5.8

financial sector

5.8

3.15

structural policies cluster average

5.8

3.15

trade

5.8

gross, as share of GDP

4.8

gross, as share of GNI net Schooling—see Education

Sulfur dioxide emissions—see Pollution

Science and technology scientific and technical journal articles

400

2007 World Development Indicators

Surface area 5.11

See also Land area

1.1, 1.6

Survival to age 65 male and female

price basket 2.20

5.10

mobile per 1,000 people

Suspended particulate matter—see Pollution

T

1.3, 5.10

population covered

5.10

price basket

5.10

total revenue

5.10

total subscribers per employee

5.10

Tariffs all products

Television, households with

5.11

binding coverage

6.7

simple mean board rate

6.7

simple mean tariff

6.7

weighted mean tariff

6.7

Tetanus vaccinations, share of pregnant women receiving

simple mean tariff

6.7

Threatened species—see Biological diversity

weighted mean tariff

6.7

Terms of trade, net barter

6.2 2.16

manufactured products

primary products

Tourism, international

simple mean tariff

6.7

expenditures

6.15

weighted mean tariff

6.7

inbound tourists, by country

6.15

outbound tourists, by country

6.15

receipts

6.15

See also Taxes and tax policies, duties Taxes and tax policies business taxes

Trade

number of payments

5.6

arms

time to prepare, file, and pay

5.6

merchandise

total tax rate, share of profit goods and services taxes, domestic

5.6 4.12

highest marginal tax rate

5.7

as share of GDP

6.1

direction of, by region

6.3

nominal growth, by region

6.3

corporate

5.6

regional trading blocs

6.6

individual

5.6

OECD trade by commodity

6.4

real growth in, less growth in real GDP

6.1

income, profit, and capital gains taxes as share of revenue

4.12

services

international trade taxes

4.12

as share of GDP

other taxes

4.12

computer, information, communications, and other

4.6, 4.7

insurance and financial

4.6, 4.7

rates, major constraint, in investment climate social contributions tax revenue, as share of GDP

5.2 4.12 5.6

6.1

transport

4.6, 4.7

travel

4.6, 4.7

See also Balance of payments; Exports; Imports; Manufacturing; Technology—see Computers; Exports, high-technology; Internet; Research and

Merchandise; Terms of trade; Trade blocs

development; Science and technology Trade blocs, regional Telephones cost of call to U.S.

5.10

international voice traffic

5.10

exports within bloc

6.6

total exports, by bloc

6.6

Trademark applications filed

mainlines faults per 100

5.10

per 1,000 people

5.10

5.12

Trade policies—see Tariffs

2007 World Development Indicators

401

INDEX OF INDICATORS Traffic

in largest city

3.10

road traffic

3.12

in urban agglomerations

3.10

road traffic injury and mortality

2.18

total

3.10

See also Roads

selected cities

Transport—see Air transport; Railways; Roads; Traffic; Urban environment Treaties, participation in

nitrogen dioxide

3.13

particulate matter

3.13

population

3.13

sulfur dioxide

3.13

biological diversity

3.14

See also Pollution; Population; Sanitation; Water, access to improved

CFC control

3.14

source of

climate change

3.14

Convention on International Trade on Endangered Species (CITES)

3.14

V

Convention to Combat Desertification (CCD)

3.14

Kyoto Protocol

3.14

Law of the Sea

3.14

as share of GDP

ozone layer

3.14

in agriculture

4.2

Stockholm Convention on Persistent Organic Pollutants

3.14

in industry

4.2

Tuberculosis, incidence

Value added

1.3, 2.18

in manufacturing

4.2

in services

4.2

growth

U UN agencies, net concessional flows from

6.13

in agriculture

4.1

in industry

4.1

in manufacturing

4.1

in services

4.1

Undernourishment, prevalence of

2.17

per worker

UNDP, net concessional flows from

6.13

total, in manufacturing

in agriculture

incidence of long-term 2.5

total, male and female youth

Water access to improved source of, population with

by level of educational attainment primary, secondary, tertiary

4.3

W

Unemployment total, male and female

3.3

2.5

pollution—see Pollution, organic water pollutants

2.5

productivity

1.3, 2.15 3.5

1.3, 2.8 WFP, net concessional flows from

UNFPA, net concessional flows from

6.13

UNICEF, net concessional flows from

6.13

6.13

Women in development

Urban environment access to sanitation employment, informal sector

2.8

1.5

women in parliaments

1.5

World Bank, net financial flows from See also International Bank for Reconstruction and Development;

as share of total

3.10

average annual growth

3.10

2007 World Development Indicators

1.5

women in nonagricultural sector 3.10

population

402

teenage mothers

International Development Association

6.13

The world by region

Classified according to World Bank analytical grouping

Low- and middle-income economies East Asia and Pacific

Middle East and North Africa

High-income economies

Europe and Central Asia

South Asia

OECD

Latin America and the Caribbean

Sub-Saharan Africa

Other

No data

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 Northern Mariana Islands Palau Papua New Guinea Philippines Samoa Solomon Islands Thailand Timor-Leste Tonga Vanuatu Vietnam Europe and Central Asia Albania Armenia Azerbaijan Belarus Bosnia and Herzegovina Bulgaria Croatia Czech Republic Estonia Georgia Hungary Kazakhstan Kyrgyz Republic Latvia Lithuania Macedonia, FYR Moldova Poland Romania Russian Federation Serbia and Montenegro Slovak Republic Tajikistan Turkey Turkmenistan Ukraine Uzbekistan Latin America and the Caribbean Argentina

Barbados Belize Bolivia Brazil Chile 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 Trinidad and Tobago Uruguay Venezuela, RB Middle East and North Africa Algeria Djibouti Egypt, Arab Rep. Iran, Islamic Rep. Iraq Jordan Lebanon Libya Morocco Oman Syrian Arab Republic Tunisia West Bank and Gaza Yemen, Rep. 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 Equatorial Guinea 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 Denmark Finland* France* Germany*

Greece* Iceland Ireland* Italy* Japan Korea, Rep. Luxembourg* Netherlands* New Zealand Norway Portugal* Spain* Sweden Switzerland United Kingdom United States Other high income Andorra Antigua and Barbuda Aruba Bahamas, The Bahrain Bermuda Brunei Darussalam Cayman Islands Channel Islands Cyprus Faeroe Islands French Polynesia Greenland Guam Hong Kong, China Isle of Man Israel Kuwait Liechtenstein Macao, China Malta Monaco Netherlands Antilles New Caledonia Puerto Rico Qatar San Marino Saudi Arabia Singapore Slovenia* United Arab Emirates Virgin Islands (U.S.)

*Member of the European Monetary Union

ISBN 0-8213-6959-8

The World Bank 1818 H Street N.W. Washington, D.C. 20433 USA Telephone: 202 473 1000 Fax: 202 477 6391 Web site: www.worldbank.org Email: [email protected]

The World Development Indicators • Includes more than 800 indicators for 152 economies • Provides definitions, sources, and other information about the data • Organizes the data into six thematic areas

WORLD VIEW Progress toward the Millennium Development Goals

PEOPLE Gender, health, and employment

ENVIRONMENT Natural resources and environmental changes

ECONOMY New opportunities for growth

STATES & MARKETS Elements of a good investment climate

GLOBAL LINKS Evidence on globalization

Saved: 93 trees 4,354 pounds of solid waste 33,908 gallons of waste water 8,169 pounds of net greenhouse gases 65 million BTUs of total energy