WORLD DEVELOPMENT INDICATORS
2009
The world by income Low ($935 or less) Lower middle ($936–$3,705) Upper middle ($3,706–$11,455) High ($11,456 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 2007 GNI per capita
2009
WORLD DEVELOPMENT INDICATORS
Copyright 2009 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 2009
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2009
WORLD DEVELOPMENT INDICATORS
PREFACE World Development Indicators 2009 arrives at a moment of great uncertainty for the global economy. The crisis that began more than a year ago in the U.S. housing market spread to the global financial system and is now taking its toll on real output and incomes. As a consequence, an additional 50 million people will be left in extreme poverty. And if the crisis deepens and widens or is prolonged, other development indicators—school enrollments, women’s employment, child mortality—will be affected, jeopardizing progress toward the Millennium Development Goals. Statistics help us understand the events that triggered the crisis and measure its impact. Along with this year’s 91 data tables, each section of the World Development Indicators 2009 has an introduction that shows statistics in action, describing the history of the current crisis, its effect on developing economies, and the challenges they face. World view reviews the housing bubble and other asset bubbles that preceded it, the global macroeconomic imbalances that fed the bubbles, and the role of financial innovation. Economy looks at the record growth of developing economies preceding the crisis. Environment reviews the increasing impact of developing economies on the global environment. Global links discusses the transmission of the global crisis through the avenues of global integration: trade, finance, migration, and remittances. States and markets reminds us that as information and communication technologies change the way we work, they will be part of the solution to the current crisis. People contains most of the statistics for measuring progress toward the Millennium Development Goals. Its introduction, prepared by our partners at the International Labour Organization, examines new measures of decent work and productive employment now included in the Millennium Development Goals. High quality, timely, and publicly available data will be central to managing the response to the crisis. We need high frequency—quarterly or monthly—data on labor markets to better track the impacts of macroeconomic events on people. We also need to know more about the characteristics of households and their response to economic conditions. While income distribution data are improving, they are weak at both ends of the spectrum, missing the very rich and the very poor. We know little about household assets in most developing economies. There is little information on housing markets, and financial data need to be enriched with more information on nonbank financial institutions (such as insurance companies, pension funds, investment banks, and hedge funds) in many countries. Official statistical agencies need to take a long range view of their public role—to think broadly about data needs and build strategic partnerships with academia and the private sector. In a time of crisis the careful, systematic accumulation of statistical information may seem a luxury. It is not. We need better data now to guide our responses to the current crisis and to plot our course in the future. The World Bank stands ready to support countries with their statistical capacity-building efforts. We will also continue to maintain the World Development Indicators as a rich source of development information, bringing to you new and critical data areas as availability and quality improve. And as always, we welcome your comments and suggestions for making World Development Indicators 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 Sulekha Patel under the supervision of Eric Swanson and comprising Awatif Abuzeid, Mehdi Akhlaghi, Azita Amjadi, Uranbileg Batjargal, David Cielikowski, Richard Fix, Masako Hiraga, Kiyomi Horiuchi, Nino Kostava, K. Sarwar Lateef, Soong Sup Lee, Ibrahim Levent, Raymond Muhula, M.H. Saeed Ordoubadi, Beatriz Prieto-Oramas, Changqing Sun, and K.M. Vijayalakshmi, working closely with other teams in the Development Economics Vice Presidency’s Develop ment Data Group. The CD-ROM development team included Azita Amjadi, Ramgopal Erabelly, Reza Farivari, Buyant Erdene Khaltarkhuu, and William Prince. The work was carried out under the management of Shaida Badiee. The choice of indicators and the contents of the explanatory text 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 contents, please see Credits. For a listing of 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. Joseph Caponio and Amye Kenall provided 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
1jj v vii xii xx
1kk 1ll 1.2a 1.3a 1.4a
1. WORLD VIEW Introduction
1.1 1.2 1.3 1.4 1.5 1.6 1a 1b 1c 1d 1e 1f 1g 1h 1i 1j 1k 1l 1m 1n 1o 1p 1q 1r 1s 1t 1u 1v 1w 1x 1y 1z 1aa 1bb 1cc 1dd 1ee 1ff 1gg 1hh 1ii
Tables Size of the economy Millennium Development Goals: eradicating poverty and saving lives Millennium Development Goals: protecting our common environment Millennium Development Goals: overcoming obstacles Women in development Key indicators for other economies
14 18 22 26 28 32
Text figures, tables, and boxes Developing economies had their best decade of growth in 2000–07 2 Long-term trends reached new heights 2 Most developing economy exports go to high-income economies 2 Increased investment led to faster growth in low- and middleincome economies 2 Large current account surpluses and deficits were concentrated in a few economies during 2005–07 3 Current account surpluses and deficits increased 3 Trade surpluses led to large build-ups in reserves 3 Trade deficits were financed by foreign investors 3 Private capital flows to developing economies took off in 2002 . . . 4 . . . And investors perceived less risk 4 Prices of assets, especially in real estate, were rising rapidly in some countries . . . 4 . . . And so were equity asset valuations 4 Indebtedness ratios have improved for most economies 5 Growing reserves comfortably covered short-term debt liabilities 5 Commodity price rises accelerated in recent years 5 Food and fuel importers were hurt by rising prices 5 Output in the largest economies slowed or declined in the 4th quarter of 2008 6 U.S. household debt rose rapidly after 2000 6 U.S. house prices peaked in 2006 6 As housing bubbles burst, investors lost confidence 6 Savings and investment in China . . . 7 . . . And the United States 7 The five largest current account surpluses and deficits 7 U.S. foreign assets and liabilities doubled 7 Assets underlying over the counter derivatives rose sevenfold . . . 8 . . . While the market value of derivatives rose ninefold 8 U.S. domestic financial sector profits averaged almost 30 percent of before-tax profits during 2000–06 8 Derivatives can undermine capital controls, leading to linkages that make market dynamics difficult to predict 8 The number of banking crises rose after the 1970s 9 The latest crisis is affecting a large portion of global income 9 The cost of systemic financial crises can be very high 9 Borrowing costs have climbed, reflecting perceived risk 10 Equity markets have suffered large losses 10 Low-income economies depend the most on official aid, workers’ remittances, and foreign direct investment 10 Remittances are significant for many low-income economies 10
2009 World Development Indicators
11 11 11 21 25 27
2. PEOPLE 1
Introduction
2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11 2.12 2.13 2.14 2.15 2.16 2.17 2.18 2.19 2.20 2.21 2.22 2a 2b 2c 2d 2e 2f 2g 2h 2i 2j 2k 2l 2m 2.6a 2.8a 2.8b 2.8c 2.9a 2.15a 2.16a
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Fiscal positions have generally improved but remain weak for some developing economies Finding fiscal space in low-income economies Recent World Bank Group initiatives Location of indicators for Millennium Development Goals 1–4 Location of indicators for Millennium Development Goals 5–7 Location of indicators for Millennium Development Goal 8
Tables Population dynamics Labor force structure Employment by economic activity Decent work and productive employment Unemployment Children at work Poverty rates at national poverty lines Poverty rates at international poverty lines Distribution of income or consumption Assessing vulnerability and security Education inputs Participation in education Education efficiency Education completion and outcomes Education gaps by income and gender Health systems Disease prevention coverage and quality Reproductive health Nutrition Health risk factors and future challenges Health gaps by income and gender Mortality
35 40 44 48 52 56 60 64 67 72 76 80 84 88 92 96 98 102 106 110 114 118 122
Text figures, tables, and boxes Different goals—different progress 35 What is decent work? 36 Employment to population ratios have not changed much over time . . . 36 . . . But variations are wide across regions 36 High employment to population ratios in some countries reflect high numbers of working poor 37 Fewer women than men are employed all over the world 37 Many young people are in the workforce, at the expense of higher education 37 For many poor countries, there is a tradeoff between education and employment 37 Although there are large regional variations in vulnerable employment . . . 38 . . . Women are more likely than men to be in vulnerable employment 38 Share of working poor in total employment is highest in South Asia and Sub-Saharan Africa 38 Labor productivity has increased across the world 38 Scenarios for 2008 39 Children work long hours 63 While the number of people living on less than $1.25 a day has fallen, the number living on $1.25–$2.00 a day has increased 69 Poverty rates have begun to fall 69 Regional poverty estimates 70 The Gini coefficient and ratio of income or consumption of the richest quintile to the poorest quintiles are closely correlated 75 There is a large gap in educational attainment across gender and urban-rural lines 97 There is a wide gap in health expenditure per capita between high-income economies and developing economies 101
3. ENVIRONMENT 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 3.11 3.12 3.13 3.14 3.15 3.16 3a 3b 3c 3d 3e 3f 3g 3h 3i 3j 3k 3l 3m 3n
Introduction
127
3o
Tables Rural population and land use Agricultural inputs Agricultural output and productivity Deforestation and biodiversity Freshwater Water pollution Energy production and use Energy dependency and efficiency and carbon dioxide emissions Trends in greenhouse gas emissions Sources of electricity Urbanization Urban housing conditions Traffic and congestion Air pollution Government commitment Toward a broader measure of savings
134 138 142 146 150 154 158 162 166 170 174 178 182 186 188 192
3p 3q
128 128 128
3.6a
Text figures, tables, and boxes Energy use has doubled since 1971 High-income economies use almost half of all global energy The top six energy consumers use 55 percent of global energy High-income economies use more than 11 times the energy that low-income economies do Nonrenewable fuels are projected to account for 80 percent of energy use in 2030—about the same as in 2006 Fossil fuels will remain the main sources of energy through 2030 Known global oil reserves and countries with highest endowments in 2006 Production declines from existing oil fields have been rapid Economic activity, energy use, and greenhouse gas emissions move together Decarbonization of energy reversed at the beginning of the 21st century The top six carbon dioxide emitters in 2005 High-income economies are by far the greatest emitters of carbon dioxide Carbon dioxide emissions embedded in international trade Impact of Policy Scenarios: carbon dioxide concentration, temperature increase, emissions, and energy demand
3r 3s 3.1a 3.2a 3.2b 3.3a 3.3b 3.5a 3.5b
3.7a 128 129
3.8a 3.8b
129
3.9a 129 129 130 130 130
3.9b 3.10a 3.10b 3.11a 3.11b
130 131 131
3.12a 3.13a
Reductions in energy-related carbon dioxide emissions by region in the 550 and 450 parts per million Policy Scenarios relative to the Trend Scenario 131 Energy efficiency has been improving 132 Electricity generated from renewables is projected to more than double by 2030 132 Top 10 users of wind to generate electricity 133 Cost and savings under the Policy Scenarios 133 What is rural? Urban? 137 Nearly 40 percent of land globally is devoted to agriculture 141 Developing regions lag in agricultural machinery, which reduces their agricultural productivity 141 Cereal yield in low-income economies was less than 40 percent of the yield in high-income countries 145 Sub-Saharan Africa had the lowest yield, while East Asia and Pacific is closing the gap with high-income economies 145 Agriculture is still the largest user of water, accounting for some 70 percent of global withdrawals 153 The share of withdrawals for agriculture approaches 90 percent in some developing regions 153 Emissions of organic water pollutants declined in most economies from 1990 to 2005, even in some of the top emitters 157 A person in a high-income economy uses an average of more than 11 times as much energy as a person in a low-income economy 161 High-income economies depend on imported energy . . . 165 . . . mostly from middle-income economies in the Middle East and North Africa and Latin America and the Caribbean 165 The 10 largest contributors to methane emissions account for about 62 percent of emissions 169 The 10 largest contributors to nitrous oxide emissions account for about 56 percent of emissions 169 Sources of electricity generation have shifted since 1999 . . . 173 . . . with developing economies relying more on coal 173 Developing economies had the largest increase in urban population between 1990 and 2007 177 Latin America and the Caribbean had the same share of urban population as high-income economies in 2007 177 Selected housing indicators for smaller economies 181 Particulate matter concentration has fallen in all income groups, and the higher the income, the lower the concentration 185
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TABLE OF CONTENTS 4. ECONOMY 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 4a 4b 4c 4d 4e 4f 4g 4h 4i 4j 4k–4p 4q–4v 4w–4bb 4cc–4hh 4.3a 4.4a 4.5a 4.6a 4.7a 4.9a 4.10a 4.11a 4.12a 4.15a
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5. STATES AND MARKETS
Introduction
197
Tables Growth of output Structure of output Structure of manufacturing Structure of merchandise exports Structure of merchandise imports Structure of service exports Structure of service imports Structure of demand Growth of consumption and investment Central government finances Central government expenses Central government revenues Monetary indicators Exchange rates and prices Balance of payments current account
204 208 212 216 220 224 228 232 236 240 244 248 252 256 260
5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5.11 5.12
197
5d
197 198 198
5c 5e 5f
198 198
5g
Text figures, tables, and boxes Economic growth slowed in 2007 Large middle-income economies with economic growth above 10 percent Asian countries invested more East Asia and Pacific is the largest saver High-income economies still produce the largest share of manufactured goods . . . . . . And account for the largest share of manufactures exports Twelve developing economies had a cash deficit greater than 3 percent of GDP Five developing economies had a public debt to GDP ratio greater than 60 percent over 2005–07 Modest inflationary pressure affected 74 countries Real interest rates declined in 66 countries Growth in GDP and investment 2007–08, selected major developing economies Growth in industrial production 2007–08, selected major developing economies Lending and inflation rates 2007–08, selected major developing economies Central government debt 2007–08, selected major developing economies Manufacturing continues to show strong growth in East Asia through 2007 Developing economies’ share of world merchandise exports continues to expand Top 10 developing economy exporters of merchandise goods in 2007 Top 10 developing economy exporters of commercial services in 2007 The mix of commercial service imports by developing economies is changing GDP per capita is still lagging in some regions Fifteen developing economies had a government expenditure to GDP ratio of 30 percent or higher Interest payments are a large part of government expenses for some developing economies Rich economies rely more on direct taxes Top 15 economies with the largest reserves in 2007
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5a 5b
5h 199
5i 199 199 199
5j 5k
200
5l 200 200 200 215 219 223 227 231 239 243 247 251 263
Introduction
265
Tables Private sector in the economy Business environment: enterprise surveys Business environment: Doing Business indicators Stock markets Financial access, stability, and efficiency Tax policies Military expenditures and arms transfers Public policies and institutions Transport services Power and communications The information age Science and technology
270 274 278 282 286 290 294 298 302 306 310 314
Text figures, tables, and boxes Improving governance and contributing to growth Seventy percent of mobile phone subscribers are in developing economies, 2000–07 Internet use in developing economies is growing, but still lags behind use in developed economies Competition can spur growth in mobile phone service Broadband access in developed and developing economies International bandwidth has increased rapidly in Europe and Central Asia and Latin America and the Caribbean Prices for mobile phone services have declined in many countries Internet service prices have fallen in some Sub-Saharan African countries, 2005–07 East Asia & Pacific leads in share of information and communication technology goods exports India leads developing economies in information and communications technology service export shares, 2007 Developing economies have only about 4 percent of the world’s secure servers, 2008 Partnership on Measuring ICT for Development
265 266 266 266 267 267 267 267 268 268 268 269
6. GLOBAL LINKS Introduction
6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 6.10 6.11 6.12 6.13 6.14 6.15 6.16 6.17 6.18 6.19 6a 6b 6c 6d 6e 6f 6g 6h 6i 6j 6k 6l 6m 6n 6o 6p 6q
Tables Integration with the global economy Growth of merchandise trade Direction and growth of merchandise trade High-income economy trade with low- and middle-income economies Direction of trade of developing economies Primary commodity prices Regional trade blocs Tariff barriers External debt Ratios for external debt Global private financial flows Net official financial flows Financial flows from Development Assistance Committee members Allocation of bilateral aid fromDevelopment Assistance Committee members Aid dependency Distribution of net aid by Development Assistance Committee members Movement of people Characteristics of immigrants in selected OECD countries Travel and tourism Text figures, tables, and boxes The importance of trade to developing economies has increased High-income economies and a few large middle-income economies account for a majority of world exports Most developing economy exports were directed to high-income economies in 2007 Merchandise imports of Group of Seven industrial economies have declined, reflecting slowing demand for imports Primary commodity prices have been volatile over the past year For some economies food imports were equivalent to more than 7 percent of household consumption, 2005–07 average Large middle-income economies received increasing amount of portfolio equity flows in recent years Other developing economies borrowed increasing amounts from private creditors Much global FDI is directed to high-income economies and a few large middle-income economies . . . . . . But as a share of GDP, FDI net inflows are a large source of private financing for low-income economies FDI net inflows to Indonesia and Malaysia declined immediately after the East Asian financial crisis hit FDI net inflows to the Republic of Korea and Thailand remained resilient for several years after the plunge in GDP Net portfolio equity flows to large middle-income economies increased considerably Stock market capitalizations declined after the financial crisis Spreads on emerging market sovereign and corporate bonds have widened, increasing the cost of borrowing Private lending to Europe and Central Asia increased ninefold between 2003 and 2007 For middle-income economies nearly 80 percent of long-term debt was from private creditors while for low-income economies 90 percent was from official creditors
319
6r
328 332 336
6s
339 342 345 348 352 356 360 364 368
6u 6v
372 374 376 380 384 388 390
6t
6w 6x 6y–6dd 6ee–6jj 6kk–6pp 6qq–6vv 6.1a 6.3a 6.4a
320
6.5a 6.6a
320
6.7a
320
6.9a 6.10a
320 321
6.11a
321
6.12a
321 321
6.15a 6.16a
322
6.19a
322
Net nonconcessional lending to middle-income economies from international financial institutions, declining since 2002, recently increased 324 Aid is equivalent to 5 percent of the GNI of low-income economies 324 s Aid for long-term development has remained about the same as in the 1970s 324 Aid flows declined after the Nordic banking crisis in 1991 325 Two U.S. financial crises in the late 20th century—aid down, then up 325 Migration to high-income economies has increased 325 More remittance flows are now going to developing economies 325 Merchandise trade 2006–08, selected major developing economies 326 Equity price indices 2007–09, selected major developing economies 326 Bond spreads 2007–09, selected major developing economies 326 Financing through international capital markets 2007–09, selected major developing economies 326 Estimating the global emigrant stock 331 In 2007 around 70 percent of exports from low- and middleincome economies and from high-income economies were directed to high-income economies 338 High-income economies’ tariffs on imports from low- and middle-income economies fell between 1997 and 2007 but remain high for some products 341 Trading partners vary by region 344 Commodity prices increased between 2000 and the last quarter of 2008—the longest boom since 1960 347 The number of trade agreements has increased rapidly since 1990, especially bilateral agreements 351 The levels and the composition of external debt vary by regions 359 The burden of external debt service declined for most regions over 1995–2007 363 In 2007 middle-income economies received nearly 20 times more private capital flows than low-income economies did 367 Net nonconcessional lending from international financial institutions has declined in recent years as countries have paid off previous loans 371 Official development assistance from non-DAC donors, 2003–07 379 Most donors increased their proportions of untied aid between 2000 and 2007 383 High-income economies remain the main destination for international travelers, but the share of tourists visiting developing economies is rising 393
322 322 323 323 323 323
BACK Primary data documentation Statistical methods Credits Bibliography Index of indicators
395 406 408 410 418
324
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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, 2009. 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. ILO helps advance the creation of decent jobs and the kinds of economic and working conditions that give working people and business people a stake in lasting peace, prosperity, and progress. As part of its mandate, the ILO maintains an extensive statistical publication program. For more information, see www.ilo.org/.
International Monetary Fund The International Monetary Fund (IMF) is an international organization of 185 member countries established to promote international monetary cooperation, a stable system of exchange rates, and the balanced expansion of international trade and to foster economic growth and high levels of employment. The IMF reviews national, regional, and global economic and financial developments, provides policy advice to member countries and serves as a forum where they can discuss the national, regional, and global consequences of their policies. The IMF also makes financing temporarily available to member countries to help them address balance of payments problems. Among the IMF’s core missions are the collection and dissemination of high-quality macroeconomic and financial statistics as an essential prerequisite for formulating appropriate policies. The IMF provides technical assistance and training to member countries in areas of its core expertise, including the development of economic and financial data in accordance with international standards. For more information, see www.imf.org.
International Telecommunication Union The International Telecommunication Union (ITU) is the leading UN agency for information and communication technologies. ITU’s mission is to enable the growth and sustained development of telecommunications and information networks and to facilitate universal access so that people everywhere can participate in, and benefit from, the emerging information society and global economy. A key priority lies in bridging the so-called Digital Divide by building information and communication infrastructure, promoting adequate capacity building, and developing confidence in the use of cyberspace through enhanced online security. ITU also concentrates on strengthening emergency communications for disaster prevention and mitigation. For more information, see www.itu.int/.
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PARTNERS National Science Foundation The National Science Foundation (NSF) is an independent U.S. government agency whose mission is to promote the progress of science; to advance the national health, prosperity, and welfare; and to secure the national defense. NSF’s goals—discovery, learning, research infrastructure, and stewardship—provide an integrated strategy to advance the frontiers of knowledge, cultivate a world-class, broadly inclusive science and engineering workforce, expand the scientific literacy of all citizens, build the nation’s research capability through investments in advanced instrumentation and facilities, and support excellence in science and engineering research and education through a capable and responsive organization. For more information, see www.nsf.gov/.
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 to support sustainable economic
growth, boost employment, raise living standards, maintain financial stability, assist other countries’ economic development, and contribute to growth in world trade. With active relationships with some 100 other countries it has a global reach. It is best known for its publications and statistics, which cover economic and social issues from macroeconomics to trade, education, development, and science and innovation. The Development Assistance Committee (DAC, www.oecd.org/dac/) is one of the principal bodies through which the OECD deals with issues related to cooperation with developing countries. The DAC is a key forum of major bilateral donors, who work together to increase the effectiveness of their common efforts to support sustainable development. The DAC concentrates on two key areas: the contribution of international development to the capacity of developing countries to participate in the global economy and the capacity of people to overcome poverty and participate fully in their societies. For more information, see www.oecd.org/.
Stockholm International Peace Research Institute The Stockholm International Peace Research Institute (SIPRI) conducts research on questions of conflict and cooperation of importance for international peace and security, with the aim of contributing to an understanding of the conditions for peaceful solutions to international conflicts and for a stable peace. SIPRI’s main publication, SIPRI Yearbook, is an authoritive and independent source on armaments and arms control and other conflict and security issues. For more information, see www.sipri.org/.
Understanding Children’s Work As part of broader efforts to develop effective and long-term solutions to child labor, the International 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.
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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/.
United Nations Centre for Human Settlements, Global Urban Observatory The Urban Indicators Programme of the United Nations Human Settlements Programme was established to address the urgent global need to improve the urban knowledge base by helping countries and cities design, collect, and apply policy-oriented indicators related to development at the city level. With the Urban Indicators and Best Practices programs, the Global Urban Observatory is establishing a worldwide information, assessment, and capacity building network to help governments, local authorities, the private sector, and nongovernmental and other civil society organizations. For more information, see www.unhabitat.org/.
United Nations Children’s Fund The United Nations Children’s Fund (UNICEF) works with other UN bodies and with governments and nongovernmental organizations to improve children’s lives in more than 190 countries through various programs in education and health. UNICEF focuses primarily on five areas: child survival and development, basic Education and gender equality (including girls’ education), child protection, HIV/AIDS, and policy advocacy and partnerships. For more information, see www.unicef.org/.
United Nations Conference on Trade and Development The United Nations Conference on Trade and Development (UNCTAD) is the principal organ of the United Nations General Assembly in the field of trade and development. Its mandate is to accelerate economic growth and development, particularly in developing countries. UNCTAD discharges its mandate through policy analysis; intergovernmental deliberations, consensus building, and negotiation; monitoring, implementation, and follow-up; and technical cooperation. For more information, see www.unctad.org/.
United Nations Educational, Scientific, and Cultural Organization, Institute for Statistics The United Nations Educational, Scientific, and Cultural Organization (UNESCO) is a specialized agency of the United Nations that promotes international cooperation among member states and associate members
2009 World Development Indicators
xv
PARTNERS in education, science, culture and communications. The UNESCO Institute for Statistics is the organization’s statistical branch, established in July 1999 to meet the growing needs of UNESCO member states and the international community for a wider range of policy-relevant, timely, and reliable statistics on these topics. For more information, see www.uis.unesco.org/.
United Nations Environment Programme The mandate of the United Nations Environment Programme is to provide leadership and encourage partnership in caring for the environment by inspiring, informing, and enabling nations and people to improve their quality of life without compromising that of future generations. For more information, see www.unep.org/.
United Nations Industrial Development Organization The United Nations Industrial Development Organization was established to act as the central coordinating body for industrial activities and to promote industrial development and cooperation at the global, regional, national, and sectoral levels. Its mandate is to help develop scientific and technological plans and programs for industrialization in the public, cooperative, and private sectors. For more information, see www.unido.org/.
The UN Refugee Agency The UN Refugee Agency (UNHCR) is mandated to lead and coordinate international action to protect refugees and resolve refugee problems worldwide. Its primary purpose is to safeguard the rights and well-being of refugees. UNHCR also collects and disseminates statistics on refugees. For more information, see www.unhcr.org
World Bank The World Bank is a vital source of financial and technical assistance for developing countries. The World Bank is made up of two unique development institutions owned by 185 member countries—the International Bank for Reconstruction and Development (IBRD) and the International Development Association (IDA). These institutions play different but collaborative roles to advance the vision of an inclusive and sustainable globalization. The IBRD focuses on middle-income and creditworthy poor countries, while IDA focuses on the poorest countries. Together they provide low-interest loans, interest-free credits, and grants to developing countries for a wide array of purposes, including investments in education, health, public administration, infrastructure, financial and private sector development, agriculture, and environmental and natural resource management. The World Bank’s work focuses on achieving the Millennium Development Goals by working with partners to alleviate poverty. For more information, see www.worldbank.org/data/.
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2009 World Development Indicators
World Health Organization The objective of the World Health Organization (WHO), a specialized agency of the United Nations, is the attainment by all people of the highest possible level of health. It is responsible for providing leadership on global health matters, shaping the health research agenda, setting norms and standards, articulating evidence-based policy options, providing technical support to countries, and monitoring and assessing health trends. For more information, see www.who.int/.
World Intellectual Property Organization The World Intellectual Property Organization (WIPO) is a specialized agency of the United Nations dedicated to developing a balanced and accessible international intellectual property (IP) system, which rewards creativity, stimulates innovation, and contributes to economic development while safeguarding the public interest. WIPO carries out a wide variety of tasks related to the protection of IP rights. These include developing international IP laws and standards, delivering global IP protection services, encouraging the use of IP for economic development, promoting better understanding of IP, and providing a forum for debate. For more information, see www.wipo.int/.
World Tourism Organization The World Tourism Organization is an intergovernmental body entrusted by the United Nations with promoting and developing tourism. It serves as a global forum for tourism policy issues and a source of tourism know-how. For more information, see www.unwto.org/.
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/.
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PARTNERS International Institute for Strategic Studies The International Institute for Strategic Studies (IISS) provides information and analysis on strategic trends and facilitates contacts between government leaders, business people, and analysts that could lead to better public policy in international security and international relations. The IISS is a primary source of accurate, objective information on international strategic issues. For more information, see www.iiss.org/.
International Road Federation The International Road Federation (IRF) is a nongovernmental, not-for-profit organization whose mission is to encourage and promote development and maintenance of better, safer, and more sustainable roads and road networks. Working together with its members and associates, the IRF promotes social and economic benefits that flow from well planned and environmentally sound road transport networks. It helps put in place technological solutions and management practices that provide maximum economic and social returns from national road investments. The IRF works in all aspects of road policy and development worldwide with governments and financial institutions, members, and the community of road professionals. For more information, see www.irfnet.org/.
Netcraft Netcraft provides Internet security services such as antifraud and antiphishing services, application testing, code reviews, and automated penetration testing. Netcraft also provides research data and analysis on many aspects of the Internet and is a respected authority on the market share of web servers, operating systems, hosting providers, Internet service providers, encrypted transactions, electronic commerce, scripting languages, and content technologies on the Internet. For more information, see http://news.netcraft.com/.
PricewaterhouseCoopers PricewaterhouseCoopers provides industry-focused services in the fields of assurance, tax, human resources, transactions, performance improvement, and crisis management services to help address client and stakeholder issues. For more information, see www.pwc.com/.
Standard & Poor’s Standard & Poor’s is the world’s foremost provider of independent credit ratings, indexes, risk evaluation, investment research, and data. S&P’s Global Stock Markets Factbook draw on data from S&P’s Emerging Markets Database (EMDB) and other sources covering data on more than 100 markets with comprehensive market profiles for 82 countries. Drawing a sample of stocks in each EMDB market, Standard & Poor’s calculates indices to serve as benchmarks that are consistent across national boundaries. For more information, see www.standardandpoors.com/.
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2009 World Development Indicators
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 a consortium of more than 60 information technology (IT) industry associations from economies around the world. WITSA members represent over 90 percent of the world IT market. As the global voice of the IT industry, WITSA has an active role in international public policy issues affecting the creation of a robust global information infrastructure, including advocating policies that advance the industry’s growth and development, facilitating international trade and investment in IT products and services, increasing competition through open markets and regulatory reform, strengthening national industry associations through the sharing of knowledge, protecting intellectual property, encouraging cross-industry and government cooperation to enhance information security, bridging the education and skills gap, and safeguarding the viability and continued growth of the Internet and electronic commerce. For more information, see www.witsa.org/.
World Resources Institute The World Resources Institute is an independent center for policy research and technical assistance on global environmental and development issues. The institute provides—and helps other institutions provide— objective information and practical proposals for policy and institutional change that will foster environmentally sound, socially equitable development. The institute’s current areas of work include trade, forests, energy, economics, technology, biodiversity, human health, climate change, sustainable agriculture, resource and environmental information, and national strategies for environmental and resource management. For more information, see www.wri.org/.
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USERS GUIDE Tables
simple totals, where they do not), median values (m),
not be complete because of special circumstances
The tables are numbered by section and display the
weighted averages (w), or simple (unweighted) aver-
affecting the collection and reporting of data, such
identifying icon of the section. Countries and econo-
ages (u). Gap filling of amounts not allocated to coun-
as problems stemming from conflicts.
mies are listed alphabetically (except for Hong Kong,
tries may result in discrepancies between subgroup
For these reasons, although data are drawn from
China, which appears after China). Data are shown
aggregates and overall totals. For further discussion
the sources thought to be most authoritative, they
for 153 economies with populations of more than
of aggregation methods, see Statistical methods.
should be construed only as indicating trends and
1 million, as well as for Taiwan, China, in selected
characterizing major differences among economies
tables. Table 1.6 presents selected indicators for
Aggregate measures for regions
rather than as offering precise quantitative measures
56 other economies—small economies with popu-
The aggregate measures for regions cover only low- and
of those differences. Discrepancies in data presented
lations between 30,000 and 1 million and smaller
middle-income economies, including economies with
in different editions of World Development Indicators
economies if they are members of the International
populations of less than 1 million listed in table 1.6.
reflect updates by countries as well as revisions to his-
Bank for Reconstruction and Development (IBRD) or,
The country composition of regions is based on
torical series and changes in methodology. Thus read-
as it is commonly known, the World Bank. A complete
the World Bank’s analytical regions and may differ
ers are advised not to compare data series between
set of indicators for these economies is available
from common geographic usage. For regional clas-
editions of World Development Indicators or between
on the World Development Indicators CD-ROM and in
sifications, see the map on the inside back cover and
different World Bank publications. Consistent time-
WDI Online. The term country, used interchangeably
the list on the back cover flap. For further discussion
series data for 1960–2007 are available on the World
with economy, does not imply political independence,
of aggregation methods, see Statistical methods.
Development Indicators CD-ROM and in WDI Online. Except where otherwise noted, growth rates are
but refers to any territory for which authorities report separate social or economic statistics. When avail-
Statistics
in real terms. (See Statistical methods for information
able, aggregate measures for income and regional
Data are shown for economies as they were con-
on the methods used to calculate growth rates.) Data
groups appear at the end of each table.
stituted in 2007, and historical data are revised to
for some economic indicators for some economies
reflect current political arrangements. Exceptions are
are presented in fiscal years rather than calendar
noted throughout the tables.
years; see Primary data documentation. All dollar fig-
Indicators are shown for the most recent year or period for which data are available and, in most tables, for an earlier year or period (usually 1990 or
Additional information about the data is provided
ures are current U.S. dollars unless otherwise stated.
1995 in this edition). Time-series data for all 209
in Primary data documentation. That section sum-
The methods used for converting national currencies
economies are available on the World Development
marizes national and international efforts to improve
are described in Statistical methods.
Indicators CD-ROM and in WDI Online.
basic data collection and gives country-level informa-
Known deviations from standard definitions or
tion on primary sources, census years, fiscal years,
Country notes
breaks in comparability over time or across countries
statistical methods and concepts used, and other
•
are either footnoted in the tables or noted in About
background information. Statistical methods provides
the data. When available data are deemed to be
technical information on some of the general calcula-
too weak to provide reliable measures of levels and
tions and formulas used throughout the book.
include data for Hong Kong, China; Macao, China; or Taiwan, China. •
Data for Indonesia include Timor-Leste through 1999 unless otherwise noted
trends or do not adequately adhere to international standards, the data are not shown.
Unless otherwise noted, data for China do not
Data consistency, reliability, and comparability
•
Montenegro declared independence from Serbia
Considerable effort has been made to standardize
and Montenegro on June 3, 2006. When avail-
Aggregate measures for income groups
the data, but full comparability cannot be assured,
able, data for each country are shown separately.
The aggregate measures for income groups include
and care must be taken in interpreting the indicators.
However, some indicators for Serbia continue to
209 economies (the economies listed in the main
Many factors affect data availability, comparability,
include data for Montenegro through 2005; these
tables plus those in table 1.6) whenever data are
and reliability: statistical systems in many develop-
data are footnoted in the tables. Moreover, data
available. To maintain consistency in the aggregate
ing economies are still weak; statistical methods,
for most indicators from 1999 onward for Serbia
measures over time and between tables, missing
coverage, practices, and definitions differ widely; and
exclude data for Kosovo, which in 1999 became
data are imputed where possible. The aggregates
cross-country and intertemporal comparisons involve
a territory under international administration pur-
are totals (designated by a t if the aggregates include
complex technical and conceptual problems that can-
suant to UN Security Council Resolution 1244
gap-filled estimates for missing data and by an s, for
not be resolved unequivocally. Data coverage may
(1999); any exceptions are noted.
xx
2009 World Development Indicators
Classification of economies
Symbols
For operational and analytical purposes the World
..
Bank’s main criterion for classifying economies is
means that data are not available or that aggregates
gross national income (GNI) per capita (calculated
cannot be calculated because of missing data in the
by the World Bank Atlas method). Every economy is
years shown.
classified as low income, middle income (subdivided into lower middle and upper middle), or high income.
0 or 0.0
For income classifications see the map on the inside
means zero or small enough that the number would
front cover and the list on the front cover fl ap. Low-
round to zero at the displayed number of decimal
and middle-income economies are sometimes
places.
referred to as developing economies. The term is used for convenience; it is not intended to imply
/
that all economies in the group are experiencing
in dates, as in 2003/04, means that the period of time,
similar development or that other economies have
usually 12 months, straddles two calendar years and
reached a preferred or final stage of development.
refers to a crop year, a survey year, or a fiscal year.
Note that classifi cation by income does not necessarily refl ect development status. Because GNI per
$
capita changes over time, the country composition
means current U.S. dollars unless otherwise noted.
of income groups may change from one edition of World Development Indicators to the next. Once the
>
classifi cation is fi xed for an edition, based on GNI
means more than.
per capita in the most recent year for which data are available (2007 in this edition), all historical
<
data presented are based on the same country
means less than.
grouping. Low-income economies are those with a GNI per capita of $935 or less in 2007. Middle-income
Data presentation conventions •
economies are those with a GNI per capita of more
A blank means not applicable or, for an aggregate, not analytically meaningful.
than $935 but less than $11,456. Lower middle-
•
A billion is 1,000 million.
income and upper middle-income economies are
•
A trillion is 1,000 billion.
separated at a GNI per capita of $3,705. High-
•
Figures in italics refer to years or periods other
income economies are those with a GNI per capita
than those specified or to growth rates calculated
of $11,456 or more. The 16 participating member countries of the euro area are presented as a subgroup under high-income economies. Note
for less than the full period specified. •
Data for years that are more than three years from the range shown are footnoted.
that the Slovak Republic joined the euro area on January 1, 2009.
The cutoff date for data is February 1, 2009.
2009 World Development Indicators
xxi
Introduction
T
he world seems to be entering an economic crisis unlike any seen since the founding of the Bretton Woods institutions. Indeed, simultaneous crises. The bursting of a real estate bubble. The liquidity and solvency problems for major banks. The liquidity trap as consumers and businesses prefer holding cash to spending on consumption or investment. The disruptions in international capital flows. And for some countries a currency crisis. Plummeting global output and trade in the last quarter of 2008 brought the global economy to a standstill after years of remarkable growth, throwing millions out of work. The United States, as the epicenter, has seen unemployment rising to more than 11 million, an unemployment rate of 7.2 percent. Most forecasts show world GDP growth slowing to near zero or negative values, after a 3.4 percent increase in 2008. What brought about the crisis? Why is it so severe? How quickly has it spread? In this introduction, and in the introductions to sections four (Economy) and six (Global links), the data describe the events that have brought us to this point. Could the crisis have been anticipated by looking more closely at the same data? Perhaps. Perhaps not. But there is still much we can learn about how these events unfolded. The crisis must be seen in the context of dramatic changes in the global economy. First, record export-led economic growth in emerging market economies shifted the balance of global economic power, evidenced by their growing share in world output, trade, and international reserves. High savings rates outstripped their capacity to invest in their own economies while policies to sterilize large inflows and protect against financial shocks led to a large build up in international reserves. So poorer economies were financing the current account deficits of high-income economies. Second, financial integration has accompanied expanding trade, spurred by remarkable developments in information technology and financial innovation. This extended the reach of global markets, lowering costs and increasing their efficiency, but also spreading systemic shocks farther and faster. The financial crisis had its origins in a U.S. real estate asset bubble fed by a boom in subprime mortgage lending. The availability of cheap credit fed asset bubbles in other developed economies and among major emerging market economies. The rapid and massive growth of long-term, illiquid, and risky assets financed by short-term liabilities contributed to the speed with which the crisis spread across the world economy and to its severity. Global growth will be negative in 2009, and growth in developing economies will fall sharply from the 6 percent or higher rates of 2008. This reflects both a sharp decline in export demand from high-income economies and a major reduction in access to commercial finance and an increase in its cost. Slower growth will inevitably affect the ability of low-income economies to reach the Millennium Development Goals. How far the global recession extends and how long it lasts will depend on the effectiveness of policies adopted by rich and poor economies alike in the months ahead.
2009 World Development Indicators
1
Growth accelerated in the 2000s
Exports led growth
The years preceding the 2008 global crisis saw the strongest
Integration of the global economy was marked by a rapid in-
economic growth in decades (figure 1a). Global economic
crease in trade. Growth in low- and middle-income economies
output grew 4 percent a year from 2000 to 2007, led by re-
was led by exports, which grew at an average annual rate of
cord growth in low- and middle-income economies. Develop-
12 percent over 2000–07. China and India were among the
ing economies averaged 6.5 percent annual growth of GDP
fastest- growing exporters. Export growth was led by manu-
from 2000 to 2007, and growth in every region was the high-
factures in China and by services in India. Some smaller
est in three decades (figure 1b). Europe and Central Asia and
economies with exports of oil, gas, metals, minerals, or manu-
South Asia had their best decade in the most recent period
factures were also among the fastest growing. Exports from
(2000–07). East Asia and Pacific almost equaled its previous
low- and middle-income economies in 2007 made up 29 per-
peak, reached before the 1997 crisis. For others the peak
cent of the world total, up from 21 percent in 2000. Although
was in 1976— before the oil price shocks of the late 1970s
trade between low- and middle-income economies has been
and the debt crisis of the 1980s. But growth rates in high-
growing, 70 percent of low- and middle-income economies’ ex-
income economies have been on a downward path since the
ports still went to high-income economies in 2007 (figure 1c). Fast-growing, export-oriented economies attracted new
1970s. China and India have emerged in recent years as drivers of
investment (figure 1d). Some of it came from domestic sav-
global economic growth, accounting for 2.9 percentage points
ing. In low- and middle-income economies savings rose from
of the 5 percent growth in global output in 2007. Low- and
25 percent of GDP in 2000 to 32 percent in 2007. But growth
middle-income economies now contribute 43 percent of
also attracted foreign direct investment. The contribution of
global output, up from 36 percent in 2000. China and India account for 5 percentage points of that increased share.
investment to GDP growth in these economies averaged less than 1 percentage point before 2000 but rose to 2.4 percentage points over 2000–07.
Developing economies had their best decade of growth in 2000–07 Average annual growth in purchasing power parity GDP (%)
1a
Low income
Middle income
High income
8
Most developing economy exports go to high-income economies
1c
Merchandise exports from developing economies, by destination ($ trillions) 5 4
To low-income economies
6 3 4
To middle-income economies
2 2 1 0 1970–80
1980–90
1990–2000
Long-term trends reached new heights
To high-income economies
0
2000–07
Note: Data for 1970–80 are based on GDP in constant 2000 U.S. dollars converted using market exchange rates. Source: World Development Indicators data files.
1b
1990
2000
2005
2007
Source: International Monetary Fund’s Direction of Trade database.
Increased investment led to faster growth in low- and middle-income economies Contributions to GDP growth (%) 10
Annual growth in GDP per capita, 10-year moving average (%) 8 East Asia & Pacific
Net exports
1d Investment
Consumption
8
6 4
South Asia
2
6
High-income OECD
4
0 Latin America & Caribbean Middle East & North Africa Sub-Saharan Africa
–2 –4
2 Europe & Central Asia
0
–6 1970
1975
1980
1985
1990
1995
2000
2007
–2 1990
Source: World Development Indicators data files.
2
1995
2009 World Development Indicators
1995
2000
Source: World Development Indicators data files.
2005
2007
Structural imbalances emerged
Countries became more interdependent
Countries with trade surpluses accumulated capital beyond their capacity to absorb it. Many ran large current account surpluses and accumulated record reserves. Countries with trade deficits financed their current account by increased borrowing abroad. From 2005 to 2007 the five largest surplus economies accounted for 71 percent of total current account surpluses, and the five largest deficit economies, for 79 percent of total current account deficits (table 1e). China’s current account surplus rose from 2 percent of GDP in 2000 to an average of 10 percent during 2005–07 (figure 1f). Oil and gas exporters such as the Russian Federation and Saudi Arabia also saw surpluses balloon. Unlike many high-income economies, Germany went from a deficit of 1.5 percent of GDP in 2000 to a surplus of 6 percent over 2000–07. But some countries with strong export growth had equally strong import growth, with India and Mexico maintaining small current account deficits. The largest deficits were in high-income economies, with the United States accounting for more than half the world’s current account deficits. The U.S. current account deficit increased from 4.3 percent of GDP in 2000 to an average of 6 percent in 2005–07. Spain’s rose from 4 percent to 9 percent of GDP.
As the global imbalance between savings and investment
Large current account surpluses and deficits were concentrated in a few economies during 2005–07 2005–07 average ($ billions) –1,303 –749 –113 –74 –47 –43 1,428 372 256 210 95 76
Economy All deficit economies United States Spain United Kingdom Australia Italy All surplus economies China Germany Japan Saudi Arabia Russian Federation
1e
Share of all deficit/surplus economies (%)
Percent of GDP
57 9 6 4 3
–6 –9 –3 –6 –2
26 18 15 7 5
10 6 4 27 8
with surpluses, while fast-growing exporters depended on expanding markets in deficit countries. China and other surplus economies accumulated record reserves (figure 1g) and sent capital overseas. The United States and other deficit countries consumed more and financed their deficits by issuing more debt and equity (figure 1h). Savings and investment trends for China, the largest surplus country, and the United States, the largest deficit country, illustrate the growing imbalances. China’s savings rate increased, exceeding investment by 11.5 percent of GDP in 2007. In the United States private savings almost disappeared, and investment exceeded savings by 4.6 percent of GDP. Countries with large reserves invested large portions of their holdings in U.S. Treasury securities, widely regarded as very low risk. At the end of 2008 China was the largest foreign holder of U.S. Treasury securities, at $696 billion, followed by Japan, at $578 billion. Total foreign holdings of U.S. Treasury securities were $3.1 trillion, up from $2.4 trillion in 2007.
Trade surpluses led to large build-ups in reserves
1g
Reserves ($ billions)
2000
1,200 800 400
0 Taiwan, China
1f
Russian Federation
Euro zone
Japan
Saudi Arabia
China
Source: International Monetary Fund balance of payments data files.
Trade deficits were financed by foreign investors
1h
Net flows of portfolio debt and equity securities ($ billions)
Current account balance (% of GDP) 30
2007
1,600
Source: International Monetary Fund balance of payments data files and World Development Indicators data files.
Current account surpluses and deficits increased
grew, countries with large deficits borrowed from countries
2000
2007
1,000 750
20 China
Russian Federation
500
10 250 0
United Kingdom Spain
–10 2000
2001
2002
2003
2004
Source: World Development Indicators data files.
2005
0
United States
–250 2006
2007
Brazil
Japan
Euro area
United Kingdom
United States
Source: International Monetary Fund balance of payments data files.
2009 World Development Indicators
3
Foreign investments grew
Asset prices rose rapidly as well
Private capital flows to low- and middle-income economies more than quadrupled from $200 billion in 2000 to over $900 billion in 2007, reaching 6.6 percent of the economies’ collective GDP (figure 1i). Foreign domestic investment accounts for most of those flows, as multinational corporations established footholds in new markets, shifted production sites to take advantage of lower costs, or sought access to supplies of natural resources. Portfolio investment in bond and equity markets also grew. Foreign investors were drawn to emerging equity markets as the prospects for these economies improved substantially and the returns outpaced those in more developed markets. Net inflows from bonds and commercial bank lending grew from $12 billion in 2000 to $269 billion in 2007 as globalization of the banking industry continued and perceived risk in many low- and middleincome economies dropped to all-time lows (figure 1j). Brazil, China, India, and the Russian Federation attracted the largest shares of capital flows among developing economies. But foreign domestic investment flows to low-income economies also increased in recent years—some of them coming from developing economies with large current account surpluses—drawn by rising commodity prices into the oil, mineral, and other commodity sectors and into infrastructure projects.
Stock market capitalization in low- and middle-income econo-
Private capital flows to developing economies took off in 2002 . . .
1i
Private financial flows (% of GDP) 8
mies increased nearly eightfold, rising from $2 trillion in 2000 to $15 trillion in 2007, or from 35 percent of GDP to 114 percent. Stock markets in Brazil, China, India, and the Russian Federation accounted for $11 trillion. Foreign investors increased their stakes in these markets, which outperformed more developed markets. Foreign holdings of portfolio equity securities increased from $37 billion in 2001 to $364 billion in 2007 in Brazil, from $11 billion in 2000 to $292 billion in 2007 in the Russian Federation and from $17 billion to $103 billion in India, and from $43 billion in 2004 to $125 billion in 2007 in China. Other classes of assets such as housing also appreciated rapidly (figure 1k). Asset prices rose in part due to more optimistic expectations for future earnings. Price-earnings ratios, a measure of valuation for equities, rose rapidly in low- and middle-income economy stock markets (figure 1l). From 2000 to 2007 ratios rose from 11.5 to 16.6 in Brazil, from 21.6 to 50.5 in China, from 16.8 to 31.6 in India, and from 3.8 to 18.4 in the Russian Federation. And rising housing prices reflected expectations for continuing appreciation. Prices of assets, especially in real estate, were rising rapidly in some countries . . .
1k
House price indices (2002 = 100) 500 Russian Federation: average housing prices
6
400
4
300 South Africa: ABSA House Price Index
2
200
Indonesia: residential property price index (14-city composite)
Taiwan, China: Sinyi Purchase Price Index
0 1990
1995
2000
2005
2007
100
Malaysia: house prices
2002 Source: Global Development Finance data files and World Development Indicators data files.
. . . And investors perceived less risk
2003
2004
2005
2006
Singapore: property price index
2007
2008
Source: Haver Analytics.
1j
. . . And so were equity asset valuations
1l
Spread on emerging market sovereign bonds against 10-year U.S. Treasury notes (basis points) 1,800
Price-earnings ratio (Standard & Poor’s IFCG index) 50
1,500
40
China
India
1,200
30
Russian Federation
900 20 600
Brazil
10 300 0 Jan-98 Jan-99 Jan-00 Jan-01 Jan-02 Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Source: JPMorgan-Chase.
4
2009 World Development Indicators
0 2000
2001
2002
Source: Standard & Poor’s 2008.
2003
2004
2005
2006
2007
External debt declined and changed composition
Demand for primary commodities increased
The Debt Initiative for Heavily Indebted Poor Countries and the Multilateral Debt Relief Initiative have helped some of the poorest and most indebted countries, especially in SubSaharan Africa, significantly reduce their outstanding debt. External debt to GNI ratios for Sub-Saharan Africa went from more than 80 percent in the mid-1990s to less than 30 percent today (figure 1m). Elsewhere, especially in Europe and Central Asia, debt increased in recent years. For Croatia, Kazakhstan, Latvia, Romania, and a few small island economies external debt to GNI ratios reached all-time highs in 2007. As debt ratios fell, many countries gained access to private financing. Private nonguaranteed debt of low- and middleincome economies rose from 24 percent of total debt in 2000 to 37 percent in 2007. In Europe and Central Asia private nonguaranteed debt made up 55 percent of total external debt in 2007. Short-term debt in low- and middle-income economies rose from 13 percent of total debt in 2000 to 24 percent in 2007. In 2007 in East Asia and Pacific short-term debt made up 39 percent of total debt and 55 percent in China. But growing international reserves helped offset the risk of short-term financing in foreign currencies (figure 1n).
Rapid global economic growth drove demand for commodities, boosting prices, especially for oil, metals, and minerals used as inputs to manufacturing. After increasing gradually from 2000 to 2006, prices rose more rapidly in 2007 and into 2008. Food prices also rose, due in part to the production of ethanol from corn and other food crops (figure 1o). Rising commodity prices benefited exporters, especially in Latin America and the Caribbean and Sub-Saharan Africa. Aside from the terms of trade gains, the higher commodity prices increased government revenues from taxes on commodity exports and attracted foreign domestic investment into commodity exports and supporting infrastructure projects. But for food and fuel importers the spike in prices has been costly. Current account balances of most oil-importing low- and middle-income economies worsened (figure 1p). Price increases have also pushed up inflation and interest rates, with the impacts especially severe for poor people. In eight countries higher food prices between 2005 and 2007 increased poverty rates by 3 percentage points on average (Ivanic and Martin 2008). Globally, the number of people living on less than $1.25 a day may have risen by more than 100 million before commodity prices began to fall in the latter half of 2008.
Indebtedness ratios have improved for most economies
1m
External debt to GNI ratio, by region (%) 100
Commodity price rises accelerated in recent years
1o
Commodity prices (index, 2000 = 100) 400 Metals and minerals
75
300
Sub-Saharan Africa Latin America & Caribbean
Energy
50 Middle East & North Africa
200 Food
25
100
South Asia Europe & Central Asia
East Asia & Pacific
0 1990
1995
2000
2005
2007
Source: Global Development Finance data files and World Development Indicators data files.
Growing reserves comfortably covered short-term debt liabilities
1n 2000
Short-term debt, by region (% of total reserves) 100
2007
0 Jan-00
Jan-01
Jan-02
Jan-03
Jan-04
Jan-05
Jan-06
Jan-07 Dec-07
Source: World Development Indicators data files.
Food and fuel importers were hurt by rising prices
1p
Current account balance of low- and middle-income economies, excluding China and oil exporters (% of GDP) 0
75 –1 50
25
–2
0 East Asia & Pacific
Europe & Latin Middle East & Central Asia America & North Africa Caribbean Source: World Development Indicators data files.
South Asia
Sub-Saharan Africa
–3 2000
2001
2002
2003
2004
2005
2006
2007
Source: World Development Indicators data files.
2009 World Development Indicators
5
A perfect storm? The current global financial and economic crisis is unlike anything the world has seen since the Great Depression nearly eight decades ago. It embraces simultaneous crises in the housing, equity, and financial markets, triggering what could become a global recession. Output and trade declined sharply in the last quarter of 2008 (figure 1q). Projections for 2009 suggest global growth close to zero percent, with strong downside risks. Unemployment is rising sharply in both developed and emerging market economies. The International Labour Organization estimates job losses of up to 50 million in 2009. The United States lost as many as 3.6 million jobs in 2008. The crisis had its superficial roots in the rise in U.S. household debt (figure 1r), financed largely by home mortgages, many of which did not meet prime underwriting guidelines. When home prices began to fall from their peak in 2006 (figure 1s), mortgage default rates rose sharply and triggered a collapse in mortgage-backed securities. Subprime lending came to an abrupt halt, further driving down the prices of U.S. homes. Investors, their confidence undermined, withdrew funds from other illiquid markets (figure 1t), and investment banks had to liquidate assets or withdraw financing from customers, forcing further deleveraging. Thus, Output in the largest economies slowed or declined in the 4th quarter of 2008
1q 2008 Q1 2008 Q3
GDP (% change from previous year) 15
2008 Q2 2008 Q4
the subprime mortgage crisis became a fully fledged financial crisis (Lin 2009). The impacts were felt throughout the increasingly integrated global financial markets, attacking stock markets globally and reducing credit availability. Global stock markets lost an estimated $30 trillion in market capitalization in 2008 over their inflated 2007 levels. Rising unemployment and the wealth effects of falling asset prices contributed to a sharp decline in consumer spending. Developing countries suddenly faced a sharp decline in demand for their exports and a drop in commodity prices. As recessionary trends developed, remittances from migrant workers declined, and migrants began returning home. Three major factors account for the scale of the crisis. Underlying the bubbles in global real estate and stock markets were growing macroeconomic imbalances that fed liquidity into the system, lowering real interest rates and fueling the asset price bubbles. Financial innovations pioneered by major global investment banks turned out to be transmission mechanisms for instability (Lin 2009). And the failure of national financial regulators to effectively regulate global financial markets encouraged investors to take exorbitant risk. U.S. house prices peaked in 2006
1s
U.S. house price index (1980 = 100) 400
10 300 5 200
0 –5
100
–10 0 2000 Q1
–15 United States
Japan
Euro area
China
Source: U.S. Department of Commerce, Japan Cabinet Office, Eurostat, China National Bureau of Statistics, Haver Analytics, and World Bank staff calculations.
U.S. household debt rose rapidly after 2000
1r
Household debt as share of disposable personal income (%) 160
2002 Q1
2004 Q1
2006 Q1
2008 Q4
Source: U.S. Office of Federal Housing Enterprise Oversight.
As housing bubbles burst, investors lost confidence
1t
Stock market capitalization ($ trillions) 80 Total household debt
120
Home mortgages outstanding
80
40
40
Consumer credit
0 1998
2000
2002
2004
Source: Board of Governors of the Federal Reserve System data files.
6
60
2009 World Development Indicators
2006
2007
20 0 Jan-07
Jun-07
Jan-08
Source: World Federation of Exchanges data files.
Jun-08
Jan-09
Macroeconomic imbalances Global savings and investment rates have been fairly stable in recent years at 20–22 percent. But these global rates masked a significant shift in the source of savings. In developing economies savings rates rose 7 percentage points of aggregate GDP between 2000 and 2007, exceeding investment rates, while in high-income, Organisation for Economic Co-operation and Development countries, savings rates fell by about 2 percentage points of aggregate GDP. The growing pool of savings in part of the world reflected the rising new incomes of oil exporters, boosted by record prices, deliberate policies to build up foreign exchange reserves by Asian countries wishing to avoid repeating the experience of the late 1990s, and the excess household and corporate savings in China (figure 1u). Surpluses in Germany, Japan, and some Asian countries were matched by substantial savings deficits, mainly in the United States (figures 1v and 1w). U.S. savings rates fell nearly 4 percentage points between 2000 and 2007, producing a savings-investment imbalance of close to 5 percentage points of GDP. The rest of the world’s savings surpluses left the United States awash in liquidity. U.S.-owned assets abroad doubled between 2000 and 2007 to 128 percent of GDP, reflecting the importance of the United States as a source of both foreign Savings and investment in China . . .
1u
direct investment and portfolio flows (figure 1x). U.S. liabilities rose from 77 percent of GDP to 145 percent over the same period, increasing the negative net liabilities to 17 percent of 2007 GDP. Foreign official assets held in the United States more than tripled, to $3.3 trillion, or 24 percent of GDP, the bulk of it in U.S. government securities, the counterpart to the buildup in reserves in developing and high-income Asia. This kept U.S. interest rates low and stimulated a global “search for yield.” It also led investors to underprice risk and shift to risky assets, stimulating a boom in real estate and stock markets globally. Some see the savings surpluses in developing economies as policy driven, ascribing a passive role to policymakers in industrial economies who benefited from these surpluses. Others see U.S. structural deficits as reflecting profligate spending. Indeed, personal savings in the United States were a mere 1.7 percent of GDP in 2000, falling further to 0.4 percent by 2007 reflecting consumption growth in “substantial excess of income growth” (Summers 2006). But until the crisis rudely interrupted the party, both surplus and deficit countries benefited: the first from high export-led growth; the second from low interest rates and cheap consumer goods, which held inflation down despite large fiscal deficits. The five largest current account surpluses and deficits
1w
Current account balance, 2005–07 average ($ billions) 400
Gross savings and investment (% of GDP) 60 Gross savings (% of GDP)
200 0
50
–200 –400
40 Gross capital formation (% of GDP)
30 1980
–600 –800
1985
1990
1995
2000
2007
Source: World Development Indicators data files.
China Germany Japan
Saudi Russian Italy Arabia Federation
Australia United Spain Kingdom
United States
Source: International Monetary Fund balance of payments data files.
. . . And the United States
1v
Gross savings and investment (% of GDP)
U.S. foreign assets and liabilities doubled
1x
U.S. international investment position (% of GDP) 160
30
Foreign-owned assets in the United States (% of GDP) Gross capital formation (% of GDP)
120
20 80
U.S.-owned assets abroad (% of GDP)
Gross savings (% of GDP)
10
40 0 1980
0 1985
1990
1995
Source: World Development Indicators data files.
2000
2006
2000
2001
2002
2003
2004
2005
2006
2007
Source: Department of Commerce, Bureau of Economic Analysis data files.
2009 World Development Indicators
7
The role of financial innovation What distinguishes this crisis from previous crises is the speed and depth of the transmission channels, as a U.S.based crisis turned global in a matter of months. This reflects the transformation of the financial system during this boom period by the dramatic growth in the share of assets held outside the traditional banking system. The mortgage market, for example, was transformed by an “originate and distribute” model. Mortgage loans, made by loan originators, were resold to financial institutions, which “sliced and diced” pools of mortgages and aggregated them into collateralized debt obligations resold in turn to investors all over the world. Derivatives, or financial instruments whose value is derived from the value of an underlying asset (commodities, equities, stocks, mortgages, real estate, loans, bonds) or an index (of interest rates, stock prices, or consumer prices), enable those who trade in them to mitigate risk through hedging or to speculate. Derivatives can be bought and sold through over the counter trades between two parties, or they can be exchange traded. Over the counter derivatives had a notional value of some $684 trillion in June 2008 (figure 1y), representing the value of the underlying assets against which the derivatives were issued. But the risk is better measured by the cost of replacing all such contracts at the prevailing market price: their gross global market value rose from $2.5 trillion in June 2000 to a still astronomical $20.4 trillion in June 2008 (figure 1z). Assets underlying over the counter derivatives rose sevenfold . . .
1y
Notional amounts oustanding ($ trillions)
Derivatives were pioneered globally by investment banks, stimulated by high fees. Financial sector profits in the U.S. averaged 29 percent of before-tax profi ts between 2000 and 2006 (figure 1aa). U.S investment banks quickly grew to rival commercial banks but were not subject to the same regulation. “The scale of long-term risky and illiquid assets financed by very short-term liabilities made many of the vehicles and institutions in this parallel financial system vulnerable to a classic type of run, but without the protections, such as deposit insurance, that the banking system has in place to reduce such risks” (Geithner 2008). Following the collapse of the real estate market and the loss of confidence in mortgage- backed securities, investors began pulling out of these markets, creating liquidity and solvency crises for investment banks. Underlying these developments lay the failure to properly regulate financial institutions, weaknesses in internal risk management systems, and the failure of credit rating agencies to correctly rate risk. At the end of 2007, there were reportedly 12 triple-A rated companies in the world, but as many as 64,000 structured finance instruments were rated triple-A (Blankfein 2009). Given the size of the market in these new instruments, it is questionable whether any single national authority can regulate cross-border transactions (fi gure 1bb). U.S. domestic financial sector profits averaged almost 30 percent of before-tax profits during 2000–06
1aa
Share of before-tax profits (%) 40
800 Unallocateda 600
30
Credit default swaps Commodity contracts Equity linked contracts
400
20 200
10 Interest rate contracts Foreign exchange contracts
0
Jun-00
Jun-01
Jun-02
Jun-03
Jun-04
Jun-05
Jun-06
Jun-07
Jun-08
a. Includes over the counter derivatives of nonreporting institutions, based on the latest Triennial Central Bank Survey of Foreign Exchange and Derivatives Market Activity in 2007. Source: Bank for International Settlements data files.
. . . While the market value of derivatives rose ninefold
1z
1995
1997
1999
2001
20
Unallocateda
2003
2005
2007
Source: Department of Commerce, Bureau of Economic Analysis data files.
Derivatives can undermine capital controls, leading to linkages that make market dynamics difficult to predict Market average daily turnover in over the counter derivatives, 2007 ($ billions) 500
Gross market values ($ trillions) 25
1bb
Total Foreign exchange Interest rate
400
Credit default swaps
15
Commodity contracts Equity linked contracts
300
10
200
5
Interest rate contracts Foreign exchange contracts
0 Jun-00
Jun-01
Jun-02
Jun-03
Jun-04
Jun-05
Jun-06
Jun-07
Jun-08
a. Includes over the counter derivatives of nonreporting institutions, based on the latest Triennial Central Bank Survey of Foreign Exchange and Derivatives Market Activity in 2007. Source: Bank for International Settlements data files.
8
0
2009 World Development Indicators
100 0 Emerging China Asia
India
Korea, Latin Brazil Mexico Central Russian South Turkey Rep. America Europe Feder- Africa ation
Source: Bank for International Settlements 2007.
Why financial crises occur so often Banking systems are inherently prone to crises. Banks borrow short (take in deposits) and lend long. They rely on depositors not to withdraw their deposits all at the same time. But depositor confidence in banks can be shaken by the rising threat of nonperforming loans or by political and economic developments. Even sound banks can be hurt by rumors and a general loss of confidence. When that happens, depositors may demand their deposits, causing a run on banks or a liquidity crisis. If banks sell their assets to maintain their ability to repay depositors, that may reduce the price of their assets and thus their equity base, a solvency crisis. A recent study notes that banking crises “have long been an equal opportunity menace,” (Reinhart and Rogoff 2008, p. 2) affecting developed and developing economies alike. It examines crises beginning with Denmark’s financial panic during the Napoleonic war to the current global financial crisis in 66 economies. The Great Depression of the 1930s was the crisis that affected the greatest number of countries in the 109 years ending in 2008. The period immediately after World War II, between the late 1940s and the early 1970s, when financial markets were repressed and capital controls were extensive, was marked by relative calm. Banking crises recurred again after the 1970s following financial and international capital account liberalization (figure 1cc). Major crises in this period included the Latin American debt crisis of the 1980s, the U.S. The number of banking crises rose after the 1970s
1cc
savings and loan crisis of 1984, Japan’s asset bust in 1992, and the Asian financial crisis of 1997–98 (figure 1dd). Some key characteristics of banking crises include: • Periods of high international capital mobility, which have repeatedly produced international banking crises, possibly because they were accompanied by inadequate regulation and supervision (Caprio and Klingebiel 1996). • Preceding period of sustained surges in capital inflows. • Preceding boom in real housing prices, followed by a marked decline in the year of the crisis and beyond. • Preceding expansion in the number of fi nancial institutions. The cost of bailing out banks following a systemic crisis (the exhaustion of much or all of banking capital) is often high (figure 1ee). A study of 117 systemic banking crises in 93 economies between the late 1970s and 2002 shows that the cost to countries of major crises could amount to as much as 55% of GDP (as with Indonesia in its 1997–2002 crisis; Caprio and Klingebiel 2003). This does not include the cost to depositors and borrowers of wider interest rate spreads from bad loans on balance sheets. The data need to be used with caution, however, because in some cases costs include corporate restructuring while in others they relate to restructuring and capitalization of banks. The cost of systemic financial crises can be very high
1ee
Cost of crisis (% of GDP)
Countries with banking crisis 60
Tanzania 1980–87 Hungary 1991–95 Finland 1991–94
40
Taiwan, China 1997–98 Czech Republic 1991–1994 Paraguay 1995–99
20
Bulgaria 1995–1997 Mauritania 1984–93 Malaysia 1997–2002
0 1970
Spain 1977–85 1975
1980
1985
1990
1995
2000
2007
Senegal 1988–91 Benin 1988–90
Source: Reinhart and Rogoff 2008.
Venezuela 1994–95 Mexico 1994–97
The latest crisis is affecting a large portion of global income
Ecuador 1998–2002
1dd
Uruguay 1981–84 Japan 1991–2002
Proportion of global income of countries with banking crises (%) 60 U.S. savings and loan crisis
Japan banking crisis
40
Côte d’Ivoire 1988–91 U.S.-led global financial crisis
Asian crisis
Korea, Rep. 1997–2002 Israel 1977–83 Turkey 2000–2002 Macedonia 1993–94 Thailand 1997–2002 Chile 1981–86
20
Jamaica 1995–2000 China 1990s Indonesia 1997–2002
0 1970
Argentina 1980–82 1975
1980
1985
1990
1995
2000
2007
Source: Reinhart and Rogoff 2008 and World Development Indicators data files.
0
10
20
30
40
50
60
Source: Caprio and Klingebiel 2003.
2009 World Development Indicators
9
The crisis spreads quickly . . .
. . . And developing economies feel the pain
Past crises show that equity prices typically fall 55 percent over 3.5 years (Reinhart and Rogoff 2008). Housing prices decline an average 35 percent over 6 years. Unemployment rises 7 percentage points over 4 years. Output falls by 9 percent over 2 years. And the real value of government debt rises an average of 86 percent. This pattern is beginning to play out in the major high-income economies. In the fourth quarter of 2008 U.S. gross domestic output contracted by an annualized rate of 6.2 percent, Euro zone countries by 5.9 percent, and Japan by 12.1 percent. Many low- and middle-income economies have begun to feel the impact as high-income economy demand for their exports declines. The troubles of the financial sector have increased risk aversion and reduced liquidity, impairing or reversing capital flows to low- and middle-income economy borrowers and equity markets (figures 1ff and 1gg). The subsidiaries of troubled banks are likely to curtail lending, pushing corporations with debt falling due into risk of insolvency. Foreign direct investment flows may also decline as corporations adjust to an increasingly uncertain environment and as plunging commodity prices make some ventures less appealing. Higher unemployment will also reduce workers’ remittance flows to low- and middle-income economies.
Low-income economies are the most vulnerable to potential losses of official aid, workers’ remittances, and foreign direct investment, which often make up a large share of their GDP (table 1hh). But the slowdown in trade could also hurt low-income commodity exporters such as The Gambia, Guinea-Bissau, Nigeria, Mauritania, Mongolia, Papua New Guinea, and Zimbabwe, which are expected to suffer large terms of trade losses as prices fall. Lower commodity prices will reduce both export revenues and fiscal revenues—and discourage foreign direct investment. But food and fuel importers, which have endured soaring prices since January 2007, will get some relief—oil importers such as Kyrgyz Republic and Tajikistan and food importers such as Benin, Eritrea, Ghana, Guinea, Haiti, Madagascar, Niger, Senegal, and Togo may benefit from improved terms of trade. Remittances have proved surprisingly resilient, rising again in 2008. But they are expected to fall as unemployment rises in high-income economies and some migrants return home. For many low-income economies, remittances are a big part of total capital flows—10 percent of GDP in more than a quarter of them (figure 1ii). And if the pattern of past financial crises is a guide, there is also a risk that official aid will decline. Low-income economies rely heavily on official aid flows, with median official aid at 15 percent of GDP in 2005–07.
Borrowing costs have climbed, reflecting perceived risk
1ff
Low-income economies depend the most on official aid, workers’ remittances, and foreign direct investment
1hh
External financing, 2007 (% of GDP)
Spread on emerging market sovereign bonds against 10-year U.S. Treasury notes (basis points) 1,000
LowMiddleincome income economies economies
Source 750
Workers’ remittances and compensation of employees, receipts
5.7
1.8
500
Official aid
5.0
0.3
Foreign direct investment, net inflows
4.2
3.7
Portfolio equity investment
1.6
0.9
250
Bonds 0 Jan-08
Mar-08
May-08
Jul-08
Sep-08
Nov-08
Jan-09
0.2
0.6
Commercial banks and other lending, net flows
–0.1
1.6
Net exports of goods and services
–6.3
2.6
Source: World Development Indicators data files.
Source: JPMorgan-Chase.
Equity markets have suffered large losses
1gg
Remittances are significant for many low-income economies
1ii
Number of low-income countries 15
MSCI equity price indices (January 2008 = 100) 120
90
10 Group of Seven
60 5 Emerging market economies
30 0 0 Jan-08
0–5 Mar-08
May-08
Jul-08
Source: Morgan-Stanley.
10
2009 World Development Indicators
Sep-08
Nov-08
Jan-09
6–10 11–15 16–20 21–25 26–30 31–35 36–40 41–45 46–50 Private current transfers (% of GDP)
Source: International Monetary Fund balance of payments data files and World Development Indicators data files.
Coping with the crisis
Protecting the vulnerable
In November 2008 China introduced a $585 billion economic stimulus package to counter the global crisis. Other middle-income economies also have stimulus plans. Fiscal responses to the crisis must address short-term risks to macroeconomic stability and long-term fiscal sustainability—while protecting the vulnerable segments of society and the longer term investments that sustain economic growth and human development. About 40 percent of low- and middle-income economies have good fiscal and current account positions, including many larger economies, and may be able to expand fiscal policy without jeopardizing solvency (table 1jj). In addition to strong fiscal and external positions, a successful fiscal stimulus requires administrative capability to design and implement new programs, or expand existing ones (box 1kk). Getting the timing and size right for a discretionary fiscal stimulus is not easy. Packages often cannot be delivered quickly enough, and expenditures may go to wasteful projects, especially when subject to political pressure. Where administrative capacity is weak, easier to implement options are boosting existing safety net programs, supplementing or replacing faltering foreign financing of infrastructure projects already under way with domestic financing, creating jobs through public works projects, increasing fiscal transfers to subnational governments, and facilitating central bank support of trade financing.
Poor people in developing economies are highly exposed to the global crisis. World Bank estimates for 2009 suggest that lower growth rates will trap 46 million more people below the $1.25 a day poverty line than expected before the crisis. An extra 53 million people will be living on less than $2 a day, and child mortality rates could soar. It is estimated that 200,000–400,000 more children a year, a total of 1.4–2.8 million from 2009 to 2015, may die if the crisis persists. Poor consumers are the first to be hurt by lower demand for labor and falling remittances. In addition, shrinking fiscal revenues and potential decreases in offi cial aid flows threaten to reduce access to social safety nets and to such social services as health care and education. Households may have to sell productive assets, pull children out of school, and reduce calorie intake, which can lead to acute malnutrition. The long-term consequences can be severe and in some cases irreversible, especially for women and children. Almost 40 percent of low- and middle-income economies are highly exposed to the poverty effects of the crisis. Yet three-quarters of them cannot raise funds domestically or internationally to finance programs to curb the effects of the downturn.
Fiscal positions have generally improved but remain weak for some developing economies
1jj
Fiscal position (% of GDP; median) Country group Low-income economiesb Large economies East Asia and Pacific Europe and Central Asia Latin America and Caribbean Middle East and North Africa South Asia Sub-Saharan Africa Small economies Middle-income economies
Public debt 2007 40.5 36.7 40.6 31.7 37.6 41.2 57.1 28.0 61.0 34.1
Maximum debt 2002–07 87.9 87.9 70.1 77.5 55.0 57.8 66.4 93.9 87.1 51.1
Fiscal balance 2007a –2.4 –1.9 –2.5 –2.6 –0.4 –4.8 –3.4 –1.7 –3.6 –0.6
Recent World Bank Group initiatives
1ll
Establish a vulnerability fund. The World Bank has proposed a vulnerability fund financed by high-income economies to assist countries that cannot afford to protect the vulnerable. The fund’s priorities would be to invest in safety net programs and infrastructure and to finance small and medium-size enterprises and microfinance institutions. Substantially increase lending by the International Bank for Reconstruction and Development (IBRD). IBRD could make new commitments of up to $100 billion over the next three years. Fast track funds from the International Development Association (IDA). A facility is now in place to speed $2 billion to help the poorest countries deal with the effects of the crisis.
a. After official grants. b. IDA-eligible economies. Source: IMF 2008b; World Development Indicators data files.
Respond to the food crisis. Nearly $900 million is approved or in the pipeline to help developing countries cope with the impact of high food prices through a $1.2 billion food facility.
Finding fiscal space in low-income economies
Ensure trade flows. The International Finance Corporation (IFC), a member of the World Bank Group that focuses on the private sector, plans to double its existing Global Trade Finance Program to $3 billion over three years and to mobilize funds from other sources.
1kk
Low-income economies can allow fiscal deficits to temporarily increase if they can access financing, but this generally has not been the case in past downturns. Median public debt among low-income economies was 41 percent of GDP in 2007. A quarter of developing economies had public debt of less than 21 percent. Among larger Sub-Saharan African economies median debt was 28 percent. The ability to borrow depends on the size of the fiscal deficit, the level of government debt, the country’s growth prospects, the government’s reputation for fiscal management, the structure of debt (maturity, currency), and recent debt history. Financing a larger fiscal deficit is generally easier if the country’s starting external balance and reserve position are strong. A fiscal stimulus package tends to increase the external deficit by bolstering domestic demand. For commodity-exporting countries current account and fiscal deficits tend to rise when commodity prices fall, as at present. Thus a large imbalance or a low level of reserves will tend to limit the size of the fiscal stimulus that is possible.
Bolster distressed banking systems. IFC is putting in place a global equity fund to recapitalize distressed banks. IFC expects to invest $1 billion over three years, and Japan plans to invest $2 billion. Keep infrastructure projects on track. IFC expects to invest at least $300 million over three years and mobilize $1.5 billion to provide rollover financing and recapitalize viable infrastructure projects in distress. Support microfinance. IFC and Germany have launched a $500 million facility to support microfinance institutions facing difficulties as a result of the crisis. Shift advisory support to help companies weather the crisis. IFC is refocusing advisory services to help clients cope with the crisis. It estimates a financing need of at least $40 million over three years.
2009 World Development Indicators
11
Millennium Development Goals Goals and targets from the Millennium Declaration Indicators for monitoring progress Goal 1
Eradicate extreme poverty and hunger
Target 1.A Halve, between 1990 and 2015, the proportion of people whose income is less than $1 a day
1.1 1.2 1.3
Target 1.B Achieve full and productive employment and decent work for all, including women and young people
1.4 1.5 1.6 1.7
Target 1.C Halve, between 1990 and 2015, the proportion of people who suffer from hunger
1.8 1.9
Goal 2
2.1 2.2 2.3 3.1 3.2 3.3
Ratios of girls to boys in primary, secondary, and tertiary education Share of women in wage employment in the nonagricultural sector Proportion of seats held by women in national parliament
Reduce child mortality
Target 4.A Reduce by two-thirds, between 1990 and 2015, the under-five mortality rate
Goal 5
Net enrollment ratio in primary education Proportion of pupils starting grade 1 who reach last grade of primary education Literacy rate of 15- to 24-year-olds, women and men
Promote gender equality and empower women
Target 3.A Eliminate gender disparity in primary and secondary education, preferably by 2005, and in all levels of education no later than 2015
Goal 4
Growth rate of GDP per person employed Employment to population ratio Proportion of employed people living below $1 (PPP) a day Proportion of own-account and contributing family workers in total employment Prevalence of underweight children under five years of age Proportion of population below minimum level of dietary energy consumption
Achieve universal primary education
Target 2.A Ensure that by 2015 children everywhere, boys and girls alike, will be able to complete a full course of primary schooling Goal 3
Proportion of population below $1 purchasing power parity (PPP) a day1 Poverty gap ratio [incidence × depth of poverty] Share of poorest quintile in national consumption
4.1 4.2 4.3
Under-five mortality rate Infant mortality rate Proportion of one-year-old children immunized against measles
Improve maternal health
Target 5.A Reduce by three-quarters, between 1990 and 2015, 5.1 the maternal mortality ratio 5.2
Maternal mortality ratio Proportion of births attended by skilled health personnel
Target 5.B Achieve by 2015 universal access to reproductive health
Contraceptive prevalence rate Adolescent birth rate Antenatal care coverage (at least one visit and at least four visits) Unmet need for family planning
5.3 5.4 5.5 5.6
Goal 6
Combat HIV/AIDS, malaria, and other diseases
Target 6.A Have halted by 2015 and begun to reverse the spread of HIV/AIDS
6.1 6.2 6.3 6.4
Target 6.B Achieve by 2010 universal access to treatment for HIV/AIDS for all those who need it
6.5
Target 6.C Have halted by 2015 and begun to reverse the incidence of malaria and other major diseases
6.6 6.7
HIV prevalence among population ages 15–24 years Condom use at last high-risk sex Proportion of population ages 15–24 years with comprehensive, correct knowledge of HIV/AIDS Ratio of school attendance of orphans to school attendance of nonorphans ages 10–14 years Proportion of population with advanced HIV infection with access to antiretroviral drugs
Incidence and death rates associated with malaria Proportion of children under age five sleeping under insecticide-treated bednets 6.8 Proportion of children under age five with fever who are treated with appropriate antimalarial drugs 6.9 Incidence, prevalence, and death rates associated with tuberculosis 6.10 Proportion of tuberculosis cases detected and cured under directly observed treatment short course
The Millennium Development Goals and targets come from the Millennium Declaration, signed by 189 countries, including 147 heads of state and government, in September 2000 (www. un.org/millennium/declaration/ares552e.htm) as updated by the 60th UN General Assembly in September 2005. The revised Millennium Development Goal (MDG) monitoring framework shown here, including new targets and indicators, was presented to the 62nd General Assembly, with new numbering as recommended by the Inter-agency and Expert Group on MDG Indicators at its 12th meeting on 14 November 2007. The goals and targets are interrelated and should be seen as a whole. They represent a partnership between the developed countries and the developing countries “to create an environment—at the national and global levels alike—which is conducive to development and the elimination of poverty.” All indicators should be disaggregated by sex and urban-rural location as far as possible.
12
2009 World Development Indicators
Goals and targets from the Millennium Declaration Indicators for monitoring progress Goal 7
Ensure environmental sustainability
Target 7.A Integrate the principles of sustainable development into country policies and programs and reverse the loss of environmental resources Target 7.B Reduce biodiversity loss, achieving, by 2010, a significant reduction in the rate of loss
7.1 7.2 7.3 7.4 7.5 7.6 7.7
Proportion of land area covered by forest Carbon dioxide emissions, total, per capita and per $1 GDP (PPP) Consumption of ozone-depleting substances Proportion of fish stocks within safe biological limits Proportion of total water resources used Proportion of terrestrial and marine areas protected Proportion of species threatened with extinction
Target 7.C Halve by 2015 the proportion of people without sustainable access to safe drinking water and basic sanitation
7.8
Target 7.D Achieve by 2020 a significant improvement in the lives of at least 100 million slum dwellers
7.10 Proportion of urban population living in slums2
Goal 8
7.9
Proportion of population using an improved drinking water source Proportion of population using an improved sanitation facility
Develop a global partnership for development
Target 8.A Develop further an open, rule-based, predictable, nondiscriminatory trading and financial system
Some of the indicators listed below are monitored separately for the least developed countries (LDCs), Africa, landlocked developing countries, and small island developing states.
(Includes a commitment to good governance, development, and poverty reduction—both nationally and internationally.)
Official development assistance (ODA) 8.1 Net ODA, total and to the least developed countries, as percentage of OECD/DAC donors’ gross national income 8.2 Proportion of total bilateral, sector-allocable ODA of OECD/DAC donors to basic social services (basic education, primary health care, nutrition, safe water, and Target 8.B Address the special needs of the least developed sanitation) countries 8.3 Proportion of bilateral official development assistance of OECD/DAC donors that is untied (Includes tariff and quota-free access for the least developed countries’ exports; enhanced program of 8.4 ODA received in landlocked developing countries as a proportion of their gross national incomes debt relief for heavily indebted poor countries (HIPC) 8.5 ODA received in small island developing states as a and cancellation of official bilateral debt; and more proportion of their gross national incomes generous ODA for countries committed to poverty reduction.) Market access Target 8.C Address the special needs of landlocked 8.6 Proportion of total developed country imports (by value developing countries and small island developing and excluding arms) from developing countries and least states (through the Programme of Action for developed countries, admitted free of duty the Sustainable Development of Small Island 8.7 Average tariffs imposed by developed countries on Developing States and the outcome of the 22nd agricultural products and textiles and clothing from special session of the General Assembly) developing countries 8.8 Agricultural support estimate for OECD countries as a percentage of their GDP 8.9 Proportion of ODA provided to help build trade capacity Target 8.D Deal comprehensively with the debt problems of developing countries through national and Debt sustainability international measures in order to make debt 8.10 Total number of countries that have reached their HIPC sustainable in the long term decision points and number that have reached their HIPC completion points (cumulative) 8.11 Debt relief committed under HIPC Initiative and Multilateral Debt Relief Initiative (MDRI) 8.12 Debt service as a percentage of exports of goods and services Target 8.E In cooperation with pharmaceutical companies, provide access to affordable essential drugs in developing countries
8.13 Proportion of population with access to affordable essential drugs on a sustainable basis
Target 8.F In cooperation with the private sector, make available the benefits of new technologies, especially information and communications
8.14 Telephone lines per 100 population 8.15 Cellular subscribers per 100 population 8.16 Internet users per 100 population
1. Where available, indicators based on national poverty lines should be used for monitoring country poverty trends. 2. The proportion of people living in slums is measured by a proxy, represented by the urban population living in households with at least one of these characteristics: lack of access to improved water supply, lack of access to improved sanitation, overcrowding (3 or more persons per room), and dwellings made of nondurable material.
2009 World Development Indicators
13
1.1
Size of the economy Population Surface Population area density
millions 2007
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Cote 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 34 17 40 3 21 8 9 159 10 11 9 10 4 2 192 8 15 8 14 19 33 4 11 17 1,318 7 44 62 4 4 19 4 11 10 5 10 13 75 7 5 1 79 5 62 1 2 4 82 23 11 13 9 2 10
thousand sq. km 2007
people per sq. km 2007
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 9,598 1 1,142 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
.. 116 14 14 14 107 3 101 104 1,218 47 351 82 9 74 3 23 71 54 331 82 40 4 7 9 22 141 6,647 40 28 11 87 61 79 103 134 129 201 48 76 331 48 32 79 17 112 5 171 63 236 103 87 123 38 60 349
2009 World Development Indicators
Gross national income
Gross national income per capita
PPP gross national incomea
Gross domestic product
$ billions 2007b
Rank 2007
$ 2007b
Rank 2007
$ billions 2007
Per capita $ 2007
Rank 2007
% growth 2006–07
Per capita % growth 2006–07
8.1 10.5 122.5 43.0 238.7 7.9 751.5 348.9 22.6 74.9 40.9 436.9 5.1 12.0 14.3 11.5 1,122.1 35.1 6.4 0.9 8.0 19.5 1,307.5 1.6 5.8 135.8 3,126.0 218.6 180.4 8.6 5.8 24.7 17.8 46.4 .. 150.7 302.8 34.6 41.5 119.5 19.6 1.3 17.2 17.6 234.3 2,466.6g 9.3 0.5 9.3 3,207.3 13.8 288.1 32.8 3.7 0.3 5.0
120 111 48 67 30 123 15 25 84 56 69 20 141 106 101 107 10 73 130 188 122 90 9 174 137 46 4 32 37 121 136 81 95 65 .. 40 26 74 68 49 89 178 97 96 31 6 116 193 117 3 104 27 79 150 202 143
..c 3,300 3,620 2,540 6,040 2,630 35,760 41,960 2,640 470 4,220 41,110 570 1,260 3,790e 6,120 5,860 4,580 430 110 550 1,050 39,650 370 540 8,190 2,370 31,560 4,100e 140 1,540 5,520 920 10,460 ..f 14,580 55,440 3,560 3,110 1,580 2,850 270 12,830 220 44,300 38,810g 7,020 320 2,120 38,990 590 25,740 2,450 400 200 520
.. 116 108 128 85 125 29 19 124 184 100 21 178 151 105 84 86 97 186 209 179 156 22 189 180 76 132 33 103 207 145 90 161 66 .. 56 9 109 120 144 121 202 61 205 18 25 80 195 135 24 177 40 130 188 206 182
26d 23 259d 72.3 512.4 17.7 702.0 305.6 56.3 211.4 104.3 375.3 11.8 39.5 30.3 24.2 1,775.6 85.0 16.5 2.8 24.9 39.3 1,170.7 3.1 13.8 204.7 7,150.5 304.3 363.4 17.9 10.4 46.9d 31.1 68.9 .. 234.5 201.0 61.8d 94.8 405.3 38.6d 3.0d 25.3 61.7 183.9 2,088.8 17.8 1.9 21.0 2,857.7 31.0 311.5 60.3d 10.5 0.8 10.1d
..d 7,240 7,640d 4,270 12,970 5,870 33,400 36,750 6,570 1,330 10,750 35,320 1,310 4,150 8,020 12,880 9,270 11,100 1,120 330 1,720 2,120 35,500 710 1,280 12,330 5,420 43,940 8,260 290 2,750 10,510d 1,620 15,540 .. 22,690 36,800 6,350d 7,110 5,370 5,640d 620d 18,830 780 34,760 33,850 13,410 1,140 4,760 34,740 1,320 27,830 4,520d 1,120 470 1,050d
.. 107 104 135 78 117 34 21 114 177 90 26 180 138 102 79 97 87 186 206 170 161 25 201 181 82 120 13 101 207 152 92 175 68 .. 53 20 115 112 121 118 204 62 196 28 32 76 185 129 30 178 43 131 186 205 189
5.3 6.0 3.1 21.1 8.7 13.8 3.3 3.4 25.0 6.4 8.2 2.8 4.6 4.6 6.8 5.3 5.4 6.2 4.0 3.6 10.2 3.5 2.7 4.2 0.6 5.1 13.0 6.4 7.5 6.5 –1.6 7.8 1.7 5.6 .. 6.6 1.8 8.5 2.7 7.1 4.7 1.3 6.3 11.1 4.4 2.2 5.6 6.3 12.4 2.5 6.3 4.0 5.7 1.5 2.7 3.2
.. 5.7 1.6 18.3 7.6 13.8 1.7 2.9 23.9 4.7 8.5 2.0 1.5 2.8 7.0 4.0 4.2 6.7 1.0 –0.3 8.3 1.5 1.7 2.3 –2.1 4.1 12.4 5.3 6.2 3.5 –3.6 6.3 –0.2 5.6 .. 5.9 1.3 7.3 1.6 5.2 3.3 –1.8 6.5 8.4 4.0 1.6 4.0 3.6 13.3 2.6 4.2 3.6 3.2 –0.6 –0.3 1.4
Population Surface Population area density
millions 2007
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
7 10 1,125 226 71 .. 4 7 59 3 128 6 15 38 24 48 3 5 6 2 4 2 4 6 3 2 20 14 27 12 3 1 105 4 3 31 21 49 2 28 16 4 6 14 148 5 3 162 3 6 6 28 88 38 11 4
thousand sq. km 2007
112 93 3,287 1,905 1,745 438 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 1,031 2 1,964 34 1,567 447 799 677 824 147 42 268 130 1,267 924 324 310 796 76 463 407 1,285 300 313 92 9
people per sq. km 2007
63 112 378 125 44 .. 63 332 202 247 351 65 6 66 198 491 149 27 25 37 400 66 39 3 54 80 34 148 81 10 3 621 54 116 2 69 27 74 3 197 484 16 46 11 162 15 8 211 45 14 15 22 295 124 116 445
Gross national income
$ billions 2007b
11.3 117.5 1,071.0 372.6 251.5 .. 207.9 159.2 1,988.2 8.9 4,828.9 16.3 77.7 24.0 .. 955.8 99.9 3.2 3.7 22.6 23.8 2.1 0.5 55.5 33.0 7.1 6.4 3.5 170.5 6.1 2.6 7.0 989.5 4.1 3.4 70.7 7.1 .. 7.2 9.9 747.8 114.5 5.5 4.0 136.3 364.3 32.8 140.2 18.4 5.4 10.5 95.0 142.1 375.3 201.1 ..
Gross national income per capita
PPP gross national incomea
WORLD VIEW
Size of the economy
1.1 Gross domestic product
Rank 2007
$ 2007b
Rank 2007
$ billions 2007
Per capita $ 2007
Rank 2007
% growth 2006–07
Per capita % growth 2006–07
109 51 11 23 29 .. 34 39 7 119 2 99 55 82 .. 14 53 157 151 85 83 171 194 62 77 126 131 153 38 134 166 127 13 147 156 57 125 .. 124 115 16 52 138 148 45 24 75 43 93 139 112 54 42 21 36 ..
1,590 11,680 950 1,650 3,540 ..h 47,610 22,170 33,490 3,330i 37,790 2,840 5,020 640 ..c 19,730 38,420 610 630 9,920 5,800 1,030 140 9,010 9,770 3,470 320 250 6,420 500 840 5,580 9,400 1,210 j 1,290 2,290 330 ..c 3,450 350 45,650 27,080 990 280 920 77,370 12,860 860 5,500 850 1,710 3,410 1,620 9,850 18,950 ..k
143 64 159 141 110 .. 13 45 30 115 26 122 94 174 .. 48 23 176 175 69 87 157 207 73 71 111 195 204 81 183 167 89 72 153 149 133 194 .. 112 193 17 39 158 200 161 3 59 165 92 166 139 113 142 70 50 ..
25.6d 175.6 3,082.5 804.5 769.7 .. 164.6 188.9 1,792.6 14.2d 4,440.2 29.5 148.7 58.1 .. 1,203.6 136.7 10.4 12.2 35.9 41.2 3.9 1.0 90.6d 56.8 18.4 18.2 10.5 351.2 12.8 6.3 14.4 1,464.4 10.6 8.3 125.1 15.5 .. 10.6 29.8 646.5 107.3 14.1d 9.0 260.8 252.6 55.1 412.9 35.5d 11.8d 27.7 200.9 326.4 590.9 231.1 ..
3,610d 17,470 2,740 3,570 10,840 .. 37,700 26,310 30,190 5,300d 34,750 5,150 9,600 1,550 .. 24,840 52,610 1,980 2,080 15,790 10,040 1,940 280 14,710d 16,830 9,050 930 760 13,230 1,040 2,000 11,410 13,910 2,800 3,170 4,050 730 .. 5,100 1,060 39,470 25,380 2,510d 630 1,760 53,650 21,650 2,540 10,610d 1,870d 4,520 7,200 3,710 15,500 21,790 ..
143 64 153 144 89 .. 18 45 38 123 29 124 95 176 .. 50 4 165 162 67 93 166 208 71 66 100 193 198 77 190 164 86 74 151 146 139 199 .. 125 188 17 48 157 203 169 5 57 155 91 168 131 108 142 69 59 ..
6.3 1.1 9.1 6.3 7.8 .. 6.0 5.4 1.5 –7.3 2.1 6.0 8.9 7.0 .. 5.0 6.3 8.2 7.9 10.3 2.0 4.9 9.4 6.8 8.8 5.0 6.2 7.9 6.3 2.8 1.9 4.7 3.2 3.0 10.2 2.7 7.3 5.0 5.9 3.2 3.5 3.0 3.9 3.2 5.9 3.7 7.2 6.0 11.5 6.2 6.8 8.9 7.2 6.6 1.8 ..
4.3 1.3 7.6 5.1 6.4 .. 3.4 3.5 0.7 –7.7 2.1 2.6 7.7 4.2 .. 4.6 3.7 7.3 6.0 10.9 1.0 4.3 5.4 4.8 9.4 4.9 3.4 5.2 4.6 –0.2 –0.6 4.0 2.2 3.8 9.2 1.5 5.3 4.1 4.2 1.5 3.3 1.9 2.6 –0.1 3.6 2.6 5.6 3.7 9.8 4.2 4.9 7.6 5.2 6.7 1.5 ..
2009 World Development Indicators
15
1.1
Size of the economy Population Surface Population area density
millions 2007
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
thousand sq. km 2007
people per sq. km 2007
Gross national income
$ billions 2007b
22 238 94 137.7 142 17,098 9 1,069.8 10 26 395 3.1 12 373.7 24 2,000l 12 197 64 10.3 7 78 95 33.5 6 72 82 1.5 5 1 6,660 148.4 5 49 112 63.3 2 20 100 43.4 9 638 14 .. 48 1,219 39 273.9 45 505 90 1,314.5 20 66 310 30.8 39 2,506 16 36.7 1 17 67 2.9 9 450 22 437.9 8 41 189 459.2 20 185 108 35.3 7 143 48 3.1 40 947 46 16.3m 64 513 125 217.2 1 15 71 1.6 7 57 121 2.4 1 5 260 19.3 10 164 66 32.8 74 784 96 593.0 5 488 11 .. 31 241 157 11.3 47 604 80 118.9 4 84 52 .. 61 244 252 2,464.3 302 9,632 33 13,886.4 3 176 19 21.2 27 447 63 19.7 27 912 31 207.6 85 329 275 65.4 4 6 616 4.5 22 528 42 19.4 12 753 16 9.2 13 391 35 4.5 6,610 s 133,946 s 51 w 52,850.4 t 1,296 21,846 61 744.3 4,258 77,006 57 12,393.5 3,435 35,510 100 6,542.9 824 41,497 20 5,853.9 5,554 98,852 58 13,141.1 1,912 16,299 121 4,172.8 446 23,972 19 2,697.2 561 20,421 28 3,252.1 313 8,778 36 883.5 1,522 5,140 318 1,338.7 800 24,242 34 761.0 1,056 35,094 32 39,685.9 324 2,585 129 11,611.1
Gross national income per capita
PPP gross national incomea
Rank 2007
$ 2007b
Rank 2007
$ billions 2007
Per capita $ 2007
44 12 161 22 113 76 176 41 59 66 .. 28 8 80 70 164 19 18 72 160 98 33 175 168 92 78 17 .. 108 50 .. 5 1 86 87 35 58 142 91 118 145
6,390 7,530 320 15,450 830 4,540 260 32,340 11,720 21,510 ..c 5,720 29,290 1,540 950 2,560 47,870 60,820 1,780 460 410m 3,400 1,510 360 14,480 3,210 8,030 ..h 370 2,560 ..k 40,660 46,040 6,390 730 7,550 770 1,290 870 770 340 7,995 w 574 2,910 1,905 7,107 2,366 2,182 6,052 5,801 2,820 880 951 37,570 35,818
82 79 195 54 168 98 203 31 63 46 .. 88 36 145 159 126 12 7 137 185 187 114 147 191 57 118 77 .. 189 126 .. 20 16 82 172 78 169 148 163 169 191
266.2 2,036.5 8.4 554.4 20.5 72.6 3.9 220.0 103.7 52.9 .. 452.3 1,380.0 84.0 72.5 5.6 343.0 335.3 88.1 11.5 48.7 502.8 3.3d 5.1 29.9d 73.0 946.7 21.0d 32.1 316.7 .. 2,063.8 13,827.2 36.6 65.3d 337.8 215.4 .. 49.3 14.2 .. 65,752.3 t 1,929.7 25,666.2 15,748.8 9,943.8 27,592.5 9,503.1 5,018.7 5,426.0 2,318.7 3,853.6 1,495.5 38,386.0 10,554.9
12,350 14,330 860 22,910 1,650 9,830 660 47,950 19,220 26,230 .. 9,450 30,750 4,200 1,880 4,890 37,490 44,410 4,430 1,710 1,200 7,880 3,090d 770 22,420d 7,140 12,810 4,350d 1,040 6,810 .. 34,050 45,840 11,020 2,430d 12,290 2,530 .. 2,200 1,190 .. 9,947 t 1,489 6,027 4,585 12,072 4,968 4,969 11,262 9,678 7,402 2,532 1,869 36,340 32,560
Gross domestic product
Rank 2007
% growth 2006–07
Per capita % growth 2006–07
81 73 194 52 173 94 202 10 61 46 .. 96 37 137 167 128 19 12 133 171 182 103 147 197 56 110 80 130 190 113 .. 27 11 88 158 83 156 .. 160 183 ..
6.0 8.1 6.0 3.4 4.8 7.5 6.8 7.7 10.4 6.8 .. 5.1 3.8 6.8 10.2 3.5 2.7 3.3 6.6 7.8 7.1 4.8 7.8 1.9 5.5 6.3 4.6 .. 7.9 7.6 9.4 3.0 2.0 7.4 9.5 8.4 8.5 6.3 3.6 6.0 –5.3 3.8 w 6.4 8.2 10.2 5.8 8.1 11.4 6.9 5.7 5.9 8.4 6.2 2.5 2.7
6.2 8.4 3.0 1.2 1.9 8.0 4.9 3.3 10.3 6.2 .. 4.1 2.1 6.1 7.7 2.8 2.0 2.4 4.0 6.2 4.5 4.1 4.5 –0.7 5.1 5.3 3.3 .. 4.3 8.2 5.7 2.4 1.0 7.1 7.9 6.6 7.2 2.7 0.6 4.0 –6.0 2.6 w 4.2 7.2 9.1 5.0 6.8 10.5 6.7 4.4 4.1 6.8 3.8 1.8 2.1
a. PPP is purchasing power parity; see Definitions. b. Calculated using the World Bank Atlas method. c. Estimated to be low income ($935 or less). d. Based on regression; others are extrapolated from the 2005 International Comparison Program benchmark estimates. e. Included in the aggregates for lower middle-income economies based on earlier data. f. Estimated to be upper middle income ($3,706–$11,455). g. Includes the French overseas departments of French Guiana, Guadeloupe, Martinique, and Réunion. h. Estimated to be lower middle income ($936–$3,705). i. Included in the aggregates for upper middle-income economies based on earlier data. j. Excludes Transnistria. k. Estimated to be high income ($11,456 or more). l. Provisional estimate. m. Covers mainland Tanzania only.
16
2009 World Development Indicators
About the data
WORLD VIEW
Size of the economy
1.1
Definitions
Population, land area, income, output, and growth in
allowing comparison of real levels of expenditure
• Population is based on the de facto definition of
output are basic measures of the size of an economy.
between countries, just as conventional price indexes
population, which counts all residents regardless of
They also provide a broad indication of actual and
allow comparison of real values over time.
legal status or citizenship—except for refugees not
potential resources. Population, land area, income
PPP rates are calculated by simultaneously compar-
permanently settled in the country of asylum, who
(as measured by gross national income, GNI), and
ing the prices of similar goods and services among a
are generally considered part of the population of
output (as measured by gross domestic product,
large number of countries. In the most recent round
their country of origin. The values shown are midyear
GDP) are therefore used throughout World Develop-
of price surveys conducted by the International Com-
estimates. See also table 2.1. • Surface area is
ment Indicators to normalize other indicators.
parison Program (ICP), 146 countries and territories
a country’s total area, including areas under inland
Population estimates are generally based on
participated in the data collection, including China
bodies of water and some coastal waterways. • Pop-
extrapolations from the most recent national cen-
for the first time, India for the first time since 1985,
ulation density is midyear population divided by land
sus. For further discussion of the measurement of
and almost all African countries. The PPP conver-
area in square kilometers. • Gross national income
population and population growth, see About the data
sion factors presented in the table come from three
(GNI) is the sum of value added by all resident pro-
for table 2.1 and Statistical methods.
sources. For 45 high- or upper middle-income coun-
ducers plus any product taxes (less subsidies) not
The surface area of an economy includes inland
tries conversion factors are provided by Eurostat
included in the valuation of output plus net receipts
bodies of water and some coastal waterways. Sur-
and the Organisation for Economic Co-operation
of primary income (compensation of employees and
face area thus differs from land area, which excludes
and Development (OECD), with PPP estimates for
property income) from abroad. Data are in current
bodies of water, and from gross area, which may
34 European countries incorporating new price data
U.S. dollars converted using the World Bank Atlas
include offshore territorial waters. Land area is par-
collected since 2005. For the remaining 2005 ICP
method (see Statistical methods). • GNI per capita is
ticularly important for understanding an economy’s
countries the PPP estimates are extrapolated from
GNI divided by midyear population. GNI per capita in
agricultural capacity and the environmental effects
the 2005 ICP benchmark results, which account for
U.S. dollars is converted using the World Bank Atlas
of human activity. (For measures of land area and
relative price changes between each economy and
method. • Purchasing power parity (PPP) GNI is GNI
data on rural population density, land use, and agri-
the United States. For countries that did not partici-
converted to international dollars using PPP rates. An
cultural productivity, see tables 3.1–3.3.) Innova-
pate in the 2005 ICP round, the PPP estimates are
international dollar has the same purchasing power
tions in satellite mapping and computer databases
imputed using a statistical model.
over GNI that a U.S. dollar has in the United States.
have resulted in more precise measurements of land and water areas. GNI measures total domestic and foreign value added claimed by residents. GNI comprises GDP
For more information on the results of the 2005
• Gross domestic product (GDP) is the sum of value
ICP, see the introduction to World View. The final
added by all resident producers plus any product
report of the program is available at www.worldbank.
taxes (less subsidies) not included in the valuation
org/data/icp.
of output. Growth is calculated from constant price
plus net receipts of primary income (compensation
All 209 economies shown in World Development
of employees and property income) from nonresident
Indicators are ranked by size, including those that
sources. The World Bank uses GNI per capita in U.S.
appear in table 1.6. The ranks are shown only in
dollars to classify countries for analytical purposes
table 1.1. No rank is shown for economies for which
and to determine borrowing eligibility. For definitions
numerical estimates of GNI per capita are not pub-
of the income groups in World Development Indica-
lished. Economies with missing data are included in
tors, see Users guide. For discussion of the useful-
the ranking at their approximate level, so that the rel-
ness of national income and output as measures of
ative order of other economies remains consistent.
GDP data in local currency. • GDP per capita is GDP divided by midyear population.
productivity or welfare, see About the data for tables 4.1 and 4.2.
Data sources
When calculating GNI in U.S. dollars from GNI
Population estimates are prepared by World Bank
reported in national currencies, the World Bank fol-
staff from a variety of sources (see Data sources
lows the World Bank Atlas conversion method, using
for table 2.1). Data on surface and land area are
a three-year average of exchange rates to smooth
from the Food and Agriculture Organization (see
the effects of transitory fluctuations in exchange
Data sources for table 3.1). GNI, GNI per capita,
rates. (For further discussion of the World Bank Atlas
GDP growth, and GDP per capita growth are esti-
method, see Statistical methods.) GDP and GDP per
mated by World Bank staff based on national
capita growth rates are calculated from data in con-
accounts data collected by World Bank staff during
stant prices and national currency units.
economic missions or reported by national statis-
Because exchange rates do not always reflect dif-
tical offices to other international organizations
ferences in price levels between countries, the table
such as the OECD. PPP conversion factors are
also converts GNI and GNI per capita estimates into
estimates by Eurostat/OECD and by World Bank
international dollars using purchasing power parity
staff based on data collected by the ICP.
(PPP) rates. PPP rates provide a standard measure
2009 World Development Indicators
17
1.2
Millennium Development Goals: eradicating poverty and saving lives Eradicate extreme poverty and hunger Share of poorest quintile Vulnerable in national employment consumption Unpaid family workers and or income own-account workers % % of total employment 1995– 2007a,b 1990 2007
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
.. 7.8 6.9 2.0 3.4d 8.6 5.9 8.6 13.3 9.4 8.8 8.5 6.9 1.8 6.9 3.1 3.0 8.7 7.0 9.0 7.1 5.6 7.2 5.2 6.3 4.1 5.7 5.3 2.3 5.5 5.0 4.2 5.0 8.7 .. 10.2 8.3 4.0 3.4 9.0 3.3 .. 6.8 9.3 9.6 7.2 6.1 4.8 5.4 8.5 5.2 6.7 3.4 5.8 7.2 2.5
2009 World Development Indicators
.. .. .. .. .. .. 10 .. .. .. .. .. .. 40 .. .. 29 .. .. .. .. .. .. .. 94 .. .. 6 28 .. .. 25 .. .. .. 7 .. 39 36 28 35 .. 2 .. .. .. 48 .. .. .. .. 40 .. .. .. ..
.. .. 35 .. 20 .. 9 9 53 85 .. 10 .. .. .. .. 27 8 .. .. 87 .. 10 .. .. 25 .. 7 41 .. .. 20 .. 18 .. 12 .. 42 34 25 36 .. 6 52 .. 7 .. .. 62 .. .. 28 .. .. .. ..
Prevalence of malnutrition Underweight % of children under age 5
Achieve universal primary education
Promote gender equality
Reduce child mortality
Primary completion rate %
Ratio of girls to boys enrollments in primary and secondary school %
Under-fi ve mortality rate per 1,000
1990
2000–07a
1991
2007c
1991
2007c
1990
2007
.. .. .. .. .. .. .. .. .. 64.3 .. .. .. 8.9 .. .. .. .. 29.6 .. .. 18.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 8.4 .. 8.2 11.1 .. .. .. .. .. .. .. .. .. 24.1 .. 27.8 .. .. ..
.. 17.0 10.2 27.5 2.3 4.2 .. .. 14.0 39.2 1.3 .. 21.5 5.9 1.6 10.7 2.2 1.6 35.2 38.9 28.4 15.1 .. 21.8 33.9 0.6 6.8 .. 5.1 33.6 11.8 .. 16.7 .. .. 2.1 .. 4.2 6.2 5.4 6.1 34.5 .. 34.6 .. .. 8.8 15.8 .. .. 18.8 .. 17.7 22.5 21.9 18.9
.. .. 80 35 .. .. .. .. .. .. 94 79 21 71 .. 89 90 90 20 46 .. 53 .. 27 18 .. 105 102 70 46 54 79 43 .. 99 .. 98 62 .. .. 61 .. .. .. 97 104 .. .. .. .. 61 .. .. 17 .. 27
.. 96 95 .. 97 98 .. 103 .. 72 92 87 64 101 .. 95 106 98 33 39 85 55 .. 24 31 95 .. 102 107 51 72 91 45 96 93 94 101 89 106 98 91 46 100 46 97 .. .. 72 92 97 71 103 77 64 .. ..
.. 96 83 .. .. .. 101 95 100 .. .. 101 49 .. .. 109 .. 99 62 82 73 83 99 60 42 100 87 103 108 .. 85 101 65 .. 106 98 101 .. .. 81 102 .. .. 68 109 102 .. 66 98 99 79 99 .. 45 .. 94
.. 97 99 .. 104 104 97 97 .. 103 101 98 73 98 99 101 103 97 82 90 90 85 98 .. 64 99 100 98 104 73 90 102 .. 96 99 101 101 104 100 95 101 78 100 83 102 100 .. 100 98 98 95 98 93 74 .. ..
.. 46 69 258 29 56 10 10 98 151 24 10 184 125 22 57 58 19 206 189 119 139 8 171 201 21 45 .. 35 200 104 18 151 13 13 13 9 66 57 93 60 147 18 204 7 9 92 153 47 9 120 11 82 231 240 152
.. 15 37 158 16 24 6 4 39 61 13 5 123 57 14 40 22 12 191 180 91 148 6 172 209 9 22 .. 20 161 125 11 127 6 7 4 4 38 22 36 24 70 6 119 4 4 91 109 30 4 115 4 39 150 198 76
Eradicate extreme poverty and hunger Share of poorest quintile Vulnerable in national employment consumption Unpaid family workers and or income own-account workers % % of total employment 1995– 2007a,b 1990 2007
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
2.5 8.6 8.1 7.1 6.4 .. 7.4 5.7 6.5 5.2 10.6 7.2 7.4 4.7 .. 7.9 .. 8.1 8.5 6.8 .. 3.0 6.4 .. 6.8 6.1 6.2 7.0 6.4 6.5 6.2 .. 4.6 7.3 7.2 6.5 5.4 .. 1.5 6.1 7.6 6.4 3.8 5.9 5.1 9.6 .. 9.1 2.5 4.5 3.4 3.9 5.6 7.3 5.8 ..
49 7 .. .. .. .. 20 .. 16 42 19 .. .. .. .. .. .. .. .. .. .. 38 .. .. .. .. 84 .. 29 .. .. 12 26 .. .. .. .. .. .. .. .. 13 .. .. .. .. .. .. 34 .. 23 36 .. 28 19 ..
.. 7 .. 63 43 .. 11 7 22 35 11 .. 36 .. .. 25 .. 47 .. 7 .. .. .. .. .. 22 86 .. 22 .. .. 17 29 32 .. 52 .. .. 21 .. .. 12 45 .. .. 6 .. 62 28 .. 47 40 45 19 19 ..
Prevalence of malnutrition Underweight % of children under age 5
WORLD VIEW
Millennium Development Goals: eradicating poverty and saving lives
1.2
Achieve universal primary education
Promote gender equality
Reduce child mortality
Primary completion rate %
Ratio of girls to boys enrollments in primary and secondary school %
Under-fi ve mortality rate per 1,000
1990
2000–07a
1991
2007c
1991
2007c
1990
2007
.. 2.3 .. 31.0 .. .. .. .. .. .. .. 4.8 .. 20.1 .. .. .. .. .. .. .. .. .. .. .. .. 35.5 24.4 .. 29.0 .. .. 13.9 .. .. 8.1 .. .. 21.5 .. .. .. 9.6 41.0 35.1 .. .. 39.0 .. .. 2.8 8.8 .. .. .. ..
8.6 .. 43.5 24.4 .. .. .. .. .. 3.1 .. 3.6 4.9 16.5 17.8 .. .. 2.7 36.4 .. .. 16.6 20.4 .. .. 1.8 36.8 18.4 .. 27.9 30.4 .. 3.4 3.2 5.3 9.9 21.2 29.6 17.5 38.8 .. .. 7.8 39.9 27.2 .. .. 31.3 .. .. .. 5.2 20.7 .. .. ..
64 87 64 91 91 .. .. .. 104 90 101 101 .. .. .. 98 .. .. 45 .. .. 59 .. .. 89 .. 33 29 91 13 34 107 88 .. .. 48 26 .. .. 51 .. 100 42 18 .. 100 74 .. .. 46 68 .. 88 96 95 ..
88 96 86 99 105 .. 96 101 100 82 .. 99 104 e 93 .. 101 98 95 77 92 82 78 55e .. 93 97 62 55 98 49 59 94 104 93 110 83 46 .. 77 78e .. .. 73 40 72 96 88 62 99 .. 95 101 94 97 104 ..
106 100 70 93 85 78 104 105 100 102 101 101 102 94 .. 99 97 .. 76 101 .. 123 .. .. .. .. 98 81 101 57 71 102 97 106 109 70 71 97 106 59 97 100 109 53 77 102 89 .. .. 80 98 96 100 100 103 ..
106 99 91 98 114 .. 103 101 99 101 100 102 99e 96 .. 96 100 100 86 100 103 104 .. 105 100 99 96 100 104 78 102 102 99 102 107 87 85 .. 104 98e 98 103 102 71 84 100 99 78 101 .. 99 101 102 99 101 ..
58 17 117 91 72 53 9 12 9 33 6 40 60 97 55 9 15 74 163 17 37 102 205 41 16 38 168 209 22 250 130 24 52 37 98 89 201 130 87 142 9 11 68 304 230 9 32 132 34 94 41 78 62 17 15 ..
24 7 72 31 33 .. 4 5 4 31 4 24 32 121 55 5 11 38 70 9 29 84 133 18 8 17 112 111 11 196 119 15 35 18 43 34 168 103 68 55 5 6 35 176 189 4 12 90 23 65 29 20 28 7 4 ..
2009 World Development Indicators
19
1.2
Millennium Development Goals: eradicating poverty and saving lives Eradicate extreme poverty and hunger Share of poorest quintile Vulnerable in national employment consumption Unpaid family workers and or income own-account workers % % of total employment 1995– 2007a,b 1990 2007
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
8.2 6.4 5.4 .. 6.2 8.3f 6.1 5.0 8.8 8.2 .. 3.1 7.0 6.8 .. 4.5 9.1 7.6 .. 7.7 7.3 6.1 6.7 7.6 5.5 5.9 5.2 6.0 6.1 9.0 .. 6.1 5.4 4.5 7.1 4.9 7.1 .. 7.2 3.6 4.6
9 1 .. .. 83 .. .. 8 .. 12 .. .. 22 .. .. .. .. 9 .. .. .. 70 .. .. 22 .. .. .. .. .. .. .. .. .. .. .. .. .. .. 65 .. .. w .. .. .. .. .. .. .. 30 .. .. .. .. ..
32 6 .. .. .. 23 .. 10 10 13 .. 3 12 41 .. .. .. 10 .. .. 88 53 .. .. 16 .. 36 .. .. .. .. .. .. 25 .. 30 74 36 .. .. .. .. w .. .. .. 22 .. .. 19 31 37 .. .. .. 12
Prevalence of malnutrition Underweight % of children under age 5 1990
.. .. 24.3 .. 21.9 .. .. .. .. .. .. .. .. 29.3 .. .. .. .. .. .. 25.1 17.4 .. 21.2 4.7 8.5 8.7 .. 19.7 .. .. .. .. .. .. .. 36.9 .. .. 21.2 8.0 .. w .. .. .. .. .. .. .. .. .. .. .. .. ..
2000–07a
3.5 .. 18.0 .. 14.5 1.8 28.3 3.3 .. .. 32.8 .. .. 22.8 38.4 9.1 .. .. .. 14.9 16.7 7.0 40.6 .. 4.4 .. 3.5 .. 19.0 4.1 .. .. 1.3 6.0 4.4 .. 20.2 .. .. 23.3 14.0 23.2 w 28.0 22.0 24.8 .. 24.1 12.8 .. 4.4 .. 41.1 26.6 .. ..
Achieve universal primary education
Promote gender equality
Reduce child mortality
Primary completion rate %
Ratio of girls to boys enrollments in primary and secondary school %
Under-fi ve mortality rate per 1,000
1991
100 .. 35 55 43 .. .. .. .. .. .. 76 103 102 42 60 96 53 89 .. 62 .. .. 35 101 74 90 .. .. 94 103 .. .. 94 .. 81 .. .. .. .. 97 79 w .. 84 83 90 78 101 93 84 78 62 51 .. 101
2007c
101 .. 35 93 49 .. 81 .. 93 .. .. 92 99 106 50 67 .. 88 114 95 112e 101 69 57 88 120 96 .. 54 101 105 .. 95 99 97 98 .. 83 60 88 .. 86 w 65 93 91 101 85 98 98 100 90 80 60 97 ..
1991
99 104 92 84 69 .. 67 .. .. .. .. 104 104 102 77 98 102 97 85 .. 97 97 .. 59 101 86 81 .. 82 .. 104 102 100 .. 94 105 .. .. .. .. 92 86 w .. 85 82 99 83 89 100 98 78 70 79 100 101
2007c
100 99 100 94 92 102 86 .. 100 100 .. 100 103 .. 89e 95 100 97 96 89 .. 104 95 75 101 104 90 .. 98 100 101 102 100 106 98 103 .. 104 66 96 97 96 w 87 97 95 103 95 99 102 103 96 89 86 104 ..
1990
2007
32 27 195 44 149 .. 290 8 15 11 203 64 9 32 125 96 7 9 37 117 157 31 184 150 34 52 82 99 175 25 15 10 11 25 74 32 56 38 127 163 95 93 w 164 75 81 47 101 56 49 55 77 125 183 12 10
15 15 181 25 114 8 262 3 8 4 142 59 4 21 109 91 3 5 17 67 116 7 97 100 35 21 23 50 130 24 8 6 8 14 41 19 15 27 73 170 90 68 w 126 45 50 24 74 27 23 26 38 78 146 7 4
a. Data are for the most recent year available. b. See table 2.9 for survey year and whether share is based on income or consumption expenditure. c. Provisional data. d. Urban data. e. Data are for 2008. f. Includes Montenegro.
20
2009 World Development Indicators
About the data
WORLD VIEW
Millennium Development Goals: eradicating poverty and saving lives
1.2
Definitions
Tables 1.2–1.4 present indicators for 17 of the 21
and undernourished mothers who give birth to under-
• Share of poorest quintile in national consumption
targets specified by the Millennium Development
weight children.
or income is the share of the poorest 20 percent of
Goals. Each of the eight goals includes one or more
Progress toward universal primary education is
the population in consumption or, in some cases,
targets, and each target has several associated
measured by the primary completion rate. Because
income. • Vulnerable employment is the sum of
indicators for monitoring progress toward the target.
many school systems do not record school comple-
unpaid family workers and own-account workers as
Most of the targets are set as a value of a specific
tion on a consistent basis, it is estimated from the
a percentage of total employment. • Prevalence of
indicator to be attained by a certain date. In some
gross enrollment rate in the final grade of primary
malnutrition is the percentage of children under age
cases the target value is set relative to a level in
school, adjusted for repetition. Official enrollments
five whose weight for age is more than two standard
1990. In others it is set at an absolute level. Some
sometimes differ significantly from attendance, and
deviations below the median for the international
of the targets for goals 7 and 8 have not yet been
even school systems with high average enrollment
reference population ages 0–59 months. The data
quantified.
ratios may have poor completion rates.
are based on the new international child growth stan-
The indicators in this table relate to goals 1–4.
Eliminating gender disparities in education would
dards for infants and young children, called the Child
Goal 1 has three targets between 1990 and 2015:
help increase the status and capabilities of women.
Growth Standards, released in 2006 by the World
to halve the proportion of people whose income is
The ratio of female to male enrollments in primary
Health Organization. • Primary completion rate is
less than $1 a day, to achieve full and productive
and secondary school provides an imperfect measure
the percentage of students completing the last year
employment and decent work for all, and to halve the
of the relative accessibility of schooling for girls.
of primary school. It is calculated as the total num-
proportion of people who suffer from hunger. Esti-
The targets for reducing under-five mortality rates
ber of students in the last grade of primary school,
mates of poverty rates are in tables 2.7 and 2.8.
are among the most challenging. Under-five mortal-
minus the number of repeaters in that grade, divided
The indicator shown here, the share of the poorest
ity rates are harmonized estimates produced by a
by the total number of children of official graduation
quintile in national consumption, is a distributional
weighted least squares regression model and are
age. • Ratio of girls to boys enrollments in primary
measure. Countries with more unequal distribu-
available at regular intervals for most countries.
and secondary school is the ratio of the female to
tions of consumption (or income) have a higher rate
Most of the 60 indicators relating to the Millennium
male gross enrollment rate in primary and secondary
of poverty for a given average income. Vulnerable
Development Goals can be found in World Develop-
school. • Under-five mortality rate is the probability
employment measures the portion of the labor force
ment Indicators. Table 1.2a shows where to find the
that a newborn baby will die before reaching age five,
that receives the lowest wages and least security in
indicators for the first four goals. For more informa-
if subject to current age-specific mortality rates. The
employment. No single indicator captures the con-
tion about data collection methods and limitations,
probability is expressed as a rate per 1,000.
cept of suffering from hunger. Child malnutrition is a
see About the data for the tables listed there. For
symptom of inadequate food supply, lack of essen-
information about the indicators for goals 5–8, see
tial nutrients, illnesses that deplete these nutrients,
About the data for tables 1.3 and 1.4.
1.2a
Location of indicators for Millennium Development Goals 1–4 Goal 1. Eradicate extreme poverty and hunger 1.1 Proportion of population below $1.25 a day 1.2 Poverty gap ratio 1.3 Share of poorest quintile in national consumption 1.4 Growth rate of GDP per person employed 1.5 Employment to population ratio 1.6 Proportion of employed people living below $1 per day 1.7 Proportion of own-account and unpaid family workers in total employment 1.8 Prevalence of underweight in children under age five 1.9 Proportion of population below minimum level of dietary energy consumption Goal 2. Achieve universal primary education 2.1 Net enrollment ratio in primary education 2.2 Proportion of pupils starting grade 1 who reach last grade of primary 2.3 Literacy rate of 15- to 24-year-olds Goal 3. Promote gender equality and empower women 3.1 Ratio of girls to boys in primary, secondary, and tertiary education 3.2 Share of women in wage employment in the nonagricultural sector 3.3 Proportion of seats held by women in national parliament Goal 4. Reduce child mortality 4.1 Under-five mortality rate 4.2 Infant mortality rate 4.3 Proportion of one-year-old children immunized against measles
Table 2.8 2.8 1.2, 2.9 2.4 2.4 — 1.2, 2.4 1.2, 2.19, 2.21 2.19 Data sources 2.12 2.13 2.14
The indicators here and throughout this book have been compiled by World Bank staff from primary and secondary sources. Efforts have been made
1.2, 2.12* 1.5, 2.3* 1.5
to harmonize the data series used to compile this
1.2, 2.21, 2.22 2.21, 2.22 2.17, 2.21
un.org/millenniumgoals), but some differences in
— No data are available in the World Development Indicators database. * Table shows information on related indicators.
table with those published on the United Nations Millennium Development Goals Web site (www.
timing, sources, and definitions remain. For more information see the data sources for the indicators listed in table 1.2a.
2009 World Development Indicators
21
1.3
Millennium Development Goals: protecting our common environment Improve maternal health Maternal mortality ratio Modeled estimate per 100,000 live births 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
.. 92 180 1,400 77 76 4 4 82 570 18 8 840 290 3 380 110 11 700 1,100 540 1,000 7 980 1,500 16 45 .. 130 1,100 740 30 810 7 45 4 3 150 210 130 170 450 25 720 7 8 520 690 66 4 560 3 290 910 1,100 670
Combat HIV/AIDS and other diseases
Contraceptive prevalence rate % of married women ages 15–49 1990 2002–07b
.. .. 47 .. .. .. .. .. .. 40 .. 78 .. 30 .. 33 59 .. .. .. .. 16 .. .. .. 56 85 86 66 8 .. .. .. .. .. 78 78 56 53 47 47 .. .. 4 77 81 .. 12 .. 75 13 .. .. .. .. 10
2009 World Development Indicators
.. 60 61 .. .. 53 .. .. 51 56 73 .. 17 58 36 .. .. .. 17 9 40 29 .. 19 3 58 85 .. 78 .. 21 96 13 .. 77 .. .. 73 73 59 67 8 .. 15 .. .. .. .. 47 .. 17 .. 43 9 10 32
Ensure environmental sustainability
HIV Incidence prevalence of tuberculosis Carbon dioxide emissions % of per capita population per 100,000 metric tons people ages 15–49 2007 2007 1990 2005
.. .. 0.1 2.1 0.5 0.1 0.2 0.2 0.2 .. 0.2 0.2 1.2 0.2 <0.1 23.9 0.6 .. 1.6 2.0 0.8 5.1 0.4 6.3 3.5 0.3 0.1 .. 0.6 .. 3.5 0.4 3.9 <0.1 0.1 .. 0.2 1.1 0.3 .. 0.8 1.3 1.3 2.1 0.1 0.4 5.9 0.9 0.1 0.1 1.9 0.2 0.8 1.6 1.8 2.2
.. 17 57 287 31 72 6 12 77 223 61 12 91 155 51 731 48 39 226 367 495 192 5 345 299 12 98 62 35 392 403 11 420 40 6 9 8 69 101 21 40 95 38 378 6 14 406 258 84 6 203 18 63 287 220 306
.. 2.2 3.0 0.4 3.4 1.2 17.2 7.5 6.4 0.1 10.6 9.9 0.1 0.8 1.6 1.6 1.4 8.6 0.1 0.0 0.0 0.1 15.4 0.1 0.0 2.7 2.1 4.6 1.7 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 10.1 6.4 6.5 0.2 3.2 12.3 0.2 7.1 0.6 0.2 0.2 0.1
.. 1.1 4.2 0.6 3.9 1.4 18.1 8.9 4.4 0.3 6.5 9.8 0.3 1.0 6.9 2.5 1.7 5.7 0.1 0.0 0.0 0.2 16.6 0.1 0.0 4.1 4.3 5.7 1.4 0.0 0.6 1.7 0.5 5.2 2.2 11.7 8.5 2.0 2.2 2.4 1.0 0.2 13.5 0.1 10.1 6.2 1.2 0.2 1.1 9.5 0.3 8.6 0.9 0.2 0.2 0.2
Proportion of species threatened with extinction % 2008
0.7 1.5 2.1 1.4 1.9 0.9 4.7 1.9 0.8 1.9 0.7 1.3 1.5 0.8 13.1 0.5 1.3 1.1 1.0 1.5 29.8 5.4 1.8 0.6 1.0 2.4 2.4 13.2 1.2 2.5 1.0 1.9 3.9 1.8 4.2 1.5 1.6 2.1 10.4 4.1 1.8 15.0 0.6 1.3 1.3 2.5 2.1 2.2 1.0 2.2 3.7 2.1 2.4 2.2 2.4 2.3
Develop a global partnership for development
Access to improved sanitation facilities % of population 1990 2006
.. .. 88 26 81 .. 100 100 .. 26 .. .. 12 33 .. 38 71 99 5 44 8 39 100 11 5 84 48 .. 68 15 .. 94 20 99 98 100 100 68 71 50 73 3 95 4 100 .. .. .. 94 100 6 97 70 13 .. 29
.. 97 94 50 91 91 100 100 80 36 93 .. 30 43 95 47 77 99 13 41 28 51 100 31 9 94 65 .. 78 31 20 96 24 99 98 99 100 79 84 66 86 5 95 11 100 .. 36 52 93 100 10 98 84 19 33 19
Internet users per 100 peoplea 2007
.. 14.9 10.3 2.9 25.9 5.7 68.1 67.4 10.8 0.3 29.0 65.9 1.7 10.5 28.0 5.3 35.2 30.9 0.6 0.7 0.5 2.0 72.8 0.3 0.6 31.1 16.1 57.2 27.5 0.4 1.9 33.6 1.6 44.7 11.6 48.3 80.7 17.2 13.2 14.0 11.1 2.5 63.7 0.4 78.8 51.2 6.2 5.9 8.2 72.3 3.8 32.9 10.1 0.5 2.2 10.4
Improve maternal health Maternal mortality ratio Modeled estimate per 100,000 live births 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
280 6 450 420 140 .. 1 4 3 170 6 62 140 560 370 14 4 150 660 10 150 960 1,200 97 11 10 510 1,100 62 970 820 15 60 22 46 240 520 380 210 830 6 9 170 1,800 1,100 7 64 320 130 470 150 240 230 8 11 18
Combat HIV/AIDS and other diseases
Contraceptive prevalence rate % of married women ages 15–49 1990 2002–07b
47 .. 43 50 49 14 60 68 .. 55 58 40 .. 27 62 79 .. .. .. .. .. 23 .. .. .. .. 17 13 50 .. 3 75 .. .. .. 42 .. 17 29 23 76 .. .. 4 6 74 9 15 .. .. 48 59 36 49 .. ..
65 .. 56 61 79 .. .. .. .. 69 .. 57 51 39 .. .. .. 48 38 .. 58 37 11 .. .. 14 27 42 .. 8 .. 76 71 68 66 63 17 34 55 48 .. .. 72 11 13 .. .. 30 .. .. 73 71 51 .. .. ..
0.7 0.1 0.3 0.2 0.2 .. 0.2 0.1 0.4 1.6 .. .. 0.1 .. .. <0.1 .. 0.1 0.2 0.8 0.1 23.2 1.7 .. 0.1 <0.1 0.1 11.9 0.5 1.5 0.8 1.7 0.3 0.4 0.1 0.1 12.5 0.7 15.3 0.5 0.2 0.1 0.2 0.8 3.1 0.1 .. 0.1 1.0 1.5 0.6 0.5 .. 0.1 0.5 ..
59 17 168 228 22 .. 13 8 7 7 21 7 129 353 344 90 24 121 151 53 19 637 277 17 68 29 251 346 103 319 318 22 20 141 205 92 431 171 767 173 8 7 49 174 311 6 13 181 47 250 58 126 290 25 30 4
1.3
Ensure environmental sustainability
HIV Incidence prevalence of tuberculosis Carbon dioxide emissions % of per capita population per 100,000 metric tons people ages 15–49 2007 2007 1990 2005
0.5 5.8 0.8 0.8 4.0 2.6 8.7 7.1 7.0 3.3 8.7 3.2 17.6 0.2 12.1 5.6 20.4 2.8 0.1 5.4 3.1 .. 0.2 8.7 6.6 8.1 0.1 0.1 3.1 0.1 1.4 1.4 4.5 5.4 4.7 1.0 0.1 0.1 0.0 0.0 9.3 6.5 0.6 0.1 0.5 7.1 5.6 0.6 1.3 0.6 0.5 1.0 0.7 9.1 4.3 ..
1.1 5.6 1.3 1.9 6.5 .. 10.2 9.2 7.7 3.8 9.6 3.8 11.9 0.3 3.5 9.4 36.9 1.1 0.3 2.8 4.2 .. 0.1 9.5 4.1 5.1 0.2 0.1 9.3 0.0 0.6 2.7 4.1 2.1 3.4 1.6 0.1 0.2 1.3 0.1 7.7 7.2 0.7 0.1 0.8 11.4 12.5 0.9 1.8 0.7 0.7 1.4 0.9 7.9 5.9 ..
Proportion of species threatened with extinction % 2008
3.5 1.8 3.3 3.4 1.0 11.0 1.8 4.3 2.2 7.7 4.9 3.4 1.1 3.9 1.3 1.7 6.3 0.8 1.2 1.4 1.2 0.6 3.8 1.6 0.9 0.9 6.4 3.3 6.9 1.0 2.9 24.3 3.2 1.3 1.1 1.9 2.9 2.7 2.1 1.1 1.3 5.1 1.3 1.0 4.3 1.5 4.2 1.7 2.9 3.6 0.5 2.8 6.6 1.2 2.8 3.6
WORLD VIEW
Millennium Development Goals: protecting our common environment
Develop a global partnership for development
Access to improved sanitation facilities % of population 1990 2006
45 100 14 51 83 .. .. .. .. 83 100 .. 97 39 .. .. .. .. .. .. .. .. 40 97 .. .. 8 46 .. 35 20 94 56 .. .. 52 20 23 26 9 100 .. 42 3 26 .. 85 33 .. 44 60 55 58 .. 92 ..
66 100 28 52 .. .. .. .. .. 83 100 85 97 42 .. .. .. 93 48 78 .. 36 32 97 .. 89 12 60 94 45 24 94 81 79 50 72 31 82 35 27 100 .. 48 7 30 .. .. 58 74 45 70 72 78 .. 99 ..
2009 World Development Indicators
Internet users per 100 peoplea 2007
6.0 51.9 7.2 5.8 32.4 .. 56.1 27.9 53.9 56.1 69.0 19.7 12.3 8.0 0.0 75.9 33.8 14.3 1.7 55.0 38.3 3.5 0.5 4.3 49.2 27.3 0.6 1.0 55.7 0.8 1.0 27.0 22.7 18.4 12.3 21.4 0.9 0.1 4.9 1.4 84.2 69.2 2.8 0.3 6.8 84.8 13.1 10.8 22.3 1.8 8.7 27.4 6.0 44.0 40.1 25.4
23
1.3
Millennium Development Goals: protecting our common environment Improve maternal health Maternal mortality ratio Modeled estimate per 100,000 live births 2005
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
24 28 1,300 18 980 14 c 2,100 14 6 6 1,400 400 4 58 450 390 3 5 130 170 950 110 380 510 45 100 44 130 550 18 37 8 11 20 24 57 150 .. 430 830 880 400 w 780 260 300 97 440 150 44 130 200 500 900 10 5
Combat HIV/AIDS and other diseases
Contraceptive prevalence rate % of married women ages 15–49 1990 2002–07b
.. 34 21 .. .. .. .. 65 74 .. 1 57 .. .. 9 20 .. .. .. .. 10 .. .. 34 .. 50 63 .. 5 .. .. .. 71 .. .. .. 53 .. 10 15 43 57 w 22 61 63 52 54 75 .. 56 42 40 15 72 ..
70 .. 17 .. 12 41 5 .. .. .. 15 60 .. 68 8 51 .. .. 58 38 26 77 20 17 43 .. 71 48 24 67 .. 84 .. .. 65 .. 76 50 28 34 60 60 w 33 68 69 .. 60 78 .. .. 62 53 23 .. ..
Ensure environmental sustainability
HIV Incidence prevalence of tuberculosis Carbon dioxide emissions % of per capita population per 100,000 metric tons people ages 15–49 2007 2007 1990 2005
0.1 1.1 2.8 .. 1.0 0.1 1.7 0.2 <0.1 <0.1 0.5 18.1 0.5 .. 1.4 26.1 0.1 0.6 .. 0.3 6.2 1.4 .. 3.3 1.5 0.1 .. <0.1 5.4 1.6 .. 0.2 0.6 0.6 0.1 .. 0.5 .. .. 15.2 15.3 0.8 w 2.1 0.6 0.3 1.7 0.9 0.2 0.6 0.5 0.1 0.3 5.0 0.3 0.3
115 110 397 46 272 32 574 27 17 13 249 948 30 60 243 1,198 6 6 24 231 297 142 322 429 11 26 30 68 330 102 16 15 4 22 113 34 171 20 76 506 782 139 w 269 129 134 108 162 136 84 50 41 174 369 16 13
6.7 15.3 0.1 12.1 0.4 6.2d 0.1 13.8 9.7 9.0 0.0 9.4 5.5 0.2 0.2 0.6 5.8 6.4 2.8 4.4 0.1 1.8 .. 0.2 13.8 1.6 2.5 8.7 0.0 13.2 29.3 9.9 19.2 1.3 6.1 5.9 0.3 .. 0.8 0.3 1.6 4.3e w 0.7 2.8 1.8 6.9 2.4 1.9 10.4 2.3 2.5 0.7 0.9 11.8 8.4
4.1 10.5 0.1 16.5 0.4 6.5d 0.2 13.2 6.8 7.4 0.1 8.7 7.9 0.6 0.3 0.8 5.4 5.5 3.6 0.8 0.1 4.3 0.2 0.2 24.7 2.2 3.4 8.6 0.1 6.9 30.1 9.1 19.5 1.7 4.3 5.6 1.2 .. 1.0 0.2 0.9 4.5e w 0.6 3.3 2.8 5.5 2.7 3.6 7.0 2.5 3.7 1.1 0.8 12.6 8.1
Proportion of species threatened with extinction % 2008
1.6 1.3 1.6 3.8 2.2 .. 3.2 9.7 1.1 2.1 3.2 1.6 3.8 14.0 2.4 0.8 1.4 1.4 2.0 0.8 5.1 3.4 .. 1.2 1.7 2.1 1.4 10.7 2.5 1.1 14.1 2.8 5.7 2.6 1.0 1.1 3.5 .. 12.6 0.7 0.9
Develop a global partnership for development
Access to improved sanitation facilities % of population 1990 2006
72 87 29 91 26 .. .. 100 100 .. .. 55 100 71 33 .. 100 100 81 .. 35 78 .. 13 93 74 85 .. 29 96 97 .. 100 100 93 83 29 .. 28 42 44 51 w 26 48 41 77 44 48 88 68 67 18 26 100 ..
72 87 23 99 28 92 11 100 100 .. 23 59 100 86 35 50 100 100 92 92 33 96 41 12 92 85 88 .. 33 93 97 .. 100 100 96 .. 65 80 46 52 46 60 w 39 60 55 83 55 66 89 78 77 33 31 100 ..
Internet users per 100 peoplea 2007
23.9 21.1 1.1 26.4 6.6 20.3 0.2 65.7 55.9 52.6 1.1 8.3 51.3 3.9 9.1 3.7 79.7 76.3 17.4 7.2 1.0 21.0 0.1 5.0 16.0 16.8 16.5 1.4 2.5 21.5 51.8 71.7 73.5 29.1 4.5 20.8 21.0 9.6 1.4 4.2 10.1 21.8 w 5.2 15.2 12.4 26.6 13.1 14.6 21.4 26.9 17.1 6.6 4.4 65.7 59.2
a. Data are from the International Telecommunication Union’s (ITU) World Telecommunication Development Report database. Please cite ITU for third-party use of these data. b. Data are for the most recent year available. c. Includes Montenegro. d. Includes Kosovo and Montenegro. e. Includes emissions not allocated to specific countries.
24
2009 World Development Indicators
About the data
The Millennium Development Goals address concerns common to all economies. Diseases and environmental degradation do not respect national boundaries. Epidemic diseases, wherever they occur, pose a threat to people everywhere. And environmental damage in one location may affect the well-being of plants, animals, and humans far away. The indicators in the table relate to goals 5, 6, and 7 and the targets of goal 8 that address access to new technologies. For the other targets of goal 8, see table 1.4. The target of achieving universal access to reproductive health has been added to goal 5 to address the importance of family planning and health services in improving maternal health and preventing maternal death. Women with multiple pregnancies are more likely to die in childbirth. Access to contraception is an important way to limit and space births. Measuring disease prevalence or incidence can be difficult. Most developing economies lack reporting systems for monitoring diseases. Estimates are often derived from survey data and report data from sentinel sites, extrapolated to the general population. Tracking diseases such as HIV/AIDS, which has a long latency
WORLD VIEW
Millennium Development Goals: protecting our common environment
1.3
Definitions
between contraction of the virus and the appearance of symptoms, or malaria, which has periods of dormancy, can be particularly difficult. The table shows the estimated prevalence of HIV among adults ages 15–49. Prevalence among older populations can be affected by life-prolonging treatment. The incidence of tuberculosis is based on case notifications and estimates of cases detected in the population. Carbon dioxide emissions are the primary source of greenhouse gases, which contribute to global warming, threatening human and natural habitats. In recognition of the vulnerability of animal and plant species, a new target of reducing biodiversity loss has been added to goal 7. Access to reliable supplies of safe drinking water and sanitary disposal of excreta are two of the most important means of improving human health and protecting the environment. Improved sanitation facilities prevent human, animal, and insect contact with excreta. Internet use includes narrowband and broadband Internet. Narrowband is often limited to basic applications; broadband is essential to promote e-business, e-learning, e-government, and e-health.
• Maternal mortality ratio is the number of women who die from pregnancy-related causes during pregnancy and childbirth, per 100,000 live births. Data are from various years and adjusted to a common 2005 base year. The values are modeled estimates (see About the data for table 2.18). • Contraceptive prevalence rate is the percentage of women ages 15–49 married or in-union who are practicing, or whose sexual partners are practicing, any form of contraception. • HIV prevalence is the percentage of people ages 15–49 who are infected with HIV. • Incidence of tuberculosis is the estimated number of new tuberculosis cases (pulmonary, smear positive, and extrapulmonary). • Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include emissions produced during consumption of solid, liquid, and gas fuels and gas flaring (see table 3.8). • Proportion of species threatened with extinction is the total number of threatened mammal (excluding whales and porpoises), bird, and higher native, vascular plant species as a percentage of the total number of known species of the same categories.
1.3a
Location of indicators for Millennium Development Goals 5–7 Goal 5. Improve maternal health 5.1 Maternal mortality ratio 5.2 Proportion of births attended by skilled health personnel 5.3 Contraceptive prevalence rate 5.4 Adolescent fertility rate 5.5 Antenatal care coverage 5.6 Unmet need for family planning Goal 6. Combat HIV/AIDS, malaria, and other diseases 6.1 HIV prevalence among population ages 15–24 6.2 Condom use at last high-risk sex 6.3 Proportion of population ages 15–24 with comprehensive, correct knowledge of HIV/AIDS 6.4 Ratio of school attendance of orphans to school attendance of nonorphans ages 10–14 6.5 Proportion of population with advanced HIV infection with access to antiretroviral drugs 6.6 Incidence and death rates associated with malaria 6.7 Proportion of children under age 5 sleeping under insecticide-treated bednets 6.8 Proportion of children under age 5 with fever who are treated with appropriate antimalarial drugs 6.9 Incidence, prevalence, and death rates associated with tuberculosis 6.10 Proportion of tuberculosis cases detected and cured under directly observed treatment short course Goal 7. Ensure environmental sustainability 7.1 Proportion of land area covered by forest 7.2 Carbon dioxide emissions, total, per capita, and per $1 purchasing power parity GDP 7.3 Consumption of ozone-depleting substances 7.4 Proportion of fish stocks within safe biological limits 7.5 Proportion of total water resources used 7.6 Proportion of terrestrial and marine areas protected 7.7 Proportion of species threatened with extinction 7.8 Proportion of population using an improved drinking water source 7.9 Proportion of population using an improved sanitation facility 7.10 Proportion of urban population living in slums
Table 1.3, 2.18 2.18, 2.21 1.3, 2.18, 2.21 2.18 1.5, 2.18, 2.21 2.18
• Access to improved sanitation facilities is the percentage of the population with at least adequate access to excreta disposal facilities (private or shared, but not public) that can effectively prevent human, animal, and insect contact with excreta (facilities do not have to include treatment to render sewage outflows innocuous). Improved facilities range from simple but protected pit latrines to flush toilets with a sewerage connection. To be effective,
1.3*, 2.20* 2.20* —
facilities must be correctly constructed and properly maintained. • Internet users are people with access to the worldwide network.
— — — 2.17 2.17 1.3, 2.20 2.17
Data sources
3.1
The indicators here and throughout this book have been compiled by World Bank staff from primary
3.8 3.9* — 3.5 — 1.3 1.3, 2.17, 3.5 1.3, 2.17, 3.11 —
— No data are available in the World Development Indicators database. * Table shows information on related indicators.
and secondary sources. Efforts have been made to harmonize the data series used to compile this table with those published on the United Nations Millennium Development Goals Web site (www. un.org/millenniumgoals), but some differences in timing, sources, and definitions remain. For more information see the data sources for the indicators listed in table 1.3a.
2009 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 Zealand Norway Switzerland United States
Net % of donor GNI 2007
For basic social servicesa % of total sector-allocable ODA 2007
0.32 0.29
9.1 31.2
0.50 0.43 0.81 0.39 0.38 0.37 0.16 0.55 0.19 0.91 0.81 0.22 0.37 0.93 0.36 0.17 0.27 0.95 0.37 0.16
9.0 20.6 10.1 13.9 6.0 10.0 15.0 35.1 9.8 33.9 18.1 3.1 15.6 13.1 57.6 3.8 32.0 21.2 6.3 31.6
Least developed countries’ access to high-income markets Goods (excluding arms) admitted free of tariffs % of exports from least developed countries 2000 2006
95.9 39.0 97.8
49.1 85.9c 99.0 99.4 50.3
100.0 99.7 97.8
26.7 99.2c 99.0 96.7 76.6
Support to agriculture
Average tariff on exports of least developed countries Agricultural products % 2000 2006
Textiles %
Clothing %
2000
2006
2000
2006
% of GDP 2007b
0.2 0.3 3.0
0.0 0.1 2.4
5.7 6.0 0.0
0.0 0.2 0.1
22.5 19.3 0.0
0.0 1.7 1.2
0.28 0.68 0.91
4.7 0.0 c 3.6 6.1 6.9
4.4 13.1c 0.2 2.6 6.4
5.0 9.3c 4.6 0.0 7.0
2.7 0.0 c 0.0 0.0 5.8
0.4 12.9c 1.4 0.0 14.1
0.1 0.0 c 1.0 0.0 11.3
1.04 0.22 0.79 1.11 0.73
Heavily indebted poor countries (HIPCs) HIPC HIPC HIPC decision completion Initiative pointd pointd assistancee
Afghanistan Benin Boliviag Burkina Fasog,h Burundi Cameroon Central African Republic Chad Congo, Dem. Rep. Congo, Rep. Ethiopiah Gambia, The Ghana Guinea Guinea-Bissau Guyanag Haiti
Jul. 2007 Jul. 2000 Feb. 2000 Jul. 2000 Aug. 2005 Oct. 2000 Sep. 2007 May 2001 Jul. 2003 Apr. 2006 Nov. 2001 Dec. 2000 Feb. 2002 Dec. 2000 Dec. 2000 Nov. 2000 Nov. 2006
Floating Mar. 2003 Jun. 2001 Apr. 2002 Jan. 2009 Apr. 2006 Floating Floating Floating Floating Apr. 2004 Dec. 2007 Jul. 2004 Floating Floating Dec. 2003 Floating
MDRI assistancef
$ millions
$ millions
571 366 1,856 772 908 1,768 611 227 7,636 1,847 2,575 93 2,910 761 581 852 147
.. 604 1,596 603 53i 747 .. .. .. .. 1,458 199 2,095 .. .. 402 ..
HIPC HIPC HIPC decision completion Initiative pointd pointd assistancee
Honduras Liberia Madagascar Malawih Malig Mauritania Mozambiqueg Nicaragua Nigerh Rwandah São Tomé & Principeh Senegal Sierra Leone Tanzania Togo Ugandag Zambia
Jul. 2000 Mar. 2008 Dec. 2000 Dec. 2000 Sep. 2000 Feb. 2000 Apr. 2000 Dec. 2000 Dec. 2000 Dec. 2000 Dec. 2000 Jun. 2000 Mar. 2002 Apr. 2000 Nov. 2008 Feb. 2000 Dec. 2000
Apr. 2005 Floating Oct. 2004 Aug. 2006 Mar. 2003 Jun. 2002 Sep. 2001 Jan. 2004 Apr.2004 Apr. 2005 Mar. 2007 Apr. 2004 Dec. 2006 Nov. 2001 Floating May 2000 Apr. 2005
MDRI assistancef
$ millions
$ millions
776 2,845 1,167 1,310 752 868 2,992 4,618 899 908 163 682 857 2,828 270 1,434 3,489
1,543 .. 1,292 705 1,043 450 1,057 954 519 225 26 1,374 352 2,038 .. 1,805 1,632
a. Includes primary education, basic life skills for youth, adult and early childhood education, basic health care, basic health infrastructure, basic nutrition, infectious disease control, health education, health personnel development, population policy and administrative management, reproductive health care, family planning, sexually transmitted disease control including HIV/AIDS, personnel development for population and reproductive health, basic drinking water supply and basic sanitation, and multisector aid for basic social services. b. Provisional data. c. Calculated by World Bank staff using the World Integrated Trade Solution based on the United Nations Conference on Trade and Development’s Trade Analysis and Information Systems database. d. Refers to the Enhanced HIPC Initiative. e. Total HIPC assistance (committed debt relief) assuming of full participation of creditors, in end-2007 net present value terms. Topping-up assistance and assistance provided under the original HIPC Initiative were committed in net present value terms as of the decision point and are converted to end-2007 terms. f. Multilateral Debt Relief Initiative (MDRI) assistance has been delivered in full to all post-completion point countries, shown in end-2007 net present value terms. g. Also reached completion point under the original HIPC Initiative. The assistance includes original debt relief. h. Assistance includes topping up at completion point. i. Excludes $15 million (in nominal terms) of committed debt relief by the International Monetary Fund.
26
2009 World Development Indicators
About the data
WORLD VIEW
Millennium Development Goals: overcoming obstacles
1.4
Definitions
Achieving the Millennium Development Goals requires
lines with “international peaks”). The averages in the
• Net offi cial development assistance (ODA) is
an open, rule-based global economy in which all
table include ad valorem duties and equivalents.
grants and loans (net of repayments of principal) that
countries, rich and poor, participate. Many poor
Subsidies to agricultural producers and exporters
meet the DAC definition of ODA and are made to coun-
countries, lacking the resources to finance develop-
in OECD countries are another barrier to developing
tries on the DAC list of recipients. • ODA for basic
ment, burdened by unsustainable debt, and unable
economies’ exports. Agricultural subsidies in OECD
social services is aid reported by DAC donors for
to compete globally, need assistance from rich coun-
economies are estimated at $365 billion in 2007.
basic education, primary health care, nutrition, pop-
tries. For goal 8—develop a global partnership for
The Debt Initiative for Heavily Indebted Poor Coun-
ulation policies and programs, reproductive health,
development—many indicators therefore monitor the
tries (HIPCs), an important step in placing debt relief
and water and sanitation services. • Goods admitted
actions of members of the Organisation for Economic
within the framework of poverty reduction, is the first
free of tariffs are exports of goods (excluding arms)
Co-operation and Development’s (OECD) Develop-
comprehensive approach to reducing the external
from least developed countries admitted without tar-
ment Assistance Committee (DAC).
debt of the world’s poorest, most heavily indebted
iff. • Average tariff is the unweighted average of the
Official development assistance (ODA) has risen
countries. A 1999 review led to an enhancement of
effectively applied rates for all products subject to
in recent years as a share of donor countries’ gross
the framework. In 2005, to further reduce the debt
tariffs. • Agricultural products are plant and animal
national income (GNI), but the poorest economies need
of HIPCs and provide resources for meeting the Mil-
products, including tree crops but excluding timber
additional assistance to achieve the Millennium Devel-
lennium Development Goals, the Multilateral Debt
and fish products. • Textiles and clothing are natural
opment Goals. After rising to a record $107 billion in
Relief Initiative (MDRI), proposed by the Group of
and synthetic fibers and fabrics and articles of cloth-
2005, net ODA disbursements from DAC donors fell
Eight countries, was launched.
ing made from them. • Support to agriculture is the
3.3 percent in 2007 to $103.5 billion in nominal terms.
Under the MDRI four multilateral institutions—the
value of gross transfers from taxpayers and consum-
One important action that high-income economies can
International Development Association (IDA), Interna-
ers arising from policy measures, net of associated
take is to reduce barriers to exports from low- and mid-
tional Monetary Fund (IMF), African Development Fund
budgetary receipts, regardless of their objectives and
dle-income economies. The European Union has begun
(AfDF), and Inter-American Development Bank (IDB)
impacts on farm production and income or consump-
to eliminate tariffs on developing economy exports of
—provide 100 percent debt relief on eligible debts
tion of farm products. • HIPC decision point is the
“everything but arms,” and the United States offers
due to them from countries having completed the HIPC
date when a heavily indebted poor country with an
special concessions to Sub-Saharan African exports.
Initiative process. Data in the table refer to status as of
established track record of good performance under
However, these programs still have many restrictions.
February 2009 and might not show countries that have
adjustment programs supported by the IMF and the
Average tariffs in the table refl ect high-income
since reached the decision or completion point. Debt
World Bank commits to additional reforms and a
OECD member tariff schedules for exports of coun-
relief under the HIPC Initiative has reduced future debt
poverty reduction strategy. • HIPC completion point
tries designated least developed countries by the
payments by $51.3 billion for 34 countries that have
is the date when a country successfully completes
United Nations. Although average tariffs have been
reached the decision point. And 23 countries that have
the key structural reforms agreed on at the decision
falling, averages may disguise high tariffs on specific
reached the completion point have received additional
point, including implementing a poverty reduction
goods (see table 6.8 for each country’s share of tariff
assistance of $22.8 billion under the MDRI.
strategy. The country then receives the bulk of debt
1.4a
Location of indicators for Millennium Development Goal 8 Goal 8. Develop a global partnership for development 8.1 Net ODA as a percentage of DAC donors’ gross national income 8.2 Proportion of ODA for basic social services 8.3 Proportion of ODA that is untied 8.4 Proportion of ODA received in landlocked countries as a percentage of GNI 8.5 Proportion of ODA received in small island developing states as a percentage of GNI 8.6 Proportion of total developed country imports (by value, excluding arms) from least developed countries admitted free of duty 8.7 Average tariffs imposed by developed countries on agricultural products and textiles and clothing from least developed countries 8.8 Agricultural support estimate for OECD countries as a percentage of GDP 8.9 Proportion of ODA provided to help build trade capacity 8.10 Number of countries reaching HIPC decision and completion points 8.11 Debt relief committed under new HIPC initiative 8.12 Debt services as a percentage of exports of goods and services 8.13 Proportion of population with access to affordable, essential drugs on a sustainable basis 8.14 Telephone lines per 100 people 8.15 Cellular subscribers per 100 people 8.16 Internet users per 100 people
Table 1.4, 6.13 1.4, 6.14b* 6.14b — — 1.4
relief under the HIPC Initiative without further policy conditions. • HIPC Initiative assistance is the debt relief committed as of the decision point in end-2007 net present value. • MDRI assistance is the debt relief from IDA, IMF, AfDF, and IDB, delivered to countries having reached the HIPC completion point in end-2007 net present value. Data sources Data on ODA are from the OECD. Data on goods
1.4, 6.8* 1.4 — 1.4 1.4 6.10* — 1.3*, 5.10 1.3*, 5.10 5.11
— No data are available in the World Development Indicators database. * Table shows information on related indicators.
admitted free of tariffs and average tariffs are from the World Trade Organization, in collaboration with the United Nations Conference on Trade and Development and the International Trade Centre. These data are available at www.mdg-trade. org. Data on subsidies to agriculture are from the OECD’s Producer and Consumer Support Estimates, OECD Database 1986–2007. Data on the HIPC Initiative and MDRI are from the World Bank’s Economic Policy and Debt Department.
2009 World Development Indicators
27
1.5
Women in development Female population
Life expectancy at birth
Pregnant women receiving prenatal care
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
28
% of total 2007
Male 2007
Female 2007
.. 50.3 49.5 50.7 51.1 53.5 50.2 51.0 51.4 48.8 53.5 51.0 49.6 50.2 51.4 50.3 50.7 51.6 50.0 51.1 51.2 50.0 50.5 51.2 50.3 50.5 48.4 52.1 50.8 50.5 50.4 49.2 49.3 51.9 50.0 51.2 50.5 49.9 49.9 49.9 50.9 50.9 53.9 50.3 51.0 51.3 50.0 49.9 52.8 51.1 49.3 50.5 51.2 49.5 50.6 50.5
.. 73 71 41 72 68 79 77 64 63 65 77 56 63 72 50 69 69 51 48 57 50 78 43 49 75 71 79 69 45 54 76 48 72 76 74 76 69 72 69 69 56 67 52 76 78 56 59 67 77 60 77 67 54 45 59
.. 80 74 44 79 75 84 83 71 65 76 83 58 68 77 51 76 76 54 51 62 51 83 46 52 82 75 85 77 48 57 81 49 79 80 80 81 75 78 74 75 60 79 54 83 85 57 60 75 82 60 82 74 58 48 63
2009 World Development Indicators
% 2002–07a
.. 97 89 80 99 93 .. .. 77 51 99 .. 84 79 99 .. 97 .. 85 92 69 82 .. 69 39 .. 90 .. 94 85 86 92 85 100 100 .. .. 99 84 70 86 70 .. 28 .. .. .. 98 94 .. 92 .. 84 82 78 85
Teenage mothers
Women in nonagricultural sector
Unpaid family workers
Women in parliaments
% of women ages 15–19 2002–07a
% of nonagricultural wage employment 2006
Male Female % of male % of female employment employment 2002–07a 2002–07a
% of total seats 1990 2008
.. .. .. .. .. 5 .. .. 6 33 .. .. 21 16 .. .. .. .. 23 .. 8 28 .. .. 37 .. .. .. 21 24 27 .. .. 4 .. .. .. 21 .. 9 .. 14 .. 17 .. .. .. .. .. .. 14 .. .. 32 .. 14
.. .. 17 .. 45 46 49 47 50 .. .. 46 .. .. 35 42 .. 53 .. .. 52 .. 50 .. .. 39 .. 48 49 .. .. 41 .. 44 c 43 46 49 39 42 21 49 .. 53 47 51 48 .. .. 49 47 .. 42 38 .. .. ..
.. .. 7.1 .. 0.7b .. 0.2 2.0 16.8 9.7 .. 0.4 .. 12.6 3.0 2.2 4.6b 0.7 .. .. .. .. 0.1 .. .. 0.9 .. 0.1b 3.2 .. .. 1.3 .. 1.1 .. 0.2 0.3 2.8 4.4 8.6b 8.8 .. 0.0d 7.8 0.6 0.3 .. .. 19.0 0.4 .. 3.7 21.3 .. .. ..
.. .. 13.6 .. 1.6b .. 0.4 2.9 16.8 60.1 .. 2.9 .. 34.8 11.0 2.2 8.1b 1.6 .. .. .. .. 0.2 .. .. 2.8 .. 1.1b 6.1 .. .. 2.8 .. 3.7 .. 1.1 1.0 4.9 11.1 32.6b 9.9 .. 0.0 d 12.7 0.4 1.0 .. .. 39.0 1.8 .. 10.7 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 ..
28 7 8 37 40 8 27 32 11 15 29 35 11 17 12 11 9 22 15 31 16 14 21 11 5 15 21 .. 8 8 7 37 9 22 43 16 38 20 25 2 17 22 21 22 42 18 17 9 6 32 11 15 12 19 14 4
Female population
Life expectancy at birth
Pregnant women receiving prenatal care
years
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 total 2007
Male 2007
Female 2007
50.3 52.4 48.2 50.1 49.3 .. 50.1 50.5 51.4 50.7 51.2 48.6 52.2 50.2 50.7 50.0 40.1 50.7 50.2 53.9 51.0 52.9 50.0 48.2 53.4 50.1 50.3 50.3 49.2 51.3 49.4 50.4 51.2 52.1 50.1 50.8 51.5 50.5 50.7 50.4 50.5 50.7 50.2 49.3 50.0 50.3 44.1 48.6 49.6 49.3 49.5 49.9 49.6 51.7 51.6 52.1
67 69 63 69 69 .. 77 79 79 70 79 71 61 53 65 76 76 64 63 66 70 43 45 72 65 72 58 48 72 52 62 69 73 65 64 69 42 59 52 63 78 78 70 58 46 78 74 65 73 55 70 69 70 71 75 74
74 77 66 73 73 .. 82 83 84 75 86 74 72 55 69 82 80 72 66 77 74 42 47 77 77 77 61 48 77 57 66 76 77 72 70 73 42 65 53 64 82 82 76 56 47 83 77 66 78 60 74 74 74 80 82 83
% 2002–07a
92 .. 74 93 .. .. .. .. .. 91 .. 99 100 88 .. .. .. 97 .. .. 96 90 .. .. .. 98 80 92 79 70 .. .. .. 98 99 68 85 .. 95 44 .. .. 90 46 58 .. .. 61 .. .. 94 91 88 .. .. ..
WORLD VIEW
Women in development
1.5
Teenage mothers
Women in nonagricultural sector
Unpaid family workers
Women in parliaments
% of women ages 15–19 2002–07a
% of nonagricultural wage employment 2006
Male Female % of male % of female employment employment 2002–07a 2002–07a
% of total seats 1990 2008
22 .. 16 10 .. .. .. .. .. .. .. 4 7 23 .. .. .. .. .. .. .. 20 32 .. .. .. 34 34 .. 36 .. .. .. 6 .. 7 41 .. .. 19 .. .. .. 39 25 .. .. 9 .. .. .. 26 8 .. .. ..
45 48 18 29 .. .. 48 49 43 48 42 26 49 .. .. 42 .. 52 50 53 .. .. .. .. 54 40 38 .. 38 35 .. 38 39 54 53 28 .. .. 47 .. 47 47 .. .. 21 49 .. 11 43 .. .. 36 42 47 47 41
12.1 0.3 .. 7.8 5.4 .. 0.4 0.1 1.3 0.5 1.1 .. 1.0 .. .. 1.2 .. 8.8 .. 1.5 .. .. .. .. 1.1 7.0 32.1 .. 2.7 18.4 .. 0.9 4.9 1.3 18.4 17.0 .. .. 3.2 .. 0.2 0.8 12.2 .. .. 0.2 .. 18.6 2.3 .. 10.8 b 4.7b 9.0 2.8 0.7 0.0 d
8.3 0.7 .. 33.6 32.7 .. 0.9 0.4 2.6 2.2 7.3 .. 1.3 .. .. 12.7 .. 19.3 .. 1.6 .. .. .. .. 2.4 14.9 73.0 .. 8.8 10.2 .. 4.7 10.0 3.4 31.7 55.3 .. .. 5.8 .. 1.0 1.5 9.1 .. .. 0.3 .. 61.9 4.0 .. 8.9b 9.9b 18.0 6.0 1.5 0.0 d
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 ..
2009 World Development Indicators
23 11 9 12 3 26 13 14 21 13 9 6 16 9 20 14 3 26 25 20 5 25 13 8 23 32 8 13 11 10 22 17 23 22 4 11 35 .. 27 33 39 33 19 12 7 36 0 23 17 1 13 29 21 20 28 ..
29
1.5
Women in development Female population
Life expectancy at birth
Pregnant women receiving prenatal care
years % of total 2007
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
51.3 53.7 51.8 45.0 50.2 50.5 50.7 49.7 51.5 51.2 50.4 50.8 50.7 50.7 49.6 51.6 50.4 51.3 49.5 50.4 50.3 51.3 49.2 50.5 50.8 49.7 49.6 50.8 50.0 53.9 32.4 51.0 50.8 51.7 50.3 49.8 50.0 49.1 49.4 50.2 50.3 49.6 w 49.8 49.3 48.9 51.2 49.4 48.9 52.1 50.6 49.7 48.4 50.2 50.6 51.1
Male 2007
69 62 45 71 61 71 41 78 71 74 47 49 78 69 57 40 79 79 72 64 51 66 60 57 68 72 69 59 51 63 77 77 75 72 64 71 72 72 61 42 44 67 w 56 67 67 67 65 70 65 70 68 63 50 77 77
Female 2007
76 74 48 75 65 76 44 83 78 82 49 52 84 76 60 39 83 84 76 69 54 75 62 60 72 76 74 68 52 74 81 82 81 80 70 77 76 75 64 42 43 71 w 59 72 71 75 69 74 74 76 72 66 52 82 83
% 2002–07a
94 .. 94 .. 87 98 81 .. .. .. 26 92 .. 99 70 85 .. .. 84 79 78 98 61 84 96 .. 81 99 94 99 .. .. .. .. 99 .. 91 99 41 93 94 81 w 67 86 84 .. 81 90 .. 95 76 69 72 .. ..
Teenage mothers
Women in nonagricultural sector
Unpaid family workers
Women in parliaments
% of women ages 15–19 2002–07a
% of nonagricultural wage employment 2006
Male Female % of male % of female employment employment 2002–07a 2002–07a
% of total seats 1990 2008
.. .. 4 .. 19 .. .. .. .. .. .. .. .. .. .. 23 .. .. .. .. 26 .. .. .. .. .. .. .. 25 .. .. .. .. .. .. .. 3 .. .. 32 21
a. Data are for the most recent year available. b. Limited coverage. c. Data are for 2007. d. Less than 0.5.
30
2009 World Development Indicators
47 51 .. 13 .. 42 23 50 50 48 .. 43 43 45 .. .. 50 47 .. .. .. 47 .. .. 43 .. 21 .. .. 55 .. 50 47 45 .. 41 46 17 .. .. .. .. w .. .. .. 45 .. .. 48 .. .. 18 .. 46 45
6.5 0.1 .. .. .. 3.1 14.8 0.4 0.1b 3.1 .. 0.3 0.7 4.4b .. .. 0.3 1.7 .. .. 9.7 14.0 .. .. 0.3 .. 5.6 .. 10.3b 0.4 .. 0.2 0.1 0.9b .. 0.6 18.9 6.6 .. .. 10.4 .. w .. .. .. 3.3 .. .. 2.4 4.0 .. .. .. 0.5 0.8
19.9 0.1 .. .. .. 11.9 21.6 1.3 0.1b 7.1 .. 0.6 1.6 21.7b .. .. 0.3 3.2 .. .. 13.0 29.9 .. .. 1.7 .. 38.2 .. 40.5b 0.3 .. 0.5 0.1 3.0 b .. 1.6 47.2 31.5 .. .. 13.6 .. w .. .. .. 7.3 .. .. 6.3 7.5 .. .. .. 2.4 2.1
34 .. 17 .. 13 .. .. 5 .. .. 4 3 15 5 .. 4 38 14 9 .. .. 3 .. 5 17 4 1 26 12 .. 0 6 7 6 .. 10 18 .. 4 7 11 13 w .. 13 13 13 13 17 .. 12 4 6 .. 12 12
9 14 56 0 22 22 13 25 19 13 8 33 36 6 18 11 47 29 12 18 30 12 29 11 27 23 9 16 31 8 23 20 17 12 18 19 26 .. 0d 15 15 18 w 18 17 15 19 17 18 15 22 9 20 18 22 25
About the data
WORLD VIEW
Women in development
1.5
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 developing
workloads at home and in nonpaid domestic and
prevailing patterns of mortality at the time of its birth
countries. Gender inequalities in the allocation of
market activities, and labor market discrimination
were to stay the same throughout its life. • Pregnant
such resources as education, health care, nutrition,
often limit women’s participation in paid economic
women receiving prenatal care are the percentage
and political voice matter because of the strong
activities, lower their productivity, and reduce their
of women attended at least once during pregnancy
association with well-being, productivity, and eco-
wages. When women are in salaried employment,
by skilled health personnel for reasons related to
nomic growth. These patterns of inequality begin at
they tend to be concentrated in the nonagricultural
pregnancy. • Teenage mothers are the percentage of
an early age, with boys routinely receiving a larger
sector. However, in many developing countries
women ages 15–19 who already have children or are
share of education and health spending than do girls,
women are a large part of agricultural employment,
currently pregnant. • Women in nonagricultural sec-
for example.
often as unpaid family workers. Among people who
tor are female wage employees in the nonagricultural
Because of biological differences girls are
are unsalaried, women are more likely than men to
sector as a percentage of total nonagricultural wage
expected to experience lower infant and child mor-
be unpaid family workers, while men are more likely
employment. • Unpaid family workers are those who
tality rates and to have a longer life expectancy
than women to be self-employed or employers. There
work without pay in a market-oriented establishment
than boys. This biological advantage may be over-
are several reasons for this.
or activity operated by a related person living in the
shadowed, however, by gender inequalities in nutri-
Few women have access to credit markets, capital,
same household. • Women in parliaments are the
tion and medical interventions and by inadequate
land, training, and education, which may be required
percentage of parliamentary seats in a single or
care during pregnancy and delivery, so that female
to start a business. Cultural norms may prevent
lower chamber held by women.
rates of illness and death sometimes exceed male
women from working on their own or from super-
rates, particularly during early childhood and the
vising other workers. Also, women may face time
reproductive years. In high-income countries women
constraints due to their traditional family respon-
tend to outlive men by four to eight years on aver-
sibilities. Because of biases and misclassification
age, while in low-income countries the difference is
substantial numbers of employed women may be
narrower—about two to three years. The difference
underestimated or reported as unpaid family workers
in child mortality rates (table 2.22) is another good
even when they work in association or equally with
indicator of female social disadvantage because
their husbands in the family enterprise.
nutrition and medical interventions are particularly
Women are vastly underrepresented in decision-
important for the 1–4 age group. Female child mor-
making positions in government, although there is
tality rates that are as high as or higher than male
some evidence of recent improvement. Gender parity
child mortality rates may indicate discrimination
in parliamentary representation is still far from being
against girls.
realized. In 2008 women accounted for 18 percent
Having a child during the teenage years limits
of parliamentarians worldwide, compared with 9 per-
girls’ opportunities for better education, jobs, and
cent in 1987. Without representation at this level, it
income. Pregnancy is more likely to be unintended
is difficult for women to influence policy.
during the teenage years, and births are more likely
For information on other aspects of gender, see
to be premature and are associated with greater
tables 1.2 (Millennium Development Goals: eradicat-
Data sources
risks of complications during delivery and of death.
ing poverty and saving lives), 1.3 (Millennium Devel-
Data on female population and life expectancy are
In many countries maternal mortality (tables 1.3 and
opment Goals: protecting our common environment),
from the World Bank’s population database. Data
2.18) is a leading cause of death among women of
2.3 (Employment by economic activity), 2.4 (Decent
on pregnant women receiving prenatal care are
reproductive age. Most maternal deaths result from
work and productive employment), 2.5 (Unemploy-
from household surveys, including Demographic
preventable causes—hemorrhage, infection, and
ment), 2.6 (Children at work), 2.10 (Assessing vulner-
and Health Surveys by Macro International and
complications from unsafe abortions. Prenatal care
ability and security), 2.13 (Education efficiency), 2.14
Multiple Indicator Cluster Surveys by the United
is essential for recognizing, diagnosing, and promptly
(Education completion and outcomes), 2.15 (Educa-
Nations Children’s Fund (UNICEF), and UNICEF’s
treating complications that arise during pregnancy.
tion gaps by income and gender), 2.18 (Reproductive
State of the World’s Children 2009. Data on teen-
In high-income countries most women have access
health), 2.20 (Health risk factors and future chal-
age mothers are from Demographic and Health
to health care during pregnancy, but in developing
lenges), 2.21 (Health gaps by income and gender),
Surveys by Macro International. Data on labor
countries an estimated 200 million women suffer
and 2.22 (Mortality).
force and employment are from the International
pregnancy-related complications, and over half a mil-
Labour Organization’s Key Indicators of the Labour
lion die every year (Glasier and others 2006). This
Market, fi fth edition. Data on women in parlia-
is reflected in the differences in maternal mortality
ments are from the Inter-Parliamentary Union.
ratios between high- and low-income countries.
2009 World Development Indicators
31
1.6
Key indicators for other economies Population Surface Population area density
thousands 2007
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
65 82 85 101 331 753 294 304 64 657 389 530 54 149 628 855 833 73 508 48 834 263 57 106 173 739 311 77
Gross national income
PPPa Per capita $ millions $ 2007 2007
thousand sq. km 2007
people per sq. km 2007
$ millions 2007b
Per capita $ 2007b
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 0.2 1.9 9.3 23.2 0.8 28.1 1.4 18.3 4.0 410.5 0.3 0.5 215.0 103.0 0.6
325 175 193 561 33 1,060 684 13 1,280 14 74 132 206 785 338 92 36 97 18 35 46 72 0g 311 321 4 3 136
.. .. 980 .. .. 12,607 .. 1,144 .. 1,166 10,211 1,287 .. 10,241 425 19,617f 908 292 6,527 .. 3,125 .. .. 414 .. 926 17,959 3,517
..c .. .. .. .. ..d 11,650 1,487e 17,680e .. .. ..d .. .. ..d 17,390 19,720 27,210 4,711e 16,140e ..d 6,080e 3,760 1,847e .. .. ..d 1,770 3,275 4,980 26,740 19,540 50,200 2,430 1,557 2,940 .. .. ..d 68,640 .. .. 680 721 1,150 24,940 f 20,549 24,040 1,090 1,885 2,260 6,930e 4,030 501e 12,860 10,770 21,220 .. .. ..d 3,750 3,538 4,240 .. .. ..d ..d .. .. 5,480e 3,920 579e .. .. ..d 2,580e 1,250 1,907e 57,750 10,596 34,070 45,810 .. ..
About the data
Gross domestic product
Life Adult expectancy literacy rate at birth
% growth 2006–07
Per capita % growth 2006–07
years 2007
.. .. –1.2 .. 2.8 7.8 .. 1.2 4.6 19.1 0.6 7.0 .. 5.9 –1.0 4.4 4.0 3.2 12.5 .. –6.6 .. .. 3.0 .. 9.1 3.8 7.7
.. .. –2.1 .. 1.6 5.6 .. –0.9 4.3 17.5 –1.3 4.6 .. 5.7 –3.3 3.3 2.2 2.6 9.9 .. –7.1 .. .. 2.9 .. 9.2 1.4 6.7
.. .. .. .. 73 76 77 76 79 66 77 71 .. 79 65 79 55 .. 52 79 69 74 .. 69 76 67 81 ..
Carbon dioxide emissions
% ages 15 thousand and older metric tons 2007 2005
.. .. 99 98 .. 89 .. .. .. 53 95 84 99 .. 75 98 .. .. .. .. .. .. .. .. .. .. .. ..
.. .. 421 2,308 2,107 19,668 1,315 817 572 414 5,892 286 315 .. 88 7,017 374 114 4,335 656 1,641 685 557 234 .. 1,491 2,184 ..
Definitions
The table shows data for 56 economies with popula-
• Population is based on the de facto definition of
net receipts of primary income (compensation of
tions between 30,000 and 1 million and for smaller
population, which counts all residents regardless
employees and property income) from abroad. Data
economies if they are members of the World Bank.
of legal status or citizenship—except for refugees
are in current U.S. dollars converted using the World
Where data on gross national income (GNI) per capita
not permanently settled in the country of asylum,
Bank Atlas method (see Statistical methods). • GNI
are not available, the estimated range is given. For
who are generally considered part of the popula-
per capita is GNI divided by midyear population.
more information on the calculation of GNI (gross
tion of their country of origin. The values shown are
GNI per capita in U.S. dollars is converted using
national product, or GNP, in the System of National
midyear estimates. See also table 2.1. • Surface
the World Bank Atlas method. • Purchasing power
Accounts 1968) and purchasing power parity (PPP)
area is a country’s total area, including areas under
parity (PPP) GNI is GNI converted to international
conversion factors, see About the data for table
inland bodies of water and some coastal waterways.
dollars using PPP rates. An international dollar has
1.1. Additional data for the economies in the table
• Population density is midyear population divided
the same purchasing power over GNI that a U.S.
are available on the World Development Indicators
by land area in square kilometers. • Gross national
dollar has in the United States. • Gross domestic
CD-ROM or at WDI Online.
income (GNI) is the sum of value added by all resi-
product (GDP) is the sum of value added by all
dent producers plus any product taxes (less sub-
resident producers plus any product taxes (less
sidies) not included in the valuation of output plus
subsidies) not included in the valuation of output.
32
2009 World Development Indicators
Population Surface Population area density
thousands 2007
Kiribati Liechtenstein Luxembourg Macao, China Maldives Malta Marshall Islands Mayotte Micronesia, Fed. Sts. Monaco Montenegro Netherlands Antilles New Caledonia Northern Mariana Islands Palau Qatar Samoa San Marino Sao Tome and Principe Seychelles Solomon Islands St. Kitts and Nevis St. Lucia St. Vincent & Grenadines Suriname Tonga Vanuatu Virgin Islands (U.S.)
95 35 480 480 305 409 58 186 111 33 599 191 242 84 20 836 181 31 158 85 495 49 168 120 458 102 226 108
thousand sq. km 2007
0.8 0.2 2.6 0.0 0.3 0.3 0.2 0.4 0.7 0.0 14.0 0.8 18.6 0.5 0.5 11.0 2.8 0.1 1.0 0.5 28.9 0.3 0.6 0.4 163.3 0.8 12.2 0.4
people per sq. km 2007
117 220 185 17,026 1,018 1,279 324 497 159 16,769 43 239 13 182 44 76 64 510 165 185 18 188 275 309 3 142 19 310
Gross national income
Gross domestic product
1.6
Life Adult expectancy literacy rate at birth
$ millions 2007b
Per capita $ 2007b
PPPa Per capita $ millions $ 2007 2007
% growth 2006–07
Per capita % growth 2006–07
years 2007
107 .. 34,234 .. 974 6,825 189 .. 253 .. 3,154 .. .. .. 167 .. 489 1,430 138 762 374 488 928 507 2,166 254 417 ..
1,120 ..d 72,430 ..d 3,190 16,680 3,240 ..c 2,280 ..d 5,270 ..d ..d ..d 8,270 ..d 2,700 46,770 870 8,960 750 9,990 5,520 4,210 4,730 2,480 1,840 ..d
194 e 2,040e .. .. 29,239 61,860 .. .. 1,500 4,910 9,192 22,460 .. .. .. .. 3,010e 334 e .. .. 7,056 11,780 .. .. .. .. .. .. .. .. .. .. 4,350e 789e .. .. 258 1,630 1,313e 15,440e 1,710e 845e 668e 13,680e 9,240e 1,552e 7,170e 863e 7,640e 3,498e 3,880e 397e 3,410e 771e .. ..
1.7 .. 4.5 27.3 6.6 3.8 3.5 .. –3.2 .. 10.7 .. .. .. 2.5 6.1 6.1 4.5 6.0 6.3 10.2 3.3 3.2 6.7 5.3 –0.3 5.0 ..
0.1 .. 2.9 26.6 4.9 3.1 1.2 .. –3.5 .. 11.1 .. .. .. 1.9 1.8 5.6 3.1 4.1 5.8 7.7 2.5 2.0 6.2 4.7 –0.6 2.6 ..
61 .. 79 81 68 80 .. .. 69 .. 75 75 76 .. 69 76 72 82 65 73 64 .. 74 72 70 73 70 79
WORLD VIEW
Key indicators for other economies
Carbon dioxide emissions
% ages 15 thousand and older metric tons 2007 2005
.. .. .. 94 97 92 .. .. .. .. .. 96 96 .. .. 93 99 .. 88 .. .. .. .. .. 90 99 78 ..
26 .. 11,314 2,235 714 2,550 84 .. .. .. .. 3,891 2,638 .. 114 49,816 150 .. 103 579 176 136 370 191 2,374 117 88 ..
a. PPP is purchasing power parity, see Definitions. b. Calculated using the World Bank Atlas method. c. Estimated to be upper middle income ($3,706–$11,455). d. Estimated to be high income ($11,456 or more). e. Based on regression; others are extrapolated from the 2005 International Comparison Program benchmark estimates. f. Excludes Turkish Cypriot side. g. Less than 0.5.
Growth is calculated from constant price GDP data in local currency. • GDP per capita is GDP divided by midyear population. • Life expectancy at birth is the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.
Data sources
• Adult literacy rate is the percentage of adults
The indicators here and throughout the book
ages 15 and older who can, with understanding,
are compiled by World Bank staff from primary
read and write a short, simple statement about their
and secondary sources. More information about
everyday life. • Carbon dioxide emissions are those
the indicators and their sources can be found in
stemming from the burning of fossil fuels and the
the About the data, Definitions, and Data sources
manufacture of cement. They include carbon dioxide
entries that accompany each table in subsequent
produced during consumption of solid, liquid, and
sections.
gas fuels and gas fl aring.
2009 World Development Indicators
33
Text figures, tables, and
Introduction
D
ecent and productive work
Sustainable development is about improving the quality of people’s lives and expanding their ability to shape their futures. These generally call for higher per capita incomes, but they also involve equitable education and job opportunities, better health and nutrition, and a more sustainable natural environment. The Millennium Development Goals are the world’s time-bound targets to measure and monitor the progress of countries in improving people’s welfare. They address extreme poverty in its many dimensions—income, hunger, and disease—while promoting education, gender equality, health, and sanitation. At the midpoint between their adoption in 2000 and the target date of 2015, the goals related to human development (primary school completion rate, under-five and maternal mortality) have recorded slower progress than those related to economic growth and infrastructure development (income poverty, gender parity, access to clean water and sanitation; figure 2a). Income from work is the main determinant of living conditions and well-being (World Bank 1995). Therefore, breaking the cycle of poverty involves creating local wealth and new cycles of opportunity through decent and productive employment. Economic growth is a vehicle for poverty reduction, but economic advances in a variety of countries over the past decade have not helped lift a majority of people and their families out of poverty because many poor people were deprived of opportunities to benefit from economic expansion. That is why decent and productive work has been added as a target, with four specific indicators. Target 1b under Goal 1 is to achieve full and productive employment and decent work for all, including women and young people. The indicators cover employment, vulnerable employment, the working poor, and labor productivity. 2a
Different goals—different progress Distance to goal achieved in 2006 Distance to goal to be on track in 2006 Goal 1a. Deep poverty Goal 1b. Hunger Goal 2. Primary education Goal 3. Gender parity at school Goal 4. Child mortality Goal 5. Maternal mortality Goal 7c. Access to safe water Goal 7c. Access to sanitation 0%
20%
40%
60%
80%
100%
Source: World Bank and IMF 2008a.
2009 World Development Indicators
35
New indicators on decent and productive work
Employment to population ratios
Measuring the achievement of decent and productive work in its many facets (box 2b) is challenging. Nevertheless, it is clear that increased opportunities for decent and productive employment have led to greater earnings for many workers in highincome economies. In contrast, jobs in the formal economy are often beyond the reach of most people in low-income economies, where many workers still continue to toil long hours in poor conditions with low remuneration and low productivity. A set of indicators has been adopted to assess the achievement of the Millennium Development Goal target for full and productive employment and decent work for all: employment to population ratios, the share of vulnerable employment in total employment, the share of working poor (earning less than $1.25 a day) in total employment, and labor productivity growth rates. The four indicators should: • Provide relevant measures of progress toward the new target. • Provide a basis for international comparison. • Link to country monitoring systems. • Be based on international standards, recommendations, and best practices. • Be constructed from well established data sources, quantifiable and consistent, enabling measurement over time.
employed as a percentage of the population for the corre-
What is decent work?
The employment to population ratio—the number of people
2b
The International Labour Organization defines decent work as productive work for women and men in conditions of freedom, equity, security, and human dignity. Endorsed by the international commu-
sponding age group (ages 15 and older or ages 15–24) and sex—is a good indicator of the efficiency of an economy in providing jobs. Although there is no optimal employment to population ratio, most economies have ratios in the range of 55–75 percent. Ratios above 80 percent could point to an abundance of low-quality jobs. The ratios for people ages 15 and older changed little between 1991 and 2007 (figure 2c), but they hide wide variations across regions (figure 2d). Developed economies have lower ratios than developing economies because their higher productivity and incomes require fewer workers to meet the needs of the entire population. For developing economies there is no correct, or optimal, ratio. During the development process employment to population ratios and poverty indicators can both be high because people must work to survive, which is the case for some countries in South Asia and Sub-Saharan Africa (figure 2e). When unemployment rates are very high, signifying
Employment to population ratios have not changed much over time. . .
2c 1991
Employment to population ratio, ages 15 and older (%) 100
2007
75
nity, decent work involves opportunities for productive work and delivers a fair income, guarantees equal opportunities and equal treatment for all, provides security in the workplace and protection
50 25
for workers and their families, offers better prospects for personal development and social integration, and gives people the freedom to
0 Low income
express their concerns, to organize, and to participate in decisions that affect their lives.
Lower Upper Low and middle income middle income middle income
High income
Source: ILO database Key Indicators of the Labour Market, 5th edition.
The Decent Work Agenda strives for equitable economic growth through a coherent blend of social and economic goals, balanced
. . . But variations are wide across regions
2d
and integrated at the global, regional, national, sectoral, and local levels. Its four strategic objectives are mutually supportive:
Employment to population ratio, ages 15 and older (%) 100
1991
2008
• Employment—the principal route out of poverty is productive work. • Rights—without them, men and women will not be empowered to
75 50
escape poverty. • Protection—social protection safeguards against poverty. • Dialogue—participation of employer and worker organizations are a key element in shaping government policy for poverty reduction.
25 0 East Asia & Pacific
Source: Adapted from International Labour Organization (www.ilo.org/decentwork/). Source: ILO 2008.
36
2009 World Development Indicators
Europe & Central Asia
Latin America & Caribbean
Middle East & North Africa
South Asia
Sub-Saharan Africa
High income
that people are looking for work but not finding it, efforts
and Pacific, where the high employment to population ratio is
are needed to increase the employment to population ratio.
in part explained by the high employment to population ratio
Efforts to boost ratios are also needed when unemployment
for women, and the gender gap in employment to popula-
rates are low as a result of discouragement, indicating that
tion ratios is the lowest of all regions. Gender gaps in these
people may have given up hope of finding a job. On the other
ratios may be the result of women choosing not to work, but
hand, increases in employment to population ratios should
especially in developing countries women are likely to face
be moderate, since sharp spikes could be the result of a
cultural or other constraints to labor market participation. If
decline in productivity.
such constraints become less binding over time, ratios for
Two regions experienced increases in employment to
women will gradually increase.
population ratios over this period: Latin America and the
High ratios among youth, as in some East Asian and
Caribbean and Middle East and North Africa. In both, the
Pacific economies, indicate that more young people are work-
increases were fueled by increases in the labor force partici-
ing rather than attending school and investing in their future
pation of women. But despite this, the employment to popu-
(figure 2g). A reduction in youth employment to population
lation ratio in the Middle East and North Africa remained the
ratios can be a positive trend if related to increased enroll-
lowest in the world.
ments. Ratios generally are negatively correlated to school
Globally, employment to population ratios are lower for
enrollment: the higher the enrollment, the lower the employ-
women than for men, resulting in a large untapped potential
ment (figure 2h). But Nigeria and Sudan have low second-
of female labor (figure 2f). The Middle East and North Africa
ary school enrollments and low youth employment, indicat-
has the largest gender gap, attributable to low participation
ing that young people are neither working nor preparing for
of women in the workforce. The opposite is true in East Asia
future work.
High employment to population ratios in some countries reflect high numbers of working poor Employment to population ratio, ages 15 and older, selected countries (%) 100
2e 1991
2007
Many young people are in the workforce, at the expense of higher education Enrollment ratios and employment to population ratio, ages 15–24, by region, 2007 (%) 100
75
75
50
50
25
25
2g
Employment to population ratio, ages 15–24, total (%) Gross enrollment ratio, secondary Gross enrollment ratio, tertiary, total
0
0 Burundi
Uganda
Madagascar
Middle East South Sub-Saharan Latin Europe & North Asia Africa & Central America & Africa Caribbean Asia Source: UNESCO Institute for Statistics and ILO database Key Indicators of the Labour Market, 5th edition. East Asia & Pacific
Guinea
Source: ILO database Key Indicators of the Labour Market, 5th edition.
Fewer women than men are employed all over the world Employment to population ratio, ages 15 and older, by sex and region, 2008 (%) 100
2f Men
Women
For many poor countries, there is a tradeoff between education and employment
2h
Employment to population ratio, ages 15–24, 2007 (%) 80 Uganda Cambodia
60
75
Niger
40
50 25
20
0
0
Nigeria
Sudan
Namibia
Latin Middle East East Asia Europe & Pacific & Central America & & North Caribbean Africa Asia Source: ILO 2008.
South Sub-Saharan High Asia Africa income
0
20
40
60
80
100
120
Gross enrollment ratio, secondary, 2005–07 (% of relevant age group) Source: UNESCO Institute for Statistics and ILO database Key Indicators of the Labour Market, 5th edition.
2009 World Development Indicators
37
Share of vulnerable employment in total employment
The share of working poor in total employment
The share of vulnerable employment in total employment captures the proportion of workers who are less likely to have access to social security, income protection, and effective coverage under labor legislation—and are thus more likely to lack critical elements of decent work. Such elements include mechanisms for dialogue that could improve their working conditions or ensure rights at work. The fact that the vulnerable are most likely to lack social protection and safety nets in times of low economic demand can increase their poverty, a big concern in the current global economic crisis. Vulnerable employment accounted for just over half of world employment in 2007 (50.5 percent), down from 52.6 percent in 2000. It is very high in South Asia and SubSaharan Africa (figure 2i), accounting for three-quarters of all jobs, and in East Asia and Pacific. The lowest share of vulnerable employment outside high-income economies is in Europe and Central Asia. Women are more likely than men to be vulnerable (figure 2j), and the difference is more than 10 percentage points in Sub-Saharan Africa, South Asia, and the Middle East and North Africa. Shares of women in vulnerable employment are very low in high-income regions. Although there are large regional variations in vulnerable employment . . .
2i
Vulnerable employment as a share of total employment, by region (%) 100
1991
2007
Share of working poor in total employment is highest in South Asia and Sub-Saharan Africa
2k
Share of working poor in total employment, by region (%) 100
75
75
50
50
25
25
0
1991
2007
0 East Asia & Pacific
Europe & Central Asia
Latin America & Caribbean
Middle East & North Africa
South Asia
Sub-Saharan Africa
High income
Source: ILO 2008.
East Asia & Pacific
Europe & Central Asia
Latin America & Caribbean
Middle East & North Africa
South Asia
Sub-Saharan Africa
High income
Source: ILO 2008.
. . . Women are more likely than men to be in vulnerable employment Vulnerable employment as a share of total employment, by sex and region (%) 100
2j 1991 men 2007 men
1991 women 2007 women
Labor productivity has increased across the world
2l
GDP per person employed, by region ($ thousands) 80
1991
2000
2007
60
75
40
50
20
25
0
0 East Asia & Pacific
Europe & Central Asia
Latin America & Caribbean
Middle East & North Africa
Source: ILO 2008.
38
Working poor are employed people living in a household in which each member is estimated to be below the poverty line ($1.25 a day). Measurements of the working poor indicate the lack of decent work: if a person’s work does not provide a sufficient income to lift the family out of poverty, this work does not fulfill the income component of decent work. Unemployment is not an option for the poor, who have no savings or other sources of income and cannot rely on safety nets. Measuring decent work remains a challenge. Harsh labor market conditions in South Asia and SubSaharan Africa are reflected in the share of working poor in total employment (figure 2k). Almost 60 percent of workers in Sub-Saharan Africa and 40 percent in South Asia are extremely poor ($1.25 a day). It will take many years in these regions to make decent work for all a realistic objective. East Asia and Pacific advanced most toward improving the incomes of workers, reducing the share of working poor from 36 percent to 13 percent. The global financial crisis, which has turned into a global jobs crisis, will likely increase the share of working poor and the share of vulnerable employment in developing economies. But with 2008 data not yet available for many countries, it is difficult to estimate the impact (box 2m).
2009 World Development Indicators
South Asia
Sub-Saharan Africa
High income
East Asia & Pacific
Source: ILO 2008.
Europe & Central Asia
Latin America & Caribbean
Middle East & North Africa
South Asia
Sub-Saharan Africa
High income
Labor productivity growth rates
Challenge of measuring decent work
Labor productivity assesses the likelihood that an economy
The multifaceted Decent Work Agenda links full and produc-
provides the opportunity to create and sustain decent em-
tive employment with rights at work, social protection, and a
ployment with fair and equitable remuneration and better
social dialogue. A major challenge lies in refining indicators
working conditions. Higher productivity improves the social
of the qualitative elements of decent work and collecting the
and economic environment, reducing poverty through invest-
data. Future action will include:
ments in human and physical capital, social protection, and
• Compiling definitions for statistical indicators based on agreed international statistical standards and pro-
technological progress.
viding guidance on interpreting indicators, including
Higher productivity comes from enterprises’ combining of
limitations and possible pitfalls.
capital, labor, and technology. There has been an increase in •
labor productivity since 2000 across the world.
Carrying out developmental work on statistical indica-
Productivity gains have been greatest in high-income
tors in areas highlighted by experts, such as mater-
economies and in Europe and Central Asia (figure 2l). Produc-
nity protection, paid annual leave, sick leave, and
tivity increased slightly in Latin America and the Caribbean, and the share of working poor subsequently fell. The fairly
sustainable enterprises. •
Generating reliable and reproducible indicators to com-
low and often volatile productivity changes in Sub-Saharan
ply with fundamental principles and rights at work.
Africa may explain the limited decline in workers in vulnerable
The indicators are a start at measuring progress toward
employment there. The number of working poor is unlikely to
decent work, but challenges remain, particularly for develop-
decline without increased productivity.
ing economies, at different stages of statistical development and with different statistical capacities.
2m
Scenarios for 2008 In response to the financial crisis and dwindling access to funding, many businesses are reducing operating costs by postponing investments and shedding workers. The economic weight and market size of the high-income economies, and the global linkages of the financial sector, mean that the crisis is hitting funding and export markets in other parts of the world, especially those for commodities. Since data on working poverty and vulnerable employment are lacking for 2008, global scenarios for 2008 are presented instead (ILO 2009). Both the share of working poor and workers in vulnerable employment will have increased in 2008, reversing the encouraging trends to 2007. The slight decline in vulnerable employment in recent years raised hopes that in 2008, for the first time, the share of workers in vulnerable Two global scenarios for vulnerable employment in 2008
Figure 1
Percent
employment would fall below 50 percent (figure 1). But a second scenario finds it likely to rise to 52.6 percent. The projections in figure 2 would result in a decrease in the share of extreme working poverty in total employment from 2007 in the first and second scenarios. However, in the third scenario, the share would increase 4 percent over the share from 2007 (figure 2). Given the sharp decline in economic growth for many economies in 2008, the third scenario may be most likely. The negative impact in these scenarios is realistic, since people who lose their wage and salaried employment will most likely end up out of work altogether or working as own-account workers and unpaid contributing family workers. And new entrants to labor markets will have fewer opportunities in wage and salaried jobs, most likely ending up in vulnerable employment. Global scenarios for working poor ($1.25 a day) in 2008
Figure 2
Percent
60
30
2007 (23.0)
2007 (50.5) 27.0
50 49.6
52.6
40
20 20.2
21.2
30 20
10
10 0
Scenario 1 Source: ILO 2009.
Scenario 2
Scenario 1. Based on labor market data to date and IMF November 2008 revised estimates for economic growth. Scenario 2. Based on a simultaneous increase in vulnerable employment in all economies equal to half the largest increase since 1991 and IMF November 2008 revised estimates for economic growth.
0
Scenario 1 Source: ILO 2009.
Scenario 2
Scenario 3
Scenario 1. Based on labor market data to date and IMF November 2008 revised estimates for economic growth. Scenario 2. Based on a 5 percent higher poverty line. Scenario 3. Based on a 10 percent higher poverty line.
2009 World Development Indicators
39
Tables
2.1
Population dynamics Population
Average annual population growth
Population age composition
Dependency ratio
%
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
1990
millions 2007
2015
.. 3.3 25.3 10.5 32.6 3.5 17.1 7.7 7.2 113 10.2 10 5.2 6.7 4.3 1.4 149.5 8.7 8.9 5.7 9.7 12.2 27.8 3 6.1 13.2 1,135.2 5.7 33.2 37.9 2.4 3.1 12.8 4.8 10.6 10.4 5.1 7.3 10.3 55.1 5.1 3.2 1.6 48.0 5.0 56.7 0.9 1.0 5.5 79.4 15.6 10.2 8.9 6.0 1.0 7.1
.. 3.2 33.9 16.9 39.5 3.0 21.0 8.3 8.6 158.6 9.7 10.6 9.0 9.5 3.8 1.9 191.6 7.7 14.8 8.5 14.4 18.5 33.0 4.3 10.8 16.6 1,318.3 6.9 44.0 62.4 3.8 4.5 19.3 4.4 11.3 10.3 5.5 9.7 13.3 75.5 6.9 4.8 1.3 79.1 5.3 61.7 1.3 1.7 4.4 82.3 23.5 11.2 13.3 9.4 1.7 9.6
.. 3.3 38.0 20.7 42.5 3.0 22.5 8.4 9.1 180.0 9.3 10.7 11.3 10.9 3.7 2.1 209.4 7.2 18.6 11.2 16.6 21.5 35.3 5.0 13.4 17.8 1,375.7 7.4 48.4 78.5 4.5 5.0 22.3 4.4 11.2 10.3 5.5 10.6 14.6 86.2 7.6 6.2 1.3 96.0 5.4 63.3 1.5 2.1 4.2 81.1 27.3 11.2 16.2 11.4 2.2 11.0
2009 World Development Indicators
% 1990–2007 2007–15
.. –0.2 1.7 2.8 1.1 –1.0 1.2 0.4 1.0 2.0 –0.3 0.4 3.3 2.1 –0.8 1.9 1.5 –0.8 3.0 2.4 2.3 2.4 1.0 2.2 3.3 1.4 0.9 1.1 1.7 2.9 2.6 2.2 2.4 –0.4 0.4 0.0a 0.4 1.7 1.5 1.8 1.7 2.5 –0.9 2.9 0.3 0.5 2.2 3.4 –1.3 0.2 2.4 0.6 2.4 2.6 3.0 1.8
.. 0.4 1.5 2.5 0.9 0.1 0.8 0.1 0.8 1.6 –0.6 0.1 2.8 1.6 –0.1 1.2 1.1 –0.8 2.9 3.5 1.8 1.9 0.8 1.8 2.7 0.9 0.5 0.9 1.2 2.9 2.1 1.3 1.9 –0.2 –0.1 –0.1 0.1 1.1 1.1 1.7 1.3 3.0 –0.3 2.4 0.2 0.3 1.5 2.5 –0.6 –0.2 1.9 0.0 b 2.4 2.4 3.0 1.7
Ages 0–14 2007
.. 25 28 46 26 19 19 15 23 34 15 17 44 37 17 35 27 13 46 44 36 41 17 42 46 24 21 14 29 47 42 27 41 15 18 14 19 33 32 33 33 43 15 44 17 18 35 41 18 14 38 14 43 43 48 37
Ages 15–64 2007
.. 66 67 51 64 69 67 68 69 62 71 66 54 58 69 62 66 69 51 53 61 55 70 54 51 68 71 73 65 50 55 67 56 68 70 71 66 61 62 62 61 55 68 53 67 65 61 55 68 66 58 67 53 54 49 59
Ages 65+ 2007
.. 9 5 2 10 12 13 17 7 4 14 17 3 5 14 3 6 17 3 3 3 4 13 4 3 9 8 12 5 3 3 6 3 17 12 15 16 6 6 5 6 2 17 3 16 16 5 4 14 20 4 19 4 3 3 4
% of working-age population Young Old 2007 2007
.. 38 42 90 40 28 28 23 34 55 21 25 82 64 25 56 41 19 90 84 59 74 24 78 91 35 29 20 45 95 76 41 74 22 26 20 28 54 51 52 55 78 22 82 26 28 58 74 26 21 66 21 80 80 97 63
.. 14 7 5 16 17 20 25 10 6 20 26 5 8 21 6 10 25 6 5 5 6 19 7 6 13 11 16 8 5 6 9 6 26 17 20 24 9 10 8 9 4 24 6 24 25 8 7 21 30 6 28 8 6 6 7
Crude death rate
Crude birth rate
per 1,000 people 2007
per 1,000 people 2007
.. 6 5 21 8 10 7 9 6 8 14 9 11 8 10 14 6 15 14 16 9 14 7 18 15 5 7 6 6 18 11 4 15 12 8 10 10 6 5 6 6 9 13 13 9 8 12 10 12 10 9 10 6 12 18 9
.. 16 21 47 18 13 14 9 18 25 10 11 40 27 9 25 19 10 44 47 26 35 11 36 45 15 12 10 19 50 35 18 35 9 10 11 12 23 21 24 23 39 12 38 11 13 26 35 11 8 30 10 33 40 50 28
Population
Average annual population growth
Population age composition
Dependency ratio
%
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
millions 2007
2015
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 20.1 42.9 2.1 4.4 4.1 2.7 3.0 1.6 2.1 4.4 3.7 1.9 12.0 9.4 18.1 7.7 1.9 1.1 83.2 4.4 2.1 24.2 13.5 40.1 1.4 19.1 15.0 3.4 4.1 7.8 94.5 4.2 1.8 108.0 2.4 4.1 4.2 21.8 61.2 38.1 9.9 3.5
7.1 10.1 1,124.8 225.6 71.0 .. 4.4 7.2 59.4 2.7 127.8 5.7 15.5 37.5 23.8 48.5 2.7 5.2 5.9 2.3 4.1 2.0 3.7 6.2 3.4 2.0 19.7 13.9 26.5 12.3 3.1 1.3 105.3 3.8 2.6 30.9 21.4 48.8 2.1 28.1 16.4 4.2 5.6 14.2 148.0 4.7 2.6 162.5 3.3 6.3 6.1 27.9 87.9 38.1 10.6 3.9
8.3 9.8 1,249.6 245.1 78.9 .. 4.8 8.1 58.4 2.8 124.5 6.8 16.8 46.1 24.4 49.2 3.2 5.8 6.7 2.2 4.4 2.1 4.7 7.1 3.3 2.0 24.1 17.0 30.0 15.7 3.8 1.3 113.7 3.7 2.8 33.9 24.7 51.9 2.3 32.2 16.5 4.5 6.3 18.5 175.6 4.9 3.0 192.3 3.8 7.3 7.0 30.7 101.0 37.5 10.7 4.1
% 1990–2007 2007–15
2.2 –0.2 1.7 1.4 1.6 .. 1.3 2.5 0.3 0.7 0.2 3.5 –0.3 2.8 1.0 0.7 1.3 1.0 2.1 –0.9 1.9 1.3 3.3 2.0 –0.5 0.4 2.9 2.3 2.3 2.8 2.8 1.0 1.4 –0.8 1.3 1.4 2.7 1.1 2.3 2.3 0.5 1.2 1.8 3.5 2.6 0.6 2.0 2.4 1.9 2.5 2.2 1.5 2.1 0.0 b 0.4 0.6
1.9 –0.3 1.3 1.0 1.3 .. 1.2 1.6 –0.2 0.5 –0.3 2.1 1.0 2.6 0.3 0.2 2.1 1.2 1.7 –0.5 1.0 0.6 2.8 1.8 –0.5 0.0a 2.5 2.5 1.5 3.0 2.4 0.6 1.0 –0.4 1.0 1.2 1.8 0.8 1.5 1.7 0.1 0.8 1.4 3.3 2.1 0.5 2.0 2.1 1.5 1.8 1.6 1.2 1.7 –0.2 0.1 0.5
Ages 0–14 2007
39 15 32 28 27 .. 21 28 14 31 14 36 24 43 23 18 23 30 38 14 28 40 47 30 16 19 43 47 30 48 40 24 30 19 27 29 44 26 37 38 18 21 37 48 44 19 32 36 30 40 35 31 35 15 16 21
Ages 15–64 2007
57 69 63 67 69 .. 68 62 66 62 66 61 69 55 68 72 75 65 58 69 65 55 51 66 69 70 54 50 65 49 57 70 64 70 69 65 52 68 59 58 67 67 59 49 53 66 65 60 64 58 60 64 61 71 67 66
Ages 65+ 2007
4 16 5 6 4 .. 11 10 20 7 21 3 8 3 9 10 2 6 4 17 7 5 2 4 16 11 3 3 5 4 4 7 6 11 4 5 3 6 4 4 15 12 4 3 3 15 3 4 6 2 5 6 4 13 17 13
% of working-age population Young Old 2007 2007
68 22 51 42 39 .. 30 45 21 50 21 59 35 78 34 24 31 46 65 20 43 72 93 46 23 27 81 94 47 97 70 34 46 27 39 45 85 39 64 65 27 31 62 98 82 29 50 59 47 69 58 48 59 22 23 32
7 22 8 9 6 .. 16 16 30 12 32 5 11 5 13 14 3 9 6 25 11 9 4 6 23 16 6 6 7 7 6 10 10 16 6 8 6 8 6 6 22 19 7 7 6 22 4 7 10 4 8 9 7 19 25 20
PEOPLE
Population dynamics
2.1 Crude death rate
Crude birth rate
per 1,000 people 2007
per 1,000 people 2007
6 13 8 6 6 .. 6 6 10 6 9 4 10 12 10 5 2 7 7 15 7 19 18 4 14 9 10 15 4 15 8 7 5 12 6 6 20 10 12 8 8 7 5 14 17 9 3 7 5 10 6 6 5 10 10 8
2009 World Development Indicators
28 10 24 19 18 .. 16 21 9 17 9 29 20 39 13 10 18 23 27 10 18 29 50 23 10 11 36 41 21 48 32 14 19 11 22 21 39 18 26 28 11 15 25 49 40 12 22 27 21 30 25 21 26 10 10 13
41
2.1
Population dynamics Population
Average annual population growth
Population age composition
Dependency ratio
%
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
1990
millions 2007
2015
23.2 148.3 7.3 16.4 7.9 7.6 4.1 3.0 5.3 2.0 6.7 35.2 38.8 17.1 25.9 0.8 8.6 6.7 12.7 5.3 25.5 54.3 0.7 4.0 1.2 8.2 56.2 3.7 17.8 51.9 1.9 57.2 249.6 3.1 20.5 19.8 66.2 2.0 12.3 8.1 10.5 5,259.1 s 866.5 3,457.9 2,751.9 706.0 4,324.4 1,596.0 436.2 435.1 223.7 1,120.2 513.2 934.7 301.6
21.5 142.1 9.7 24.2 12.4 7.4 5.8 4.6 5.4 2.0 8.7 47.9 44.9 20.0 38.6 1.1 9.1 7.6 19.9 6.7 40.4 63.8 1.1 6.6 1.3 10.2 73.9 5.0 30.9 46.5 4.4 61.0 301.6 3.3 26.9 27.5 85.2 3.7 22.4 11.9 13.4 6,610.3 s 1,295.8 4,258.2 3,434.5 823.7 5,554.0 1,912.4 445.6 560.6 313.2 1,522.0 800.0 1,056.3 324.2
20.5 135.6 12.1 28.3 15.4 7.3 6.9 4.8 5.4 2.0 10.9 49.5 45.7 20.5 45.6 1.2 9.4 7.7 23.5 7.7 48.9 66.6 1.4 8.0 1.4 11.2 81.0 5.5 40.6 43.6 5.3 62.5 324.1 3.4 30.0 31.1 94.1 4.5 28.2 13.8 14.8 7,210.6 s 1,532.9 4,586.3 3,721.4 865.0 6,119.2 2,026.0 447.3 614.2 358.7 1,711.6 961.4 1,091.3 325.5
a. More than –0.05. b. Less than 0.05. c. Includes Kosovo.
42
2009 World Development Indicators
% 1990–2007 2007–15
–0.4 –0.3 1.7 2.3 2.7 –0.2 2.1 2.4 0.1 0.1 1.5 1.8 0.9 0.9 2.3 2.3 0.4 0.7 2.6 1.4 2.7 1.0 2.1 3.0 0.5 1.3 1.6 1.8 3.2 –0.6 5.0 0.4 1.1 0.4 1.6 1.9 1.5 3.7 3.5 2.3 1.4 1.3 w 2.4 1.2 1.3 0.9 1.5 1.1 0.1 1.5 2.0 1.8 2.6 0.7 0.4
–0.6 –0.6 2.8 2.0 2.7 –0.1 2.1 0.5 0.0a –0.2 2.8 0.4 0.2 0.3 2.1 0.7 0.3 0.2 2.1 1.6 2.4 0.5 3.2 2.5 0.4 1.1 1.2 1.3 3.4 –0.8 2.4 0.3 0.9 0.2 1.4 1.5 1.3 2.5 2.9 1.9 1.3 1.1 w 2.1 0.9 1.0 0.6 1.2 0.7 0.0 b 1.1 1.7 1.5 2.3 0.4 0.1
Ages 0–14 2007
15 15 43 33 42 18 c 43 18 16 14 44 32 15 23 40 39 17 16 36 38 44 21 45 43 21 25 27 30 49 14 20 18 20 23 32 31 28 45 45 46 38 28 w 39 27 27 24 30 23 19 29 32 33 43 18 15
Ages 15–64 2007
70 72 55 65 54 67c 54 73 72 70 53 64 69 70 56 58 66 68 61 58 53 71 53 54 72 69 67 65 48 70 79 66 67 63 64 64 66 52 53 52 58 65 w 57 67 67 67 64 70 69 64 63 62 54 67 67
Ages 65+ 2007
15 13 2 3 4 15c 3 9 12 16 3 4 17 7 4 3 18 16 3 4 3 8 3 3 7 6 6 5 2 16 1 16 12 14 5 5 6 3 2 3 4 7w 4 7 6 9 6 7 11 6 4 5 3 15 18
% of working-age population Young Old 2007 2007
22 21 78 50 77 27c 80 25 22 20 83 50 21 33 71 67 26 24 58 65 84 30 85 79 30 36 41 46 101 20 25 27 31 37 49 47 42 88 85 88 66 43 w 69 40 41 36 46 33 28 45 51 53 80 26 23
21 19 4 4 8 22c 6 12 16 23 5 7 25 10 6 6 27 24 5 7 6 12 5 6 9 9 9 7 5 23 1 25 18 22 7 8 8 6 4 6 6 12 w 6 10 10 13 9 10 17 10 7 8 6 22 27
Crude death rate
Crude birth rate
per 1,000 people 2007
per 1,000 people 2007
12 15 17 4 9 14 22 5 10 9 17 17 9 6 10 21 10 8 3 6 13 8 9 10 8 6 7 8 13 16 1 9 8 9 5 5 5 4 7 19 18 8w 11 7 7 9 8 7 12 6 6 8 15 8 9
10 11 44 25 35 9 46 10 10 10 43 22 11 19 32 29 12 10 27 27 39 15 42 37 15 17 19 22 47 10 16 13 14 15 21 22 19 36 38 39 28 20 w 33 18 19 17 22 14 14 20 24 25 39 12 10
About the data
PEOPLE
Population dynamics
2.1
Definitions
Population estimates are usually based on national
Dependency ratios account for variations in the
• Population is based on the de facto definition of
population censuses, but the frequency and quality
proportions of children, elderly people, and working-
population, which counts all residents regardless of
vary by country. Most countries conduct a complete
age people in the population. Calculations of young
legal status or citizenship—except for refugees not
enumeration no more often than once a decade.
and old-age dependency suggest the dependency
permanently settled in the country of asylum, who
Estimates for the years before and after the census
burden that the working-age population bears in
are generally considered part of the population of
are interpolations or extrapolations based on demo-
relation to children and the elderly. But dependency
their country of origin. The values shown are mid-
graphic models. Errors and undercounting occur
ratios show only the age composition of a popula-
year estimates for 1990 and 2007 and projections
even in high-income countries; in developing coun-
tion, not economic dependency. Some children and
for 2015. • Average annual population growth is
tries errors may be substantial because of limits in
elderly people are part of the labor force, and many
the exponential change for the period indicated. See
the transport, communications, and other resources
working-age people are not.
Statistical methods for more information. • Popula-
required to conduct and analyze a full census.
Vital rates are based on data from birth and death
tion age composition is the percentage of the total
The quality and reliability of official demographic
registration systems, censuses, and sample surveys
population that is in specific age groups. • Depen-
data are also affected by public trust in the govern-
by national statistical offices and other organiza-
dency ratio is the ratio of dependents—people
ment, government commitment to full and accurate
tions, or on demographic analysis. The 2007 esti-
younger than 15 or older than 64—to the working-
enumeration, confidentiality and protection against
mates for many countries are projections based on
age population—those ages 15–64. • Crude death
misuse of census data, and census agencies’ indepen-
extrapolations of levels and trends from earlier years
dence from political influence. Moreover, comparability
or interpolations of population estimates and projec-
of population indicators is limited by differences in the
tions from the United Nations Population Division.
concepts, definitions, collection procedures, and esti-
Data for most high-income countries are provisional
mation methods used by national statistical agencies
estimates based on vital registers.
and other organizations that collect the data.
Vital registers are the preferred source for these
Of the 153 economies in the table and the 56 econo-
data, but in many developing countries systems for
mies in table 1.6, 180 (about 86 percent) conducted a
registering births and deaths are absent or incom-
census during the 2000 census round (1995–2004). A
plete because of defi ciencies in the coverage of
quarter of countries have completed a census for the
events or geographic areas. Many developing coun-
2010 census round (2005–14). All told, 195 countries
tries carry out special household surveys that ask
(93 percent) have conducted a census during 1995–
respondents about recent births and deaths. Esti-
2008. The currentness of a census and the availability
mates derived in this way are subject to sampling
of complementary data from surveys or registration
errors and recall errors.
systems are objective ways to judge demographic
The United Nations Statistics Division monitors
data quality. Some European countries’ registration
the completeness of vital registration systems. The
systems offer complete information on population in
share of countries with at least 90 percent complete
the absence of a census. See Primary data documenta-
vital registration rose from 45 percent in 1988 to
tion for the most recent census or survey year and for
61 percent in 2007. Still, some of the most populous
the completeness of registration.
developing countries—China, India, Indonesia, Bra-
rate and crude birth rate are the number of deaths and the number of live births occurring during the year, per 1,000 people, estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the population growth rate in the absence of migration.
Data sources
Current population estimates for developing coun-
zil, Pakistan, Bangladesh, Nigeria—lack complete
tries that lack recent census data and pre- and post-
vital registration systems. From 2000 to 2007, on
The World Bank’s population estimates are com-
census estimates for countries with census data are
average 64 percent of births, 62 percent of deaths,
piled and produced by its Human Development
provided by the United Nations Population Division
and 45 percent of infant deaths were registered and
Network and Development Data Group in consulta-
and other agencies. The standard estimation method
reported to the United Nations Statistics Division.
tion with its operational staff and country offices.
requires fertility, mortality, and net migration data,
International migration is the only other factor
Important inputs to the World Bank’s demographic
often collected from sample surveys, which can be
besides birth and death rates that directly deter-
work come from the United Nations Population
small or limited in coverage. Population estimates
mines a country’s population growth. From 1990 to
Division’s World Population Prospects: The 2006
are from demographic modeling and so are suscep-
2005 the number of migrants in high-income coun-
Revision; census reports and other statistical
tible to biases and errors from shortcomings in the
tries rose 40 million. About 190 million people (3
publications from national statistical offi ces;
model and in the data. Population projections use
percent of the world population) live outside their
household surveys conducted by national agen-
the cohort component method.
home country. Estimating migration is difficult. At
cies, Macro International, and the U.S. Centers for
The growth rate of the total population conceals
any time many people are located outside their home
Disease Control and Prevention; Eurostat, Demo-
age-group differences in growth rates. In many devel-
country as tourists, workers, or refugees or for other
graphic Statistics (various years); Secretariat of
oping countries the once rapidly growing under-15
reasons. Standards for the duration and purpose of
the Pacific Community, Statistics and Demography
population is shrinking. Previously high fertility rates
international moves that qualify as migration vary,
Programme; and U.S. Bureau of the Census, Inter-
and declining mortality rates are now reflected in the
and estimates require information on flows into and
national Database.
larger share of the working-age population.
out of countries that is difficult to collect.
2009 World Development Indicators
43
2.2
Labor force structure Labor force participation rate
Labor force
% ages 15 and older Male
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
2007
1990
2007
1990
2007
1990–2007
1990
2007
.. 84 75 90 79 79 76 70 78 89 75 61 88 85 83 78 85 64 90 90 85 79 76 88 84 77 85 80 77 86 84 85 89 75 73 80 75 82 78 74 80 88 72 89 71 65 83 86 83 73 74 67 89 90 87 81
.. 71 78 89 76 68 72 68 71 85 66 60 86 83 67 63 82 57 90 90 87 75 73 87 77 72 80 70 79 90 83 79 85 60 69 68 71 73 79 71 79 86 65 91 65 62 80 84 74 66 73 65 85 89 90 83
.. 67 23 74 29 66 52 43 66 62 60 36 51 46 69 44 39 57 76 91 77 53 58 69 57 32 73 47 44 60 57 36 42 52 36 61 62 26 33 24 51 55 61 63 59 46 63 70 67 46 73 36 28 80 56 49
.. 50 37 75 50 56 57 52 60 57 53 46 59 66 53 48 60 46 77 90 75 52 63 67 71 39 71 53 64 54 56 43 39 45 45 51 61 57 52 24 47 55 54 80 58 50 62 70 55 51 72 43 45 79 54 39
.. 1.7 7.0 4.5 12.0 1.8 8.5 3.5 3.4 50.9 5.3 3.9 1.9 2.5 2.5 0.5 59.3 4.2 3.9 2.8 4.3 4.4 14.7 1.3 2.3 5.0 650.6 2.9 13.4 14.6 0.9 1.2 4.6 2.4 4.5 5.7 2.9 2.5 3.5 15.9 2.0 1.2 0.8 19.7 2.6 24.8 0.4 0.4 3.1 39.3 6.4 4.2 2.8 2.8 0.4 2.6
.. 1.4 13.9 7.5 18.3 1.5 10.9 4.2 4.3 74.3 4.9 4.7 3.7 4.4 1.9 0.7 97.7 3.4 6.7 4.2 7.5 7.0 18.5 1.9 4.3 7.0 785.7 3.6 22.1 23.5 1.5 2.0 7.1 2.0 5.2 5.2 2.9 4.2 5.9 24.0 2.8 1.9 0.7 37.9 2.7 28.0 0.6 0.8 2.3 41.4 10.5 5.2 4.9 4.5 0.6 3.6
.. –0.9 4.0 2.9 2.5 –1.1 1.5 1.0 1.4 2.2 –0.5 1.0 3.9 3.3 –1.7 2.4 2.9 –1.3 3.2 2.4 3.2 2.7 1.3 2.2 3.6 2.0 1.1 1.4 2.9 2.8 2.8 3.0 2.5 –1.2 0.9 –0.5 0.0a 3.1 3.2 2.4 2.2 2.8 –1.0 3.8 0.2 0.7 2.6 3.5 –1.6 0.3 3.0 1.3 3.2 2.7 2.8 2.0
.. 43.4 23.6 46.4 28.4 48.0 41.2 40.8 48.2 39.5 48.9 39.0 38.2 36.1 46.7 37.7 32.1 48.4 47.4 52.5 52.4 40.8 44.1 46.6 41.4 30.6 44.8 36.3 35.9 42.5 41.4 29.2 29.6 43.2 33.2 45.5 46.1 24.0 29.5 24.4 41.2 40.4 50.3 42.4 47.5 43.3 44.0 45.4 48.2 40.9 49.4 36.2 23.7 47.2 40.3 39.4
.. 41.9 31.9 46.6 41.1 49.9 44.8 44.7 48.1 39.2 48.8 44.4 40.5 45.0 46.1 43.7 43.3 46.2 46.8 51.4 48.7 41.3 46.6 45.5 48.7 36.0 45.7 45.3 44.2 38.6 41.1 34.5 30.6 44.5 39.2 44.2 46.7 43.8 40.0 25.3 38.8 41.0 49.8 47.3 48.0 46.0 43.8 45.8 46.3 44.8 48.9 40.4 37.1 47.1 38.4 33.4
2009 World Development Indicators
Labor force participation rate
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
Labor force
% ages 15 and older Male
PEOPLE
Labor force structure
Ages 15 and older average annual % growth
Total millions
Female
1990
2007
1990
2007
1990
2007
87 66 85 81 81 74 70 62 66 80 77 68 78 90 79 73 81 74 83 77 83 85 85 78 74 73 85 80 81 69 84 82 84 74 65 82 84 88 65 80 70 74 85 87 75 73 81 86 81 75 83 76 83 72 73 61
82 59 82 86 75 .. 73 61 61 74 72 72 75 87 78 73 81 75 80 69 77 75 85 78 61 66 88 80 80 65 80 77 80 48 61 80 77 86 59 76 71 75 87 88 71 71 77 85 80 73 84 82 80 61 70 58
37 47 35 50 22 12 36 41 36 66 50 11 62 75 51 47 34 58 80 63 22 68 54 17 59 54 80 76 43 34 58 40 34 61 55 24 86 69 49 48 43 54 39 41 37 57 20 11 37 71 52 48 47 55 50 31
37 44 34 50 32 .. 53 50 39 55 48 16 65 74 58 49 43 53 79 54 25 68 55 26 51 42 82 76 45 37 60 42 41 45 58 25 88 69 49 59 57 61 38 39 39 62 26 21 48 71 71 64 50 47 56 38
1.6 4.6 321.9 75.3 15.6 4.4 1.3 1.7 24.0 1.1 63.9 0.7 7.8 9.9 9.6 19.1 0.8 1.8 1.8 1.4 1.0 0.7 0.8 1.2 1.9 0.9 5.5 3.9 7.0 2.0 0.8 0.5 29.9 2.1 0.7 7.7 6.2 20.2 0.4 7.1 6.9 1.7 1.4 2.6 28.4 2.2 0.6 30.1 0.9 1.8 1.7 8.3 23.5 18.1 4.8 1.2
2.6 4.3 447.7 110.5 27.8 .. 2.2 2.9 25.3 1.2 65.7 1.6 8.2 17.4 12.4 24.3 1.4 2.3 2.9 1.2 1.5 0.9 1.4 2.3 1.6 0.9 9.5 5.7 11.6 3.2 1.3 0.6 44.4 1.4 1.1 11.2 9.9 27.9 0.7 11.7 8.5 2.3 2.2 4.7 45.3 2.5 1.0 56.2 1.5 2.7 3.1 14.1 36.9 17.3 5.6 1.5
1990–2007
2.6 –0.4 1.9 2.3 3.4 .. 2.9 3.2 0.3 0.3 0.2 5.0 0.3 3.3 1.5 1.4 2.9 1.5 2.6 –1.2 2.4 1.5 3.3 3.6 –1.1 –0.1 3.3 2.3 2.9 2.8 3.1 1.4 2.3 –2.2 2.5 2.3 2.8 1.9 2.7 2.9 1.2 1.7 2.8 3.6 2.7 0.8 3.0 3.7 2.9 2.6 3.6 3.1 2.7 –0.2 0.9 1.4
Female % of labor force 1990
2007
30.0 44.6 27.6 38.4 20.2 13.3 34.3 40.6 37.0 46.6 40.6 12.6 47.0 46.0 40.6 39.3 21.6 46.1 49.6 49.6 22.8 50.9 39.3 15.5 48.1 42.7 48.8 50.7 34.4 34.9 42.0 33.1 30.0 48.6 46.2 23.5 54.7 44.7 45.2 37.9 39.1 43.1 32.1 32.5 34.0 44.7 14.2 10.9 30.9 46.7 38.0 39.1 36.6 45.4 42.8 35.8
31.7 45.2 28.2 36.9 29.2 .. 42.5 46.3 40.4 44.0 41.2 16.9 49.3 46.4 44.0 40.8 24.0 42.8 50.5 48.2 25.6 52.4 39.8 23.5 49.5 39.3 48.8 50.1 35.2 38.3 42.9 36.1 35.6 51.3 49.1 24.7 56.1 45.3 46.3 45.2 45.0 45.9 31.0 30.7 35.9 47.1 19.6 18.7 37.1 49.2 45.2 43.9 38.3 45.4 46.3 42.3
2009 World Development Indicators
45
2.2
Labor force structure Labor force participation rate
Labor force
% ages 15 and older Male 1990
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
67 76 88 80 90 .. 65 79 79 76 89 64 69 79 78 79 72 79 81 84 93 87 81 89 76 76 81 75 92 72 92 75 76 72 85 82 81 67 70 81 80 81 w 84 83 84 78 83 84 76 82 77 85 82 74 69
a. Less than 0.05.
46
2009 World Development Indicators
Female 2007
60 69 79 80 86 60 67 76 69 65 89 60 68 75 72 69 69 75 78 67 90 81 83 87 77 71 71 71 90 65 93 70 72 75 70 81 76 67 66 81 80 78 w 82 79 80 73 79 80 67 80 74 82 80 70 65
1990
55 60 86 15 61 .. 66 51 66 60 52 44 34 46 24 66 63 49 18 75 89 76 52 53 39 21 34 63 80 57 25 53 57 43 76 32 74 10 15 59 68 52 w 56 53 55 46 53 69 57 38 21 36 58 49 42
Ages 15 and older average annual % growth
Total millions 2007
46 57 81 19 62 43 65 54 52 52 54 47 47 43 31 62 61 60 21 56 87 66 58 52 55 26 24 59 82 53 40 56 59 53 58 52 69 14 22 60 60 53 w 56 52 53 50 53 67 50 53 26 36 60 52 48
1990
2007
1990–2007
10.8 77.0 3.2 5.1 3.3 .. 1.6 1.6 2.9 1.1 2.6 11.6 15.9 7.3 7.3 0.3 4.7 3.6 3.3 2.4 12.5 31.6 0.3 1.5 0.5 2.4 21.0 1.5 8.0 26.0 1.0 29.5 129.3 1.3 9.7 7.0 31.4 0.4 2.5 3.1 4.2 2,352.2 t 342.8 1,565.5 1,270.7 294.8 1,908.3 858.7 206.6 165.1 62.4 421.5 194.1 443.9 135.8
9.7 75.8 4.4 8.7 5.3 3.1 2.2 2.4 2.7 1.0 3.4 17.4 22.0 9.0 11.9 0.5 4.9 4.2 6.4 2.6 19.9 36.6 0.4 2.6 0.7 3.7 24.2 2.2 13.5 23.3 2.7 31.4 156.6 1.6 11.8 12.7 44.4 0.8 5.4 4.6 5.7 3,098.8 t 542.8 2,039.7 1,661.6 378.1 2,582.5 1,081.5 207.2 262.2 106.2 607.9 317.5 516.3 154.3
–0.6 –0.1 2.0 3.2 2.9 .. 2.0 2.7 –0.3 –0.3 1.7 2.4 1.9 1.2 2.9 2.7 0.2 1.0 4.0 0.4 2.7 0.9 2.0 3.2 2.4 2.5 0.8 2.4 3.1 –0.6 6.2 0.4 1.1 1.2 1.1 3.5 2.0 4.1 4.6 2.3 1.9 1.6 w 2.7 1.6 1.6 1.5 1.8 1.4 0.0 a 2.7 3.1 2.2 2.9 0.9 0.7
Female % of labor force 1990
2007
46.7 48.5 51.7 11.2 40.1 .. 51.6 39.1 47.2 46.3 37.6 41.9 34.4 36.1 23.4 51.1 47.7 39.4 18.3 48.2 50.1 47.3 38.1 38.6 34.9 21.6 29.4 47.3 47.4 49.4 9.8 43.3 44.3 39.8 48.3 27.8 48.4 11.9 17.5 42.6 46.4 39.3 w 40.1 38.6 38.6 38.7 38.9 44.2 46.1 32.1 21.3 28.2 42.3 41.4 39.9
45.0 49.5 52.9 14.9 42.2 42.5 50.3 41.1 44.6 45.9 38.9 45.2 41.5 37.2 30.4 50.1 47.0 45.8 20.8 46.6 49.8 46.8 40.4 38.3 42.6 26.5 26.8 46.9 47.8 49.3 14.5 45.5 45.7 43.5 46.0 38.9 47.9 16.9 24.4 43.2 43.3 40.3 w 40.7 39.5 38.9 42.1 39.7 44.5 45.5 40.8 26.1 29.1 43.5 43.4 43.8
About the data
PEOPLE
Labor force structure
2.2
Definitions
The labor force is the supply of labor available for pro-
further information on source, reference period, or
• Labor force participation rate is the proportion
ducing goods and services in an economy. It includes
definition, consult the original source.
of the population ages 15 and older that is eco-
people who are currently employed and people who
The labor force participation rates in the table are
nomically active: all people who supply labor for the
are unemployed but seeking work as well as first-time
from the ILO database, Key Indicators of the Labour
production of goods and services during a specified
job-seekers. Not everyone who works is included,
Market, 5th edition. These harmonized estimates
period. • Total labor force is people ages 15 and
however. Unpaid workers, family workers, and stu-
use strict data selection criteria and enhanced
older who meet the ILO definition of the economi-
dents are often omitted, and some countries do not
methods to ensure comparability across countries
cally active population. It includes both the employed
count members of the armed forces. Labor force size
and over time, including collection and tabulation
and the unemployed. • Average annual percentage
tends to vary during the year as seasonal workers
methodologies and methods applied to such country-
growth of the labor force is calculated using the
enter and leave.
specific factors as military service requirements.
exponential endpoint method (see Statistical meth-
Data on the labor force are compiled by the Inter-
Estimates are based mainly on labor force surveys,
ods for more information). • Female labor force as as
national Labour Organization (ILO) from labor force
with other sources (population censuses and nation-
a percentage of the labor force shows the extent to
surveys, censuses, establishment censuses and
ally reported estimates) used only when no survey
which women are active in the labor force.
surveys, and administrative records such as employ-
data are available.
ment exchange registers and unemployment insur-
Participation rates indicate the relative size of
ance schemes. For some countries a combination
the labor supply. Beginning in the 2008 edition of
of these sources is used. Labor force surveys are
World Development Indicators, the indicator covers
the most comprehensive source for internationally
the population ages 15 and older, to include peo-
comparable labor force data. They can cover all
ple who continue working past age 65. In previous
noninstitutionalized civilians, all branches and sec-
editions the indicator was for the population ages
tors of the economy, and all categories of workers,
15–64, so participation rates are not comparable
including people holding multiple jobs. By contrast,
across editions.
labor force data from population censuses are often
The labor force estimates in the table were calcu-
based on a limited number of questions on the eco-
lated by applying labor force participation rates from
nomic characteristics of individuals, with little scope
the ILO database to World Bank population estimates
to probe. The resulting data often differ from labor
to create a series consistent with these population
force survey data and vary considerably by country,
estimates. This procedure sometimes results in
depending on the census scope and coverage. Estab-
labor force estimates that differ slightly from those
lishment censuses and surveys provide data only on
in the ILO’s Yearbook of Labour Statistics and its data-
the employed population, not unemployed workers,
base Key Indicators of the Labour Market.
workers in small establishments, or workers in the
Estimates of women in the labor force and employ-
informal sector (ILO, Key Indicators of the Labour
ment are generally lower than those of men and are
Market 2001–2002).
not comparable internationally, reflecting that demo-
The reference period of a census or survey is
graphic, social, legal, and cultural trends and norms
another important source of differences: in some
determine whether women’s activities are regarded
countries data refer to people’s status on the day
as economic. In many countries many women work
of the census or survey or during a specific period
on farms or in other family enterprises without pay,
before the inquiry date, while in others data are
and others work in or near their homes, mixing work
recorded without reference to any period. In devel-
and family activities during the day.
oping countries, where the household is often the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working. Differing definitions of employment age also affect
Data sources
comparability. For most countries the working age is
Data on labor force participation rates are from
15 and older, but in some countries children younger
the ILO database Key Indicators of the Labour
than 15 work full- or part-time and are included in
Market, 5th edition. Labor force numbers were
the estimates. Similarly, some countries have an
calculated by World Bank staff, applying labor
upper age limit. As a result, calculations may sys-
force participation rates from the ILO database
tematically over- or underestimate actual rates. For
to population estimates.
2009 World Development Indicators
47
2.3
Employment by economic activity Agriculture
Male % of male employment 1990–92a 2003–06a
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 .. 23c .. 11 .. .. .. .. 4 66 20 c .. .. .. ..
.. .. 23 .. 2c .. 5 6c 41 50 .. 2c .. .. .. 29 25c 11 .. .. .. .. 4c .. .. 17 .. 0b 32 .. .. 21 .. 12d 28 5 4 21 11c 28 30 .. 7c 84 7 5 .. .. 52 3 .. 12c .. .. .. ..
2009 World Development Indicators
Industry
Female % of female employment 1990–92a
.. .. .. .. 0 b,c .. 4 8 .. 85 .. 2c .. 1c .. .. 25c .. .. .. .. 68 2c .. .. 6 .. 0b 1c .. .. 5 .. .. .. 7 3 3 2c 52 15 .. 13c .. 6 .. .. .. .. 4 59 26c .. .. .. ..
2003–06a
.. .. 11 .. 1c .. 3 6c 37 59 .. 2c .. .. .. 13 16c 7 .. .. .. .. 2c .. .. 6 .. 0b 8 .. .. 5 .. 14 d 10 3 2 3 4c 39 3 .. 4c 76 3 2 .. .. 57 2 .. 14 c .. .. .. ..
Male % of male employment 1990–92a
.. .. .. .. 40 c .. 32 47 .. 16 .. 41c .. 42c .. .. 27c .. .. .. .. 14 31c .. .. 32 .. 37 35c .. .. 27 .. .. .. 55 37 23 29c 25 23 .. 42c .. 38 .. .. .. .. 50 10 32c .. .. .. ..
2003–06a
.. .. 24 .. 33c .. 31 40 c 15 12 .. 35c .. .. .. 28 27c 39 .. .. .. .. 32c .. .. 29 .. 22 21 .. .. 26 .. 40d 23 49 34 26 27c 23 25 .. 44 c 5 38 35 .. .. 14 41 .. 30 c .. .. .. ..
Services
Female % of female employment 1990–92a
.. .. .. .. 18 c .. 12 20 .. 9 .. 16c .. 17c .. .. 10 c .. .. .. .. 4 11c .. .. 15 .. 27 25c .. .. 25 .. .. .. 33 16 21 17c 10 23 .. 30 c .. 15 .. .. .. .. 24 10 17c .. .. .. ..
2003–06a
.. .. 25 .. 11c .. 9 13c 9 18 .. 11c .. .. .. 17 13c 29 .. .. .. .. 11c .. .. 12 .. 7 16 .. .. 13 .. 18d 14 27 12 15 12c 6 22 .. 24 c 8 12 12 .. .. 4 16 .. 10 c .. .. .. ..
Male % of male employment 1990–92a
.. .. .. .. 59c .. 61 46 .. 25 .. 56c .. 55c .. .. 43c .. .. .. .. 26 64 c .. .. 45 .. 63 63c .. .. 41 .. .. .. 36 56 52 62c 41 29 .. 36c .. 51 .. .. .. .. 47 23 48 c .. .. .. ..
2003–06a
.. .. 53 .. 66c .. 65 55c 44 38 .. 62c .. .. .. 43 48 c 50 .. .. .. .. 64 c .. .. 54 .. 77 48 .. .. 52 .. 48d 50 46 62 53 62c 49 45 .. 49c 10 56 60 .. .. 34 56 .. 58 c .. .. .. ..
Female % of female employment 1990–92a
.. .. .. .. 81c .. 84 72 .. 2 .. 81c .. 82c .. .. 65c .. .. .. .. 23 87c .. .. 79 .. 73 74 c .. .. 69 .. .. .. 61 81 76 81c 37 63 .. 57c .. 78 .. .. .. .. 72 32 56c .. .. .. ..
2003–06a
.. .. 64 .. 88 c .. 88 81c 54 23 .. 86c .. .. .. 71 71c 64 .. .. .. .. 88 c .. .. 83 .. 93 76 .. .. 82 .. 67d 76 71 86 82 84 c 55 75 .. 72c 16 84 85 .. .. 38 82 .. 76c .. .. .. ..
Agriculture
Male % of male employment 1990–92a 2003–06a
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 .. .. 15 34 .. .. .. .. .. 45 75 5 13 .. .. .. 7 .. 45 35 .. 3c 1c 53c .. 10 c 5
51c 7c .. 41d 23 14 9 3 5 25 4 4 35 .. .. 7 .. 39 .. 15c .. .. .. .. 17c 20 77 .. 16 50 .. 11 21 41 43 40 .. .. .. .. 4 9 41 .. .. 5 .. 38 22 .. 39c 1c 45 18 c 11c 3
Industry
Female % of female employment 1990–92a
6c .. .. 57 .. .. 3 2 9 16 7 .. .. 20 c .. 18 .. .. .. .. .. .. .. .. 15 .. .. .. 20 .. .. 13 11 .. .. .. .. .. 52 91 3 8 .. .. .. 3 .. 69 3 .. 0 b,c 0 b,c 32c .. 13c 0b
2003–06a
13c 3c .. 41d 34 33 1 1 3 9 5 2 32 .. .. 9 .. 39 .. 8c .. .. .. .. 11c 19 79 .. 11 30 .. 9 5 40 37 61 .. .. .. .. 2 5 10 .. .. 2 .. 67 4 .. 20 c 0 b,c 25 17c 13c 0b
Male % of male employment 1990–92a
18 c .. .. 15 .. .. 33 38 37 25 40 .. .. 23c .. 40 .. .. .. .. .. .. .. .. 46 .. .. .. 31 .. .. 36 25 .. .. .. .. .. 21 4 33 31 .. .. .. 34 .. 20 20 .. 33c 30 c 17c .. 39c 27
2003–06a
20 c 42c .. 21d 31 20 39 31 39 27 35 23 24 .. .. 34 .. 23 .. 35c .. .. .. .. 37c 34 7 .. 35 18 .. 34 30 21 19 21 .. .. .. .. 30 32 19 .. .. 32 .. 21 22 .. 19c 31c 17 39c 41c 25
PEOPLE
2.3
Employment by economic activity
Services
Female % of female employment 1990–92a
25c .. .. 13 .. .. 18 15 22 12 27 .. .. 9c .. 28 .. .. .. .. .. .. .. .. 31 .. .. .. 32 .. .. 48 19 .. .. .. .. .. 8 1 10 13 .. .. .. 10 .. 15 11 .. 19c 13c 14 c .. 24 c 19
2003–06a
23c 21c .. 15d 28 7 12 11 18 5 18 12 10 .. .. 17 .. 11 .. 16c .. .. .. .. 21c 30 6 .. 27 15 .. 29 19 12 15 16 .. .. .. .. 8 11 17 .. .. 8 .. 15 9 .. 10 c 13c 12 17c 19c 11
Male % of male employment 1990–92a
29c .. .. 31 .. .. 48 57 55 39 54 .. .. 58 c .. 46 .. .. .. .. .. .. .. .. 29 .. .. .. 46 .. .. 48 41 .. .. .. .. .. 34 20 60 56 .. .. .. 58 .. 35 45 .. 64 c 69c 29c .. 51c 67
2003–06a
29c 51c .. 38d 46 66 51 65 56 48 59 73 41 .. .. 59 .. 38 .. 49c .. .. .. .. 46c 46 16 .. 49 32 .. 55 49 38 38 39 .. .. .. .. 62 59 33 .. .. 63 .. 41 56 .. 42c 68 c 39 43c 48 c 72
Female % of female employment 1990–92a
69c .. .. 31 .. .. 78 83 70 72 65 .. .. 71c .. 54 .. .. .. .. .. .. .. .. 54 .. .. .. 48 .. .. 39 70 .. .. .. .. .. 40 8 82 80 .. .. .. 86 .. 16 85 .. 80 c 87c 55c .. 63c 80
2009 World Development Indicators
2003–06a
63c 76c .. 44 d 37 60 86 88 79 86 77 84 58 .. .. 74 .. 50 .. 75c .. .. .. .. 68 c 51 15 .. 62 55 .. 62 76 48 48 23 .. .. .. .. 86 84 52 .. .. 90 .. 18 86 .. 70 c 86c 64 66c 68 c 89
49
2.3
Employment by economic activity Agriculture
Male % of male employment 1990–92a 2003–06a
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
29 .. .. .. .. .. .. 1 .. .. .. .. 11c .. .. .. 5c 4c 23 .. 78 c 60 .. .. 15 .. 33 .. 91 .. .. 3 4 7c .. 17 .. .. 44 .. .. .. w .. .. .. .. .. .. .. 21 .. .. .. 6 7
31 12 .. 5 .. 21e .. 0 6c 9 .. 13 6c .. .. .. 3c 5c 23 .. .. 44 .. .. 6 .. 22 .. 60c .. .. 2 2 7c .. 16c 56 12 .. .. .. .. w .. .. .. 20 .. .. 19 21 .. .. .. 4 5
Industry
Female % of female employment 1990–92a
38 .. .. .. .. .. .. 0b .. .. .. .. 8c .. .. .. 2c 4c 54 .. 90 c 62 .. .. 6 .. 72 .. 91 .. .. 1 1 1c .. 2 .. .. 83 .. .. .. w .. .. .. .. .. .. .. 14 .. .. .. 5 7
2003–06a
33 8 .. 0b .. 20e .. 0 3c 9 .. 7 4c .. .. .. 1c 3c 49 .. .. 41 .. .. 2 .. 52 .. 77c .. .. 1 1 2c .. 2c 60 34 .. .. .. .. w .. .. .. 14 .. .. 19 10 .. .. .. 3 3
Male % of male employment 1990–92a
44 .. .. .. .. .. .. 36 .. .. .. .. 41c .. .. .. 40 c 37c 28 .. 7c 18 .. .. 34 .. 26 .. 4 .. .. 41 34 36c .. 32 .. .. 14 .. .. .. w .. .. .. .. .. .. .. 30 .. .. .. 38 42
2003–06a
35 38 .. 11 .. 37e .. 36 50 c 47 .. 33 41c .. .. .. 34 c 32c 29 .. .. 22 .. .. 41 .. 28 .. 11c .. .. 33 30 29c .. 25c 21 28 .. .. .. .. w .. .. .. 30 .. .. 34 27 .. .. .. 34 38
Services
Female % of female employment 1990–92a
30 .. .. .. .. .. .. 32 .. .. .. .. 16c .. .. .. 12c 15c 8 .. 1c 13 .. .. 14 .. 11 .. 6 .. .. 16 14 21c .. 16 .. .. 2 .. .. .. w .. .. .. .. .. .. .. 14 .. .. .. 19 21
2003–06a
25 21 .. 1 .. 20e .. 21 25c 25 .. 14 12c .. .. .. 9c 11c 8 .. .. 19 .. .. 16 .. 15 .. 5c .. .. 9 10 13c .. 11c 14 8 .. .. .. .. w .. .. .. 17 .. .. 20 15 .. .. .. 13 14
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. d. Data are for 2007. e. Data are for 2008.
50
2009 World Development Indicators
Male % of male employment 1990–92a
28 .. .. .. .. .. .. 63 .. .. .. .. 49c .. .. .. 55c 59c 49 .. 15c 22 .. .. 51 .. 41 .. 5 .. .. 55 62 57c .. 52 .. .. 38 .. .. .. w .. .. .. .. .. .. .. 49 .. .. .. 56 50
2003–06a
34 50 .. 85 .. 42e .. 63 44 c 43 .. 54 52c .. .. .. 63c 63c 48 .. .. 34 .. .. 52 .. 50 .. 29c .. .. 65 68 64 c .. 59c 23 59 .. .. .. .. w .. .. .. 49 .. .. 47 52 .. .. .. 62 56
Female % of female employment 1990–92a
33 .. .. .. .. .. .. 68 .. .. .. .. 76c .. .. .. 86c 81c 38 .. 8c 25 .. .. 80 .. 17 .. 3 .. .. 82 85 78 c .. 82 .. .. 13 .. .. .. w .. .. .. .. .. .. .. 71 .. .. .. 76 72
2003–06a
42 71 .. 99 .. 60e .. 79 72c 65 .. 79 84 c .. .. .. 90 c 86c 43 .. .. 41 .. .. 82 .. 33 .. 18 c .. .. 90 90 86c .. 86c 26 56 .. .. .. .. w .. .. .. 68 .. .. 62 76 .. .. .. 84 82
About the data
PEOPLE
Employment by economic activity
2.3
Definitions
The International Labour Organization (ILO) classi-
aggregated into three broad groups: agriculture,
• Agriculture corresponds to division 1 (ISIC revi-
fies economic activity using the International Stan-
industry, and services. Such broad classification may
sion 2) or tabulation categories A and B (ISIC revi-
dard Industrial Classification (ISIC) of All Economic
obscure fundamental shifts within countries’ indus-
sion 3) and includes hunting, forestry, and fishing.
Activities, revision 2 (1968) and revision 3 (1990).
trial patterns. A slight majority of countries report
• Industry corresponds to divisions 2–5 (ISIC revi-
Because this classification is based on where work
economic activity according to the ISIC revision 2
sion 2) or tabulation categories C–F (ISIC revision
is performed (industry) rather than type of work per-
instead of revision 3. The use of one classification or
3) and includes mining and quarrying (including oil
formed (occupation), all of an enterprise’s employees
the other should not have a significant impact on the
production), manufacturing, construction, and public
are classified under the same industry, regardless
information for the three broad sectors presented
utilities (electricity, gas, and water). • Services corre-
of their trade or occupation. The categories should
in the table.
spond to divisions 6–9 (ISIC revision 2) or tabulation
sum to 100 percent. Where they do not, the differ-
The distribution of economic wealth in the world
categories G–P (ISIC revision 3) and include whole-
ences are due to workers who cannot be classified
remains strongly correlated with employment by
sale and retail trade and restaurants and hotels;
by economic activity.
economic activity. The wealthier economies are
transport, storage, and communications; financing,
Data on employment are drawn from labor force
those with the largest share of total employment in
insurance, real estate, and business services; and
surveys, household surveys, official estimates, cen-
services, whereas the poorer economies are largely
community, social, and personal services.
suses and administrative records of social insurance
agriculture based.
schemes, and establishment surveys when no other
The distribution of economic activity by gender
information is available. The concept of employment
reveals some clear patterns. Men still make up the
generally refers to people above a certain age who
majority of people employed in all three sectors, but
worked, or who held a job, during a reference period.
the gender gap is biggest in industry. Employment in
Employment data include both full-time and part-time
agriculture is also male-dominated, although not as
workers.
much as industry. Segregating one sex in a narrow
There are many differences in how countries define
range of occupations significantly reduces economic
and measure employment status, particularly mem-
efficiency by reducing labor market flexibility and thus
bers of the armed forces, self-employed workers, and
the economy’s ability to adapt to change. This seg-
unpaid family workers. Where members of the armed
regation is particularly harmful for women, who have
forces are included, they are allocated to the service
a much narrower range of labor market choices and
sector, causing that sector to be somewhat over-
lower levels of pay than men. But it is also detri-
stated relative to the service sector in economies
mental to men when job losses are concentrated
where they are excluded. Where data are obtained
in industries dominated by men and job growth is
from establishment surveys, data cover only employ-
centered in service occupations, where women have
ees; thus self-employed and unpaid family workers
better chances, as has been the recent experience
are excluded. In such cases the employment share
in many countries.
of the agricultural sector is severely underreported.
There are several explanations for the rising impor-
Caution should be also used where the data refer
tance of service jobs for women. Many service jobs—
only to urban areas, which record little or no agricul-
such as nursing and social and clerical work—are
tural work. Moreover, the age group and area covered
considered “feminine” because of a perceived simi-
could differ by country or change over time within a
larity to women’s traditional roles. Women often do
country. For detailed information on breaks in series,
not receive the training needed to take advantage of
consult the original source.
changing employment opportunities. And the greater
Countries also take different approaches to the
availability of part-time work in service industries may
treatment of unemployed people. In most countries
lure more women, although it is unclear whether this
unemployed people with previous job experience are
is a cause or an effect.
classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries. The ILO’s Yearbook of Labour Statistics and its data-
Data sources
base Key Indicators of the Labour Market report data
Data on employment are from the ILO database
by major divisions of the ISIC revision 2 or revision 3.
Key Indicators of the Labour Market, 5th edition.
In the table the reported divisions or categories are
2009 World Development Indicators
51
2.4
Decent work and productive employment Employment to population ratio
Gross enrollment ratio, secondary
Vulnerable employment
Labor productivity
Unpaid family workers and own-account workers Total
Youth
% ages 15 and older
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
% ages 15–24
% of relevant age group
1991
2007
1991
2007
1991
2007a
.. 53 39 76 54 39 57 54 57 75 59 46 71 62 52 49 56 46 81 84 78 59 59 72 66 51 76 63 52 67 65 57 62 46 53 58 62 44 52 43 59 65 63 72 59 50 58 72 58 56 69 46 56 82 66 56
.. 51 51 76 58 40 62 57 61 68 53 49 72 70 41 46 65 47 81 83 79 59 64 71 69 51 73 59 63 66 64 59 60 47 56 56 63 53 60 42 58 65 57 81 57 51 59 72 56 54 65 50 63 82 66 56
.. 39 25 69 43 24 58 61 38 67 40 31 64 48 28 39 54 28 77 73 68 39 57 57 50 34 72 54 37 58 48 48 51 25 39 45 65 28 39 22 42 58 44 65 45 28 37 58 28 58 40 31 52 75 57 37
.. 36 34 68 39 26 64 53 39 56 34 27 59 49 16 26 53 26 74 72 75 35 61 57 49 24 56 39 44 61 45 44 45 29 32 29 62 33 39 22 41 53 30 74 44 29 34 54 22 43 40 28 53 73 62 48
.. 88 60 11 72 .. 83 102 88 20 93 101 10 44 .. 48 58 86 7 5 25 26 101 11 7 73 40 80 50 21 46 45 21 .. 94 91 109 .. 55 71 36 .. 104 13 116 98 39 17 95 98 34 94 23 10 6 21
.. .. 83 17 84 89 150 102 .. .. 95 110 32 82 85 76 105 105 16 15 42 25 .. .. 19 91 76 86 85 33 .. 87 .. 91 93 96 120 79 70 .. 64 29 100 30 112 114 .. 49 90 102 49 103 56 35 .. ..
2009 World Development Indicators
Male % of male employment
Female % of female employment
GDP per person employed % growth
1990
2007
1990
2007
1992
2008
.. .. .. .. .. .. 12 .. .. .. .. .. .. 32b .. .. 29 b .. .. .. .. .. .. .. .. .. .. .. 30 b .. .. 26 .. .. .. .. .. 42 33b .. .. .. 2 .. .. .. .. .. .. .. .. .. .. .. .. ..
.. .. .. .. 22b .. 11 9 41 85 .. 11 .. .. .. .. 30 10 .. .. .. .. 12b .. .. 25 .. 10 41 .. .. 20 .. 18b .. 15 .. 49 29 b 20 29 .. 8 48 .. 8 .. .. 64 .. .. 28 .. .. .. ..
.. .. .. .. .. .. 9 .. .. .. .. .. .. 50 b .. .. 30 b .. .. .. .. .. .. .. .. .. .. .. 26 b .. .. 21 .. .. .. .. .. 30 41b .. .. .. 3 .. .. .. .. .. .. .. .. .. .. .. .. ..
.. .. .. .. 17b .. 7 9 66 87 .. 9 .. .. .. .. 24 7 .. .. .. .. 9b .. .. 24 .. 4 41 .. .. 20 .. 18b .. 9 .. 30 41b 44 44 .. 4 56 .. 5 .. .. 65 .. .. 27 .. .. .. ..
.. –3.9 –0.1 –10.3 9.6 –39.5 0.4 0.3 –23.7 5.2 –8.7 0.3 –2.1 0.0 0.8 0.8 –8.2 –11.6 –3.0 –1.0 4.7 –5.8 2.1 –9.0 3.0 6.9 11.8 5.7 –0.1 –13.6 –0.4 4.2 –3.1 –11.5 .. –1.1 1.7 6.4 –3.1 1.8 6.9 15.3 –18.5 –11.7 3.8 1.7 –7.5 –0.3 –43.3 3.8 –1.2 –2.0 3.4 –0.9 –2.2 –10.2
.. 4.9 0.1 7.8 5.0 11.2 0.6 0.2 9.3 2.3 9.6 0.3 1.6 2.7 4.7 –0.7 3.8 4.6 1.2 0.2 3.8 1.1 –0.6 0.4 –3.5 2.8 8.6 1.8 2.0 5.0 5.9 1.7 0.6 1.6 .. 3.6 0.7 4.4 2.8 4.5 1.6 –2.5 2.1 7.7 1.1 0.5 –0.7 2.3 4.1 –0.7 3.8 1.6 1.0 2.2 0.5 0.0
Employment to population ratio
Gross enrollment ratio, secondary
Vulnerable employment
Youth
% ages 15 and older
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
% ages 15–24
% of relevant age group
Male % of male employment
2.4 Labor productivity
Unpaid family workers and own-account workers Total
PEOPLE
Decent work and productive employment
Female % of female employment
GDP per person employed % growth
1991
2007
1991
2007
1991
2007a
1990
2007
1990
2007
1992
2008
59 50 59 63 46 35 45 46 45 62 63 36 64 72 62 59 62 60 80 59 46 51 66 46 55 38 79 72 60 48 54 56 57 59 51 46 79 75 46 61 53 57 53 60 52 60 53 48 50 70 62 58 59 54 59 38
56 47 55 62 48 .. 60 51 46 58 57 39 64 73 65 59 66 59 78 57 46 56 66 49 53 35 83 72 61 46 47 55 58 44 52 46 77 75 42 62 61 65 59 60 51 65 51 51 60 70 73 68 61 49 58 42
49 39 47 46 33 23 38 25 30 39 43 24 46 61 46 36 30 41 74 42 32 43 57 28 35 19 65 49 47 39 43 46 50 38 40 40 66 62 24 54 55 55 41 50 28 49 30 39 34 58 51 45 42 32 53 21
43 21 39 41 35 .. 47 27 26 32 41 19 42 59 40 29 32 40 64 35 28 43 57 28 20 14 70 49 44 34 31 37 43 17 35 34 64 54 14 45 65 56 48 51 24 56 29 43 40 55 58 52 40 26 36 29
33 86 42 45 57 44 100 92 83 65 97 82 100 46 .. 90 43 100 23 92 62 24 .. .. 92 .. 17 8 57 8 14 55 53 78 82 36 7 23 45 34 120 90 42 7 24 103 45 25 62 12 31 67 71 87 66 ..
61 96 55 66 73 .. 112 92 100 87 101 89 92d 50 .. 98 91 86 44 99 81 37 .. 94 99 84 26 28 69 32 25 88 87 89 92 56 18 .. 59 48 d 118 120 66 11 32 113 90 33 70 .. 66 94 83 100 97 ..
48b 8 .. .. .. .. 25 .. .. 46 15 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 31 .. .. 13 29 .. .. .. .. .. .. .. .. 15 .. .. .. .. .. .. 44 .. 17b 30 b .. .. 18b ..
.. 8 .. 60 40 .. 16 9 27 38 10 .. .. .. .. 23 .. 47 .. 9 .. .. .. .. .. 24 84 .. 23 .. .. 18 28 35 .. 47 .. .. .. .. .. 14 45 .. .. 8 .. 58 30 .. 45 33b 44 21 18 ..
50 b 7 .. .. .. .. 9 .. .. 37 26 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 25 .. .. 7 15 .. .. .. .. .. .. .. .. 10 .. .. .. .. .. .. 19 .. 31b 46b .. .. 21b ..
.. 6 .. 68 56 .. 5 5 16 31 12 .. .. .. .. 28 .. 47 .. 6 .. .. .. .. .. 20 89 .. 21 .. .. 15 32 30 .. 65 .. .. .. .. .. 10 46 .. .. 3 .. 75 24 .. 50 47b 47 18 19 ..
–0.4 –3.0 2.4 4.3 2.6 .. 2.5 1.3 0.9 0.9 –0.3 8.2 –1.5 –5.2 .. 3.9 52.3 –14.3 3.8 –31.7 1.0 5.6 –34.1 –5.5 –20.0 –5.7 –2.8 –10.6 6.2 5.2 –0.9 –2.5 –0.2 –28.6 –12.8 –7.1 –13.1 7.1 3.6 –0.6 –0.3 0.1 –5.5 –10.0 0.2 4.1 1.6 4.6 1.9 11.8 –2.1 –3.6 –3.4 4.1 3.3 ..
1.9 2.3 5.4 4.2 1.4 .. 0.0 2.3 –0.4 –0.7 0.4 1.7 2.8 –1.1 .. 1.9 3.0 5.6 4.3 1.7 6.2 4.8 4.3 4.5 8.3 4.3 3.1 5.0 3.6 1.8 2.7 2.9 0.6 7.6 7.9 4.2 4.3 3.0 0.2 1.7 0.5 –0.4 0.4 2.4 2.4 1.0 2.8 1.8 7.4 4.2 2.2 6.7 3.1 1.3 –0.2 ..
2009 World Development Indicators
53
2.4
Decent work and productive employment Employment to population ratio
Gross enrollment ratio, secondary
Vulnerable employment
Labor productivity
Unpaid family workers and own-account workers Total
Youth
% ages 15 and older 1991
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
57 58 87 51 67 50 c 64 64 56 56 65 40 43 52 46 54 65 68 47 54 88 78 64 65 45 41 53 56 85 59 71 58 61 54 55 52 76 29 38 57 70 63 w 66 64 66 55 64 74 56 55 43 59 64 57 50
2007
50 59 80 51 66 49c 64 62 53 56 66 41 53 55 47 51 61 64 45 55 78 72 67 64 62 42 43 59 83 54 75 59 62 58 58 60 71 32 39 61 67 61 w 65 62 63 56 62 71 54 61 45 56 64 58 52
% ages 15–24 1991
42 34 79 27 59 28 c 39 56 43 38 58 20 36 32 29 34 59 69 38 36 79 70 51 57 33 29 48 34 78 37 43 66 56 43 36 36 75 19 23 40 48 53 w 55 54 56 43 54 67 38 47 28 48 50 47 41
% of relevant age group
2007
24 33 64 25 54 30 c 42 37 30 33 57 14 38 35 24 26 45 63 33 55 70 47 58 52 47 23 31 34 76 34 47 56 52 38 38 38 52 16 22 47 51 45 w 51 43 44 38 45 52 32 46 28 42 49 44 37
a. Provisional data. b. Limited coverage. c. Includes Montenegro. d. Data are for 2008.
54
2009 World Development Indicators
1991
2007a
92 93 9 44 15 .. 17 .. .. 89 .. 69 105 71 21 42 90 99 48 102 5 33 .. 20 82 45 48 .. 11 94 68 87 92 84 99 53 32 .. .. 23 49 51 w 25 51 47 68 45 47 85 51 57 38 21 92 ..
86 84 18 94 24 88 32 .. 96 95 .. 96 119 .. 35d 47 103 93 72 84 .. 83 53 39 76 85 79 .. 18 94 92 98 94 101 102 79 .. 92 46 43 40 66 w 38 70 65 91 61 73 88 89 71 49 32 101 ..
Male % of male employment 1990
7b 1 .. .. 77 .. .. 10 .. .. .. .. 20 .. .. .. .. 8 .. .. .. 67 .. .. 22 .. .. .. .. .. .. .. .. .. .. .. .. .. .. 56 .. .. w .. .. .. .. .. .. .. 30 .. .. .. .. ..
2007
32 6 .. .. .. 25 .. 12 13b 14 .. 2 13 39 b .. .. .. 10 .. .. 82b 51 .. .. 17 .. 32 .. .. .. .. .. .. 26 .. 28 .. 34 .. .. .. .. w .. .. .. 23 .. .. 19 31 34 .. .. .. 14
Female % of female employment 1990
11b 1 .. .. 91 .. .. 6 .. .. .. .. 24 .. .. .. .. 11 .. .. .. 74 .. .. 21 .. .. .. .. .. .. .. .. .. .. .. .. .. .. 81 .. .. w .. .. .. .. .. .. .. 28 .. .. .. .. ..
2007
33 6 .. .. .. 20 .. 7 5b 13 .. 3 10 44b .. .. .. 11 .. .. 93b 56 .. .. 13 .. 50 .. .. .. .. .. .. 24 .. 33 .. 47 .. .. .. .. w .. .. .. 20 .. .. 18 31 52 .. .. .. 9
GDP per person employed % growth 1992
–13.3 –19.2 13.7 2.0 –1.6 .. –19.5 3.5 –8.0 1.4 .. –6.0 3.7 5.1 4.3 –0.5 3.3 0.3 8.1 –27.4 –2.8 6.8 .. –6.5 –4.8 4.7 4.4 –8.7 0.2 –8.8 –3.3 2.7 2.7 4.4 –11.4 0.7 5.8 .. 2.6 –3.9 .. –0.4 w –0.5 –1.7 3.1 –7.2 –1.6 8.5 –11.9 –1.8 1.2 3.0 –4.0 2.0 2.2
2008
9.4 6.3 5.2 2.7 0.7 .. 3.8 –0.9 4.6 3.6 .. 2.7 7.1 4.3 5.2 1.3 0.6 1.5 1.5 3.2 4.2 3.3 .. –2.4 2.7 2.1 0.1 7.2 6.1 3.0 4.7 1.0 3.0 9.7 5.7 2.7 4.0 .. –0.2 3.2 .. 3.1 w 3.8 6.1 7.7 3.9 5.8 8.6 4.6 3.4 3.2 6.5 3.7 1.9 1.4
About the data
PEOPLE
Decent work and productive employment
2.4
Definitions
Four targets were added to the UN Millennium Dec-
small group of countries. The labor force survey is
• Employment to population ratio is the proportion
laration at the 2005 World Summit High-Level Ple-
the most comprehensive source for internationally
of a country’s population that is employed. People
nary Meeting of the 60th Session of the UN General
comparable employment, but there are still some
ages 15 and older are generally considered the work-
Assembly. One was full and productive employment
limitations for comparing data across countries and
ing-age population. People ages 15–24 are generally
and decent work for all, which is seen as the main
over time even within a country. Information from
considered the youth population. • Gross enrollment
route for people to escape poverty. The four indi-
labor force surveys is not always consistent in what
ratio, secondary, is the ratio of total enrollment in
cators for this target have an economic focus, and
is included in employment. For example, informa-
secondary education, regardless of age, to the popu-
three of them are presented in the table.
tion provided by the Organisation for Economic Co-
lation of the age group that officially corresponds to
The employment to population ratio indicates
operation and Development relates only to civilian
secondary education. • Vulnerable employment is
how efficiently an economy provides jobs for people
employment, which can result in an underestimation
unpaid family workers and own-account workers as a
who want to work. A high ratio means that a large
of “employees” and “workers not classified by sta-
percentage of total employment. • Labor productiv-
propor tion of the population is employed. But a
tus,” especially in countries with large armed forces.
ity is the growth rate of gross domestic product
lower employment to population ratio can be seen
While the categories of unpaid family workers and
(GDP) divided by total employment in the economy.
as a positive sign, especially for young people, if it
self-employed workers, which include own-account
is caused by an increase in their education. This
workers, would not be affected, their relative shares
indicator has a gender bias because women who do
would be. Geographic coverage is another factor that
not consider their work employment or who are not
can limit cross-country comparisons. The employment
perceived as working tend to be undercounted. This
by status data for most Latin American countries cov-
bias has different effects across countries.
ers urban areas only. Similarly, in some countries
Comparability of employment ratios across coun-
in Sub-Saharan Africa, where limited information is
tries is also affected by variations in definitions of
available anyway, the members of producer coopera-
employment and population (see About the data for
tives are usually excluded from the self-employed
table 2.3). The biggest difference results from the
category. For detailed information on definitions and
age range used to define labor force activity. The
coverage, consult the original source.
population base for employment ratios can also vary
Labor productivity is used to assess a country’s
(see table 2.1). Most countries use the resident,
economic ability to create and sustain decent employ-
noninstitutionalized population of working age living
ment opportunities with fair and equitable remunera-
in private households, which excludes members of
tion. Productivity increases obtained through invest-
the armed forces and individuals residing in men-
ment, trade, technological progress, or changes in
tal, penal, or other types of institutions. But some
work organization can increase social protection
countries include members of the armed forces in
and reduce poverty, which in turn reduce vulner-
the population base of their employment ratio while
able employment and working poverty. Productivity
excluding them from employment data (International
increases do not guarantee these improvements,
Labour Organization, Key Indicators of the Labour
but without them—and the economic growth they
Market, 5th edition).
bring—improvements are highly unlikely. For compa-
The proportion of unpaid family workers and own-
rability of individual sectors labor productivity is esti-
account workers in total employment is derived from
mated according to national accounts conventions.
information on status in employment. Each status
However, there are still significant limitations on the
group faces different economic risks, and unpaid
availability of reliable data. Information on consis-
family workers and own-account workers are the
tent series of output in both national currencies and
most vulnerable—and therefore the most likely to
purchasing power parity dollars is not easily avail-
fall into poverty. They are the least likely to have for-
able, especially in developing countries, because the
mal work arrangements, are the least likely to have
definition, coverage, and methodology are not always
social protection and safety nets to guard against
consistent across countries. For example, countries
economic shocks, and often are incapable of gen-
employ different methodologies for estimating the
erating sufficient savings to offset these shocks. A
missing values for the nonmarket service sectors
Data on employment to population ratio, vulner-
high proportion of unpaid family workers in a country
and use different definitions of the informal sector.
able employment, and labor productivity are from
Data sources
indicates weak development, little job growth, and
the International Labour Organization database
often a large rural economy.
Key Indicators of the Labour Market, 5th edition.
Data on employment by status are drawn from labor force surveys and household surveys, supple-
Data on gross enrollment ratios are from the UNESCO Institute for Statistics.
mented by offi cial estimates and censuses for a
2009 World Development Indicators
55
2.5
Unemployment Unemployment
Total % of total labor force 1990–92a 2004–07a
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
.. .. 23.0 .. 6.7b .. 10.8 3.6 .. .. .. 6.7 1.5 5.5b 17.6 .. 6.4b .. .. 0.5 .. .. 11.2b .. .. 4.4 2.3b 2.0 9.5 .. .. 4.1 6.7 .. .. .. 9.0 20.7 8.9 9.0 7.9 .. 3.7 .. 11.6 10.0 .. .. .. 6.6 4.7 7.8 .. .. .. 12.7
.. .. 12.3 .. 9.5b 9.6 4.4 4.4 .. 4.3 .. 7.6 .. .. 31.1 17.6 8.9b 8.9 .. .. .. .. 6.0 b .. .. 8.9 4.0 b 4.0 10.9 .. .. 4.6 .. 9.6 1.9 5.3 3.6 17.9 7.9 9.0 6.6 .. 4.7 5.4 6.8 8.0 .. .. 13.3 8.6 .. 8.1 3.1 .. .. ..
2009 World Development Indicators
Male % of male labor force 1990–92a 2004–07a
.. .. 24.2 .. 6.4b .. 11.4 3.5 .. .. .. 4.8 2.2 5.5b 15.5 .. 5.4b .. .. 0.7 .. .. 12.0 b .. .. 3.9 .. 2.0 6.8 .. .. 3.5 .. .. .. .. 8.3 12.0 6.0 6.4 8.4 .. 3.9 .. 13.3 7.9 .. .. .. 5.3 3.7 4.9 .. .. .. 11.9
.. .. .. .. 7.8b 5.7 4.0 3.9 .. 3.4 .. 6.7 .. .. 28.9 15.3 6.8b 8.6 .. .. .. .. 6.4b .. .. .. .. 4.5 8.7 .. .. 3.3 .. 8.3 1.7 4.2 3.2 11.3 5.8 6.0 8.5 .. 5.4 2.7 6.4 7.4 .. .. 13.9 8.5 .. 5.0 2.8 .. .. ..
Female % of female labor force 1990–92a 2004–07a
.. .. 20.3 .. 7.0 b .. 10.0 3.8 .. .. .. 9.5 0.6 5.6b 21.6 .. 7.9b .. .. 0.3 .. .. 10.2b .. .. 5.3 .. 1.9 13.0 .. .. 5.4 .. .. .. .. 9.9 35.2 13.2 17.0 7.2 .. 3.5 .. 9.6 12.7 .. .. .. 8.4 5.5 12.9 .. .. .. 13.8
.. .. .. .. 11.6b 13.8 4.8 5.0 .. 7.0 .. 8.7 .. .. 34.8 19.9 11.7b 9.3 .. .. .. .. 5.6b .. .. .. .. 3.4 13.8 .. .. 6.8 .. 11.2 2.2 6.7 4.0 28.8 10.8 18.6 3.9 .. 3.9 8.2 7.3 8.5 .. .. 12.6 8.8 .. 12.6 3.7 .. .. ..
Long-term unemployment
Unemployment by educational attainment
% of total unemployment Total Male Female 2004–07a 2004–07a 2004–07a
% of total unemployment Primary Secondary Tertiary 2004–07a 2004–07a 2004–07a
.. .. .. .. .. .. 15.5b 26.8 .. .. .. 50.0 .. .. .. .. .. .. .. .. .. .. 7.5b .. .. .. .. .. .. .. .. .. .. 58.8 .. 53.4 18.2 .. .. .. .. .. .. .. 23.0 40.4 .. .. .. 56.6 .. 50.3 .. .. .. ..
.. .. .. .. .. .. 16.5b 26.6 .. .. .. 49.1 .. .. .. .. .. .. .. .. .. .. 8.4b .. .. .. .. .. .. .. .. .. .. 54.6 .. 51.7 18.4 .. .. .. .. .. .. .. 26.5 40.6 .. .. .. 57.5 .. 42.1 .. .. .. ..
.. .. .. .. .. .. 14.4b 27.1 .. .. .. 51.0 .. .. .. .. .. .. .. .. .. .. 6.3b .. .. .. .. .. .. .. .. .. .. 61.8 .. 54.7 17.9 .. .. .. .. .. .. .. 19.5 40.1 .. .. .. 55.6 .. 54.9 .. .. .. ..
.. 98.3 59.3 .. 37.3b 5.2 48.0 37.9 b 6.3 33.0 10.0 42.1 .. .. .. .. 51.6b 41.8 .. .. .. .. 27.7b .. .. 17.0 .. 40.8 76.6 .. .. 65.2 .. 20.4 43.0 26.8 35.9 .. 74.0 .. .. .. 23.1 35.9 35.5 39.9 .. .. 5.1 33.1 .. 29.3 .. .. .. ..
.. .. 23.0 .. 41.8b 83.0 34.1 52.1b 78.9 24.4 39.0 38.2 .. .. .. .. 33.6b 49.7 .. .. .. .. 41.1b .. .. 57.9 .. 41.4 .. .. .. 27.3 .. 67.8 52.4 68.8 35.1 .. .. .. .. .. 57.8 13.3 45.9 39.6 .. .. 52.5 56.3 .. 48.4 .. .. .. ..
.. 1.7 11.4 .. 19.7b 11.9 17.9 10.0 b 14.9 15.9 51.0 19.7 .. .. .. .. 3.6b 8.6 .. .. .. .. 31.2b .. .. 24.8 .. 16.6 20.6 .. .. 6.4 .. 11.8 4.6 4.3 23.0 .. 23.6 .. .. .. 16.6 3.2 18.6 19.9 .. .. 42.3 10.6 .. 21.8 .. .. .. ..
Unemployment
Total % of total labor force 1990–92a 2004–07a
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 9.9 .. 2.8 11.1 .. 15.2 11.2b 11.5 15.7 2.2 .. .. .. .. 2.5 .. .. .. .. .. .. .. .. .. .. .. .. 3.7 .. .. .. 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 16.9
4.2 7.4 5.0 b 9.1 10.5 .. 4.6 7.3b 6.1 9.4 3.9 12.4 8.4 .. .. 3.2 1.7 8.3 1.4 6.0 8.1 .. 5.6 .. 4.3 34.9 2.6 7.8 3.1 8.8 33.0 8.5 3.4 5.1 2.8 10.0 .. .. 21.9 .. 3.6 3.6b 5.2 .. .. 2.5 .. 5.3 6.8 .. 5.6b 6.7b 6.3 9.6 8.0 10.9
Male % of male labor force 1990–92a 2004–07a
3.3 11.0 .. 2.7 9.5 .. 15.2 9.2b 8.1 9.5 2.1 .. .. .. .. 2.8 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 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 19.1
3.2 7.1 4.9b 8.1 9.3 .. 4.8 6.7b 4.9 5.5 4.0 11.8 7.0 .. .. 3.7 .. 7.7 1.3 6.3 .. .. 6.8 .. 4.3 34.5 1.7 5.4 3.2 7.2 25.2 5.3 3.2 6.2 .. 10.1 .. .. 19.4 .. 3.2 3.3b 5.4 .. .. 2.5 .. 4.5 5.3 .. 4.2b 5.6b 6.4 9.0 6.6 12.0
Female % of female labor force 1990–92a 2004–07a
3.0 8.7 .. 3.0 24.4 .. 15.2 13.9 b 17.3 22.8 2.2 .. .. .. .. 2.1 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 4.0 .. .. 25.3b .. 8.8 19.0 .. 7.3 9.6b 19.5 .. .. 5.1 .. 14.0 22.3 5.9 3.8b 12.5b 9.9 14.7 5.0 b 13.3
6.2 7.7 5.3b 10.8 15.7 .. 4.3 7.9b 7.9 14.3 3.7 16.5 9.8 .. .. 2.6 .. 9.0 1.4 5.4 .. .. 4.2 .. 4.4 35.5 3.5 10.0 3.4 10.9 .. 14.4 3.7 3.9 .. 10.0 .. .. 25.0 .. 4.1 3.9b 4.9 .. .. 2.4 .. 8.4 9.3 .. 7.6b 8.0 b 6.0 10.3 9.6 9.5
PEOPLE
Unemployment
2.5
Long-term unemployment
Unemployment by educational attainment
% of total unemployment Total Male Female 2004–07a 2004–07a 2004–07a
% of total unemployment Primary Secondary Tertiary 2004–07a 2004–07a 2004–07a
.. 47.5 .. .. .. .. 30.3 .. 49.9 .. 32.0 .. .. .. .. 0.6 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 2.7b .. .. .. .. .. .. .. 41.7 5.7b .. .. .. 8.8 .. .. .. .. .. .. .. 45.9 47.3 ..
.. 47.3 .. .. .. .. 36.0 .. 47.3 .. 40.3 .. .. .. .. 0.7 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 3.0 b .. .. .. .. .. .. .. 43.9 6.1b .. .. .. 10.2 .. .. .. .. .. .. .. 45.8 48.2 ..
.. 47.9 .. .. .. .. 21.9 .. 52.3 .. 19.4 .. .. .. .. 0.3 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 2.3b .. .. .. .. .. .. .. 39.8 5.4b .. .. .. 7.1 .. .. .. .. .. .. .. 46.0 46.7 ..
.. 33.1 29.0 44.4 41.8 .. 39.8 12.2 46.5 9.7 67.2 .. 7.1 .. .. 15.2 19.4 13.3 .. 24.3 .. .. .. .. 14.2 .. 67.7 .. 13.3 .. .. 44.2 50.7 .. 35.1 51.1b .. .. .. .. 41.3 30.6 72.8 .. .. 25.4 .. 14.3 36.0 .. 49.9 30.0 b 13.6 16.4 68.1 ..
.. 58.7 37.7 40.7 34.7 .. 37.2 12.8 40.6 4.3 .. .. 49.0 .. .. 49.7 41.4 77.1 .. 59.9 .. .. .. .. 70.4 .. .. .. 61.6 .. .. 48.5 24.5 .. 45.8 22.4b .. .. .. .. 39.7 38.8 2.1 .. .. 49.2 .. 11.4 39.6 .. 38.0 31.9 b 46.2 73.2 15.4 ..
2009 World Development Indicators
.. 8.1 33.3 9.6 19.6 .. 18.2 72.5 11.3 8.4 32.8 .. 43.9 .. .. 35.2 9.6 9.6 .. 14.6 .. .. .. .. 15.4 .. 9.3 .. 25.1 .. .. 6.4 22.9 .. 18.5 21.6b .. .. .. .. 17.0 26.9 18.0 .. .. 20.6 .. 26.0 24.0 .. 9.9 37.6b 39.4 10.4 13.2 ..
57
2.5
Unemployment Unemployment
Total % of total labor force 1990–92a 2004–07a
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
.. 5.3 0.3 .. .. .. .. 2.7 .. .. .. .. 18.1 13.3b .. .. 5.7 2.8 6.8 .. 3.6b 1.4 .. .. 19.6 .. 8.5 .. .. .. .. 9.7 7.5b 9.0 b .. 7.7 .. .. .. 18.9 .. .. w .. .. .. 6.3 .. 2.5 .. 6.7 12.8 .. .. 7.4 9.5
6.4 6.1 .. 5.6 .. 13.3c 3.4 4.0 11.0 4.6 .. 23.0 8.3 6.0 b .. .. 6.1 3.6 .. .. 4.7 1.2 .. .. 6.5 14.2 9.9 .. .. 6.8 3.1 5.2 4.6b 9.2b .. 7.5 2.1 21.6 .. .. 4.2 6.4 w .. 6.4 5.7 8.7 6.4 4.5 7.8 8.8 12.1 5.3 .. 5.5 7.5
Male % of male labor force 1990–92a 2004–07a
.. 5.4 0.6 .. .. .. .. 2.7 .. .. .. .. 13.9 10.1b .. .. 6.7 2.3 5.2 .. 2.8b 1.3 .. .. 17.0 .. 8.8 .. .. .. .. 11.5 7.9b 6.8b .. 8.2 .. .. .. 16.3 .. .. w .. .. .. 5.9 .. .. .. 5.4 10.8 .. .. 7.0 7.5
7.2 6.4 .. 4.2 .. 11.7c 4.5 3.7 9.8 3.9 .. 20.0 6.4 4.3b .. .. 5.8 2.9 .. .. .. 1.3 .. .. 4.4 13.1 9.8 .. .. 7.0 2.5 5.5 4.7b 6.6b .. 7.1 1.9 22.1 .. .. 4.2 .. w .. .. .. 8.0 .. .. 8.1 6.9 10.4 5.1 .. 5.2 6.6
Female % of female labor force 1990–92a 2004–07a
.. 5.2 0.2 .. .. .. .. 2.6 .. .. .. .. 25.8 19.9 b .. .. 4.6 3.5 14.0 .. 4.3b 1.5 .. .. 23.9 .. 7.8 .. .. .. .. 7.3 7.0 b 11.8b .. 6.8 .. .. .. 22.4 .. .. w .. .. .. 7.1 .. .. .. 8.4 21.7 .. .. 7.9 12.5
a. Data are for the most recent year available. b. Limited coverage. c. Data are for 2008.
58
2009 World Development Indicators
5.4 5.8 .. 13.2 .. 15.2c 2.3 4.3 12.5 6.1 .. 26.6 10.9 9.0 b .. .. 6.4 4.5 .. .. .. 1.1 .. .. 9.6 17.3 10.2 .. .. 6.6 7.1 4.9 4.5b 12.4b .. 8.1 2.4 19.0 .. .. 4.1 .. w .. .. .. 9.6 .. .. 7.4 11.5 18.4 6.0 .. 5.8 8.6
Long-term unemployment
Unemployment by educational attainment
% of total unemployment Total Male Female 2004–07a 2004–07a 2004–07a
% of total unemployment Primary Secondary Tertiary 2004–07a 2004–07a 2004–07a
.. .. .. .. .. 70.5 .. .. 70.8 .. .. .. 27.6 .. .. .. 13.0 40.8 .. .. .. .. .. .. .. .. 30.4 .. .. .. .. 24.7 10.0 b .. .. .. .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. .. 25.6 45.2
.. .. .. .. .. 79.3 .. .. 72.3 .. .. .. 23.9 .. .. .. 14.5 37.9 .. .. .. .. .. .. .. .. 27.1 .. .. .. .. 29.7 10.7b .. .. .. .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. .. 27.3 44.4
.. .. .. .. .. 82.2 .. .. 69.4 .. .. .. 30.5 .. .. .. 11.4 43.0 .. .. .. .. .. .. .. .. 39.5 .. .. .. .. 18.2 9.0 b .. .. .. .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. .. 23.1 45.5
25.8 13.7 .. 12.3 40.2 .. .. 31.0 29.2b 25.0 .. 36.2 54.8 45.4b .. .. 32.2 28.8 .. 66.5 .. 40.5 .. .. .. 79.1 52.3 .. .. 8.5 24.3 37.3 18.7b 59.1 .. .. .. 54.3 .. .. .. .. w .. .. .. 37.8 .. .. 23.4 53.4 .. 27.8 .. 35.3 41.4
66.3 54.2 .. 43.9 6.9 .. .. 25.6 65.3b 60.4 .. 56.3 23.6 22.0 b .. .. 46.0 53.2 .. 28.8 .. 45.5 .. .. .. .. 28.2 .. .. 52.2 36.0 47.7 35.5b 27.0 .. .. .. 14.2 .. .. .. .. w .. .. .. 43.6 .. .. 53.1 32.2 .. 34.5 .. 41.3 42.9
6.1 32.1 .. 40.0 2.5 .. .. 43.2 5.3b 12.5 .. 4.5 20.4 32.6b .. .. 17.1 17.9 .. 4.6 .. 0.1 .. .. .. 13.6 12.7 .. .. 39.3 21.6 14.3 45.7b 13.8 .. .. .. 23.5 .. .. .. .. w .. .. .. 13.7 .. .. 22.3 12.9 .. 32.2 .. 26.7 14.9
About the data
PEOPLE
Unemployment
2.5
Definitions
Unemployment and total employment are the broad-
generate statistics that are more comparable inter-
• Unemployment is the share of the labor force with-
est indicators of economic activity as reflected by
nationally. But the age group, geographic coverage,
out work but available for and seeking employment.
the labor market. The International Labour Organiza-
and collection methods could differ by country or
Definitions of labor force and unemployment may
tion (ILO) defines the unemployed as members of the
change over time within a country. For detailed infor-
differ by country (see About the data). • Long-term
economically active population who are without work
mation, consult the original source.
unemployment is the number of people with continu-
but available for and seeking work, including people
Women tend to be excluded from the unemploy-
ous periods of unemployment extending for a year or
who have lost their jobs or who have voluntarily left
ment count for various reasons. Women suffer more
longer, expressed as a percentage of the total unem-
work. Some unemployment is unavoidable. At any
from discrimination and from structural, social, and
ployed. • Unemployment by educational attainment
time some workers are temporarily unemployed—
cultural barriers that impede them from seeking
is the unemployed by level of educational attainment
between jobs as employers look for the right workers
work. Also, women are often responsible for the
as a percentage of the total unemployed. The levels
and workers search for better jobs. Such unemploy-
care of children and the elderly and for household
of educational attainment accord with the ISCED97
ment, often called frictional unemployment, results
affairs. They may not be available for work during
of the United Nations Educational, Scientific, and
from the normal operation of labor markets.
the short reference period, as they need to make
Cultural Organization.
Changes in unemployment over time may reflect
arrangements before starting work. Furthermore,
changes in the demand for and supply of labor; they
women are considered to be employed when they
may also refl ect changes in reporting practices.
are working part-time or in temporary jobs, despite
Paradoxically, low unemployment rates can disguise
the instability of these jobs or their active search for
substantial poverty in a country, while high unemploy-
more secure employment.
ment rates can occur in countries with a high level of
Long-term unemployment is measured by the
economic development and low rates of poverty. In
length of time that an unemployed person has been
countries without unemployment or welfare benefits
without work and looking for a job. The data in the
people eke out a living in vulnerable employment. In
table are from labor force surveys. The underlying
countries with well developed safety nets workers
assumption is that shorter periods of joblessness
can afford to wait for suitable or desirable jobs. But
are of less concern, especially when the unem-
high and sustained unemployment indicates serious
ployed are covered by unemployment benefi ts or
inefficiencies in resource allocation.
similar forms of support. The length of time that a
The ILO definition of unemployment notwithstand-
person has been unemployed is difficult to measure,
ing, reference periods, the criteria for people consid-
because the ability to recall that time diminishes as
ered to be seeking work, and the treatment of people
the period of joblessness extends. Women’s long-
temporarily laid off or seeking work for the first time
term unemployment is likely to be lower in countries
vary across countries. In many developing countries
where women constitute a large share of the unpaid
it is especially difficult to measure employment and
family workforce.
unemployment in agriculture. The timing of a survey,
Unemployment by level of educational attainment
for example, can maximize the effects of seasonal
provides insights into the relation between the edu-
unemployment in agriculture. And informal sector
cational attainment of workers and unemployment
employment is difficult to quantify where informal
and may be used to draw inferences about changes
activities are not tracked.
in employment demand. Information on educational
Data on unemployment are drawn from labor force
attainment is the best available indicator of skill
sample surveys and general household sample
levels of the labor force. Besides the limitations to
surveys, censuses, and offi cial estimates, which
comparability raised for measuring unemployment,
are generally based on information from different
the different ways of classifying the education level
sources and can be combined in many ways. Admin-
may also cause inconsistency. Education level is
istrative records, such as social insurance statistics
supposed to be classifi ed according to Interna-
and employment office statistics, are not included
tional Standard Classifi cation of Education 1997
in the table because of their limitations in cover-
(ISCED97). For more information on ISCED97, see
age. Labor force surveys generally yield the most
About the data for table 2.11.
comprehensive data because they include groups not covered in other unemployment statistics, particularly people seeking work for the first time. These
Data sources
surveys generally use a definition of unemployment
Data on unemployment are from the ILO database
that follows the international recommendations more
Key Indicators of the Labour Market, 5th edition.
closely than that used by other sources and therefore
2009 World Development Indicators
59
2.6
Children at work Survey year
Children in employment
% of children ages 7–14 in employment
% of children ages 7–14
Afghanistan Albania Algeria Angolab Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroonc Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep.c Congo, Rep Costa Ricac Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republicc Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
60
2000 2001 2004
2000 2006 2005 2006 2005 2006 2004 2004 2000 2001 2001 2000 2004 2003
2005 2000 2005 2004 2006
2005 2004 2005 2003
2005
2005
2003 2004 1994 2000 2005
Total
Male
Female
Work only
Study and work
.. 36.6 .. 30.1 12.9 .. .. .. 9.7 16.2 11.7 .. 74.4 22.0 10.6 .. 7.0 .. 50.0 37.0 52.3 15.9 .. 67.0 60.4 4.1 .. .. 4.0 39.8 30.1 5.7 45.7 .. .. .. .. 5.8 12.0 7.9 12.7 .. .. 56.0 .. .. .. 43.5 .. .. 6.0 .. 16.8 48.3 67.5 33.4
.. 41.1 .. 30.0 15.7 .. .. .. 12.0 25.7 12.1 .. 72.8 23.9 11.7 .. 9.4 .. 49.0 38.4 52.4 14.5 .. 66.5 64.4 5.1 .. .. 6.2 39.9 29.9 8.1 47.7 .. .. .. .. 9.0 14.6 11.5 17.1 .. .. 64.3 .. .. .. 33.9 .. .. 6.0 .. 23.1 47.2 67.4 37.3
.. 31.8 .. 30.1 9.8 .. .. .. 7.3 6.4 11.2 .. 76.1 20.1 9.5 .. 4.6 .. 51.0 35.7 52.1 17.4 .. 67.6 56.2 3.1 .. .. 1.8 39.8 30.2 3.5 43.6 .. .. .. .. 2.7 9.3 4.3 8.1 .. .. 47.1 .. .. .. 52.3 .. .. 5.9 .. 10.5 49.5 67.5 29.6
.. 43.1 .. 26.6 4.8 .. .. .. 4.2 37.8 0.0 .. 36.1 8.1 0.1 .. 7.2 .. 98.1 48.3 16.5 52.5 .. 54.9 49.1 3.2 .. .. 32.8 35.7 9.9 44.6 46.8 .. .. .. .. 6.2 27.0 21.0 19.5 .. .. 69.4 .. .. .. 32.1 .. .. 71.2 .. 31.3 98.6 63.7 17.7
.. 56.9 .. 73.4 95.2 .. .. .. 95.8 62.2 100.0 .. 63.9 91.9 99.9 .. 92.8 .. 1.9 51.7 83.5 47.5 .. 45.1 50.9 96.8 .. .. 67.2 64.3 90.1 55.4 53.2 .. .. .. .. 93.8 73.0 79.0 80.5 .. .. 30.6 .. .. .. 67.9 .. .. 28.8 .. 68.7 1.4 36.3 82.3
2009 World Development Indicators
Employment by economic activitya
Status in employmenta
% of children ages 7–14 in employment
% of children ages 7–14 in employment SelfUnpaid employed Wage family
Agriculture Manufacturing
.. .. .. .. .. .. .. .. .. .. .. .. .. 84.4 .. .. 60.8 .. 97.2 .. 76.1 88.2 .. .. .. 24.1 .. .. .. .. .. 40.3 .. .. .. .. .. 18.5 70.0 .. 51.0 .. .. 94.6 .. .. .. .. .. .. 78.8 .. 66.1 .. .. ..
.. .. .. .. .. .. .. .. .. .. .. .. .. 4.3 .. .. 6.6 .. 0.4 .. 5.0 2.1 .. .. .. 6.9 .. .. .. .. .. 9.5 .. .. .. .. .. 9.8 4.7 .. 12.5 .. .. 1.5 .. .. .. .. .. .. 2.8 .. 9.1 .. .. ..
Services
.. .. .. .. .. .. .. .. .. .. .. .. .. 10.1 .. .. 30.9 .. 2.2 .. 18.0 7.1 .. .. .. 66.9 .. .. .. .. .. 49.0 .. .. .. .. .. 57.5 23.7 .. 35.4 .. .. 3.7 .. .. .. .. .. .. 15.2 .. 23.5 .. .. ..
.. .. .. .. 34.2 .. .. .. .. – .. .. .. 1.2 .. .. 6.8 .. 1.3 3.9 1.5 .. .. .. .. .. .. .. 12.6 .. .. 15.8 .. .. .. .. .. 23.8 6.0 1.5 .. .. 1.7 .. .. .. .. .. .. 10.8 .. 3.4 .. .. ..
.. 1.4 .. 6.2 8.1 .. .. .. 2.1 17.0 9.2 .. .. 4.4 1.6 .. 21.5 .. 0.4 85.3 4.7 .. .. 2.0 1.8 .. .. .. 39.1 6.6 4.2 57.7 2.4 .. .. .. .. 19.5 15.8 11.4 15.3 .. .. 2.4 .. .. .. 1.1 .. .. 5.5 .. 17.8 .. 0.9 1.8
.. 93.1 .. 80.1 56.2 .. .. .. 88.9 77.8 78.8 .. .. 92.9 92.1 .. 58.0 .. 98.3 .. 90.2 .. .. 56.4 77.2 .. .. .. 48.3 76.7 84.5 26.6 88.0 .. .. .. .. 56.2d 75.5 87.4 78.4 .. .. 95.8 .. .. .. 87.3 .. .. 78.4 .. 78.9 .. 81.1 79.4
Survey year
Children in employment
% of children ages 7–14 in employment
% of children ages 7–14
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 Mexicoe Moldova Mongolia Morocco Mozambiquec Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panamac Papua New Guinea Paraguayc Peru Philippines Poland Portugal Puerto Rico
2004 2004–05 2000 2006
2002
2006 2000
2006
2000 2007
2005 2001 2006 2006
2004 2000 2005 1998–99 1996 1999 1999
2005 2006
2003 2005 2000 2001 2001
Total
Male
Female
Work only
Study and work
6.8 .. 4.2 8.9 .. 14.7 .. .. .. 1.1 .. .. 3.6 37.7 .. .. .. 5.2 .. .. .. 30.8 37.4 .. .. 11.8 25.6 40.3 .. 49.5 .. .. 8.9 33.5 12.4 13.2 1.8 .. 15.4 47.2 .. .. 10.1 47.1 .. .. .. .. 5.1 .. 15.3 24.1 13.3 .. 3.6 ..
10.4 .. 4.2 8.8 .. 17.9 .. .. .. 1.5 .. .. 4.4 40.1 .. .. .. 5.8 .. .. .. 34.2 37.8 .. .. 14.8 26.1 41.3 .. 55.0 .. .. 12.2 34.1 14.1 13.5 1.9 .. 16.2 42.2 .. .. 16.2 49.2 .. .. .. .. 7.7 .. 22.6 25.7 16.3 .. 4.6 ..
3.2 .. 4.2 9.1 .. 11.3 .. .. .. 0.6 .. .. 2.8 35.2 .. .. .. 4.6 .. .. .. 27.5 37.1 .. .. 8.6 25.1 39.4 .. 44.1 .. .. 5.6 32.8 10.7 12.8 1.7 .. 14.7 52.4 .. .. 3.9 45.0 .. .. .. .. 2.2 .. 7.7 22.3 10.0 .. 2.6 ..
48.6 .. 84.9 24.9 .. 32.4 .. .. .. 17.1 .. .. 1.6 14.1 .. .. .. 7.9 .. .. .. 17.6 45.0 .. .. 2.8 85.1 10.5 .. 59.5 .. .. 34.1 3.8 8.7 93.2 100.0 .. 9.5 35.6 .. .. 30.8 66.5 .. .. .. .. 38.4 .. 24.2 4.8 14.8 .. 3.6 ..
51.4 .. 15.2 75.1 .. 67.6 .. .. .. 82.9 .. .. 98.4 85.9 .. .. .. 92.1 .. .. .. 82.4 55.0 .. .. 97.2 14.9 89.5 .. 40.5 .. .. 65.9 96.2 91.3 6.8 0.0 .. 90.5 64.4 .. .. 69.2 33.5 .. .. .. .. 61.6 .. 75.7 95.2 85.2 .. 96.4 ..
PEOPLE
Children at work
2.6
Employment by economic activitya
Status in employmenta
% of children ages 7–14 in employment
% of children ages 7–14 in employment SelfUnpaid employed Wage family
Agriculture Manufacturing
63.4 .. 69.4 .. .. .. .. .. .. 31.3 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 94.0 .. .. .. .. .. 38.1 .. .. 60.6 .. .. 91.5 87.0 .. .. 70.5 .. .. .. .. .. 57.6 .. 60.8 72.6 64.3 .. 48.5 ..
8.3 .. 16.0 .. .. .. .. .. .. 7.7 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 1.0 .. .. .. .. .. 12.3 .. .. 8.3 .. .. 0.4 1.4 .. .. 9.7 .. .. .. .. .. 3.1 .. 6.2 2.8 4.1 .. 11.2 ..
Services
24.7 .. 12.4 .. .. .. .. .. .. 51.4 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 2.4 .. .. .. .. .. 48.0 .. .. 10.1 .. .. 8.0 11.1 .. .. 19.3 .. .. .. .. .. 38.1 .. 32.1 24.5 30.6 .. 33.3 ..
2.7 .. 7.1 .. .. .. .. .. .. 21.6 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 6.8 .. .. .. .. .. 3.7 .. .. 2.1 .. .. 0.1 4.2 .. .. 1.2 4.8 .. .. .. .. 12.4 .. 9.3 1.9 4.1 .. .. ..
19.9 .. 6.8 17.8 .. 7.0 .. .. .. 35.1 .. .. 4.0 .. .. .. .. 3.7 .. .. .. 3.6 1.7 .. .. 3.9 1.5 6.7 .. 1.6 .. .. 52.0 2.9 3.9 10.0 .. .. 4.5 3.3 .. .. 13.8 74.5 .. .. .. .. 24.9 .. 24.8 6.8 22.8 .. .. ..
2009 World Development Indicators
73.8 .. 59.3 75.8 d .. 85.3 .. .. .. 43.3 .. .. 75.0 .. .. .. .. 81.9 .. .. .. 83.3 79.3 .. .. 89.5 91.4 75.5 .. 80.4 .. .. 44.2 82.0 91.3 81.7 .. .. 95.0 92.4 .. .. 85.0 f .. .. .. .. .. 50.3f .. 65.8 91.4 73.1 .. .. ..
61
2.6
Children at work Survey year
Children in employment
% of children ages 7–14 in employment
% of children ages 7–14
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudang Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste 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 2005 2005 2005
2006 1999 1999 2000 2000
2006 2005 2001 2005 2006 2000 1999 2005–06 2005
2005 2005 2006 1999 2005 1999
Total
Male
Female
Work only
Study and work
1.4 .. 33.1 .. 18.5 6.9 62.7 .. .. .. 43.5 27.7 .. 17.0 19.1 11.2 .. .. 6.6 8.9 40.4 15.1 .. 38.7 3.9 .. 4.5 .. 38.2 17.3 .. .. .. .. 5.1 5.4 21.3 .. 13.1 47.9 14.3
1.7 .. 36.1 .. 24.4 7.2 63.6 .. .. .. 45.5 29.0 .. 20.4 21.5 11.4 .. .. 8.8 8.7 41.5 15.7 .. 39.8 5.2 .. 5.2 .. 39.8 18.0 .. .. .. .. 5.3 7.1 21.0 .. 12.4 48.9 15.3
1.1 .. 30.3 .. 12.6 6.6 61.8 .. .. .. 41.5 26.4 .. 13.4 16.8 10.9 .. .. 4.3 9.1 39.2 14.4 .. 37.4 2.8 .. 3.8 .. 36.5 16.6 .. .. .. .. 4.9 3.6 21.6 .. 14.0 46.8 13.3
20.7 .. 27.5 .. 61.9 2.1 29.9 .. .. .. 53.5 5.1 .. 5.4 55.9 14.0 .. .. 34.6 9.0 40.0 4.2 .. 29.8 12.8 .. 66.8 .. 7.7 0.1 .. .. .. .. 1.0 24.7 11.9 .. 64.3 25.9 12.0
79.3 .. 72.5 .. 38.1 97.9 70.1 .. .. .. 46.5 94.9 .. 94.6 44.1 86.0 .. .. 65.4 91.0 60.0 95.8 .. 70.2 87.2 .. 33.2 .. 92.3 99.9 .. .. .. .. 99.0 75.3 88.1 .. 35.7 74.1 88.0
Employment by economic activitya
Status in employmenta
% of children ages 7–14 in employment
% of children ages 7–14 in employment SelfUnpaid employed Wage family
Agriculture Manufacturing
97.1 .. .. .. 79.1 .. .. .. .. .. .. .. .. 71.2 .. .. .. .. .. .. 78.5 .. .. 82.9 .. .. 65.4 .. 95.5 .. .. .. .. .. .. 28.3 .. .. 92.0 95.9 ..
0.0 .. .. .. 5.0 .. .. .. .. .. .. .. .. 13.1 .. .. .. .. .. .. 0.2 .. .. 1.3 .. .. 15.9 .. 1.4 .. .. .. .. .. .. 8.0 .. .. 1.0 0.6 ..
Services
2.3 .. .. .. 14.0 .. .. .. .. .. .. .. .. 15.0 .. .. .. .. .. .. 21.3 .. .. 15.1 .. .. 18.7 .. 3.0 .. .. .. .. .. .. 61.1 .. .. 6.2 3.5 ..
4.5 .. .. .. 6.3 .. .. .. .. .. .. 7.1 .. 2.9 .. .. .. .. .. .. 0.9 .. .. 5.0 .. .. 3.7 .. 1.4 .. .. .. .. .. .. 18.9 .. .. 4.1 2.6 3.4
.. .. 2.9 .. 4.4 5.2 1.0 .. .. .. 1.6 7.1 .. 8.3 7.3 10.4 .. .. 21.5 24.2 1.0 13.5 .. 1.6 29.8 .. 34.9 .. 1.5 3.1 .. .. .. .. 3.8 25.3 5.9 .. 5.4 0.7 28.4
92.9 d .. 85.7 .. 84.1 89.4 71.1 .. .. .. 94.8 85.8 .. 88.0 81.3 85.9 .. .. 68.8 71.3 98.2d 80.0 .. 93.4 64.9 .. 61.4 .. 97.1 79.3 .. .. .. .. 78.6 54.0 91.2 .. 86.8 96.5 68.2
a. Shares may not sum to 100 percent because of a residual category not included in the table. b. Covers only Angola-secured territory. c. Covers children ages 10–14. d. Refers to family workers, regardless of whether they are paid. e. Covers children ages 12–14. f. Refers to unpaid workers, regardless of whether they are family workers. g. Covers northern Sudan only.
62
2009 World Development Indicators
About the data
PEOPLE
Children at work
2.6
Definitions
The data in the table refer to children’s work in
working for payment in cash or in kind or is involved in
• Survey year is the year in which the underlying
the sense of “economic activity”—that is, children
unpaid work, whether a child is working for someone
data were collected. • Children in employment are
in employment, which is a broader concept than
who is not a member of the household, whether a
children involved in any economic activity for at least
child labor (see ILO forthcoming for details on this
child is involved in any type of family work (on the
one hour in the reference week of the survey. • Work
distinction).
farm or in a business), and the like. The ages used
only refers to children who are employed and not
In line with the definition of economic activity
in country surveys to define child labor range from 5
attending school. • Study and work refer to chil-
adopted by the 13th International Conference of
to 17 years. The data in the table have been recalcu-
dren attending school in combination with employ-
Labour Statisticians, the threshold for classifying a
lated to present statistics for children ages 7–14.
ment. • Employment by economic activity is the
person as employed is to have been engaged at least
Although efforts are made to harmonize the defi -
distribution of children in employment by the major
one hour in any activity during the reference period
nition of employment and the questions on employ-
industrial categories (ISIC revision 2 or revision 3).
relating to the production of goods and services
ment used in survey questionnaires, significant dif-
• Agriculture corresponds to division 1 (ISIC revi-
set by the 1993 United Nations System of National
ferences remain in the survey instruments used to
sion 2) or categories A and B (ISIC revision 3) and
Accounts. Children seeking work are thus not included
collect data on children in employment and in the
includes agriculture and hunting, forestry and log-
in employment. Economic activity covers all market
sampling design underlying these surveys. Differ-
ging, and fishing. • Manufacturing corresponds to
production and certain types of nonmarket production,
ences exist not only across different household sur-
division 3 (ISIC revision 2) or category D (ISIC revi-
including production of goods for own use. It excludes
veys in the same country, but also across the same
sion 3). • Services correspond to divisions 6–9 (ISIC
unpaid household services (commonly called “house-
type of survey carried out in different countries.
revision 2) or categories G–P (ISIC revision 3) and
hold chores”)—that is, the production of domestic
Because of the differences in the underlying sur-
include wholesale and retail trade, hotels and restau-
and personal services by household members for
vey instruments and dates, estimates of working
rants, transport, financial intermediation, real estate,
consumption within their own household.
children are not fully comparable across countries.
public administration, education, health and social
The data are from household surveys conducted
Caution should be used in drawing conclusions
work, other community services, and private house-
by the International Labor Organization (ILO), the
concerning relative levels of child economic activity
hold activity. • Self-employed workers are people
United Nations Children’s Fund (UNICEF), the World
across countries or regions based on the data.
whose remuneration depends directly on the profits
Bank, and national statistical offi ces. These sur-
The table aggregates the distribution of children
derived from the goods and services they produce,
veys yield a variety of data on education, employ-
in employment by the industrial categories of the
with or without other employees, and include employ-
ment, health, expenditure, and consumption indica-
International Standard Industrial Classification
ers, own-account workers, and members of produc-
tors that relate to children’s work.
(ISIC): agriculture, manufacturing, and services.
ers cooperatives. • Wage workers (also known as
Household survey data generally include informa-
A residual category—which includes mining and
employees) are people who hold explicit (written or
tion on work type—for example, whether a child is
quarrying; electricity, gas, and water; construction;
oral) or implicit employment contracts that provide
extraterritorial organization and other inadequately
basic remuneration that does not depend directly on
defined activities—is not presented. Both ISIC revi-
the revenue of the unit for which they work. • Unpaid
sion 2 and revision 3 are used, depending solely on
family workers are people who work without pay in a
the codification applied by each country in describ-
market-oriented establishment operated by a related
ing the economic activity. The use of two different
person living in the same household.
2.6a
Children work long hours
Average work time among children ages 7–14 who study and work, 2005 (hours per week) 40
classifications does not affect the definition of the groups presented in the table. 30
20
Data sources
The table aggregates the distribution of children
Data on children at work are estimates produced
in employment by status in employment. Status in
by the Understanding Children’s Work project
employment is based on the International Classifica-
based on household survey data sets made avail-
tion of Status in Employment (1993), which shows
able by the ILO’s International Programme on the
the distribution of children in employment by three
Elimination of Child Labour under its Statistical
major categories: self-employed workers, wage work-
Monitoring Programme on Child Labour, UNICEF
ers (also known as employees), and unpaid family
under its Multiple Indicator Cluster Survey pro-
workers. A residual category—which includes those
gram, the World Bank under its Living Standards
not classifiable by status—is not presented.
Measurement Study program, and national sta-
10
0 Paraguay
Somalia
Zambia
Children in many countries work long hours,
tistical offices. Information on how the data were
often combining studying with working. In Para-
collected and some indication of their reliability
guay children work more than 30 hours a week,
can be found at www.ilo.org/public/english/
leaving very little time for studying or any other
standards/ipec/simpoc/, www.childinfo.org, and
activities.
www.worldbank.org/lsms. Detailed country statis-
Source: Understanding Children’s Work Project.
tics can be found at www.ucw-project.org.
2009 World Development Indicators
63
2.7
Poverty rates at national poverty lines Population below national poverty line
Afghanistan Albania Algeria Argentina Armenia Azerbaijan Bangladesh Belarus Benin Bolivia Bosnia and Herzegovina Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Chad Chile China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Croatia Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Gambia, The Georgia Ghana Guatemala Guinea Guinea-Bissau Haiti Honduras Hungary India Indonesia Jamaica Jordan Kazakhstan Kenya Kyrgyz Republic Lao PDR Latvia Lesotho Macedonia, FYR Madagascar Malawi Malaysia Mali
64
Survey year
Rural %
Urban %
National %
2007 2002 1988 1998 1998–99 1995 2000 2002 1999 1999 2001–02 1998 1997 1998 1998 1994 1996 1995–96 1996 1998 1995 2004–05 2005 1989 2002 2000 1998 1995–96 1995 1993–94 1995 1995–96 1998 2002 1998–99 1989 1994 2002 1987 1998–99 1993 1993–94 1996 1995 1997 2001 1994 2003 1997–98 2002 1994/95 2002 1997 1990–91 1989 1998
45.0 29.6 16.6 .. 50.8 .. 52.3 .. 33.0 80.1 19.9 51.4 .. 61.1 64.6 .. 59.6 48.6 .. 4.6 79.0 75.7 49.2 35.8 .. 45.3 69.0 23.3 64.8 .. 14.7 47.0 61.0 55.4 49.6 71.9 .. .. .. 71.2 .. 37.3 19.8 37.0 27.0 .. 47.0 57.5 41.0 11.6 68.9 25.3 76.0 .. .. 75.9
27.0 19.5 7.3 28.8 58.3 .. 35.1 .. 23.3 51.4 13.8 14.7 .. 22.4 66.5 .. 41.4 .. .. .. 48.0 61.5 .. 26.2 .. 18.2 30.0 22.5 38.9 .. 6.8 33.3 48.0 48.5 19.4 33.7 .. 52.6 .. 28.6 .. 32.4 13.6 18.7 19.7 .. 29.0 35.7 26.9 .. 36.7 .. 63.2 .. .. 30.1
42.0 25.4 12.2 .. 55.1 68.1 48.9 30.5 29.0 62.0 19.5 22.0 36.0 54.6 68.0 47.0 53.3 43.4 19.9 4.6 60.0 71.3 42.3 31.7 11.2 27.7 46.0 22.9 50.6 53.0 8.9 45.5 57.6 52.1 39.5 57.9 40.0 65.7 65.0 52.5 14.5 36.0 17.6 27.5 21.3 17.6 40.0 49.9 38.6 7.5 66.6 21.4 73.3 54.0 15.5 63.8
2009 World Development Indicators
Survey year
2005 1995 2002 2001 2001 2005 2004 2003 2002 2002–03 2001 2003 2004 2001 1998 2004 1999
2004 2004 2004 2001 1999–20 00 2002
1999–2000 2003 2003 2005–06 2000
1995 2004 1997 1999–2000 2005 2000 2002 2002 1997 2005 2002–03 2004 2002/03 2003 1999 1997–98
Poverty gap at national poverty line
Rural %
Urban %
National %
.. 24.2 30.3 .. 48.7 42.0 43.8 .. 46.0 82.2 .. 41.0 .. 52.4 .. 38.0 49.9 .. .. .. 79.0 .. .. 28.3 .. 55.7 .. .. 49.8 .. .. 45.0 63.0 52.7 39.2 74.5 .. .. 66.0 70.4 .. 30.2 .. 25.1 18.7 .. 53.0 50.8 .. 12.7 60.5 22.3 76.7 66.5 .. ..
.. 11.2 14.7 53.0 51.9 55.0 28.4 .. 29.0 53.9 .. 17.5 .. 19.2 .. 18.0 22.1 .. .. .. 55.0 .. .. 20.8 .. 34.7 .. .. 28.5 .. .. 37.0 57.0 56.2 10.8 27.1 .. .. .. 29.5 .. 24.7 .. 12.8 12.9 .. 49.0 29.8 .. .. 41.5 .. 52.1 54.9 .. ..
.. 18.5 22.6 .. 50.9 49.6 40.0 17.4 39.0 64.6 .. 21.5 12.8 46.4 .. 35.0 40.2 .. 17.0 2.8 64.0 .. .. 23.9 11.1 42.2 45.2 16.7 37.2 .. .. 44.2 61.3 54.5 28.5 56.2 .. .. .. 50.7 17.3 28.6 16.0 18.7 14.2 15.4 52.0 43.1 33.0 5.9 56.3 21.7 71.3 65.3 .. ..
Survey year
2005 1995 2002 2001 2001 2005 2003 2002 2001–02 2002–03 2001 2003 2004 1995–96 1998 1999 2004–05 2004 2004 2001 1999–2000 2002 1995 1999–2000 2003 2005–06 2000 2000 2004 1997 1999–2000 2004 2002 2002 2005 2002–03 2004 2003 1999
Rural %
Urban %
National %
.. 5.3 4.5 .. .. .. 9.8 .. 14.0 43.4 4.9 28.4 .. 17.6 .. 7.8 .. 26.3 .. .. 44.0 34.9 .. 10.8 .. 24.0 .. .. 24.2 .. 6.6 12.0 .. .. 13.5 .. .. .. .. 34.5 4.1 5.6 .. .. 4.7 4.5 .. 12.0 .. .. .. 6.5 36.1 .. .. ..
.. 2.3 1.8 28.5 .. .. 6.5 .. 8.0 23.8 2.8 17.8 .. 5.1 .. 1.2 .. .. .. .. 26.0 26.2 .. 7.0 .. 12.9 .. .. 11.1 .. 1.8 10.0 .. .. 3.1 .. .. 17.5 .. 9.1 .. 6.9 .. .. 2.9 2.0 .. 7.0 .. .. .. .. 21.4 .. .. ..
.. 4.0 3.2 .. 15.1 15.5 9.0 .. 12.0 31.2 4.6 19.6 4.2 15.3 .. 6.7 .. 27.5 5.7 .. 34.0 32.2 .. 8.6 .. 16.8 18.0 3.0 16.5 .. 3.1 12.0 25.9 .. 9.6 22.6 .. 25.7 .. 22.3 .. .. 2.9 .. 3.3 3.1 .. 10.0 8.0 1.2 .. 6.7 32.8 .. .. ..
Population below national poverty line
Survey year
Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Nepal Nicaragua Niger Nigeria Pakistan Panama Papua New Guinea Paraguaya Peru Philippines Poland Romania Russian Federation Rwanda Senegal Sierra Leone Slovak Republic Sri Lanka Swaziland Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Uganda Ukraine Uruguay Uzbekistan Venezuela, RB Vietnam Yemen, Rep. Zambia Zimbabwe
1996 1992 2002 2001 1998 1990–91 1996–97 1995–96 1998 1989–93 1985 1993 1997 1996 1990 2001 1994 1996 1995 1998 1993 1992 1989 2004 1995–96 2000–01 1999 1991 1994 2001 1987–89 1992 1990 1994 1999–2000 2000 1994 2000–01 1989 1998 1998 1998 1990–91
Rural %
Urban %
National %
65.5 .. 34.8 64.1 32.6 18.0 71.3 43.3 68.5 66.0 49.5 33.4 64.9 41.3 28.5 77.1 45.4 .. .. .. .. 40.4 .. .. 27.0 75.0 .. 40.8 .. .. .. 20.0 13.1 .. 37.4 34.9 .. 33.6 .. 45.5 45.0 83.1 35.8
30.1 .. 11.4 58.0 39.4 7.6 62.0 21.6 30.5 52.0 31.7 17.2 15.3 16.1 19.7 42.0 18.6 .. .. .. .. 23.7 .. .. 15.0 49.0 .. 31.2 .. .. .. 24.0 3.5 .. 9.6 .. 20.2 27.8 .. 9.2 30.8 56.0 3.4
50.0 10.6 20.3 62.4 35.6 13.1 69.4 41.8 47.9 63.0 43.0 28.6 37.3 37.5 20.5 54.3 32.1 14.6 25.4 31.4 51.2 33.4 82.8 16.8 25.0 69.2 74.9 38.6 9.8 39.7 32.3 21.0 7.4 28.3 33.8 31.5 .. 31.5 31.3 37.4 41.8 72.9 25.8
Survey year
2000 2004 2002 2002 1998–99 2002–03 2003–04 2001 1992–93 1998–99
2004 1997 2001 2002 2002 1999–2000 2003–04 2002 2003 2000–01 1998
1995 2002 2002–03 2003 1998 2003 1997–99 2002 2004 1995–96
PEOPLE
Poverty rates at national poverty lines
2.7
Poverty gap at national poverty line
Rural %
Urban %
National %
61.2 .. 27.9 67.2 43.4 27.2 55.3 34.6 64.3 .. 36.4 35.9 .. .. .. 72.1 36.9 .. .. .. 65.7 .. 79.0 .. 7.9 .. .. 38.7 .. .. .. .. 13.9 34.5 41.7 28.4 .. 29.8 .. 35.6 .. 78.0 48.0
25.4 .. 11.3 42.6 30.3 12.0 51.5 9.6 28.7 .. 30.4 24.2 .. .. .. 42.9 11.9 .. .. .. 14.3 .. 56.4 .. 24.7 .. .. 29.5 .. .. .. .. 3.6 22.0 12.2 .. 24.7 22.6 .. 6.6 .. 53.0 7.9
46.3 .. 17.6 48.5 36.1 19.0 54.1 30.9 45.8 .. 34.1 32.6 .. .. .. 53.1 25.1 14.8 28.9 19.6 60.3 .. 70.2 .. 22.7 .. 44.4 35.7 13.6 .. .. .. 7.6 27.0 37.7 19.5 .. 27.2 52.0 28.9 .. 68.0 34.9
Survey year
2002 2002 2002 1998–99 2002–03 2003–04 2001
1998–99 1997 1996 1990 2004 1997 2002 2002 1992 2003–04 2004 2002 2000–01 2003 1998 2001 1987–89 1992 1990 2002 2002–03 1998 1997–99 2002 1998 2004
Rural %
Urban %
National %
.. .. 12.2 .. 13.2 6.7 20.9 8.5 25.9 .. .. 7.9 32.1 13.8 10.5 28.3 10.0 .. .. .. .. 16.4 34.0 .. 5.6 .. .. .. .. .. .. 6.2 3.3 .. 12.6 .. .. .. .. 8.7 14.7 44.0 ..
.. .. 2.8 .. 9.2 2.5 19.7 2.2 8.7 .. .. 5.0 3.9 4.3 5.6 12.4 2.6 .. .. .. .. 3.1 .. .. 1.7 .. .. .. .. .. .. 7.4 0.9 .. 3.0 .. 8.6 .. .. 1.3 8.2 22.0 ..
.. .. 6.3 16.5 11.0 4.4 20.5 7.5 17.0 .. .. 7.0 16.4 12.4 6.0 18.0 6.4 .. 7.6 5.1 .. 13.9 29.0 5.5 5.1 32.9 12.7 .. 3.0 11.9 10.0 7.3 1.7 0.3 11.3 .. .. .. 24.0 6.9 13.2 36.0 ..
a. Covers Asunción metropolitan area only.
2009 World Development Indicators
65
2.7
Poverty rates at national poverty lines
About the data
Definitions
The World Bank periodically prepares poverty
Data quality
• Survey year is the year in which the underlying data
assessments of countries in which it has an active
Poverty assessments are based on surveys fielded to
were collected. • Rural population below national
program, in close collaboration with national institu-
collect, among other things, information on income
poverty line is the percentage of the rural population
tions, other development agencies, and civil society
or consumption from a sample of households. To be
living below the national rural poverty line. • Urban
groups, including poor people’s organizations. Pov-
useful for poverty estimates, surveys must be nation-
population below national poverty line is the per-
erty assessments report the extent and causes of
ally representative and include sufficient information
centage of the urban population living below the
poverty and propose strategies to reduce it. Since
to compute a comprehensive estimate of total house-
national urban poverty line. • National population
1992 the World Bank has conducted about 200 pov-
hold consumption or income (including consumption
below national poverty line is the percentage of the
erty assessments, which are the main source of the
or income from own production), from which it is pos-
country’s population living below the national poverty
poverty estimates presented in the table. Countries
sible to construct a correctly weighted distribution
line. National estimates are based on population-
report similar assessments as part of their Poverty
of consumption or income per person. There remain
weighted subgroup estimates from household sur-
Reduction Strategies.
many potential problems with household survey data,
veys. • Poverty gap at national poverty line is the
The poverty assessments are the best available
including selective nonresponse and differences in
mean shortfall from the poverty line (counting the
source of information on poverty estimates using
the menu of consumption items presented and the
nonpoor as having zero shortfall) as a percentage of
national poverty lines. They often include separate
length of the period over which respondents must
the poverty line. This measure reflects the depth of
assessments of urban and rural poverty. Data are
recall their expenditures. These issues are dis-
poverty as well as its incidence.
derived from nationally representative household
cussed in About the data for table 2.8.
surveys conducted by national statistical offices or by private agencies under the supervision of govern-
National poverty lines
ment or international agencies and obtained from
National poverty lines are used to make estimates
government statistical offices and World Bank Group
of poverty consistent with the country’s specific eco-
country departments.
nomic and social circumstances and are not intended
Some poverty assessments analyze the current
for international comparisons of poverty rates. The
poverty status of a country using the latest available
setting of national poverty lines reflects local percep-
household survey data, while others use survey data
tions of the level of consumption or income needed
for several years to analyze poverty trends. Thus,
not to be poor. The perceived boundary between
poverty estimates for more than one year might be
poor and not poor rises with the average income of
derived from a single poverty assessment. A poverty
a country and so does not provide a uniform measure
assessment might not use all available household
for comparing poverty rates across countries. Never-
surveys, or survey data might become available at
theless, national poverty estimates are clearly the
a later date even though data were collected before
appropriate measure for setting national policies for
the poverty assessment date. Thus poverty assess-
poverty reduction and for monitoring their results.
ments may not fully represent all household survey data.
Almost all the national poverty lines use a food bundle based on prevailing diets that attains pre-
Over the last 20 years there has been considerable
determined nutritional requirements for good health
expansion in the number of countries that field sur-
and normal activity levels, plus an allowance for non-
veys and in the frequency of the surveys. The quality
food spending. The rise in poverty lines with average
of their data has improved greatly as well.
income is driven more by the gradient in the non-
Data sources
food component of the poverty lines than in the food
The poverty measures are prepared by the World
Data availability
component, although there is still an appreciable
Bank’s Development Research Group, based on
The number of data sets within two years of any given
share attributable to the gradient in food poverty
data from World Bank’s country poverty assess-
year rose dramatically, from 13 between 1978 and
lines. While nutritional requirements tend to be fairly
ments and country Poverty Reduction Strategies.
1982 to 158 between 2001 and 2006. Data cover-
similar even across countries at different levels of
Summaries of poverty assessments are available
age is improving in all regions, but the Middle East
economic development, richer countries tend to use
at www.worldbank.org/povertynet, by selecting
and North Africa and Sub-Saharan Africa continue to
a more expensive food bundle—more meat and veg-
“Poverty assessments” from the left side bar.
lag. The database, maintained by a team in the World
etables, less starchy staples, and more processed
Poverty assessment documents are available at
Bank’s Development Research Group, is updated
foods generally—for attaining the same nutritional
www-wds.worldbank.org, under “By topic,” “Pov-
annually as new survey data become available, and
needs.
erty reduction,” “Poverty assessment.” Further
a major reassessment of progress against poverty is
discussion of how national poverty lines vary
made about every three years. A complete overview
across countries can be found in Ravallion, Chen,
of data availability by year and country is available at
and Sangraula’s “Dollar a Day Revisited” (2008).
http://iresearch.worldbank.org/povcalnet/.
66
2009 World Development Indicators
International poverty line in local currency
Albania Algeria Angola Argentina Armenia Azerbaijan Bangladesh Belarus Benin Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Cape Verde Central African Republic Chad Chile China Colombia Comoros Congo, Dem. Rep. Congo, Rep. Costa Rica Croatia Czech Republic Côte d’Ivoire Djibouti Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Estonia Ethiopia Gabon Gambia, The Georgia Ghana Guatemala Guinea-Bissau Guinea Guyana Haiti Honduras Hungary India Iran, Islamic Rep. Jamaica Jordan Kazakhstan
$1.25 a day
$2 a day
2005
2005
75.51 48.42b 88.13 1.69 245.24 2,170.94 31.87 949.53 343.99 23.08 3.21 1.09 4.23 1.96 0.92 303.02 558.79 2,019.12 368.12 97.72 384.33 409.46 484.20 5.11g 1,489.68 368.01 395.29 469.46 348.70 b 5.58 19.00 407.26 134.76 25.50 b 0.63 2.53 6.02b 11.04 3.44 554.69 12.93 0.98 5,594.78 5.68b 355.34 1,849.46 131.47b 24.21b 12.08b 171.90 19.50i 3,393.53 54.20 b 0.62 81.21
PEOPLE
Poverty rates at international poverty lines
2.8
International poverty line
Survey year
120.82 2002a 77.48b 1988a 141.01 2.71 2002c,d 392.38 2002a 3,473.51 2001a 50.99 2000a 1,519.25 2002a 550.38 36.93 5.14 2002d 1.74 2001a 6.77 1985–86a 3.14 2005d 1.47 2001a 484.83 1998a 894.07 1998a 3,230.60 1993–94a,f 588.99 1996a 156.35 614.93 1993a 655.14 774.72 2003d 8.17g 2002a 2,383.48 2003d 588.82 632.46 751.14 557.92b 2003d 8.92 2001a 30.39 1993d 651.62 1998a 215.61 1996a 40.79b 2003d 1.00 2005d 4.04 1999–2000a 9.62b 2003d 17.66 2002a 5.50 1999–2000a 887.50 20.69 1998a 1.57 2002a 8,951.64 1998–99a 9.08b 2002d 568.55 1993–94a 2,959.13 1994a 210.35b 1993d 38.73b 19.32b 2005d 275.03 2002a 31.20i 1993–94a 5,429.65 1998a 86.72b 2002a 0.99 2002–03a 129.93 2002a
Population below $1.25 a day %
<2 6.6 .. 9.9 15.0 6.3 57.8 e <2 .. .. 22.8 <2 35.6 7.8 2.6 70.0 86.4 48.6 51.5 .. 82.8 .. <2 28.4h 15.4 .. .. .. 5.6 <2 <2 24.1 4.8 6.1 9.8 <2 14.3 <2 55.6 .. 66.7 15.1 39.1 16.9 52.1 36.8 5.8 .. 22.2 <2 49.4h <2 <2 <2 5.2
Poverty gap at $1.25 a day %
<0.5 1.8 .. 2.9 3.1 1.1 17.3e <0.5 .. .. 12.4 <0.5 13.8 1.6 <0.5 30.2 47.3 13.8 18.9 .. 57.0 .. <0.5 8.7h 6.1 .. .. .. 2.4 <0.5 <0.5 6.7 1.6 1.5 3.2 <0.5 6.7 <0.5 16.2 .. 34.7 4.7 14.4 6.5 20.6 11.5 2.6 .. 10.2 <0.5 14.4h <0.5 <0.5 <0.5 0.9
Population below $2 a day %
8.7 23.8 .. 19.7 46.7 27.1 85.4 e <2 .. .. 34.2 <2 54.7 18.3 7.8 87.6 95.4 77.8 74.4 .. 90.7 .. 5.3 51.1h 26.3 .. .. .. 11.5 <2 <2 49.1 15.1 16.3 20.4 19.3 25.3 2.5 86.4 .. 82.0 34.2 63.3 29.8 75.7 63.8 15.0 .. 34.8 <2 81.7h 8.3 8.7 11.0 21.5
Poverty gap at $2 a day %
1.4 6.6 .. 7.4 13.6 6.8 38.7e <0.5 .. .. 18.5 <0.5 25.8 5.9 2.2 49.1 64.1 33.3 36.0 .. 68.4 .. 1.3 20.6h 10.9 .. .. .. 4.7 <0.5 <0.5 18.1 4.5 5.1 7.6 3.5 11.6 0.6 37.9 .. 50.0 12.2 28.5 12.9 37.4 26.4 5.4 .. 16.7 <0.5 35.3h 1.8 1.6 2.1 5.4
Survey year
2005a 1995a 2000a 2005c,d 2003a 2005a 2005a 2005a 2003a 2003a 2005a 2004a 1993–94a 2007d 2003a 2003a 2006a 2004a 2001a 2001a 2003a 2002–03a 2006d 2005a 2006d 2004a 2005–06a 2005a 2005d 2005a 1996d 2002a 2002a 2005d 2007d 2004–05a 2005d 2004a 2005a 2005a 2003–04a 2005a 2006a 2006d 2002–03a 2002–03a 1998d 2001d 2006d 2004a 2004–05a 2005a 2004a 2006a 2003a
Population below $1.25 a day %
Poverty gap at $1.25 a day %
Population below $2 a day %
Poverty gap at $2 a day %
<2 6.8 54.3 4.5 10.6 <2 49.6e <2 47.3 26.2 19.6 <2 31.2 5.2 <2 56.5 81.3 40.2 32.8 20.6 62.4 61.9 <2 15.9h 16.0 46.1 59.2 54.1 2.4 <2 <2 23.3 18.8 5.0 4.7 <2 11.0 <2 39.0 4.8 34.3 13.4 30.0 11.7 48.8 70.1 7.7 54.9 18.2 <2 41.6h <2 <2 <2 3.1
<0.5 1.4 29.9 1.0 1.9 <0.5 13.1e <0.5 15.7 7.0 9.7 <0.5 11.0 1.3 <0.5 20.3 36.4 11.3 10.2 5.9 28.3 25.6 <0.5 4.0h 5.7 20.8 25.3 22.8 <0.5 <0.5 <0.5 6.8 5.3 0.9 1.2 <0.5 4.8 <0.5 9.6 0.9 12.1 4.4 10.5 3.5 16.5 32.2 3.9 28.2 8.2 <0.5 10.8h <0.5 <0.5 <0.5 <0.5
7.8 23.6 70.2 11.3 43.4 <2 81.3e <2 75.3 49.5 30.3 <2 49.4 12.7 <2 81.2 93.4 68.2 57.7 40.2 81.9 83.3 2.4 36.3h 27.9 65.0 79.5 74.4 8.6 <2 <2 46.8 41.2 15.1 12.8 18.4 20.5 <2 77.5 19.6 56.7 30.4 53.6 24.3 77.9 87.2 16.8 72.1 29.7 <2 75.6h 8.0 5.8 3.5 17.2
1.4 6.4 42.3 3.6 11.3 <0.5 33.8e <0.5 33.5 18.8 15.5 <0.5 22.3 4.1 0.9 39.2 56.0 28.0 23.6 14.9 45.3 43.9 0.39 12.2h 11.9 34.2 42.4 38.8 2.3 <0.5 <0.5 17.6 14.6 4.3 4.0 3.5 8.9 <0.5 28.8 5.0 24.9 10.9 22.3 8.9 34.8 50.2 6.9 41.8 14.2 <0.5 30.4h 1.8 0.9 0.6 3.9
2009 World Development Indicators
67
2.8
Poverty rates at international poverty lines International poverty line in local currency
Kenya Kyrgyz Republic Lao PDR Latvia Lesotho Liberia Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mexico Moldova Mongolia Morocco Mozambique Namibia Nepal Nicaragua Niger Nigeria Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Romania Russian Federation Rwanda Senegal Sierra Leone Slovak Republic Slovenia South Africa Sri Lanka St. Lucia Suriname Swaziland Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine Uruguay Uzbekistan Venezuela, RB
68
$1.25 a day
$2 a day
2005
2005
40.85 65.37 16.25 26.00 4,677.02 7,483.24 0.43 0.69 4.28 6.85 0.64 1.02 2.08 3.32 29.47 47.16 945.48 1,512.76 71.15 113.84 2.64 4.23 362.10 579.36 157.08 251.33 9.56 15.30 6.03 9.65 653.12 1,044.99 6.89 11.02 14,532.12 23,251.39 6.33 10.13 33.08 52.93 14.59b 9.12b 334.16 534.66 98.23 157.17 25.89 41.42 1.22b 0.76b 3.37b 2.11b 2,659.74 4,255.59 2.07 3.31 30.22 48.36 2.69 4.31 2.15 3.44 16.74 26.78 295.93 473.49 372.81 596.49 1,745.26 2,792.42 23.53 37.66 198.25 317.20 5.71 9.14 50.05 80.08 3.80 b 2.37b 3.67b 2.29b 4.66 7.45 1.16 1.85 603.06 964.90 21.83 34.93 0.98b 0.61b 352.82 564.51 9.23b 5.77b 0.87 1.39 1.25 2.00 9,537.69b 5,961.06b 930.77 1,489.24 2.14 3.42 19.14 30.62 752.14b 470.09b 1,563.90 2,502.24
2009 World Development Indicators
International poverty line
Survey year
1997a 2002a 1997–98 2002a 1995a 2002a 2002a 2001a 1997–98d 1997d 2001a 1995–96a 2004a 2002a 2002a 2000a 1996–97a 1995–96a 2001d 1994a 1996–97a 2001–02a 2004d 2005d 2005d 2003a 2002a 2002a 2002a 1984–85a 2001a 1989–90a 1992d 2002a 1995a 1995–96a
1994–95a 2003a 1991–92a 2002a
1988d 1995a 2002a 1993d 2002a 2002a 2005c,d 2002a 2003d
Population below $1.25 a day %
19.6 34.0 49.3e <2 47.6 .. <2 <2 76.3 83.1 <2 61.2 23.4 2.8 17.1 15.5 6.3 81.3 .. 68.4 19.4 78.2 68.5 35.9 9.2 .. 9.3 8.2 22.0 <2 2.9 <2 63.3 44.2 62.8 <2 <2 21.4 16.3 .. .. 78.6 36.3 72.6 <2 .. .. <2 6.5 2.0 63.5 57.4 <2 <2 42.3 18.4
Poverty gap at $1.25 a day %
4.6 8.8 14.9e <0.5 26.7 .. <0.5 <0.5 41.4 46.0 <0.5 25.8 7.1 1.4 4.0 3.6 0.9 42.0 .. 26.7 6.7 38.6 32.1 7.9 2.7 .. 3.4 2.0 5.5 <0.5 0.8 <0.5 19.7 14.3 44.8 <0.5 <0.5 5.2 3.0 .. .. 47.7 10.3 29.7 <0.5 .. .. <0.5 1.3 <0.5 25.8 22.7 <0.5 <0.5 12.4 8.8
Population below $2 a day %
42.7 66.6 79.9e <2 61.1 .. <2 3.1 88.7 93.5 6.8 82.0 48.3 7.0 40.3 38.8 24.3 92.9 .. 88.1 37.5 91.5 86.4 73.9 18.0 .. 18.4 19.4 43.8 <2 13.0 3.7 88.4 71.3 75.0 <2 <2 39.9 46.7 .. .. 89.3 68.8 91.3 15.1 .. .. 8.6 20.4 9.6 85.7 79.8 3.4 4.5 75.6 31.7
Poverty gap at $2 a day %
14.7 24.9 34.4 e 0.6 37.3 .. <0.5 0.7 57.2 62.3 1.3 43.6 17.8 2.6 13.2 12.3 6.3 59.4 .. 46.8 14.4 56.5 49.7 26.4 6.8 .. 7.3 6.3 16.0 <0.5 3.2 0.6 41.8 31.2 54.0 <0.5 <0.5 15.0 13.7 .. .. 61.6 26.7 50.1 2.8 .. .. 1.9 5.8 2.3 44.8 40.6 0.7 0.7 30.6 14.6
Survey year
2005–06a 2004a 2002–03a 2004a 2002–03a 2007a 2004a 2003a 2005a 2004–05a,j 2004–05d 2006a 2000a 2006a 2004a 2005a 2007a 2002–03a 1993d 2003–04a 2005d 2005a 2003–04a 2004–05a 2006d 1996a 2007d 2006d 2006a 2005a 2005a 2005a 2000a 2005a 2002–03a 1996d 2004a 2000a 2002a 1995d 1999d 2000–01a 2004a 2000–01a 2004a 2001a 2006a 1992d 2000a 2005a 1998a 2005a 2005a 2006d 2003a 2006d
Population below $1.25 a day %
19.7 21.8 44.0e <2 43.4 83.7 <2 <2 67.8 73.9 <2 51.4 21.2 <2 8.1 22.4 2.5 74.7 49.1 55.1 15.8 65.9 64.4 22.6 9.5 35.8 6.5 7.9 22.6 <2 <2 <2 76.6 33.5 53.4 <2 <2 26.2 14.0 20.9 15.5 62.9 21.5 88.5 <2 52.9 38.7 4.2 2.6 2.7 24.8 51.5 <2 <2 46.3 3.5
Poverty gap at $1.25 a day %
Population below $2 a day %
Poverty gap at $2 a day %
6.1 4.4 12.1e <0.5 20.8 40.8 <0.5 <0.5 26.5 32.3 <0.5 18.8 5.7 <0.5 1.7 6.2 0.5 35.4 24.6 19.7 5.2 28.1 29.6 4.4 3.1 12.3 2.7 1.9 5.5 <0.5 <0.5 <0.5 38.2 10.8 20.3 <0.5 <0.5 8.2 2.6 7.2 5.9 29.4 5.1 46.8 <0.5 19.1 11.4 1.1 <0.5 0.9 7.0 19.1 <0.5 <0.5 15.0 1.2
39.9 51.9 76.8e <2 62.2 94.8 <2 3.2 89.6 90.4 7.8 77.1 44.1 4.8 28.9 49.0 14.0 90.0 62.2 77.6 31.8 85.6 83.9 60.3 17.8 57.4 14.2 18.5 45.0 <2 3.4 <2 90.3 60.3 76.1 <2 <2 42.9 39.7 40.6 27.2 81.0 50.8 96.6 11.5 77.5 69.3 13.5 12.8 9.0 49.6 75.6 <2 4.2 76.7 10.2
15.1 16.8 31.0e <0.5 33.0 59.5 <0.5 0.7 46.9 51.8 1.4 36.5 15.9 1.0 7.9 17.2 3.1 53.5 36.5 37.8 12.3 46.6 46.9 18.7 7.1 25.5 5.5 6.0 16.3 <0.5 0.9 <0.5 55.7 24.6 37.5 <0.5 <0.5 18.3 11.8 15.5 11.7 45.8 16.8 64.4 2.0 37.0 27.9 3.9 3.0 2.6 18.4 36.4 <0.5 0.6 33.2 3.2
International poverty line in local currency
Vietnam Yemen, Rep. Zambia
$1.25 a day
$2 a day
2005
2005
7,399.87 113.83 3,537.91
11,839.79 182.12 5,660.65
PEOPLE
2.8
Poverty rates at international poverty lines International poverty line
Survey year
2004a 1998a 2002–03a
Population below $1.25 a day %
Poverty gap at $1.25 a day %
Population below $2 a day %
Poverty gap at $2 a day %
24.2 12.9 64.6
5.1 3.0 27.1
52.5 36.3 85.1
17.9 11.1 45.8
Survey year
Population below $1.25 a day %
Poverty gap at $1.25 a day %
Population below $2 a day %
Poverty gap at $2 a day %
2006a 2005a 2004–05a
21.5 17.5 64.3
4.6 4.2 32.8
48.4 46.6 81.5
16.2 14.8 48.3
a. Expenditure based. b. PPP imputed using regression. c. Covers urban area only. d. Income based. e. Adjusted by spatial consumer price index information. f. Due to security concerns, the survey covered only 56 percent of rural villages and 65 percent of the rural population. g. PPP conversion factor based on urban prices. h. Weighted average of urban and rural estimates. i. Weighted average of urban and rural poverty lines. j. Due to change in survey design, the most recent survey is not strictly comparable with the previous one.
Regional poverty estimates and progress toward
84 percent to 16 percent, leaving 620 million fewer
$1.25 a day poverty line than had been expected
the Millennium Development Goals
people in poverty.
before the crisis.
Global poverty measured at the $1.25 a day poverty
Over the same period the poverty rate in South
Most of the people who have escaped extreme
line has been decreasing since the 1980s. The share
Asia fell from 59 percent to 40 percent (table 2.8c).
poverty remain very poor by the standards of mid-
of population living on less than $1.25 a day fell
In contrast, the poverty rate fell only slightly in Sub-
dle-income economies. The median poverty line for
10 percentage points, to 42 percent, in 1990 and
Saharan Africa—from less than 54 percent in 1981
developing countries in 2005 was $2.00 a day. The
then fell nearly 17 percentage points between 1990
to more than 58 percent in 1999 then down to
poverty rate for all developing countries measured
and 2005. The number of people living in extreme
51 percent in 2005. But the number of people living
at this line fell from nearly 70 percent in 1981 to
poverty fell from 1.9 billion in 1981 to 1.8 billion
below the poverty line has nearly doubled.
47 percent in 2005, but the number of people liv-
in 1990 to about 1.4 billion in 2005 (figure 2.8a).
Only East Asia and Pacific is consistently on track
ing on less than $2.00 a day has remained nearly
This substantial reduction in extreme poverty over
to meet the Millennium Development Goal target of
constant at 2.5 billion. The largest decrease, both
the past quarter century, however, disguises large
reducing 1990 poverty rates by half by 2015. A slight
in number and proportion, occurred in East Asia
regional differences.
acceleration over historical growth rates could lift
and Pacifi c, led by China. Elsewhere, the number of
The greatest reduction in poverty occurred in East
Latin America and the Caribbean and South Asia
people living on less than $2.00 a day increased,
Asia and Pacific, where the poverty rate declined
to the target. However, the recent slowdown in the
and the number of people living between $1.25
from 78 percent in 1981 to 17 percent in 2005 and
global economy may leave these regions and many
and $2.00 a day nearly doubled, to 1.18 billion.
the number of people living on less than $1.25 a day
countries short of the target. Preliminary estimates
In 2009 the global growth deceleration will likely
dropped more than 750 million (figure 2.8b). Much
for 2009 suggest that lower economic growth rates
leave 53 million more people below the $2 a day
of this decline was in China, where poverty fell from
will likely leave 46 million more people below the
poverty line.
While the number of people living on less than $1.25 a day has fallen, the number living on $1.25–$2.00 a day has increased 2.8a
80
2.5 People living on less than $1.25 a day, other developing regions
People living on more than $1.25 and less than $2.00 a day, all developing regions
South Asia
People living on less than $1.25 a day, East Asia & Pacific 20
0.5
Sub-Saharan Africa
60
40
1.5 1.0
2.8b
Share of population living on less than $1.25 a day, by region (%)
People living in poverty (billions) 3.0
2.0
Poverty rates have begun to fall
Middle East & North Africa
People living on less than $1.25 a day, South Asia People living on less than $1.25 a day, Sub-Saharan Africa
0 1981
1984
1987
Source: PovcalNet, World Bank.
1990
1993
1996
1999
2002
2005
East Asia & Pacific
Europe & Central Asia
Latin America & Caribbean
0 1981
1984
1987
1990
1993
1996
1999
2002
2005
Source: PovcalNet, World Bank.
2009 World Development Indicators
69
2.8
Poverty rates at international poverty lines
Regional poverty estimates Region
2.8c 1981
1984
1987
1990
1993
1996
1999
2002
2005
People living on less than 2005 PPP $1.25 a day (millions) East Asia & Pacific China Europe & Central Asia
1,072
947
822
873
845
622
635
507
316
835
720
586
683
633
443
447
363
208
7
6
5
9
20
21
24
21
17 45
Latin America & Caribbean
47
59
56
49
46
53
55
56
Middle East & North Africa
14
12
12
10
10
10
11
10
11
548
548
569
579
559
594
589
616
596
South Asia India Sub-Saharan Africa Total
420
416
428
435
444
442
447
460
456
211
241
256
295
317
355
382
390
388
1,898
1,812
1,721
1,816
1,797
1,657
1,696
1,600
1,373
Share of people living on less than 2005 PPP $1.25 a day (%) East Asia & Pacific
77.7
65.5
54.2
54.7
50.8
36.0
35.5
27.6
16.8
84.0
69.4
54.0
60.2
53.7
36.4
35.6
28.4
15.9
1.8
1.4
1.1
2.1
4.4
4.8
5.3
4.8
3.8
Latin America & Caribbean
12.9
15.3
13.7
11.3
10.1
10.9
10.9
10.7
8.2
Middle East & North Africa
7.9
6.1
5.7
4.3
4.1
4.1
4.2
3.6
3.6
59.4
55.6
54.2
51.7
46.9
47.1
44.1
43.8
40.3
China Europe & Central Asia
South Asia
59.8
55.5
53.6
51.3
49.4
46.6
44.8
43.9
41.6
Sub-Saharan Africa
India
53.4
55.8
54.5
57.6
56.9
58.8
58.4
55.0
50.9
Total
52.2
47.0
42.1
42.0
39.5
34.7
33.9
30.7
25.3
People living on less than 2005 PPP $2.00 a day (millions) East Asia & Pacific
1,278
1,280
1,238
1,273
1,262
1,108
1,105
954
728
972
963
907
961
926
792
770
655
473
35
28
25
31
47
55
66
55
41
Latin America & Caribbean
89
109
102
95
95
106
110
113
94
Middle East & North Africa
46
43
47
44
48
52
51
50
51
799
836
881
926
950
1,008
1,030
1,083
1,091
609
635
669
702
735
757
783
813
827
291
325
348
390
423
471
508
535
555
2,538
2,622
2,642
2,760
2,825
2,800
2,870
2,791
2,560
China Europe & Central Asia
South Asia India Sub-Saharan Africa Total
Share of people living on less than 2005 PPP $2.00 a day (%) East Asia & Pacific China Europe & Central Asia
92.6
88.5
81.5
79.8
75.8
64.1
61.8
51.9
38.6
97.8
92.9
83.7
84.6
78.6
65.0
61.4
51.1
36.3
8.7
6.8
5.9
7.1
10.8
12.4
14.9
12.5
9.2
Latin America & Caribbean
24.6
28.1
24.9
21.9
20.7
22.0
21.8
21.5
17.1
Middle East & North Africa
26.7
23.0
22.7
19.7
19.8
20.2
18.9
17.6
16.9
South Asia
86.5
84.8
83.9
82.7
79.7
79.8
77.2
77.0
73.9
86.6
84.8
83.8
82.6
81.7
79.8
78.4
77.5
75.6
Sub-Saharan Africa
73.8
75.5
74.0
76.0
75.9
77.9
77.6
75.6
72.9
Total
69.9
68.1
64.7
63.8
62.0
58.6
57.4
53.6
47.3
India
Source: World Bank PovcalNet.
70
2009 World Development Indicators
PEOPLE
Poverty rates at international poverty lines
2.8
About the data The World Bank produced its first global poverty esti-
The statistics reported here are based on con-
PPP exchange rates are used to estimate global
mates for developing countries for World Development
sumption data or, when unavailable, on income
poverty, because they take into account the local
Report 1990: Poverty using household survey data for
surveys. Analysis of some 20 countries for which
prices of goods and services not traded internation-
22 countries (Ravallion, Datt, and van de Walle 1991).
income and consumption expenditure data were
ally. But PPP rates were designed for comparing
Since then there has been considerable expansion in
both available from the same surveys found income
aggregates from national accounts, not for mak-
the number of countries that field household income
to yield a higher mean than consumption but also
ing international poverty comparisons. As a result,
and expenditure surveys. The World Bank’s poverty
higher inequality. When poverty measures based on
there is no certainty that an international poverty line
monitoring database now includes more than 600
consumption and income were compared, the two
measures the same degree of need or deprivation
surveys representing 115 developing countries. More
effects roughly cancelled each other out: there was
across countries. So called poverty PPPs, designed
than 1.2 million randomly sampled households were
no significant statistical difference.
to compare the consumption of the poorest people in the world, might provide a better basis for com-
interviewed in these surveys, representing 96 percent of the population of developing countries.
International poverty lines
parison of poverty across countries. Work on these
International comparisons of poverty estimates entail
measures is ongoing.
Data availability
both conceptual and practical problems. Countries
The number of data sets within two years of any given
have different definitions of poverty, and consistent
year rose dramatically, from 13 between 1978 and
comparisons across countries can be difficult. Local
1982 to 158 between 2001 and 2006. Data cover-
poverty lines tend to have higher purchasing power in
age is improving in all regions, but the Middle East
rich countries, where more generous standards are
and North Africa and Sub-Saharan Africa continue to
used, than in poor countries.
Definitions • International poverty line in local currency is the international poverty lines of $1.25 and $2.00 a day in 2005 prices, converted to local currency using the PPP conversion factors estimated by the International Comparison Program. • Survey year is the year
lag. The database, maintained by a team in the World
Poverty measures based on an international pov-
Bank’s Development Research Group, is updated
erty line attempt to hold the real value of the poverty
annually as new survey data become available, and
line constant across countries, as is done when mak-
a day are the percentages of the population living
a major reassessment of progress against poverty is
ing comparisons over time. Since World Development
on less than $1.25 a day and $2.00 a day at 2005
made about every three years. A complete overview
Report 1990 the World Bank has aimed to apply a
international prices. As a result of revisions in PPP
of data availability by year and country is available at
common standard in measuring extreme poverty,
exchange rates, poverty rates for individual countries
http://iresearch.worldbank.org/povcalnet/.
anchored to what poverty means in the world’s poor-
cannot be compared with poverty rates reported in
est countries. The welfare of people living in different
earlier editions. • Poverty gap is the mean shortfall
Data quality
countries can be measured on a common scale by
from the poverty line (counting the nonpoor as having
Besides the frequency and timeliness of survey data,
adjusting for differences in the purchasing power of
zero shortfall), expressed as a percentage of the pov-
other data quality issues arise in measuring house-
currencies. The commonly used $1 a day standard,
hold living standards. The surveys ask detailed ques-
measured in 1985 international prices and adjusted
tions on sources of income and how it was spent,
to local currency using purchasing power parities
which must be carefully recorded by trained person-
(PPPs), was chosen for World Development Report
nel. Income is generally more difficult to measure
1990 because it was typical of the poverty lines in
accurately, and consumption comes closer to the
low-income countries at the time.
in which the underlying data were collected. • Population below $1.25 a day and population below $2
erty line. This measure reflects the depth of poverty as well as its incidence.
Data sources
notion of living standards. And income can vary over
Early editions of World Development Indicators
time even if living standards do not. But consumption
used PPPs from the Penn World Tables to convert
The poverty measures are prepared by the World
data are not always available: the latest estimates
values in local currency to equivalent purchasing
Bank’s Development Research Group. The interna-
reported here use consumption for about two-thirds
power measured in U.S dollars. Later editions used
tional poverty lines are based on nationally repre-
of countries.
1993 consumption PPP estimates produced by the
sentative primary household surveys conducted by
However, even similar surveys may not be strictly
World Bank. International poverty lines were recently
national statistical offices or by private agencies
comparable because of differences in timing or in the
revised using the new data on PPPs compiled in
under the supervision of government or interna-
quality and training of enumerators. Comparisons
the 2005 round of the International Comparison
tional agencies and obtained from government
of countries at different levels of development also
Program, along with data from an expanded set of
statistical offices and World Bank Group country
pose a potential problem because of differences
household income and expenditure surveys. The new
departments. The World Bank Group has prepared
in the relative importance of the consumption of
extreme poverty line is set at $1.25 a day in 2005
an annual review of its poverty work since 1993.
nonmarket goods. The local market value of all con-
PPP terms, which represents the mean of the poverty
For details on data sources and methods used in
sumption in kind (including own production, particu-
lines found in the poorest 15 countries ranked by per
deriving the World Bank’s latest estimates, and fur-
larly important in underdeveloped rural economies)
capita consumption. The new poverty line maintains
ther discussion of the results, see Shaohua Chen
should be included in total consumption expenditure,
the same standard for extreme poverty—the poverty
and Martin Ravallion’s “The Developing World Is
but may not be. Most survey data now include valu-
line typical of the poorest countries in the world—but
Poorer Than We Thought, but No Less Successful
ations for consumption or income from own produc-
updates it using the latest information on the cost of
in the Fight against Poverty?” (2008).
tion, but valuation methods vary.
living in developing countries.
2009 World Development Indicators
71
2.9
Distribution of income or consumption Survey year
Afghanistan Albania Algeria Angola Argentinac 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
72
2005b 1995b 2000 b 2005d 2003b 1994d 2000 d 2005b 2005b 2005b 2000 d 2003b 2005b 2004b 1993–95b 2007d 2003b 2003b 2006b 2007b 2001b 2000 d 2003b 2002–03b 2006d 2005d 1996d 2006d 2005–06b 2005b 2005d 2002b 2005b 1996d 1997d 2005d 2007d 2004–05b 2005d 2004b 2005b 2000 d 1995d 2005b 2003b 2005b 2000 d 2006b 2000 d 2006d 2003b 2002b 2001d
2009 World Development Indicators
Gini index
.. 33.0 35.3 58.6 50.0 33.8 35.2 29.1 16.8 31.0 27.9 33.0 38.6 58.2 35.8 61.0 55.0 29.2 39.6 33.3 40.7 44.6 32.6 43.6 39.8 52.0 41.5 43.4 58.5 44.4 47.3 47.2 48.4 29.0 .. 25.8 24.7 50.0 54.4 32.1 49.7 .. 36.0 29.8 26.9 32.7 41.5 47.3 40.8 28.3 42.8 34.3 53.7 43.3 35.5 59.5
Percentage share of income or consumptiona
Lowest 10%
Lowest 20%
Second 20%
Third 20%
Fourth 20%
Highest 20%
Highest 10%
.. 3.2 2.8 0.6 1.2 3.7 2.0 3.3 6.1 4.3 3.6 3.4 2.9 0.5 2.8 1.3 1.1 3.5 3.0 4.1 3.0 2.4 2.6 2.1 2.6 1.6 2.4 2.0 0.8 2.3 2.1 1.5 2.0 3.6 .. 4.3 2.6 1.5 1.2 3.9 1.0 .. 2.7 4.1 4.0 2.8 2.5 2.0 1.9 3.2 1.9 2.5 1.3 2.4 2.9 0.9
.. 7.8 6.9 2.0 3.4 8.6 5.9 8.6 13.3 9.4 8.8 8.5 6.9 1.8 6.9 3.1 3.0 8.7 7.0 9.0 7.1 5.6 7.2 5.2 6.3 4.1 5.7 5.3 2.3 5.5 5.0 4.2 5.0 8.7 .. 10.2 8.3 4.0 3.4 9.0 3.3 .. 6.8 9.3 9.6 7.2 6.1 4.8 5.4 8.5 5.2 6.7 3.4 5.8 7.2 2.5
.. 12.2 11.5 5.7 7.8 12.3 12.0 13.3 16.2 12.6 13.6 13.0 10.9 5.9 11.5 5.8 6.9 13.5 10.6 11.9 10.6 9.3 12.7 9.4 10.4 7.7 9.8 9.4 6.0 9.2 8.4 8.6 8.7 13.3 .. 14.3 14.7 8.0 7.2 12.6 8.1 .. 11.6 13.2 14.1 12.6 10.1 8.6 10.4 13.7 9.8 11.9 7.2 9.6 11.6 5.9
.. 16.6 16.3 10.8 13.3 15.7 17.2 17.4 18.7 16.1 17.8 16.3 15.1 11.4 16.2 9.6 11.8 17.4 14.7 15.4 14.0 13.7 17.2 14.3 15.0 12.2 14.7 13.9 11.0 13.8 13.0 13.9 12.9 17.5 .. 17.5 18.2 12.9 11.8 16.1 13.6 .. 16.2 16.8 17.5 17.2 14.6 13.2 15.4 17.8 14.8 16.8 12.0 14.1 16.0 10.5
.. 22.6 22.8 19.7 21.6 20.7 23.6 22.9 21.7 21.1 23.1 20.8 21.2 20.2 22.6 16.4 19.6 22.3 20.6 21.0 19.6 20.5 23.0 21.7 21.8 19.3 22.0 20.7 19.1 20.9 20.5 21.7 19.3 22.8 .. 21.7 22.9 20.6 19.2 20.9 21.6 .. 22.5 21.4 22.1 22.8 21.2 20.6 22.4 23.1 21.9 23.0 19.5 20.8 22.1 18.1
.. 40.9 42.4 61.9 53.9 42.8 41.3 37.8 30.2 40.8 36.7 41.4 45.9 60.7 42.8 65.0 58.7 38.1 47.1 42.8 48.8 50.9 39.9 49.4 46.6 56.8 47.8 50.7 61.6 50.6 53.1 51.8 54.1 37.7 .. 36.2 35.8 54.5 58.5 41.5 53.4 .. 43.0 39.4 36.7 40.2 47.9 52.8 46.4 36.9 48.3 41.5 57.8 49.7 43.0 63.0
.. 25.9 26.9 44.7 37.3 28.9 25.4 23.0 17.5 26.6 22.0 28.1 31.0 44.1 27.4 51.2 43.0 23.8 32.4 28.0 34.2 35.5 24.8 33.0 30.8 41.7 31.4 34.9 45.9 34.7 37.1 35.5 39.6 23.1 .. 22.7 21.3 38.7 43.3 27.6 37.0 .. 27.7 25.6 22.6 25.1 32.7 36.9 30.6 22.1 32.5 26.0 42.4 34.4 28.0 47.8
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 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
2006d 2004b 2004–05b 2005b 2005b 2000 d 2001d 2000 d 2004b 1993d 2006b 2003b 2005b 1998d 2004b 2002–03b 2004b 2003b 2007b 2004b 2003b 2005b 2004–05b 2004 d 2006b 2000 b 2006b 2004b 2005b 2007b 2002–03b 1993d 2003–04b 1999d 1997d 2005d 2005b 2003–04b 2000 d 2004–05b 2006d 1996b 2007d 2006d 2006b 2005b 1997d
Gini index
55.3 30.0 36.8 39.4 38.3 .. 34.3 39.2 36.0 45.5 24.9 37.7 33.9 47.7 .. 31.6 .. 32.9 32.6 35.7 .. 52.5 52.6 .. 35.8 39.0 47.2 39.0 37.9 39.0 39.0 .. 48.1 35.6 33.0 40.9 47.1 .. 74.3 47.3 30.9 36.2 52.3 43.9 42.9 25.8 .. 31.2 54.9 50.9 53.2 49.6 44.0 34.9 38.5 ..
PEOPLE
Distribution of income or consumption
2.9
Percentage share of income or consumptiona
Lowest 10%
Lowest 20%
Second 20%
Third 20%
Fourth 20%
Highest 20%
Highest 10%
0.7 3.5 3.6 3.0 2.6 .. 2.9 2.1 2.3 2.1 4.8 3.0 3.1 1.8 .. 2.9 .. 3.6 3.7 2.7 .. 1.0 2.4 .. 2.7 2.4 2.6 2.9 2.6 2.7 2.5 .. 1.8 3.0 2.9 2.7 2.1 .. 0.6 2.7 2.5 2.2 1.4 2.3 2.0 3.9 .. 3.9 0.8 1.9 1.1 1.5 2.4 3.0 2.0 ..
2.5 8.6 8.1 7.1 6.4 .. 7.4 5.7 6.5 5.2 10.6 7.2 7.4 4.7 .. 7.9 .. 8.1 8.5 6.8 .. 3.0 6.4 .. 6.8 6.1 6.2 7.0 6.4 6.5 6.2 .. 4.6 7.3 7.2 6.5 5.4 .. 1.5 6.1 7.6 6.4 3.8 5.9 5.1 9.6 .. 9.1 2.5 4.5 3.4 3.9 5.6 7.3 5.8 ..
6.7 13.1 11.3 10.7 10.9 .. 12.3 10.5 12.0 9.0 14.2 11.1 11.9 8.8 .. 13.6 .. 12.0 12.3 11.6 .. 7.2 11.4 .. 11.5 10.6 9.6 10.8 10.8 10.7 10.5 .. 8.6 11.6 12.2 10.5 9.2 .. 2.8 8.9 13.2 11.4 7.7 9.8 9.7 14.0 .. 12.8 6.6 7.7 7.6 8.0 9.1 11.7 11.0 ..
12.1 17.1 14.9 14.4 15.6 .. 16.3 15.9 16.8 13.8 17.6 15.2 16.6 13.3 .. 18.0 .. 16.2 16.2 16.3 .. 12.5 15.7 .. 16.3 15.6 13.1 14.9 15.8 15.2 15.4 .. 13.2 16.0 17.1 14.5 13.1 .. 5.5 12.5 17.2 15.8 12.3 13.9 14.7 17.2 .. 16.3 12.1 12.1 12.2 13.2 13.7 16.2 15.5 ..
20.4 22.5 20.4 20.5 22.2 .. 21.9 23.0 22.8 20.9 22.0 21.1 23.0 20.3 .. 23.1 .. 22.3 21.6 22.6 .. 21.0 21.6 .. 22.7 22.5 17.7 20.9 22.8 21.6 22.3 .. 20.3 22.0 23.4 20.6 19.0 .. 12.0 18.4 23.3 22.6 19.4 20.1 21.9 22.0 .. 21.3 20.8 19.3 19.4 21.0 21.2 22.4 21.9 ..
58.4 38.7 45.3 47.3 45.0 .. 42.0 44.9 42.0 51.2 35.7 45.4 41.3 53.0 .. 37.5 .. 41.4 41.4 42.7 .. 56.4 45.0 .. 42.8 45.2 53.5 46.4 44.4 46.0 45.7 .. 53.3 43.1 40.2 47.9 53.3 .. 78.3 54.2 38.7 43.8 56.9 50.3 48.6 37.2 .. 40.5 58.0 56.4 57.4 54.0 50.4 42.4 45.9 ..
42.2 24.1 31.1 32.3 29.6 .. 27.2 28.8 26.8 35.6 21.7 30.7 25.9 37.8 .. 22.5 .. 25.9 27.0 27.4 .. 39.4 30.1 .. 27.4 29.5 41.5 31.7 28.5 30.5 29.6 .. 37.9 28.2 24.8 33.2 39.2 .. 65.0 40.4 22.9 27.8 41.8 35.7 32.4 23.4 .. 26.5 41.4 40.9 42.3 37.9 33.9 27.2 29.8 ..
2009 World Development Indicators
73
2.9
Distribution of income or consumption Survey year
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbiae Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe
2005b 2005b 2000 b 2005b 2003b 2003b 1998d 1996d 2004b 2000 b 2000 d 2002b 2001b 2000 d 2000d 2004b 2000–01b 2004b 2001b 2006b 1992d 2000 b 2005b 1998b 2005b 2005b 1999d 2000d 2006d 2003b 2006d 2006b 2005b 2004–05b 1995b
Gini index
31.5 37.5 46.7 .. 39.2 30.0 42.5 42.5 25.8 31.2 .. 57.8 34.7 41.1 .. 50.7 25.0 33.7 .. 33.6 34.6 42.5 39.5 34.4 40.3 40.8 43.2 40.8 42.6 28.2 .. 36.0 40.8 46.2 36.7 43.4 37.8 .. 37.7 50.7 50.1
Percentage share of income or consumptiona
Lowest 10%
Lowest 20%
Second 20%
Third 20%
Fourth 20%
Highest 20%
Highest 10%
3.3 2.6 2.3 .. 2.5 3.4 2.6 1.9 3.1 3.4 .. 1.3 2.6 2.9 .. 1.8 3.6 2.9 .. 3.2 3.1 2.6 2.9 3.3 2.1 2.4 1.9 2.5 2.6 3.8 .. 2.1 1.9 1.7 2.9 1.7 3.1 .. 2.9 1.3 1.8
8.2 6.4 5.4 .. 6.2 8.3 6.1 5.0 8.8 8.2 .. 3.1 7.0 6.8 .. 4.5 9.1 7.6 .. 7.7 7.3 6.1 6.7 7.6 5.5 5.9 5.2 6.0 6.1 9.0 .. 6.1 5.4 4.5 7.1 4.9 7.1 .. 7.2 3.6 4.6
12.8 11.0 9.0 .. 10.6 13.0 9.7 9.4 14.9 12.8 .. 5.6 12.1 10.4 .. 8.0 14.0 12.2 .. 12.0 11.8 9.8 10.4 11.7 10.3 10.2 9.8 10.2 9.8 13.4 .. 11.4 10.7 8.7 11.5 9.6 10.8 .. 11.3 7.8 8.1
16.8 15.9 13.2 .. 15.3 17.3 14.0 14.6 18.6 17.0 .. 9.9 16.4 14.4 .. 12.3 17.6 16.3 .. 16.4 16.3 14.2 14.8 16.1 15.5 14.9 14.6 14.9 14.1 17.6 .. 16.0 15.7 14.0 15.7 14.8 15.2 .. 15.3 12.8 12.2
22.3 22.7 19.6 .. 22.0 23.0 20.9 22.0 22.9 22.6 .. 18.8 22.5 20.5 .. 19.4 22.7 22.6 .. 22.4 22.3 21.0 21.3 22.2 22.7 21.8 21.6 21.7 20.7 22.9 .. 22.5 22.4 21.8 21.5 22.1 21.6 .. 21.0 20.6 19.3
39.9 44.1 52.8 .. 45.9 38.4 49.3 49.0 34.8 39.4 .. 62.7 42.0 48.0 .. 55.9 36.6 41.3 .. 41.4 42.3 49.0 46.8 42.4 45.9 47.2 48.8 47.2 49.3 37.2 .. 44.0 45.8 51.1 44.2 48.6 45.4 .. 45.3 55.2 55.7
25.3 28.4 38.2 .. 30.1 23.4 33.6 32.8 20.8 24.6 .. 44.9 26.6 33.3 .. 40.8 22.2 25.9 .. 26.4 27.0 33.7 31.3 27.1 29.9 31.6 33.2 31.8 34.1 22.5 .. 28.5 29.9 34.8 29.5 32.7 29.8 .. 30.8 38.9 40.3
a. Percentage shares by quintile may not sum to 100 percent because of rounding. b. Refers to expenditure shares by percentiles of population, ranked by per capita expenditure. c. Urban data. d. Refers to income shares by percentiles of population, ranked by per capita income. e. Includes Montenegro.
74
2009 World Development Indicators
About the data
PEOPLE
Distribution of income or consumption
2.9
Definitions
Inequality in the distribution of income is reflected
but achieving strict comparability is still impossible
• Survey year is the year in which the underlying
in the percentage shares of income or consumption
(see About the data for tables 2.7 and 2.8).
data were collected. • Gini index measures the
accruing to portions of the population ranked by
Two sources of noncomparability should be noted
extent to which the distribution of income (or con-
income or consumption levels. The portions ranked
in particular. First, the surveys can differ in many
sumption expenditure) among individuals or house-
lowest by personal income receive the smallest
respects, including whether they use income or con-
holds within an economy deviates from a perfectly
shares of total income. The Gini index provides a con-
sumption expenditure as the living standard indi-
equal distribution. A Lorenz curve plots the cumula-
venient summary measure of the degree of inequal-
cator. The distribution of income is typically more
tive percentages of total income received against the
ity. Data on the distribution of income or consump-
unequal than the distribution of consumption. In
cumulative number of recipients, starting with the
tion come from nationally representative household
addition, the definitions of income used differ more
poorest individual. The Gini index measures the area
surveys. Where the original data from the house-
often among surveys. Consumption is usually a
between the Lorenz curve and a hypothetical line
hold survey were available, they have been used to
much better welfare indicator, particularly in devel-
of absolute equality, expressed as a percentage of
directly calculate the income or consumption shares
oping countries. Second, households differ in size
the maximum area under the line. Thus a Gini index
by quintile. Otherwise, shares have been estimated
(number of members) and in the extent of income
of 0 represents perfect equality, while an index of
from the best available grouped data.
sharing among members. And individuals differ in
100 implies perfect inequality. • Percentage share
The distribution data have been adjusted for
age and consumption needs. Differences among
of income or consumption is the share of total
household size, providing a more consistent mea-
countries in these respects may bias comparisons
income or consumption that accrues to subgroups of
sure of per capita income or consumption. No adjust-
of distribution.
population indicated by deciles or quintiles.
ment has been made for spatial differences in cost
World Bank staff have made an effort to ensure
of living within countries, because the data needed
that the data are as comparable as possible. Wher-
for such calculations are generally unavailable. For
ever possible, consumption has been used rather
further details on the estimation method for low- and
than income. Income distribution and Gini indexes for
middle-income economies, see Ravallion and Chen
high-income economies are calculated directly from
(1996).
the Luxembourg Income Study database, using an
Because the underlying household surveys differ in method and type of data collected, the distribu-
estimation method consistent with that applied for developing countries.
tion data are not strictly comparable across countries. These problems are diminishing as survey methods improve and become more standardized, The Gini coefficient and ratio of income or consumption of the richest quintile to the poorest quintiles are closely correlated
2.9a
Gini coefficient (%) 80 70 60 50 40 30 20 10 0 0
10
20
30
40
50
60
Ratio of income or consumption of richest quintile to poorest quintile
Data sources
There are many ways to measure income or consumption inequality. The Gini coefficient shows inequal-
Data on distribution are compiled by the World
ity over the entire population; the ratio of income or consumption of the richest quintile to the poorest
Bank’s Development Research Group using pri-
quintiles shows differences only at the tails of the population distribution. Both measures are closely
mary household survey data obtained from govern-
correlated and provide similar information. At low levels of inequality the Gini coefficient is a more sensi-
ment statistical agencies and World Bank country
tive measure, but above a Gini value of 45–55 percent the inequality ratio rises faster.
departments. Data for high-income economies are
Source: World Development Indicators data files.
from the Luxembourg Income Study database.
2009 World Development Indicators
75
2.10
Assessing vulnerability and security Youth unemployment
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
76
Female-headed households
Male % of male labor force ages 15–24
Female % of female labor force ages 15–24
% of total
2003–05a
2003–05a
2004–07a
.. .. 43 .. 22b .. 11b 11 .. 7 .. 21 .. .. .. .. 14b 23 .. .. .. .. 14b .. .. 15 .. 14 12 .. .. 11 .. 21c .. 19 6 .. 12b .. 13 .. 16 4 21 21b .. .. 27 16 .. 18 .. .. .. ..
.. .. 46 .. 28b .. 11b 10 .. 6 .. 19 .. .. .. .. 23b 21 .. .. .. .. 11b .. .. 21 .. 8 19 .. .. 22 .. 28 c .. 19 10 .. 21b .. 9 .. 15 11 19 25b .. .. 31 14 .. 35 .. .. .. ..
.. .. .. .. .. 36 .. .. 25 10 54 .. 23 .. .. .. .. .. .. .. 24 24 .. .. 20 .. .. .. 19 21 23 .. .. .. 46 .. .. 35 .. 12 .. .. .. 23 .. .. .. .. .. .. .. .. .. 17 .. 44
2009 World Development Indicators
Pension contributors
Year
2004 2002 2004 2002 2005 2005 2007 2004 1992 2005 1996 2002 2004 2004 1994 1993 1993 1993 2005 2004 1990 2003 2005 2006 1992 2004 1997 2007 2007 2007 2000 2004 2004 2005 2004 2005 2005 1995 2003 2004 2005 2004 2005 2005 1993 2004
Public expenditure on pensions
% of labor force
% of workingage population
.. 48.9 36.7 .. 35.0 64.4 92.6 96.4 36.8 2.8 97.0 94.2 4.8 10.1 36.0 .. 52.6 64.0 3.1 3.3 .. 13.7 90.5 1.5 1.1 58.0 20.5 .. 24.5 .. 5.8 55.3 9.3 75.2 .. 84.5 94.4 31.0 27.0 55.5 29.8 .. 95.2 .. 88.7 89.9 15.0 3.8 29.9 88.2 9.1 85.2 24.0 1.5 1.9 ..
.. 33.0 22.1 .. 25.9 48.3 69.6 68.7 30.2 2.1 94.0 61.6 .. 7.8 27.0 .. 39.1 63.0 3.0 3.0 .. 11.5 71.4 1.3 1.0 35.2 17.2 .. 18.8 .. 5.6 37.6 9.1 51.0 .. 67.3 86.9 20.7 20.8 27.7 19.7 .. 68.6 .. 67.2 61.4 14.0 2.9 22.7 65.5 7.1 58.5 18.0 1.8 1.5 ..
Year
2005 2005 2002 2007 2004 2004 2005 2000 2001 2002 2003 2006 2000 2004 2004 2005 1992 1991 2001 2004 2004 1997 2001 1996 2006 2004 2006 1997 2007 1992 2005 2005 2000 2002 2004 2006 2001 2003 2007 2005 2005
2004 2005 2002 2005 2005 2005
% of GDP
0.5 5.4 3.2 .. 8.0 3.4 4.9 14.7 3.3 0.5 12.1 11.0 1.5 4.5 8.8 .. 12.6 8.9 0.3 0.2 .. 0.8 4.8 0.8 0.1 2.9 2.7 .. 2.7 .. 0.9 2.4 0.3 11.3 12.6 9.4 8.5 0.8 2.5 4.1 1.9 0.3 6.0 0.3 8.0 14.0 .. .. 3.0 12.6 1.3 12.0 1.0 .. 2.1 ..
Year
2000 2007
2006 2002
2004
2006
2005 2005
2007
2003
Average pension % of average wage
.. .. .. .. 43.8 20.3 .. .. 24.3 .. 41.6 .. .. .. .. .. .. 42.9 .. .. .. .. .. .. .. 53.5 .. .. .. .. .. .. .. 32.4 .. 40.7 .. .. .. .. .. .. 35.4 .. .. .. .. .. 13.0 .. .. .. .. .. .. ..
Youth unemployment
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
Female-headed households
Pension contributors
Male % of male labor force ages 15–24
Female % of female labor force ages 15–24
% of total
2003–05a
2003–05a
2004–07a
Year
5b 20 10 b 25 20 .. 9 17 22 22 10 b .. 13 .. .. 12 .. 14 .. 12 .. .. .. .. 16 63 7 .. .. .. .. 21 6 19 20 16 .. .. .. .. 10 9b 11 .. .. 13 .. 11 19 .. 12b 21b 15 37 14 25b
11b 19 11b 34 32 .. 7 19 27 36 7b .. 16 .. .. 9 .. 18 .. 14 .. .. .. .. 15 62 7 .. .. .. .. 34 7 18 21 14 .. .. .. .. 10 10 b 16 .. .. 12 .. 15 30 .. 21b 21b 19 39 19 21b
26 .. 14 .. .. .. .. .. .. 41 .. .. .. .. .. .. .. 25 .. .. .. 37 31 .. .. 8 22 25 .. 12 .. .. .. 34 17 17 .. .. .. 23 .. .. .. 19 .. .. .. 10 .. .. .. 22 .. .. .. ..
2006 2007 2004 2002 2001 2005 1992 2005 2004 2005 2004 2004 2005 2005 2006 2003 2003 2005 2004 2004 2000 1993 2000 1990 1995 2000 2002 2000 2002 2003 1995
2003 2005 2005 2006 2005 2005 2004 1998 2004 2003 2007 2005 2005
2.10
Public expenditure on pensions
% of labor force
% of workingage population
16.1 93.0 9.0 15.5 35.1 .. 88.0 82.0 92.4 17.4 95.3 32.2 33.8 8.0 .. 78.0 .. 42.2 .. 92.4 33.1 5.7 .. 65.5 79.7 63.8 5.4 .. 65.0 2.5 5.0 51.4 34.5 60.6 61.4 22.4 2.0 .. .. 2.1 90.3 .. 17.9 1.3 1.7 90.8 .. 6.4 51.6 .. 11.6 16.3 20.8 84.9 91.4 ..
12.4 62.6 5.7 11.3 20.0 .. 63.9 63.0 58.4 12.6 75.0 18.6 26.4 6.7 .. 55.0 .. 28.9 .. 66.5 19.9 3.6 .. 38.1 56.0 38.9 4.8 .. .. 2.0 4.0 33.6 22.7 43.1 49.1 12.8 2.1 .. .. 1.4 70.4 .. 11.5 1.2 1.2 75.7 .. 4.0 40.7 .. 9.1 12.3 15.5 54.5 71.9 ..
Year
1994 2005 2007 2000 2003 1996 2005 1996 2005 2001 2004 2003 2005 1990 2006 2002 2003
2001 2003 2006 1990 1999 1991 1992 1999 2005 2003 2002 2003 1996
2003 2005 2005 1996 2006 1991 2003 1993 1996 2001 2000 1993 2005 2004
% of GDP
0.6 11.1 2.0 .. 1.1 .. 3.4 5.9 14.7 .. 9.5 2.2 4.9 1.1 .. 2.0 3.5 4.8 .. 7.5 2.1 .. .. 2.1 6.8 8.5 0.2 .. 6.5 0.4 0.2 4.4 0.9 8.0 5.8 1.9 0.0 .. .. 0.3 8.4 7.2 2.5 0.7 0.1 8.4 .. 0.9 4.3 .. 1.2 2.6 1.0 14.1 10.4 ..
Year
2005
2003
2003 2005
2005 2006
2003
2007
2009 World Development Indicators
Average pension % of average wage
.. 39.8 .. .. .. .. .. .. .. .. .. .. 24.9 .. .. .. .. 27.5 .. 33.1 .. .. .. .. 30.9 55.0 .. .. .. .. .. .. .. 20.9 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 47.1 .. ..
77
PEOPLE
Assessing vulnerability and security
2.10
Assessing vulnerability and security Youth unemployment
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Female-headed households
Pension contributors
Male % of male labor force ages 15–24
Female % of female labor force ages 15–24
% of total
2003–05a
2003–05a
2004–07a
Year
.. .. 34 .. 23 29 .. .. .. .. .. .. .. .. 19 48 .. .. .. .. 25 30 .. .. .. .. .. .. 30 .. .. .. .. .. 18 .. .. .. .. .. 38
2007
21 .. .. .. .. .. .. 4 31 11 .. 56 17 20 b .. .. 23 9 .. .. .. 5 .. .. .. 31 19 .. .. 15 .. 13 12b 25 .. 24 4 39 .. .. .. .. w .. .. .. 21 .. .. .. 14 .. 11 .. 14 18
18 .. .. .. .. .. .. 6 29 12 .. 65 24 37b .. .. 22 9 .. .. .. 5 .. .. .. 29 19 .. .. 14 .. 10 10 b 35 .. 35 5 45 .. .. .. .. w .. .. .. 26 .. .. .. 21 .. 12 .. 13 21
2004 2003 2003 2004 2000 2003 1995
2005 2004 1995 2005 2005 2004 1996 2005 1997 2004 2004 2007 2004 2007 2005 2005 2004 2004 2005 2000 2005 2000 1995
% of labor force
% of workingage population
53.4 .. 4.8 .. 5.3 46.0 d 4.6 70.0 78.5 86.0 .. .. 91.0 35.6 12.1 .. 91.0 100.0 17.4 .. 2.0 27.2 .. 15.9 55.6 45.3 55.0 .. 10.7 68.2 .. 92.7 92.5 55.0 .. 31.8 13.2 18.8 10.0 5.9 12.0
36.3 .. 4.1 .. 3.9 32.2d 3.6 .. 55.3 68.7 .. .. 63.2 22.2 12.0 .. 72.3 79.1 11.4 .. 2.0 21.8 .. 15.0 .. 25.4 30.5 .. 9.3 47.4 .. 71.4 72.5 44.3 .. 23.8 10.8 7.8 5.5 4.9 10.0
a. Data are for the most recent year available. b. Limited coverage. c. Data are for 2007. d. Includes Montenegro.
78
2009 World Development Indicators
Public expenditure on pensions
Year
2003 2004 1998 2003 2003 1996 2005 2003
2005 2002
2005 2005 2004 1996
1997 1996 2003 2003 1996 2003 2005 2005 2003 2007 2005 2001 1998 2001 1999 2006 2002
% of GDP
6.9 5.8 .. 0.2 1.3 12.4 d .. 1.4 6.5 10.1 .. .. 10.4 2.0 .. .. 11.4 9.2 1.3 3.0 .. .. .. 0.6 0.6 4.3 3.2 2.3 0.3 15.4 .. 7.6 7.3 10.0 6.5 2.7 1.6 0.8 0.9 0.2 2.3
Year
2005 2003
Average pension % of average wage
41.5 29.2 .. .. .. ..
2005 2005
2006
2000 2003
2007
2007
2006 2005
.. .. 44.7 44.3 .. .. 58.6 .. .. .. .. 40.0 .. 25.7 .. .. .. .. .. .. 61.3 .. .. 48.3 .. .. 29.2 .. 40.0 .. .. .. .. .. ..
About the data
2.10
Definitions
As traditionally measured, poverty is a static con-
residency, or income status. In contribution-related
• Youth unemployment is the share of the labor force
cept, and vulnerability a dynamic one. Vulnerabil-
schemes, however, eligibility is usually restricted
ages 15–24 without work but available for and seek-
ity reflects a household’s resilience in the face of
to individuals who have contributed for a minimum
ing employment. • Female-headed households are
shocks and the likelihood that a shock will lead to a
number of years. Definitional issues—relating to the
the percentage of households with a female head.
decline in well-being. Thus, it depends primarily on
labor force, for example—may arise in comparing
• Pension contributors are the share of the labor
the household’s assets and insurance mechanisms.
coverage by contribution-related schemes over time
force or working-age population (here defined as
Because poor people have fewer assets and less
and across countries (for country-specific informa-
ages 15 and older) covered by a pension scheme.
diversified sources of income than do the better-off,
tion, see Hinz and Pallares-Miralles forthcoming).
• Public expenditure on pensions is all government
fluctuations in income affect them more.
The share of the labor force covered by a pension
expenditures on cash transfers to the elderly, the
Enhancing security for poor people means reduc-
scheme may be overstated in countries that do not
disabled, and survivors and the administrative costs
ing their vulnerability to such risks as ill health, pro-
try to count informal sector workers as part of the
of these programs. • Average pension is the aver-
viding them the means to manage risk themselves,
labor force.
age pension payment of all pensioners of the main
and strengthening market or public institutions for
Public interventions and institutions can provide
managing risk. Tools include microfinance programs,
services directly to poor people, although whether
public provision of education and basic health care,
these interventions and institutions work well for the
and old age assistance (see tables 2.11 and 2.16).
poor is debated. State action is often ineffective,
Poor households face many risks, and vulnerability
in part because governments can influence only a
is thus multidimensional. The indicators in the table
few of the many sources of well-being and in part
focus on individual risks—youth unemployment,
because of difficulties in delivering goods and ser-
female-headed households, income insecurity in
vices. The effectiveness of public provision is further
old age—and the extent to which publicly provided
constrained by the fiscal resources at governments’
services may be capable of mitigating some of these
disposal and the fact that state institutions may not
risks. Poor people face labor market risks, often hav-
be responsive to the needs of poor people.
ing to take up precarious, low-quality jobs and to
The data on public pension spending cover the
increase their household’s labor market participa-
pension programs of the social insurance schemes
tion by sending their children to work (see tables
for which contributions had previously been made.
2.4 and 2.6). Income security is a prime concern
In many cases noncontributory pensions or social
for the elderly.
assistance targeted to the elderly and disabled are
Youth unemployment is an important policy issue
also included. A country’s pattern of spending is cor-
for many economies. Experiencing unemployment
related with its demographic structure—spending
may permanently impair a young person’s produc-
increases as the population ages.
pension schemes divided by the average wage of all formal sector workers.
tive potential and future employment opportunities. The table presents unemployment among youth ages 15–24, but the lower age limit for young people in a country could be determined by the minimum age for leaving school, so age groups could differ across countries. Also, since this age group is likely to include school leavers, the level of youth unemployment varies considerably over the year as a result of different school opening and closing dates. The youth unemployment rate shares similar limitations on comparability as the general unemployment rate. For further information, see About the data for table 2.5 and the original source.
Data sources Data on youth unemployment are from the ILO
The definition of female-headed household differs
database Key Indicators of the Labour Market,
greatly across countries, making cross-country com-
5th edition. Data on female-headed household are
parison difficult. In some cases it is assumed that a
from Demographic and Health Surveys by Macro
woman cannot be the head of any household with an
International. Data on pension contributors and
adult male, because of sex-biased stereotype. Cau-
pension spending are from Richard Paul Hinz and
tion should be used in interpreting the data.
Montserrat Pallares-Miralles’ International Pat-
Pension scheme coverage may be broad or even uni-
terns of Pension Provision II (forthcoming).
versal where eligibility is determined by citizenship,
2009 World Development Indicators
79
PEOPLE
Assessing vulnerability and security
2.11
Education inputs Public expenditure per student
Primary 1991
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
80
.. .. 26.5 .. .. .. .. 18.2 .. .. 30.1 15.8 .. .. .. .. .. .. .. 13.4 .. .. .. 11.9 8.0 .. .. .. .. .. .. 7.8 .. .. 21.6 .. .. .. .. .. .. .. .. 22.1 21.7 11.8 .. 13.2 .. .. .. 7.5 .. .. .. 9.1
2009 World Development Indicators
2007a
.. .. .. 3.7 12.0 .. 17.3 23.5 .. .. 14.4 20.2 13.4 .. .. 16.1 15.4 24.5 36.0 19.9 .. 7.6 .. 7.5 7.1 11.1 .. 12.5 15.6 .. 3.0 .. .. .. 51.1 12.6 25.1 10.3 .. .. 9.0 9.6 19.4 12.5 18.0 17.4 .. .. .. 16.3 18.4 14.1 10.5 .. .. ..
% of GDP per capita Secondary 1999 2007a
.. .. .. .. .. .. 15.4 29.9 15.4 13.4 .. 23.7 26.3 11.7 .. .. 9.5 18.8 .. .. 11.5 16.8 .. .. 28.3 14.8 11.5 17.7 16.9 .. .. 23.2 55.5 .. 41.4 21.7 38.1 .. 9.7 .. 7.9 38.5 27.9 .. 25.8 28.5 .. .. .. 20.5 .. 13.5 4.3 .. .. ..
.. .. .. 36.9 19.6 .. 15.4 26.3 .. .. 27.0 33.4 .. .. .. 41.2 13.2 23.4 23.3 77.5 .. 41.6 .. .. 29.2 12.4 .. 16.5 12.6 .. .. .. .. .. 60.1 22.9 35.0 4.7 .. .. 10.5 9.6 23.0 8.9 32.3 27.0 .. .. .. 21.5 29.1 18.2 6.0 .. .. ..
Public expenditure on education
Tertiary 1999
.. .. .. .. 17.7 .. 27.2 51.6 .. 50.1 .. 38.3 170.4 44.1 .. .. 57.1 17.9 .. 1,078.2 43.7 64.4 47.1 .. .. 19.4 90.1 .. 39.6 .. 379.5 55.0 216.6 41.5 86.6 33.7 65.9 .. .. .. 9.4 444.1 32.6 .. 40.3 29.7 .. .. .. .. .. 22.8 .. .. .. ..
2007a
.. .. .. 78.3 .. .. 23.1 50.0 .. 46.2 18.3 35.1 165.4 .. .. 449.6 35.1 24.8 236.5 363.1 8.5 126.3 .. 305.2 348.2 11.8 .. 47.3 52.7 .. .. .. .. .. 43.5 27.2 55.3 .. .. .. 15.5 .. 18.3 785.5 34.4 33.3 .. .. .. .. 213.4 21.5 19.3 192.9 .. ..
% of GDP 2007a
.. .. .. 2.6 .. 2.7 4.8 5.4 2.6 2.6 5.2 6.0 3.9 .. .. 8.1 4.5 4.5 4.5 5.1 1.6 3.9 4.9 1.4 1.9 3.2 .. 3.5 4.9 .. 1.8 4.9 .. 4.6 13.3 4.3 8.3 2.4 .. 3.8 3.0 2.4 4.9 5.5 6.3 5.7 .. .. 2.7 4.5 5.4 3.5 3.1 1.7 .. ..
Trained Primary teachers school in primary pupil-teacher education ratio
% of total government expenditure
% of total
pupils per teacher
2007a
2007a
2007a
.. .. .. .. .. 15.0 .. 10.9 12.6 15.8 9.3 12.1 18.0 .. .. 21.0 14.5 .. 15.4 17.7 12.4 17.0 .. .. 10.1 16.0 .. 23.2 12.6 .. 8.1 20.6 .. 9.3 20.6 9.5 15.5 11.0 .. 12.6 13.1 .. 14.6 23.3 12.5 10.6 .. .. 7.8 9.7 .. 9.2 .. .. .. ..
.. .. 99.2 .. .. 77.5 .. .. .. .. 99.8 .. 71.8 .. .. 86.9 .. .. 87.7 87.4 98.4 36.3 .. .. 26.8 .. .. 94.6 .. 96.0 86.6 89.5 100.0 .. 100.0 .. .. 88.3 71.6 .. 93.3 87.1 .. .. .. .. .. 76.3 .. .. 56.3 .. .. 98.8 .. ..
.. .. 24 41 17 19 .. 12 13 .. 16 11 44 .. .. 24 21 16 48 52 51 44 .. 102b 60 26 18 17 28 38 58 19 41 17 10 16 .. 24 23 27 40 48 11 .. 16 19 .. 41 .. 14 35 11 30 45 .. ..
Public expenditure per student
Primary 1991
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
.. .. .. .. .. .. 11.5 .. 14.9 9.9 .. .. .. .. .. 11.8 35.4 .. .. .. .. .. .. .. .. .. .. 7.2 10.1 .. .. 10.1 4.8 .. .. 15.4 .. .. .. .. 12.1 17.2 .. .. .. 32.7 10.5 .. 11.3 .. .. .. .. .. 16.3 ..
2007a
.. 25.7 8.9 .. 15.4 .. 14.7 20.7 23.1 14.6 22.2 15.4 .. 22.4 .. 18.8 9.2 .. 9.1 .. 8.3 25.0 6.0 .. 15.9 .. 9.5 .. .. 21.3 9.6 10.3 15.1 33.6 14.9 14.6 15.1 .. 21.4 15.3b 17.7 17.8 9.8 28.7 .. 18.9 15.1 .. 12.4 .. .. 7.0 8.6 23.7 23.2 ..
% of GDP per capita Secondary 1999 2007a
.. 19.1 24.7 .. 9.9 .. 16.8 22.4 27.7 23.6 20.9 15.8 .. 15.2 .. 15.7 .. .. 4.3 23.7 .. 71.6 .. .. .. .. .. .. 22.6 53.0 35.3 15.3 14.2 .. .. 44.5 .. 6.8 36.2 13.1 21.1 24.3 .. 60.9 .. 26.8 21.9 .. 19.1 .. 18.4 10.7 10.8 16.5 27.5 ..
.. 23.1 16.7 .. 22.3 .. 21.8 20.5 26.9 21.5 22.4 19.0 .. 22.1 .. 23.4 14.1 .. 4.7 .. 8.8 49.8 .. .. 20.2 .. 12.7 .. .. 31.7 24.2 17.4 15.6 40.7 14.8 39.3 86.9 .. 22.0 11.3b 24.2 20.6 4.5 46.1 .. 28.8 12.7 .. 15.1 .. .. 9.0 9.1 22.2 34.7 ..
Public expenditure on education
Tertiary 1999
.. 34.2 90.8 .. 34.8 .. 28.5 31.7 27.6 79.0 15.1 .. .. 209.4 .. 8.4 .. 27.7 66.5 27.9 14.2 1,295.1 .. 23.9 34.2 .. 182.1 .. 84.3 227.7 77.8 40.4 47.8 .. .. 94.8 .. 27.5 156.9 141.6 42.8 41.6 .. .. .. 45.8 .. .. 33.6 .. 58.9 20.9 15.1 21.1 28.1 ..
2007a
.. 23.8 57.8 .. 27.7 .. 24.8 23.1 22.3 .. 19.2 .. 8.0 .. .. 9.3 79.8 22.3 25.2 .. 14.8 1,141.5 .. .. 18.2 .. 145.2 .. .. .. 39.2 29.8 40.0 41.4 .. 73.9 .. .. 141.3 .. 39.9 26.4 .. 371.4 .. 49.2 14.0 .. .. .. .. 10.5 11.5 21.4 27.1 ..
% of GDP 2007a
.. 5.5 3.2 3.5 5.5 .. 4.8 6.3 4.4 5.3 3.5 .. 2.9 7.1 .. 4.4 3.6 5.6 3.2 .. 2.7 13.3 .. .. 5.0 .. 3.4 .. .. 4.6 2.9 3.9 5.5 8.3 5.1 5.5 5.2 .. .. 3.8 b 5.2 6.2 .. 3.4 .. 7.0 4.0 2.9 .. .. .. 2.5 2.5 5.5 5.4 ..
PEOPLE
Education inputs
2.11 Trained Primary teachers school in primary pupil-teacher education ratio
% of total government expenditure
% of total
pupils per teacher
2007a
2007a
2007a
.. 10.9 .. 17.5 19.5 .. 13.9 .. 9.2 8.8 9.5 .. .. 17.9 .. 15.3 12.9 .. 15.8 .. 9.6 29.8 .. .. 14.7 .. 16.4 .. .. 16.8 10.1 12.7 .. 19.8 .. 26.1 21.0 .. .. .. 11.5 15.5 .. 17.6 .. 16.7 31.1 11.2 .. .. .. 15.4 15.2 .. 11.3 ..
.. .. .. .. 70.8 .. .. .. .. .. .. .. .. .. .. .. 100.0 62.4 89.7 .. 12.2 66.1 40.2b .. .. .. 55.2 .. .. 100.0 100.0 100.0 .. .. 98.7 100.0 63.2 99.0 94.8 61.4b .. .. 73.6 98.2 51.2 .. 100.0 84.6 90.8 .. .. .. .. .. .. ..
2009 World Development Indicators
28 10 .. 20 19 .. 17 13 11 28 19 .. 17b 40 .. 27 10 24 30 12 14 40 24b .. 14 19 49 .. 17 52 43 22 28 16 32 27 65 29 30 38b .. 16 33 40 40 .. 14 39 25 36 .. 22 35 11 11 ..
81
2.11
Education inputs Public expenditure per student
Primary
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
1991
2007a
.. .. .. .. 18.9 .. .. .. .. 17.4 .. 20.2 11.3 .. .. 6.7 45.8 36.1 .. .. .. 11.6 .. .. .. .. 10.7 .. .. .. .. 15.0 .. 7.8 .. .. .. .. .. .. 20.7 .. m .. .. .. .. .. .. .. .. .. .. .. 15.8 14.9
10.7 .. 10.2 18.5 17.9 .. .. 9.3b 14.8 25.1 .. 15.6 19.1 .. .. 15.4 25.7 24.5 20.3 9.4b .. .. 27.6 9.8 .. 20.9 .. .. .. 15.8 4.4 18.9 22.2 8.8 .. 9.1 .. .. .. 2.3 .. 15.3 m .. .. .. 14.5 .. .. .. 12.0 .. .. 11.8 18.9 18.0
a. Provisional data. b. Data are for 2008.
82
2009 World Development Indicators
% of GDP per capita Secondary 1999 2007a
16.0 .. 30.2 .. .. .. .. .. 18.3 26.0 .. 20.0 24.4 .. .. 26.1 26.6 27.7 21.7 6.5 .. 15.5 .. 31.1 12.3 27.1 14.3 .. .. 11.2 11.5 24.3 22.5 11.3 .. .. .. .. .. 19.4 19.6 .. m .. .. .. 18.1 .. 8.1 .. 13.1 .. 13.4 .. 22.4 24.4
16.0 .. 35.1 18.4 32.9 .. .. 14.1b 15.2 32.0 .. 16.7 23.4 .. .. 43.7 33.5 28.3 .. 14.4b .. .. .. 20.0 .. 24.2 .. .. .. 24.3 6.2 20.3 24.6 10.8 .. 8.1 .. .. .. 8.1 .. .. m .. .. .. 19.7 .. .. .. 13.2 .. .. .. 23.1 26.3
Public expenditure on education
Tertiary 1999
2007a
32.6 .. 699.4 .. .. .. .. .. 32.6 28.3 .. 60.7 19.6 .. .. 388.4 52.7 54.5 .. 27.4 .. 35.1 .. .. 149.3 89.4 45.5 .. .. 36.5 41.5 26.2 27.0 19.1 .. .. .. .. .. 164.3 196.1 .. m .. .. .. 38.6 .. 37.8 .. 37.1 .. 90.8 .. 32.6 29.1
23.7 12.6 372.8 .. 225.2 .. .. .. 24.2 22.7 .. 44.3 22.8 .. .. 343.6 41.5 56.2 .. 11.8 .. 28.0 .. 162.5 .. 55.9 .. .. .. 25.5 .. 32.3 25.4 18.8 .. 24.4 .. .. .. .. .. .. m .. .. .. 24.2 .. .. .. .. .. .. .. 25.4 26.0
% of GDP 2007a
3.5 3.1 4.9 .. 4.8 4.2b 3.8 2.9 b 3.9 5.8 .. 5.4 4.2 .. .. 7.6 7.1 5.8 .. 3.7b .. 4.3 .. 3.7 .. 7.2 .. .. .. 5.4 1.4 5.5 5.7 2.9 .. 3.7 .. .. .. 1.5 .. 4.5 m .. 4.5 3.2 4.5 .. .. 4.1 3.5 .. .. 4.1 5.1 5.2
Trained Primary teachers school in primary pupil-teacher education ratio
% of total government expenditure
% of total
pupils per teacher
2007a
2007a
2007a
.. .. 98.1 91.5 100.0 .. 49.4 96.1 .. .. .. .. .. .. 58.7 90.8 .. .. .. 87.4 99.4b .. .. 14.6 .. .. .. .. 84.8 99.8 100.0 .. .. .. 100.0 84.0 95.6 100.0 .. .. ..
17 17 69 11 34 13 44 20 17 15 .. 30 14 23 37 33 10 13 .. 22 53b 18 31 39 17 19 .. .. 49 16 17 18 14 20 18 19 21 30 .. 49 38 25 w 41 24 26 20 28 20 18 23 24 .. 45 15 14
.. .. 19.0 .. 26.3 9.4b .. 15.3b .. 12.7 .. 17.4 11.0 .. .. .. .. .. .. 19.3b .. 25.0 .. 15.8b .. 20.8 .. .. .. 20.2 28.3 12.5 13.7 11.6 .. .. .. .. .. .. .. 14.2 m .. 14.2 .. 13.0 .. .. 13.1 13.1 .. .. .. 12.5 11.2
About the data
PEOPLE
Education inputs
2.11
Definitions
Data on education are compiled by the United
private spending adds to the complexity of collecting
• Public expenditure per student is public current
Nations Educational, Scientific, and Cultural Organi-
accurate data on public spending.
and capital spending on education divided by the
zation (UNESCO) Institute for Statistics from official
The share of trained teachers in primary educa-
number of students by level as a percentage of gross
responses to surveys and from reports provided by
tion measures the quality of the teaching staff. It
domestic product (GDP) per capita. • Public expen-
education authorities in each country. The data are
does not take account of competencies acquired by
diture on education is current and capital public
used for monitoring, policymaking, and resource
teachers through their professional experience or
expenditure on education as a percentage of GDP
allocation. However, coverage and data collection
self-instruction or of such factors as work experi-
and as a percentage of total government expendi-
methods vary across countries and over time within
ence, teaching methods and materials, or classroom
ture. • Trained teachers in primary education are
countries, so comparisons should be made with
conditions, which may affect the quality of teaching.
the percentage of primary school teachers who have
caution.
Since the training teachers receive varies greatly
received the minimum organized teacher training
(pre-service or in-service), care should be taken in
(pre-service or in-service) required for teaching in
making comparisons across countries.
their country. • Primary school pupil-teacher ratio
For most countries the data on education spending in the table refer to public spending—government spending on public education plus subsidies for pri-
The primary school pupil-teacher ratio refl ects
is the number of pupils enrolled in primary school
vate education—and generally exclude foreign aid for
the average number of pupils per teacher. It differs
divided by the number of primary school teachers
education. They may also exclude spending by reli-
from the average class size because of the differ-
(regardless of their teaching assignment).
gious schools, which play a significant role in many
ent practices countries employ, such as part-time
developing countries. Data for some countries and
teachers, school shifts, and multigrade classes. The
some years refer to ministry of education spending
comparability of pupil-teacher ratios across coun-
only and exclude education expenditures by other
tries is affected by the definition of teachers and by
ministries and local authorities.
differences in class size by grade and in the number
Many developing countries seek to supplement
of hours taught, as well as the different practices
public funds for education, some with tuition fees
mentioned above. Moreover, the underlying enroll-
to recover part of the cost of providing education
ment levels are subject to a variety of reporting errors
services or to encourage development of private
(for further discussion of enrollment data, see About
schools. Fees raise diffi cult questions of equity,
the data for table 2.12). While the pupil-teacher ratio
efficiency, access, and taxation, however, and some
is often used to compare the quality of schooling
governments have used scholarships, vouchers, and
across countries, it is often weakly related to the
other public finance methods to counter criticism.
value added of schooling systems.
For greater detail, consult the country- and indicatorspecific notes in the original source.
In 1998 UNESCO introduced the new International Standard Classification of Education 1997 (ISCED
The share of public expenditure devoted to edu-
1997). Consistent historical time series with reclas-
cation allows an assessment of the priority a gov-
sification of the pre–ISCED 1997 series were pro-
ernment assigns to education relative to other
duced for a selection of indicators in 2008. The full
public investments, as well as a government’s
set of the historical series is forthcoming.
commitment to investing in human capital develop-
In 2006 the UNESCO Institute for Statistics also
ment. It also reflects the development status of a
changed its convention for citing the reference year
country’s education system relative to that of oth-
of education data and indicators to the calendar year
ers. However, returns on investment to education,
in which the academic or financial year ends. Data
especially primary and lower secondary education,
that used to be listed for 2006, for example, are
cannot be understood simply by comparing current
now listed for 2007. This change was implemented
education indicators with national income. It takes
to present the most recent data available and to
a long time before currently enrolled children can
align the data reporting with that of other interna-
productively contribute to the national economy
tional organizations (in particular the Organisation
(Hanushek 2002).
for Economic Co-operation and Development and
Data on education finance are generally of poor quality. This is partly because ministries of education,
Eurostat). Data sources
from which the UNESCO Institute for Statistics col-
Data on education inputs are from the UNESCO
lects data, may not be the best source for education
Institute for Statistics, which compiles inter-
finance data. Other agencies, particularly ministries
national data on education in cooperation with
of finance, need to be consulted, but coordination is
national commissions and national statistical
not easy. It is also difficult to track actual spending
services.
from the central government to local institutions. And
2009 World Development Indicators
83
2.12
Participation in education Gross enrollment ratio
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
84
% of relevant age group Primary Secondary
Net enrollment ratio
Adjusted net enrollment ratio, primary
% of primary-schoolage children Male Female
% of relevant age group Primary Secondary
Tertiary
Children out of school
thousand primary-schoolage children Male Female
2007a
2007a
2007a
2007a
1991
2007a
1991
2007a
2007a
2007a
2007a
2007a
.. .. 30 61 66 37 104 90 .. .. 103 121 6 50 10 15 69 82 3 2 13 21 68 .. 1 55 39 66 41 3 10 61 3 50 111 114 95 32 100 17 49 14 93 3 62 116 .. 22 57 106 60 69 29 10 .. ..
.. .. 110 199 112 110 105 102 .. .. 97 102 96 109 98 107 137 100 65 114 119 110 98 80 b 74 104 111 98 116 85 106 110 72 99 102 100 99 107 118 105 118 55 99 91 98 110 .. 86 99 103 98 102 113 91 .. ..
.. .. 83 17 84 89 150 102 .. .. 95 110 32 82 85 76 105 105 16 15 42 25 .. .. 19 91 76 86 85 33 .. 87 .. 91 93 96 120 79 70 .. 64 29 100 30 112 114 .. 49 90 102 49 103 56 35 .. ..
.. .. 24 3 64 34 73 50 .. 7 69 63 5 .. .. 5 25 46 3 2 5 7 .. 1 1 47 22 34 32 4 .. 25 8 44 109 50 80 .. .. 35 22 .. 65 3 93 56 .. .. 37 .. 6 95 18 5 .. ..
.. 96 89 50 94 .. 99 88 89 76 85 96 41 .. 79 88 84 .. 27 53 72 69 98 52 34 89 98 92 68 54 82 87 45 70 94 87 98 .. 98 86 .. 15 .. 22 98 100 94 46 97 84 54 95 64 27 38 21
.. .. 95 .. 99 85 96 97 .. .. 91 97 80 95 .. 84 94 92 52 81 89 .. .. 61b .. .. .. 91 87 .. 54 .. .. 90 98 93 96 82 97 96 92 41 94 71 97 99 .. 76 94 98 72 99 95 74 .. ..
.. .. 53 .. .. .. 80 .. .. .. .. 86 .. .. .. 39 .. .. .. .. .. .. 89 .. .. 55 .. .. 34 .. .. 38 .. .. 73 .. 87 .. .. .. .. .. .. .. 93 .. .. .. .. .. .. 83 .. .. .. ..
.. .. .. .. 78 86 87 .. .. .. 87 87 .. 71 .. 56 79 88 12 .. 31 .. .. .. .. .. .. 79 67 .. .. 64 .. 87 86 .. 89 61 59 .. 54 25 91 24 96 99 .. 36 82 .. 45 92 38 28 .. ..
.. .. 97 .. 100 92 96 97 .. .. 91 97 90 96 .. 83 94 94 58 82 .. .. .. 71b .. .. .. 97 91 .. 56 .. .. 98 99 91 95 84 .. 100 93 45 97 75 97 99 .. 74 96 98 73 100 98 80 .. ..
.. .. 95 .. 98 95 97 98 .. .. 89 98 75 97 .. 86 97 94 48 80 .. .. .. 51b .. .. .. 93 91 .. 52 .. .. 100 99 94 97 86 .. 95 94 40 97 69 97 99 .. 78 93 99 71 100 95 70 .. ..
.. .. 61 .. 5 5 36 6 .. .. 18 9 71 30 .. 27 383 8 514 116 .. .. .. 103b .. .. .. 7 219 .. 129 .. .. 2 4 22 10 104 .. 10 32 167 1 1,667 6 18 .. 33 7 28 477 1 17 146 .. ..
.. .. 88 .. 31 2 27 3 .. .. 21 8 173 22 .. 22 214 9 608 128 .. .. .. 174b .. .. .. 16 194 .. 142 .. .. 0c 6 15 7 90 .. 222 27 181 1 2,054 5 9 .. 27 11 19 490 1 53 216 .. ..
2009 World Development Indicators
Gross enrollment ratio
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
% of relevant age group Primary Secondary
Net enrollment ratio
Adjusted net enrollment ratio, primary
% of primary-schoolage children Male Female
% of relevant age group Primary Secondary
Tertiary
PEOPLE
Participation in education
2.12 Children out of school
thousand primary-schoolage children Male Female
2007a
2007a
2007a
2007a
1991
2007a
1991
2007a
2007a
2007a
2007a
2007a
36 86 40 37 54 .. .. 91 104 92 86 32 39b 49 .. 101 77 16 13 89 66 18 125b 9 69 33 8 .. 63 3 2 99 106 70 54 60 .. .. 32 57b 90 92 52 2 15 90 31 52 70 .. 34 68 45 57 79 ..
117 97 112 114 121 .. 104 110 103 95 100 97 109b 106 .. 105 98 95 118 95 95 114 83b 110 95 98 141 116 100 83 103 101 113 94 100 107 111 .. 109 124b 107 102 116 53 97 98 80 84 113 55 111 116 110 98 115 ..
61 96 55 66 73 .. 112 92 100 87 101 89 92b 50 .. 98 91 86 44 99 81 37 .. 94 99 84 26 28 69 32 25 88 87 89 92 56 18 .. 59 48b 118 120 66 11 32 113 90 33 70 .. 66 94 83 100 97 ..
.. 69 12 17 31 .. 59 58 67 .. 57 39 47b .. .. 93 18 43 9 74 52 4 .. .. 76 30 3 0c 29 4 4 14b 26 41 48 11 1 .. 6 .. 60 80 .. 1 10 78 25 5 45 .. 26 35 28 66 55 ..
88 87 .. 96 92 94 90 .. 100 96 100 95 88 .. .. 100 49 92 62 94 66 72 .. .. .. .. 64 49 93 25 36 91 98 86 90 56 42 99 86 63 95 98 70 24 55 100 69 33 92 66 94 88 96 96 98 ..
96 88 89 95 94 .. 95 97 99 90 100 90 90 b 75 .. 98 88 84 86 90 83 72 31b .. 89 92 98 87 100 63 80 95 98 88 89 89 70 .. 87 80 b 98 99 90 45 63 98 73 66 98 .. 94 96 91 96 98 ..
21 .. .. 39 .. .. 80 .. .. 64 97 .. .. .. .. 86 .. .. .. .. .. 15 .. .. .. .. .. .. .. 6 .. .. 45 .. .. .. .. .. .. .. 84 85 .. 6 .. 88 .. .. .. .. 26 .. .. .. .. ..
.. 90 .. 60 77 .. 87 89 94 78 99 82 86b 43 .. 96 77 81 35 .. 73 24 .. .. 92 81 17 24 69 .. 16 73 70 81 81 .. 3 .. 49 .. 88 .. 43 9 .. 96 79 32 64 .. 57 72 60 94 82 ..
96 94 96 99 .. .. 94 96 100 91 .. 93 99b 76 .. .. 95 93 88 90 84 71 32b .. 92 97 99 84 .. 70 79 95 100 90 96 92 72 .. 84 82b 99 99 91 52 69 98 74 73 99 .. 95 .. 91 96 99 ..
98 95 92 96 .. .. 95 98 99 91 .. 95 100 b 77 .. .. 93 92 84 94 83 74 30 b .. 92 97 100 91 .. 56 83 96 99 90 99 87 67 .. 89 78b 98 100 92 39 60 98 76 57 99 .. 95 .. 93 97 99 ..
21 12 2,529 142 .. .. 13 13 5 16 .. 31 6b 708 .. .. 5 16 44 4 38 54 221b .. 7 2 17 198 .. 312 51 3 12 8 5 157 578 .. 30 338b 7 1 38 574 3,608 5 46 2,705 2 .. 23 .. 553 55 2 ..
12 11 4,613 544 .. .. 10 9 12 15 .. 22 2b 662 .. .. 7 16 59 3 38 47 226b .. 6 1 3 117 .. 452 38 2 61 9 1 237 671 .. 21 376b 14 1 34 689 4,582 4 41 4,116 3 .. 19 .. 400 44 3 ..
2009 World Development Indicators
85
2.12
Participation in education Gross enrollment ratio
Preprimary 2007a
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
72 83 .. 11 9 59 5 .. 93 81 .. 43 121 .. 27b 17 95 99 10 9b 35b 94 10 4 85 .. 13 .. 3 94 85 72 61 79 27 62 .. 30 1 .. .. 41 w 22 44 39 68 37 42 52 65 33 36 14 78 106
% of relevant age group Primary Secondary 2007a
105 96 147 98 84 97 147 .. 100 100 .. 103 105 108 72b 106 96 97 126 100 b 112b 106 91 97 95 108 94 .. 117 100 107 105 98 115 95 106 .. 80 87 119 101 105 w 94 111 111 111 106 110 97 118 105 108 94 101 ..
a. Provisional data. b. Data are for 2008. c. Less than 0.5.
86
2009 World Development Indicators
2007a
86 84 18 94 24 88 32 .. 96 95 .. 96 119 .. 35b 47 103 93 72 84 .. 83 53 39 76 85 79 .. 18 94 92 98 94 101 102 79 .. 92 46 43 40 66 w 38 70 65 91 61 73 88 89 71 49 32 101 ..
Net enrollment ratio
52 70 3 30 6 .. .. .. 45 83 .. 15 67 .. .. 4 79 46 .. 20 b 1 50 .. 5 11 31 35 .. .. 76 23 59 82 46 10 52 .. 46 9 .. .. 25 w 6 24 19 42 19 21 53 31 25 10 5 67 ..
% of primary-schoolage children Male Female
% of relevant age group Primary Secondary
Tertiary 2007a
Adjusted net enrollment ratio, primary
1991
81 98 67 59 45 .. 43 .. .. 96 .. 90 100 84 .. 75 100 84 91 77 51 88 .. 64 89 93 89 .. 51 81 99 98 97 91 78 91 90 .. .. 78 84 81 w 56 87 85 90 79 96 89 86 83 69 53 95 ..
2007a
93 .. 94 85 72 95 .. .. 92 95 .. 86 100 .. .. 78 95 89 .. 98b 98 94 63 77 85 96 91 .. .. 89 91 98 92 100 91 92 .. 73 75 94 88 86 w 73 91 90 94 85 93 91 94 90 85 70 95 ..
1991
.. .. 8 31 .. .. .. .. .. .. .. 45 .. .. .. 30 85 80 43 .. .. .. .. 15 .. .. 42 .. .. .. 60 80 84 .. .. .. .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. .. .. ..
2007a
73 .. .. 73 20 .. 23 .. .. 90 .. 72 94 .. .. 32 99 82 66 81 .. 76 .. .. 65 .. 69 .. 16 84 79 92 88 .. 92 68 .. 89 37 41 37 58 w 34 62 .. 76 54 .. 81 70 67 .. 25 90 ..
2007a
95 .. 92 85 73 95 .. .. 92 97 .. 91 100 .. .. 78 95 93 .. 99 99 .. 64 84 89 97 93 .. .. 90 99 99 92 98 95 94 .. 77 85 95 88 90 77 95 94 96 89 94 94 95 93 92 73 96 ..
2007a
96 .. 95 84 73 95 .. .. 92 97 .. 92 99 .. .. 79 95 94 .. 96 97 .. 62 74 90 98 89 .. .. 90 98 100 94 97 92 94 .. 78 65 96 89 87 w 70 93 92 96 86 94 92 96 90 87 68 96 ..
Children out of school
thousand primary-schoolage children Male Female 2007a
2007a
21 .. 56 245 254 8 .. .. 10 2 .. 332 1 .. .. 23 17 18 .. 2 50 .. 35 83 8 18 291 .. .. 85 2 16 1,004 4 60 105 .. 56 275 60 149
19 .. 38 252 253 7 .. .. 9 1 .. 274 6 .. .. 22 17 17 .. 15 93 .. 36 139 7 9 439 .. .. 82 3 0c 723 4 86 90 .. 52 632 48 132
About the data
PEOPLE
Participation in education
2.12
Definitions
School enrollment data are reported to the United
enrolled in each grade because of repetition rather
• Gross enrollment ratio is the ratio of total enroll-
Nations Educational, Scientific, and Cultural Organi-
than a successful education system. The net enroll-
ment, regardless of age, to the population of the age
zation (UNESCO) Institute for Statistics by national
ment ratio excludes overage and underage students
group that officially corresponds to the level of educa-
education authorities and statistical offices. Enroll-
to capture more accurately the system’s coverage
tion shown. • Preprimary education refers to the ini-
ment ratios help monitor whether a country is on
and internal efficiency but does not account for chil-
tial stage of organized instruction, designed primarily
track to achieve the Millennium Development Goal
dren who fall outside the official school age because
to introduce very young children to a school-type envi-
of universal primary education by 2015 (a net pri-
of late or early entry rather than grade repetition.
ronment. • Primary education provides children with
mary enrollment ratio of 100 percent), and whether
Differences between gross and net enrollment
basic reading, writing, and mathematics skills along
an education system has the capacity to meet the
ratios show the incidence of overage and underage
with an elementary understanding of such subjects
needs of universal primary education, as indicated
enrollments.
as history, geography, natural science, social sci-
in part by gross enrollment ratios.
Adjusted net primary enrollment (called total net
ence, art, and music. • Secondary education com-
Enrollment ratios, while a useful measure of par-
primary enrollment in the 2008 edition), recently
pletes the provision of basic education that began
ticipation in education, have limitations. They are
added as a Millennium Development Goal indica-
at the primary level and aims at laying the founda-
based on annual school surveys, which are typically
tor, captures primary-school-age children who have
tions for lifelong learning and human development
conducted at the beginning of the school year and do
progressed to secondary education, which the tradi-
by offering more subject- or skill-oriented instruction
not reflect actual attendance or dropout rates during
tional net enrollment ratio excludes.
using more specialized teachers. • Tertiary educa-
the year. And school administrators may exaggerate
The data on children out of school (primary-school-
tion refers to a wide range of post-secondary educa-
enrollments, especially if there is a financial incen-
age children not enrolled in primary or secondary
tion institutions, including technical and vocational
tive to do so.
education) are compiled by the UNESCO Institute for
education, colleges, and universities, whether or not
Also, as international indicators, the gross and net
Statistics using administrative data. Children out of
leading to an advanced research qualification, that
primary enrollment ratios have an inherent weak-
school include dropouts, children never enrolled, and
normally require as a minimum condition of admis-
ness: the length of primary education differs across
children of primary age enrolled in preprimary educa-
sion the successful completion of education at the
countries, although the International Standard Clas-
tion. Large numbers of children out of school create
secondary level. • Net enrollment ratio is the ratio
sification of Education tries to minimize the differ-
pressure to enroll children and provide classrooms,
of total enrollment of children of official school age
ence. A relatively short duration for primary educa-
teachers, and educational materials, a task made
based on the International Standard Classification of
tion tends to increase the ratio; a relatively long one
difficult in many countries by limited education bud-
Education 1997 to the population of the age group
to decrease it (in part because more older children
gets. However, getting children into school is a high
that officially corresponds to the level of education
drop out).
priority for countries and crucial for achieving the
shown. • Adjusted net enrollment ratio, primary,
Millennium Development Goal of universal primary
is the ratio of total enrollment of children of official
education.
school age for primary education who are enrolled in
Overage or underage enrollments are frequent, particularly when parents prefer children to start school at other than the official age. Age at enrollment may
In 2006 the UNESCO Institute for Statistics
primary or secondary education to the total primary-
be inaccurately estimated or misstated, especially
changed its convention for citing the reference
school-age population. • Children out of school
in communities where registration of births is not
year. For more information, see About the data for
are the number of primary-school-age children not
strictly enforced.
table 2.11.
enrolled in primary or secondary school.
Other problems of cross-country comparison stem from errors in school-age population estimates. Agesex structures drawn from censuses or vital registrations, the primary data sources on school-age population, commonly underenumerate (especially young children) to circumvent laws or regulations. Errors are also introduced when parents round children’s ages. While census data are often adjusted for age bias, adjustments are rarely made for inadequate vital registration systems. Compounding these problems, pre- and postcensus estimates of school-age children are model interpolations or projections that may miss important demographic events (see discussion of demographic data in About the data for table 2.1). Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio
Data sources Data on gross and net enrollment ratios and out of school children are from the UNESCO Institute for Statistics.
may reflect a substantial number of overage children
2009 World Development Indicators
87
2.13
Education efficiency Gross intake rate in grade 1
Cohort survival rate
Repeaters in primary school
Transition to secondary school
% of grade 1 students Reaching grade 5
% of relevant age group Male Female
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
88
Male
Reaching last grade of primary education Female
Male
Female
2007a
2007a
1991
2006a
1991
2006a
2006a
2006a
.. .. 102 .. 109 130 106 102 94 .. 103 98 122 122 .. 124 .. 100 86 144 141 118 .. 99 c 109 100 88 88 123 114 89 101 76 97 98 109 97 123 141 105 111 44 96 144 97 .. .. 88 109 104 105 100 124 97 .. ..
.. .. 100 .. 108 133 105 100 92 .. 101 99 108 122 .. 120 .. 100 76 137 132 103 .. 73c 79 99 87 83 121 99 86 102 64 97 98 108 98 116 139 102 107 38 95 128 96 .. .. 95 103 103 110 99 122 90 .. ..
.. .. 95 .. .. .. 98 .. .. .. .. 90 54 .. .. 81 .. .. 71 65 .. .. 95 24 56 94 58 .. .. 58 56 83 75 .. .. .. 94 .. .. .. .. .. .. 16 100 69 .. .. .. .. 81 100 .. 64 .. ..
.. .. 95 .. 88 .. .. .. .. .. .. 96 72 .. .. 80 .. .. 79 65 61 .. .. 53 34 99 .. 99 85 .. .. 86 83 .. 97 100 100 66 80 96 72 59 97 64 99 .. .. .. 86 .. .. 97 69 87 .. ..
.. .. 94 .. .. .. 99 .. .. .. .. 92 56 .. .. 87 .. .. 68 58 .. .. 98 22 41 91 78 .. .. 50 65 85 70 .. .. .. 94 .. .. .. .. .. .. 23 100 95 .. .. .. .. 79 100 .. 48 .. ..
.. .. 97 .. 91 .. .. .. .. .. .. 97 71 .. .. 85 .. .. 82 68 64 .. .. 45 32 99 .. 100 92 .. .. 89 73 .. 97 100 100 71 83 97 76 61 97 65 100 .. .. .. 90 .. .. 100 67 79 .. ..
.. .. 89 .. 85 98 .. .. 98 .. 99 93 67 .. .. 71 .. 95 71 56 53 .. .. 43 27 .. .. 99 85 .. .. 82 83 99 97 100 92 58 79 94 67 59 96 57 99 .. .. .. 83 98 .. 97 63 82 .. ..
.. .. 95 .. 89 97 .. .. 100 .. 100 94 63 .. .. 78 .. 95 74 61 56 .. .. 35 23 .. .. 100 92 .. .. 86 66 100 97 100 92 65 82 96 71 61 97 59 100 .. .. .. 89 99 .. 100 62 72 .. ..
2009 World Development Indicators
% of enrollment Male Female 2007a
2007a
.. .. 14 .. 8 0b .. .. .. .. 0b 3 8 1 .. .. .. 3 12 32 13 20 .. 28 22 3 0b 1 4 16 21 9 22 0b 1 1 0 7 2 4 8 15 3 7 1 .. .. 6 0b 1 6 1 13 9 .. ..
.. .. 8 .. 5 0b .. .. .. .. 0b 3 8 1 .. .. .. 2 12 32 10 20 .. 28 24 2 0b 1 3 16 21 6 21 0b 0b 1 0 4 1 2 5 14 1 5 0b .. .. 6 0b 1 6 1 11 10 .. ..
% Male
Female
2006a
2006a
.. .. 78 .. 92 99 .. .. 99 .. 100 .. 72 .. .. 97 .. 96 47 37 81 35 .. 44 56 96 .. 100 99 .. 58 100 49 100 98 99 100 93 81 .. 91 78 96 90 98 .. .. 95 98 100 .. 100 94 69 .. ..
.. .. 84 .. 94 100 .. .. 100 .. 100 .. 70 .. .. 98 .. 96 44 24 78 37 .. 51 42 98 .. 100 100 .. 58 97 48 100 98 99 100 98 77 .. 92 76 99 87 99 .. .. 93 100 99 .. 99 90 59 .. ..
Gross intake rate in grade 1
Cohort survival rate
Repeaters in primary school
PEOPLE
Education efficiency
2.13 Transition to secondary school
% of grade 1 students Reaching grade 5
% of relevant age group Male Female
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
Male
Reaching last grade of primary education Female
Male
Female
2007a
2007a
1991
2006a
1991
2006a
2006a
2006a
136 97 133 123 109 .. 97 95 105 94 99 89 117c 112 .. 106 97 97 135 95 89 105 100 c .. 98 99 171 137 98 92 115 100 112 96 124 116 166 136 102 125c 103 105 173 72 106 100 77 125 115 .. 113 109 131 97 108 ..
131 96 126 119 106 .. 99 98 104 92 99 90 117c 108 .. 109 94 97 126 95 87 99 100 c .. 94 99 168 147 98 79 120 102 110 96 126 112 156 135 103 127c 101 104 163 58 90 100 78 100 113 .. 110 110 121 98 109 ..
.. .. .. 34 91 .. 99 .. .. .. 100 .. .. .. .. 99 .. .. .. .. .. 58 .. .. .. .. 22 71 97 71 76 97 35 .. .. 75 36 .. 60 51 .. .. 11 61 .. 99 97 .. .. 70 73 .. .. .. .. ..
64 .. 66 83 .. .. 97 100 99 .. .. 97 .. 81 .. 99 100 .. 62 .. 97 68 .. .. .. .. 42 44 99 83 63 99 94 .. 86 85 68 68 84 60 d 99 .. 50 74 .. 100 98 68 90 .. 86 90 70 .. .. ..
.. .. .. 78 89 .. 100 .. .. .. 100 .. .. .. .. 100 .. .. .. .. .. 73 .. .. .. .. 21 57 97 67 75 98 71 .. .. 76 32 .. 65 51 .. .. 37 65 .. 100 96 .. .. 68 75 .. .. .. .. ..
69 .. 65 86 .. .. 100 99 100 .. .. 96 .. 85 .. 99 99 .. 61 .. 100 80 .. .. .. .. 43 43 100 79 65 99 95 .. 83 83 60 72 90 64 d 100 .. 57 69 .. 100 99 72 90 .. 90 89 78 .. .. ..
58 97 66 78 .. .. .. 100 99 .. .. 96 99d .. .. 99 100 96 62 98 94 53 .. .. 97 98 42 37 .. 75 54 98 91 96 86 79 48 68 73 60 d .. .. 46 72 .. 99 97 68 88 .. 82 86 66 .. .. ..
64 98 65 81 .. .. .. 99 100 .. .. 95 100 d .. .. 99 99 97 61 98 99 71 .. .. 97 99 43 35 .. 70 55 98 93 96 83 76 41 72 80 64 d .. .. 55 67 .. 100 98 72 89 .. 86 84 75 .. .. ..
% of enrollment Male Female 2007a
8 2 3 4 3 .. 1 2 0b 3 .. 1 0 b,c 6 .. 0b 1 0b 18 4 11 24 7c .. 1 0b 20 21 .. 17 3 4 5 0b 1 14 6 1 19 17c .. .. 11 5 3 0 1 2 7 .. 6 9 3 1 .. ..
2007a
6 2 3 3 1 .. 1 1 0b 2 .. 1 0 b,c 6 .. 0b 1 0b 16 2 8 18 6c .. 0b 0b 18 20 .. 17 3 3 3 0b 0b 10 6 0b 14 17c .. .. 8 5 3 0 2 2 4 .. 4 8 2 0b .. ..
% Male
Female
2006a
2006a
68 99 86 88 89 .. .. 73 100 100 .. 97 100 d .. .. 99 100 99 79 97 85 68 .. .. 98 100 61 76 100 52 57 65 95 99 95 80 56 75 75 81d .. .. .. 42 .. 99 97 69 100 .. 89 97 100 .. .. ..
74 99 82 89 77 .. .. 72 99 97 .. 96 100 d .. .. 99 100 99 76 97 90 68 .. .. 99 99 60 71 99 47 47 77 93 99 97 79 61 70 80 81d .. .. .. 37 .. 100 97 75 98 .. 89 94 98 .. .. ..
2009 World Development Indicators
89
2.13
Education efficiency Gross intake rate in grade 1
Cohort survival rate
Repeaters in primary school
Transition to secondary school
% of grade 1 students Reaching grade 5
% of relevant age group Male Female
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Male
Female
Male
Female
2007a
2007a
1991
2006a
1991
2006a
2006a
2006a
97 101 209 98 98 .. 188 .. 102 95 .. 117 104 112 90 c 111 96 88 123 106 116c 71 113 97 96 97 95 .. 145 101 108 .. 105 100 95 106 .. 80 122 126 .. 114 w 118 114 114 111 115 99 99 122 108 131 115 103 104
97 100 205 99 103 .. 172 .. 101 96 .. 109 104 112 80 c 103 95 92 119 102 114 c 83 111 90 92 100 92 .. 147 100 106 .. 102 101 92 104 .. 79 102 129 .. 108 w 108 110 110 107 109 97 97 117 105 122 105 102 103
.. .. 61 82 .. .. .. .. .. .. .. .. .. 92 90 74 100 .. 97 .. 81 .. .. 52 .. 94 98 .. .. .. 80 .. .. 96 .. .. .. .. .. .. 72 .. w .. .. .. .. .. 55 .. .. .. .. .. .. ..
.. .. .. .. 65 .. .. .. .. .. .. .. 100 93 72 81 .. .. .. .. 85 .. .. 58 90 96 89 .. 49 .. 100 .. 96 92 .. 96 .. .. 67 94 .. .. w .. .. .. .. .. .. .. .. .. 67 .. .. ..
.. .. 59 84 .. .. .. .. .. .. .. .. .. 93 99 80 100 .. 95 .. 82 .. .. 42 .. 77 97 .. .. .. 80 .. .. 98 .. .. .. .. .. .. 81 .. w .. .. .. .. .. 78 .. .. .. .. .. .. ..
.. .. .. .. 65 .. .. .. .. .. .. .. 100 94 69 87 .. .. .. .. 89 .. .. 50 92 97 90 .. 49 .. 100 .. 98 95 .. 100 .. .. 65 84 .. .. w .. .. .. .. .. .. .. .. .. 66 .. .. ..
93 .. .. .. 54 .. .. .. 97 .. .. .. 100 93 64 66 .. .. 95 100 81 .. .. 49 80 94 95 .. 26 97 100 .. .. 91 99 95 .. 99 61 83 .. .. w .. .. .. .. .. .. .. .. .. 67 .. .. 98
94 .. .. .. 53 .. .. .. 98 .. .. .. 100 94 60 75 .. .. 96 97 85 .. .. 39 87 95 93 .. 25 99 100 .. .. 94 99 100 .. 99 57 67 .. .. w .. .. .. .. .. .. .. .. .. 66 .. .. 99
a. Provisional data. b. Less than 0.5. c. Data are for 2008. d. Data are for 2007.
90
Reaching last grade of primary education
2009 World Development Indicators
% of enrollment Male Female 2007a
3 1 15 3 11 .. 10 .. 3 1 .. 8 3 1 3 19 .. 2 8 0b 4 12 15 23 6 7 3 .. 13 0b 2 0 0 8 0b 6 .. 1 5 7 .. 4w 8 3 3 .. 4 2 1 .. 7 4 9 .. 2
2007a
2 1 15 3 10 .. 10 .. 2 0b .. 8 2 1 3 15 .. 1 6 0b 4 6 14 24 4 5 3 .. 13 0b 2 0 0 6 0b 4 .. 1 4 6 .. 3w 8 3 3 .. 4 1 1 .. 4 4 9 .. 1
% Male
Female
2006a
2006a
98 .. .. .. 52 .. .. .. 98 .. .. 87 .. 96 90d 88 .. 99 95 100 c 64 d 85 .. 56 94 86 93 .. 42 100 98 .. .. 76 100 98 .. 97 83 54 .. .. w .. .. .. .. .. .. .. .. 86 83 .. .. ..
98 .. .. .. 48 .. .. .. 98 .. .. 89 .. 97 98d 89 .. 100 96 97c 52d 89 .. 49 92 90 90 .. 43 100 99 .. .. 87 100 98 .. 98 82 64 .. .. w .. .. .. .. .. .. .. .. 83 81 .. .. ..
About the data
PEOPLE
Education efficiency
2.13
Definitions
The United Nations Educational, Scientific, and Cul-
and measuring students’ learning progress against
• Gross intake rate in grade 1 is the number of
tural Organization (UNESCO) Institute for Statistics
those standards through standardized assessments,
new entrants in the first grade of primary education
estimates indicators of students’ progress through
actions that many countries do not systematically
regardless of age as a percentage of the population
school. These indicators measure an education sys-
undertake.
of the official primary school entrance age. • Cohort
tem’s success in reaching all students, efficiently
Data on repeaters are often used to indicate an
survival rate is the percentage of children enrolled
moving students from one grade to the next, and
education system’s internal efficiency. Repeaters not
in the first grade of primary school who eventually
imparting a particular level of education.
only increase the cost of education for the family
reach grade 5 or the last grade of primary educa-
The gross intake rate indicates the level of access
and the school system, but also use limited school
tion. The estimate is based on the reconstructed
to primary education and the education system’s
resources. Country policies on repetition and promo-
cohort method (see About the data). • Repeaters in
capacity to provide access to primary education.
tion differ; in some cases the number of repeaters is
primary school are the number of students enrolled
Low gross intake rates in grade 1 reflect the fact
controlled because of limited capacity. Care should
in the same grade as in the previous year as a per-
that many children do not enter primary school even
be taken in interpreting this indicator.
centage of all students enrolled in primary school.
though school attendance, at least through the pri-
The transition rate from primary to secondary
• Transition to secondary school is the number of
mary level, is mandatory in all countries. Because
school conveys the degree of access or transition
new entrants to the first grade of secondary school
the gross intake rate includes all new entrants
between the two levels. As completing primary edu-
in a given year as a percentage of the number of
regardless of age, it can exceed 100 percent. Once
cation is a prerequisite for participating in lower
students enrolled in the final grade of primary school
enrolled, students drop out for a variety of reasons,
secondary school, growing numbers of primary
in the previous year.
including low quality schooling, lack of relevant cur-
completers will inevitably create pressure for more
riculum (real or perceived by parents or students),
available places at the secondary level. A low transi-
repetition, discouragement over poor performance,
tion rate can signal such problems as an inadequate
and direct and indirect schooling costs. Students’
examination and promotion system or insufficient
progress to higher grades may also be limited by the
secondary school capacity. The quality of data on
availability of teachers, classrooms, and materials.
the transition rate is affected when new entrants and
The cohort survival rate is the estimated proportion
repeaters are not correctly distinguished in the first
of an entering cohort of grade 1 students that even-
grade of secondary school. Students who interrupt
tually reaches grade 5 or the last grade of primary
their studies after completing primary school could
education. It measures an education system’s hold-
also affect data quality.
ing power and internal efficiency. Rates approaching
In 2006 the UNESCO Institute for Statistics
100 percent indicate high retention and low dropout
changed its convention for citing the reference
levels. Cohort survival rates are typically estimated
year. For more information, see About the data for
from data on enrollment and repetition by grade for
table 2.11.
two consecutive years. This procedure, called the reconstructed cohort method, makes three simplifying assumptions: dropouts never return to school; promotion, repetition, and dropout rates remain constant over the period in which the cohort is enrolled in school; and the same rates apply to all pupils enrolled in a grade, regardless of whether they previously repeated a grade (Fredricksen 1993). Crosscountry comparisons should thus be made with caution, because other flows—caused by new entrants, reentrants, grade skipping, migration, or transfers during the school year—are not considered. Research suggests that five to six years of schooling, which is how long primary education lasts in most countries, is a critical threshold for achieving sustainable basic literacy and numeracy skills. But the indicator only indirectly reflects the quality of schooling, and a high rate does not guarantee
Data sources
these learning outcomes. Measuring actual learn-
Data on education efficiency are from the UNESCO
ing outcomes requires setting curriculum standards
Institute for Statistics.
2009 World Development Indicators
91
2.14
Education completion and outcomes Primary completion rate
% of relevant age group Total
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
Male
1991
2007a
1991
2007a
.. .. 80 35 .. .. .. .. .. .. 94 79 21 71 .. 89 90 90 20 46 .. 53 .. 27 18 .. 105 102 70 46 54 79 43 .. 99 .. 98 62 .. .. 61 .. .. .. 97 104 .. .. .. .. 61 .. .. 17 .. 27
.. 96 95 .. 97 98 .. 103 .. 72 92 87 64 101 .. 95 106 98 33 39 85 55 .. 24 31 95 .. 102 107 51 72 91 45 96 93 94 101 89 106 98 91 46 100 46 97 .. .. 72 92 97 71 103 77 64 .. ..
.. .. 86 .. .. .. .. .. .. .. .. 76 28 78 .. 82 .. 88 24 49 .. 57 .. 35 29 .. .. .. 67 58 59 77 55 .. .. .. 98 .. .. .. 60 .. .. .. 98 .. .. .. .. .. 69 .. .. 25 .. 29
.. 97 94 .. 95 96 .. 103 .. 70 93 86 76 102 .. 91 .. 98 37 42 85 61 .. 30 41 96 .. 104 105 61 75 90 53 97 93 95 101 87 105 101 89 52 102 51 97 .. .. 70 .. 97 73 104 80 73 .. ..
2009 World Development Indicators
Female 1991 2007a
.. .. 73 .. .. .. .. .. .. .. .. 82 13 64 .. 97 .. 92 15 43 .. 49 .. 18 7 .. .. .. 73 34 49 81 32 .. .. .. 98 .. .. .. 62 .. .. .. 97 .. .. .. .. .. 54 .. .. 9 .. 26
.. 96 96 .. 99 100 .. 102 .. 74 92 88 52 100 .. 98 .. 98 29 36 85 50 .. 19 21 95 .. 99 109 41 70 93 36 95 93 93 102 91 107 96 93 41 98 41 97 .. .. 73 .. 98 68 103 74 55 .. ..
Youth literacy rate
Adult literacy rate
% ages 15–24
% ages 15 and older
Male 1990 2005–07b
Female 1990 2005–07b
.. .. 86 .. 98 100 .. .. .. 52 100 .. 55 96 .. 86 .. .. 27 59 .. .. .. 63 .. 98 97 .. 89 .. .. .. 60 100 .. .. .. .. 97 71 85 .. 100 39 .. .. 94 .. .. .. .. 99 82 .. .. ..
.. .. 62 .. 99 100 .. .. .. 38 100 .. 27 92 .. 92 .. .. 14 48 .. .. .. 35 .. 99 91 .. 92 .. .. .. 38 100 .. .. .. .. 96 54 85 .. 100 28 .. .. 92 .. .. .. .. 99 71 .. .. ..
.. 99 94 .. 99 100 .. .. 100 71 100 .. 63 100 .. 93 97 98 47 .. 90 .. .. .. 53 99 99 .. 97 .. .. 98 .. 100 100 .. .. 95 95 88 93 .. 100 .. .. .. 98 .. .. .. 80 99 88 .. .. ..
.. 99 91 .. 99 100 .. .. 100 73 100 .. 41 99 .. 95 99 97 33 .. 83 .. .. .. 35 99 99 .. 98 .. .. 98 .. 100 100 .. .. 97 96 82 94 .. 100 .. .. .. 96 .. .. .. 76 99 83 .. .. ..
Male 2005–07b
Female 2005–07b
.. 99 84 .. 98 100 .. .. 100 59 100 .. 53 96 .. 83 90 99 37 .. 86 .. .. .. 43 97 96 .. 92 .. .. 96 .. 99 100 .. .. 89 87 75 85 .. 100 .. .. .. 90 .. .. .. 72 98 79 .. .. ..
.. 99 66 .. 98 99 .. .. 99 48 100 .. 28 86 .. 83 90 98 22 .. 68 .. .. .. 21 96 90 .. 93 .. .. 96 .. 98 100 .. .. 90 82 58 80 .. 100 .. .. .. 82 .. .. .. 58 96 68 .. .. ..
Primary completion rate
% of relevant age group Total
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
Male
1991
2007a
1991
2007a
Female 1991 2007a
64 87 64 91 91 .. .. .. 104 90 101 101 .. .. .. 98 .. .. 45 .. .. 59 .. .. 89 .. 33 29 91 13 34 107 88 .. .. 48 26 .. .. 51 .. 100 42 18 .. 100 74 .. .. 46 68 .. 88 96 95 ..
88 96 86 99 105 .. 96 101 100 82 .. 99 104 d 93 .. 101 98 95 77 92 82 78 55d .. 93 97 62 55 98 49 59 94 104 93 110 83 46 .. 77 78 d .. .. 73 40 72 96 88 62 99 .. 95 101 94 97 104 ..
67 93 75 .. 97 .. .. .. 104 86 101 101 .. .. .. 98 .. .. .. .. .. 42 .. .. .. .. 33 36 91 15 41 107 .. .. .. 57 32 .. .. .. .. 101 .. 22 .. 100 78 .. .. 51 68 .. .. .. 94 ..
85 96 88 99 98 .. 91 100 100 81 .. 100 104 d 94 .. 106 98 95 81 93 80 65 60d .. 92 96 62 55 98 59 59 92 104 93 108 87 53 .. 73 79d .. .. 70 47 80 95 88 70 98 .. 94 101 90 .. 102 ..
61 95 52 .. 85 .. .. .. 104 94 102 101 .. .. .. 98 .. .. .. .. .. 76 .. .. .. .. 34 21 91 10 27 107 .. .. .. 39 21 .. .. .. .. 99 .. 13 .. 100 70 .. .. 42 69 .. .. .. 95 ..
90 96 83 99 113 .. 101 101 99 84 .. 98 105d 92 .. 95 98 94 72 91 83 92 50d .. 93 98 61 56 98 40 60 95 104 93 113 79 39 .. 81 78d .. .. 77 32 65 97 88 53 99 .. 96 101 97 .. 107 ..
PEOPLE
Education completion and outcomes
2.14
Youth literacy rate
Adult literacy rate
% ages 15–24
% ages 15 and older
Male 1990 2005–07b
Female 1990 2005–07b
.. 99 74c 97 92 .. .. .. .. .. .. .. 100 .. .. .. .. .. .. 100 .. .. 56 99 100 99 .. 70 96 .. .. 91 96 100 .. 71 .. .. 86 68 .. .. .. .. 81 .. .. .. 95 .. 96 97 96 100 99 92
.. 99 49c 95 81 .. .. .. .. .. .. .. 100 .. .. .. .. .. .. 100 .. .. 47 91 100 99 .. 49 95 .. .. 92 95 100 .. 46 .. .. 90 33 .. .. .. .. 62 .. .. .. 95 .. 95 94 97 100 99 94
93 98 87 97 97 .. .. .. 100 91 .. 99 100 .. .. .. 98 100 89 100 98 .. 68 100 100 99 .. 84 98 47 70 95 98 100 94 84 58 .. 91 85 .. .. 85 52 89 .. 99 79 97 63 99 98 94 100 100 ..
95 99 77 96 96 .. .. .. 100 98 .. 99 100 .. .. .. 99 100 79 100 99 .. 76 98 100 99 .. 82 98 31 62 97 98 100 97 67 47 .. 94 73 .. .. 89 23 85 .. 98 58 96 65 99 97 95 99 100 ..
Male 2005–07b
Female 2005–07b
84 99 77 95 87 .. .. .. 99 81 .. 95 100 .. .. .. 95 100 82 100 93 .. 60 94 100 99 .. 79 94 35 63 90 94 100 97 69 57 .. 89 70 .. .. 78 43 80 .. 89 68 94 62 96 95 93 100 97 ..
83 99 54 89 77 .. .. .. 99 91 .. 87 99 .. .. .. 93 99 63 100 86 .. 51 78 100 95 .. 65 90 18 48 85 91 99 98 43 33 .. 87 44 .. .. 78 15 64 .. 77 40 93 53 93 85 94 99 93 ..
2009 World Development Indicators
93
2.14
Education completion and outcomes Primary completion rate
% of relevant age group Total
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Male
1991
2007a
1991
2007a
Female 1991 2007a
100 .. 35 55 43 .. .. .. .. .. .. 76 103 102 42 60 96 53 89 .. 62 .. .. 35 101 74 90 .. .. 94 103 .. .. 94 .. 81 .. .. .. .. 97 79 w .. 84 83 90 78 101 93 84 78 62 51 .. 101
101 .. 35 93 49 .. 81 .. 93 .. .. 92 99 106 50 67 .. 88 114 95 112d 101 69 57 88 120 96 .. 54 101 105 .. 95 99 97 98 .. 83 60 88 .. 86 w 65 93 91 101 85 98 98 100 90 80 60 97 ..
100 .. 40 60 52 .. .. .. .. .. .. 71 104 103 47 57 96 53 94 .. 62 .. .. 48 98 79 93 .. .. .. 104 .. .. 91 .. 76 .. .. .. .. 99 86 w .. 90 90 90 85 105 93 84 84 75 57 .. 100
101 .. 36 96 51 .. 92 .. 94 .. .. 92 99 106 54 64 .. 88 116 .. 115d 99 69 67 86 122 101 .. 57 101 103 .. 94 98 99 96 .. 83 74 94 .. 88 w 70 94 93 101 87 98 99 100 93 83 65 97 ..
100 .. 31 51 33 .. .. .. .. .. .. 80 103 102 37 63 96 54 84 .. 63 .. .. 22 104 70 86 .. .. .. 103 .. .. 96 .. 86 .. .. .. .. 96 75 w .. 79 76 90 73 97 92 85 72 52 47 .. 100
101 .. 35 91 47 .. 70 .. 92 .. .. 92 99 107 46 69 .. 89 113 .. 109d 104 69 48 90 117 91 .. 51 101 106 .. 96 100 96 100 .. 83 46 83 .. 84 w 60 92 90 101 83 98 96 101 88 77 55 97 ..
Youth literacy rate
Adult literacy rate
% ages 15–24
% ages 15 and older
Male 1990 2005–07b
Female 1990 2005–07b
99 100 .. 94 49 99e .. 99 .. 100 .. .. 100 .. .. 83 .. .. .. 100 86 .. .. .. 99 .. 97 .. 77 .. .. .. .. .. .. 95 94 .. 83 67 97 88 w 70 90 89 96 97 99 93 86 85 71 71 100 ..
99 100 .. 81 28 98e .. 99 .. 100 .. .. 100 .. .. 84 .. .. .. 100 78 .. .. .. 99 .. 88 .. 63 .. .. .. .. .. .. 96 93 .. 35 66 94 79 w 56 81 78 96 92 98 94 76 69 48 59 99 ..
97 100 .. 98 58 .. 64 100 .. 100 .. 95 100 97 .. .. .. .. 95 100 79 98 .. .. 100 97 99 100 88 100 94 .. .. 98 .. 98 .. 99 93 82 94 91 w 79 94 93 98 90 98 99 97 93 84 77 100 ..
98 100 .. 96 45 .. 44 100 .. 100 .. 96 100 98 .. .. .. .. 92 100 76 98 .. .. 100 94 94 100 84 100 97 .. .. 99 .. 99 .. 99 67 68 88 87 w 69 91 89 98 85 98 99 97 86 74 67 100 ..
Male 2005–07b
98 100 .. 89 52 .. 50 97 .. 100 .. 89 99 93 .. .. .. .. 90 100 79 96 .. .. 99 86 96 100 82 100 89 .. .. 97 .. 95 .. 97 77 81 94 88 w 72 90 88 95 86 96 99 92 82 74 71 99 ..
a. Provisional data. b. Data are for the most recent year available. c. Includes the Indian-held part of Jammu and Kashmir. d. Data are for 2008. e. Includes Montenegro.
94
2009 World Development Indicators
Female 2005–07b
97 99 .. 79 33 .. 27 92 .. 100 .. 87 97 89 .. .. .. .. 76 100 66 93 .. .. 98 69 81 99 66 100 91 .. .. 98 .. 95 .. 90 40 61 88 79 w 55 80 77 93 75 90 90 90 65 52 54 99 ..
About the data
PEOPLE
Education completion and outcomes
2.14
Definitions
Many governments publish statistics that indi-
Institute for Statistics has established literacy as
• Primary completion rate is the percentage of stu-
cate how their education systems are working and
an outcome indicator based on an internationally
dents completing the last year of primary school. It is
developing—statistics on enrollment and such effi -
agreed definition.
calculated by taking the total number of students in
ciency indicators as repetition rates, pupil-teacher
The literacy rate is the percentage of people who
the last grade of primary school, minus the number of
ratios, and cohort progression. The World Bank and
can, with understanding, both read and write a
repeaters in that grade, divided by the total number
the United Nations Educational, Scientific, and Cul-
short, simple statement about their everyday life. In
of children of official completing age. • Youth literacy
tural Organization (UNESCO) Institute for Statistics
practice, literacy is difficult to measure. To estimate
rate is the percentage of people ages 15–24 that
jointly developed the primary completion rate indica-
literacy using such a definition requires census or
can, with understanding, both read and write a short,
tor. Increasingly used as a core indicator of an educa-
survey measurements under controlled conditions.
simple statement about their everyday life. • Adult
tion system’s performance, it reflects an education
Many countries estimate the number of literate
literacy rate is the literacy rate among people ages
system’s coverage and the educational attainment
people from self-reported data. Some use educa-
15 and older.
of students. The indicator is a key measure of edu-
tional attainment data as a proxy but apply different
cation outcome at the primary level and of progress
lengths of school attendance or levels of completion.
toward the Millennium Development Goals and the
Because definitions and methodologies of data col-
Education for All initiative. However, because curri-
lection differ across countries, data should be used
cula and standards for school completion vary across
cautiously.
countries, a high primary completion rate does not necessarily mean high levels of student learning.
The reported literacy data are compiled by the UNESCO Institute for Statistics based on national
The primary completion rate reflects the primary
censuses and household surveys during 1985–2007.
cycle as defined by the International Standard Clas-
For countries that have not reported national esti-
sification of Education, ranging from three or four
mates, the UNESCO Institute for Statistics derived
years of primary education (in a very small number
the modeled estimates. For detailed information on
of countries) to five or six years (in most countries)
sources, definitions, and methodology, consult the
and seven (in a small number of countries).
original source.
The table shows the proxy primary completion rate,
Literacy statistics for most countries cover the pop-
calculated by subtracting the number of repeaters
ulation ages 15 and older, but some include younger
in the last grade of primary school from the total
ages or are confined to age ranges that tend to inflate
number of students in that grade and dividing by the
literacy rates. The literacy data in the narrower age
total number of children of official graduation age.
range of 15–24 better captures the ability of partici-
Data limitations preclude adjusting for students who
pants in the formal education system and reflects
drop out during the final year of primary school. Thus
recent progress in education. The youth literacy rate
proxy rates should be taken as an upper estimate of
reported in the table measures the accumulated out-
the actual primary completion rate.
comes of primary education over the previous 10
There are many reasons why the primary comple-
years or so by indicating the proportion of people who
tion rate can exceed 100 percent. The numerator
have passed through the primary education system
may include late entrants and overage children who
and acquired basic literacy and numeracy skills.
have repeated one or more grades of primary school as well as children who entered school early, while the denominator is the number of children of official completing age. Other data limitations contribute to completion rates exceeding 100 percent, such as the use of population estimates of varying reliability, the conduct of school and population surveys at different times of year, and other discrepancies in the numbers used in the calculation. Basic student outcomes include achievements in reading and mathematics judged against established standards. In many countries national assessments are enabling the ministry of education to monitor progress in these outcomes. Internationally comparable assessments are not yet available, except for
Data sources Data on primary completion rates and literacy rates are from the UNESCO Institute for Statistics.
a few, mostly industrialized, countries. The UNESCO
2009 World Development Indicators
95
2.15
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 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
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 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
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 .. .. 128 .. 84 180 45 109 104 .. 240 127 11 77 68 137 114 131 216 95 145 .. 121 83 138
a. Less than 0.5.
96
2009 World Development Indicators
93 156 112 95 97 187 100 118 14 85 119 39 103 120 117 257 .. 90 124 39 200 72 92 .. .. 123 .. 87 226 89 85 134 .. 249 108 69 106 173 106 94 102 197 231 127 .. 105 119 114
177 107 51 92 20 78 94 57 15 126 56 41 149 96 42 61 155 71 62 10 94 87 103 101 125 104 133 59 103 36 98 79 138 116 79 15 67 45 103 112 103 100 63 106 102 139 74 104
181 120 115 98 98 134 122 130 98 99 147 103 156 103 154 186 140 108 122 38 152 122 104 99 130 118 138 134 126 101 116 150 116 160 104 77 111 127 114 109 102 126 119 120 114 127 112 109
9 3 1 6 1 2 3 2 0a 6 2 2 6 6 1 1 5 4 2 1 3 3 7 10 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 8 6 11 6 7 9 6 5 11 6 6 11 11 7 5 8 9 9 5 8 10 11 12 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
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 14 86 1 10 3 17 2 15 18 14 8 16 11 29 41 46 7 27 7 84 58 16 36
98 70 45 90 52 45 69 18 36 90 29 41 87 87 65 44 60 66 76 27 40 87 97 98 100 57 88 47 52 37 78 17 65 59 88 46 70 55 77 93 88 28 55 43 87 97 79 80
96 47 23 75 24 18 36 8 15 70 12 25 57 77 21 15 35 38 41 18 13 64 86 97 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
98 58 15 75 20 17 37 6 8 77 12 17 69 71 24 12 40 41 40 9 18 55 89 97 99 36 87 16 14 11 46 7 34 28 59 13 37 22 54 72 79 14 34 21 86 84 43 57
14 25 66 24 87 50 42 65 91 8 72 70 14 24 84 87 8 57 58 95 64 35 19 11 24 24 21 60 29 75 26 59 22 33 46 90 56 72 21 9 17 43 74 28 29 8 61 22
13 10 21 5 32 12 4 21 36 1 26 23 4 5 10 42 3 20 8 77 21 2 6 9 18 4 18 6 9 29 2 13 9 6 5 44 5 13 10 1 2 23 27 6 23 2 18 8
About the data
PEOPLE
Education gaps by income and gender
2.15
Definitions
The data in the table describe basic information on
collected was assigned a weight generated through
• Survey year is the year in which the underlying data
school participation and educational attainment by
principal-component analysis. The resulting scores
were collected. • Gross intake rate in grade 1 is
individuals in different socioeconomic groups within
were standardized to a standard normal distribution
the number of students in the first grade of primary
countries. The data are from Demographic and
with a mean of zero and a standard deviation of one.
education regardless of age as a percentage of the
Health Surveys conducted by Macro International
The standardized scores were then used to create
population of the official primary school entrance
with the support of the U.S. Agency for International
break-points defining wealth quintiles, expressed as
age. These data may differ from those in table 2.13.
Development. These large-scale household sample
quintiles of individuals in the population.
• Gross primary participation rate is the ratio of
surveys, conducted periodically in developing coun-
The selection of the asset index for defining socio-
total students attending primary school regardless
tries, collect information on a large number of health,
economic status was based on pragmatic rather than
of age to the population of the age group that offi -
nutrition, and population measures as well as on
conceptual considerations: Demographic and Health
cially corresponds to primary education. • Average
respondents’ social, demographic, and economic
Surveys do not collect income or consumption data
years of schooling are the years of formal school-
characteristics using a standard set of question-
but do have detailed information on households’ own-
ing received, on average, by youths and adults ages
naires. The data presented here draw on responses
ership of consumer goods and access to a variety
15–24. • Primary completion rate is the percentage
to individual and household questionnaires.
of goods and services. Like income or consumption,
of children of the official primary school completing
Typically, Demographic and Health Surveys collect
the asset index defines disparities primarily in eco-
age to the official primary school completing age plus
basic information on educational attainment and
nomic terms. It therefore excludes other possibilities
four who have completed the last year of primary
enrollment levels from every household member
of disparities among groups, such as those based
school or higher. These data differ from those in
ages 5 or 6 and older as background characteris-
on gender, education, ethnic background, or other
table 2.14 because the definition and methodology
tics. As the surveys are intended for the collection of
facets of social exclusion. To that extent the index
are different. • Children out of school are the per-
demographic and health information, the education
provides only a partial view of the multidimensional
centage of children ages 6–11 who are not in school.
section of the survey is not as robust and detailed
concepts of poverty, inequality, and inequity.
These data differ from those in table 2.12 because
as the health section; however, it still provides useful
Creating one index that includes all asset indica-
micro-level information on education that cannot be
tors limits the types of analysis that can be per-
explained by aggregate national-level data.
formed. In particular, the use of a unified index does
Socioeconomic status as displayed in the table
not permit a disaggregated analysis to examine
is based on a household’s assets, including owner-
which asset indicators have a more or less important
ship of consumer items, features of the household’s
association with education status. In addition, some
dwelling, and other characteristics related to wealth.
asset indicators may reflect household wealth better
Each household asset on which information was
in some countries than in others—or reflect differ-
the definition and methodology are different.
ent degrees of wealth in different countries. Taking There is a large gap in educational attainment across gender and 2.15a urban-rural lines
such information into account and creating countryspecific asset indexes with country-specific choices of asset indicators might produce a more effective
Highest level of education, Liberia, 2007 (%) 75 Urban men Rural men Urban women Rural women
and accurate index for each country. The asset index used in the table does not have this flexibility. The analysis was carried out for 48 countries. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for 32 indi-
50
cators is available in the country reports (see Data sources). The data in the table differ from data for similar indicators in preceding tables either because 25
the indicator refers to a period a few years preceding the survey date or because the indicator definition or methodology is different. Findings should be used
0
with caution because of measurement error inherent No education
Primary
Secondary or higher
in the use of survey data.
Data sources
Rural women are the most disadvantaged in
Data on education gaps by income and gender are
Liberia, with more than 55 percent having no
from an analysis of Demographic and Health Sur-
education and only 10 percent having second-
veys by Macro International and the World Bank.
ary or higher education.
Country reports are available at www.worldbank.
Source: Demographic and Health Surveys.
org/education/edstats/.
2009 World Development Indicators
97
2.16
Health systems Health expenditure
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
98
Year last national health account completed Per capita $ PPP$
Total % of GDP
Public % of total
Out of pocket % of private
2006
2006
2006
2006
2006
9.2 6.5 4.2 2.6 10.1 4.7 8.7 10.2 4.1 3.2 6.4 9.9 4.7 6.4 9.5 7.1 7.5 7.2 6.3 8.7 5.9 4.6 10.0 4.0 4.9 5.3 4.6 .. 7.3 6.8 2.1 7.7 3.8 8.2 7.7 6.9 10.8 5.6 5.3 6.3 6.6 3.6 5.2 3.9 8.2 11.0 4.5 5.0 8.4 10.6 5.1 9.5 5.8 5.8 5.8 8.4
32.4 37.3 81.1 86.8 45.5 41.2 67.7 75.9 26.1 31.8 74.9 72.5 50.2 62.8 55.2 76.5 47.9 56.7 56.9 8.6 26.0 21.2 70.4 38.3 53.9 52.7 40.7 .. 85.4 18.7 71.7 68.4 23.6 76.8e 91.6 88.0 85.9 37.0 43.6 41.4 61.8 45.9 73.3 59.3 76.0 79.7 73.0 56.8 21.5 76.9 34.2 62.0 28.7 14.1 26.3 67.6
78.5 94.9 94.6 100.0 43.8 87.6 56.4 65.8 86.4 88.3 68.8 79.0 94.9 81.0 100.0 27.5 63.8 97.1 91.5 57.4 84.7 94.8 49.0 95.0 96.2 54.8 83.1 .. 43.9 48.9 100.0 86.7 87.8 92.2 93.3 95.5 90.1 64.3 85.6 94.9 88.9 100.0 93.3 80.6 77.6 33.2 100.0 71.2 91.9 57.1 77.8 94.8 92.5 99.5 55.8 89.6
.. 187 148 71 551 98 3,302 3,974 102 12 243 3,726 26 79 296 379 427 297 27 10 30 45 3,917 14 29 473 94 .. 217 10 44 402 35 996e 362 953 5,447 206 166 92 181 8 632 7 3,232 3,937 351 15 147 3,718 33 2,280 157 20 12 42
.. 1,332 .. 187 2,723 763 4,152 5,424 1,031 119 1,997 4,821 154 699 1,102 1,054 1,460 1,709 161 92 437 202 4,651 56 379 1,290 1,124 .. 989 47 188 .. .. 2,101 .. 3,270 5,165 .. 928 1,174 .. .. 2,043 77 3,607 5,189 1,338 142 1,063 5,210 214 3,745 .. 105 69 ..
2009 World Development Indicators
2003 2001 1997 2007 2005 2004 2005 2006 2003 2007 2006 2003 2005 2006 2005
1995 2007
2006 2006 2003
2003
2006 2006 2002 2005 2002 2007 2006 2005 2006 2006 2004 2007 2006 2002 2007
2006
Health workers
Year of last health surveya
Hospital beds
Outpatient visits
per 1,000 people Nurses and Physicians midwives
per 1,000 people
per capita
2002–07b
2002–07b
2000–07b
.. 3.0 1.7 0.8 .. 4.4 4.0 7.6 8.0 0.3 11.3 5.3 0.5 1.1 3.0 2.4 2.4 6.4 0.9 0.7 0.1 1.5 3.4 1.2 0.4 2.3 2.2 .. 1.0 0.8 1.6 1.3 0.4 5.3 4.9 8.2 3.8 1.0 1.7 2.1 0.7 1.2 5.7 0.2 6.8 7.3 2.0 0.8 3.3 8.3 0.9 4.8 0.7 0.3 0.7 1.3
.. 1.5 .. .. .. 2.8 6.2 6.7 4.6 .. 13.2 7.0 .. .. 3.3 .. .. .. .. .. .. .. 6.3 .. .. .. .. .. .. .. .. .. .. 6.4 .. 15.0 4.1 .. .. .. .. .. 6.9 .. 4.3 6.9 .. .. 2.2 7.0 .. .. .. .. .. ..
.. 1.2 1.1 0.1 .. 3.7 2.5 3.7 3.6 0.3 4.8 4.2 0.0 c .. 1.4 0.4 .. 3.7 0.1 0.0 c .. 0.2 1.9 0.1 0.0 c 1.1 1.5 .. 1.4d 0.1 0.2 .. 0.1 2.7 5.9 3.6 3.6 .. .. 2.4 1.5 0.1 3.3 0.0 c 3.3 3.4 0.3 0.1 4.7 3.4 0.2 5.0 .. 0.1 0.1 ..
2002–07b
.. 4.1 2.2 1.4 .. 4.9 .. 6.6 8.4 0.3 12.5 14.2 0.8 .. 4.7 2.7 .. 4.6 0.5 0.2 .. 1.6 10.1 0.4 0.3 0.6 1.0 .. 0.6 0.5 1.0 .. 0.6 5.5 7.4 8.9 10.1 .. .. 3.4 0.8 0.6 7.0 0.2 8.9 8.0 5.0 1.3 4.0 8.0 0.9 3.6 .. 0.5 0.7 ..
2003 2005 2006 2001 2005
2006 2006 2005 2006 2003 2006 2000 1996 2003 2000 2005 2006 2006 2004 2006 2005 2007 2005 1993 2006 2006 1993 2007 2004 2005 2002/03 2002 2005
2000 2005/06 2005 2006 2002 2005 2006 2005
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
Year last national health account completed Per capita $ PPP$
Total % of GDP
Public % of total
Out of pocket % of private
2006
2006
2006
2006
6.4 8.3 3.6 2.5 6.8 3.5f 7.5 8.0 9.0 4.7 8.1 9.7g 3.6 4.6 3.5 6.4 2.2 6.4 4.0 6.6 8.8 6.8 4.8 2.4 6.2 8.0 3.2 12.9 4.3 5.8 2.2 3.9 6.6 9.4 5.7 5.3 5.0 2.2 8.7 5.1 9.4 9.3 9.6 5.9 3.8 8.7 2.3 2.0 7.3 3.2 7.6 4.4 3.8 6.2 10.2 ..
47.8 70.9 25.0 50.5 50.7 78.1f 78.3 56.0 77.2 53.1 81.3 43.3g 64.3 47.8 85.6 55.7 78.2 43.0 18.6 59.2 44.3 58.9 25.8 66.3 70.0 70.6 62.8 69.0 44.6 49.6 69.5 51.1 44.2 46.9 73.7 26.2 70.8 13.1 43.5 30.5 80.0 77.8 48.2 54.7 29.7 83.6 82.3 16.4 68.8 82.0 38.3 58.3 32.9 70.0 70.5 ..
87.1 77.6 91.4 70.4 94.8 100.0 f 57.2 75.3 88.5 63.7 80.8 75.9 98.4 80.0 100.0 81.0 91.6 94.1 76.1 97.2 76.1 68.9 65.7 100.0 98.3 100.0 52.5 28.4 73.2 99.5 100.0 80.6 93.9 97.7 44.0 77.3 40.6 99.4 5.7 85.2 29.3 74.6 98.1 96.5 90.4 95.2 57.7 97.9 80.6 41.5 87.7 77.5 83.5 85.4 77.3 ..
99 929 29 39 215 .. 3,871 1,675 2,813 180 2,759 238g 190 29 .. 1,168 803 35 24 582 494 51 7 219 547 249 9 21 259 31 19 230 527 90 70 113 16 5 281 17 3,872 2,421 92 16 33 6,267 332 16 380 29 117 149 52 555 1,864 ..
2006
.. 2,761 426 213 3,057 .. 4,270 3,028 3,190 .. 4,693 988g 1,608 205 .. 3,341 1,614 524 401 2,320 1,694 403 46 .. 2,046 1,522 60 114 1,518 152 128 998 1,208 862 984 288 75 .. 957 215 5,520 3,370 .. 78 184 5,952 906 187 .. .. 777 587 314 2,031 3,014 ..
2005 2006 2001 2004 2001
2006 2006 2001 2007 2002 2007 2006 2005 2005
2006 2003 2006 2006 2004 2004 2006 2003 2001 1997 2001 2000 2003 2007 2006 2004 2004 2002 2005 1998 2003 2000 2005 2005 2007 2006 2006
Health workers
Year of last health surveya
2.16 Hospital beds
Outpatient visits
per 1,000 people Nurses and Physicians midwives
per 1,000 people
per capita
2002–07b
2002–07b
2000–07b
1.0 7.1 0.9 0.6 1.7 .. 5.6 6.0 3.9 2.0 14.0 1.9 8.1 1.4 13.2 8.6 1.9 4.9 1.2 7.5 3.4 1.3 .. 3.7 8.1 4.6 0.3 1.1 1.8 0.3 0.4 3.0 1.6 5.2 6.1 0.9 0.8 0.6 3.3 0.2 4.8 6.2 1.0 0.3 0.5 4.0 2.0 1.0 2.2 .. 1.3 1.2 1.1 5.2 3.5 ..
.. 12.9 .. .. .. .. .. 7.1 6.1 .. 14.4 .. 6.6 .. .. .. .. 3.6 .. 5.5 .. .. .. .. 6.6 6.0 0.5 .. .. .. .. .. 2.5 6.0 .. .. .. .. .. .. 5.4 4.4 .. .. .. .. .. .. .. .. .. .. .. 6.1 3.9 ..
.. 3.0 0.6 0.1 0.9 .. 2.9 3.7 3.7 0.9 2.1 2.4 3.9 0.1 3.3 1.6 1.8 2.4 0.4 3.1 2.4 0.1 0.0 c 1.3 4.0 2.6 0.3 0.0 c 0.7 0.1 0.1 1.1 1.5 2.7 2.6 0.5 0.0 c 0.4 0.3 0.2 3.7 2.2 0.4 0.0 c 0.3 3.8 1.7 0.8 .. .. 1.1 .. 1.2 2.0 3.4 ..
2002–07b
.. 9.2 1.3 0.8 1.6 .. 19.5 6.2 7.2 1.7 9.5 3.2 7.6 1.2 4.1 1.9 3.7 5.8 1.0 5.6 1.3 0.6 0.3 4.8 7.7 4.3 0.3 0.6 1.8 0.6 0.6 3.7 .. 6.2 3.5 0.8 0.3 1.0 3.1 0.5 14.6 8.9 1.1 0.2 1.7 16.2 3.7 0.5 .. .. 1.8 .. 6.1 5.2 4.6 ..
2005 2005/06 2002/03 2000 2006
2005 2007 2006 2004 2000 1996 2005/06 2006 2000 2004 2007 2000 2005 2003/04 2006 2006 2000/01 1995 2005 2005 2003/04 2003 2000 2006/07 2006
2001 2006 2007 1995 2006/07 2003 1996 2004 2004 2003
1995/96
2009 World Development Indicators
99
PEOPLE
Health systems
2.16
Health systems Health expenditure
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, R.B. Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Total % of GDP
Public % of total
Out of pocket % of private
2006
2006
2006
4.5 5.3 10.9 3.3 5.8 8.2 4.0 3.3 7.1 8.4 .. 8.0 8.4 4.2 3.8 6.3 9.2 10.8 3.9 5.0 6.4 3.5 17.7 6.0 4.4 5.1 4.8 3.8 7.0 6.9 2.5 8.2 15.3 8.2 4.7 4.9 6.6 .. 4.5 6.2 9.3 9.8 w 4.3 5.4 4.5 6.3 5.3 4.3 5.5 6.9 5.7 3.5 5.7 11.2 9.9
76.9 63.2 42.5 77.0 56.9 69.7 36.4 33.1 70.6 72.2 .. 37.7 71.2 47.5 36.8 65.8 81.7 59.1 47.8 23.8e 57.8 64.5 86.0 21.2 56.5 44.2 72.5 66.5 25.4 55.4 70.4 87.3 45.8 43.5 50.2 49.5 32.3 .. 46.0 60.7 48.7 60.0 w 36.8 50.1 44.1 54.7 49.5 42.1 66.2 50.0 51.3 25.8 41.6 61.6 76.9
96.8 81.5 38.6 13.4 77.0 87.9 56.4 93.8 79.8 42.5 .. 17.5 74.7 86.7 100.0 41.4 87.9 75.3 100.0 96.6 54.3 76.6 37.2 84.2 88.0 81.7 84.2 100.0 51.0 88.8 69.4 91.7 23.5 31.1 97.1 88.6 90.2 .. 95.2 67.2 50.3 43.5 w 84.6 77.6 85.0 70.7 78.0 82.1 85.6 72.2 90.5 91.4 46.8 36.5 60.0
Year last national health account completed Per capita $ PPP$ 2006
256 367 33 492 44 336 12 1,017 735 1,607 .. 425 2,328 62 37 155 3,973 5,660 66 25e 23 113 52 21 600 156 352 146 24 160 1,018 3,332 6,719 476 30 332 46 .. 40 58 38 722 w 23 140 75 412 114 83 304 374 133 26 53 4,033 3,268
2006
1,244 2,217 270 1,386 199 1,717 88 3,037 2,788 3,230 .. 1,100 3,935 677 167 1,420 4,588 5,446 482 455 324 825 .. 67 .. 624 866 .. 165 1,327 .. 4,259 6,719 1,616 .. .. 658 .. 367 244 .. 1,466 w 205 945 794 1,581 789 939 1,631 1,355 1,364 368 224 4,969 4,460
2006 2007 2006 2005 2005
2006 2006 1998 2006 2006
2006 2007
2006 2006 2002 2000 2005 2005 2001 2004 2000 2007 2006
2006 2003 2006 2001
Health workers
Year of last health surveya
Hospital beds
Outpatient visits
per 1,000 people Nurses and Physicians midwives
per 1,000 people
per capita
2002–07b
2002–07b
2002–07b
2000–07b
1.9 4.3 0.1 1.4 0.1 2.0 0.0 c 1.5 3.1 2.4 .. 0.8 3.3 0.6 0.3 0.2 3.3 4.0 0.5 2.0 0.0 c .. 0.1 0.0 c .. 1.3 1.6 2.5 0.1 3.1 1.7 2.2 2.3 3.7 2.7 .. 0.6 .. 0.3 0.1 0.2 .. w .. .. .. .. .. 1.5 3.2 .. .. 0.6 .. 2.6 3.5
4.2 8.5 0.4 3.0 0.3 4.3 0.5 4.4 6.6 8.0 .. 4.1 7.6 1.7 0.9 6.3 10.9 .. 1.4 5.0 0.4 .. 2.2 0.4 1.8 2.9 2.9 4.7 0.7 8.5 3.5 .. .. 0.9 10.9 .. 0.8 .. 0.7 2.0 0.7 .. w .. .. .. .. .. 1.0 6.7 .. .. 1.3 .. .. ..
6.5 9.7 1.6 2.2 0.1 4.1 0.4 3.2 6.8 4.8 .. 2.8 3.4 3.1 0.7 2.1 .. 5.5 1.5 5.4 d 1.1 2.2 .. 0.9 2.7 1.8 2.7 4.3 1.1 8.7 1.9 3.9 3.1 2.9 4.7 0.9 2.7 .. 0.7 2.0 3.0 2.6 w .. 2.3 1.8 5.0 1.7 2.2 7.1 .. .. 0.9 .. 6.1 5.7
5.6 9.0 .. .. .. .. .. .. 12.5 6.6 .. .. 9.5 .. .. .. 2.8 .. .. 8.3d .. .. .. .. .. .. 4.6 3.7 .. 10.8 .. 4.9 9.0 .. 8.7 .. .. .. .. .. .. .. w .. .. .. .. .. .. 7.5 .. .. .. .. 8.6 6.8
1999 1996 2005 2007 2005 2005-06 2005 2005
2006 1998 1987 2006 2000
2006 2005 2006 2005/06 2006 2006 2006 2003 2006 2006 2007
monthly 2006 2000 2006 2006 2006 2005 2005/06
a. Survey name can be found in Primary data documentation. b. Data are for the most recent year available. c. Less than 0.05. d. Data are for 2008. e. Data are for 2007. f. Excludes northern Iraq. g. Includes contributions from the United Nations Relief and Works Agency for Palestine Refugees.
100
2009 World Development Indicators
2.16
About the data
Health systems—the combined arrangements of
expenditures. In general, low-income economies
international agencies and nongovernmental organi-
institutions and actions whose primary purpose is to
have a higher share of private health expenditure
zations), and social (or compulsory) health insurance
promote, restore, or maintain health (WHO 2000)—
than do middle- and high-income countries. High
funds. • Out-of-pocket health expenditure, part of
are increasingly being recognized as key to combating
out-of-pocket expenditures may discourage people
private health expenditure, is direct household out-
disease and improving the health status of popula-
from accessing preventive or curative care and can
lays, including gratuities and in-kind payments, for
tions. The World Bank’s 2007 “Healthy Development:
impoverish households that cannot afford needed
health practitioners and pharmaceutical suppliers,
Strategy for Health, Nutrition, and Population
care. Health financing data are collected through
therapeutic appliances, and other goods and ser-
Results” emphasizes the need to strengthen health
national health accounts, which systematically,
vices whose primary intent is to restore or enhance
systems, which are weak in many countries, in order
comprehensively, and consistently monitoring health
health. • Health expenditure per capita is total
to increase the effectiveness of programs aimed at
system resource flows. To establish a national health
health expenditure divided by population in U.S.
reducing specific diseases and further reduce mor-
account, countries must define the boundaries of the
dollars and in international dollars converted using
bidity and mortality (World Bank 2007c). To evaluate
health system and classify health expenditure infor-
2005 purchasing power parity (PPP) rates for health
health systems, the World Health Organization (WHO)
mation along several dimensions, including sources
expenditure. • Year last national health account
has recommended that key components—such as
of financing, providers of health services, functional
completed is the latest year for which the health
financing, service delivery, workforce, information,
use of health expenditures, and benefi ciaries of
expenditure data are available using the national
and governance—be monitored using several key
expenditures. The accounting system can then pro-
health account approach. • Physicians include gen-
indicators (WHO 2008a). The data in the table are
vide an accurate picture of resource envelopes and
eralist and specialist medical practitioners.• Nurses
a subset of these indicators. Monitoring health sys-
financial flows and allow analysis of the equity and
and midwives include professional nurses and
tems allows the effectiveness, efficiency, and equity
efficiency of financing to inform policy.
midwives, auxiliary nurses and midwives, enrolled
of different health system models to be compared.
Many low-income countries use Demographic and
nurses and midwives, and other personnel, such as
Health system data also help identify weaknesses
Health Surveys or Multiple Indicator Cluster Surveys
dental nurses and primary care nurses. • Year of
and strengths and areas that need investment, such
funded by donors to obtain health system data. Data
last health survey is the latest year the national sur-
as additional health facilities, better health informa-
on health worker (physicians, nurses, and midwives)
vey that collects health information was conducted.
tion systems, or better trained human resources.
density shows the availability of medical personnel.
• Hospital beds are inpatient beds for both acute
Health expenditure data are broken down into
The WHO estimates that at least 2.5 physicians,
and chronic care available in public, private, general,
public and private expenditures, with private expen-
nurses, and midwives per 1,000 people are needed
and specialized hospitals and rehabilitation centers.
diture further broken down into out-of-pocket expen-
to provide adequate coverage with primary care inter-
• Outpatient visits per capita are the number of
diture (direct payments by households to providers),
ventions associated with achieving the Millennium
visits to health care facilities per capita, including
which make up the largest proportion of private
Development Goals (WHO 2006). The WHO compiles
repeat visits.
data from household and labor force surveys, cenThere is a wide gap in health expenditure per capita between high-income economies and developing economies
suses, and administrative records. Data comparability is limited by differences in definitions and train-
2.16a
ing of medical personnel varies. In addition, human resources tend to be concentrated in urban areas, so
Health expenditure per capita ($) 5,000
2002
2006
4,000
3,000
2,000
Data sources
that average densities do not provide a full picture of
Data on health expenditures and year last national
health personnel available to the entire population.
health account completed are mostly from the
Availability and use of health services, shown by
WHO’s National Health Account database (www.
hospital beds per 1,000 people and outpatient visits
who.int/nha/en), supplemented by country data.
per capita, reflect both demand and supply side fac-
Data on health expenditure per capita in current
tors. In the absence of a consistent definition these
dollars are from WHO’s National Health Account
are crude indicators of the extent of physical, finan-
database. Data on health expenditure per capita
cial, and other barriers to health care.
in PPP dollars are World Bank staff estimates based on the WHO’s National Health Account
1,000
Definitions
0
database and the 2005 round of the International
• Total health expenditure is the sum of public and
Comparison Program. Data on physicians, nurses
private health expenditure. It covers the provision of
and midwives, hospital beds, and outpatient vis-
Health expenditure per capita by high-income
health services (preventive and curative), family plan-
its are from the WHO, OECD, and TransMONEE,
economies is 300 times more than that by
ning and nutrition activities, and emergency aid for
supplemented by country data. Information on
developing economies, and the gap has been
health but excludes provision of water and sanitation.
health survey is from various sources including
increasing.
• Public health expenditure is recurrent and capital
Macro International and the United Nations Chil-
Source: World Health Organization.
spending from central and local governments, exter-
dren’s Fund.
High income
Middle income
Low income
nal borrowing and grants (including donations from
2009 World Development Indicators
101
PEOPLE
Health systems
2.17
Disease prevention coverage and quality Access to an improved water source
Access to improved sanitation facilities
% of population 1990 2006
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
102
.. .. 94 39 94 .. 100 100 68 78 100 .. 63 72 97 93 83 99 34 70 19 49 100 58 .. 91 67 .. 89 43 .. .. 67 99 .. 100 100 84 73 94 69 43 100 13 100 .. .. .. 76 100 56 96 79 45 .. 52
.. 97 85 51 96 98 100 100 78 80 100 .. 65 86 99 96 91 99 72 71 65 70 100 66 48 95 88 .. 93 46 71 98 81 99 91 100 100 95 95 98 84 60 100 42 100 100 87 86 99 100 80 100 96 70 57 58
% of population 1990 2006
.. .. 88 26 81 .. 100 100 .. 26 .. .. 12 33 .. 38 71 99 5 44 8 39 100 11 5 84 48 .. 68 15 .. 94 20 99 98 100 100 68 71 50 73 3 95 4 100 .. .. .. 94 100 6 97 70 13 .. 29
2009 World Development Indicators
.. 97 94 50 91 91 100 100 80 36 93 .. 30 43 95 47 77 99 13 41 28 51 100 31 9 94 65 .. 78 31 20 96 24 99 98 99 100 79 84 66 86 5 95 11 100 .. 36 52 93 100 10 98 84 19 33 19
Child immunization rate
% of children ages 12–23 monthsb Measles DTP3 2007 2007
.. 97 92 88 99 92 94 79 97 88 99 92 61 81 96 90 99 96 94 75 79 74 94 62 23 91 94 .. 95 79 67 90 67 96 99 97 89 96 99 97 98 95 96 65 98 87 55 85 97 94 95 88 93 71 76 58
.. 98 95 83 96 88 92 85 95 90 95 99 67 81 95 97 98 95 99 74 82 82 94 54 20 94 93 .. 93 87 80 89 76 96 93 99 75 79 99 98 96 97 95 73 99 98 38 90 98 97 94 88 82 75 63 53
Children with acute respiratory infection taken to health provider
Children with diarrhea who received oral rehydration and continuous 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 2002–07c
% of children under age 5 with diarrhea 2002–07c
% of children under age 5 2002–07c
% of children under age 5 with fever 2002–07c
% of new registered cases 2006
% of new estimated cases 2007
.. 45 53 .. .. 36 .. .. 33 30 90 .. 36 52 91 .. .. .. 39 38 48 35 .. 32 12 .. .. .. 62 42 48 .. 35 .. .. .. .. 67 .. 63 62 44 .. 19 .. .. .. 69 74 .. 34 .. 64 42 57 31
.. 50 24 .. .. 59 .. .. 45 49 54 .. 42 54 53 .. .. .. 42 23 50 22 .. 47 27 .. .. .. 39 .. 39 .. 45 .. .. .. .. 42 .. 27 .. 54 .. 15 .. .. .. 38 37 .. 29 .. 22 38 25 43
.. .. .. 17.7 .. .. .. .. .. .. .. .. 20.1 .. .. .. .. .. 9.6 8.3 4.2 13.1 .. 15.1 0.6 .. .. .. .. 5.8 6.1 .. 3.0 .. .. .. .. .. .. .. .. 4.2 .. 33.1 .. .. .. 49.0 .. .. 21.8 .. .. 1.4 39.0 ..
.. .. .. 29.3 .. .. .. .. .. .. .. .. 54.0 .. .. .. .. .. 48.0 30.0 0.2 57.8 .. 57.0 44.0 .. .. .. .. 29.8 48.0 .. 36.0 .. .. .. .. .. .. .. .. 3.6 .. 9.5 .. .. .. 62.6 .. .. 60.8 .. .. 43.5 45.7 5.1
84 93 91 18 63 69 85 71 60 92 70 73 87 83 97 72 72 80 73 83 93 74 57 65 54 85 94 78 71 86 53 88 73 30 90 69 77 78 74 87 91 90 68 84 .. .. 46 58 75 71 76 .. 47 75 69 82
64 54 98 102 76 51 49 41 46 66 40 58 86 71 81 57 69 81 18 27 61 91 62 71 18 105 80 60 81 61 56 120 42 46 109 67 69 66 46 72 65 35 76 28 0 0 66 64 113 54 36 0 40 53 68 49
Access to an improved water source
% of population 1990 2006
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
72 96 71 72 92 83 .. 100 .. 92 100 97 96 41 .. .. .. .. .. 99 100 .. 57 71 .. .. 39 41 98 33 37 100 88 .. 64 75 36 57 57 72 100 97 70 41 50 100 81 86 .. 39 52 75 83 .. 96 ..
84 100 89 80 .. .. .. 100 .. 93 100 98 96 57 100 .. .. 89 60 99 100 78 64 .. .. 100 47 76 99 60 60 100 95 90 72 83 42 80 93 89 100 .. 79 42 47 100 .. 90 92 40 77 84 93 .. 99 ..
Access to improved sanitation facilities
% of population 1990 2006
45 100 14 51 83 .. .. .. .. 83 100 .. 97 39 .. .. .. .. .. .. .. .. 40 97 .. .. 8 46 .. 35 20 94 56 .. .. 52 20 23 26 9 100 .. 42 3 26 .. 85 33 .. 44 60 55 58 .. 92 ..
66 100 28 52 .. .. .. .. .. 83 100 85 97 42 .. .. .. 93 48 78 .. 36 32 97 .. 89 12 60 94 45 24 94 81 79 50 72 31 82 35 27 100 .. 48 7 30 .. .. 58 74 45 70 72 78 .. 99 ..
Child immunization rate
% of children ages 12–23 monthsb Measles DTP3 2007 2007
89 99 67 80 97 .. 87 97 87 76 98 95 99 80 99 92 99 99 40 97 53 85 95 98 97 96 81 83 90 68 67 98 96 96 98 95 77 81 69 81 96 79 99 47 62 92 97 80 89 58 80 99 92 98 95 ..
86 99 62 75 99 .. 92 96 96 85 98 98 93 81 92 91 99 94 50 98 74 83 88 98 95 95 82 87 96 68 75 97 98 92 95 95 72 86 86 82 96 88 87 39 54 93 99 83 88 60 66 80 87 99 97 ..
2.17
Children with acute respiratory infection taken to health provider
Children with diarrhea who received oral rehydration and continuous 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 2002–07c
% of children under age 5 with diarrhea 2002–07c
% of children under age 5 2002–07c
% of children under age 5 with fever 2002–07c
% of new registered cases 2006
% of new estimated cases 2007
56 .. 69 61 .. .. .. .. .. 75 .. 75 71 49 93 .. .. 62 .. .. .. 59 70 .. .. 93 48 52 .. 38 45 .. .. 60 63 38 55 66 72 43 .. .. .. 47 33 .. .. 69 .. .. .. 67 55 .. .. ..
49 .. 33 56 .. .. .. .. .. 39 .. 44 48 33 .. .. .. 22 .. .. .. 53 .. .. .. 45 47 27 .. 38 .. .. .. 48 47 46 47 65 .. 37 .. .. .. 34 28 .. .. 37 .. .. .. 71 76 .. .. ..
.. .. .. .. .. .. .. .. .. .. .. .. .. 6.0 .. .. .. .. .. .. .. .. .. .. .. .. 0.2 24.7 .. 27.1 .. .. .. .. .. .. .. .. 10.5 .. .. .. .. 7.4 1.2 .. .. .. .. .. .. .. .. .. .. ..
0.5 .. 8.2 .. .. .. .. .. .. .. .. .. .. 26.5 .. .. .. .. .. .. .. .. 58.5 .. .. .. 34.2 24.9 .. 31.7 20.7 .. .. .. .. .. 14.9 .. 9.8 0.1 .. .. .. 33.0 33.9 .. .. 3.3 .. .. .. .. 0.2 .. .. ..
86 46 86 91 83 84 .. 74 67 41 53 71 72 85 86 81 78 82 92 73 90 66 76 77 74 87 78 78 48 76 41 92 80 62 88 87 83 84 76 88 84 70 89 77 76 93 86 88 79 73 83 78 88 75 87 80
2009 World Development Indicators
87 51 68 91 68 37 0 61 0 83 77 81 69 72 64 14 90 60 78 89 62 16 69 162 90 74 69 41 80 23 39 69 99 67 76 93 49 116 84 66 11 60 97 53 23 33 125 67 98 15 58 93 75 66 87 77
103
PEOPLE
Disease prevention coverage and quality
2.17
Disease prevention coverage and quality Access to an improved water source
Access to improved sanitation facilities
% of population 1990 2006
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
76 94 65 94 67 .. .. 100 100 .. .. 81 100 67 64 .. 100 100 83 .. 49 95 .. 49 88 82 85 .. 43 .. 100 100 99 100 90 89 52 .. .. 50 78 76 w 58 75 72 88 72 68 90 84 89 73 49 99 ..
88 97 65 96 77 99e 53 100 100 .. 29 93 100 82 70 60 100 100 89 67 55 98 62 59 94 94 97 .. 64 97 100 100 99 100 88 .. 92 89 66 58 81 86 w 68 89 88 95 84 87 95 91 89 87 58 100 100
% of population 1990 2006
72 87 29 91 26 .. .. 100 100 .. .. 55 100 71 33 .. 100 100 81 .. 35 78 .. 13 93 74 85 .. 29 96 97 .. 100 100 93 83 29 .. 28 42 44 51 w 26 48 41 77 44 48 88 68 67 18 26 100 ..
72 87 23 99 28 92e 11 100 100 .. 23 59 100 86 35 50 100 100 92 92 33 96 41 12 92 85 88 .. 33 93 97 .. 100 100 96 .. 65 80 46 52 46 60 w 39 60 55 83 55 66 89 78 77 33 31 100 ..
Child immunization rate
% of children ages 12–23 monthsb Measles DTP3 2007 2007
97 99 99 96 84 95 67 95 99 96 34 83 97 98 79 91 96 86 98 85 90 96 63 80 91 98 96 99 68 98 92 86 93 96 99 55 83 .. 74 85 66 82 w 76 84 82 94 81 90 97 93 90 72 73 93 91
97 98 97 96 94 94 64 96 99 97 39 97 96 98 84 95 99 93 99 86 83 98 70 88 88 98 96 98 64 98 92 92 96 94 96 71 92 .. 87 80 62 82 w 77 82 79 96 80 89 96 92 92 69 73 95 96
Children with acute respiratory infection taken to health provider
Children with diarrhea who received oral rehydration and continuous feeding
Children sleeping under treated bednetsa
Children with fever receiving antimalarial drugs
Tuberculosis treatment success rate
DOTS detection rate
% of children under age 5 with ARI 2002–07c
% of children under age 5 with diarrhea 2002–07c
% of children under age 5 2002–07c
% of children under age 5 with fever 2002–07c
% of new registered cases 2006
% of new estimated cases 2007
.. .. 28d .. 47 93 48 .. .. .. 13 .. .. 58 .. 73 .. .. 77 64 59 84 24 23 74 .. 41 83 73 .. .. .. .. .. 68 .. 83 .. 47 68 25
.. .. 24 .. 43 31 31 .. .. .. 7 .. .. .. 56 22 .. .. 34 22 53 46 .. 22 32 .. .. 25 39 .. .. .. .. .. 28 .. 65 .. 48 48 47
.. .. 13.0 .. 16.4 .. 5.3 .. .. .. 11.4 .. .. 2.9 27.6 0.6 .. .. .. 1.3 16.0 .. 8.3 38.4 .. .. .. .. 9.7 .. .. .. .. .. .. .. 5.1 .. .. 22.8 2.9 .. w .. .. .. .. .. .. .. .. .. .. 12.3 .. ..
.. .. 12.3 .. 22.0 .. 51.9 .. .. .. 7.9 .. .. 0.3 54.2 0.6 .. .. .. 1.2 58.2 .. 47.4 47.7 .. .. .. .. 61.3 .. .. .. .. .. .. .. 2.6 .. .. 57.9 4.7 .. w 26.4 .. .. .. .. .. .. .. .. 7.3 34.9 .. ..
83 58 86 69 76 84 87 84 81 92 89 74 .. 87 82 43 63 .. 86 84 85 77 79 67 .. 91 91 84 70 59 79 .. 64 87 81 82 92 94 83 85 60 85 w 84 85 87 72 85 91 70 76 86 87 76 68 ..
85 49 25 39 48 80 37 96 44 77 64 78 0 85 31 55 0 0 80 30 51 72 61 15 .. 78 76 84 51 55 18 0 87 95 45 68 82 5 46 58 27 63 w 51 72 72 72 64 77 56 72 72 67 47 37 17
a. For malaria prevention only. b. Refers to children who were immunized before age 12 months or in some cases at any time before the survey (12–23 months). c. Data are for the most recent year available. d. Data are for 2008. e. Includes Kosovo.
104
2009 World Development Indicators
About the data
2.17
Definitions
People’s health is influenced by the environment
rehydration salts at home. However, recommenda-
• Access to an improved water source refers to peo-
in which they live. Lack of clean water and basic
tions for the use of oral rehydration therapy have
ple with reasonable access to water from an improved
sanitation is the main reason diseases transmitted
changed over time based on scientific progress, so
source, such as piped water into a dwelling, public tap,
by feces are so common in developing countries.
it is difficult to accurately compare use rates across
tubewell, protected dug well, and rainwater collection.
Access to drinking water from an improved source
countries. Until the current recommended method
Reasonable access is the availability of at least 20
and access to improved sanitation do not ensure
for home management of diarrhea is adopted and
liters a person a day from a source within 1 kilometer
safety or adequacy, as these characteristics are
applied in all countries, the data should be used
of the dwelling. • Access to improved sanitation facil-
not tested at the time of the surveys. But improved
with caution. Also, the prevalence of diarrhea may
ities refers to people with at least adequate access
drinking water technologies and improved sanitation
vary by season. Since country surveys are adminis-
to excreta disposal facilities that can effectively pre-
facilities are more likely than those characterized
tered at different times, data comparability is further
vent human, animal, and insect contact with excreta.
as unimproved to provide safe drinking water and to
affected.
Improved facilities range from protected pit latrines
prevent contact with human excreta. The data are
Malaria is endemic to the poorest countries in the
to flush toilets. • Child immunization rate refers to
derived by the Joint Monitoring Programme (JMP)
world, mainly in tropical and subtropical regions of
children ages 12–23 months who, before 12 months
of the World Health Organization (WHO) and United
Africa, Asia, and the Americas. Insecticide-treated
or at any time before the survey, had received one
Nations Children’s Fund (UNICEF) based on national
bednets, properly used and maintained, are one of
dose of measles vaccine and three doses of diphthe-
censuses and nationally representative household
the most important malaria-preventive strategies to
ria, pertussis (whooping cough), and tetanus (DTP3)
surveys. The coverage rates for water and sanita-
limit human-mosquito contact. Studies have empha-
vaccine. • Children with acute respiratory infection
tion are based on information from service users
sized that mortality rates could be reduced by about
taken to health provider are children under age 5 with
on the facilities their households actually use rather
25–30 percent if every child under age 5 in malaria-
acute respiratory infection in the two weeks before
than on information from service providers, which
risk areas such as Africa slept under a treated bed-
the survey who were taken to an appropriate health
may include nonfunctioning systems. While the esti-
net every night.
provider. • Children with diarrhea who received oral
mates are based on use, the JMP reports use as
Prompt and effective treatment of malaria is a criti-
rehydration and continuous feeding are children
access, because access is the term used in the Mil-
cal element of malaria control. It is vital that suffer-
under age 5 with diarrhea in the two weeks before the
lennium Development Goal target for drinking water
ers, especially children under age 5, start treatment
survey who received either oral rehydration therapy or
and sanitation.
within 24 hours of the onset of symptoms, to pre-
increased fluids, with continuous feeding. • Children
vent progression—often rapid—to severe malaria
sleeping under treated bednets are children under
and death.
age 5 who slept under an insecticide-treated bed-
Governments in developing countries usually finance immunization against measles and diphtheria, pertussis (whooping cough), and tetanus (DTP)
Data on the success rate of tuberculosis treatment
net to prevent malaria the night before the survey.
as part of the basic public health package. In many
are provided for countries that have implemented
• Children with fever receiving antimalarial drugs are
developing countries lack of precise information on
DOTS, the internationally recommended tubercu-
children under age 5 who were ill with fever in the two
the size of the cohort of one-year-old children makes
losis control strategy. The treatment success rate
weeks before the survey and received any appropri-
immunization coverage diffi cult to estimate from
for tuberculosis provides a useful indicator of the
ate (locally defined) antimalarial drugs. • Tuberculosis
program statistics. The data shown here are based
quality of health services. A low rate or no success
treatment success rate refers to new registered infec-
on an assessment of national immunization cover-
suggests that infectious patients may not be receiv-
tious tuberculosis cases that were cured or completed
age rates by the WHO and UNICEF. The assessment
ing adequate treatment. An essential complement
a full course of treatment. • DOTS detection rate
considered both administrative data from service
to the tuberculosis treatment success rate is the
refers to estimated new infectious tuberculosis cases
providers and household survey data on children’s
DOTS detection rate, which indicates whether there
detected by DOTS, the internationally recommended
immunization histories. Based on the data available,
is adequate coverage by the recommended case
tuberculosis detection and treatment strategy.
consideration of potential biases, and contributions
detection and treatment strategy. A country with a
of local experts, the most likely true level of immuni-
high treatment success rate may still face big chal-
zation coverage was determined for each year.
lenges if its DOTS detection rate remains low.
Acute respiratory infection continues to be a lead-
For indicators that are from household surveys, the
ing cause of death among young children, killing
year in the table refers to the survey year. For more
about 2 million children under age 5 in developing
information, consult the original sources.
countries each year. Data are drawn mostly from household health surveys in which mothers report on number of episodes and treatment for acute respiratory infection. Since 1990 diarrhea-related deaths among children have declined tremendously. Most diarrhearelated deaths are due to dehydration, and many of
Data sources Data on access to water and sanitation are from the WHO and UNICEF’s Progress on Drinking Water and Sanitation (2008). Data on immunization are from WHO and UNICEF estimates (www.who.int/ immunization_monitoring). Data on children with acute respiratory infection, with diarrhea, sleeping under treated bednets, and receiving antimalarial drugs are from UNICEF’s State of the World’s Children 2009, Childinfo, and Demographic and Health Surveys by Macro International. Data on tuberculosis are from the WHO’s Global Tuberculosis Control Report 2009.
these deaths can be prevented with the use of oral
2009 World Development Indicators
105
PEOPLE
Disease prevention coverage and quality
2.18
Reproductive health Total fertility Adolescent rate fertility rate
births per woman 1990 2007
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
106
.. 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 4.6 2.8 1.8 7.3 6.8 5.7 5.9 1.8 5.6 6.7 2.6 2.1 1.3 3.0 6.7 5.3 3.1 6.5 1.6 1.7 1.9 1.7 3.3 3.6 4.3 3.7 6.2 2.0 6.8 1.8 1.8 4.7 6.0 2.1 1.5 5.7 1.4 5.6 6.6 7.1 5.4
.. 1.8 2.4 5.8 2.3 1.7 1.9 1.4 2.0 2.8 1.3 1.8 5.4 3.5 1.2 2.9 2.2 1.4 6.0 6.8 3.2 4.3 1.6 4.6 6.2 1.9 1.7 1.0 2.5 6.3 4.5 2.1 4.5 1.4 1.5 1.4 1.9 2.4 2.6 2.9 2.7 5.0 1.6 5.3 1.8 2.0 3.1 4.7 1.4 1.4 3.8 1.4 4.2 5.4 7.1 3.8
2009 World Development Indicators
Unmet Contraceptive need for prevalence rate contraception
births per 1,000 women ages 15–19 2007
% of married women ages 15–49 2002–07a
.. 16 7 138 57 30 14 12 29 124 22 7 120 78 20 52 89 40 126 55 42 118 14 115 164 60 8 5 76 222 115 71 107 14 47 11 6 108 83 39 81 72 21 94 9 7 82 104 30 9 55 9 107 149 188 46
.. .. .. .. .. 13 .. .. 23 11 .. .. 30 23 23 .. .. .. 29 .. 25 20 .. .. 21 .. .. .. 6 24 16 .. 29 .. 8 .. .. 11 .. 10 .. 27 .. 34 .. .. .. .. .. .. 34 .. .. 21 .. 38
% of married women ages 15–49 2002–07a
.. 60 61 .. .. 53 .. .. 51 56 73 .. 17 58 36 .. .. .. 17 9 40 29 .. 19 3 58 85 .. 78 .. 21 96 13 .. 77 .. .. 73 73 59 67 8 .. 15 .. .. .. .. 47 .. 17 .. 43 9 10 32
Newborns protected against tetanus
Pregnant women receiving prenatal care
Births attended by skilled health staff
Maternal mortality ratio
per 100,000 live births % of births 2007
% 2002–07a
.. 87 70 81 .. .. .. .. .. 91 .. 94 93 71 85 78 93 65 80 78 87 81 82 54 60 .. .. .. 78 81 90 .. 76 97 .. .. .. 85 67 85 87 80 .. 85 .. .. 67 90 87 .. 88 69 80 95 92 43
.. 97 89 80 99 93 .. .. 77 51 99 .. 84 79 99 .. 97 .. 85 92 69 82 .. 69 39 .. 90 .. 94 85 86 92 85 100 100 .. .. 99 84 70 86 70 .. 28 .. .. .. 98 94 .. 92 .. 84 82 78 85
National estimates % of total a 1990 2002–07 1990–2007a
.. .. 77 .. 96 .. 100 .. .. .. .. .. .. 43 97 77 72 .. .. .. .. 58 .. .. .. .. 50 .. 82 .. .. 98 .. 100 .. .. .. 93 .. 37 52 .. .. .. .. .. .. 44 .. .. 40 .. .. 31 .. 23
.. 100 95 47 99 98 100 .. 89 18 100 .. 74 67 100 .. 97 99 54 34 44 63 100 53 14 100 98 100 96 74 83 99 57 100 100 100 .. 98 75 74 92 28 100 6 100 .. .. 57 98 100 50 .. 41 38 39 26
20 117 .. 48 16 .. .. 29 322 12 .. 397 229 9 326 53 7 484 615 472 669 .. 543 1,099 20 41 .. 75 1,289 781 36 543 10 21 8 10 159 107 84 71 998 7 673 6 10 519 730 23 8 .. 1 133 980 405 630
Modeled estimates 2005
.. 92 180 1,400 77 76 4 4 82 570 18 8 840 290 3 380 110 11 700 1,100 540 1,000 7 980 1,500 16 45 .. 130 1,100 740 30 810 7 45 4 3 150 210 130 170 450 25 720 7 8 520 690 66 4 560 3 290 910 1,100 670
Total fertility Adolescent rate fertility rate
births per woman 1990 2007
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 4.0 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 6.1 2.0 3.1 4.9 6.9 4.7 2.0 2.0 6.2 6.9 3.7 7.4 5.8 2.3 3.4 2.3 4.0 4.0 6.2 3.4 5.7 5.1 1.6 2.2 4.7 7.9 6.7 1.9 6.5 6.1 3.0 4.8 4.5 3.9 4.3 2.0 1.4 2.2
3.3 1.3 2.7 2.2 2.0 .. 1.9 2.9 1.3 2.4 1.3 3.6 2.4 5.0 1.9 1.3 2.2 2.7 3.2 1.4 2.2 3.4 5.2 2.7 1.4 1.4 4.8 5.6 2.6 6.5 4.4 1.7 2.1 1.7 1.9 2.4 5.1 2.1 3.6 3.0 1.7 2.2 2.8 7.0 5.3 1.9 3.0 3.9 2.6 3.8 3.1 2.5 3.2 1.3 1.3 1.8
Unmet Contraceptive need for prevalence rate contraception
Newborns protected against tetanus
Pregnant women receiving prenatal care
Births attended by skilled health staff
2.18 Maternal mortality ratio
per 100,000 live births
births per 1,000 women ages 15–19 2007
% of married women ages 15–49 2002–07a
% of married women ages 15–49 2002–07a
% of births 2007
% 2002–07a
93 19 62 40 20 .. 16 14 6 78 3 25 31 104 1 4 13 31 72 14 25 73 219 3 18 21 133 135 13 179 85 41 65 32 45 19 149 16 59 115 5 22 113 196 126 8 10 36 83 51 72 60 47 13 13 47
17 .. 13 9 .. .. .. .. .. .. .. 11 .. 25 .. .. .. 1 .. .. .. 31 36 .. .. 34 24 28 .. 31 .. .. .. 7 14 10 18 .. .. 25 .. .. .. 16 17 .. .. 25 .. .. .. 8 17 .. .. ..
65 .. 56 61 79 .. .. .. .. 69 .. 57 51 39 .. .. .. 48 38 .. 58 37 11 .. .. 14 27 42 .. 8 .. 76 71 68 66 63 17 34 55 48 .. .. 72 11 13 .. .. 30 .. .. 73 71 51 .. .. ..
94 .. 86 83 83 .. .. .. 52 54 86 87 .. 74 91 .. 83 82 47 .. 72 76 89 .. .. .. 72 86 89 89 60 86 87 .. 87 85 82 91 82 83 .. .. 94 72 53 .. 95 81 .. 60 81 82 65 .. .. ..
92 .. 74 93 .. .. .. .. .. 91 .. 99 100 88 .. .. .. 97 .. .. 96 90 .. .. .. 98 80 92 79 70 .. .. .. 98 99 68 85 .. 95 44 .. .. 90 46 58 .. .. 61 .. .. 94 91 88 .. .. ..
National estimates % of total a 1990 2002–07 1990–2007a
45 .. .. 32 .. 54 .. .. .. 79 100 87 .. 50 .. 98 .. .. .. .. .. .. .. .. .. .. 57 55 .. .. 40 91 .. .. .. 31 .. .. 68 7 .. .. .. 15 33 100 .. 19 .. .. 66 80 .. .. 98 ..
67 100 47 72 97 .. 100 .. 99 97 100 99 100 42 97 100 100 98 .. 100 98 55 46 .. 100 98 51 54 98 45 .. 99 93 100 99 63 48 68 81 19 100 .. 74 33 35 .. 98 39 91 42 77 71 60 100 .. 100
108 8 301 307 25 294 6 5 7 95 8 41 70 414 105 20 5 104 405 9 .. 762 .. 77 13 21 469 807 28 464 747 22 62 16 90 227 408 316 271 281 7 15 87 648 .. 6 13 533 66 .. 121 185 162 3 8 ..
2009 World Development Indicators
Modeled estimates 2005
280 6 450 420 140 .. 1 4 3 170 6 62 140 560 370 14 4 150 660 10 150 960 1,200 97 11 10 510 1,100 62 970 820 15 60 22 46 240 520 380 210 830 6 9 170 1,800 1,100 7 64 320 130 470 150 240 230 8 11 18
107
PEOPLE
Reproductive health
2.18
Reproductive health Total fertility Adolescent rate fertility rate
births per woman 1990 2007
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
1.8 1.9 7.4 5.9 6.5 1.8 6.5 1.9 2.1 1.5 6.8 3.5 1.3 2.5 5.9 5.6 2.1 1.6 5.4 5.1 6.1 2.1 5.5 6.4 2.4 3.5 3.0 4.2 7.1 1.8 4.3 1.8 2.1 2.5 4.1 3.4 3.6 6.3 8.0 6.4 5.1 3.2 w 5.6 3.0 3.1 2.7 3.5 2.4 2.3 3.2 4.8 4.2 6.3 1.8 1.5
1.3 1.4 5.9 3.2 5.1 1.4 6.5 1.3 1.3 1.4 6.0 2.7 1.4 1.9 4.2 3.6 1.9 1.5 3.1 3.3 5.2 1.9 6.5 4.8 1.6 2.0 2.2 2.5 6.7 1.2 2.3 1.9 2.1 2.0 2.4 2.6 2.1 4.6 5.5 5.2 3.7 2.5 w 4.2 2.2 2.3 2.0 2.7 1.9 1.7 2.4 2.8 2.9 5.1 1.8 1.5
Unmet Contraceptive need for prevalence rate contraception
births per 1,000 women ages 15–19 2007
32 28 40 28 87 24 160 5 20 7 66 61 9 25 57 33 4 4 35 28 121 42 54 89 35 7 37 16 152 28 18 24 42 61 34 90 17 79 71 125 59 52 w 95 42 39 56 56 17 29 77 30 67 118 22 8
a. Data are for most recent year available. b. Includes Montenegro.
108
2009 World Development Indicators
% of married women ages 15–49 2002–07a
.. .. 38 .. 32 29 .. .. .. .. .. .. .. .. 6 24 .. .. .. .. 22 .. .. .. .. .. .. .. 41 .. .. .. .. .. 8 .. 5 .. .. 27 13 .. w 21 .. .. .. .. .. .. .. .. 14 24 .. ..
% of married women ages 15–49 2002–07a
70 .. 17 .. 12 41 5 .. .. .. 15 60 .. 68 8 51 .. .. 58 38 26 77 20 17 43 .. 71 48 24 67 .. 84 .. .. 65 .. 76 50 28 34 60 60 w 33 68 69 .. 60 78 .. .. 62 53 23 .. ..
Newborns protected against tetanus
Pregnant women receiving prenatal care
Births attended by skilled health staff
Maternal mortality ratio
per 100,000 live births % of births 2007
.. .. 82 56 86 .. 94 4 73 74 68 72 72 91 72 86 86 93 92 88 88 89 59 82 .. 96 69 .. 85 .. .. .. .. .. 87 51 86 .. 52 89 78 .. w 78 .. .. .. 81 .. .. 83 78 85 75 .. ..
% 2002–07a
94 .. 94 .. 87 98 81 .. .. .. 26 92 .. 99 70 85 .. .. 84 80 78 98 61 84 96 .. 81 99 94 99 .. .. .. .. 99 .. 91 99 41 93 94 81 w 67 86 84 .. 81 90 .. 95 76 69 72 .. ..
National estimates % of total a 1990 2002–07 1990–2007a
.. .. 26 .. .. .. .. .. .. 100 .. .. .. .. 69 .. .. .. .. .. 53 .. .. 31 .. 69 .. .. 38 .. .. .. 99 .. .. .. .. .. 16 51 70 50 w .. 49 45 .. 46 48 .. 72 48 32 .. .. ..
98 100 39 96 52 99 43 100 100 100 33 92 .. 99 49 69 .. 100 93 83 43 97 18 62 98 .. 83 100 42 99 100 .. 99 99 100 95 88 99 36 43 69 65 w 42 74 69 95 62 87 95 89 80 41 45 99 ..
15 24 750 10 401 13 1,800 6 4 17 1,044 166 6 43 .. 589 5 5 65 97 578 12 .. 478 45 69 29 14 435 17 3 7 8 35 28 61 162 .. 365 729 555
Modeled estimates 2005
24 28 1,300 18 980 14b 2,100 14 6 6 1,400 400 4 58 450 390 3 5 130 170 950 110 380 510 45 100 44 130 550 18 37 8 11 20 24 57 150 .. 430 830 880 400 w 780 260 300 97 440 150 44 130 200 500 900 10 5
About the data
2.18
Definitions
Reproductive health is a state of physical and men-
indicator has changed, these data cannot be com-
• Total fertility rate is the number of children that
tal well-being in relation to the reproductive system
pared with those in editions before 2008.
would be born to a woman if she were to live to the
and its functions and processes. Means of achieving
Good prenatal and postnatal care improve mater-
end of her childbearing years and bear children in
reproductive health include education and services
nal health and reduce maternal and infant mortality.
accordance with current age-specific fertility rates.
during pregnancy and childbirth, safe and effec-
But data may not reflect such improvements because
• Adolescent fertility rate is the number of births per
tive contraception, and prevention and treatment
health information systems are often weak, mater-
1,000 women ages 15–19. • Unmet need for contra-
of sexually transmitted diseases. Complications of
nal deaths are underreported, and rates of maternal
ception is the percentage of fertile, married women
pregnancy and childbirth are the leading cause of
mortality are difficult to measure.
of reproductive age who do not want to become preg-
death and disability among women of reproductive
The share of births attended by skilled health staff
nant and are not using contraception. • Contracep-
is an indicator of a health system’s ability to provide
tive prevalence rate is the percentage of women
Total and adolescent fertility rates are based on
adequate care for pregnant women. Maternal mor-
married or in-union ages 15–49 who are practicing,
data on registered live births from vital registration
tality ratios are generally of unknown reliability, as
or whose sexual partners are practicing, any form
systems or, in the absence of such systems, from
are many other cause-specific mortality indicators.
of contraception. • Newborns protected against
censuses or sample surveys. The estimated rates
Household surveys such as Demographic and Health
tetanus are the percentage of births by women of
are generally considered reliable measures of fertility
Surveys attempt to measure maternal mortality by
child-bearing age who are immunized against teta-
in the recent past. Where no empirical information
asking respondents about survivorship of sisters.
nus. • Pregnant women receiving prenatal care are
on age- specific fertility rates is available, a model is
The main disadvantage of this method is that the
the percentage of women attended at least once
used to estimate the share of births to adolescents.
estimates of maternal mortality that it produces
during pregnancy by skilled health personnel for
For countries without vital registration systems fertil-
pertain to 12 years or so before the survey, making
reasons related to pregnancy. • Births attended by
ity rates are generally based on extrapolations from
them unsuitable for monitoring recent changes or
skilled health staff are the percentage of deliveries
trends observed in censuses or surveys from earlier
observing the impact of interventions. In addition,
attended by personnel trained to give the necessary
years.
age in developing countries.
measurement of maternal mortality is subject to
care to women during pregnancy, labor, and post-
More couples in developing countries want to limit
many types of errors. Even in high-income countries
partum; to conduct deliveries on their own; and to
or postpone childbearing but are not using effec-
with vital registration systems, misclassification of
care for newborns. • Maternal mortality ratio is the
tive contraception. These couples have an unmet
maternal deaths has been found to lead to serious
number of women who die from pregnancy-related
need for contraception. Common reasons are lack
underestimation.
causes during pregnancy and childbirth per 100,000
of knowledge about contraceptive methods and
The national estimates of maternal mortality
concerns about possible side effects. This indica-
ratios in the table are based on national surveys,
tor excludes women not exposed to the risk of unin-
vital registration records, and surveillance data or
tended pregnancy because of menopause, infertility,
are derived from community and hospital records.
or postpartum anovulation.
live births.
Data sources
The modeled estimates are based on an exercise by
Data on fertility rates are compiled and estimated
Contraceptive prevalence reflects all methods—
the World Health Organization (WHO), United Nations
by the World Bank’s Development Data Group.
ineffective traditional methods as well as highly
Children’s Fund (UNICEF), United Nations Population
Inputs come from the United Nations Population
effective modern methods. Contraceptive prevalence
Fund (UNFPA), and World Bank. For countries with
Division’s World Population Prospects: The 2006
rates are obtained mainly from household surveys,
complete vital registration systems with good attribu-
Revision, census reports and other statistical
including Demographic and Health Surveys, Multiple
tion of cause of death, the data are used as reported.
publications from national statistical offi ces,
Indicator Cluster Surveys, and contraceptive preva-
For countries with national data either from complete
and household surveys such as Demographic
lence surveys (see Primary data documentation for
vital registration systems with uncertain or poor attri-
and Health Surveys. Data on women with unmet
the most recent survey year). Unmarried women are
bution of cause of death or from household surveys
need for contraception and contraceptive preva-
often excluded from such surveys, which may bias
reported maternal mortality was adjusted, usually by
lence rates are from household surveys, including
the estimates.
a factor of underenumeration and misclassification.
Demographic and Health Surveys by Macro Inter-
An important cause of infant mortality in some
For countries with no empirical national data (about
national and Multiple Indicator Cluster Surveys by
developing countries, neonatal tetanus can be pre-
35 percent of countries), maternal mortality was esti-
UNICEF. Data on tetanus vaccinations, pregnant
vented through immunization of the mother during
mated with a regression model using socioeconomic
women receiving prenatal care, births attended
pregnancy. As in last year’s edition, the data on
information, including fertility, birth attendants, and
by skilled health staff, and national estimates of
tetanus in the table are estimated by the “protec-
GDP. Neither set of ratios can be assumed to provide
maternal mortality ratios are from UNICEF’s State
tion at birth” model, which tracks the immunization
an exact estimate of maternal mortality for any of the
of the World’s Children 2009 and Childinfo and
status of women of child-bearing age. The estimates
countries in the table.
Demographic and Health Surveys by Macro Inter-
account for the number of vaccine doses received
For the indicators that are from household surveys,
national. Modeled estimates for maternal mortal-
and the time since the mother’s last immunization.
the year in the table refers to the survey year. For
ity ratios are from WHO, UNICEF, UNFPA and the
A currently immune woman’s child is considered
more information, consult the original sources.
World Bank’s Maternal Mortality in 2005 (2007).
protected. Because the methodology behind this
2009 World Development Indicators
109
PEOPLE
Reproductive health
2.19
Nutrition Prevalence of undernourishment
Prevalence of child Prevalence Lowmalnutrition of overweight birthweight children babies
Exclusive breastfeeding
Consumption Vitamin A of iodized supplemensalt tation
Prevalence of anemia
% % of population 1990–92 2003–05
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
110
.. <5 <5 66 <5 46 <5 <5 27 36 <5 <5 28 24 <5 20 10 <5 14 44 38 34 <5 47 59 7 15c .. 15 29 40 <5 15 <5 5 <5 <5 27 24 <5 9 67 <5 71 <5 <5 5 20 47 <5 34 <5 14 19 20 63
.. <5 <5 46 <5 21 <5 <5 12 27 <5 <5 19 22 <5 26 6 <5 10 63 26 23 <5 43 39 <5 9c .. 10 76 22 <5 14 <5 <5 <5 <5 21 15 <5 10 68 <5 46 <5 <5 <5 30 13 <5 9 <5 16 17 32 58
2009 World Development Indicators
% of children under age 5 Underweight Stunting 2000–07a 2000–07a
.. 17.0 10.2 27.5 2.3 4.2 .. .. 14.0 39.2 1.3 .. 21.5 5.9 1.6 10.7 2.2 1.6 35.2 38.9 28.4 15.1 .. 21.8 33.9 0.6 6.8 .. 5.1 33.6 11.8 .. 16.7 .. .. 2.1 .. 4.2 6.2 5.4 6.1 34.5 .. 34.6 .. .. 8.8 15.8 .. .. 18.8 .. 17.7 22.5 21.9 18.9
.. 39.2 21.6 50.8 8.2 18.2 .. .. 24.1 47.8 4.5 .. 39.1 32.5 11.8 29.1 7.1 8.8 43.1 63.1 43.7 35.4 .. 44.6 44.8 2.1 21.8 .. 16.2 44.4 31.2 .. 40.1 .. .. 2.6 .. 11.7 29.0 23.8 24.6 43.7 .. 50.7 .. .. 26.3 27.6 .. .. 35.6 .. 54.3 39.3 36.1 29.7
% of children under age 5 2000–07a
.. 30.0 15.4 5.3 9.9 11.7 .. .. 6.2 0.9 9.7 .. 3.0 9.2 25.6 10.4 7.3 13.6 5.4 1.4 1.7 8.7 .. 10.8 4.4 9.8 9.2 .. 4.2 6.5 8.5 .. 9.0 .. .. 4.4 .. 8.6 5.1 14.1 5.8 1.6 .. 5.1 .. .. 5.6 2.7 .. .. 4.5 .. 5.6 5.1 5.1 3.9
% of births 2002–07a
% of children under 6 months 2002–07a
% of households 2002–07a
.. 7 6 .. 7 8 .. .. .. 22 4 .. 15 7 5 .. 8 .. 16 11 14 11 .. 13 22 6 2 .. 6 .. 13 7 17 5 5 .. .. 11 .. 14 7 14 .. 20 .. .. .. 20 5 .. 9 .. 12 12 24 25
.. 40 7 .. .. 33 .. .. 12 37 9 .. 43 54 18 .. .. .. 7 45 60 21 .. 23 2 85 51 .. 47 36 19 .. 4 .. 26 .. .. 8 40 38 24 52 .. 49 .. .. .. 41 11 .. 54 .. 51 27 16 41
.. 60 61 .. .. 97 .. .. 54 84 55 .. 55 90 62 .. .. 100 34 98 73 49 .. 62 56 .. 94 .. .. .. 82 .. 84 .. 88 .. .. 19 .. 78 62 68 .. 20 .. .. .. 7 87 .. 32 .. 40 51 1 3
% of children Children Pregnant 6–59 months under age 5 women a 2007 2000–05 2000–05a
.. .. .. 36 .. .. .. .. 95b 95 .. .. 73 39 .. .. .. .. 95 83 76 95 .. 78 54 .. .. .. .. 79 95 .. 63 .. .. .. .. .. .. 87b 20 51 .. 88 .. .. 90 93 .. .. 95 .. 33 95 66 42
.. 31 43 .. 18 24 8 11 32 47 27 9 82 52 27 .. 55 27 92 56 63 68 8 .. 71 24 20 .. 28 71 66 .. 69 23 27 18 9 35 38 30 18 70 23 75 11 8 44 .. 41 8 76 12 38 79 75 65
.. 34 43 57 25 12 12 15 38 47 26 13 73 37 35 21 29 30 68 47 66 51 12 .. 60 28 29 .. 31 67 55 .. 55 28 39 22 12 40 38 45 .. 55 23 63 15 11 46 .. 42 12 65 19 22 63 58 63
Prevalence of undernourishment
Prevalence of child Prevalence Lowmalnutrition of overweight birthweight children babies
Exclusive breastfeeding
2.19
Consumption Vitamin A of iodized supplemensalt tation
Prevalence of anemia
% % of population 1990–92 2003–05
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
19 <5 24 19 <5 .. <5 <5 <5 11 <5 <5 <5 33 21 <5 20 17 27 <5 <5 15 30 <5 <5 <5 32 45 <5 14 10 7 <5 <5 30 5 59 44 29 21 <5 <5 52 38 15 <5 .. 22 18 .. 16 28 21 <5 <5 ..
12 <5 21 17 <5 .. <5 <5 <5 5 <5 <5 <5 32 32 <5 <5 <5 19 <5 <5 15 40 <5 <5 <5 37 29 <5 11 8 6 <5 <5 29 <5 38 19 19 15 <5 <5 22 29 9 <5 .. 23 17 .. 11 15 16 <5 <5 ..
% of children under age 5 Underweight Stunting 2000–07a 2000–07a
8.6 .. 43.5 24.4 .. .. .. .. .. 3.1 .. 3.6 4.9 16.5 17.8 .. .. 2.7 36.4 .. .. 16.6 20.4 .. .. 1.8 36.8 18.4 .. 27.9 30.4 .. 3.4 3.2 5.3 9.9 21.2 29.6 17.5 38.8 .. .. 7.8 39.9 27.2 .. .. 31.3 .. .. .. 5.2 20.7 .. .. ..
29.9 .. 47.9 28.6 .. .. .. .. .. 4.5 .. 12.0 17.5 35.8 44.7 .. .. 18.1 48.2 .. .. 45.2 39.4 .. .. 11.5 52.8 52.5 .. 38.5 39.5 .. 15.5 11.3 27.5 23.1 47.0 40.6 29.6 49.3 .. .. 25.2 54.8 43.0 .. .. 41.5 .. .. .. 31.3 33.8 .. .. ..
% of children under age 5 2000–07a
5.8 .. 1.9 5.1 .. .. .. .. .. 7.5 .. 4.7 14.8 5.8 0.9 .. .. 10.7 2.7 .. .. 6.8 4.2 .. .. 16.2 6.2 10.2 .. 4.7 3.8 .. 7.6 9.1 14.2 13.3 6.3 2.4 4.6 0.6 .. .. 7.1 3.5 6.2 .. .. 4.8 .. .. .. 11.8 2.4 .. .. ..
% of births 2002–07a
% of children under 6 months 2002–07a
% of households 2002–07a
10 .. 28 9 .. .. .. .. .. 12 .. 12 6 10 7 .. .. 5 .. .. .. 13 .. .. .. 6 17 14 9 19 .. 14 8 6 6 15 15 .. .. 21 .. .. .. 27 14 .. 9 .. 10 .. 9 10 20 .. .. ..
30 .. 46 40 23 .. .. .. .. 15 .. 22 17 13 65 .. .. 32 .. .. .. 36 29 .. .. 16 67 57 .. 38 .. 21 .. 46 57 31 30 15 24 53 .. .. .. 9 17 .. .. 37 .. .. 22 63 34 .. .. ..
.. .. 51 73 99 .. .. .. .. .. .. .. 92 .. 40 .. .. 76 .. .. 92 91 .. .. .. 94 75 50 .. 79 .. .. 91 60 83 21 54 60 .. .. .. .. 97 46 97 .. .. 17 .. .. 94 91 45 .. .. ..
% of children Children Pregnant 6–59 months under age 5 women a 2007 2000–05 2000–05a
40 .. 33 87 .. .. .. .. .. .. .. .. .. 22 95 .. .. 95 83 .. .. 85 85 .. .. .. 95 90 .. 95 95 .. 68 .. 95 .. 48 94 68 95 .. .. 95 95 74 .. .. 95 4 7 .. .. 83 .. .. ..
30 19 74 44 35 .. 10 12 11 .. 11 28 .. .. .. .. 32 .. 48 27 .. 49 .. 34 24 .. 68 73 32 83 68 .. .. 41 21 32 75 63 41 .. 9 11 17 81 .. 6 .. 51 .. 60 30 50 36 23 13 ..
2009 World Development Indicators
.. 21 50 44 .. .. 15 17 15 .. 15 39 26 .. .. 23 31 34 56 25 32 25 .. 34 24 32 50 47 38 73 53 .. .. 36 37 37 52 50 31 .. 13 18 33 66 .. 9 43 39 .. 55 39 43 44 25 17 ..
111
PEOPLE
Nutrition
2.19
Nutrition Prevalence of undernourishment
Prevalence of child Prevalence Lowmalnutrition of overweight birthweight children babies
Exclusive breastfeeding
Consumption Vitamin A of iodized supplemensalt tation
Prevalence of anemia
% % of population 1990–92 2003–05
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
<5 <5 45 <5 28 <5d 45 .. <5 <5 .. <5 <5 27 31 12 <5 <5 <5 34 28 29 18 45 11 <5 <5 9 19 <5 <5 <5 <5 5 5 10 28 .. 30 40 40 17 w 31 16 19 6 19 18 7 12 7 25 31 5 5
<5 <5 40 <5 26 <5d 47 .. <5 <5 .. <5 <5 21 21 18 <5 <5 <5 34 35 17 22 37 10 <5 <5 6 15 <5 <5 <5 <5 <5 14 12 14 15 32 45 40 14 w 27 13 14 6 16 11 6 9 7 22 29 5 5
% of children under age 5 Underweight Stunting 2000–07a 2000–07a
3.5 .. 18.0 .. 14.5 1.8 28.3 3.3 .. .. 32.8 .. .. 22.8 38.4 9.1 .. .. .. 14.9 16.7 7.0 40.6 .. 4.4 .. 3.5 .. 19.0 4.1 .. .. 1.3 6.0 4.4 .. 20.2 .. .. 23.3 14.0 23.2 w 28.0 22.0 24.8 .. 24.1 12.8 .. 4.4 .. 41.1 26.6 .. ..
12.8 .. 51.7 .. 20.1 8.1 46.9 4.4 .. .. 42.1 .. .. 18.4 47.6 36.6 .. .. .. 33.1 44.4 15.7 55.7 .. 5.3 .. 15.6 .. 44.8 22.9 .. .. 3.9 13.9 19.6 .. 35.8 .. .. 52.5 35.8 34.7 w 43.8 31.8 34.9 .. 36.0 25.8 .. 15.8 .. 47.3 44.3 .. ..
% of children under age 5 2000–07a
8.3 .. 6.7 .. 2.4 19.3 5.9 2.6 .. .. 4.7 .. .. 1.0 5.2 14.9 .. .. .. 6.7 4.9 8.0 5.7 .. 4.9 .. 9.1 .. 4.9 26.5 .. .. 8.0 9.4 12.8 .. 2.5 .. .. 5.9 9.1 5.7 w 4.8 6.1 5.8 .. 5.7 7.1 .. 7.4 .. 2.2 5.8 .. ..
% of births 2002–07a
8 6 6 .. 19 5 24 .. .. .. 11 .. .. .. .. 9 .. .. 9 10 10 9 12 12 19 .. .. 4 14 4 .. .. 8 8 5 9 7 7 .. 12 11 14 w 15 15 16 8 15 6 6 9 12 27 14 .. ..
% of children under 6 months 2002–07a
16 .. 88 .. 34 15 8 .. .. .. 9 7 .. .. 34 32 .. .. 29 25 41 5 31 28 13 .. 21 11 60 6 .. .. .. 54 26 .. 17 27 12 61 22 38 w 33 41 43 .. 38 43 .. .. 26 44 31 .. ..
% of households 2002–07a
74 35 88 .. 41 .. 45 .. .. .. 1 .. .. 94 11 80 .. .. 79 46 43 47 58 25 28 .. 64 87 96 18 .. .. .. .. 53 .. 93 86 30 77 91 69 w 61 71 71 72 69 86 50 85 68 51 62 .. ..
% of children Children Pregnant 6–59 months under age 5 women a 2007 2000–05 2000–05a
.. .. 89 .. 94 .. 95 .. .. .. 89 33 .. 64 90 59 .. .. .. 92 93 .. 57 95 .. .. .. .. 64 .. .. .. .. .. 84 .. 95b .. 47b 95 83 .. w 82 .. .. .. .. .. .. .. .. 50 77 .. ..
40 27 .. 33 70 .. 83 19 23 14 .. .. 13 30 85 47 9 6 41 38 72 .. 32 52 30 .. 33 36 64 22 28 .. 3 19 38 33 34 .. 68 53 .. .. w .. .. .. 38 .. 20 29 .. .. 74 .. .. 10
30 21 .. 32 58 .. 60 24 25 19 .. 22 18 29 58 24 13 .. 39 45 58 .. 23 50 30 .. 40 30 41 27 28 15 6 27 .. 40 32 .. 58 .. .. .. w .. .. .. 29 .. 29 30 .. .. 50 .. 13 14
a. Data are for the most recent year available. b. Country’s vitamin A supplementation programs do not target children all the way up to 59 months of age. c. Includes Hong Kong, China; Macao, China; and Taiwan, China. d. Includes Montenegro.
112
2009 World Development Indicators
About the data
2.19
Definitions
Data on undernourishment are from the Food and
Low birthweight, which is associated with maternal
• Prevalence of undernourishment is the percentage
Agriculture Organization (FAO) of the United Nations
malnutrition, raises the risk of infant mortality and
of the population whose dietary energy consump-
and measure food deprivation based on average food
stunts growth in infancy and childhood. There is also
tion is continuously below a minimum requirement
available for human consumption per person, the
emerging evidence that low-birthweight babies are
for maintaining a healthy life and carrying out light
level of inequality in access to food, and the mini-
more prone to noncommunicable diseases such as
physical activity with an acceptable minimum weight
mum calories required for an average person.
diabetes and cardiovascular diseases. Estimates of
for height. • Prevalence of child malnutrition is the
From a policy and program standpoint, however,
low-birthweight infants are drawn mostly from hos-
percentage of children under age 5 whose weight for
this measure has its limits. First, food insecurity
pital records and household surveys. Many births
age (underweight) or height for age (stunting) is more
exists even where food availability is not a problem
in developing countries take place at home and are
than two standard deviations below the median for
because of inadequate access of poor households
seldom recorded. A hospital birth may indicate higher
the international reference population ages 0–59
to food. Second, food insecurity is an individual
income and therefore better nutrition, or it could indi-
months. Height is measured by recumbent length
or household phenomenon, and the average food
cate a higher risk birth, possibly skewing the data on
for children up to two years old and by stature while
available to each person, even corrected for possible
birthweights downward. The data should therefore be
standing for older children. Data are for the WHO child
effects of low income, is not a good predictor of food
used with caution.
growth standards released in 2006. • Prevalence of
insecurity among the population. And third, nutrition
Improved breastfeeding can save an estimated 1.3
over weight children is the percentage of children
security is determined not only by food security but
million children a year. Breast milk alone contains
under age 5 whose weight for height is more than
also by the quality of care of mothers and children
all the nutrients, antibodies, hormones, and antioxi-
two standard deviations above the median for the
and the quality of the household’s health environ-
dants an infant needs to thrive. It protects babies
international reference population of the correspond-
ment (Smith and Haddad 2000).
from diarrhea and acute respiratory infections, stimu-
ing age as established by the WHO child growth stan-
Estimates of child malnutrition, based on weight for
lates their immune systems and response to vaccina-
dards released in 2006. • Low-birthweight babies
age (underweight) and height for age (stunting), are
tion, and may confer cognitive benefits. The data on
are the percentage of newborns weighing less than
from national survey data. The proportion of under-
breastfeeding are derived from national surveys.
2.5 kilograms within the first hours of life, before sig-
weight children is the most common malnutrition
Iodine defi ciency is the single most important
nificant postnatal weight loss has occurred. • Exclu-
indicator. Being even mildly underweight increases
cause of preventable mental retardation, and it
sive breastfeeding is the percentage of children less
the risk of death and inhibits cognitive development
contributes significantly to the risk of stillbirth and
than six months old who were fed breast milk alone
in children. And it perpetuates the problem across
miscarriage. Widely used and inexpensive, iodized
(no other liquids) in the past 24 hours. • Consump-
generations, as malnourished women are more
salt is the best source of iodine, and a global cam-
tion of iodized salt is the percentage of households
likely to have low-birthweight babies. Height for age
paign to iodize edible salt is significantly reducing the
that use edible salt fortified with iodine. • Vitamin A
reflects linear growth achieved pre- and postnatally;
risks (www.childinfo.org). The data on iodized salt are
supplementation is the percentage of children ages
a deficit indicates long-term, cumulative effects of
derived from household surveys.
6–59 months old who received at least one dose of
inadequate health, diet, or care. Stunting is often
Vitamin A is essential for immune system function-
vitamin A in the previous six months, as reported by
used as a proxy for multifaceted deprivation and as
ing. Vitamin A deficiency, a leading cause of blind-
mothers. • Prevalence of anemia, children under
an indicator of long-term changes in malnutrition.
ness, also causes a 23 percent greater risk of dying
age 5, is the percentage of children under age 5
Estimates of overweight children are also from
from a range of childhood ailments such as measles,
whose hemoglobin level is less than 110 grams per
national survey data. Overweight children have
malaria, and diarrhea. Giving vitamin A to new breast-
liter at sea level. • Prevalence of anemia, pregnant
become a growing concern in developing countries.
feeding mothers helps protect their children during
women, is the percentage of pregnant women whose
Research shows an association between childhood
the first months of life. Food fortification with vitamin
hemoglobin level is less than 110 grams per liter
obesity and a high prevalence of diabetes, respiratory
A is being introduced in many developing countries.
at sea level.
disease, high blood pressure, and psychosocial and
Data on anemia are compiled by the WHO based
orthopedic disorders (de Onis and Blössner 2000).
mainly on nationally representative surveys between
New international growth reference standards for
1993 and 2005, which measured hemoglobin in the
infants and young children were released in 2006 by
blood. WHO’s hemoglobin thresholds were then used
Data on undernourishment are from ww.fao.org/
the World Health Organization (WHO) to monitor chil-
to determine anemia status based on age, sex, and
faostat/foodsecurity/index_en.htm. Data on
dren’s nutritional status. They are also key in moni-
physiological status. Children under age 5 and preg-
malnutrition and overweight children are from
toring health targets for the Millennium Development
nant women have the highest risk for anemia. Data
the WHO’s Global Database on Child Growth and
Goals. Differences in growth to age 5 are influenced
should be used with caution because surveys dif-
Malnutrition (www.who.int/nutgrowthdb). Data on
more by nutrition, feeding practices, environment,
fer in quality, coverage, age group interviewed, and
low-birthweight babies, breastfeeding, iodized salt
and healthcare than by genetics or ethnicity. The
treatment of missing values across countries and
consumption, and vitamin A supplementation are
previously reported data were based on the U.S.
over time.
from the United Nations Children’s Fund’s State of
Data sources
National Center for Health Statistics–WHO growth
For indicators from household surveys, the year in
the World’s Children 2009 and Childinfo. Data on
reference. Because of the change in standards, the
the table refers to the survey year. For more informa-
anemia are from the WHO’s Worldwide Prevalence
data in this edition should not be compared with data
tion, consult the original sources.
of Anemia 1993–2005 (2008).
in editions prior to 2008.
2009 World Development Indicators
113
PEOPLE
Nutrition
2.20
Health risk factors and future challenges Prevalence of smoking
% of adults Male 2008
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
114
.. 41 27 .. 34 55 28 46 .. 43 64 30 .. 34 49 .. 20 48 14 16 38 10 19 .. 13 42 60 22 27 11 10 26 12 39 36 37 36 16 24 25 22 16 50 7 32 37 .. 17 57 37 7 64 25 .. .. ..
Female 2008
.. 4 0b .. 24 4 22 40 1 1 21 24 .. 26 35 .. 13 28 1 11 6 1 18 .. 1 31 4 4 11 1 0b 7 1 29 26 25 31 11 6 1 3 1 28 1 24 27 .. 1 6 26 1 40 4 9 .. 4
2009 World Development Indicators
Incidence of Prevalence tuberculosis of diabetes
Prevalence of HIV
Total % of population ages 15–49
Female % of total population with HIV
per 100,000 people
% of population ages 20–79
2007
2007
1990
2007
.. 17 57 287 31 72 6 12 77 223 61 12 91 155 51 731 48 39 226 367 495 192 5 345 299 12 98 62 35 392 403 11 420 40 6 9 8 69 101 21 40 95 38 378 6 14 406 258 84 6 203 18 63 287 220 306
.. 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
.. .. .. 0.3 0.2 .. 0.1 <0.1 .. .. .. 0.1 0.1 0.1 .. 4.7 0.4 .. 1.9 1.7 0.7 0.8 0.2 1.8 0.7 <0.1 .. .. 0.1 .. 5.1 0.1 2.2 .. .. .. 0.1 0.6 0.1 .. 0.1 0.1 .. 0.7 .. 0.1 0.9 .. .. <0.1 0.1 0.1 <0.1 0.2 0.2 1.2
.. .. .. .. .. .. 0.1 25.0 28.6 2.1 60.9 61.1 0.5 25.0 26.7 0.1 <27.8 <41.7 0.2 <7.1 6.7 0.2 27.3 29.6 0.2 .. 16.7 .. .. 16.7 0.2 27.5 30.0 0.2 26.2 27.3 1.2 63.3 62.7 0.2 24.6 27.8 <0.1 .. .. 23.9 59.3 60.7 0.6 34.4 33.8 .. .. .. 1.6 45.4 50.8 2.0 59.2 58.9 0.8 25.8 28.6 5.1 61.2 60.0 0.4 26.5 27.4 6.3 66.7 65.0 3.5 60.7 61.1 0.3 26.0 28.1 25.5c 29.0 c 0.1c .. .. .. 0.6 26.9 29.4 .. .. .. 3.5 58.4 58.9 0.4 27.5 28.1 3.9 58.2 59.5 <0.1 .. .. 0.1 <43.5 29.0 .. <38.5 <33.3 0.2 .. 22.9 1.1 54.0 50.8 0.3 25.8 28.4 .. 26.8 28.9 0.8 25.7 28.5 1.3 60.0 60.0 1.3 <28.6 24.2 2.1 59.5 59.6 0.1 <50.0 <41.7 0.4 25.0 27.1 5.9 58.3 58.7 0.9 59.0 60.0 0.1 20.0 37.0 0.1 27.3 28.8 1.9 58.3 60.0 0.2 26.5 27.3 0.8 97.9 98.1 1.6 59.6 59.3 1.8 59.2 58.0 2.2 45.7 52.7
2001
2007
Condom use
Youth % of population ages 15–24
% of population ages 15–24
Male 2007
Female 2007
Male 2000–07a
Female 2000–07a
.. .. 0.1 0.2 0.6 0.2 0.2 0.2 0.3 .. 0.3 0.2 0.3 0.2 .. 5.1 1.0 .. 0.5 0.4 0.8 1.2 0.4 1.1 2.0 0.3 0.1c .. 0.7 .. 0.8 0.4 0.8 .. 0.1 <0.1 0.2 0.3 0.4 .. 0.9 0.3 1.6 0.5 0.1 0.4 1.3 0.2 0.1 0.1 0.4 0.2 .. 0.4 0.4 0.6
.. .. 0.1 0.3 0.3 0.1 <0.1 0.1 0.1 .. 0.1 0.1 0.9 0.1 .. 15.3 0.6 .. 0.9 1.3 0.3 4.3 0.2 5.5 2.8 0.2 0.1c .. 0.3 .. 2.3 0.2 2.4 .. 0.1 .. 0.1 0.6 0.2 .. 0.5 0.9 0.7 1.5 <0.1 0.2 3.9 0.6 0.1 0.1 1.3 0.1 1.5 1.2 1.2 1.4
.. .. .. .. .. 32 .. .. 25 .. .. .. 39 29 .. .. .. .. 54 .. 31 52 .. .. 18 .. .. .. .. .. 36 .. .. .. .. .. .. 58 .. .. .. .. .. 18 .. .. .. .. .. .. 45 .. .. 35 .. 28
.. .. .. .. .. 7 .. .. 1 .. .. .. 10 10 .. .. .. .. 17 .. 3 24 .. .. 7 .. .. .. 23 .. 16 .. .. .. .. .. .. 19 .. .. .. 2 .. 2 .. .. .. .. .. .. 19 .. .. 10 .. 20
Prevalence of smoking
% of adults Male 2008
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 28 62 24 .. 27 31 33 19 44 62 43 24 59 53 34 47 61 54 29 48 .. 32 45 40 .. 19 51 14 16 36 37 46 46 26 20 44 36 29 38 30 .. 41 9 34 24 30 52 46 33 43 39 44 41 ..
Female 2008
3 34 1 4 2 .. 26 18 19 8 14 10 10 1 .. 6 2 2 14 24 7 34 .. 2 21 32 .. 2 3 1 1 1 12 6 7 0b 2 12 9 26 30 28 5 11 0b 30 0b 3 20 28 14 23 9 27 31 ..
Incidence of Prevalence tuberculosis of diabetes
Prevalence of HIV
Total % of population ages 15–49
Female % of total population with HIV
2.20 Condom use
Youth % of population ages 15–24
% of population ages 15–24
per 100,000 people
% of population ages 20–79
2007
2007
1990
2007
2001
2007
Male 2007
Female 2007
Male 2000–07a
Female 2000–07a
59 17 168 228 22 .. 13 8 7 7 21 7 129 353 344 90 24 121 151 53 19 637 277 17 68 29 251 346 103 319 318 22 20 141 205 92 431 171 767 173 8 7 49 174 311 6 13 181 47 250 58 126 290 25 30 4
9.1 7.6 6.7 2.3 7.8 .. 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
1.3 .. 0.1 .. .. .. .. <0.1 0.4 0.3 .. .. .. .. .. .. .. .. .. .. <0.1 0.8 0.4 .. .. .. .. 2.1 0.1 0.2 <0.1 <0.1 0.2 .. .. .. 1.4 0.4 1.2 <0.1 0.1 0.1 <0.1 0.1 0.7 <0.1 .. .. 0.4 .. <0.1 0.1 .. .. 0.2 ..
0.7 0.1 0.3 0.2 0.2 .. 0.2 0.1 0.4 1.6 .. .. 0.1 .. .. <0.1 .. 0.1 0.2 0.8 0.1 23.2 1.7 .. 0.1 <0.1 0.1 11.9 0.5 1.5 0.8 1.7 0.3 0.4 0.1 0.1 12.5 0.7 15.3 0.5 0.2 0.1 0.2 0.8 3.1 0.1 .. 0.1 1.0 1.5 0.6 0.5 .. 0.1 0.5 ..
25.7 <35.7 38.5 10.8 26.7 .. 26.1 60.0 25.7 26.4 22.2 .. <29.4 .. .. 26.5 .. <50 <45.5 <23.8 <45.5 58.3 59.1 .. <35.7 .. 23.8 56.4 23.3 60.5 25.8 <27.8 27.1 <50.0 .. 27.5 59.4 33.4 60.7 21.8 25.6 <16.7 25.6 29.3 60.0 <41.7 .. 26.0 26.9 34.7 26.4 26.8 <50 26.0 26.6 ..
28.5 <30.3 38.3 20.0 28.2 .. 27.3 59.2 27.3 29.2 24.0 .. 27.5 .. .. 27.7 .. 26.2 24.1 27.0 <33.3 57.7 59.4 .. <45.5 .. 26.2 58.3 26.6 60.2 27.9 29.2 28.5 29.5 <20.0 28.1 57.9 41.7 61.1 25.0 27.2 <35.7 28.0 30.4 58.3 <33.3 .. 28.7 28.9 39.6 29.0 28.4 26.8 28.9 27.6 ..
0.7 0.1 0.3 0.3 0.2 .. 0.2 <0.1 0.4 1.7 .. .. 0.2 .. .. <0.1 .. 0.2 0.2 0.9 0.1 5.9 0.4 .. 0.1 .. 0.2 2.4 0.6 0.4 0.9 1.8 0.3 0.4 0.1 0.1 2.9 0.7 3.4 0.5 0.2 0.1 0.3 0.9 0.8 0.1 .. 0.1 1.1 0.6 0.7 0.5 .. 0.1 0.5 ..
0.4 <0.1 0.3 0.1 0.1 .. 0.1 0.1 0.2 0.9 .. .. 0.1 .. .. <0.1 .. 0.1 0.1 0.5 0.1 14.9 1.3 .. 0.1 .. 0.1 8.4 0.3 1.1 0.5 1.0 0.2 0.2 .. 0.1 8.5 0.6 10.3 0.3 0.1 .. 0.1 0.5 2.3 0.1 .. 0.1 0.6 0.7 0.3 0.3 .. 0.1 0.3 ..
.. .. .. .. .. .. .. .. .. .. .. .. .. 39 .. .. .. .. .. .. .. 44 19 .. .. .. 8 32 .. 29 .. .. .. 55 .. .. 27 .. 65 24 .. .. .. .. 38 .. .. .. .. .. .. .. 13 .. .. ..
7 .. .. 1 .. .. .. .. .. .. .. 4 .. 9 .. .. .. .. .. .. .. 26 9 .. .. .. 2 9 .. 4 .. .. .. 22 .. .. 12 .. 42 8 .. .. 7 .. 8 .. .. .. .. .. .. 9 3 .. .. ..
2009 World Development Indicators
115
PEOPLE
Health risk factors and future challenges
2.20
Health risk factors and future challenges Prevalence of smoking
% of adults Male 2008
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
41 70 .. 25 14 44 d 32 26 42 32 .. 25 36 25 24 13 20 31 43 .. 21 37 26 .. 32 47 52 27 18 64 26 37 26 37 24 33 43 41 77 18 21 40 w 29 44 44 41 41 57 56 29 31 30 15 34 37
Female 2008
25 27 8 3 1 44 d 4 5 20 21 .. 8 31 0b 2 3 25 22 .. .. 2 3 1 .. 6 1 19 1 2 23 2 35 22 28 1 27 2 3 29 2 2 8w 4 6 3 18 6 4 21 15 3 2 2 22 27
Incidence of Prevalence tuberculosis of diabetes
Prevalence of HIV
Total % of population ages 15–49
per 100,000 people
% of population ages 20–79
2007
2007
1990
2007
7.6 7.6 1.5 16.7 4.6 7.1d 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 1.7 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 5.8 w 4.7 5.9 5.5 7.4 5.6 4.2 7.3 7.1 8.6 6.9 3.6 6.8 6.4
.. .. 9.2 .. 0.1 <0.1 0.2 .. .. .. <0.1 0.8 0.4 .. 0.8 0.9 0.1 0.4 .. .. 4.8 1.0 .. 0.7 0.2 .. .. .. 13.7 .. .. <0.1 0.5 0.1 .. .. 0.1 .. .. 8.9 14.2 0.3 w 1.5 0.1 0.1 .. 0.4 0.1 .. 0.3 .. 0.1 2.1 0.3 0.2
0.1 1.1 2.8 .. 1.0 0.1 1.7 0.2 <0.1 <0.1 0.5 18.1 0.5 .. 1.4 26.1 0.1 0.6 .. 0.3 6.2 1.4 .. 3.3 1.5 0.1 .. <0.1 5.4 1.6 .. 0.2 0.6 0.6 0.1 .. 0.5 .. .. 15.2 15.3 0.8 w 2.1 0.6 0.3 1.7 0.9 0.2 0.6 0.5 0.1 0.3 5.0 0.3 0.3
115 110 397 46 272 32 574 27 17 13 249 948 30 60 243 1,198 6 6 24 231 297 142 322 429 11 26 30 68 330 102 16 15 4 22 113 34 171 20 76 506 782 139 w 269 129 134 108 162 136 84 50 41 174 369 16 13
Female % of total population with HIV 2001
50.7 50.0 22.1 25.5 60.6 60.0 .. .. 60.9 59.4 25.5 28.1 59.4 58.8 <34.5 29.3 .. .. .. .. 26.5 27.9 58.7 59.3 20.8 20.0 <33.3 37.8 56.0 58.6 60.7 58.8 43.4 46.8 33.2 36.8 .. .. <20.8 21.0 61.7 58.5 36.9 41.7 .. .. 61.0 57.5 57.5 59.2 <45.5 27.8 .. .. .. .. 58.9 59.3 35.7 44.2 .. .. .. .. 18.0 20.9 25.4 28.0 <35.7 28.8 .. .. 24.7 27.1 .. .. .. .. 54.7 57.1 58.8 56.7 30.8 w 32.9 w 37.5 40.6 30.8 32.6 30.7 32.7 31.1 32.2 32.1 34.2 25.4 28.5 28.6 30.5 32.1 32.8 28.0 28.6 32.9 34.6 57.0 56.9 23.3 24.9 25.8 26.9
a. Data are for the most recent year available. b. Less than 0.5. c. Includes Hong Kong, China. d. Includes Montenegro.
116
2009 World Development Indicators
2007
Condom use
Youth % of population ages 15–24 Male 2007
0.2 1.3 0.5 .. 0.3 0.1 0.4 0.2 .. .. 0.6 4.0 0.6 <0.1 0.3 5.8 0.1 0.4 .. 0.4 0.5 1.2 .. 0.8 0.3 0.1 .. .. 1.3 1.5 .. .. 0.7 0.6 0.1 .. 0.6 .. .. 3.6 2.9 0.5 w 0.7 0.4 0.3 1.0 0.5 0.2 0.8 0.7 .. 0.3 1.1 0.5 0.3
Female 2007
0.2 0.6 1.4 .. 0.8 0.1 1.3 0.1 .. .. 0.3 12.7 0.2 .. 1.0 22.6 0.1 0.5 .. 0.1 0.9 1.2 .. 2.4 1.0 <0.1 .. .. 3.9 1.5 .. .. 0.3 0.3 0.1 .. 0.3 .. .. 11.3 7.7 0.7 w 1.6 0.5 0.3 1.4 0.8 0.2 0.5 0.4 .. 0.3 3.3 0.2 0.2
% of population ages 15–24 Male 2000–07a
Female 2000–07a
.. .. 19 .. 48 .. .. .. .. .. .. 57 .. .. .. 66 .. .. .. .. 36 .. .. .. .. .. .. .. 38 .. .. .. .. .. 18 .. 16 .. .. 36 52
.. .. 5 .. 5 .. .. .. .. .. .. 46 .. .. .. 44 .. .. .. .. 13 .. .. .. .. .. .. 1 15 .. .. .. .. .. 2 .. 8 .. .. 19 9
About the data
2.20
Definitions
The limited availability of data on health status is a
occur in young adults, with young women especially
• Prevalence of smoking is the percentage of men
major constraint in assessing the health situation in
vulnerable.
and women who smoke cigarettes. The age range var-
developing countries. Surveillance data are lacking
The Joint United Nations Programme on HIV/AIDS
ies, but in most countries is 18 and older or 15 and
for many major public health concerns. Estimates
(UNAIDS) and the WHO estimate HIV prevalence from
older. • Incidence of tuberculosis is the estimated
of prevalence and incidence are available for some
sentinel surveillance, population-based surveys, and
number of new tuberculosis cases (pulmonary, smear
diseases but are often unreliable and incomplete.
special studies. The estimates in the table are more
positive, extrapulmonary). • Prevalence of diabetes
National health authorities differ widely in capacity
reliable than previous estimates because of expanded
refers to the percentage of people ages 20–79 who
and willingness to collect or report information. To
sentinel surveillance and improved data quality. Find-
have type 1 or type 2 diabetes. • Prevalence of HIV
compensate for this and improve reliability and inter-
ings from population-based HIV surveys, which are
is the percentage of people who are infected with
national comparability, the World Health Organization
geographically more representative than sentinel sur-
HIV. Total and youth rates are percentages of the
(WHO) prepares estimates in accordance with epide-
veillance and include both men and women, influenced
relevant age group. Female rate is as a percentage
miological models and statistical standards.
a downward adjustment to prevalence rates based on
of the total population with HIV. • Condom use is the
Smoking is the most common form of tobacco use
sentinel surveillance. And assumptions about the aver-
percentage of the population ages 15–24 who used a
and the prevalence of smoking is therefore a good
age time people living with HIV survive without antiret-
condom at last intercourse in the last 12 months.
measure of the tobacco epidemic (Corrao and others
roviral treatment were improved in the most recent
2000). Tobacco use causes heart and other vascular
model. Thus, estimates in this edition should not be
diseases and cancers of the lung and other organs.
compared with estimates in previous editions.
Given the long delay between starting to smoke and
Estimates from recent Demographic and Health
the onset of disease, the health impact of smoking
Surveys that have collected data on HIV/AIDS dif-
in developing countries will increase rapidly only in
fer somewhat from those of UNAIDS and the WHO,
the next few decades. Because the data present a
which are based on surveillance systems that focus
one-time estimate, with no information on intensity
on pregnant women who attend sentinel antenatal
or duration of smoking, and because the definition of
clinics. Caution should be used in comparing the
adult varies, the data should be used with caution.
two sets of estimates. Demographic and Health Sur-
Tuberculosis is one of the main causes of adult
veys are household surveys that use a representative
deaths from a single infectious agent in developing
sample from the whole population, whereas surveil-
countries. In developed countries tuberculosis has
lance data from antenatal clinics are limited to preg-
reemerged largely as a result of cases among immi-
nant women. Household surveys also frequently pro-
grants. The estimates of tuberculosis incidence in
vide better coverage of rural populations. However,
the table are based on an approach in which reported
respondents who refuse to participate or are absent
cases are adjusted using the ratio of case notifi -
from the household add considerable uncertainty to
cations to the estimated share of cases detected
survey-based HIV estimates, because the possible
by panels of 80 epidemiologists convened by the
association of absence or refusal with higher HIV
WHO.
prevalence is unknown. UNAIDS and the WHO esti-
Diabetes, an important cause of ill health and a
mate HIV prevalence for the adult population (ages
risk factor for other diseases in developed countries,
15–49) by assuming that prevalence among pregnant
is spreading rapidly in developing countries. Highest
women is a good approximation of prevalence among
among the elderly, prevalence rates are rising among
men and women. However, this assumption might not
younger and productive populations in developing
apply to all countries or over time. Other potential
countries. Economic development has led to the
biases are associated with the use of antenatal clinic
spread of Western lifestyles and diet to develop-
data, such as differences among women who attend
ing countries, resulting in a substantial increase in
antenatal clinics and those who do not.
Data sources Data on smoking are from Omar Shafey, Michael
diabetes. Without effective prevention and control
Data on condom use are from household surveys
Eriksen, Hana Ross, and Judith Mackay’s Tobacco
programs, diabetes will likely continue to increase.
and refer to condom use at last intercourse. How-
Atlas, 3rd edition (2009). Data on tuberculosis are
Data are estimated based on sample surveys.
ever, condoms are not as effective at preventing the
from the WHO’s Global Tuberculosis Control Report
Adult HIV prevalence rates reflect the rate of HIV
transmission of HIV unless used consistently. Some
2009. Data on diabetes are from the International
infection in each country’s population. Low national
surveys have asked directly about consistent use,
Diabetes Federation’s Diabetes Atlas, 3rd edition.
prevalence rates can be misleading, however. They
but the question is subject to recall and other biases.
Data on prevalence of HIV are from UNAIDS and
often disguise epidemics that are initially concen-
Caution should be used in interpreting the data.
the WHO’s 2008 Report on the Global AIDS Epi-
trated in certain localities or population groups and
For indicators from household surveys, the year in
threaten to spill over into the wider population. In
the table refers to the survey year. For more informa-
many developing countries most new infections
tion, consult the original sources.
demic. Data on condom use are from Demographic and Health Surveys by Macro International.
2009 World Development Indicators
117
PEOPLE
Health risk factors and future challenges
2.21
Health gaps by income and gender Survey year
Prevalence of child malnutrition
Child immunization rate
Moderate underweight % of children under age 5
% of children ages 12–23 monthsa
New reference
Armenia Bangladesh Benin Bolivia Brazil Burkina Faso Cambodia Cameroon Central African Republic Chad Colombia Comores 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
Old reference
Measles
Poorest quintile
Richest quintile
Poorest quintile
Richest quintile
Poorest quintile
3 36 18 7 7 19 27 .. 20 24 7 15 17 7 4 .. 25 10 17 21 17 14 28 .. .. 3 17 6 24 18 20 18 11 16 17 34 9 27 20 28 3 9 .. 15 .. .. 14 17 .. .. 16 11 .. .. 18 10
2 19 6 1 2 13 23 .. 11 16 2 12 7 1 2 .. 22 4 6 9 9 4 16 .. .. 5 6 5 18 9 10 11 2 5 6 20 2 18 9 14 1 1 .. 8 .. .. 8 8 .. .. 7 8 .. .. 12 5
3 41 21 10 10 26 35 .. 25 27 11 22 21 9 5 .. 32 14 22 26 22 18 33 .. .. 5 22 10 29 24 26 23 13 21 22 40 13 30 24 33 5 13 .. 19 .. .. 20 23 .. .. 21 15 .. 36 24 16
1 24 9 1 3 16 28 .. 15 19 3 14 10 1 2 .. 29 7 10 10 13 6 21 .. .. 6 7 7 24 12 13 15 3 7 9 26 2 26 10 19 1 1 .. 12 .. .. 11 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
Infant mortality rate
DTP3
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
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
2009 World Development Indicators
per 1,000
Poorest quintile
Richest quintile
Poorest quintile
Richest quintile
52 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
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
a. Refers to children who were immunized at any time before the survey. b. The data contain large sampling errors because of the small number of cases.
118
Under-five mortality rate
Survey year
Armenia Bangladesh Benin Bolivia Brazil Burkina Faso Cambodia Cameroon Central African Republic Chad Colombia Comores 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
Old reference Moderate underweight % of children under age 5
% of children ages 12–23 monthsa Measles
Infant mortality rate
DTP3
2.21 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
a. Refers to children who were immunized at any timebefore the survey.
2009 World Development Indicators
119
PEOPLE
Health gaps by income and gender
2.21
Health gaps by income and gender Survey year
Pregnant women receiving prenatal care
Contraceptive prevalence rate
%
modern methods % of married women ages 15–49
Poorest quintile
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
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
Richest quintile
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
Births attended by skilled health staffa
Total fertility rate
Exclusive breastfeeding
% of total
births per woman
% of children under 4 months
Poorest quintile
Richest quintile
Poorest quintile
16 45 4 23 56 2 13 2 1 0 60 7 1 59 43 0c 3 6 9 5 1 17 29 49 28 49 12 44 2 20 4 0c 51 14 29 24 50 1 4 1 21 37 24 2 1 34 11 3 24 51 11 46 58 1 11 41
29 50 15 49 77 27 25 27 9 7 72 19 13 70 61 19 23 18 26 60 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
Richest quintile
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
Poorest quintile
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
Richest quintile
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
a. Based on births in the fi ve years before the survey. b. The data contain large sampling errors because of the small number of cases. c. Less than 0.5.
120
2009 World Development Indicators
Poorest quintile
Richest quintile
.. 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 20 39 36
.. 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 28b 59 .. .. 13 70 b 46b
About the data
2.21
Definitions
The data in the table describe the health status and
specific asset indexes with country-specific choices
• Survey year is the year in which the underlying
use of health services by individuals in different
of asset indicators might produce a more effective
data were collected. • Prevalence of child malnutri-
socioeconomic groups and by sex within countries.
and accurate index for each country. The asset index
tion is the percentage of children under age 5 whose
The data are from Demographic and Health Surveys
used in the table does not have this flexibility.
weight for age is two to three standard deviations
conducted by Macro International with the support
The analysis was carried out for 56 countries,
below the median reference standard for their age.
of the U.S. Agency for International Development.
with the results issued in country reports. The table
The table presents malnutrition data using both the
These large-scale household sample surveys, con-
shows the estimates for the poorest and richest quin-
old reference standards and the new international
ducted periodically in developing countries, collect
tiles and by sex only; the full set of estimates for up
child growth standards released in 2006 by the
information on many health, nutrition, and popula-
to 117 indicators is available in the country reports
World Health Organization. For more information
tion measures as well as on respondents’ social,
(see Data sources).
about the change in standards, see About the data
demographic, and economic characteristics using a
Demographic and Health Surveys try to collect
for table 2.19. • Child immunization rate is the per-
standard set of questionnaires. The data presented
internationally comparable data, but the age group
centage of children ages 12–23 months at the time
here draw on responses to individual and household
of the reference population could differ across coun-
of the survey who, at any time before the survey,
questionnaires.
tries. Caution should be used when comparing the
had received measles vaccine and three doses of
Socioeconomic status as displayed in the table is
data. The estimates in the table are based on survey
diphtheria, tetanus, and pertussis (whooping cough)
based on a household’s assets, including ownership
data, which refer to a period preceding the survey
vaccine (DTP3). • Infant mortality rate is the num-
of consumer items, features of the household’s dwell-
date, or use a definition or methodology different
ber of infants dying before reaching one year of age,
ing, and other characteristics related to wealth. Each
from the estimates in tables 2.17–2.19 and 2.22.
per 1,000 live births. • Under-five mortality rate is
household asset on which information was collected
Thus the estimates may differ from those in the other
the probability that a newborn baby will die before
was assigned a weight generated through principal-
tables, and caution should be used in interpreting
reaching age 5, per 1,000, if subject to current age-
component analysis. The resulting scores were stan-
the data.
specific mortality rates. • Pregnant women receiv-
dardized in relation to a standard normal distribution
ing prenatal care are the percentage of women with
with a mean of zero and a standard deviation of one.
one or more births during the five years preceding the
The standardized scores were then used to create
survey who were attended by skilled health personnel
break-points defining wealth quintiles, expressed as
at least once during pregnancy for reasons related
quintiles of individuals in the population rather than
to pregnancy. • Contraceptive prevalence rate is
quintiles of individuals at risk with respect to any
the percentage of women married or in-union ages
one health indicator.
15–49 who are practicing, or whose sexual partners
The choice of the asset index for defining socio-
are practicing, any modern method of contracep-
economic status was based on pragmatic rather than
tion. • Births attended by skilled health staff are
conceptual considerations: Demographic and Health
the percentage of deliveries attended by personnel
Surveys do not collect income or consumption data
trained to give the necessary supervision, care, and
but do have detailed information on households’ own-
advice to women during pregnancy, labor, and the
ership of consumer goods and access to a variety
postpartum period; to conduct deliveries on their
of goods and services. Like income or consumption,
own; and to care for newborns. Skilled health staff
the asset index defines disparities primarily in eco-
include doctors, nurses, and trained midwives, but
nomic terms. It therefore excludes other possibilities
exclude trained or untrained traditional birth atten-
of disparities among groups, such as those based
dants. • Total fertility rate is the number of children
on gender, education, ethnic background, or other
that would be born to a woman if she were to live to
facets of social exclusion. To that extent the index
the end of her childbearing years and bear children
provides only a partial view of the multidimensional
in accordance with current age-specific fertility rates.
concepts of poverty, inequality, and inequity.
• Exclusive breastfeeding refers to the percentage
Creating one index that includes all asset indicators limits the types of analysis that can be performed.
of children ages 0–3 months who received only breast milk in the 24 hours preceding the survey.
In particular, the use of a unified index does not permit a disaggregated analysis to examine which asset
Data sources
indicators are more closely associated with health
Data on health gaps by income and gender are from
status or use of health services. In addition, some
Davidson R. Gwatkin and others’ Socio- Economic
asset indicators may reflect household wealth better
Differences in Health, Nutrition, and Population
in some countries than in others—or reflect differ-
(2007). Country reports are available at www.
ent degrees of wealth in different countries. Taking
worldbank.org/povertyandhealth/countrydata.
such information into account and creating country-
2009 World Development Indicators
121
PEOPLE
Health gaps by income and gender
2.22 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
122
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 2007
per 1,000 1990 2007
per 1,000 Male Female 2000–07a,b 2000–07a,b
per 1,000 Male Female 2005–07a 2005–07a
% of cohort Male Female 2007 2007
1990
2007
.. 72 67 40 72 68 77 76 66 55 71 76 53 59 72 63 67 72 50 46 55 55 77 50 51 74 68 77 68 46 57 76 53 72 75 71 75 68 69 62 66 49 69 48 75 77 61 51 70 75 57 77 63 47 42 55
.. 76 72 43 75 72 81 80 67 64 70 80 57 66 75 51 72 73 52 49 60 50 81 45 51 78 73 82 73 46 55 79 48 76 78 77 78 72 75 71 72 58 73 53 79 81 57 59 71 80 60 80 70 56 46 61
2009 World Development Indicators
.. 37 54 150 25 48 8 8 78 105 20 8 111 89 18 45 49 15 112 113 87 85 7 113 120 18 36 .. 28 127 67 16 104 11 11 11 8 53 43 68 47 88 12 122 6 7 60 104 41 7 76 9 60 137 142 105
.. 13 33 116 15 22 5 4 34 47 12 4 78 48 13 33 20 10 104 108 70 87 5 113 124 8 19 .. 17 108 79 10 89 5 5 3 4 31 20 30 21 46 4 75 3 4 60 82 27 4 73 4 29 93 118 57
.. 46 69 258 29 56 10 10 98 151 24 10 184 125 22 57 58 19 206 189 119 139 8 171 201 21 45 .. 35 200 104 18 151 13 13 13 9 66 57 93 60 147 18 204 7 9 92 153 47 9 120 11 82 231 240 152
.. 15 37 158 16 24 6 4 39 61 13 5 123 57 14 40 22 12 191 180 91 148 6 172 209 9 22 .. 20 161 125 11 127 6 7 4 4 38 22 36 24 70 6 119 4 4 91 109 30 4 115 4 39 150 198 76
.. 3 .. .. .. 8 .. .. 9 24 .. .. 64 20 .. .. .. .. 110 .. 20 73 .. 74 96 .. .. .. 4 70 49 .. .. .. .. .. .. 6 5 10 .. 55 .. 56 .. .. 32 46 5 .. 51 .. .. 89 110 33
.. 1 .. .. .. 3 .. .. 5 29 .. .. 65 26 .. .. .. .. 113 .. 20 72 .. 82 101 .. .. .. 3 64 43 .. .. .. .. .. .. 4 5 10 .. 50 .. 56 .. .. 33 39 4 .. 35 .. .. 86 88 36
.. 106 121 479 166 195 84 111 216 231 330 111 279 235 145 567 229 221 283 404 346 414 94 559 355 129 151 77 202 435 391 114 420 156 117 148 116 219 169 155 207 418 283 361 133 123 379 219 213 107 285 91 236 272 447 306
.. 51 102 434 79 87 48 55 102 198 115 61 235 176 76 567 120 92 183 370 243 420 57 533 308 64 90 33 95 400 367 61 403 61 72 66 69 131 90 91 125 319 92 325 57 57 383 181 81 56 281 41 130 235 401 237
.. 81 78 30 74 68 88 84 62 62 52 84 53 63 76 32 67 70 47 40 50 41 86 28 43 80 75 87 71 36 45 82 38 76 82 78 83 68 75 73 70 42 59 45 83 84 49 58 67 84 57 85 67 52 35 55
.. 90 81 36 87 83 93 93 77 67 82 92 59 71 86 36 80 86 58 45 61 43 92 34 49 89 83 94 83 40 50 89 43 89 88 90 89 79 85 83 80 54 85 50 92 93 51 63 84 92 59 93 79 59 41 63
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
2.22
Life expectancy at birth
Infant mortality rate
Under-five mortality rate
Child mortality rate
Adult mortality rate
Survival to age 65
years
per 1,000 live births 1990 2007
per 1,000 1990 2007
per 1,000 Male Female 2000–07a,b 2000–07a,b
per 1,000 Male Female 2005–07a 2005–07a
% of cohort Male Female 2007 2007
1990
2007
66 69 60 62 65 62 75 77 77 72 79 67 68 59 70 71 75 68 55 69 69 59 43 68 71 71 51 49 70 48 58 69 71 67 61 64 44 59 62 54 77 75 64 47 47 77 70 60 72 55 68 66 66 71 74 75
70 73 65 71 71 .. 79 81 81 73 83 73 66 54 67 79 78 68 64 71 72 43 46 74 71 74 59 48 74 54 64 72 75 69 67 71 42 62 53 64 80 80 73 57 47 80 76 65 76 57 72 71 72 75 78 78
45 15 80 60 54 42 8 10 8 28 5 33 51 64 42 8 13 62 120 14 32 81 138 35 10 33 103 124 16 148 81 20 42 30 71 69 135 91 57 99 7 8 52 143 120 7 25 102 27 69 34 58 43 19 11 ..
20 6 54 25 29 .. 4 4 3 26 3 21 28 80 42 4 9 34 56 7 26 68 93 17 7 15 70 71 10 117 75 13 29 16 35 32 115 74 47 43 4 5 28 83 97 3 11 73 18 50 24 17 23 6 3 ..
58 17 117 91 72 53 9 12 9 33 6 40 60 97 55 9 15 74 163 17 37 102 205 41 16 38 168 209 22 250 130 24 52 37 98 89 201 130 87 142 9 11 68 304 230 9 32 132 34 94 41 78 62 17 15 ..
24 7 72 31 33 .. 4 5 4 31 4 24 32 121 55 5 11 38 70 9 29 84 133 18 8 17 112 111 11 196 119 15 35 18 43 34 168 103 68 55 5 6 35 176 189 4 12 90 23 65 29 20 28 7 4 ..
8 .. 9 13 .. .. .. .. .. 5 .. 3 5 42 .. .. .. 8 .. .. .. 22 57 .. .. 2 45 52 .. 117 50 .. .. 7 11 9 61 .. 24 21 .. .. 10 138 57 .. .. 14 .. .. .. 11 14 .. .. ..
9 .. 12 11 .. .. .. .. .. 6 .. 2 4 39 .. .. .. 4 .. .. .. 19 51 .. .. 1 45 54 .. 114 48 .. .. 4 10 11 64 .. 19 18 .. .. 9 135 57 .. .. 22 .. .. .. 8 9 .. .. ..
238 256 257 168 152 .. 88 80 84 220 90 164 361 417 179 109 86 279 233 311 153 723 460 148 346 135 287 526 152 255 172 207 142 294 262 148 625 298 518 230 81 92 209 166 434 86 99 174 139 422 174 200 158 209 128 134
139 107 164 118 101 .. 56 38 44 139 44 113 144 396 127 45 52 131 190 114 101 720 425 92 116 80 227 519 87 178 106 106 79 138 171 98 613 190 512 206 59 59 120 180 416 53 73 142 74 306 128 123 104 80 53 53
66 67 60 72 73 .. 85 87 86 70 87 73 50 43 66 82 85 57 62 64 74 19 33 75 63 77 55 34 75 49 64 68 78 59 59 74 24 54 37 62 86 87 70 60 37 87 82 66 78 41 72 70 73 72 83 80
2009 World Development Indicators
78 86 69 80 81 .. 91 93 93 79 94 81 77 48 75 92 90 75 68 86 82 22 38 84 86 85 62 37 85 59 74 82 86 77 71 82 27 67 41 66 91 91 80 58 39 92 87 69 87 55 79 80 82 89 91 91
123
PEOPLE
Mortality
2.22
Mortality Life expectancy at birth
Infant mortality rate
Under-five mortality rate
Child mortality rate
Adult mortality rate
Survival to age 65
years
per 1,000 live births 1990 2007
per 1,000 1990 2007
per 1,000 Male Female 2000–07a,b 2000–07a,b
per 1,000 Male Female 2005–07a 2005–07a
% of cohort Male Female 2007 2007
1990
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
70 69 32 68 57 71 39 74 71 73 42 62 77 70 53 58 78 77 68 63 51 67 47 58 70 70 66 63 50 70 73 76 75 73 69 71 66 69 54 48 61 65 w 54 65 64 69 63 67 69 68 64 59 50 76 76
2007
73 68 46 73 63 73 43 80 74 78 48 50 81 72 59 40 81 82 74 67 52 71 61 58 70 74 72 63 51 68 79 79 78 76 67 74 74 73 63 42 43 69 w 57 70 69 71 67 72 70 73 70 64 51 79 80
27 23 117 35 72 .. 169 7 12 8 121 49 8 26 79 70 6 7 30 91 96 26 138 89 30 41 67 81 106 22 13 8 9 21 61 27 40 33 90 99 62 63 w 103 55 58 39 69 42 41 44 58 87 108 10 8
13 13 109 20 59 7 155 2 7 3 88 46 4 17 69 66 3 4 15 57 73 6 77 65 31 18 21 45 82 20 7 5 7 12 36 17 13 24 55 103 59 47 w 80 35 38 21 51 22 21 22 32 59 89 6 4
32 27 195 44 149 .. 290 8 15 11 203 64 9 32 125 96 7 9 37 117 157 31 184 150 34 52 82 99 175 25 15 10 11 25 74 32 56 38 127 163 95 93 w 164 75 81 47 101 56 49 55 77 125 183 12 10
15 15 181 25 114 8 262 3 8 4 142 59 4 21 109 91 3 5 17 67 116 7 97 100 35 21 23 50 130 24 8 6 8 14 41 19 15 27 73 170 90 68 w 126 45 50 24 74 27 23 26 38 78 146 7 4
.. .. 90 3 69 4 134 .. .. .. 53 13 .. .. 38 32 .. .. 5 18 56 .. .. 55 .. .. 9 19 75 4 .. .. .. .. 11 .. 5 3 10 89 21
.. .. 87 4 69 3 124 .. .. .. 54 9 .. .. 30 30 .. .. 3 13 52 .. .. 43 .. .. 9 17 62 1 .. .. .. .. 7 .. 4 3 11 74 21
201 429 456 140 171 157c 405 81 196 149 385 623 106 233 305 772 78 78 124 211 433 264 266 278 240 124 152 298 429 385 74 96 141 142 240 179 137 129 254 620 687 219 w 306 201 197 225 224 163 303 196 164 248 417 117 112
85 158 408 90 102 83c 345 46 76 57 335 598 44 99 265 760 48 46 84 139 401 159 232 236 190 72 85 142 416 142 49 60 82 67 136 94 91 92 205 619 719 155 w 269 127 125 138 159 102 125 107 112 169 390 63 54
69 43 34 76 63 74 c 33 86 71 80 39 27 85 67 53 15 88 87 78 63 41 64 57 54 66 78 73 53 41 51 86 85 81 77 61 73 78 78 59 25 22 68 w 53 68 68 64 65 74 58 71 72 60 41 83 84
85 77 40 84 73 85c 40 92 88 91 44 33 94 83 59 18 93 93 85 73 46 77 62 61 73 86 84 72 44 80 91 91 88 88 74 84 84 84 66 27 22 76 w 58 78 77 80 74 81 81 82 80 69 45 91 92
a. Data are for the most recent year available. b. Refers to a survey year. Values were estimated directly from surveys and cover the 5 or 10 years preceding the survey. c. Includes Kosovo.
124
2009 World Development Indicators
About the data
2.22
Definitions
Mortality rates for different age groups (infants, chil-
their reference dates and then extrapolate the trend
• Life expectancy at birth is the number of years a
dren, and adults) and overall mortality indicators (life
to the present. (For further discussion of childhood
newborn infant would live if prevailing patterns of mor-
expectancy at birth or survival to a given age) are
mortality estimates, see UNICEF, WHO, World Bank,
tality at the time of its birth were to stay the same
important indicators of health status in a country.
and United Nations Population Division 2007; for a
throughout its life. • Infant mortality rate is the num-
Because data on the incidence and prevalence of
graphic presentation and detailed background data,
ber of infants dying before reaching one year of age,
diseases are frequently unavailable, mortality rates
see www.childmortality.org/).
per 1,000 live births in a given year. • Under-five mor-
are often used to identify vulnerable populations.
Infant and child mortality rates are higher for boys
tality rate is the probability per 1,000 that a newborn
And they are among the indicators most frequently
than for girls in countries in which parental gender
baby will die before reaching age 5, if subject to current
used to compare socioeconomic development across
preferences are insignificant. Child mortality cap-
age-specific mortality rates. • Child mortality rate is
countries.
tures the effect of gender discrimination better than
the probability per 1,000 of dying between ages 1 and
The main sources of mortality data are vital reg-
infant mortality does, as malnutrition and medical
5—that is, the probability of a 1-year-old dying before
istration systems and direct or indirect estimates
interventions are more important in this age group.
reaching age 5—if subject to current age-specific mor-
based on sample surveys or censuses. A “complete”
Where female child mortality is higher, as in some
tality rates. • Adult mortality rate is the probability per
vital registration system—covering at least 90 per-
countries in South Asia, girls probably have unequal
1,000 of dying between the ages of 15 and 60—that
cent of vital events in the population—is the best
access to resources. Child mortality rates in the
is, the probability of a 15-year-old dying before reach-
source of age-specific mortality data. Where reliable
table are not compatible with infant mortality and
ing age 60—if subject to current age-specific mortality
age-specific mortality data are available, life expec-
under-fi ve mortality rates because of differences
rates between those ages. • Survival to age 65 refers
tancy at birth is directly estimated from the life table
in methodology and reference year. Child mortality
to the percentage of a hypothetical cohort of newborn
constructed from age- specific mortality data.
data were estimated directly from surveys and cover
infants that would survive to age 65, if subject to cur-
But complete vital registration systems are fairly
the 10 years preceding the survey. In addition to
rent age-specific mortality rates.
uncommon in developing countries. Thus estimates
estimates from Demographic Health Surveys, new
must be obtained from sample surveys or derived
estimates derived from Multiple Indicator Cluster
by applying indirect estimation techniques to reg-
Surveys (MICS) 3 have been added to the table; they
Data on infant and under-five mortality rates are
istration, census, or survey data (see Primary data
cover the 5 years preceding the survey.
the estimates by the Inter-agency Group for Child
Data sources
documentation). Survey data are subject to recall
Rates for adult mortality and survival to age 65
Mortality Estimation (which comprises the World
error, and surveys estimating infant deaths require
come from life tables. Adult mortality rates increased
Health Organization, UNICEF, United Nations Popu-
large samples because households in which a birth
notably in a dozen countries in Sub-Saharan Africa
lation Division, World Bank, Harvard University,
or an infant death has occurred during a given year
between 1995–2000 and 2000–05 and in several
U.S. Census Bureau, Economic Commission for
cannot ordinarily be pre-selected for sampling. Indi-
countries in Europe and Central Asia during the first
Latin America and the Caribbean, Measure DHS,
rect estimates rely on model life tables that may be
half of the 1990s. In Sub-Saharan Africa the increase
and other universities and research institutes)
inappropriate for the population concerned. Because
stems from AIDS-related mortality and affects both
and are based mainly on household surveys, cen-
life expectancy at birth is estimated using infant mor-
sexes, though women are more affected. In Europe
suses, and vital registration data, supplemented
tality data and model life tables for many develop-
and Central Asia the causes are more diverse (high
by the World Bank’s estimates based on house-
ing countries, similar reliability issues arise for this
prevalence of smoking, high-fat diet, excessive alco-
hold surveys and vital registration and sample reg-
indicator. Extrapolations based on outdated surveys
hol use, stressful conditions related to the economic
istration data. Data on child mortality rates are
may not be reliable for monitoring changes in health
transition) and affect men more.
from Demographic and Health Surveys by Macro
status or for comparative analytical work.
PEOPLE
Mortality
The percentage of a hypothetical cohort surviv-
International (Measure DHS) and Multiple Indica-
Estimates of infant and under-five mortality tend
ing to age 65 reflects both child and adult mortality
tor Cluster Surveys by UNICEF. Other estimates
to vary by source and method for a given time and
rates. Like life expectancy, it is a synthetic mea-
are compiled and produced by the World Bank’s
place. Years for available estimates also vary by
sure based on current age-specific mortality rates.
Human Development Network and Development
country, making comparison across countries and
It shows that even in countries where mortality is
Data Group in consultation with its operational
over time diffi cult. To make infant and under-fi ve
high, a certain share of the current birth cohort will
staff and country offices. Important inputs to the
mortality estimates comparable and to ensure con-
live well beyond the life expectancy at birth, while in
World Bank’s demographic work come from the
sistency across estimates by different agencies,
low-mortality countries close to 90 percent will reach
United Nations Population Division’s World Popula-
the United Nations Children’s Fund (UNICEF) and
at least age 65.
tion Prospects: The 2006 Revision, census reports
the World Bank (now working together with other
and other statistical publications from national
organizations as the Inter-agency Group for Child
statistical offices and Eurostat, Demographic and
Mortality Estimation) developed and adopted a
Health Surveys by Macro International, and the
statistical method that uses all available informa-
Human Mortality Database by the University of
tion to reconcile differences. The method uses the
California, Berkeley, and the Max Planck Institute
weighted least squares method to fit a regression
for Demographic Research (www.mortality.org).
line to the relationship between mortality rates and
2009 World Development Indicators
125 Text figures, tables, and boxes
Introduction
E
nergy and a changing climate
The world economy needs ever-increasing amounts of energy to sustain economic growth, raise living standards, and reduce poverty. But today’s trends in energy use are not sustainable. As the world’s population grows and economies become more industrialized, nonrenewable energy sources will become scarcer and more costly. And carbon dioxide emissions from the use of fossil fuels will continue to build in the atmosphere, accelerating global warming. Energy-related carbon dioxide now accounts for 61–65 percent of global greenhouse gas emissions (IEA 2008a; IPCC 2007a; WRI 2005). Global warming will have particularly pernicious effects for developing economies, with their high exposure and low adaptive capacity. Where energy comes from, how we produce it, and how much we use will profoundly affect development in the 21st century. This introduction focuses on recent trends in energy use and carbon dioxide emissions—and projections through 2030. There is now a consensus that action is needed to curb the growth in human-made greenhouse-gas emissions (IPCC 2007b; IEA 2008a). A new post-2012 policy regime on global climate change—to be agreed in Copenhagen in late 2009—aims to set a quantified global goal for stabilizing greenhouse gases in the atmosphere and to establish robust policy mechanisms that ensure the goal is achieved. Without government initiatives on energy or climate change, global temperatures may rise as much as 6°C by the end of the century. This outcome of the Intergovernmental Panel on Climate Change Trend Scenario can be compared with a 3°C rise under a Policy Scenario in which greenhouse gasses are stabilized at 550 parts per million (ppm) of carbon dioxide equivalent and a 2°C rise under a Policy Scenario in which concentrations are stabilized at 450 ppm. The consequences of the Trend Scenario go well beyond what the international community regards as acceptable. The global financial and economic crisis, while reducing the demand for energy in the short run, may also slow efforts at energy saving by lowering the price of oil and other fossil energy sources. And by discouraging investments in fossil fuel substitutes and more energy- efficient production processes, the crisis may leave the world on a higher carbon dioxide emission path. The world’s largest economy and biggest contributor to carbon dioxide emissions has new leadership that could make climate change a top priority and commit resources to finding alternative sources of cleaner energy.
2009 World Development Indicators
127
Energy use: unsustainable trend, unacceptable future
particularly oil, make projections of energy demand diffi -
In 2006 global energy use from all sources reached 11.5 bil-
cult. The International Energy Agency forecasts that energy
lion metric tons of oil equivalent—twice as high as its 1971
demand in 2030 will be 45 percent higher than energy use
level (figure 3a). High-income economies, with just 15 per-
in 2006, for an average annual growth of 1.6 percent, or just
cent of world population, use almost half of global energy (fig-
a little slower than the 1.9 percent from 1980 to 2006 (IEA
ure 3b). Energy use grew by 2.4 percent a year in low-income
2008a, b). More than 80 percent of the energy used in 2006
economies, 2.0 percent in middle-income economies, and
was from nonrenewable fuels—carbon dioxide–emitting oil,
1.6 percent in high-income economies over 1990–2006. It
coal, and gas. In the absence of new policies this share is
rose 4.4 percent a year in China and 3.5 percent in India. The
projected to remain above 80 percent in 2030, with demand
United States, Russian Federation, Germany, Japan, China,
for coal—cheaper and more abundant—growing faster than
and India are the top energy consumers, accounting for 55
that for oil and gas (figure 3e and table 3f).
percent of global energy use (figure 3c and table 3.7). On
Growing 2 percent a year on average, world demand for
average, high-income economies use more than 11 times the
coal is projected to be 60 percent higher in 2030 than in
energy per capita of low-income economies, with huge dif-
2006. Most of the increase in demand comes from the power
ferences across countries and within countries and regions
generation sector. China and India together account for 85
(figure 3d).
percent of this increase. Oil demand grows far more slowly
The accelerating trend in energy use and the potential
than demand for other fossil fuels, mainly because of high
consequences have been matters of concern for the interna-
final prices. Yet, oil remains the dominant fuel in the primary
tional community. The recent global financial crisis, economic
mix, even with the drop in its share from 34 percent in 2006
downturn, and significant fluctuations in the price of energy,
to 30 percent in 2030.
Energy use has doubled since 1971
3a
Energy use (billions of metric tons of oil equivalent) 12
The top six energy consumers use 55 percent of global energy
3c
Energy use per capita (thousands of kilograms of oil equivalent) 8
9
Other high income
6
United States
1971
1990
2006
6 4
China India
3
2 Russian Federation Rest of the world
0 1971
1975
1980
1985
1990
1995
2000
Source: World Development Indicators data files.
High-income economies use almost half of all global energy
Low income 5% India 5%
China 16%
United States
Russian Federation
Germany
Japan
China
India
Source: Table 3.7.
3b
Energy use, 2006
0
2006
High-income economies use more than 11 times the energy that low-income economies do Energy use per capita, by income group (thousands of kilograms of oil equivalent)
1971
3d 1990
2006
6
Other high income 29%
4
2 Other middle income 29%
Source: Table 3.7.
128
2009 World Development Indicators
United States 20%
0
Low income
Source: Table 3.7.
Lower Upper middle income middle income
High income
World
Uncertain supply It is not going to be easy to meet the expected growth in
fields, which are typically smaller, more complex, and more
energy demand, particularly for oil, despite seemingly large
costly to develop.
available reserves (table 3g). Based on a field by field analy-
Despite the improved environment for emerging, climate-
sis of production trends, the cost of investment, opportu-
friendly, renewable energy sources and technologies, many
nities for expanding capacity, and possible constraints and
barriers remain. The costs of some technologies are high
risks—both above and below ground—the International
at their early stages, when economies of scale cannot be
Energy Agency has drawn attention to the possibility of an
realized. Research and development were limited until the
oil-supply crunch by the middle of the 2010s, if upstream
recent oil price rise. Concerns are growing about the impact
investments fall short of requirements. A growing number of
on food supplies with more use of crops for energy. And there
oil companies and analysts have suggested that oil produc-
is skepticism about the net contribution of biofuels to lower
tion may peak within the next two decades, a result of ris-
greenhouse gas emissions (FAO 2008).
ing costs, political and geological factors, and limits on the
In many countries climate change has risen to the top of
investment that can be mobilized (IEA 2008a). The rate of
the political agenda—the result of a growing body of evidence
decline in production from existing fields—especially large,
on global warming and ever more startling predictions of the
mature fields that have been the mainstay of global output
ecological consequences (IPCC 2007b). The commitment of
for several decades—has been faster than anticipated (fig-
the new U.S. administration to containing the impact of global
ure 3h). How output from these fields evolves—with or with-
climate change and the changing attitudes toward wind and
out the deployment of enhanced recovery techniques—will
solar energy offer promise of reducing the carbon footprint of
have major implications for the required investment in new
energy use.
Nonrenewable fuels are projected to account for 80 percent of energy use in 2030—about the same as in 2006 3e Energy demand, by source (thousands of megatons of oil equivalent)
2006
2030
6
4
2
0 Coal
Oil
Gas
Nuclear
Hydro
Biomass and waste
Other renewables
Source: Table 3.7.
3f
1980
2006
2030
Annual growth, 2006–30 (%)
7,224
11,730
17,014
1.6
Coal
24.8
26.0
28.8
2.0
Oil
43.0
34.3
30.0
1.0
Gas
17.1
20.5
21.6
1.8
2.6
6.2
5.3
0.9
Total (million metric tons oil equivalent) Share (% of total)
Nuclear Hydropower Biomass and waste Other renewables Source: IEA 2008a.
Country
3g
Oil reserves (billions of barrels)
Share of world total (%)
264.3 178.9 132.5 115.0 101.5 97.8 79.7 60.0 262.8 1,292.5
20.4 13.8 10.3 8.9 7.9 7.6 6.2 4.6 20.3
Saudi Arabia Canada Iran Iraq Kuwait United Arab Emirates Venezuela, RB Russian Federation Rest of the world Total
Source: Deutch, Lauvergeon, and Prawiraatmadja 2007.
Fossil fuels will remain the main sources of energy through 2030
Fuel
Known global oil reserves and countries with highest endowments in 2006
Production declines from existing oil fields have been rapid
3h OPEC Non-OPEC
Production-weighted average post-peak observed decline, by type of producer and year of first production (%) 0
–4
2.0
2.2
2.4
1.9
10.4
10.1
9.8
1.4
0.2
0.6
2.1
7.2
–8 –12 –16
Pre-1970s
1970s
1980s
1990s
2000–07
Source: IEA 2008a.
2009 World Development Indicators
129
Energy and climate change Economic activity, energy use, and carbon dioxide emissions move together (figure 3i). The world is already experiencing the impact of rising average global temperature on physical and biological systems, and the situation is worsening. The 13 warmest years since 1880 have occurred in the last 16 years (IPCC 2007a; Rosenzweig and others 2008). There is a risk of reaching unpredictable tipping points, such as a rise in Arctic temperatures precipitating a massive release of methane from permafrost zones. Thawing permafrost could also threaten oil and gas extraction infrastructure and pipeline stability.
Current carbon dioxide levels In the 1980s global energy-related carbon dioxide emissions (up 1.7 percent annually) rose more slowly than primary energy demand (up 1.9 percent annually), mainly because the shares of natural gas, nuclear power, and renewables in the power mix expanded. But this decarbonization of energy reversed at the beginning of the 21st century as the share of nuclear energy fell while that of coal rose. In the Trend Scenario recarbonization of the energy sector is projected to continue until after 2020, when changing supply patterns Economic activity, energy use, and greenhouse gas emissions move together
3i
again cause emissions growth to fall below the rate of growth of primary energy use (figure 3j). But as the world becomes wealthier, energy-related carbon dioxide emissions continue to rise in absolute terms. World carbon dioxide emissions per capita fell until around 2000, but have since risen rapidly. In the absence of new policies, this upward trend is projected to continue through 2030. Government policies, including those to address climate change, air pollution, and energy security, have slowed the growth in emissions in some countries. But in most, emissions are still rising fast. In 2005 per capita emissions were greatest in the United States, followed by the Russian Federation, Japan, and Germany (table 3.8 and figure 3k). China’s per capita emissions were 4.3 tons—close to the global average and about one-third of the level of high-income economies (figure 3l)—while India’s were 1.3 tons. Carbon dioxide emissions are attributed to the country or region consuming the fossil fuel. Yet the consumption benefits from the goods and services produced using the fossil fuel are often realized in a country other than that in which the emissions arise. This concerns some emerging market economies, which tend to be more export-oriented, The top six carbon dioxide emitters in 2005
3k
Carbon dioxide emissions per capita (metric tons) 25
Index (1971 = 100) 350 Real GDP
300
1971
1990
2005
20 15
250 Energy use Fossil fuel energy consumption
200
10 5
150
Carbon dioxide emissions
100 1971
0 1975
1980
1985
1990
1995
2000
Source: World Development Indicators data files.
Russian Federation
Japan
Germany
China
India
Source: Table 3.8.
Decarbonization of energy reversed at the beginning of the 21st century Average annual growth in world primary energy demand and energy-related carbon dioxide emissions in the Trend Scenario (%) 3
United States
2006
3j Energy demand Energy-related carbon dioxide emissions
High-income economies are by far the greatest emitters of carbon dioxide Carbon dioxide emissions, by income group (metric tons) 15
3l 1971
1990
2005
12 9
2
6 1
3 0
0
1980–90
1990–2000
2000–10
Source: IEA 2008a.
130
2009 World Development Indicators
2010–20
2020–30
Low income Source: Table 3.8.
Lower Upper middle income middle income
High income
World
with energy-intensive manufactured exports. A detailed inputoutput analysis in China tracked the distribution of fuels, raw materials, and intermediate goods to and from industries throughout the economy. Taking carbon intensities and trade data into account, it estimated the energy-related carbon dioxide emissions embedded in domestic production for export at 34 percent of its 2004 emissions (IEA 2007). With China’s production facilities expanding rapidly, the figures for later years could be higher (box 3m).
Trend and Policy Scenarios Annual greenhouse gas emissions are projected to grow from 44 gigatons of carbon dioxide equivalent in 2005 to 60 gigatons in 2030, a 35 percent increase. The share of energy- related carbon dioxide emissions in total emissions is forecast to increase from 61 percent in 2005 to 68 percent in 2030 (IEA 2008a). With emissions of greenhouse gases building in the atmosphere faster than natural processes can remove them, concentrations rise. The Trend Scenario puts us on a path to doubling aggregate concentrations by the end of the century, increasing global average temperatures up to 6°C (IPCC 2007a; IEA 2008a). Carbon dioxide emissions embedded in international trade
3m
Energy and energy-related carbon dioxide emissions are embedded in imports as well as exports, and some goods and services are more emissions-intensive than others. There are ways of calculating the emissions embedded in international trade, none of them fully accurate because of the lack of complete, reliable, and up-to-date data. A detailed input-output analysis for China reveals the complexity, involving calculating carbon intensity all along the production chain and across the economy, including outsourcing (IEA 2007; Houser and others 2008). At the global level the percentage of exports in GDP can be used as a simple proxy for the share of energy-related carbon dioxide emissions embedded in domestic production for export. The countries for which up-to-date trade data are available represent 83 percent of total world energy-related carbon dioxide emissions (IEA 2008a). The International Energy Agency estimate of the share of emissions embedded in exports in 2006 ranges from 15 percent for North America to 48 percent for the Middle East. The difference reflects variations in the amount and type of exports and the carbon intensity of energy use. The shares for China (44 percent) and Asian countries other than China and India (41 percent) are next highest. Of the 23 gigatons of energy-related carbon dioxide emissions in the International Energy Agency sample, one-third were embedded in production for export. China alone accounted for 2.3 gigatons (31 percent) of this, and Europe and the Russian Federation combined for another 1.7 gigatons (23 percent). Africa and Latin America each accounted for just 2 percent of embedded emissions (IEA 2008a).
In the Trend Scenario rising global use of fossil energy continues to drive up energy-related carbon dioxide emissions over at least the next two decades. Emissions grew by 2.5 gigatons from 1990 to 2000, when their growth accelerated, and increased a further 4.5 gigatons to 28 gigatons by 2006. They are projected to increase a further 45 percent by 2030, approaching 41 gigatons in the Trend Scenario. This acceleration in carbon dioxide emissions calls for urgent stabilization measures. There is not yet an international consensus on longterm stabilization targets. Most discussions center on stabilization levels between 450 ppm and 550 ppm of carbon dioxide equivalent and their consequences (table 3n and figure 3o; IPCC 2007). The required reduction in energy-related emissions varies with level of international participation by economies sorted by income groups. In both the 450 ppm and 550 ppm Policy Scenarios, even after allowing for international emissions trading and active engagement by non– Organisation for Economic Co-operation and Development (OECD) countries, International Energy Agency projections show that OECD countries would have to substantially reduce emissions domestically (IEA 2008a). Impact of Policy Scenarios: carbon dioxide concentration, temperature increase, emissions, and energy demand Global emissions by 2030 (gigatons) Carbon dioxide concentration (parts per million)
Temperature increase
Energyrelated carbon dioxide
Total greenhouse gases
3n
Global energy demand (metric tons oil equivalent) 2020
2030
550
3ºC
33
48
14,360
15,480
450
2ºC
26a
36
14,280
14,360
a. Emissions peak in 2020 at 32.5 gigatons and then decline to 25.7 gigatons in 2030. Source: IPCC 2007b; IEA 2008a.
Reductions in energy-related carbon dioxide emissions by region in the 550 and 450 parts per million Policy Scenarios relative to the Trend Scenario
3o
Carbon dioxide emissions (gigatons) 45 Trend Scenario
40 35
550 Policy Scenario
30 450 Policy Scenario
25 20 2006
2009
2010
2015
2020
2025
2030
Source: IEA 2008a.
2009 World Development Indicators
131
Need for cleaner, more efficient energy Adequate energy supplies are required for economies to grow and poverty to be reduced, but the current reliance on fossil fuels is not sustainable. Transitioning to new energy sources poses a significant challenge to all economies. Humanity’s future on this planet may depend on finding ways to supply the world’s growing energy needs without irreparably harming the environment. This could be achieved through new energy technologies, greater energy efficiency, and alternative renewable sources that provide a low-carbon path to growth. For the low-carbon growth needed to stabilize carbon dioxide emissions, technological innovations are crucial. Because much of today’s energy-using capital stock will be replaced only gradually, it will take time before most of the impact of recent and future technological developments that improve energy efficiency are felt. Rates of capital-stock turnover differ greatly by industry and sector. Most of today’s cars, trucks, heating and cooling systems, and industrial boilers will be replaced by 2030. But most buildings, roads, railways, and airports and many power stations and refineries will still be in use unless governments encourage or force early retirement. Despite the slow turnover, refurbishment in some cases could significantly improve energy efficiency at an acceptable net economic cost. On the supply side technological advances can improve the technical and economic efficiency of producing and supplying energy. In some cases they are expected to reduce unit costs and to lead to new and cleaner ways of producing and delivering energy services. Some major new supply-side technologies that are approaching commercialization are expected to become available to some degree before 2030 (IEA 2008a). • Carbon capture and storage. This technology mitigates emissions of carbon dioxide from power plants
Energy efficiency has been improving
3p 1990
2005 PPP$ per kilogram of oil equivalent
2006
8
and other industrial facilities, but it has not yet been deployed on a significant scale (IEA 2008a). The basic technology already exists to capture carbon dioxide gas and transport and store it permanently in geological formations. Four large-scale carbon capture and storage projects are operating around the world, each separating around 1 megaton of carbon dioxide per year from produced natural gas: Sleipner and Snohvit in Norway, Weyburn in Canada (with the carbon dioxide sourced in the United States), and In Salah in Algeria. Yet there are technical, economic, and legal barriers to more widespread deployment, particularly high energy intensity and the cost. Second-generation biofuels. New biofuel technologies —notably hydrolysis and gasification of woody lignocellulosic feedstock to produce ethanol—are expected to reach commercialization by around 2020. Although the technology already exists, experts believe that more research is needed to improve process efficiencies. There is virtually no commercial production of ethanol yet from cellulosic biomass, but several OECD countries are researching it. A recent Food and Agriculture Organization report is skeptical about the net contribution of biofuels to reduction of greenhouse gasses and blames biofuels production for last year’s large food price increases (FAO 2008). Coal-to-liquids. The conversion of coal to oil products through gasification and synthesis—much like gas to liquid production—has been done commercially for many decades. Yet global production remains limited because it has been uneconomical, mainly because of the large amounts of energy and water used in the process, the high cost of building plants, and the volatility of oil and coal prices.
•
•
Electricity generated from renewables is projected to more than double by 2030 Hydropower Solar
Electricity generated (terrawatt hours)
3q
Wind Geothermal
Biomass and waste Tide and wave
1,200 6 900 4 600 2 300 0
Low income
Lower middle income
Upper middle income
Source: Table 3.8.
132
2009 World Development Indicators
High income
Euro area
World
0 2006–15 Source: IEA 2008a.
2015–30
Energy efficiency
•
In recent years there has been an encouraging trend in producing more from each unit of energy (figure 3p), a powerful and cost-effective way to get on the path to a sustainable energy future. Greater energy efficiency can reduce the need for investing in energy infrastructure, cut fuel costs, increase competitiveness, and improve consumer welfare. And by reducing greenhouse gas emissions and air pollution, it can be good for the environment. The International Energy Agency estimates that implementing a host of 25 policy recommendations for promoting energy efficiency could reduce annual carbon dioxide emissions 8.2 gigatons by 2030—equivalent to one-fifth of global energy-related carbon dioxide emissions in the Trend Scenario (IEA 2008b). The recommendations cover policies and technologies for buildings, appliances, transport, and industry as well as end-use applications such as lighting.
Renewable energy The share of renewables in global primary energy demand, excluding traditional biomass, is projected to climb from 7 percent in 2006 to 10 percent by 2030 in the Trend Scenario (IEA 2008a). This assumes that costs come down as renewable technologies mature, that higher fossil fuel prices make renewables more competitive, and that policy support is strong. The renewables industry could eliminate its reliance on subsidies and bring emerging technologies into the mainstream. • World renewables-based electricity generation— mostly hydro and wind power—is projected to more than double by 2030 (figure 3q). • Many countries have already begun exploiting wind to generate electricity (figure 3r). Global wind power is projected to increase 11-fold, becoming the second largest source renewable after hydropower by 2030. Top 10 users of wind to generate electricity
3r
The cost of power generation from renewables is expected to fall. Greater deployment spurs technological progress and increases economies of scale, lowering investment costs. The costs of the more mature technologies, including geothermal and onshore wind, are assumed to fall least. Renewables account for just under half of the total projected investment in electricity generation. The cost of stabilizing carbon dioxide is significant, but there are also significant savings (box 3s). And the cost of inaction would be far higher. Cost and savings under the Policy Scenarios
Denmark United States Portugal Germany United Kingdom India Italy Spain France China
3s
The 550 parts per million (ppm) Policy Scenario requires spending $4.1 trillion more on energy efficiency and power plants and reducing consumption of fossil fuels by 22 gigatons of oil equivalent over 2010–30 through more efficient energy use. The International Energy Agency estimates that the net undiscounted savings in the 550 ppm Policy Scenario, compared with the Trend Scenario, amount to more than $4 trillion. The 450 ppm Policy Scenario requires additional investment of $3.6 trillion in power plants and $5.7 trillion in energy efficiency over 2010– 30 relative to the Trend Scenario. This additional investment is much higher in 2021–30 than in 2010–20 (see figure). In the 450 ppm Policy Scenario substantially higher investment is needed in power plants. Also, investment in energy efficiency rises considerably, particularly beyond 2020. During that period improving energy efficiency in buildings will require the highest investment. In the 450 ppm Policy Scenario the additional investment in power plants and demand-side efficiency corresponds to 0.55 percent of cumulative world GDP over 2010–30, compared with 0.24 percent in the 550 ppm Policy Scenario. Change in power plant and energy efficiency investments in the Policy Scenarios relative to the Trend Scenario 2007 $ trillions 5
Share of wind in total power generation, 2006 (%)
450 ppm Scenario (additional to 550)
550 ppm Scenario
4 3 2 1 0 0
Source: IEA 2008a.
•
Biomass, geothermal, and solar thermal met around 6 percent of global heating demand in 2006, a share projected to rise to 7 percent by 2030. Where resources are abundant and conventional energy sources expensive, renewables-based heating can be cost competitive with conventional heating systems. The share of biofuels in road transport fuels worldwide is projected to rise from 1.5 percent in 2006 to 5 percent in 2030, spurred by subsidies and high oil prices. Most of the growth comes from the United States, European Union, China, and Brazil.
3
6
9
12
15
2010–20 2020–30 Power plants
2010–20 2020–30 Energy efficiency
Source: IEA 2008a.
2009 World Development Indicators
133
Tables
3.1
Rural population and land use Rural population
% of total 1990 2007
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
.. 64 48 63 13 33 15 34 46 80 34 4 66 44 61 58 25 34 86 94 87 59 23 63 79 17 73 1 32 72 46 49 60 46 27 25 15 45 45 57 51 84 29 87 39 26 31 62 45 27 64 41 59 72 72 72
.. 54 35 44 8 36 11 33 48 73 27 3 59 35 53 41 15 29 81 90 79 44 20 62 74 12 58 0 26 67 39 37 52 43 24 27 14 32 35 57 40 80 31 83 37 23 15 44 47 26 51 39 52 66 70 55
2009 World Development Indicators
Land area
Land use
average annual % growth 1990–2007
thousand sq. km 2007
Forest area 1990 2005
.. –1.2 –0.1 0.7 –1.6 –0.4 –0.2 0.2 1.3 1.5 –1.7 –1.4 2.7 0.7 –1.6 –0.1 –1.6 –1.6 2.6 2.1 1.8 0.7 0.0 2.0 2.9 –0.7 –0.5 .. 0.5 2.5 1.7 0.5 1.5 –0.8 –0.2 0.4 –0.3 –0.3 0.1 1.9 0.3 2.2 –0.6 2.7 0.1 –0.2 –1.9 1.4 –1.0 0.1 1.1 0.3 1.6 2.1 2.9 0.2
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 9,327.5 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 12.0 21.9 45.8 11.2 6.8 36.0 23.2b 30.0 57.9 43.1 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 37.9 18.7 34.1 10.5 28.4 49.9 0.0 18.1 15.9 51.4 14.7 72.9 26.4 85.1 44.2 39.7 30.8 32.7 25.6 43.8 30.1 78.8 4.2
% of land area
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 2.7 0.0 1.0 3.7 2.3 0.9 0.5b 0.9 0.1 2.9 0.0 0.8 2.7 0.2 14.0 0.6 2.6 0.7 0.1 0.0 0.3 0.8 .. 1.5 0.5 0.1 4.9 11.0 2.0 7.4 3.1 0.2 9.3 4.8 0.4 12.5 0.0 0.3 0.6 0.0 2.2 0.6 0.5 4.8 1.3 6.6 8.3 4.5 2.0 4.2 11.6
0.2 4.5 0.4 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 0.1 0.0 0.5 1.4 .. 1.5 0.5 0.1 6.5 11.3 2.1 6.1 3.1 0.2 10.3 4.4 0.5 12.1 0.0 0.3 0.8 0.0 2.1 0.7 0.5 3.8 0.6 9.7 8.8 5.6 2.7 8.9 11.6
Arable land 1990 2005
12.1 21.1 3.0 2.3 9.6 17.7 6.2 17.3 20.5 70.2 29.3 23.9 b 14.6 1.9 16.6 0.7 6.0 34.9 12.9 36.2 20.9 12.8 5.0 3.1 2.6 3.7 13.3 .. 3.0 2.9 1.4 5.1 7.6 21.7 27.6 41.1 60.4 18.6 5.8 2.3 26.5 4.9 26.3 10.0 7.4 32.7 1.1 18.2 11.4 34.3 11.9 22.5 12.0 3.0 10.7 28.3
12.1 21.1 3.1 2.6 10.4 17.6 6.4 16.8 22.3 61.1 26.3 27.9 24.9 2.8 19.5 0.7 7.0 29.2 17.7 37.8 21.0 12.8 5.0 3.1 3.3 2.6 15.4 .. 1.8 3.0 1.4 4.4 11.0 19.8 33.4 39.4 52.7 16.9 4.9 3.0 31.9 6.3 13.9 13.1 7.3 33.6 1.3 35.0 11.5 34.1 18.4 20.4 13.3 4.9 10.7 28.3
Arable land hectares per 100 people 1990–92 2003–05
.. 18.7 24.5 21.2 75.2 16.1a 248.9 17.3 22.6a 5.7 58.4 a 8.2 33.0 34.9 27.0a 21.5 33.1 43.4 35.9 14.2 28.4 36.7 147.4 49.1 40.7 12.7 11.1 .. 6.2 12.9 15.0 5.6 18.2 32.9a 32.8 30.1 42.6 9.2 12.0 4.2 10.4 14.6 52.1a 15.1 42.2 31.1 27.0 21.3 17.1a 14.3 19.7 24.9 12.2 12.1 21.2 8.9
.. 18.4 23.1 21.1 74.0 16.4 240.6 17.0 22.2 5.3 56.2 8.1 33.0 33.9 26.9 20.8 32.0 42.0 35.9 13.0 27.0 34.2 142.7 46.8 40.1 12.2 11.0 .. 5.1 11.8 14.0 5.3 18.8 27.6 32.7 29.9 41.8 8.8 10.1 4.1 10.0 14.0 40.9 16.7 42.5 30.5 25.6 21.9 17.8 14.4 19.0 24.1 11.6 13.2 19.4 8.5
Rural population
% of total 1990 2007
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
60 34 75 69 44 30 43 10 33 51 37 28 44 82 42 26 2 62 85 31 17 86 55 24 32 42 76 88 50 77 60 56 29 53 43 52 79 75 72 91 31 15 48 85 65 28 34 69 46 85 51 31 51 39 52 28
53 33 71 50 32 .. 39 8 32 47 34 22 42 79 38 19 2 64 70 32 13 75 41 23 33 34 71 82 31 68 59 58 23 58 43 44 64 68 64 83 19 14 44 84 52 23 28 64 28 87 40 29 36 39 41 2
average annual % growth 1990–2007
1.4 –0.4 1.3 –0.6 –0.3 .. 0.7 1.7 0.0 0.2 –0.3 2.0 –0.5 2.5 0.4 –1.2 0.2 1.1 1.0 –0.7 0.4 0.5 1.6 1.6 –0.4 –1.0 2.4 1.8 –0.7 2.1 2.7 1.2 0.1 –0.4 1.3 0.5 1.4 0.6 1.5 1.7 –2.5 0.5 1.2 3.4 1.4 –0.7 1.0 1.9 –1.1 2.7 0.8 1.0 0.0 0.0 –1.0 –15.1
Land area
ENVIRONMENT
Rural population and land use
3.1
Land use
% of land area thousand sq. km 2007
Forest area 1990 2005
111.9 89.6 2,973.2 1,811.6 1,628.6 437.4 68.9 21.6 294.1 10.8 364.5 88.2 2,699.7 569.1 120.4 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 1,030.7 2.0 1,944.0 32.9 1,566.5 446.3 786.4 657.6 823.3 143.0 33.9 267.7 121.4 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 45.1 11.8 0.2 42.1 0.1 31.3 35.6 23.5 41.4 68.1 11.5 0.4 19.2 35.5 9.7 7.3 9.6 25.4 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
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 33.0 10.0 6.5 9.8 24.5 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 0.8 1.5 1.6 0.1 0.4 0.3 0.4 11.9 0.1 2.2 0.2 0.7 2.2 1.0 1.2 16.0 0.0 0.0 3.0 1.0 14.2 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 1.0 0.6 0.0 3.5 8.6 10.2 0.9 1.0 0.1 0.8 1.7 2.0 0.2 0.4 0.4 0.2 13.9 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 2.1 0.3 1.4 0.0 0.9 1.0 7.1 1.9 0.0 3.3 .. 0.1 1.0 2.0 1.4 0.2 0.5 16.8 1.2 7.1 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 13.0 8.8 19.0 19.8 0.2 6.9 3.5 27.2 17.9 10.4 4.2 1.0 46.0 23.8 4.7 19.3 5.2 1.7 0.4 49.3 12.5 52.8 0.9 19.5 4.4 14.5 0.8 16.0 25.9 9.9 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 10.2 13.1 17.6 14.6 26.3 16.1 12.0 2.1 8.3 9.2 23.3 16.4 0.8 6.7 4.3 17.5 18.2 10.9 4.0 1.0 30.4 22.3 5.1 27.6 5.5 3.9 0.5 49.3 12.9 56.2 0.7 19.0 5.6 15.3 1.0 16.5 26.8 5.6 15.9 11.4 35.1 2.8 0.2 27.6 7.4 0.5 10.6 2.9 19.1 39.6 13.8 8.0
Arable land hectares per 100 people 1990–92 2003–05
16.9 45.2 15.5 10.3 24.0 22.0 29.7 5.3 14.7 6.7 3.5 3.9 148.7a 15.7 11.4 3.6 0.6 27.2a 17.0 41.0a 4.7 17.3 12.0 33.3 58.8a 27.9a 17.6 18.4 7.6 45.3 18.5 8.3 25.4 45.1a 49.1 29.7 21.6 21.4 42.7 9.4 5.7 38.5 37.1 125.7 22.6 19.6 1.6 15.2 18.1 3.8 61.2 14.2 7.3 35.3 15.4 1.7
2009 World Development Indicators
15.9 45.5 14.8 10.6 24.0 .. 29.5 4.8 13.6 6.6 3.4 3.6 149.3 15.1 11.7 3.4 0.6 25.9 17.8 44.1 4.7 16.8 11.4 30.6 49.0 27.9 16.3 19.8 7.1 42.6 17.1 8.1 24.6 47.1 46.7 28.4 21.8 21.1 40.9 8.9 5.6 36.7 35.7 113.1 22.6 19.0 2.2 14.1 17.3 3.9 70.2 13.7 6.9 32.6 13.3 1.8
135
3.1
Rural population and land use Rural population
% of total 1990 2007
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
47 27 95 23 61 50 67 0 44 50 70 48 25 83 73 77 17 27 51 68 81 71 79 70 92 42 41 55 89 33 21 11 25 11 60 16 80 32 79 61 71 57 w 75 61 68 31 63 71 37 29 48 75 72 27 29
46 27 82 17 58 48 63 0 44 51 64 40 23 85 57 75 16 27 46 74 75 67 73 59 87 34 32 52 87 32 22 10 19 8 63 7 73 28 70 65 63 51 w 68 52 58 25 56 57 36 22 43 71 64 23 27
Land area
average annual % growth 1990–2007
Land use
% of land area thousand sq. km 2007
–0.5 230.0 –0.1 16,381.4 0.9 24.7 0.5 2,000.0 c 2.4 192.5 –0.3 91.0 1.7 71.6 .. 0.7 0.1 48.1 0.2 20.1 1.0 627.3 0.7 1,214.5 0.5 499.2 1.1 64.6 0.9 2,376.0 2.2 17.2 –0.1 410.3 0.6 40.0 2.0 183.8 1.8 140.0 2.2 885.8 0.6 510.9 1.7 14.9 2.0 54.4 0.2 5.1 0.1 155.4 0.1 769.6 1.4 469.9 3.1 197.1 –0.8 579.4 5.3 83.6 –0.3 241.9 –0.6 9,161.9 –1.6 175.0 1.9 425.4 –2.8 882.1 0.9 310.1 3.0 6.0 2.8 528.0 2.6 743.4 0.8 386.9 0.6 w 129,644.6 s 1.8 21,216.9 0.3 74,923.2 0.4 34,404.7 –0.4 40,518.4 0.7 96,140.1 –0.3 15,870.6 0.0 23,109.9 –0.2 20,156.5 1.3 8,643.7 1.5 4,781.3 1.9 23,578.1 –0.3 33,504.5 –0.1 2,513.0
Forest area 1990 2005
27.8 49.4 12.9 1.4 48.6 .. 42.5 3.4 40.0 59.5 13.2 7.6 27.0 36.4 32.1 27.4 66.7 28.9 2.0 2.9 46.8 31.2 65.0 12.6 45.8 4.1 12.6 8.8 25.0 16.1 2.9 10.8 32.6 5.2 7.2 59.0 28.8 .. 1.0 66.1 57.5 31.2 w 26.8 33.7 25.6 40.6 32.2 28.8 38.2 48.8 2.3 16.5 28.5 28.4 32.5
27.7 49.4 19.5 1.4 45.0 26.3d 38.5 3.3 40.1 62.8 11.4 7.6 35.9 29.9 28.4 31.5 67.1 30.5 2.5 2.9 39.8 28.4 53.7 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 30.4 w 24.7 32.7 25.0 39.3 31.0 28.4 38.3 45.4 2.4 16.8 26.5 28.8 37.2
Permanent cropland 1990 2005
2.6 0.1 12.4 0.0 0.1 .. 0.8 1.5 1.0 1.8 0.0 0.7 9.7 15.9 0.0 0.7 0.0 0.5 4.0 0.9 1.1 6.1 3.9 1.7 9.0 12.5 3.9 0.1 9.4 1.9 0.2 0.3 0.2 0.3 0.9 0.9 3.2 19.1 0.2 0.0 0.3 0.9 w 1.0 1.0 1.5 0.6 1.0 2.2 0.4 0.9 0.8 1.8 0.8 0.7 4.7
a. Data are not available for all three years. b. Includes Luxembourg. c. Provisional estimate. d. Includes Montenegro.
136
2009 World Development Indicators
2.3 0.1 11.1 0.1 0.2 3.2d 1.1 0.3 0.5 1.3 0.0 0.8 9.9 15.5 0.1 0.8 0.0 0.6 4.7 0.9 1.3 7.0 4.6 2.6 9.2 13.9 3.6 0.1 11.2 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 1.2 1.2 1.8 0.7 1.2 2.9 0.4 1.0 0.9 2.6 0.9 0.7 4.3
Arable land 1990 2005
41.2 8.1 35.7 1.6 12.1 .. 6.8 1.5 32.5 9.9 1.6 11.1 30.7 13.5 5.4 10.5 6.9 9.8 26.6 6.1 10.2 34.2 7.4 38.6 14.4 18.7 32.0 2.9 25.4 57.6 0.4 27.4 20.3 7.2 10.5 3.2 16.4 18.4 2.9 7.1 7.5 10.8 w 9.0 11.1 13.5 9.1 10.6 12.1 12.4 6.5 5.8 42.6 6.6 11.4 26.9
40.4 7.4 48.6 1.6 13.2 33.6d 8.4 0.9 28.9 8.7 2.2 12.1 27.4 14.2 8.2 10.3 6.6 10.3 26.5 6.6 10.4 27.8 8.2 45.8 14.6 17.6 31.0 4.9 27.4 56.0 0.8 23.7 19.0 7.8 11.0 3.0 21.3 17.8 2.9 7.1 8.3 11.0 w 10.2 11.2 14.3 8.6 11.0 13.5 11.0 7.2 6.1 41.9 8.0 11.0 25.5
Arable land hectares per 100 people 1990–92 2003–05
42.4 84.9a 11.8 17.0 22.9 .. 10.8 0.0 27.1 8.6a 15.1 33.0 32.2 4.8 48.1 16.7 30.3 5.7 27.1 14.9a 25.9 25.9 15.2 45.1 5.7 29.0 34.8 40.5a 20.0 66.9a 2.0 9.8 61.6 41.5 18.0a 10.5 8.2 3.5 8.1 49.3 25.2 23.0 w 18.1 20.8 15.0 44.7 20.2 11.6 58.4 27.6 18.3 14.5 25.5 37.3 20.6
43.2 84.9 13.2 15.7 21.8 42.4 d 11.0 0.0 26.0 8.7 16.5 31.8 32.0 4.7 51.2 15.9 29.8 5.5 25.9 14.4 24.5 22.7 13.2 41.2 5.7 27.9 33.2 46.9 18.9 68.4 1.6 9.6 59.7 41.5 18.2 10.1 8.0 3.2 7.4 46.7 24.7 22.3 w 17.6 20.2 14.7 43.3 19.6 11.4 57.7 26.8 17.6 13.8 25.0 36.4 20.2
About the data
ENVIRONMENT
Rural population and land use
3.1
Definitions
With 3 billion people, including 70 percent of the
Satellite images show land use that differs from
• Rural population is calculated as the difference
world’s poor people, living in rural areas, adequate
that of ground-based measures in area under cultiva-
between the total population and the urban popula-
indicators to monitor progress in rural areas are
tion and type of land use. Moreover, land use data
tion (see Definitions for tables 2.1 and 3.11). • Land
essential. However, few indicators are disaggre-
in some countries (India is an example) are based
area is a country’s total area, excluding area under
gated between rural and urban areas (for some that
on reporting systems designed for collecting tax rev-
inland water bodies and national claims to the con-
are, see tables 2.7, 3.5, and 3.11). The table shows
enue. With land taxes no longer a major source of
tinental shelf and to exclusive economic zones.
indicators of rural population and land use. Rural
government revenue, the quality and coverage of land
In most cases definitions of inland water bodies
population is approximated as the midyear nonurban
use data have declined. Data on forest area may be
includes major rivers and lakes. (See table 1.1 for
population. While a practical means of identifying the
particularly unreliable because of irregular surveys
the total surface area of countries.) • Land use can
rural population, it is not precise (see box 3.1a for
and differences in definitions (see About the data
be broken into several categories, three of which
further discussion).
for table 3.4). FAO’s Global Forest Resources Assess-
are presented in the table (not shown are land used
The data in the table show that land use patterns
ment 2005 aims to address this limitation. The
as permanent pasture and land under urban devel-
are changing. They also indicate major differences
FAO has been coordinating global forest resources
opments). • Forest area is land under natural or
in resource endowments and uses among countries.
assessments every 5–10 years since 1946. Global
planted stands of trees, whether productive or not.
True comparability of the data is limited, however,
Forest Resources Assessment 2005, conducted dur-
• Permanent cropland is land cultivated with crops
by variations in definitions, statistical methods, and
ing 2003–05, covers 229 countries and territories
that occupy the land for long periods and need not
quality of data. Countries use different definitions of
at three points: 1990, 2000, and 2005. The most
be replanted after each harvest, such as cocoa, cof-
rural and urban population and land use. The Food
comprehensive assessment of forests, forestry, and
fee, and rubber. Land under flowering shrubs, fruit
and Agriculture Organization of the United Nations
the benefits of forest resources in both scope and
trees, nut trees, and vines is included, but land under
(FAO), the primary compiler of the data, occasion-
number of countries and people involved, it exam-
trees grown for wood or timber is not. • Arable land
ally adjusts its definitions of land use categories
ines status and trends for about 40 variables on the
is land defined by the FAO as under temporary crops
and revises earlier data. Because the data reflect
extent, condition, uses, and values of forests and
(double-cropped areas are counted once), temporary
changes in reporting procedures as well as actual
other wooded land.
meadows for mowing or for pasture, land under mar-
changes in land use, apparent trends should be inter-
ket or kitchen gardens, and land temporarily fallow.
preted cautiously.
Land abandoned as a result of shifting cultivation is excluded.
What is rural? Urban?
3.1a
The rural population identified in table 3.1 is approximated as the difference between total population and urban population, calculated using the urban share reported by the United Nations Population Division. There is no universal standard for distinguishing rural from urban areas, and any urban-rural dichotomy is an oversimplification (see About the data for table 3.11). The two distinct images—isolated farm, thriving metropolis—represent poles on a continuum. Life changes along a variety of dimensions, moving from the most remote forest outpost through fields and pastures, past tiny hamlets, through small towns with weekly farm markets, into intensively cultivated areas near large towns and small cities, eventually reaching the center of a megacity. Along the way access to infrastructure, social services, and nonfarm employment increase, and with them population density and income. Because rurality has many dimensions, for policy purposes the rural-urban dichotomy presented in tables 3.1, 3.5, and 3.11 is inadequate. A 2005 World Bank Policy Research Paper proposes an operational definition of rurality based on population density and distance to large cities (Chomitz, Buys, and Thomas 2005). The report argues that these criteria are important gradients along which economic behavior and appropriate development interventions vary substantially. Where population densities are low, markets of all kinds are thin, and the unit cost of delivering most social services and many types of infrastructure is high. Where large urban
Data sources
areas are distant, farm-gate or factory-gate prices of outputs will be low and input prices will be high, and
Data on urban population shares used to estimate
it will be difficult to recruit skilled people to public service or private enterprises. Thus, low population
rural population come from the United Nations
density and remoteness together define a set of rural areas that face special development challenges.
Population Division’s World Urbanization Pros-
Using these criteria and the Gridded Population of the World (CIESIN 2005), the authors’ estimates of
pects: The 2007 Revision. Data on land area and
the rural population for Latin America and the Caribbean differ substantially from those in table 3.1. Their
land use are from the FAO’s electronic files. The
estimates range from 13 percent of the population, based on a population density of less than 20 people
FAO gathers these data from national agencies
per square kilometer, to 64 percent, based on a population density of more than 500 people per square
through annual questionnaires and country official
kilometer. Taking remoteness into account, the estimated rural population would be 13–52 percent. The
publications and websites and by analyzing the
estimate for Latin America and the Caribbean in table 3.1 is 22 percent.
results of national agricultural censuses.
2009 World Development Indicators
137
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
138
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 1990–92b 2003–05b
thousand hectares
hundred grams per hectare of arable land
% of total employment
Tractors per 100 sq. km of arable land
58.3 41.1 16.3 46.1 46.6 44.7c 60.5 42.5 53.4 c 73.5 45.3c 44.0 d 20.6 32.9 43.0 c 45.9 28.9 55.7 34.9 82.9 25.5 19.7 7.5 8.0 38.4 21.0 57.0 .. 40.5 10.1 30.8 55.7 59.8 43.0 c 61.5 .. 65.4 71.6 28.6 2.7 71.1 .. 32.4 c .. 7.9 55.3 20.0 63.2 46.5c 49.8 55.7 71.3 39.5 48.9 53.2 57.9
58.3 40.9 17.1 46.2 47.2 49.3 57.5 40.0 57.5 69.2 42.7 46.0 31.9 34.6 42.1 45.8 31.2 48.5 39.8 90.9 29.6 19.7 7.4 8.4 38.9 20.4 59.5 .. 38.2 10.1 30.9 56.5 63.4 50.8 60.0 55.2 62.0 70.7 26.9 3.5 82.2 75.1 19.1 33.0 7.4 53.9 20.0 80.7 43.3 48.8 64.8 65.2 42.9 51.0 58.0 57.7
2009 World Development Indicators
33.9 55.6 6.4 2.3 5.6 49.9c 4.2 0.3 68.0 c 33.8 2.1c 2.2d 0.6 5.5 0.2c 0.2 4.6 29.6 0.6 1.2 6.6 0.3 1.4 0.0 0.5 57.1 36.9 .. 14.3 0.1 0.3 15.2 1.1 0.2c 22.6 .. 16.9 16.5 27.9 100.0 4.9 .. 0.5c .. 2.8 11.0 1.1 0.9 39.9c 4.0 0.7 31.1 6.8 7.0 4.1 8.0
33.8 c 50.5c 6.9c 2.2c .. 51.5c 4.9 2.5c 69.1 56.1c 2.0 c 4.7c 0.4 c 4.1c 0.3c 0.3c 4.4 c 16.6c 0.5c 1.5c 7.0 c 0.4 c 1.5c 0.1c 0.8 c 81.0 c 35.6c .. 24.0 c 0.1c 0.4 c 20.2c 1.1c 0.7c 19.5c 0.7c 9.7 20.8 c 28.3 100.0 c 4.9c 3.5c 0.7c 2.5c 2.9 13.3c 1.4 c 0.6c 44.0 c 4.0 c 0.5c 37.9c 6.3c 5.4 c 4.5c 8.4 c
1990–92
2005–07
1990–92b
2003–05b
2,283.3 242.6 3,104.9 892.6 8,509.6 162.8c 12,813.8 903.2 627.0c 10,985.4 2,578.0c 354.3d 659.9 632.9 305.1c 140.1 19,632.5 2,179.3 2,724.5 218.8 1,800.8 816.1 20,864.4 104.0 1,241.9 741.6 93,430.3 .. 1,598.1 1,867.6 9.1 83.1 1,434.0 592.7c 235.0 .. 1,581.3 134.2 861.0 2,410.2 452.6 .. 453.6c 4,585.8 1,050.5 9,211.6 14.4 89.5 248.5c 6,673.0 1,077.6 1,455.2 768.2 774.2 112.4 406.5
2,943.7 141.0 2,664.7 1,476.4 9,332.5 180.3 18,999.9 802.2 764.7 11,547.8 2,261.7 319.7 938.1 839.9 313.4 84.3 19,055.2 1,602.5 3,259.9 211.8 2,586.3 1,138.9 16,038.3 182.3 2,510.6 619.1 83,522.6 .. 1,083.1 1,972.9 17.0 57.5 788.5 556.2 287.9 1,569.5 1,485.1 160.0 849.7 2,975.4 344.6 402.9 282.0 8,727.3 1,155.9 9,142.7 19.8 207.7 258.8 6,710.2 1,397.7 1,167.5 810.4 1,705.7 138.3 440.2
58.5 903.3 144.5 28.8 72.7 502.0 c 274.8 1,994.7 440.0 c 1,135.6 2,292.9c 4,545.8d 78.4 41.7 0.0 c 21.6 655.6 1,194.1 60.3 33.7 18.7 34.1 476.3 5.1 24.6 1,214.7 2,321.0 .. 1,822.5 8.3 34.6 4,521.9 150.8 1,514.2c 1,288.2 .. 2,249.3 1,003.3 508.5 3,977.2 1,335.5 .. 1,010.9c .. 1,647.0 2,918.1 25.1 43.7 905.7c 2,615.8 37.6 2,289.4 1,072.1 16.3 15.2 35.0
47.5 924.3 166.1 28.5 479.7 232.0 492.8 3,192.1 109.9 1,723.4 1,455.0 .. 2.9 60.8 452.7 .. 1,570.0 1,608.2 75.0 16.4 30.7 94.3 581.5 .. .. 2,910.5 3,148.0 .. 3,310.0 .. .. 8,528.3 203.0 1,368.6 193.0 1,404.1 1,159.3 .. 1,731.2 6,706.7 904.3 12.8 724.0 119.7 1,286.0 2,081.2 56.9 81.5 195.7 2,145.3 78.0 1,691.3 1,285.4 26.6 .. ..
1990–92
.. .. .. .. 0.4 .. 5.5 7.5 32.5 66.4 c 21.7 2.8 .. 1.7 .. .. 25.6c 19.7 .. .. .. 60.6c 4.2 .. .. 18.8 53.5 0.8 1.4 .. .. 25.2 .. .. 25.1c 10.1 5.4 19.5c 7.0 36.2 17.9 .. 19.5 .. 8.8 .. .. .. .. 4.0 c 62.0 c 22.7 .. .. .. ..
2003–05
.. 58.3 21.1c .. 1.2 46.5c 3.8 5.4 39.6 51.7c .. 1.9 .. .. .. 21.2c 20.9c 9.6 .. .. .. .. 2.7 .. .. 13.4 .. 0.3 21.3 .. .. 15.0 .. 16.8 21.5c 4.3 3.0 14.4 8.9 29.9c 18.7c .. 5.8 44.1c 4.9 4.0 .. .. 54.4 2.4 .. 13.4 .. .. .. ..
1990–92
2003–05
0 177 128 35 99 293c 67 2,367 195c 2 207c 1,474 d 1 25 235c 143 142 128 3 2 3 1 162 0 1 144 64 .. 98 4 15 259 20 35c 246 .. 625 25 54 251 60 .. 455c .. 900 784 28 2 296c 1,253 15 774 33 48 1 2
0 125 133 31 86 289 65 2,396 126 1 101 1,137 1 20 287 159 134 100 4 2 9 1 161 0 0 274 71 .. 97 4 14 311 27 1,574 205 292 511 23 112 324 52 8 646 2 784 653 29 3 222 795 9 970 30 48 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
ENVIRONMENT
Agricultural inputs
3.2
Agricultural landa
Irrigated land
Land under cereal production
Fertilizer consumption
Agricultural employment
Agricultural machinery
% of land area 1990–92 2003–05
% of cropland 1990–92b 2003–05b
thousand hectares
hundred grams per hectare of arable land
% of total employment
Tractors per 100 sq. km of arable land
29.8 70.7 60.9 23.5 38.5 21.9 70.2 26.7 55.4 44.0 15.5 12.0 82.0 c 47.3 21.0 21.9 7.9 52.6c 7.2 40.8 c 31.1 76.7 27.1 8.8 54.1c 51.4 c 62.5 40.2 22.7 26.3 38.5 55.7 53.8 77.9c 79.9 68.2 60.7 15.8 47.0 29.0 58.9 65.0 33.5 27.0 79.4 3.3 3.5 33.7 28.7 2.0 56.0 17.1 37.4 61.6 42.8 47.5
26.2 65.4 60.6 26.3 36.1 22.9 62.4 24.4 50.7 47.4 12.9 11.5 76.9 47.4 24.9 19.2 8.6 56.2 8.5 26.5 38.1 76.9 27.0 8.8 42.5 48.8 70.2 48.3 24.0 32.4 38.6 55.7 55.3 76.7 83.3 68.1 61.8 17.1 47.2 29.5 56.8 64.5 43.5 30.4 80.4 3.4 5.1 35.2 30.0 2.3 60.7 16.6 40.9 52.8 41.2 25.1
3.8 4.1 28.3 14.5 39.9 63.0 .. 44.4 22.9 11.0 54.3 25.0 9.8 c 1.1 58.2 47.1 60.0 72.6c 16.2 1.1c 28.1 0.6 0.5 21.8 0.5c 12.1c 30.7 1.2 4.8 3.7 11.8 16.0 22.0 .. 5.8 13.2 2.8 10.1 0.7 .. 61.0 .. 4.0 0.5 0.7 .. 71.6 .. 4.8 .. 2.9 29.9 15.7 .. .. ..
5.6c 2.5 32.9c 12.4 c 47.2 58.6c .. .. 25.8 c 0.0 c 35.8 27.5 15.7c 1.8 c 50.3c 47.6c 72.2c 73.1 16.5c 2.1c 31.3c 0.9c 0.5c 21.9c 0.4 c 9.0 c 30.6c 2.2c 0.0 c 4.9 c .. 20.8 c 22.8 c 10.7c 7.0 c 15.4 c 2.6c 17.0 c 1.0 c 47.0 60.2c .. 2.8 c 0.5c 0.8 c .. 90.0 c 84.2 6.2c .. 1.7c 27.8 c 14.5c 0.6c 23.8 c ..
1990–92
2005–07
1990–92b
502.3 2,803.5 100,759.8 13,861.2 9,611.9 3,506.1 298.0 107.8 4,346.9 2.6 2,438.6 111.9 22,152.4c 1,765.9 1,569.0 1,367.8 0.4 578.0c 625.3 696.7c 41.5 177.6 135.0 355.0 1,134.0c 235.2c 1,321.0 1,442.6 699.3 2,392.7 132.9 0.5 10,075.0 675.6c 620.0 5,373.9 1,508.6 5,282.9 206.4 2,957.2 185.0 153.5 299.3 7,010.6 16,416.7 361.4 2.4 11,776.8 182.4 1.9 454.7 682.5 6,957.4 8,522.7 780.1 0.5
367.8 2,903.3 99,409.0 15,404.7 9,074.1 4,110.7 278.3 86.3 3,899.0 1.6 2,006.9 64.5 14,512.3 2,190.8 1,293.8 1,045.2 1.4 601.0 888.8 .. 64.9 160.7 120.0 352.8 974.1 180.3 1,590.9 1,791.8 685.7 3,184.4 222.7 0.1 9,918.6 915.4 135.7 5,308.6 2,180.5 8,454.5 284.0 3,339.1 215.5 123.5 500.7 8,798.4 19,572.3 320.9 4.5 12,871.3 181.9 3.2 801.0 1,160.7 6,737.7 8,371.6 359.5 0.3
203.2 796.3 757.6 1,329.8 749.6 347.0 6,591.0 2,835.7 2,195.5 1,737.0 3,778.5 969.4 135.5c 209.2 3,522.3 4,931.8 666.7 242.4 c 31.0 995.3c 1,639.3 167.3 2.5 457.6 540.7c 0.0 c 34.0 350.6 .. 91.0 132.2 2,731.5 686.4 776.5c 111.5 352.9 12.0 78.8 0.0 339.6 6,297.8 1,911.4 269.9 1.2 142.2 2,362.0 2,440.6 962.0 666.2 621.5 91.9 245.9 935.5 894.8 1,122.9 ..
2003–05b
544.8 1,152.6 1,140.1 1,548.1 928.7 .. 4,529.4 20,007.8 1,726.2 611.4 4,108.8 7,294.8 68.2 375.5 .. 5,011.5 10,631.1 236.7 .. 435.2 1,467.0 .. .. 506.2 1,470.0 399.8 32.4 235.6 8,199.7 .. .. 2,300.9 714.4 121.8 38.5 569.7 51.0 10.5 21.6 262.3 9,126.6 6,740.8 317.2 3.7 63.9 2,461.0 3,424.2 1,594.9 426.1 1,805.9 580.9 853.6 1,449.1 1,296.5 2,018.6 ..
1990–92
42.1 11.3c 68.1 54.9 25.6 .. 14.1 3.7 8.4 27.3c 6.8 .. .. 19.0 c .. 16.7 .. 35.5 .. .. .. .. .. .. 18.8 .. .. .. 23.9 c .. .. 14.7c 24.7c .. .. .. .. 69.4 c 48.2c 81.9 4.3 10.7 38.7 .. .. 5.9 .. 48.9 25.8 c .. 1.7 1.0 45.3 25.2 15.6 3.5
2003–05
37.2 5.3 .. 44.5 24.9 c 17.0 c 6.3 2.0 4.6c 19.0 4.5 3.6c 34.4 c .. .. 8.3 0.0 c 43.4 .. 13.0 .. .. .. .. 15.9 19.4 78.0 c .. 14.6c 41.5c .. 10.0 15.9 41.4 40.6 45.0 .. .. .. .. 2.9 7.6 29.0 c .. .. 3.5 .. 42.7 16.4 .. 31.5c 0.7 37.1 17.9 12.1 2.1
1990–92
2003–05
31 158 65 3 136 72 1,667 763 1,619 242 4,297 352 62c 20 297 275 215 189c 11 364 c 188 57 8 187 256c 730 c 5 8 161 11 8 36 128 310 c 73 46 14 12 47 23 2,056 323 20 0 8 1,723 42 133 103 59 72 36 72 821 569 478
50 259 165 2 162 127 1,409 757 2,312 177 4,417 328 21 25 234 1,344 70 159 11 548 446 61 9 224 660 954 2 6 241 5 8 52 130 222 35 58 14 11 39 24 1,647 499 16 0 10 1,526 39 185 148 52 43 36 111 1,119 1,259 438
2009 World Development Indicators
139
3.2 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbiae Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
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 1990–92b 2003–05b
thousand hectares
hundred grams per hectare of arable land
% of total employment
Tractors per 100 sq. km of arable land
64.4 13.5c 75.6 57.5 41.9 .. 38.3 2.2 .. 28.0 c 70.2 80.2 60.8 36.2 51.9 75.8 8.2 46.9 73.7 32.1c 38.4 41.9 21.9 58.7 25.7 58.4 51.8 68.6c 61.0 72.4 c 3.7 75.0 46.6 84.7 65.2c 24.7 21.0 62.5 33.4 31.4 34.1 37.3 w 36.7 37.1 45.5 30.1 37.0 48.3 28.1 34.4 23.5 54.7 42.1 37.9 49.7
63.8 13.2 78.6 80.8 42.6 54.8 40.0 1.2 42.3 25.0 70.7 82.0 58.3 36.5 57.2 80.9 7.8 38.1 75.6 30.4 38.8 36.3 22.9 66.7 25.9 63.0 53.3 70.2 63.9 71.4 6.7 70.2 45.3 85.4 65.6 24.6 30.8 61.8 33.6 34.4 39.9 38.2 w 38.7 38.2 47.0 30.7 38.3 50.7 28.4 35.7 23.6 54.7 43.9 38.0 47.3
.. .. 0.3 44.2 3.3 .. 5.2 .. .. .. 19.2 8.3 .. 28.0 14.1 24.1 4.1 6.0 14.3 72.9 c 1.4 21.0 .. 0.3 3.3 .. .. .. 0.1 7.6c .. 2.5 11.3 10.2 87.3c 13.9 44.6 .. 24.3 0.7 3.6 17.0 w 9.5 22.8 27.5 10.1 20.0 .. 16.7 11.3 31.6 28.8 3.4 .. 13.9
3.2 3.6 0.6c 42.7c 4.8 c 0.9 4.7c .. 3.8 1.2 15.7c 9.5c 20.3c 38.8 c 10.2c 26.0 c 4.3c 5.8 c 24.3c 68.3c 1.8 c 28.2c .. 0.3c 3.3c 7.2 19.7 79.5c 0.1c 6.6c 29.9c 3.0 c 12.5c 14.9c 84.9c 16.9c 33.7c 7.0 c 33.0 c 2.9c 5.2c 18.0 w 18.6 20.8 28.1 8.9 20.4 .. 10.9 12.5 33.7 39.2 3.5 .. 16.5
1990–92
2005–07
1990–92b
2003–05b
5,842.3 5,178.7 788.5 428.8 59,541.3c 41,831.1 114.1 417.4 c 258.2 333.8 19.9 .. 1,061.8 664.1 1,445.8 1,060.3 1,153.8 1,130.9 65.1 134.4 .. 1,945.8c .. 1,199.5 451.7 883.5 22.6 .. .. .. 54,333.3 141,594.4 .. 772.3 .. 766.2 112.5c 97.3 3,167.8 c 3,522.5 531.4 624.6 8.8 .. 5,735.9 3,570.9 548.6 520.7 7,588.5 6,357.7 1,185.9 1,601.6 834.3 908.0 2,016.4 2,873.1 6,266.9 9,192.4 51.2 35.6 69.1 59.4 688.5 .. 1,184.3 989.4 1,112.0 1,050.6 207.3 163.2 4,031.8 2,147.6 3,811.9 3,222.8 621.5 856.8 266.0c 392.8 1,488.4 c .. 3,003.3 4,906.7 53.5 69.7 10,593.6 11,402.1 598.2 1,406.9 83.7 107.7 .. .. 610.2 736.6 56.3 61.1 6.4 2.0 1,111.4 1,848.1 1,524.7 1,484.7 329.9 460.9 13,759.9 13,328.6 756.7 1,095.5 331.3c 1,022.1 1,296.3c .. 1,097.6 1,669.7 1.5 15.0 12,542.3c 13,875.5 806.9c 186.5 1.4 0.0 4,810.4 5,531.3 3,548.5 2,880.3 3,323.1 3,020.0 64,547.3 57,213.3 1,014.8 1,562.1 509.4 571.2 610.2 1,387.4 1,225.3c 1,601.1 1,631.6c .. 798.7 1,092.0 1,388.3 1,747.5 6,726.1 8,399.2 1,299.3 3,308.9 .. 32.7c .. .. 738.2 746.2 127.2 50.6 813.4 837.2 131.1 0.0 1,430.8 2,095.0 508.1 328.6 704,455.8 s 689,120.1 s 945.2 w 1,192.9 w 103,608.2 131,441.1 339.8 448.7 454,663.6 419,524.9 965.8 1,233.0 287,054.6 283,677.2 1,169.7 1,615.2 167,609.0 135,847.6 603.6 703.9 558,271.9 550,965.9 833.7 1,090.7 142,265.1 139,631.3 1,821.0 2,627.6 137,268.0 110,119.5 564.7 359.7 47,712.7 48,655.5 585.7 1,108.1 30,590.3 30,102.1 641.4 1,055.7 129,690.1 131,101.8 767.2 1,166.3 70,745.7 91,355.7 129.0 119.2 146,184.0 138,154.2 1,200.2 1,474.7 32,674.3 31,370.1 2,302.5 2,046.1
1990–92
30.6 .. .. .. .. .. .. 0.3c .. .. .. .. 10.7 44.3 .. .. 3.3 4.2 28.2c 57.9 84.2c 61.7 .. .. 11.8 .. 46.5 .. 91.5c 20.0 .. 2.2 2.9 1.5 .. 12.6 73.8 c .. 52.6c .. .. 42.6 w .. 51.6 54.6 .. 52.4 54.5 .. 18.6 .. 65.6 .. 5.6 7.3
2003–05
1990–92
2003–05
33.1 146 183 10.4 98 c 44 .. 1 0 .. 20 28 .. 2 3 .. .. 948 .. 3 1 0.2 637 1,083 5.2 .. 158 8.9 .. .. .. 16 10 10.3c 101 43 5.5 494 705 33.9 c 180 221 .. 8 9 .. 251 222 2.1 604 616 4.0 2,870 2,632 27.0 c 137 220 67.2 415c 239 .. 8 23 43.3 39 256 .. 8 7 .. 0 0 4.9 606 683 .. 88 140 32.5 287 426 .. 465c 224 69.1c 9 9 19.8 153c 114 .. 50 59 1.3 762 872 1.6 245 272 4.6c 259 266 .. 402c 362 10.7c 176 186 58.8 c 60 247 15.8 c 441 715 .. 40 43 .. 11 11 .. 61 75 .. w 186 w 208 w .. 40 47 .. 115 144 .. 102 122 17.5 75 127 .. 166 163 .. 56 96 20.8 190 186 16.5 121 118 .. 116 151 .. 67 153 .. 19 13 3.4 413 432 4.5 993 1,002
a. Includes permanent pastures, arable land, and land under permanent crops. b. Data for two periods may not be comparable (see About the data). c. Data are not available for all three years. d. Includes Luxembourg. e. Includes Montenegro.
140
2009 World Development Indicators
About the data
ENVIRONMENT
Agricultural inputs
3.2
Definitions
Agriculture is still a major sector in many economies,
machinery. There is no single correct mix of inputs:
• Agricultural land is the share of land area that
and agricultural activities provide developing coun-
appropriate levels and application rates vary by coun-
is permanent pastures, arable, or under permanent
tries with food and revenue. But agricultural activities
try and over time and depend on the type of crops,
crops. Permanent pasture is land used for fi ve or
also can degrade natural resources. Poor farming
the climate and soils, and the production process
more years for forage, including natural and culti-
practices can cause soil erosion and loss of soil
used.
vated crops. Arable land includes land defined by the
fertility. Efforts to increase productivity by using
The data shown here and in table 3.3 are collected
FAO as land under temporary crops (double-cropped
chemical fertilizers, pesticides, and intensive irriga-
by the Food and Agriculture Organization of the
areas are counted once), temporary meadows for
tion have environmental costs and health impacts.
United Nations (FAO) through annual questionnaires.
mowing or for pasture, land under market or kitchen
Excessive use of chemical fertilizers can alter the
The FAO tries to impose standard definitions and
gardens, and land temporarily fallow. Land aban-
chemistry of soil. Pesticide poisoning is common in
reporting methods, but complete consistency across
doned as a result of shifting cultivation is excluded.
developing countries. And salinization of irrigated
countries and over time is not possible. For example,
Land under permanent crops is land cultivated with
land diminishes soil fertility. Thus, inappropriate use
permanent pastures are quite different in nature and
crops that occupy the land for long periods and need
of inputs for agricultural production has far-reaching
intensity in African countries and dry Middle Eastern
not be replanted after each harvest, such as cocoa,
effects.
countries. Thus, despite standard definitions, data
coffee, and rubber. Land under flowering shrubs, fruit
The table provides indicators of major inputs to
on agricultural land in different climates may not be
trees, nut trees, and vines is included, but land under
agricultural production: land, fertilizer, labor, and
comparable. Data on agricultural employment, in par-
trees grown for wood or timber is not. • Irrigated land
ticular, should be used with caution. In many coun-
refers to areas purposely provided with water, includ-
tries much agricultural employment is informal and
ing land irrigated by controlled flooding. • Cropland is
unrecorded, including substantial work performed
arable land and permanent cropland (see table 3.1).
by women and children. To address some of these
• Land under cereal production refers to harvested
concerns, this indicator is heavily footnoted in the
areas, although some countries report only sown or
database in sources, definition, and coverage.
cultivated area. • Fertilizer consumption is the quan-
Nearly 40 percent of land globally is devoted to agriculture
3.2a
Total land area in 2005: 130 million sq. km
Others 31.4%
Permanent pastures 26.1%
Arable land 11.0% Forests 30.4% Permanent crops 1.1% Note: Agricultural land includes permanent pastures, arable land, and land under permanent crops. Source: Tables 3.1 and 3.2.
Developing regions lag in agricultural machinery, which reduces their 3.2b agricultural productivity
Fertilizer consumption measures the quantity of
tity of plant nutrients per unit of arable land. Fertil-
plant nutrients. Consumption is calculated as pro-
izer products cover nitrogen, potash, and phosphate
duction plus imports minus exports. Because some
(including ground rock phosphate). Traditional nutri-
chemical compounds used for fertilizers have other
ents—animal and plant manures—are not included.
industrial applications, the consumption data may
The time reference for fertilizer consumption is the
overstate the quantity available for crops. The FAO
crop year (July through June). • Agricultural employ-
recently revised the time series for fertilizer con-
ment is employment in agriculture, forestry, hunting,
sumption and irrigation but only for 2002 onward,
and fishing (see table 2.3). • Agricultural machinery
and data for 2002–05 are not available for all coun-
refers to wheel and crawler tractors (excluding gar-
tries. The fertilizer data from the FAO’s previous
den tractors) in use in agriculture at the end of the
releases are not necessarily comparable with later
calendar year specified or during the first quarter of
data. In the previous release data were based on
the following year.
the total consumption of fertilizers, but in the recent release data are based on the nutrients in fertilizers.
Tractors per 100 square kilometers of arable land 500
Caution should thus be used when comparing data 1990–92
2003–05
over time. To smooth annual fluctuations in agricultural activity, the indicators in the table have been averaged
400
over three years. 300
200
100
0 East Asia & Pacific
Europe Latin & America Central & Asia Caribbean
Source: Table 3.2.
Middle East & North Africa
South Asia
SubHigh Saharan income Africa
Data sources Data on agricultural inputs are from electronic files that the FAO makes available to the World Bank.
2009 World Development Indicators
141
3.3
Agricultural output and productivity Crop production index
1999–2001 = 100 1990–92 2003–05
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
97.4 87.5 86.3 60.3 67.3 106.4a 60.3 93.4 135.4 a 75.6 113.0a 77.6b 58.4 63.7 106.5a 96.4 77.3 148.7 74.0 112.6 65.4 71.5 .. 74.0 68.4 78.5 70.0 .. 96.6 124.5 80.0 71.3 73.9 80.3a 112.1 .. 103.7 118.9 79.5 69.5 102.2 .. 123.5a .. 97.8 94.1 87.3 56.0 115.6a 84.0 59.1 86.0 77.7 74.0 71.3 108.7
2009 World Development Indicators
135.9 102.4 136.6 154.3 112.6 126.6 97.9 98.5 129.3 107.7 126.0 107.3 118.2 119.6 106.7 112.4 125.2 102.6 124.6 105.3 109.6 108.9 .. 96.5 111.2 115.9 113.9 .. 112.1 96.8 106.4 103.8 96.5 90.6 112.3 95.8 99.8 106.4 103.1 110.7 91.3 80.4 92.0 108.6 104.0 97.4 102.3 97.2 94.3 96.5 118.4 89.1 103.2 110.8 104.6 98.3
Food production index
1999–2001 = 100 1990–92 2003–05
78.3 72.0 83.3 64.9 73.9 104.4 a 68.7 95.8 103.3a 74.0 127.2a 87.8b 62.6 68.9 115.4 a 114.4 71.0 144.9 73.3 112.3 64.6 74.2 .. 69.8 72.1 73.8 61.3 .. 82.5 121.3 78.9 71.3 74.1 92.3a 116.6 .. 93.8 99.5 72.9 66.7 85.2 .. 151.3a .. 103.6 95.6 89.2 60.1 97.3a 97.5 61.1 91.5 75.8 72.9 73.3 98.5
116.5 109.4 123.1 136.3 107.0 122.6 98.9 98.1 127.9 107.5 114.6 100.1 119.7 115.3 106.9 106.2 122.6 99.2 114.7 105.3 110.8 108.2 .. 108.8 110.7 114.2 116.7 .. 112.2 97.1 108.8 104.5 101.7 93.3 107.5 94.6 102.3 108.9 110.1 113.3 103.5 90.9 99.0 111.0 105.5 97.7 101.8 97.8 104.3 98.9 118.3 91.7 106.7 113.7 105.5 101.6
Livestock production index
1999–2001 = 100 1990–92 2003–05
64.1 63.9 80.7 75.6 89.1 101.3a 83.3 97.3 94.7a 73.8 141.2a 93.8b 87.9 76.9 137.8a 118.2 65.3 143.3 68.0 134.7 65.9 85.5 .. 68.1 84.8 67.9 49.4 .. 80.4 101.4 75.8 79.7 75.6 126.4a 130.4 .. 89.0 79.1 65.8 65.4 74.3 .. 167.5a .. 106.6 97.5 86.8 98.7 79.3a 108.4 90.7 101.4 76.5 60.6 81.1 69.8
99.5 113.0 106.6 99.9 97.3 115.5 94.9 97.8 122.0 105.1 104.2 96.9 112.4 108.4 107.6 104.7 121.1 97.6 115.6 99.9 110.3 100.6 .. 117.6 107.6 111.1 122.5 .. 112.6 96.3 118.8 100.7 124.7 101.0 89.0 93.4 103.5 111.6 117.2 119.0 108.3 100.3 103.0 116.6 106.3 98.2 100.0 103.4 113.7 100.6 111.0 98.1 103.9 115.6 109.8 111.8
Cereal yield
Agricultural productivity
kilograms per hectare 1990–92 2005–07
Agriculture value added per worker 2000 $ 1990–92 2003–05
1,153 2,372 915 378 2,652 1,843a 1,739 5,400 2,113a 2,567 2,739a 6,122b 880 1,384 3,548a 312 1,916 3,633 783 1,370 1,356 1,166 2,559 883 636 3,949 4,307 .. 2,492 794 688 3,188 863 3,975a 2,092 .. 5,448 4,078 1,724 5,738 1,871 .. 1,304 a 1,234 3,246 6,370 1,712 1,114 1,998 a 5,578 1,084 3,589 1,882 1,423 1,529 997
1,650 3,580 1,465 526 4,077 1,605 1,411 5,917 2,637 3,769 2,767 8,248 1,173 1,939 3,922 508 3,206 3,010 1,132 1,316 2,482 1,355 3,083 1,100 907 6,073 5,322 .. 3,792 772 788 3,110 1,785 5,215 2,742 4,480 5,863 4,426 2,751 7,624 2,785 436 2,644 1,557 3,471 6,731 1,648 1,122 1,650 6,538 1,365 3,867 1,501 1,433 1,544 921
.. 778 1,911 165 6,767 1,476a 20,839 12,048 1,084a 254 1,977a .. 326 670 .. 536 1,507 2,500 110 108 .. 389 28,243 287 173 3,573 258 .. 3,080 184 .. 3,143 598 4,915a .. .. 15,190 2,268 1,686 1,528 1,633 .. 3,002a .. 18,818 22,234 1,176 224 2,443a 13,724 293 7,536 2,120 142 205 ..
.. 1,449 2,225 174 10,072 3,692 29,908 21,920 1,143 338 3,153 39,243 519 773 8,270 390 3,119 7,159 173 70 314 648 44,133 381 215 5,309 407 .. 2,749 149 .. 4,506 795 9,975 .. 5,521 38,441 4,586 1,676 2,072 1,638 71 3,129 158 31,276 44,080 1,592 235 1,791 25,657 320 8,818 2,623 190 238 ..
Crop production index
1999–2001 = 100 1990–92 2003–05
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
92.5 115.0 80.2 82.7 74.8 102.0 94.1 97.6 97.5 85.0 113.0 100.2 168.7a 84.9 125.7 88.3 33.9 67.4 a 62.3 137.3a 109.7 67.9 62.4 79.5 77.5a 107.1a 92.3 57.3 74.4 73.9 64.5 110.7 83.0 134.6a 260.7 100.9 64.2 61.7 72.0 73.7 93.4 78.7 76.2 73.3 68.7 121.1 62.8 80.9 109.9 78.5 85.3 52.4 84.4 110.9 104.0 167.7
129.4 111.2 103.2 118.8 117.1 118.5 105.2 111.9 97.2 97.2 93.0 134.9 110.9 103.3 110.1 93.1 122.3 103.7 113.8 120.3 94.1 105.6 99.2 98.5 104.5 99.9 105.6 94.4 121.4 118.2 90.5 103.3 106.8 112.5 114.2 139.3 108.7 119.6 113.2 114.2 98.0 105.1 118.6 110.7 105.0 104.5 87.3 106.6 108.2 102.5 124.6 109.2 113.0 91.1 97.7 117.4
Food production index
1999–2001 = 100 1990–92 2003–05
87.2 119.5 76.3 83.1 72.8 99.0 94.1 83.2 96.2 85.3 109.4 85.9 173.9a 85.3 128.7 80.3 30.0 82.8a 59.3 199.4 a 100.0 85.7 77.7 77.8 133.1a 106.2a 91.6 48.9 71.0 78.7 85.7 98.6 76.6 152.9a 103.3 93.5 69.4 61.8 106.2 75.3 100.9 78.1 63.7 73.5 69.2 102.8 63.0 71.0 87.8 79.9 77.2 56.6 77.5 112.5 95.6 127.5
158.2 105.1 105.3 120.4 114.1 118.8 98.2 115.2 96.8 98.9 96.5 119.3 112.7 106.4 112.0 96.8 115.0 103.7 115.4 111.1 103.0 102.8 97.5 99.2 103.7 101.1 104.6 95.7 120.1 113.6 107.1 106.5 108.8 113.1 72.2 126.3 106.5 121.3 130.8 112.9 93.4 115.5 123.6 105.3 105.2 100.1 93.0 109.5 103.1 107.7 116.3 112.1 114.6 97.8 97.9 97.6
Livestock production index
1999–2001 = 100 1990–92 2003–05
69.1 126.7 69.3 85.5 68.2 92.6 94.3 72.9 95.0 86.8 106.9 70.4 178.0a 85.4 145.2 68.1 27.9 109.1a 60.2 253.7a 65.6 108.7 89.9 75.8 184.7a 103.8 a 93.2 85.3 81.0 81.7 90.0 70.7 71.2 214.5a 93.2 81.0 94.5 64.2 114.1 80.1 104.4 81.0 57.1 71.7 76.8 98.1 65.6 67.4 69.5 80.9 86.7 68.0 61.9 115.4 85.5 118.3
180.3 95.8 112.6 134.2 105.2 117.8 97.1 117.5 95.8 102.0 99.7 99.6 117.2 110.4 120.9 101.7 110.9 101.8 112.9 102.9 126.7 100.0 109.7 100.5 103.0 104.2 101.0 102.1 118.2 116.8 110.5 116.4 110.1 105.2 69.6 102.2 101.3 128.5 135.2 110.0 91.4 115.2 124.0 92.3 107.0 99.0 104.1 112.5 98.1 113.1 97.8 116.5 118.2 106.7 98.2 93.5
ENVIRONMENT
Agricultural output and productivity
3.3
Cereal yield
Agricultural productivity
kilograms per hectare 1990–92 2005–07
Agriculture value added per worker 2000 $ 1990–92 2003–05
1,371 4,551 1,947 3,826 1,523 872 6,653 3,132 4,340 1,298 5,713 1,167 1,338 a 1,645 5,073 5,885 3,112 2,772a 2,355 1,641a 2,001 703 951 706 1,938a 2,652a 1,935 871 2,827 840 802 4,117 2,520 2,928a 967 1,094 330 2,739 388 1,831 7,145 5,257 1,543 323 1,135 3,744 2,206 1,818 1,862 2,504 1,905 2,463 2,070 2,958 1,939 1,100
1,519 5,135 2,464 4,374 2,481 794 7,185 3,153 5,311 1,211 6,013 1,263 1,158 1,723 3,638 6,291 2,621 2,523 3,517 2,669 2,666 546 1,156 617 2,625 2,938 2,493 1,416 3,336 1,109 763 7,666 3,145 2,113 839 986 949 3,629 420 2,271 7,753 7,071 1,880 437 1,460 3,924 3,214 2,639 1,939 3,672 2,201 3,603 3,164 3,029 2,882 1,826
1,193 4,105 324 484 1,954 .. .. .. 11,528 2,016 20,445 1,892 1,795a 334 .. 5,679 .. 675a 360 1,790a .. 428 .. .. .. 2,256a 186 72 386 208 574 3,942 2,256 1,286a 870 1,430 107 .. 820 191 24,914 19,155 .. 152 .. 19,500 1,005 594 2,363 500 1,596 930 905 1,502a 4,642 ..
2009 World Development Indicators
1,483 6,987 392 583 2,561 1,756 17,107 .. 23,967 1,889 35,668 1,360 1,557 333 .. 11,286 13,521a 979 459 2,704 29,950 423 .. .. 4,760 3,487 174 116 525 241 356 5,011 2,793 816 907 1,746 148 .. 1,103 207 42,049 27,189 2,071 157a .. 37,039 1,302a 696 3,904 595 2,052 1,481 1,075 2,182 6,220 ..
143
3.3
Agricultural output and productivity Crop production index
1999–2001 = 100 1990–92 2003–05
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
93.0 125.9a 110.2 121.4 72.5 .. 128.9 157.1 .. 94.0a 102.8 79.8 88.1 86.9 68.8 105.6 102.0 112.2 73.9 122.8a 92.8 81.9 93.7 73.8 116.4 105.6 88.3 110.9a 77.9 128.5a 23.5 105.0 88.4 71.3 108.0a 79.6 60.2 .. 75.2 80.8 69.4 82.0 w 76.1 80.5 76.7 91.7 79.8 71.9 113.8 78.0 79.4 80.0 75.5 89.9 90.6
116.4 115.9 115.1 115.0 88.9 116.3c 115.7 105.3 105.6 111.8 106.3 104.9 103.4 101.3 115.9 99.9 101.5 97.4 115.7 147.7 107.3 106.3 106.8 113.4 78.5 122.7 103.8 126.0 107.2 117.2 55.7 98.4 105.8 .. 115.6 98.6 120.4 104.6 100.9 104.3 65.1 109.4 w 109.3 112.0 111.7 113.0 111.6 114.4 109.8 116.3 117.3 104.2 106.1 101.9 98.5
Food production index
1999–2001 = 100 1990–92 2003–05
103.0 142.0a 106.7 93.9 74.0 .. 121.3 402.4 .. 82.1a 85.6 84.7 85.3 89.1 66.6 107.1 98.3 107.4 75.4 132.7a 89.0 83.5 102.0 75.1 92.7 90.0 89.2 63.0a 80.0 145.5a 29.7 105.4 85.7 76.7 97.6a 74.6 62.1 .. 72.3 84.2 76.2 81.1 w 75.1 78.6 71.5 96.0 78.1 64.8 128.3 74.2 76.5 75.6 76.9 89.6 93.6
114.3 110.3 116.4 111.6 90.1 107.0 c 114.5 114.0 101.4 104.8 105.1 107.7 106.2 103.0 112.0 104.6 99.4 99.6 120.6 150.1 106.9 104.5 112.0 108.2 112.9 115.0 105.3 142.2 107.8 111.7 64.5 98.0 104.2 .. 118.1 94.8 122.1 109.9 105.3 105.3 84.7 109.7 w 110.4 112.9 113.5 111.5 112.6 116.6 108.7 114.8 115.7 106.3 107.2 101.9 99.8
a. Data are not available for all three years. b. Includes Luxembourg. c. Includes Montenegro.
144
2009 World Development Indicators
Livestock production index
1999–2001 = 100 1990–92 2003–05
120.3 161.8a 81.2 67.5 87.5 .. 93.4 413.8 .. 75.7a 83.3 93.9 80.0 93.6 67.4 129.6 95.9 105.7 75.4 192.6a 82.9 86.2 101.4 93.4 73.4 60.5 92.1 56.1a 82.3 171.7a 71.5 105.8 83.4 83.5 100.1a 73.4 57.9 .. 66.3 79.9 90.0 82.2 w 76.5 78.3 64.6 103.6 78.1 53.1 152.2 72.5 71.2 69.2 83.1 90.0 97.1
110.3 103.8 123.6 108.4 104.4 95.1c 106.9 114.4 97.2 101.1 104.9 109.8 110.5 115.5 109.4 110.2 98.1 100.4 121.7 152.3 109.5 103.8 129.0 109.1 142.6 99.1 108.0 139.7 111.4 103.5 122.2 97.6 103.1 .. 115.8 91.7 131.1 117.9 112.0 98.9 99.8 110.1 w 112.4 114.9 117.6 110.0 114.7 122.0 106.8 112.9 110.3 112.1 108.8 101.8 100.8
Cereal yield
Agricultural productivity
kilograms per hectare 1990–92 2005–07
Agriculture value added per worker 2000 $ 1990–92 2003–05
2,777 1,743a 1,088 4,212 803 .. 1,223 .. 1,031a 3,279a 622 1,602 2,310 2,950 596 1,299 4,272 6,102 947 1,037a 1,276 2,186 1,694 791 3,159 1,401 2,192 2,210a 1,487 2,834 a 2,042 6,321 4,875 2,445 1,777a 2,561 3,097 1,105 906 1,251 1,125 2,839 w 1,596 2,639 2,789 1,992 2,421 3,816 1,961 2,234 1,544 1,977 1,003 4,259 4,621
2,680 1,865 1,117 4,464 968 3,839 1,023 .. 4,081 5,498 432 3,081 3,050 3,636 681 1,196 4,791 6,364 1,749 2,275 1,162 2,907 1,230 1,152 2,647 1,320 2,529 3,065 1,527 2,368 2,000 7,082 6,512 4,223 4,042 3,365 4,726 2,091a 885 1,697 674 3,301 w 2,105 3,093 3,354 2,553 2,858 4,681 2,211 3,308 2,206 2,581 1,228 5,072 5,419
2,196 1,825a 167 7,875 225 .. .. 22,695 .. 12,042a .. 1,786 9,511 679 414 1,231 22,533 19,884 2,344 346a 238 497 .. 312 1,666 2,422 1,770 1,222a 155 1,195a 10,454 22,664 20,793 5,714 1,272a 4,483 214 .. 271 159 240 746 w 270 467 371 1,987 433 295 1,756 2,125 1,583 335 262 14,543 12,587
4,646 2,519 182 15,780 215 1,679c .. 40,419 5,026 .. .. 2,495 18,619 702 667 1,330 35,378 23,588 3,261 409 295 624 .. 347 1,745 2,700 1,846 .. 175 1,702 25,841 26,942 42,744 7,973 1,800 6,331 305 .. 328a 204 222 908 w 319 650 513 2,819 579 438 2,108 3,055 2,204 406 278 25,594 22,134
About the data
ENVIRONMENT
Agricultural output and productivity
3.3
Definitions
The agricultural production indexes in the table are
single enterprise, estimates of the amounts retained
• Crop production index is agricultural production
prepared by the Food and Agriculture Organization of
for seed and feed are subtracted from the produc-
for each period relative to the base period 1999–
the United Nations (FAO). The FAO obtains data from
tion data to avoid double counting. The resulting
2001. It includes all crops except fodder crops. The
official and semiofficial reports of crop yields, area
aggregate represents production available for any
regional and income group aggregates for the FAO’s
under production, and livestock numbers. If data are
use except as seed and feed. The FAO’s indexes
production indexes are calculated from the under-
unavailable, the FAO makes estimates. The indexes
may differ from those from other sources because
lying values in international dollars, normalized to the
are calculated using the Laspeyres formula: produc-
of differences in coverage, weights, concepts, time
base period 1999–2001. • Food production index
tion quantities of each commodity are weighted by
periods, calculation methods, and use of interna-
covers food crops that are considered edible and
average international commodity prices in the base
tional prices.
that contain nutrients. Coffee and tea are excluded
period and summed for each year. Because the FAO’s
To facilitate cross-country comparisons, the FAO
because, although edible, they have no nutritive
indexes are based on the concept of agriculture as a
uses international commodity prices to value produc-
value. • Livestock production index includes meat
tion. These prices, expressed in international dollars
and milk from all sources, dairy products such as
(equivalent in purchasing power to the U.S. dollar),
cheese, and eggs, honey, raw silk, wool, and hides
are derived using a Geary-Khamis formula applied
and skins. • Cereal yield, measured in kilograms
to agricultural outputs (see System of National
per hectare of harvested land, includes wheat, rice,
Accounts 1993, sections 16.93–96). This method
maize, barley, oats, rye, millet, sorghum, buckwheat,
assigns a single price to each commodity so that, for
and mixed grains. Production data on cereals refer
example, one metric ton of wheat has the same price
to crops harvested for dry grain only. Cereal crops
regardless of where it was produced. The use of inter-
harvested for hay or harvested green for food, feed,
national prices eliminates fluctuations in the value
or silage, and those used for grazing, are excluded.
of output due to transitory movements of nominal
The FAO allocates production data to the calendar
exchange rates unrelated to the purchasing power
year in which the bulk of the harvest took place. But
of the domestic currency.
most of a crop harvested near the end of a year will
Cereal yield in low-income economies was less than 40 percent of the yield 3.3a in high-income countries Kilograms per hectare (thousands) 6
1990–92
2005–07
5 4 3 2 1 0 World
Low Lower Upper High income middle middle income income income
Euro area
Source: Table 3.3.
Sub-Saharan Africa had the lowest yield, while East Asia and Pacific is closing the gap with high-income economies Kilograms per hectare (thousands) 6
1990–92
Data on cereal yield may be affected by a variety of
be used in the following year. • Agricultural produc-
reporting and timing differences. Millet and sorghum,
tivity is the ratio of agricultural value added, mea-
which are grown as feed for livestock and poultry in
sured in 2000 U.S. dollars, to the number of workers
Europe and North America, are used as food in Africa,
in agriculture. Agricultural productivity is measured
Asia, and countries of the former Soviet Union. So
by value added per unit of input. (For further discus-
some cereal crops are excluded from the data for
sion of the calculation of value added in national
some countries and included elsewhere, depending
accounts, see About the data for tables 4.1 and 4.2.)
on their use. To smooth annual fluctuations in agri-
Agricultural value added includes that from forestry
cultural activity, the indicators in the table have been
and fishing. Thus interpretations of land productivity
averaged over three years.
should be made with caution.
3.3b
2005–07
5 4 3
Data sources 2
Data on agricultural production indexes, cereal yield, and agricultural workers are from electronic
1
files that the FAO makes available to the World 0
Bank. The files may contain more recent informaEast Europe Latin Middle South Sub- High Asia & & America East & Asia Saharan income Pacific Central & North Africa Asia Caribbean Africa
Source: Table 3.3.
tion than published versions. Data on agricultural value added are from the World Bank’s national accounts files.
2009 World Development Indicators
145
3.4
Deforestation and biodiversity Forest area
Average annual deforestationa
thousand sq. km
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
1990
2005
13 8 18 610 353 3 1,679 38 9 9 75 8d 33 628 22 137 5,200 33 72 3 129 245 3,101 232 131 153 1,571 .. 614 1,405 227 26 102 21 21 26 4 14 138 0e 4 16 22 147 222 145 219 4 28 107 74 33 47 74 22 1
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
2009 World Development Indicators
Higher plantsb
GEF benefits index for biodiversity
0–100 (no biodiversity Total Total known Threatened known Threatened to maximum biodiversity) species species species species
% 1990–2000 2000–05
2.5 0.3 –1.8 0.2 0.4 1.0 0.2 –0.2 0.0 0.0 –0.5 .. 2.1 0.4 0.1 0.9 0.5 –0.1 0.3 3.7 1.1 0.9 0.0 0.1 0.6 –0.4 –1.2 .. 0.1 0.4 0.1 0.8 –0.1 0.0 –1.7 0.0 –0.9 0.0 1.5 –3.0 1.5 0.2 –0.3 0.7 –0.1 –0.5 0.0 –0.4 0.0 –0.3 2.0 –0.9 1.2 0.7 0.4 0.6
Animal species
3.1 –0.6 –1.2 0.2 0.4 1.5 0.1 –0.1 0.0 0.3 –0.1 .. 2.5 0.5 0.0 1.0 0.6 –1.4 0.3 5.2 2.0 1.0 0.0 0.1 0.7 –0.4 –2.2 .. 0.1 0.2 0.1 –0.1 –0.1 –0.1 –2.2 –0.1 –0.6 0.0 1.7 –2.6 1.7 0.3 –0.4 1.1 0.0 –0.3 0.0 –0.4 0.0 0.0 2.0 –0.8 1.3 0.5 0.5 0.7
2004
2008
2004
578 376 472 1,226 1,413 380 1,227 513 446 735 297 519 644 1,775 390 739 2,290 485 581 713 648 1,258 683 850 635 604 1,801 363 2,288 1,578 763 1,070 931 461 423 474 508 260 1,856 599 571 607 334 1,127 501 665 798 668 366 613 978 530 877 855 560 312
30 45 72 62 152 35 568 62 38 89 17 29 34 80 55 18 343 47 13 48 82 157 77 17 21 95 351 37 382 126 37 131 73 78 115 39 28 81 340 59 29 38 14 86 19 117 43 31 46 59 56 95 133 61 29 91
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
Nationally protected areas
Marine protected areas
thousand sq. km
% of total land area
thousand sq. km
% of surface area 2004
2008
2008
2006 c
2006c
2004
2 0 3 26 44 1 55 4 0 12 .. 1 14 71 1 0 382 0 2 2 31 355 2 15 2 40 446 6 223 65 35 111 105 1 163 4 3 30 1,839 2 26 3 0 22 1 8 108 4 0 12 117 11 83 22 4 29
3.4 0.2 2.9 8.3 17.7 0.2 87.7 0.3 0.8 1.4 0.0 0.0 0.2 12.5 0.4 1.4 100.0 0.8 0.3 0.3 3.5 12.5 21.5 1.5 2.2 15.3 66.6 0.0 51.5 19.9 3.6 9.7 3.4 0.6 12.5 0.1 0.2 6.0 29.3 2.9 0.9 0.8 0.1 8.4 0.2 5.3 3.0 0.1 0.6 0.6 1.9 2.8 8.0 2.3 0.6 5.2
2.2 0.2 118.8 125.5 173.6 2.4 734.1 23.5 4.0 0.9 10.8 1.0 26.1 218.5 0.2 174.4 1,515.2 11.0 38.2 1.5 41.5 39.9 473.2 94.7 114.9 27.8 1,437.8 0.3 282.7 196.1 48.7 11.2 38.9 3.1 1.5 12.5 2.5 11.8 62.6 53.2 0.2 5.0 20.0 186.2 29.5 55.6 34.9 0.3 2.7 75.8 36.3 4.0 35.4 15.0 2.9 0.1
0.3 0.7 5.0 10.1 6.3 8.7 9.6 28.5 4.8 0.7 5.2 3.2 23.6 20.2 0.5 30.8 17.9 10.1 14.0 6.0 23.5 8.6 5.2 15.2 9.1 3.7 15.4 24.7 25.5 8.6 14.3 21.8 12.2 5.6 1.4 16.1 5.8 24.4 22.6 5.3 1.0 5.0 47.1 18.6 9.7 10.1 13.5 3.5 3.9 21.7 15.9 3.1 32.6 6.1 10.2 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 26.5 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
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
74 18 639 1,166 111 8 4 2 84 3 250 1 34 37 82 64 0e 8 173 28 1 0 41 2 20 9 137 39 224 141 4 0e 690 3 115 43 200 392 88 48 3 77 65 19 172 91 0e 25 44 315 212 702 106 89 31 4
2005
46 20 677 885 111 8 7 2 100 3 249 1 33 35 62 63 0e 9 161 29 1 0 32 2 21 9 128 34 209 126 3 0e 642 3 103 44 193 322 77 36 4 83 52 13 111 94 0e 19 43 294 185 687 72 92 38 4
Higher plantsb
GEF benefits index for biodiversity
0–100 (no biodiversity Total Total known Threatened known Threatened to maximum biodiversity) species species species species
% 1990–2000 2000–05
3.0 –0.6 –0.6 1.7 0.0 –0.2 –3.3 –0.6 –1.2 0.1 0.0 0.0 0.1 0.3 1.8 0.1 –3.4 –0.2 0.5 –0.3 –0.8 –3.4 1.6 0.0 –0.3 0.0 0.5 0.9 0.4 0.7 2.7 0.3 0.5 –0.2 0.7 –0.1 0.3 1.3 0.9 2.1 –0.4 –0.6 1.6 3.7 2.7 –0.2 0.0 1.8 0.2 0.5 0.9 0.1 2.8 –0.2 –1.5 –0.1
Animal species
3.1 –0.7 0.0 2.0 0.0 –0.1 –1.9 –0.8 –1.1 0.1 0.0 0.0 0.2 0.3 1.9 0.1 –2.7 –0.3 0.5 –0.4 –0.8 –2.7 1.8 0.0 –0.8 0.0 0.3 0.9 0.7 0.8 3.4 0.5 0.4 –0.2 0.8 –0.2 0.3 1.4 0.9 1.4 –0.3 –0.2 1.3 1.0 3.3 –0.2 0.0 2.1 0.1 0.5 0.9 0.1 2.1 –0.3 –1.1 0.0
ENVIRONMENT
Deforestation and biodiversity
3.4
Nationally protected areas
Marine protected areas
thousand sq. km
% of total land area
thousand sq. km
% of surface area 2004
2004
2008
2004
2008
2008
2006 c
2006c
2004
900 455 1,602 2,271 656 498 471 649 610 333 763 490 642 1,510 474 512 381 265 919 393 447 370 759 413 298 380 427 865 1,083 758 615 151 1,570 253 527 559 913 1,335 811 477 539 424 813 616 1,189 525 557 820 1,145 980 864 2,222 812 534 606 348
102 55 313 464 75 40 15 79 119 61 190 43 55 172 44 54 23 22 77 23 38 11 60 31 20 34 262 141 225 21 44 65 579 28 38 76 93 118 55 72 26 124 59 20 79 32 50 78 121 158 39 238 253 38 147 47
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
110 1 246 386 1 0 1 0 19 209 12 0 16 103 3 0 .. 14 21 0 0 1 46 1 .. 0 281 14 686 6 .. 88 261 0 0 2 46 38 24 7 0 21 39 2 171 2 6 2 194 142 10 275 216 4 16 53
7.2 0.2 39.9 81.0 7.3 1.6 0.6 0.8 3.8 4.4 36.0 0.4 5.1 8.8 0.7 1.7 0.1 1.1 5.0 0.0 0.2 0.3 2.6 1.6 0.0 0.2 29.2 3.5 13.9 1.5 1.3 3.3 68.7 0.0 4.2 3.5 7.2 10.0 5.2 2.1 0.2 20.2 3.3 0.9 6.0 1.3 3.7 4.9 10.9 25.4 2.8 33.4 32.3 0.5 5.5 4.0
21.9 5.2 151.7 203.1 103.9 0.0 0.8 3.4 19.4 1.6 34.5 9.3 77.4 69.1 3.2 3.5 0.0 6.2 37.5 10.4 0.0 0.1 15.2 1.2 3.6 1.8 15.2 18.4 59.7 26.0 2.5 0.1 102.5 0.5 217.9 4.7 45.3 35.5 42.8 22.8 4.3 64.7 21.3 84.1 56.5 15.5 0.2 65.3 7.6 36.2 23.4 175.9 30.0 75.4 4.6 0.3
19.6 5.8 5.1 11.2 6.4 0.0 1.1 15.6 6.6 15.0 9.5 10.6 2.9 12.1 2.6 3.5 0.0 3.2 16.3 16.7 0.4 0.2 15.8 0.1 5.7 7.1 2.6 19.5 18.2 2.1 0.2 3.3 5.3 1.4 13.9 1.1 5.8 5.4 5.2 16.0 12.7 24.2 17.6 6.6 6.2 5.1 0.1 8.5 10.2 8.0 5.9 13.7 10.1 24.6 5.0 3.3
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
2009 World Development Indicators
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
147
3.4
Deforestation and biodiversity Forest area
Average annual deforestationa
thousand sq. km 1990
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
2005
64 64 8,090 8,088 3 5 27 27 93 87 27f 26f 0e 0e 19 19 12 13 83 71 92 92 135 179 24 19 764 675 5 5 274 275 12 12 4 5 4 4 414 353 160 145 10 8 7 4 2 2 6 11 97 102 41 41 49 36 93 96 2 3 26 28 2,986 3,031 9 15 31 33 520 477 94 129 .. 0e 5 5 491 425 222 175 40,457 s 39,426 s 5,697 5,251 25,266 24,520 8,822 8,609 16,443 15,911 30,962 29,771 4,580 4,507 8,834 8,857 9,834 9,147 200 211 789 801 6,727 6,247 9,495 9,656 817 936
Higher plantsb
GEF benefits index for biodiversity
0–100 (no biodiversity Total Total known Threatened known Threatened to maximum biodiversity) species species species species
% 1990–2000 2000–05
0.0 0.0 –0.8 0.0 0.5 –0.3f 0.0 0.0 –0.3 1.0 0.0 –2.0 1.2 0.8 –0.9 0.0 –0.4 –1.5 0.0 1.0 0.7 1.2 3.4 0.3 –4.1 –0.4 0.0 1.9 –0.2 –2.4 –0.7 –0.1 –4.5 –0.4 0.6 –2.3 .. 0.0 0.9 1.5 0.2 w 0.5 0.2 0.2 0.2 0.3 0.3 0.0 0.5 –0.4 –0.2 0.4 –0.1 –1.1
Animal species
0.0 0.0 –6.9 0.0 0.5 –0.3f 0.0 –0.1 –0.4 1.0 0.0 –1.7 1.5 0.8 –0.9 0.0 –0.4 –1.3 0.0 1.1 0.4 1.3 4.5 0.2 –1.9 –0.2 0.0 2.2 –0.1 –0.1 –0.4 –0.1 –1.3 –0.5 0.6 –2.0 0.0 0.0 1.0 1.7 0.2 w 0.7 0.2 0.0 0.2 0.3 –0.2 0.0 0.5 –0.3 0.1 0.6 –0.1 –0.6
Nationally protected areas
Marine protected areas
thousand sq. km
% of total land area
thousand sq. km
% of surface area
2006c
2004
2004
2004
2008
2004
2008
2008
2006 c
466 941 871 527 803 477f 473 419 437 824 1,149 647 504 1,254 614 542 475 432 427 1,431 1,271 .. 740 551 438 581 421 1,375 445 298 660 1,356 532 434 1,745 1,116 .. 459 1,025 883
64 153 49 45 55 42 44 44 80 55 323 170 177 47 16 30 44 59 27 299 157 .. 33 38 52 121 44 131 58 27 38 937 66 33 166 152 .. 47 38 35
3,400 11,400 2,288 2,028 2,086 4,082f 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 54 2 .. 17 74 49 280 17 11 3 3 0 14 240 86 0 10 1 0 3 3 38 1 .. 14 244 1 15 69 147 0 159 8 17
0.7 34.1 0.9 3.2 1.0 0.2f 0.1 0.1 0.2 6.1 20.7 6.8 7.9 5.1 0.1 0.3 0.2 0.9 0.7 14.8 8.0 0.6 0.3 2.2 0.5 6.2 1.8 2.8 0.5 0.2 3.5 94.2 1.2 1.1 25.3 12.1 0.0 3.2 3.8 1.9
5.2 1,113.4 2.0 818.3 21.6 3.3f 0.0 9.6 1.3 1.9 73.7 41.4 11.3 114.1 0.5 42.4 11.8 1.2 19.6 342.7 101.7 0.9 6.0 0.2 2.4 12.7 12.6 62.9 19.4 0.2 47.5 1,379.2 0.6 8.7 638.1 16.0 .. 0.0 300.5 57.2 14,042.4 s 2,179.4 7,908.5 3,734.7 4,173.8 10,087.9 2,221.7 1,401.3 3,365.2 294.8 266.6 2,538.3 3,954.5 262.5
2.2 6.8 8.1 38.1 11.2 3.2f 4.2 20.0 6.7 0.3 6.1 8.3 17.5 4.8 3.1 10.3 29.5 0.7 14.0 38.7 19.9 6.3 11.1 4.7 1.5 1.6 2.7 31.9 3.3 0.2 19.6 15.1 0.3 2.0 72.3 5.2 .. 0.0 40.4 14.8 11.0 w 10.8 10.6 11.0 10.3 10.7 14.0 6.1 16.7 3.6 5.6 11.3 11.8 10.6
6.1 301.8 .. 5.2 0.9 0.1f 0.0 .. 0.0 3.3 3.4 1.8 2.3 0.3 .. 4.3 .. .. .. 2.3 5.8 .. .. 0.1 0.2 4.5 .. .. 3.1 .. 22.5 909.5 0.1 .. 21.3 0.7 .. .. .. .. 4,348.8 s 57.5 1,218.4 559.4 659.0 1,275.8 192.0 321.4 495.6 85.1 20.9 160.7 3,073.0 19.6
2.6 1.8 .. 0.3 0.4 0.1f 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 .. 1.7 1.8 1.6 1.6 1.3 1.4 2.7 1.1 0.5 .. 8.9 0.8
a. Negative values indicate an increase in forest area. b. Flowering plants only. c. Data reported by the World Conservation Monitoring Centre in 2006 are the most recent year available. d. Includes Luxembourg. e. Less than 0.5. f. Includes Montenegro.
148
2009 World Development Indicators
About the data
ENVIRONMENT
Deforestation and biodiversity
3.4
Definitions
Biological diversity is defined in terms of variability in
Global Environment Facility’s (GEF) benefits index for
• Forest area is land under natural or planted stands
genes, species, and ecosystems. A 2008 comprehen-
biodiversity is a comprehensive indicator of national
of trees, whether productive or not. • Average
sive assessment of world species shows that at least
biodiversity status and is used to guide its biodiversity
annual deforestation is the permanent conversion
1,141 of 5,487 known mammals are threatened with
priorities. The indicator incorporates information on
of natural forest area to other uses, including agri-
extinction. As threats to biodiversity mount, the inter-
individual species range maps available from the IUCN
culture, ranching, settlements, and infrastructure.
national community is increasingly focusing on con-
for virtually all mammals (5,487), amphibians (5,915),
Deforested areas do not include areas logged but
serving diversity. Deforestation is a major cause of
and endangered birds (1,098); country data from the
intended for regeneration or areas degraded by fuel-
loss of biodiversity, and habitat conservation is vital
World Resources Institute for reptiles and vascular
wood gathering, acid precipitation, or forest fires.
for stemming this loss. Conservation efforts have
plants; country data from FishBase for 31,190 fish
• Animal species are mammals (excluding whales
focused on protecting areas of high biodiversity.
species; and the ecological characteristics of 867
and porpoises) and birds (included within a country’s
The Food and Agriculture Organization of the United
world terrestrial ecoregions from WWF International.
breeding or wintering ranges). • Higher plants are
Nations (FAO) Global Forest Resources Assessment
For each country the biodiversity indicator incorpo-
native vascular plant species. • Threatened species
2005 provides detailed information on forest cover in
rates the best available and comparable information
are the number of species classified by the IUCN as
2005 and adjusted estimates of forest cover in 1990
in four relevant dimensions: represented species,
endangered and vulnerable. • GEF benefits index
and 2000. The current survey uses a uniform defini-
threatened species, represented ecoregions, and
for biodiversity is a composite index of relative bio-
tion of forest. Because of space limitations, the table
threatened ecoregions. To combine these dimensions
diversity potential based on the species represented
does not break down forest cover between natural for-
into one measure, the indicator uses dimensional
in each country and their threat status and diversity
est and plantation, a breakdown the FAO provides for
weights that reflect the consensus of conservation
of habitat types. The index has been normalized from
developing countries. Thus the deforestation data in
scientists at the GEF, IUCN, WWF International, and
0 (no biodiversity potential) to 100 (maximum bio-
the table may underestimate the rate at which natural
other nongovernmental organizations.
diversity potential). • Nationally protected areas are
forest is disappearing in some countries.
The World Conservation Monitoring Centre (WCMC)
totally or partially protected areas of at least 1,000
Measures of species richness are a straight-
compiles data on protected areas, numbers of cer-
hectares that are designated as scientific reserves
forward way to indicate an area’s importance for
tain species, and numbers of those species under
with limited public access, national parks, natural
biodiversity. The number of threatened species is
threat from various sources. Because of differences
monuments, nature reserves or wildlife sanctuaries,
also an important measure of the immediate need
in definitions, reporting practices, and reporting peri-
protected landscapes, and areas managed mainly for
for conservation in an area. Global analyses of the
ods, cross-country comparability is limited.
sustainable use. Marine areas, unclassified areas,
status of threatened species have been carried out
Nationally protected areas are defined using the
littoral (intertidal) areas, and sites protected under
for few groups of organisms. Only for mammals,
six IUCN management categories for areas of at
local or provincial law are excluded. Total area pro-
birds, and amphibians has the status of virtually all
least 1,000 hectares: scientific reserves and strict
tected is a percentage of total land area (see table
known species been assessed. Threatened species
nature reserves with limited public access; national
3.1). • Marine protected areas are areas of intertidal
are defined using the World Conservation Union’s
parks of national or international significance and not
or subtidal terrain—and overlying water and asso-
(IUCN) classifi cation: endangered (in danger of
materially affected by human activity; natural monu-
ciated flora and fauna and historical and cultural
extinction and unlikely to survive if causal factors
ments and natural landscapes with unique aspects;
features—that have been reserved to protect part
continue operating) and vulnerable (likely to move
managed nature reserves and wildlife sanctuaries;
or all of the enclosed environment.
into the endangered category in the near future if
protected landscapes (which may include cultural
causal factors continue operating).
landscapes); and areas managed mainly for the sus-
Unlike birds and mammals, it is difficult to accu-
tainable use of natural systems to ensure long-term
rately count plants. The number of plant species is
protection and maintenance of biological diversity.
Data sources
highly debated. The 2008 IUCN Red List of Threat-
The data in the table cover these six categories as
Data on forest area and deforestation are from the
ened Species, the result of more than 20 years’ work
well as terrestrial protected areas that were not
FAO’s Global Forest Resources Assessment 2005.
by botanists worldwide, is the most comprehensive
assigned to a category by the IUCN. Designating land
Data on species are from the electronic files of
list of threatened species on a global scale. Only 5
as a protected area does not mean that protection
the United Nations Environment Programme and
percent of plant species have been evaluated, and
is in force. And for small countries that only have
WCMC and 2008 IUCN Red List of Threatened Spe-
70 percent of these are threatened with extinction.
protected areas smaller than 1,000 hectares, the
cies. The GEF benefits index for biodiversity is from
Plant species data may not be comparable across
size limit in the definition leads to an underestimate
Kiran Dev Pandey, Piet Buys, Ken Chomitz, and
countries because of differences in taxonomic con-
of protected areas.
David Wheeler’s “Biodiversity Conservation Indica-
cepts and coverage and so should be used with cau-
Due to variations in consistency and methods of
tors: New Tools for Priority Setting at the Global
tion. However, the data identify countries that are
collection, data quality is highly variable across coun-
Environment Facility” (2006). Data on protected
major sources of global biodiversity and that show
tries. Some countries update their information more
areas are from the United Nations Environment
national commitments to habitat protection.
frequently than others, some have more accurate
Programme and WCMC, as compiled by the World
data on extent of coverage, and many underreport
Resources Institute.
More than information about species richness is needed to set priorities for conserving biodiversity. The
the number or extent of protected areas.
2009 World Development Indicators
149
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
150
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
2007
2007
2007b
2007b
2007b
2007b
2007b
2007b
2006
2006
23.3 1.7 6.1 0.4 29.2 3.0 23.9 2.1 12.2 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 0.6 0.2 5.6 2.5 40.0 0.1 0.0 1.6 47.1 1.0 7.8 2.0 1.5 0.2 1.0
42.3
98 62 65 60 74 66 75 1 76 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 95 5 94 3 10 42 65 65 20 66 80 80 90 82 94
0 11 13 17 9 4 10 64 19 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 13 68 10 3 13 2 5 1
2 27 22 23 17 30 15 35 4 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 5 57 6 14 16 50 23 22 12 24 16 6 8 13 5
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
.. 8,456 332 8,696 6,987 3,023 23,412 6,614 947 662 3,834 1,129 1,141 31,892 9,409 1,276 28,277 2,742 846 1,184 8,346 14,731 86,426 32,463 1,394 53,270 2,132 .. 48,014 14,423 58,937 25,189 3,988 8,499 3,386 1,272 1,099 2,153 32,385 24 2,590 578 9,475 1,543 20,232 2,893 123,291 1,758 13,224 1,301 1,291 5,182 8,181 24,093 9,441 1,354
2009 World Development Indicators
6.4 54.0
0.2 10.6 32.5 4.9 3.8 150.5 75.6 7.5 .. 1.3 0.5 .. 8.1 1.1 50.0 6.4 2.9 3.4 0.4 1.6 0.0 1.5 1.4 22.4 .. 0.5 0.0 0.0 2.4 1.2 .. 21.5 19.6 21.2 16.1 3.9 3,794.4 7.2 20.8 1.2 4.6 2.3 22.4 0.1 1.0 2.8 44.0 3.2 13.4 1.8 0.7 1.1 7.6
..
..
..
2.2 9.0 26.1 9.7 0.6 16.9 91.9 0.8 0.6 4.6 .. 18.2 5.8 .. 31.8 10.9 1.2 3.3 2.5 0.9 10.2 15.8 38.4 6.0 6.0 1.9 .. 8.8 12.0 76.1 6.0 11.2 .. .. 22.0 126.0 5.8 0.9 1.5 10.3 1.2 35.6 1.6 49.2 33.2 42.2 13.8 2.7 40.4 5.1 16.2 9.6 2.1 1.2 3.9
97 87 62 98 99 100 100 95 85 100 100 78 96 100 100 97 100 97 84 80 88 100 90 71 98 98 .. 99 82 95 99 98 100 95 100 100 97 98 99 94 74 100 96 100 100 95 91 100 100 90 100 99 91 82 70
97 81 39 80 96 100 100 59 78 99 .. 57 69 98 90 58 97 66 70 61 47 99 51 40 72 81 .. 77 29 35 96 66 98 78 100 100 91 91 98 68 57 99 31 100 100 47 81 97 100 71 99 94 59 47 51
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
ENVIRONMENT
Freshwater
3.5 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
2007
2007
2007b
2007b
2007b
2007b
2007b
2007b
2006
2006
0.9 7.6 645.8 82.8 93.3 66.0 1.1 2.0 44.4 0.4 88.4 0.9 35.0 2.7 9.0 18.6 0.9 10.1 3.0 0.3 1.3 0.1 0.1 4.3 0.3 .. 15.0 1.0 9.0 6.5 1.7 0.7 78.2 2.3 0.4 12.6 0.6 33.2 0.3 10.2 7.9 2.1 1.3 2.2 8.0 2.2 1.3 169.4 0.8 0.1 0.5 20.1 28.5 16.2 11.3 ..
0.9 127.3 51.2 2.9 72.6 187.5 2.3 260.5 24.3 4.4 20.6 138.0 46.4 13.2 13.5 28.7 .. 21.7 1.6 1.8 27.3 1.0 0.1 721.0 1.7 .. 4.4 6.3 1.6 10.9 425.0 26.4 19.1 231.0 1.3 43.4 0.6 3.8 4.9 5.1 72.2 0.6 0.7 62.3 3.6 0.6 94.4 308.0 0.6 0.0 0.5 1.2 6.0 30.2 29.6 ..
80 32 86 91 92 79 0 58 45 49 62 65 82 79 55 48 54 94 90 13 60 20 55 83 7 .. 96 80 62 90 88 68 77 33 52 87 87 98 71 96 34 42 83 95 69 11 88 96 28 1 71 82 74 8 78 ..
12 59 5 1 1 15 77 6 37 17 18 4 17 4 25 16 2 3 6 33 11 40 18 3 15 .. 2 5 21 1 3 3 5 58 27 3 2 1 5 1 60 9 2 0 10 67 1 2 5 42 8 10 9 79 12 ..
8 9 8 8 7 7 23 36 18 34 20 31 2 17 20 36 44 3 4 53 29 40 27 14 78 .. 3 15 17 9 9 30 17 10 20 10 11 1 24 3 6 48 15 4 21 23 10 2 67 56 20 8 17 13 10 ..
8.3 6.3 0.7 2.0 1.4 0.4 85.3 67.0 24.7 19.6 52.8 12.1 0.5 5.0 .. 27.5 42.9 0.1 0.6 26.1 15.7 17.1 5.1 8.0 42.3 .. 0.3 1.7 10.4 0.4 0.6 6.9 7.4 0.6 2.5 2.9 6.7 .. 11.4 0.5 48.5 24.1 3.0 0.8 5.7 76.8 16.9 0.4 14.2 49.6 14.4 2.6 2.7 10.6 10.0 ..
95 100 96 89 99 .. 100 100 100 97 100 99 99 85 100 97 .. 99 86 100 100 93 72 72 .. 100 76 96 100 86 70 100 98 96 90 100 71 80 99 94 100 100 90 91 65 100 85 95 96 88 94 92 96 100 99 ..
74 100 86 71 84 .. .. 100 .. 88 100 91 91 49 100 71 .. 83 53 96 100 74 52 68 .. 99 36 72 96 48 54 100 85 85 48 58 26 80 90 88 100 .. 63 32 30 100 73 87 81 32 52 63 88 .. 100 ..
96 6 1,261 2,838 129 35 49 1 183 9 430 1 75 21 67 65 .. 46 190 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 55 147 801 94 1,616 479 54 38 7
13,527 597 1,122 12,578 1,809 .. 11,223 104 3,074 3,514 3,365 119 4,871 552 2,817 1,338 .. 8,873 32,495 7,355 1,172 2,607 53,290 97 4,610 2,651 17,133 1,159 21,846 4,865 128 2,182 3,885 264 13,322 940 4,693 18,051 2,971 7,051 672 77,336 33,854 247 1,493 81,119 539 339 44,130 126,658 15,358 57,925 5,450 1,406 3,582 1,801
2009 World Development Indicators
151
3.5
Freshwater Renewable internal freshwater resourcesa
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
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
2007
2007
2007b
2007b
2007b
2007b
2007b
2007b
2006
2006
99 100 82 97 93 99d 83 100 100 .. 63 100 100 98 78 87 100 100 95 93 81 99 86 97 99 98 .. 90 97 100 100 100 100 98 .. 98 90 68 90 98 96 w 84 97 96 98 94 96 99 97 95 94 81 100 100
76 88 61 .. 65 .. 32 .. 100 .. 10 82 100 79 64 51 100 100 83 58 46 97 40 93 84 95 .. 60 97 100 100 94 100 82 .. 90 88 65 41 72 77 w 60 83 82 83 76 81 88 73 81 84 46 98 100
42 4,313 10 2 26 44c 160 1 13 19 6 45 111 50 30 3 171 40 7 66 84 210 12 4 4 227 1 39 53 0 145 2,800 59 16 722 367 .. 2 80 12 43,464 s 5,985 27,963 14,116 13,847 33,947 9,454 5,167 13,425 225 1,819 3,858 9,516 930
1,963 30,350 976 99 2,079 5,419c 27,358 131 2,334 9,251 690 936 2,478 2,499 778 2,306 18,692 5,351 352 9,837 2,078 3,290 1,748 2,881 410 3,072 274 1,261 1,142 34 2,377 9,283 17,750 608 26,287 4,304 .. 94 6,728 915 6,624 w 4,619 6,589 4,117 16,993 6,128 4,945 11,806 23,965 728 1,196 4,823 9,313 2,907
23.2 76.7 0.2 23.7 2.2 .. 0.4 .. .. .. 3.3 12.5 35.6 12.6 37.3 1.0 3.0 2.6 16.7 12.0 5.2 87.1 0.2 0.3 2.6 40.1 24.7 .. 37.5 4.0 9.5 479.3 3.2 58.3 8.4 71.4 .. 3.4 1.7 4.2 3,850.0 s 554.6 2,374.8 2,929.5 1,937.8 437.0 959.0 368.4 264.9 275.6 941.1 120.5 920.5 200.0
54.8 1.8 1.6 986.1 8.6 .. 0.2 .. .. .. 55.0 27.9 32.0 25.2 124.4 39.5 1.7 6.4 238.4 18.0 6.2 41.5 1.5 8.1 62.9 17.7 1,812.5 .. 70.7 2,665.3 6.6 17.1 5.3 357.0 1.2 19.5 .. 161.9 2.2 34.3 9.0 w 9.4 8.5 8.7 13.8 3.2 10.2 7.2 2.0 122.3 51.7 3.2 10.4 22.3
57 18 68 88 93 .. 92 .. .. .. 99 63 68 95 97 97 9 2 88 92 89 95 45 6 82 74 98 .. 52 83 3 41 96 93 47 68 .. 90 76 79 70 w 90 76 78 80 57 74 60 71 86 89 87 43 38
34 63 8 3 3 .. 3 .. .. .. 0 6 19 2 1 1 54 74 4 5 0 2 2 26 4 11 1 .. 35 2 75 46 1 2 7 24 .. 2 7 7 20 w 5 15 13 12 26 20 30 10 6 4 3 42 48
9 19 24 9 4 .. 5 .. .. .. 0 31 13 2 3 2 37 24 9 4 10 2 53 68 14 15 2 .. 12 15 22 13 3 5 46 8 .. 8 17 14 10 w 5 9 8 8 17 7 10 19 8 6 10 15 15
1.6 3.4 11.6 9.9 2.2 .. 1.7 .. .. .. .. 10.6 16.3 1.3 0.3 1.4 83.0 97.2 1.3 0.1 2.0 1.4 8.2 26.3 7.4 7.0 0.1 .. 0.8 24.5 152.1 20.4 6.6 0.2 14.0 0.4 .. 2.8 1.9 1.6 10.3 w 1.0 3.7 3.2 2.5 8.8 3.4 3.7 9.9 2.7 1.1 4.1 31.6 33.7
a. Excludes river flows from other countries because of data unreliability. b. Refers to data reported to the Food and Agriculture Organization as of 2007. See Primary data documentation for year of most recent water withdrawals survey. c. Includes Montenegro. d. Includes Kosovo.
152
2009 World Development Indicators
About the data
ENVIRONMENT
Freshwater
3.5
Definitions
The data on freshwater resources are based on
Caution should also be used in comparing data
• Renewable internal freshwater resources flows
estimates of runoff into rivers and recharge of
on annual freshwater withdrawals, which are subject
are internal renewable resources (internal river
groundwater. These estimates are based on differ-
to variations in collection and estimation methods.
flows and groundwater from rainfall) in the country.
ent sources and refer to different years, so cross-
In addition, inflows and outflows are estimated at
• Renewable internal freshwater resources per cap-
country comparisons should be made with caution.
different times and at different levels of quality and
ita are calculated using the World Bank’s population
Because the data are collected intermittently, they
precision, requiring caution in interpreting the data,
estimates (see table 2.1). • Annual freshwater with-
may hide signifi cant variations in total renewable
particularly for water-short countries, notably in the
drawals are total water withdrawals, not counting
water resources from year to year. The data also
Middle East and North Africa.
evaporation losses from storage basins. Withdraw-
fail to distinguish between seasonal and geographic
Water productivity is an indication only of the
als also include water from desalination plants in
variations in water availability within countries. Data
effi ciency by which each country uses its water
countries where they are a significant source. With-
for small countries and countries in arid and semiarid
resources. Given the different economic structure
drawals can exceed 100 percent of total renewable
zones are less reliable than those for larger countries
of each country, these indicators should be used
resources where extraction from nonrenewable aqui-
and countries with greater rainfall.
carefully, taking into account the countries’ sectoral
fers or desalination plants is considerable or where
activities and natural resource endowments.
water reuse is significant. Withdrawals for agriculture
Agriculture is still the largest user of water, accounting for some 70 percent of global withdrawals Percent 100
Industry
Domestic
3.5a
Agriculture
80
60
40
20
0 Low income
Lower middle income
Upper middle income
High income
World
The data on access to an improved water source
and industry are total withdrawals for irrigation and
measure the percentage of the population with ready
livestock production and for direct industrial use
access to water for domestic purposes. The data
(including for cooling thermoelectric plants). With-
are based on surveys and estimates provided by
drawals for domestic uses include drinking water,
governments to the Joint Monitoring Programme of
municipal use or supply, and use for public services,
the World Health Organization (WHO) and the United
commercial establishments, and homes. • Water
Nations Children’s Fund (UNICEF). The coverage
productivity is calculated as GDP in constant prices
rates are based on information from service users
divided by annual total water withdrawal. • Access
on actual household use rather than on information
to an improved water source is the percentage of the
from service providers, which may include nonfunc-
population with reasonable access to an adequate
tioning systems. Access to drinking water from an
amount of water from an improved source, such as
improved source does not ensure that the water is
piped water into a dwelling, plot, or yard; public tap
safe or adequate, as these characteristics are not
or standpipe; tubewell or borehole; protected dug
tested at the time of survey. While information on
well or spring; and rainwater collection. Unimproved
access to an improved water source is widely used,
sources include unprotected dug wells or springs,
it is extremely subjective, and such terms as safe,
carts with small tank or drum, bottled water, and
improved, adequate, and reasonable may have dif-
tanker trucks. Reasonable access is defined as the
ferent meaning in different countries despite offi -
availability of at least 20 liters a person a day from
cial WHO definitions (see Definitions). Even in high-
a source within 1 kilometer of the dwelling.
Source: Table 3.5.
The share of withdrawals for agriculture approaches 90 percent in some developing regions Percent 100
Industry
Domestic
income countries treated water may not always be safe to drink. Access to an improved water source is
3.5b
Agriculture
equated with connection to a supply system; it does not take into account variations in the quality and cost (broadly defined) of the service.
80
60
Data sources Data on freshwater resources and withdrawals
40
are from the Food and Agriculture Organization of the United Nations AQUASTAT data. The GDP
20
estimates used to calculate water productivity are from the World Bank national accounts data-
0 East Asia & Pacific
Europe Latin & America Central & Asia Caribbean
Source: Table 3.5.
Middle East & North Africa
South SubAsia Saharan Africa
base. Data on access to water are from WHO and UNICEF’s Progress on Drinking Water and Sanitation (2008).
2009 World Development Indicators
153
3.6
Water pollution Emissions of organic water pollutants
thousand kilograms per day 1990 2005a
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
5.9 2.4 107.0 4.5 181.4 37.9 186.1 90.5 41.3 250.8 .. 107.8 .. 11.3 50.7 2.5 780.4 124.3 .. 1.6 3.6 14.0 300.9 1.0 .. .. 7,038.1 86.1 .. .. 2.5 27.2 7.9 48.5 173.0 176.8 84.3 88.6 28.6 206.5 5.5 2.4 21.7 18.5 72.0 326.5 2.0 0.8 .. 806.6 .. 50.9 21.6 .. .. 0.1
0.2 2.5 .. .. 155.5 7.1 111.7 85.2 18.1 303.0 .. 99.6 .. 11.5 .. 3.4 .. 100.6 .. .. .. 10.0 310.3 .. .. 96.5 6,088.7 34.3 87.0 .. .. 31.2 .. 41.2 .. 152.4 62.0 88.6 44.7 206.5 .. 2.9 16.5 24.1 59.2 604.7 .. .. .. 960.3 15.4 46.5 .. .. .. 0.0
2009 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
2005a
2005a
2005a
2005a
2005a
2005a
2005a
2005a
2005a
0.16 0.25 0.25 0.19 0.21 0.11 0.18 0.15 0.15 0.15 .. 0.17 .. 0.24 0.14 0.30 0.19 0.17 .. 0.24 0.21 0.28 0.17 0.18 .. .. 0.14 0.12 .. .. 0.32 0.20 0.22 0.17 0.25 0.15 0.18 0.18 0.24 0.19 0.22 0.19 0.15 0.23 0.19 0.11 0.25 0.34 .. 0.13 .. 0.19 0.23 .. .. 0.01
0.21 0.23 .. .. 0.23 0.28 0.18 0.14 0.18 0.14 .. 0.17 .. 0.25 .. 0.34 .. 0.17 .. .. .. 0.19 0.16 .. .. 0.25 0.14 0.20 0.20 .. .. 0.22 .. 0.17 .. 0.13 0.17 0.18 0.28 0.19 .. 0.21 0.15 0.22 0.15 0.16 .. .. .. 0.14 0.17 0.20 .. .. .. 0.01
.. 0.0 .. .. 3.8 .. 12.4 5.5 9.6 0.7 .. 6.2 .. 0.9 .. 0.0 .. 3.6 .. .. .. 0.4 4.4 .. .. 7.1 20.4 1.2 2.3 .. .. 1.6 .. 3.3 .. 5.4 1.0 0.1 1.8 5.8 .. 0.3 0.3 1.6 1.6 3.2 .. .. .. 3.8 3.1 4.5 .. .. .. 0.0
19.7 0.0 .. .. 8.4 .. 22.8 7.2 2.4 2.3 .. 7.7 .. 9.8 .. 2.9 .. 4.2 .. .. .. 5.2 9.1 .. .. 6.4 10.9 43.5 8.9 .. .. 10.0 .. 7.2 .. 4.6 12.3 1.3 7.8 4.0 .. 4.1 7.3 6.9 17.0 7.4 .. .. .. 7.1 2.8 7.8 .. .. .. 2.0
27.9 0.0 .. .. 15.8 .. 6.7 9.2 19.3 3.0 .. 17.5 .. 13.1 .. 0.0 .. 7.1 .. .. .. 36.1 10.6 .. .. 13.5 14.8 3.9 17.3 .. .. 8.2 .. 9.5 .. 9.9 13.8 2.3 12.8 13.9 .. 8.6 7.8 10.7 8.5 16.1 .. .. .. 12.1 15.0 13.2 .. .. .. 0.0
14.1 33.4 .. .. 30.5 77.6 43.5 12.4 18.1 7.6 .. 15.7 .. 35.4 .. 70.5 .. 17.6 .. .. .. 48.8 13.9 .. .. 35.5 28.1 30.5 21.3 .. .. 65.7 .. 17.9 .. 11.4 17.6 18.6 46.4 20.0 .. 31.8 15.8 29.7 9.4 16.1 .. .. .. 11.7 19.2 22.8 .. .. .. 0.0
11.7 0.0 .. .. 3.5 .. 0.2 5.2 6.5 2.6 .. 5.4 .. 7.7 .. 0.0 .. 4.3 .. .. .. 0.0 2.8 .. .. 3.6 0.5 0.1 5.3 .. .. 0.1 .. 5.9 .. 4.9 4.2 1.4 4.4 8.2 .. 14.8 4.7 8.3 4.1 3.7 .. .. .. 3.4 4.2 6.9 .. .. .. 0.0
23.3 66.6 .. .. 14.3 22.4 5.3 4.8 13.7 79.3 .. 7.4 .. 18.4 .. 5.6 .. 31.4 .. .. .. 3.8 7.9 .. .. 9.3 15.5 16.2 24.1 .. .. 10.2 .. 16.2 .. 8.3 2.4 73.1 12.3 31.1 .. 24.1 10.9 28.6 3.1 5.4 .. .. .. 2.6 10.0 20.0 .. .. .. 0.0
.. 0.0 .. .. 2.1 .. 2.8 5.9 1.2 0.5 .. 2.2 .. 5.3 .. 0.0 .. 3.0 .. .. .. 5.0 6.7 .. .. 6.7 0.9 0.2 0.9 .. .. 1.3 .. 4.8 .. 4.0 4.0 0.1 2.2 0.6 .. 0.0 16.9 1.4 7.1 2.3 .. .. .. 2.1 34.3 2.0 .. .. .. 0.0
3.1 0.0 .. .. 21.6 .. 6.3 49.7 29.3 4.2 .. 37.9 .. 9.5 .. 21.1 .. 28.7 .. .. .. 0.8 44.6 .. .. 18.0 8.8 4.6 19.9 .. .. 2.9 .. 35.1 .. 51.6 44.8 3.1 12.3 16.4 .. 16.4 36.4 12.8 49.3 45.8 .. .. .. 57.2 11.4 22.9 .. .. .. 98.0
Emissions of organic water pollutants
thousand kilograms per day 1990 2005a
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 7.0 1,410.6 721.8 131.6 7.7 36.0 43.9 378.3 18.7 1,451.4 15.0 1.3 42.6 .. 366.9 9.1 28.9 0.5 39.8 14.7 .. 0.6 .. 54.0 32.4 11.0 37.2 104.7 .. .. 0.3 370.8 29.2 10.2 .. 20.4 7.7 7.4 26.4 137.0 46.7 10.5 .. 70.8 51.8 3.8 104.1 10.3 5.7 15.3 56.1 118.4 446.7 140.6 19.0
.. 123.2 1,519.8 731.0 163.2 7.7 33.6 42.8 481.3 .. 1,133.1 27.1 1.7 56.1 .. 317.0 11.9 11.5 0.5 29.9 14.7 13.2 .. .. 42.9 .. 88.9 32.7 187.6 .. .. 0.4 .. 22.4 .. 72.8 10.2 6.2 .. 26.8 119.2 55.6 .. 0.4 .. 49.2 6.5 .. 12.9 .. 10.8 .. 98.5 364.2 107.2 9.2
ENVIRONMENT
Water pollution
3.6
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
2005a
2005a
2005a
2005a
2005a
2005a
2005a
2005a
2005a
0.23 0.36 0.20 0.18 0.16 0.27 0.19 0.18 0.13 0.29 0.14 0.18 0.40 0.23 .. 0.12 0.16 0.14 0.44 0.12 0.19 .. 0.30 .. 0.15 0.18 .. 0.40 .. .. .. 0.05 0.19 0.44 0.18 .. 0.27 0.17 0.35 0.14 0.20 0.24 0.27 .. 0.22 0.20 0.15 0.18 0.30 0.25 0.20 0.20 0.26 0.16 0.14 0.15
.. 0.15 0.20 0.18 0.15 0.27 0.20 0.18 0.12 .. 0.15 0.18 0.41 0.24 .. 0.12 0.17 0.19 0.44 0.18 0.19 0.13 .. .. 0.17 .. 0.14 0.39 0.12 .. .. 0.06 0.20 0.45 .. 0.16 0.31 0.18 .. 0.16 0.18 0.22 .. 0.32 .. 0.20 0.18 .. 0.31 .. 0.28 .. 0.24 0.16 0.15 0.18
.. 2.5 12.2 1.3 7.0 13.1 1.2 2.2 3.5 .. 3.1 2.7 0.0 .. .. 4.3 2.1 7.1 0.0 2.4 0.5 1.0 .. .. 0.8 .. 0.3 .. 2.8 .. .. 0.0 7.8 0.0 .. 0.9 1.1 56.5 .. 1.6 1.3 2.2 .. .. .. 3.9 3.9 .. 1.6 .. 3.1 .. 6.7 3.1 2.3 1.9
.. 6.5 7.6 3.8 2.9 25.6 11.9 8.5 5.3 .. 7.2 6.4 50.0 11.5 .. 5.5 16.6 6.2 26.3 7.3 7.5 0.5 .. .. 4.9 .. 1.6 1.4 4.7 .. .. 13.7 12.5 3.4 .. 3.0 7.1 4.6 .. 3.9 13.1 10.4 .. 17.0 .. 14.6 6.2 .. 10.2 .. 9.3 .. 6.9 5.1 7.0 14.9
.. 10.3 9.2 13.1 12.5 29.9 12.1 15.0 10.4 .. 11.2 15.0 0.0 5.4 .. 12.3 11.1 8.3 0.0 5.2 6.0 1.4 .. .. 7.1 .. 12.4 3.7 15.1 .. .. 0.0 10.4 0.0 .. 9.3 2.7 13.2 .. 7.2 15.6 8.4 .. 4.4 .. 7.6 16.1 .. 8.2 .. 16.7 .. 15.2 10.7 6.2 21.9
.. 16.1 53.7 21.7 16.1 16.9 24.5 19.7 8.7 .. 15.2 21.9 47.6 66.8 .. 6.5 50.2 23.6 73.7 21.7 25.5 3.4 .. .. 20.3 .. 7.6 82.1 9.2 .. .. 0.0 55.6 95.6 .. 16.0 81.2 14.9 .. 19.2 18.8 28.9 .. 76.9 .. 21.0 22.1 .. 53.8 .. 42.6 .. 34.7 19.0 14.5 34.4
.. 3.7 0.3 3.5 14.0 5.4 2.7 0.0 5.4 .. 3.7 11.3 0.0 0.1 .. 3.1 0.4 15.2 0.0 3.6 12.9 0.5 .. .. 4.2 .. 2.8 0.6 3.7 .. .. .. 0.2 0.0 .. 7.1 0.1 0.4 .. 29.9 3.7 3.0 .. 0.3 .. 3.8 23.4 .. 5.6 .. 5.9 .. 7.3 5.6 8.6 0.2
.. 12.1 12.7 30.5 11.8 9.1 2.1 9.1 15.0 .. 5.6 16.1 0.0 12.8 .. 10.2 11.6 12.0 0.0 13.5 16.7 90.8 .. .. 21.3 .. 58.9 7.5 8.1 .. .. 0.0 7.5 0.0 .. 45.5 5.8 2.9 .. 29.4 2.9 7.2 .. .. .. 2.1 6.2 .. 7.5 .. 11.0 .. 3.4 11.8 25.3 15.5
.. 3.5 0.3 8.0 0.7 .. 3.7 1.5 2.9 .. 2.0 2.7 0.0 1.7 .. 0.9 2.8 1.8 0.0 20.2 4.5 .. .. .. 11.1 .. 6.3 1.1 7.6 .. .. 0.0 0.9 .. .. 1.9 1.4 1.7 .. 2.0 2.6 8.3 .. 0.8 .. 5.7 2.2 .. 1.7 .. 4.5 .. 0.0 5.2 3.9 1.4
.. 45.3 3.9 18.1 34.9 .. 41.8 43.9 48.9 .. 52.0 23.9 2.4 1.8 .. 57.2 5.2 25.9 0.0 26.1 26.3 2.4 .. .. 30.3 .. 10.0 3.6 48.9 .. .. 86.3 5.1 1.0 .. 16.3 0.7 5.8 .. 6.8 42.1 31.7 .. 0.6 .. 41.2 19.8 .. 11.4 .. 6.9 .. 25.9 39.6 32.2 9.7
2009 World Development Indicators
155
3.6
Water pollution Emissions of organic water pollutants
thousand kilograms per day 1990 2005a
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe
407.0 1,521.4 7.1 18.5 6.1 .. 4.2 32.4 72.8 55.6 6.2 260.5 348.0 53.0 .. 146.0 116.8 .. 6.6 29.1 31.1 369.4 .. .. 7.0 44.6 174.9 .. 2.7 .. 5.6 599.9 2,307.0 38.7 .. 96.5 141.0 .. 1.5 15.9 29.3
235.1 1,425.9 7.1 .. 6.6 .. .. 34.3 54.6 38.4 .. 183.8 372.5 78.4 38.6 .. 100.1 .. 4.5 16.1 35.2 333.8 .. .. 7.6 55.8 177.7 .. 2.1 527.2 .. 539.7 1,960.3 15.8 .. .. 470.2 .. 1.0 .. 29.3
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
2005a
2005a
2005a
2005a
2005a
2005a
2005a
2005a
2005a
0.12 0.16 0.44 0.15 0.30 .. 0.32 0.09 0.13 0.16 0.38 0.17 0.16 0.19 .. 0.16 0.15 .. 0.45 0.17 0.24 0.15 .. .. 0.23 0.18 0.18 .. 0.27 .. 0.14 0.16 0.14 0.23 .. 0.21 0.16 .. 0.43 0.23 0.20
0.15 0.17 0.44 .. 0.29 .. .. 0.10 0.14 0.16 .. 0.17 0.15 0.18 0.29 .. 0.15 .. 0.45 0.23 0.25 0.16 .. .. 0.29 0.14 0.16 .. 0.23 0.19 .. 0.17 0.14 0.28 .. .. 0.15 .. 0.41 .. 0.20
4.8 9.8 .. .. 4.9 .. .. 1.4 7.3 33.7 .. 6.7 3.1 0.5 0.6 .. 5.3 .. 0.0 21.9 1.5 1.8 .. .. 0.0 2.5 5.2 .. .. 14.6 .. 2.5 3.4 1.2 .. .. 1.4 .. .. .. 8.0
3.2 4.8 .. .. 6.3 .. .. 24.6 4.8 14.7 .. 7.3 7.8 7.2 1.9 .. 12.4 .. 6.2 1.4 9.4 4.1 .. .. 18.1 6.1 3.0 .. 7.8 4.1 .. 12.4 9.0 3.7 .. .. 3.7 .. 79.9 .. 4.7
6.7 11.7 0.0 .. 23.8 .. .. 16.0 8.0 8.3 .. 11.7 10.6 6.6 7.0 .. 9.7 .. 0.0 5.1 2.7 13.2 .. .. 21.4 5.5 9.8 .. 7.3 10.3 .. 13.6 13.0 6.6 .. .. 6.6 .. 0.0 .. 11.0
12.7 17.5 97.0 .. 44.6 .. .. 25.4 10.5 23.7 .. 16.5 14.9 51.5 57.5 .. 9.0 .. 93.8 20.2 69.3 16.5 .. .. 39.1 35.8 15.2 .. 34.8 19.0 .. 14.4 11.8 79.2 .. .. 14.3 .. 20.1 .. 21.5
4.0 7.9 0.0 .. 3.9 .. .. 0.1 5.7 0.2 .. 5.0 7.6 0.2 14.2 .. 2.5 .. 0.0 7.6 0.1 3.4 .. .. 0.4 0.4 6.2 .. 13.3 6.4 .. 3.8 3.6 0.1 .. .. 7.1 .. 0.0 .. 6.3
28.9 6.8 0.0 .. 10.5 .. .. 3.9 14.3 10.8 .. 7.0 9.6 31.6 8.0 .. 1.4 .. 0.0 37.5 14.0 22.5 .. .. 7.6 43.3 35.7 .. 17.2 6.6 .. 4.6 5.0 7.4 .. .. 40.3 .. 0.0 .. 25.2
5.2 4.3 0.0 .. 0.8 .. .. 1.6 3.2 2.0 .. 4.6 3.8 1.1 1.7 .. 5.4 .. 0.0 0.4 1.5 2.4 .. .. 8.5 1.9 1.0 .. 0.0 2.2 .. 2.4 4.0 0.6 .. .. 3.7 .. 0.0 .. 1.7
34.4 37.3 3.0 .. 5.3 .. .. 26.9 46.3 6.7 .. 41.3 42.6 1.2 9.1 .. 54.3 .. 0.0 5.9 1.4 36.1 .. .. 4.9 4.6 24.0 .. 19.6 36.8 .. 46.2 50.3 1.2 .. .. 22.9 .. 0.0 .. 21.5
a. Data are derived using the United Nations Industrial Development Organization’s (UNIDO) industry database four-digit International Standard Industrial Classification (ISIC). Data in italics are for the most recent year available and are derived using UNIDO’s industry database at the three-digit ISIC.
156
2009 World Development Indicators
About the data
ENVIRONMENT
Water pollution
3.6
Definitions
Emissions of organic pollutants from industrial
emissions of organic water pollutants. Such data
• Emissions of organic water pollutants are mea-
activities are a major cause of degradation of water
are fairly reliable because sampling techniques for
sured as biochemical oxygen demand, or the amount
quality. Water quality and pollution levels are gener-
measuring water pollution are more widely under-
of oxygen that bacteria in water will consume in
ally measured as concentration or load—the rate of
stood and much less expensive than those for air
breaking down waste, a standard water treatment
occurrence of a substance in an aqueous solution.
pollution.
test for the presence of organic pollutants. Emis-
Polluting substances include organic matter, metals,
Hettige, Mani, and Wheeler (1998) used plant- and
sions per worker are total emissions divided by the
minerals, sediment, bacteria, and toxic chemicals.
sector-level information on emissions and employ-
number of industrial workers. • Industry shares of
The table focuses on organic water pollution result-
ment from 13 national environmental protection
emissions of organic water pollutants are emissions
ing from industrial activities. Because water pollu-
agencies and sector-level information on output
from manufacturing activities as defined by two-digit
tion tends to be sensitive to local conditions, the
and employment from the United Nations Industrial
divisions of the International Standard Industrial
national-level data in the table may not reflect the
Development Organization (UNIDO). Their economet-
Classification (ISIC) revision 3.
quality of water in specific locations.
ric analysis found that the ratio of BOD to employ-
The data in the table come from an international
ment in each industrial sector is about the same
study of industrial emissions that may have been
across countries. This finding allowed the authors to
the first to include data from developing countries
estimate BOD loads across countries and over time.
(Hettige, Mani, and Wheeler 1998). These data were
The estimated BOD intensities per unit of employ-
updated through 2005 by the World Bank’s Develop-
ment were multiplied by sectoral employment num-
ment Research Group. Unlike estimates from earlier
bers from UNIDO’s industry database for 1980–98.
studies based on engineering or economic models,
These estimates of sectoral emissions were then
these estimates are based on actual measurements
used to calculate kilograms of emissions of organic
of plant-level water pollution. The focus is on organic
water pollutants per day for each country and year.
water pollution caused by organic waste, measured in
The data in the table were derived by updating these
terms of biochemical oxygen demand (BOD), because
estimates through 2005.
the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water’s dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD. Data on water pollution are more readily available than are other emissions data because most industrial pollution control programs start by regulating
Emissions of organic water pollutants declined in most economies from 1990 to 2005, even in some of the top emitters Kilograms per day (millions)
1990–98
3.6a
2000–05
8
6
Data sources Data on water pollutants are from the 1998 study
4
by Hemamala Hettige, Muthukumara Mani, and David Wheeler, “Industrial Pollution in Economic 2
Development: Kuznets Revisited” (available at www.worldbank.org/nipr). The data were updated 0
through 2005 by the World Bank’s Development China
United States
Russian Federation
Japan
India
Germany
Indonesia
Research Group using the same methodology as
Note: Data are for the most recent year available during the period specified.
the initial study. Data on industrial sectoral employ-
Source: Table 3.6.
ment are from UNIDO’s industry database.
2009 World Development Indicators
157
3.7
Energy production and use Energy production
Total million metric tons of oil equivalent
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
1990
2006
.. 2.4 104.4 28.7 48.4 0.1 157.5 8.1 21.3 10.8 3.3 13.6 1.8 4.9 4.6 0.9 103.7 9.6 .. .. .. 11.0 273.7 .. .. 7.6 886.3 0.0 48.2 12.0 8.7 1.0 3.4 5.1 6.6 40.1 10.1 1.0 16.5 54.9 1.7 0.7 5.1 14.1 12.1 112.4 14.6 .. 1.8 186.2 4.4 9.2 3.4 .. .. 1.3
.. 1.2 173.2 79.2 83.9 0.8 267.8 9.9 38.1 20.3 3.9 15.5 1.7 14.3 4.0 1.1 206.7 11.1 .. .. 3.6 10.3 411.7 .. .. 10.0 1,749.3 0.0 84.6 17.8 15.4 2.3 9.3 4.1 5.0 33.4 29.6 1.5 29.8 77.8 2.6 0.5 3.6 20.4 18.0 137.0 12.1 .. 1.2 136.8 6.5 10.0 5.4 .. .. 2.0
2009 World Development Indicators
Energy use
Total million metric tons of oil equivalent 1990
.. 2.7 23.9 6.3 46.1 7.9 87.7 25.1 26.1 12.8 42.3 49.7 1.7 2.8 7.0 1.3 140.0 28.8 .. .. .. 5.0 209.5 .. .. 14.1 863.2 10.7 24.7 11.9 0.8 2.0 4.4 9.1 16.8 49.0 17.9 4.1 6.1 32.0 2.5 0.9 9.6 15.0 28.7 227.6 1.2 .. 12.3 355.6 5.3 22.2 4.5 .. .. 1.6
2006
.. 2.3 36.7 10.3 69.1 2.6 122.5 34.2 14.1 25.0 28.6 61.0 2.8 5.8 5.4 2.0 224.1 20.7 .. .. 5.0 7.1 269.7 .. .. 29.8 1,878.7 18.2 30.2 17.5 1.2 4.6 7.3 9.0 10.6 46.1 20.9 7.8 11.2 62.5 4.7 0.7 4.9 22.3 37.4 272.7 1.8 .. 3.3 348.6 9.5 31.1 8.2 .. .. 2.6
Clean energy production
% of total average annual % growth 1990–2006
.. 2.2 2.5 3.2 2.0 –4.8 2.2 2.0 –3.3 4.5 –2.3 1.4 3.0 4.2 1.7 2.6 3.0 –1.5 .. .. 3.6 2.3 1.7 .. .. 5.0 4.4 3.1 0.5 2.4 2.6 5.0 3.4 1.3 –1.6 0.1 0.4 4.4 3.8 4.6 3.8 –2.2 –2.8 2.6 1.8 1.2 2.2 .. –8.2 0.0 3.6 2.5 4.1 .. .. 3.3
Per capita kilograms of oil equivalent
Fossil fuel
Combustible renewables and waste
% of total energy use
1990
2006
1990
2006
1990
2006
1990
2006
.. 809 946 597 1,414 2,228 5,138 3,249 3,643 113 4,153 4,988 324 416 1,633 931 936 3,305 .. .. .. 411 7,539 .. .. 1,067 760 1,872 747 314 329 658 345 1,905 1,587 4,726 3,486 567 597 580 496 277 6,122 313 5,758 4,012 1,354 .. 2,255 4,477 343 2,188 503 .. .. 223
.. 715 1,100 620 1,766 859 5,917 4,132 1,659 161 2,939 5,782 321 625 1,427 1,054 1,184 2,688 .. .. 351 390 8,262 .. .. 1,812 1,433 2,653 695 289 327 1,040 385 2,017 944 4,485 3,850 816 851 843 697 150 3,638 289 7,108 4,444 1,391 .. 754 4,231 413 2,792 628 .. .. 272
.. 76.5 99.9 30.2 88.7 97.3 94.0 79.4 .. 45.9 95.5 75.6 5.8 69.1 93.9 66.3 51.2 84.4 .. .. .. 19.5 74.7 .. .. 74.8 75.5 99.5 68.1 12.0 35.0 48.3 24.9 86.7 65.1 93.2 89.9 75.1 79.5 94.0 32.0 19.7 .. 6.6 56.0 58.6 35.4 .. 88.7 87.0 19.0 94.8 28.8 .. .. 20.9
.. 68.5 99.7 33.9 88.4 68.3 94.7 75.6 97.9 65.8 91.9 72.5 37.1 83.1 90.6 69.2 53.7 72.8 .. .. 28.4 16.3 75.0 .. .. 73.5 85.1 96.7 73.4 4.6 36.8 48.6 35.7 84.7 88.0 83.0 87.3 80.4 88.2 95.9 44.0 27.0 88.7 8.8 52.2 52.6 39.2 .. 65.1 81.8 31.7 93.1 44.5 .. .. 23.3
.. 13.6 0.1 68.8 3.7 0.1 4.5 9.9 0.0 53.5 0.5 2.6 93.2 27.2 2.3 33.1 34.0 0.6 .. .. .. 75.9 3.9 .. .. 19.0 23.2 0.5 22.3 84.0 59.5 36.6 72.0 3.4 34.9 0.0 6.4 24.2 13.5 3.3 48.2 80.3 2.0 92.8 15.9 5.1 59.8 .. 3.7 1.3 73.1 4.0 67.9 .. .. 76.5
.. 10.1 0.2 63.9 3.7 0.0 4.1 13.1 0.0 33.7 5.0 5.9 61.1 13.8 3.4 23.2 29.6 3.9 .. .. 71.3 79.2 4.7 .. .. 15.9 12.0 0.3 14.9 92.4 57.6 15.5 63.8 4.1 11.9 4.0 12.9 18.0 5.2 2.3 31.6 73.0 10.7 90.0 20.4 4.4 56.3 .. 19.3 4.6 63.3 3.3 51.5 .. .. 75.8
.. 9.2 0.1 1.0 7.5 1.7 1.5 10.9 0.2 0.6 0.0 22.4 0.0 3.6 3.7 0.1 13.1 13.8 .. .. .. 4.5 21.4 .. .. 6.2 1.3 0.0 9.6 4.1 5.3 14.4 2.6 3.6 0.0 6.9 0.3 0.7 7.0 2.7 19.8 0.0 0.0 0.6 20.7 38.1 4.9 .. 5.3 11.6 9.2 0.9 3.4 .. .. 2.5
.. 19.0 0.1 2.2 7.6 32.7 1.3 9.6 1.5 0.5 0.0 20.0 0.0 3.2 9.4 0.0 15.0 26.5 .. .. 0.1 4.5 20.9 .. .. 9.9 3.0 0.0 12.2 3.9 2.7 35.8 1.8 5.8 0.1 15.3 2.6 1.5 5.5 1.9 24.4 0.0 0.2 1.3 18.6 44.9 4.4 .. 14.0 13.9 5.1 2.5 4.0 .. .. 0.9
Energy production
Total million metric tons of oil equivalent 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
1.7 14.3 291.1 170.0 179.8 104.9 3.5 0.4 25.3 0.5 75.2 0.2 90.5 9.0 28.9 22.6 50.4 2.5 .. 1.1 0.1 .. .. 73.2 4.9 1.5 .. .. 50.3 .. .. .. 193.4 0.1 2.7 0.8 5.6 10.7 0.2 5.5 60.5 12.0 1.5 .. 150.5 119.1 38.3 34.4 0.6 .. 4.6 10.6 13.7 99.4 3.4 ..
Energy use
Total million metric tons of oil equivalent
2006
1990
2006
2.0 10.3 435.6 307.7 309.3 101.1 1.6 2.7 27.4 0.5 101.1 0.3 131.0 14.3 22.2 43.7 150.6 1.5 .. 1.8 0.2 .. .. 102.0 3.5 1.5 .. .. 97.9 .. .. .. 256.0 0.1 3.0 0.7 10.7 22.1 0.3 8.3 60.8 13.1 2.1 .. 235.3 222.9 60.6 61.3 0.8 .. 6.7 11.5 24.7 77.9 4.3 ..
2.4 28.6 319.9 102.8 68.8 19.1 10.3 12.1 148.1 2.9 443.9 3.5 73.6 11.2 33.2 93.4 8.0 7.6 .. 7.9 2.3 .. .. 11.5 16.2 2.7 .. .. 23.3 .. .. .. 123.0 9.9 3.4 7.2 6.0 10.7 0.7 5.8 67.1 13.8 2.1 .. 70.9 21.4 4.6 43.4 1.5 .. 3.1 10.0 26.2 99.9 17.2 ..
4.3 27.6 565.8 179.1 170.9 32.0 15.5 21.3 184.2 4.6 527.6 7.2 61.4 17.9 21.7 216.5 25.3 2.8 .. 4.6 4.8 .. .. 17.8 8.5 2.8 .. .. 68.3 .. .. .. 177.4 3.4 2.8 14.0 8.8 14.3 1.5 9.4 80.1 17.5 3.5 .. 105.1 26.1 15.4 79.3 2.8 .. 4.0 13.6 43.0 97.7 25.4 ..
ENVIRONMENT
3.7
Energy production and use
Clean energy production
% of total average annual % growth 1990–2006
3.2 0.1 3.5 3.4 5.5 3.2 3.2 3.6 1.6 2.5 1.1 4.1 –2.3 2.9 –2.4 5.3 8.0 –4.9 .. –3.0 4.6 .. .. 2.3 –2.8 –0.3 .. .. 6.1 .. .. .. 2.2 –6.1 –1.9 3.9 2.7 1.9 5.1 3.2 1.2 1.6 3.1 .. 2.4 1.9 7.1 3.7 3.6 .. 1.5 2.1 3.6 –0.6 3.1 ..
Per capita kilograms of oil equivalent
Fossil fuel
Combustible renewables and waste
% of total energy use
1990
2006
1990
2006
1990
2006
1990
2006
494 2,753 377 577 1,265 1,029 2,943 2,599 2,611 1,233 3,593 1,103 4,505 479 1,649 2,178 3,762 1,713 .. 2,941 777 .. .. 2,645 4,394 1,423 .. .. 1,288 .. .. .. 1,478 2,265 1,624 298 441 266 443 304 4,489 3,991 512 .. 751 5,050 2,475 402 618 .. 731 457 427 2,620 1,742 ..
621 2,740 510 803 2,438 1,123 3,628 3,017 3,125 1,724 4,129 1,294 4,012 491 913 4,483 9,729 542 .. 2,017 1,173 .. .. 2,943 2,517 1,355 .. .. 2,617 .. .. .. 1,702 884 1,080 458 420 295 721 340 4,901 4,192 624 .. 726 5,598 6,057 499 845 .. 660 491 498 2,562 2,402 ..
31.1 82.4 55.8 54.7 98.2 98.7 85.3 97.3 93.5 83.5 84.7 98.3 97.0 19.5 93.1 83.9 99.9 93.6 .. 81.8 93.7 .. .. 98.9 76.0 98.2 .. .. 89.4 .. .. .. 88.3 .. 96.9 94.0 6.2 14.6 62.0 5.3 96.0 65.4 29.2 .. 19.7 52.8 100.0 53.3 58.4 .. 21.6 64.1 50.8 97.7 81.0 ..
53.3 80.3 69.0 67.1 98.6 99.4 91.8 97.3 90.7 89.2 81.6 98.0 98.7 20.6 90.2 80.8 100.0 62.0 .. 64.2 94.2 .. .. 99.1 62.1 82.9 .. .. 95.3 .. .. .. 89.1 88.7 95.7 94.5 6.9 25.9 67.3 11.3 92.9 70.0 39.0 .. 19.8 54.9 100.0 60.9 71.7 .. 30.5 68.5 51.0 95.3 81.1 ..
62.0 1.3 41.7 43.8 1.0 0.1 1.0 0.0 0.6 16.2 1.1 0.1 0.2 75.9 2.9 0.8 0.1 0.1 .. 8.4 4.5 .. .. 1.1 1.8 0.0 .. .. 9.1 .. .. .. 6.0 0.4 2.5 4.4 93.2 84.4 16.0 93.4 1.4 4.0 53.2 .. 79.8 4.8 0.0 43.2 28.3 .. 72.3 26.9 29.2 2.2 14.4 ..
41.5 4.3 28.3 29.2 0.5 0.1 1.4 0.0 2.6 10.5 1.3 0.0 0.1 73.6 4.8 1.1 0.0 0.1 .. 25.9 2.7 .. .. 0.9 8.8 6.0 .. .. 4.1 .. .. .. 4.6 2.2 3.8 3.2 81.6 72.1 12.7 86.2 3.3 6.0 52.2 .. 79.6 5.1 0.0 34.9 17.4 .. 52.0 17.4 26.1 5.5 11.9 ..
8.1 12.9 2.4 1.5 0.8 1.2 0.6 3.0 3.8 0.3 14.2 1.7 0.9 4.4 4.0 15.4 0.0 11.3 .. 4.9 1.9 .. .. 0.0 27.9 1.5 .. .. 1.5 .. .. .. 5.8 0.2 0.0 1.5 0.4 1.0 17.5 1.3 1.4 30.7 17.3 .. 0.5 48.6 0.0 3.5 12.8 .. 75.8 9.0 20.0 0.1 4.7 ..
5.1 13.2 2.7 3.7 0.9 0.1 1.3 3.4 4.6 0.3 17.1 1.4 1.1 5.9 5.0 18.1 0.0 45.5 .. 5.1 1.4 .. .. 0.0 27.4 5.5 .. .. 0.9 .. .. .. 6.4 0.2 0.0 1.1 14.4 2.0 8.8 2.4 1.5 24.0 8.7 .. 0.6 39.6 0.0 4.2 11.1 .. 116.5 14.0 22.9 0.2 5.2 ..
2009 World Development Indicators
159
3.7
Energy production and use Energy production
Total million metric tons of oil equivalent 1990
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
2006
40.8 28.0 1,280.3 1,220.0 .. .. 370.8 570.7 1.0 1.2 10.6 13.4 a .. .. 0.0 0.0 5.3 6.6 2.9 3.3 .. .. 114.5 158.7 34.6 31.4 4.2 5.5 8.8 30.7 .. .. 29.7 32.8 9.7 12.1 22.3 26.5 2.0 1.5 9.1 19.4 26.5 56.2 .. .. 1.1 2.0 12.6 34.6 5.7 6.6 25.8 26.3 74.9 61.6 .. .. 135.8 82.8 110.2 177.3 208.0 186.6 1,649.4 1,654.2 1.1 0.8 38.6 58.2 148.9 195.5 24.7 71.9 .. .. 9.4 18.7 4.9 6.7 8.6 8.8 8,821.7 t 11,786.0 t 451.2 734.4 4,623.1 6,447.4 2,272.0 3,782.9 2,351.1 2,664.4 5,058.6 7,147.8 1,223.4 2,372.6 1,862.0 1,772.5 608.6 926.1 563.0 827.2 348.8 535.6 475.6 762.2 3,780.4 4,663.1 477.4 463.1
a. Includes Kosovo and Montenegro.
160
2009 World Development Indicators
Energy use
Total million metric tons of oil equivalent 1990
2006
62.5 878.9 .. 61.3 1.8 19.5a .. 13.4 21.3 5.6 .. 91.2 91.2 5.5 10.7 .. 47.6 24.8 11.7 5.6 9.8 43.9 .. 1.3 6.0 5.1 52.9 19.6 .. 253.8 23.2 212.3 1,926.3 2.3 46.4 43.9 24.3 .. 2.6 5.5 9.4 8,637.3 t 400.2 3,797.2 1,973.3 1,823.9 4,181.1 1,140.5 1,695.2 457.6 190.2 390.8 313.3 4,479.4 1,075.7
40.1 676.2 .. 146.1 3.0 17.1 .. 30.7 18.7 7.3 .. 129.8 144.6 9.4 17.7 .. 51.3 28.2 18.9 3.6 20.8 103.4 .. 2.4 14.3 8.7 94.0 17.3 .. 137.4 46.9 231.1 2,320.7 3.2 48.5 62.2 52.3 .. 7.1 7.3 9.6 11,525.2 t 575.5 5,348.7 3,468.8 1,879.8 5,899.7 2,382.5 1,302.8 685.9 385.5 694.8 467.6 5,659.1 1,268.9
Clean energy production
% of total average annual % growth 1990–2006
Per capita kilograms of oil equivalent 1990
2006
Fossil fuel 1990
–2.3 2,693 1,860 96.2 –1.5 5,927 4,745 93.4 .. .. .. .. 4.7 3,744 6,170 100.0 3.8 233 250 48.0 .. 2,569a 2,303 90.7a .. .. .. .. 3.8 4,384 6,968 100.0 –0.1 4,035 3,465 81.6 2.2 2,792 3,618 70.6 .. .. .. .. 2.1 2,592 2,739 86.2 3.3 2,349 3,277 77.7 3.7 322 472 24.1 3.5 411 470 17.7 .. .. .. .. 0.5 5,557 5,650 37.8 0.7 3,695 3,770 61.5 2.9 918 975 98.0 –2.7 1,051 548 72.7 4.8 385 527 7.6 5.2 809 1,630 65.5 .. .. .. .. 4.6 328 375 17.3 5.8 4,934 10,768 99.2 3.7 630 863 87.5 3.5 943 1,288 81.9 1.1 5,352 3,524 .. .. .. .. .. –3.5 4,891 2,937 91.9 4.4 12,416 11,036 100.0 0.5 3,708 3,814 90.9 1.3 7,717 7,768 86.5 1.2 725 962 58.7 0.6 2,261 1,829 99.2 1.6 2,224 2,302 91.5 5.1 367 621 20.4 .. .. .. .. 6.4 208 326 97.0 1.7 673 625 16.6 –0.2 895 724 45.3 1.8 w 1,686 w 1,820 w 81.2 w 2.4 482 478 44.5 2.0 1,097 1,267 81.2 3.3 716 1,019 74.4 0.2 2,584 2,300 88.4 2.1 990 1,108 78.1 4.2 718 1,258 71.8 –1.5 3,887 2,930 93.2 2.4 1,054 1,240 71.4 4.3 849 1,254 97.3 3.5 350 468 54.0 2.5 685 670 41.8 1.6 4,807 5,416 84.1 1.2 3,567 3,936 80.0
2006
85.1 89.4 .. 100.0 59.5 90.2 .. 100.0 70.9 69.2 .. 87.1 82.8 41.4 21.8 .. 34.9 56.1 98.2 59.5 8.3 82.3 .. 13.4 99.8 86.6 89.1 100.0 .. 82.2 100.0 89.2 85.7 67.8 98.9 88.2 49.8 .. 98.9 11.1 29.2 80.9 w 43.1 82.4 81.0 84.8 79.0 82.1 89.1 73.0 97.8 66.8 41.1 82.9 76.4
Combustible renewables and waste
% of total energy use
1990
2006
1990
2006
1.0 1.4 .. 0.0 52.0 6.0a .. 0.0 0.8 4.8 .. 11.4 4.5 71.0 81.5 .. 11.6 3.7 0.0 0.0 91.0 33.4 .. 80.6 0.8 12.4 13.6 0.0 .. 0.1 0.0 0.3 3.2 24.3 0.0 1.2 77.7 .. 3.0 73.4 50.4 10.0 w 52.9 14.6 22.7 5.9 17.9 26.4 1.6 19.5 1.6 43.5 56.1 2.8 3.2
8.1 1.1 .. 0.0 39.6 4.7 .. 0.0 2.6 6.5 .. 10.5 3.6 54.3 77.5 .. 18.4 7.2 0.0 0.0 91.0 16.6 .. 84.5 0.2 13.3 5.5 0.0 .. 0.4 0.0 1.7 3.4 14.9 0.0 0.9 46.4 .. 1.1 78.2 63.3 9.8 w 53.8 12.3 15.2 7.0 15.9 14.7 2.2 15.9 1.2 30.4 56.3 3.4 4.9
1.6 5.2 .. 0.0 0.0 4.2a .. 0.0 15.5 26.2 .. 2.5 17.9 4.9 0.8 .. 50.5 35.5 2.0 25.5 1.4 1.0 .. 0.6 0.0 0.1 4.6 0.3 .. 8.2 0.0 8.3 10.2 26.8 1.2 7.2 1.9 .. 0.0 12.5 4.0 8.7 w 2.8 4.1 3.0 5.3 4.0 1.8 5.0 9.1 1.1 2.5 2.2 13.0 16.4
7.6 8.3 .. 0.0 0.7 5.5 .. 0.0 27.5 24.2 .. 2.7 13.8 4.2 0.7 .. 44.5 35.9 1.8 39.1 0.6 0.7 .. 0.3 0.0 0.1 5.5 0.0 .. 17.9 0.0 8.9 10.8 9.7 1.1 11.0 3.9 .. 0.0 11.0 5.0 9.2 w 3.2 5.2 3.9 7.6 5.0 3.2 8.1 11.0 0.9 2.8 2.6 13.5 18.3
About the data
ENVIRONMENT
Energy production and use
3.7
Definitions
In developing economies growth in energy use is
assumed for converting nuclear electricity into oil
• Energy production refers to forms of primary
closely related to growth in the modern sectors—
equivalents and 100 percent efficiency for converting
energy—petroleum (crude oil, natural gas liquids,
industry, motorized transport, and urban areas—
hydroelectric power.
and oil from nonconventional sources), natural gas,
but energy use also reflects climatic, geographic,
The IEA makes these estimates in consultation
solid fuels (coal, lignite, and other derived fuels),
and economic factors (such as the relative price
with national statistical offices, oil companies, elec-
and combustible renewables and waste—and pri-
of energy). Energy use has been growing rapidly in
tric utilities, and national energy experts. The IEA
mary electricity, all converted into oil equivalents
low- and middle-income economies, but high-income
occasionally revises its time series to reflect politi-
(see About the data). • Energy use refers to the use
economies still use almost five times as much energy
cal changes, and energy statistics undergo contin-
of primary energy before transformation to other
on a per capita basis.
ual changes in coverage or methodology as more
end-use fuels, which is equal to indigenous produc-
detailed energy accounts become available. Breaks
tion plus imports and stock changes, minus exports
in series are therefore unavoidable.
and fuels supplied to ships and aircraft engaged in
Energy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic
international transport (see About the data). • Fos-
Co-operation and Development (OECD) are based
sil fuel comprises coal, oil, petroleum, and natu-
on national energy data adjusted to conform to
ral gas products. • Combustible renewables and
annual questionnaires completed by OECD member
waste comprise solid biomass, liquid biomass, bio-
governments.
gas, industrial waste, and municipal waste. • Clean
Total energy use refers to the use of primary energy
energy production is noncarbohydrate energy that
before transformation to other end-use fuels (such
does not produce carbon dioxide when generated. It
as electricity and refined petroleum products). It
includes hydropower and nuclear, geothermal, and
includes energy from combustible renewables and
solar power, among others.
waste—solid biomass and animal products, gas and liquid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity. Data for combustible renewables and waste are often based on small surveys or other incomplete information and thus give only a broad impression of developments and are not strictly comparable across countries. The IEA reports include country notes that explain some of these differences (see Data sources). All forms of energy—primary energy and primary electricity—are converted into oil equivalents. A notional thermal efficiency of 33 percent is
A person in a high-income economy uses an average of more than 11 times as much energy as a person in a low-income economy Energy use per capita (thousands of kilograms of oil equivalent)
3.7a 1990
2006
6
4
Data sources 2
Data on energy production and use are from IEA electronic files and are published in IEA’s annual publications, Energy Statistics and Bal-
0
ances of Non-OECD Countries, Energy Statistics Low income
Source: Table 3.7.
Lower middle income
Upper middle income
High income
World
of OECD Countries, and Energy Balances of OECD Countries.
2009 World Development Indicators
161
3.8 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 Cote 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
Energy dependency and efficiency and carbon dioxide emissions Net energy importsa
GDP per unit of energy use
% of energy use 1990 2006
2005 PPP $ per kilogram of oil equivalent 1990 2006
.. 8 –337 –356 –5 98 –80 68 18 16 92 73 –6 –77 35 29 26 67 .. .. .. –118 –31 .. .. 46 –3 100 –95 –1 –997 49 23 43 61 18 44 75 –169 –72 32 20 47 7 58 51 –1,077 .. 85 48 18 59 24 .. .. 21
.. 47 –372 –671 –21 67 –119 71 –171 19 86 75 39 –144 27 45 8 46 .. .. 29 –46 –53 .. .. 67 7 100 –180 –2 –1,180 49 –28 54 53 27 –41 80 –165 –25 44 27 27 9 52 50 –566 .. 64 61 32 68 34 .. .. 23
2009 World Development Indicators
.. 4.8 6.6 5.4 5.3 1.3 4.6 8.0 1.3 6.1 1.5 5.0 3.2 7.4 .. 7.3 7.7 2.3 .. .. .. 5.0 3.6 .. .. 6.2 1.4 12.7 8.1 1.9 10.5 9.5 5.4 6.0 .. 3.5 7.3 5.9 9.2 5.8 7.8 1.9 1.7 1.8 4.1 6.2 11.2 .. 2.4 5.7 2.5 8.0 6.6 .. .. 7.3
.. 8.9 6.5 6.9 6.6 5.5 5.4 8.4 3.6 7.0 3.2 5.7 3.8 6.2 4.6 11.7 7.3 3.7 .. .. 4.5 5.1 4.3 .. .. 7.0 3.2 14.3 11.0 0.9 10.5 9.3 4.1 6.9 .. 4.8 8.9 7.2 8.1 5.7 7.6 4.0 5.0 2.3 4.5 7.0 9.9 .. 5.2 7.6 2.9 9.3 6.6 .. .. 4.0
Carbon dioxide emissions
Total million metric tons 1990 2005
2.6 7.3 77.0 4.6 109.7 4.2 293.1 57.6 46.1 15.4 107.8 99.1 0.7 5.5 6.9 2.2 202.6 75.3 0.6 0.2 0.5 1.6 428.5 0.2 0.1 35.3 2,399.2 26.2 57.4 4.0 1.2 2.9 5.4 24.6 32.0 161.7 49.8 9.6 16.6 75.4 2.6 .. 28.3 3.0 50.6 363.3 6.0 0.2 17.3 980.6 3.8 72.2 5.1 1.0 0.2 1.0
0.7 3.5 137.5 9.0 152.7 4.3 368.9 73.6 36.6 40.0 63.3 102.6 2.6 9.3 26.3 4.6 325.5 44.4 0.7 0.2 0.5 3.7 537.5 0.3 0.1 66.1 5,547.8 38.6 58.6 2.1 2.0 7.3 8.7 22.9 24.3 119.7 46.1 18.8 29.3 173.5 6.4 0.8 18.2 7.9 53.2 377.7 1.5 0.3 4.8 784.0 7.3 95.4 11.4 1.4 0.3 1.8
Carbon intensity kilograms per kilogram of oil equivalent energy use 1990 2005
.. 2.7 3.2 0.7 2.4 0.5 3.3 2.3 1.8 1.2 2.5 2.0 0.4 2.0 1.0 1.7 1.4 2.6 .. .. .. 0.3 2.0 .. .. 2.5 2.8 2.5 2.3 0.3 1.5 1.4 1.2 2.7 1.9 3.3 2.8 2.3 2.7 2.4 1.0 .. 3.0 0.2 1.8 1.6 4.8 .. 1.4 2.8 0.7 3.2 1.1 .. .. 0.6
.. 1.5 4.0 0.9 2.4 1.7 3.1 2.2 2.6 1.7 2.4 1.7 1.0 1.7 5.2 2.4 1.5 2.2 .. .. 0.1 0.5 2.0 .. .. 2.2 3.2 2.1 2.0 0.1 1.6 1.8 1.1 2.6 2.5 2.6 2.3 2.4 2.8 2.8 1.4 1.0 3.6 0.4 1.5 1.4 0.8 .. 1.5 2.3 0.8 3.1 1.4 .. .. 0.7
Per capita metric tons 1990 2005
.. 2.2 3.0 0.4 3.4 1.2 17.2 7.5 6.4 0.1 10.6 9.9 0.1 0.8 1.6 1.6 1.4 8.6 0.1 0.0 0.0 0.1 15.4 0.1 0.0 2.7 2.1 4.6 1.7 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 10.1 6.4 6.5 0.2 3.2 12.3 0.2 7.1 0.6 0.2 0.2 0.1
.. 1.1 4.2 0.6 3.9 1.4 18.1 8.9 4.4 0.3 6.5 9.8 0.3 1.0 6.9 2.5 1.7 5.7 0.1 0.0 0.0 0.2 16.6 0.1 0.0 4.1 4.3 5.7 1.4 0.0 0.6 1.7 0.5 5.2 2.2 11.7 8.5 2.0 2.2 2.4 1.0 0.2 13.5 0.1 10.1 6.2 1.2 0.2 1.1 9.5 0.3 8.6 0.9 0.2 0.2 0.2
kilograms per 2005 PPP $ of GDP 1990 2005
.. 0.6 0.5 0.1 0.5 0.4 0.7 0.3 1.4 0.2 1.6 0.4 0.1 0.3 .. 0.2 0.2 1.1 0.1 0.1 .. 0.1 0.6 0.1 0.0 0.4 1.9 0.2 0.3 0.2 0.1 0.2 0.2 0.5 .. 1.0 0.4 0.4 0.3 0.4 0.1 .. 1.8 0.1 0.4 0.3 0.4 0.2 0.6 0.5 0.3 0.4 0.2 0.2 0.3 0.1
0.0 0.2 0.6 0.2 0.4 0.3 0.6 0.3 1.0 0.2 0.8 0.3 0.2 0.3 1.1 0.2 0.2 0.6 0.1 0.1 0.0 0.1 0.5 0.1 0.0 0.3 1.0 0.2 0.2 0.1 0.2 0.2 0.3 0.4 .. 0.6 0.3 0.4 0.3 0.5 0.2 0.3 0.8 0.2 0.3 0.2 0.1 0.2 0.3 0.3 0.3 0.3 0.2 0.1 0.4 0.2
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
Net energy importsa
GDP per unit of energy use
% of energy use 1990 2006
2005 PPP $ per kilogram of oil equivalent 1990 2006
30 50 9 –65 –161 –451 66 96 83 84 83 95 –23 20 13 76 –530 67 .. 86 94 .. .. –534 70 47 .. .. –116 .. .. .. –57 99 20 89 6 0 67 5 10 13 29 .. –112 –456 –740 21 59 .. –48 –6 48 1 80 ..
53 63 23 –72 –81 –216 90 88 85 89 81 96 –113 21 –3 80 –495 47 .. 60 96 .. .. –474 59 47 .. .. –43 .. .. .. –44 97 –7 95 –22 –55 79 11 24 26 39 .. –124 –755 –293 23 72 .. –69 15 43 20 83 ..
5.4 4.5 3.2 3.6 4.9 .. 6.0 6.9 9.1 4.2 7.2 3.0 1.6 3.0 .. 4.9 2.8 1.5 .. 3.4 6.8 .. .. .. 2.9 5.9 .. .. 5.2 .. .. .. 6.8 1.7 1.4 9.3 0.9 1.3 8.1 2.3 5.8 4.6 3.7 .. 2.0 6.4 5.7 4.2 9.8 .. 5.5 9.8 5.7 3.1 9.1 ..
5.5 6.5 4.7 4.2 4.0 .. 10.9 7.9 9.1 3.6 7.5 3.5 2.4 2.8 .. 5.0 4.6 3.3 .. 7.3 8.1 .. .. 4.4 6.1 5.9 .. .. 4.7 .. .. .. 7.7 2.6 2.6 8.3 1.7 2.9 6.5 2.9 7.3 5.9 3.8 .. 2.5 8.6 3.6 4.6 11.6 .. 6.0 14.0 6.1 5.7 8.7 ..
ENVIRONMENT
Energy dependency and efficiency and carbon dioxide emissions
3.8
Carbon dioxide emissions
Total million metric tons 1990 2005
2.6 60.1 679.9 149.3 218.3 48.5 30.6 33.1 395.7 8.0 1,080.7 10.2 288.1 5.8 244.6 241.6 43.4 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 1.5 375.2 23.8 10.0 23.5 1.0 4.3 0.0 0.6 139.7 22.5 2.6 1.0 45.3 30.3 10.3 68.0 3.1 2.4 2.3 21.0 43.9 347.6 42.3 ..
7.4 56.4 1,402.4 419.6 451.6 84.5 42.3 63.6 452.1 10.2 1,230.0 20.5 180.9 11.1 82.6 452.2 93.6 5.6 1.4 6.5 16.9 .. 0.5 56.1 14.0 10.3 2.8 1.0 239.8 0.6 1.6 3.4 421.5 8.1 8.8 48.0 1.9 11.3 2.6 3.1 125.8 29.9 3.9 1.1 114.3 52.9 31.4 134.3 5.9 4.4 3.9 37.0 75.0 302.4 62.4 ..
Carbon intensity kilograms per kilogram of oil equivalent energy use 1990 2005
1.1 2.1 2.1 1.5 3.2 2.5 3.0 2.7 2.7 2.7 2.4 2.9 3.9 0.5 7.4 2.6 5.4 1.7 .. 1.8 3.9 .. .. 3.3 1.5 5.7 .. .. 2.4 .. .. .. 3.1 2.4 2.9 3.3 0.2 0.4 0.0 0.1 2.1 1.6 1.2 .. 0.6 1.4 2.3 1.6 2.1 .. 0.7 2.1 1.7 3.5 2.5 ..
1.9 2.0 2.6 2.4 2.9 2.8 2.8 3.0 2.4 2.6 2.3 2.9 3.2 0.6 3.9 2.1 3.3 2.0 .. 1.4 3.0 .. .. 3.2 1.6 3.7 .. .. 3.6 .. .. .. 2.4 2.3 3.4 3.6 0.2 0.8 1.8 0.3 1.5 1.7 1.2 .. 1.1 1.6 2.2 1.8 2.3 .. 1.0 2.7 1.7 3.3 2.3 ..
Per capita metric tons 1990 2005
0.5 5.8 0.8 0.8 4.0 2.6 8.7 7.1 7.0 3.3 8.7 3.2 17.6 0.2 12.1 5.6 20.4 2.8 0.1 5.4 3.1 .. 0.2 8.7 6.6 8.1 0.1 0.1 3.1 0.1 1.4 1.4 4.5 5.4 4.7 1.0 0.1 0.1 0.0 0.0 9.3 6.5 0.6 0.1 0.5 7.1 5.6 0.6 1.3 0.6 0.5 1.0 0.7 9.1 4.3 ..
1.1 5.6 1.3 1.9 6.5 .. 10.2 9.2 7.7 3.8 9.6 3.8 11.9 0.3 3.5 9.4 36.9 1.1 0.3 2.8 4.2 .. 0.1 9.5 4.1 5.1 0.2 0.1 9.3 0.0 0.6 2.7 4.1 2.1 3.4 1.6 0.1 0.2 1.3 0.1 7.7 7.2 0.7 0.1 0.8 11.4 12.5 0.9 1.8 0.7 0.7 1.4 0.9 7.9 5.9 ..
kilograms per 2005 PPP $ of GDP 1990 2005
0.2 0.5 0.7 0.4 0.6 .. 0.5 0.4 0.3 0.6 0.3 1.0 2.5 0.2 .. 0.5 0.6 1.1 0.1 0.5 0.6 .. 0.4 .. 0.5 1.0 0.1 0.1 0.5 0.1 0.9 0.2 0.4 1.4 2.0 0.4 0.2 0.3 0.0 0.0 0.4 0.4 0.3 0.2 0.3 0.2 0.4 0.4 0.2 0.3 0.1 0.2 0.3 1.1 0.3 ..
2009 World Development Indicators
0.3 0.3 0.6 0.6 0.7 .. 0.3 0.4 0.3 0.6 0.3 0.9 1.4 0.2 .. 0.4 0.8 0.6 0.1 0.2 0.4 .. 0.4 0.8 0.3 0.7 0.2 0.1 0.8 0.0 0.3 0.3 0.3 0.9 1.3 0.4 0.1 0.3 0.3 0.1 0.2 0.3 0.3 0.1 0.5 0.2 0.6 0.4 0.2 0.4 0.2 0.2 0.3 0.6 0.3 ..
163
3.8 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Energy dependency and efficiency and carbon dioxide emissions Net energy importsa
GDP per unit of energy use
% of energy use 1990 2006
2005 PPP $ per kilogram of oil equivalent 1990 2006
35 –46 .. –505 48 31b .. 100 75 48 .. –26 62 24 18 .. 38 61 –91 64 8 40 .. 19 –109 –11 51 –281 .. 47 –375 2 14 49 17 –239 –2 .. –266 10 9 –2c w –13 –22 –15 –29 –21 –7 –10 –33 –196 11 –52 16 56
30 –80 .. –291 59 38 .. 100 65 54 .. –22 78 41 –73 .. 36 57 –40 59 7 46 .. 15 –142 24 72 –257 .. 40 –278 19 29 75 –20 –214 –38 .. –164 9 9 –2c w –28 –21 –9 –42 –21 0 –36 –35 –115 23 –63 18 64
2.9 2.1 .. 5.1 5.8 5.1b .. 5.4 3.1 5.9 .. 3.0 8.4 6.3 2.5 .. 4.4 9.1 3.2 2.9 2.2 5.1 .. 2.6 2.1 6.4 8.3 .. .. 1.6 4.1 6.4 4.1 9.7 0.9 4.3 2.5 .. 8.5 1.8 .. 4.2 w 2.5 3.0 2.6 3.5 3.0 2.0 2.3 6.8 5.5 3.4 2.7 5.2 6.6
Carbon dioxide emissions
Total million metric tons 1990 2005
5.4 155.1 2.7 2,261.7 .. 0.5 3.5 197.4 6.2 3.1 4.1 65.4b .. 0.3 6.5 41.9 5.1 51.4 6.8 18.0 .. 0.0 3.2 331.9 8.5 211.8 8.0 3.8 3.9 5.4 .. 0.4 5.9 49.5 9.7 42.7 4.2 35.8 2.8 23.4 2.1 2.3 4.5 95.7 .. .. 2.0 0.8 2.0 16.9 7.8 13.3 8.9 141.5 1.4 32.0 .. 0.8 2.1 684.0 4.7 54.7 8.6 569.2 5.5 4,797.5 10.3 3.9 1.2 125.3 4.7 117.4 3.7 21.4 .. .. 6.7 9.6 2.0 2.4 .. 16.6 5.2 w 22,584.9d t 3.2 518.7 4.2 9,675.3 3.9 4,882.2 4.8 4,793.1 4.1 10,193.8 3.4 3,029.7 3.5 4,365.8 7.3 1,020.2 5.0 565.4 4.8 770.5 3.0 463.4 6.3 11,003.2 7.7 2,529.7
89.1 1,503.3 0.6 381.1 5.1 52.5b 0.9 56.3 36.6 14.8 0.6 408.8 343.7 11.0 10.6 1.0 48.5 41.2 68.4 5.2 4.7 270.9 0.2 1.3 32.7 22.0 247.9 41.6 2.3 327.1 123.7 546.4 5,776.4 5.6 112.4 148.1 101.8 .. 20.1 2.4 11.5 29,257.0d t 722.5 13,842.1 9,447.7 4,393.3 14,564.1 6,769.2 3,087.2 1,360.7 1,112.6 1,592.6 649.3 13,099.7 2,585.0
Carbon intensity kilograms per kilogram of oil equivalent energy use 1990 2005
2.5 2.6 .. 3.2 1.7 3.0 b .. 3.1 2.4 3.2 .. 3.6 2.3 0.7 0.5 .. 1.0 1.7 3.1 4.2 0.2 2.2 .. 0.6 2.8 2.6 2.7 1.6 .. 2.7 2.4 2.7 2.5 1.7 2.7 2.7 0.9 .. 3.7 0.4 1.8 2.6d w 1.3 2.5 2.5 2.6 2.4 2.7 2.6 2.2 3.0 2.0 1.5 2.5 2.4
2.3 2.3 .. 2.7 1.7 .. .. 1.8 1.9 2.0 .. 3.2 2.4 1.2 0.6 .. 0.9 1.5 3.7 1.5 0.2 2.7 .. 0.6 2.6 2.6 2.9 2.5 .. 2.3 2.7 2.3 2.5 1.9 2.4 2.5 2.0 .. 2.9 0.3 1.2 2.6d w 1.3 2.7 2.9 2.4 2.6 3.1 2.4 2.1 3.0 2.4 1.4 2.3 2.0
Per capita metric tons 1990 2005
6.7 15.3 0.1 12.1 0.4 6.2b 0.1 13.8 9.7 9.0 0.0 9.4 5.5 0.2 0.2 0.6 5.8 6.4 2.8 4.4 0.1 1.8 .. 0.2 13.8 1.6 2.5 8.7 0.0 13.2 29.3 9.9 19.2 1.3 6.1 5.9 0.3 .. 0.8 0.3 1.6 4.3d w 0.7 2.8 1.8 6.9 2.4 1.9 10.4 2.3 2.5 0.7 0.9 11.8 8.4
4.1 10.5 0.1 16.5 0.4 6.5b 0.2 13.2 6.8 7.4 0.1 8.7 7.9 0.6 0.3 0.8 5.4 5.5 3.6 0.8 0.1 4.3 0.2 0.2 24.7 2.2 3.4 8.6 0.1 6.9 30.1 9.1 19.5 1.7 4.3 5.6 1.2 .. 1.0 0.2 0.9 4.5d w 0.6 3.3 2.8 5.5 2.7 3.6 7.0 2.5 3.7 1.1 0.8 12.6 8.1
kilograms per 2005 PPP $ of GDP 1990 2005
0.9 1.2 0.1 0.6 0.3 .. 0.1 0.6 0.8 0.6 .. 1.2 0.3 0.1 0.2 0.1 0.2 0.2 1.0 1.4 0.1 0.4 .. 0.2 1.4 0.4 0.3 .. 0.1 1.6 0.6 0.4 0.6 0.2 3.1 0.6 0.4 .. 0.4 0.2 .. 0.6d w 0.3 0.9 1.0 0.8 0.8 1.3 1.1 0.3 0.5 0.6 0.6 0.5 0.4
a. Negative values indicate that a country is a net exporter. b. Includes Kosovo and Montenegro. c. Deviation from zero is due to statistical errors and changes in stock. d. Includes emissions not allocated to specific countries.
164
2009 World Development Indicators
0.4 0.9 0.1 0.8 0.3 .. 0.3 0.3 0.4 0.3 .. 1.0 0.3 0.2 0.2 0.2 0.2 0.2 0.9 0.5 0.1 0.6 0.2 0.3 1.3 0.3 0.3 1.8 0.1 1.2 0.6 0.3 0.5 0.2 2.1 0.6 0.6 .. 0.4 0.2 .. 0.5d w 0.4 0.7 0.8 0.5 0.6 0.9 0.7 0.3 0.6 0.5 0.5 0.4 0.3
ENVIRONMENT
Energy dependency and efficiency and carbon dioxide emissions
3.8
About the data
Because commercial energy is widely traded, its pro-
combustion different fossil fuels release different
estimated from a consistent time series tend to be
duction and use need to be distinguished. Net energy
amounts of carbon dioxide for the same level of
more accurate than individual values. Each year
imports show the extent to which an economy’s use
energy use: oil releases about 50 percent more car-
the CDIAC recalculates the entire time series since
exceeds its production. High-income economies are
bon dioxide than natural gas, and coal releases about
1949, incorporating recent findings and corrections.
net energy importers; middle-income economies are
twice as much. Cement manufacturing releases
Estimates exclude fuels supplied to ships and aircraft
their main suppliers.
about half a metric ton of carbon dioxide for each
in international transport because of the difficulty of
metric ton of cement produced.
apportioning the fuels among benefiting countries.
The ratio of gross domestic product (GDP) to energy use indicates energy efficiency. To produce compa-
The U.S. Department of Energy’s Carbon Diox-
The ratio of carbon dioxide per unit of energy shows
rable and consistent estimates of real GDP across
ide Information Analysis Center (CDIAC) calculates
carbon intensity, which is the amount of carbon diox-
economies relative to physical inputs to GDP—that
annual anthropogenic emissions from data on fossil
ide emitted as a result of using one unit of energy in
is, units of energy use—GDP is converted to 2005
fuel consumption (from the United Nations Statistics
the process of production. The proportion of carbon
constant international dollars using purchasing
Division’s World Energy Data Set) and world cement
dioxide per unit of GDP indicates how clean produc-
power parity (PPP) rates. Differences in this ratio
manufacturing (from the U.S. Bureau of Mines’s
tion processes are.
over time and across economies reflect structural
Cement Manufacturing Data Set). Carbon dioxide
changes in an economy, changes in sectoral energy
emissions, often calculated and reported as elemen-
Definitions
tal carbon, were converted to actual carbon dioxide
• Net energy imports are estimated as energy use
Carbon dioxide emissions, largely by-products of
mass by multiplying them by 3.664 (the ratio of the
less production, both measured in oil equivalents.
energy production and use (see table 3.7), account
mass of carbon to that of carbon dioxide). Although
• GDP per unit of energy use is the ratio of gross
for the largest share of greenhouse gases, which
estimates of global carbon dioxide emissions are
domestic product (GDP) per kilogram of oil equivalent
are associated with global warming. Anthropogenic
probably accurate within 10 percent (as calculated
of energy use, with GDP converted to 2005 constant
carbon dioxide emissions result primarily from fos-
from global average fuel chemistry and use), coun-
international dollars using purchasing power parity
sil fuel combustion and cement manufacturing. In
try estimates may have larger error bounds. Trends
(PPP) rates. An international dollar has the same
efficiency, and differences in fuel mixes.
purchasing power over GDP that a U.S. dollar has
3.8a
High-income economies depend on imported energy . . .
in the United States. Energy use refers to the use of primary energy before transformation to other
Net energy imports (% of energy use)
1990
2006
end-use fuel, which is equal to indigenous production plus imports and stock changes minus exports
Low income
and fuel supplied to ships and aircraft engaged in Lower middle income
international transport (see About the data for table 3.7). • Carbon dioxide emissions are emissions from
Upper middle income
the burning of fossil fuels and the manufacture of High income
cement and include carbon dioxide produced during consumption of solid, liquid, and gas fuels and
Euro area
gas flaring. –60
–40
–20
0
20
40
60
80
Note: Negative values indicate that the income group is a net energy exporter. Source: Table 3.8.
. . . mostly from middle-income economies in the Middle East and North Africa and Latin America and the Caribbean
3.8b
Net energy imports (% of energy use)
1990
2006
East Asia & Pacific Europe & Central Asia Latin America & Caribbean
Data sources
Middle East & North Africa
Data on energy use are from the electronic files
South Asia
of the International Energy Agency. Data on car-
Sub-Saharan Africa –250
–200
–150
–100
–50
0
50
100
bon dioxide emissions are from the CDIAC, Environmental Sciences Division, Oak Ridge National
Note: Negative values indicate that the region is a net energy exporter. Source: Table 3.8.
Laboratory, Tennessee, United States.
2009 World Development Indicators
165
3.9
Trends in greenhouse gas emissions Carbon dioxide emissions
average annual % growtha % changeb 1990– 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 Cote 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
166
–9.3 –0.9 3.9 4.1 2.0 0.3 1.3 1.5 –3.2 6.8 –3.3 –0.2 8.0 3.0 16.0 4.2 3.3 –3.1 1.8 1.1 1.0 4.5 1.8 1.8 2.9 4.7 4.6 2.4 –0.7 –5.3 1.9 5.3 2.0 1.8 –1.8 –1.5 –1.3 5.4 3.2 5.9 5.4 11.2 –2.8 8.0 1.5 0.1 –8.9 3.2 –8.8 1.8 4.7 2.3 6.2 2.0 1.9 6.9
–73.5 –51.9 78.6 93.5 39.2 3.6 25.9 27.8 –31.8 160.1 –41.3 3.6 259.0 68.2 280.4 110.0 60.6 –41.0 33.6 15.1 19.5 131.5 25.4 27.7 –2.6 87.1 131.2 47.3 2.1 –46.0 70.6 150.4 61.5 –6.9 –24.1 –26.0 –7.4 96.3 76.8 130.0 144.5 .. –35.8 165.9 5.1 4.0 –74.9 50.0 –72.3 178.1 94.3 32.0 125.0 34.1 29.8 77.9
2009 World Development Indicators
Methane emissions Total thousand metric tons of carbon dioxide equivalent 2005
.. 2,170 24,310 37,020 94,340 2,300 116,840 7,210 11,550 92,530 16,620 7,610 4,840 27,120 2,850 4,480 421,820 6,140 .. .. 14,890 15,110 103,830 .. .. 19,560 995,760 1,090 61,690 5,750 50,320 2,450 15,320 3,690 9,490 14,930 4,920 5,960 12,890 32,960 3,200 2,410 1,230 47,740 5,470 43,520 2,040 .. 4,330 58,100 8,630 7,410 8,990 .. .. 3,740
% changeb 1990– 2005
.. –2.7 30.9 171.6 14.9 –25.6 12.3 –12.2 –20.4 13.4 –13.6 –25.6 77.3 74.4 42.5 3,346.2 47.7 –35.8 .. .. .. 43.9 25.1 .. .. 37.8 11.2 –7.6 25.4 115.4 81.5 –34.1 183.2 –6.6 –4.0 –32.9 –12.9 12.9 5.9 41.8 16.8 15.3 –52.9 22.1 –26.1 –23.3 –34.6 .. –25.2 –47.1 62.5 16.0 51.9 .. .. 30.3
Nitrous oxide emissions
% of total
Total thousand metric tons of carbon dioxide equivalent
2005
2005
2005
% changeb 1990– 2005
.. 11.5 66.3 11.6 13.0 25.2 24.6 14.6 45.2 11.6 43.0 17.0 8.9 2.8 51.9 17.9 3.0 31.9 .. .. 5.4 17.9 46.6 .. .. 12.1 34.2 40.4 15.7 49.6 7.7 1.6 11.2 44.2 6.4 58.7 16.3 4.0 16.3 31.2 12.5 7.5 43.1 10.0 10.2 10.7 79.9 .. 29.6 45.7 10.7 9.7 11.3 .. .. 6.4
.. 70.0 15.3 39.1 63.9 50.9 61.5 50.1 45.4 69.2 38.8 59.7 47.5 34.5 32.6 71.9 67.1 32.7 .. .. 71.5 56.0 22.2 .. .. 29.9 50.0 0.9 55.1 11.8 26.3 58.0 20.6 29.8 62.4 17.2 67.7 62.1 57.4 44.2 48.1 77.6 35.0 77.2 30.3 71.1 4.4 .. 51.7 39.2 49.6 39.1 42.7 .. .. 61.2
.. 1,390 10,330 28,350 83,410 450 114,500 4,620 4,040 37,100 10,360 9,650 4,660 28,300 1,020 2,460 300,300 5,880 .. .. 3,820 14,540 51,390 .. .. 12,590 566,680 200 24,530 2,250 38,680 2,850 12,350 3,590 8,330 6,570 7,380 2,850 8,500 27,810 2,250 2,350 610 63,130 5,330 78,090 420 .. 3,390 69,470 10,520 13,090 7,980 .. .. 4,290
.. –40.6 17.7 454.8 28.2 –50.5 7.9 –19.5 –0.5 65.5 –32.2 –14.2 119.8 97.8 –10.5 .. 31.8 –55.6 .. .. .. 75.4 1.4 .. .. 54.1 24.5 –4.8 16.0 174.4 99.5 –17.2 402.0 5.9 –39.0 –38.8 –26.2 –31.2 –3.8 63.8 9.8 75.4 –62.6 24.4 –11.6 –11.7 –77.3 .. 0.0 –10.3 131.7 0.2 66.9 .. .. 73.7
Industrial Agricultural
Other greenhouse gas emissions
% of total
Total thousand metric tons of carbon dioxide equivalent
2005
2005
2005
% changeb 1990– 2005
.. 0.0 7.2 0.0 0.2 0.0 1.3 9.1 0.0 0.0 32.3 12.0 0.0 0.0 0.0 0.0 3.2 29.9 .. .. 0.0 0.0 4.5 .. .. 6.9 3.0 0.0 0.9 0.0 0.0 0.0 0.0 22.3 8.0 16.4 7.7 0.0 0.0 11.5 0.0 0.0 0.0 0.0 27.0 12.1 0.0 .. 17.4 13.3 0.0 3.3 0.0 .. .. 0.0
.. 97.1 89.1 35.9 97.7 93.3 94.9 85.3 93.6 91.9 65.6 65.4 68.0 43.3 82.4 96.3 74.4 64.5 .. .. 74.1 85.0 86.7 .. .. 88.7 92.7 5.0 78.0 15.6 23.2 98.9 25.0 63.8 87.4 75.0 78.6 96.1 97.6 85.6 95.1 99.1 83.6 98.6 59.5 77.3 57.1 .. 49.3 74.2 88.6 91.3 70.8 .. .. 98.4
.. 50 110 0 930 10 4,580 3,310 50 0 440 9,380 0 0 850 0 7,760 650 .. .. 0 890 11,010 .. .. 10 119,720 330 330 0 0 0 0 720 110 3,530 1,460 0 0 1,820 0 0 60 0 1,030 27,010 0 .. 10 41,980 170 1,620 0 .. .. 0
.. .. –52.2 .. –50.5 .. 74.8 173.6 –72.2 .. .. 7,115.4 .. .. 84.8 .. 46.7 .. .. .. .. 9.9 –14.1 .. .. .. 1,285.6 .. 73.7 .. .. .. .. 7.5 .. 17,550.0 461.5 .. .. –19.1 .. .. .. .. 368.2 151.5 .. .. .. 273.8 –10.5 105.1 .. .. .. ..
Industrial Agricultural
Carbon dioxide emissions
average annual % growtha % changeb 1990– 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
7.6 –0.5 4.8 5.7 4.9 3.6 2.6 4.9 0.9 2.0 0.9 4.0 –3.6 4.8 –10.4 4.2 2.7 –5.5 15.5 –5.9 4.1 .. 3.1 2.9 –4.2 –0.8 8.4 4.1 7.4 2.1 –6.1 6.2 0.5 –8.1 –1.7 4.0 4.1 6.1 51.7 9.4 –0.2 2.4 4.4 –0.4 6.2 3.4 8.3 4.5 4.4 3.6 3.7 3.3 3.9 –1.2 2.7 –8.9
186.9 –6.2 106.3 181.0 106.9 74.2 38.3 92.0 14.2 27.6 13.8 101.4 –37.2 90.5 –66.2 87.2 115.6 –55.8 520.7 –55.5 85.7 .. 1.6 48.5 –42.6 –33.9 198.9 65.2 333.9 33.9 –38.1 133.1 12.3 –66.2 –12.0 104.4 88.6 165.3 34,883.6 395.9 –10.0 33.4 47.9 1.0 152.1 74.7 206.3 97.4 88.1 82.7 71.5 76.0 70.7 –13.0 47.3 –82.1
Methane emissions Total thousand metric tons of carbon dioxide equivalent 2005
% changeb 1990– 2005
5,380 11,050 712,330 224,330 95,060 10,980 3,660 1,170 36,670 1,160 53,480 1,610 28,270 20,310 10,650 31,280 11,200 3,520 .. 2,290 980 .. .. 8,540 3,650 .. .. .. 25,510 .. .. .. 120,100 2,590 4,840 13,240 11,680 60,840 4,260 36,040 15,180 27,490 6,350 .. 78,290 12,080 4,260 110,300 3,040 .. 17,750 21,510 44,860 60,060 7,140 ..
7.2 –22.3 13.9 24.5 73.7 –1.3 –68.3 15.8 –13.3 –4.9 –7.3 49.1 –48.9 4.6 8.7 14.0 64.7 –24.8 .. –47.0 34.2 .. .. –2.4 –52.8 .. .. .. 19.8 .. .. .. 25.3 –45.8 –34.4 46.0 23.9 51.5 –1.4 6.6 –21.4 0.4 35.4 .. 31.2 58.5 110.9 33.2 2.4 .. 51.8 24.6 15.5 –33.3 –4.2 ..
Nitrous oxide emissions
% of total
Total thousand metric tons of carbon dioxide equivalent
2005
2005
2005
% changeb 1990– 2005
5.8 52.6 14.7 36.2 64.7 48.7 24.3 9.4 19.1 3.4 30.0 13.0 49.1 18.0 29.0 18.5 93.9 10.5 .. 40.6 12.2 .. .. 77.6 44.1 .. .. .. 57.2 .. .. .. 22.2 43.6 2.9 2.6 16.9 6.8 4.7 10.4 23.6 10.4 4.7 .. 45.5 61.8 76.1 14.1 4.3 .. 1.7 6.4 8.0 67.0 8.0 ..
71.9 18.3 64.8 41.2 21.8 14.7 32.0 36.8 37.7 47.4 13.4 24.2 37.9 65.0 36.4 31.1 1.5 72.2 .. 29.3 18.4 .. .. 8.9 38.1 .. .. .. 22.3 .. .. .. 39.6 30.9 83.9 41.6 64.3 70.0 89.9 80.5 49.2 82.3 80.2 .. 33.7 14.3 12.9 66.3 72.4 .. 70.9 48.1 66.7 18.4 52.9 ..
3,860 8,760 300,680 69,910 66,140 3,990 12,320 1,820 37,200 1,020 23,590 1,240 5,530 19,060 23,160 22,020 540 3,260 .. 1,390 1,020 .. .. 2,050 2,860 .. .. .. 9,920 .. .. .. 75,500 970 22,850 15,510 9,930 25,900 4,620 7,100 16,800 27,960 3,210 .. 39,030 4,680 1,140 80,040 2,070 .. 12,870 18,720 18,940 26,110 7,000 ..
8.7 –26.7 33.5 16.1 36.0 –39.3 –4.0 –4.2 4.6 –16.4 –26.2 6.9 –76.6 –12.7 152.0 132.3 116.0 –23.1 .. –48.3 37.8 .. .. –28.3 –31.3 .. .. .. –14.5 .. .. .. 7.5 –70.3 128.5 7.9 236.6 80.0 9.0 24.6 –13.0 –17.6 –14.4 .. 39.1 –11.5 31.0 44.5 –17.9 .. 29.0 30.9 5.3 –17.3 1.2 ..
Industrial Agricultural
ENVIRONMENT
Trends in greenhouse gas emissions
3.9 Other greenhouse gas emissions
% of total
Total thousand metric tons of carbon dioxide equivalent
2005
2005
2005
% changeb 1990– 2005
0.0 20.7 0.5 0.3 0.9 0.0 0.2 0.0 23.7 0.0 8.4 0.0 0.0 0.0 0.0 56.6 0.0 0.0 .. 0.0 0.0 .. .. 0.0 0.0 .. .. .. 3.9 .. .. .. 1.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 33.8 0.1 0.0 .. 0.0 37.8 0.0 0.8 0.0 .. 0.0 0.0 0.1 22.3 9.9 ..
97.9 76.0 93.0 72.6 97.6 93.0 92.6 83.5 70.5 96.1 49.3 93.5 90.2 96.4 97.5 36.1 81.5 98.8 .. 88.5 93.1 .. .. 91.7 90.2 .. .. .. 64.3 .. .. .. 90.1 94.8 99.6 75.2 99.7 66.8 99.1 88.5 51.5 99.4 96.9 .. 87.1 53.0 96.5 96.4 95.7 .. 81.8 89.4 95.6 72.5 80.7 ..
0 1,540 9,510 900 1,560 470 2,050 1,140 27,710 0 70,570 10 0 0 860 8,700 390 60 .. 110 0 .. .. 290 150 .. .. .. 530 .. .. .. 3,160 360 0 0 0 10 0 0 5,300 820 0 .. 80 1,770 0 620 0 .. 0 80 350 1,270 1,050 ..
.. 102.6 18.7 –34.8 –26.8 20.5 1,763.6 35.7 480.9 .. 165.7 .. .. .. 186.7 61.1 56.0 .. .. .. .. .. .. 190.0 .. .. .. .. –44.8 .. .. .. 63.7 .. .. .. .. .. .. .. –10.9 105.0 .. .. –33.3 –64.5 .. –11.4 .. .. .. .. 250.0 176.1 707.7 ..
Industrial Agricultural
2009 World Development Indicators
167
3.9
Trends in greenhouse gas emissions Carbon dioxide emissions
average annual % growtha % changeb 1990– 1990– 2005 2005
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbiac Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
–42.6 –3.5 –33.5 –2.6 14.6 1.6 93.0 2.2 61.9 2.9 –59.8 –2.4 181.3 5.3 34.2 0.8 –28.8 –1.6 –17.6 0.8 44.7 3,123.5 23.2 1.2 62.2 3.4 193.1 8.0 97.3 6.5 125.0 10.8 –1.9 –0.2 –3.6 –0.2 90.9 2.9 –77.7 –10.0 100.2 3.9 182.9 6.2 .. .. 79.0 4.7 93.1 4.0 65.7 3.2 75.2 3.6 30.2 2.5 183.8 7.4 –52.2 –5.1 126.3 6.9 –4.0 –0.3 20.4 1.3 42.2 1.2 13.1 1,602.7 26.2 2.6 376.0 11.9 .. .. 110.2 4.5 –3.2 –1.1 –31.2 –3.2 29.5 w 1.6 w 39.4 0.9 43.1 2.0 93.5 3.8 –8.3 –0.6 42.9 1.9 123.4 4.2 –29.3 –2.3 33.4 1.9 96.8 4.4 106.7 4.8 40.3 2.0 26.9 1.3 43.2 1.2
Methane emissions Total thousand metric tons of carbon dioxide equivalent 2005
23,260 501,380 .. 63,500 6,340 6,720 .. 1,260 5,290 1,630 .. 59,200 38,010 10,280 67,310 .. 6,460 4,150 7,960 3,270 39,460 78,840 .. 2,840 3,820 4,390 23,140 23,060 .. 75,640 34,250 39,400 810,280 17,700 51,480 65,730 75,080 .. 9,040 16,770 10,400 6,607,490 s 742,160 4,255,740 2,731,100 1,524,640 4,997,900 .. 851,260 929,970 209,070 961,480 .. 1,609,590 232,220
% changeb 1990– 2005
–45.0 –20.6 .. 59.9 14.2 –47.7 .. 70.3 –29.0 –6.3 .. 13.3 20.1 0.0 69.3 .. –15.8 –13.4 37.0 –11.4 46.9 14.4 .. 58.7 52.2 17.4 –14.5 –30.6 .. –48.3 79.2 –41.8 –5.5 25.4 23.7 58.3 41.7 .. 95.7 70.8 –4.1 7.0 w 32.7 8.6 14.2 –0.2 11.6 .. –26.2 36.0 47.8 15.3 .. –5.0 –25.9
Nitrous oxide emissions
% of total Industrial Agricultural 2005
2005
52.4 77.3 .. 91.8 4.7 16.4 .. 27.0 54.3 20.9 .. 54.3 11.3 12.3 21.5 .. 6.5 8.7 33.8 10.1 20.3 9.4 .. 14.8 78.0 32.1 15.3 81.8 .. 68.9 96.8 35.7 56.4 0.6 70.1 42.0 17.8 .. 44.5 5.7 24.8 34.8 w 21.1 32.2 27.8 39.9 30.5 30.5 69.1 10.7 52.0 14.1 24.4 47.9 22.2
30.1 7.9 .. 1.9 75.9 59.2 .. 4.8 19.5 47.9 .. 23.8 44.1 61.8 73.3 .. 41.5 68.0 34.7 68.5 63.5 76.1 .. 48.6 1.0 34.2 59.5 15.2 .. 15.7 1.7 50.7 18.4 90.3 23.2 33.6 66.8 .. 27.7 68.6 60.4 43.1 w 59.3 46.4 51.7 36.9 48.3 51.9 16.2 57.9 25.9 66.0 49.4 26.9 47.6
Total thousand metric tons of carbon dioxide equivalent 2005
% changeb 1990– 2005
11,790 –52.3 42,650 –67.0 .. .. 7,720 –6.2 10,250 64.8 4,700 –48.2 .. .. 7,970 4,327.8 2,760 –40.6 1,100 2.8 .. .. 29,250 10.5 48,520 37.5 3,130 29.9 59,750 51.6 .. .. 6,070 –4.1 2,840 –10.4 9,430 20.0 1,590 –48.9 31,690 36.0 27,990 31.2 .. .. 5,470 174.9 360 5.9 7,230 69.7 47,950 8.3 3,200 –22.9 .. .. 23,270 –66.5 2,730 193.5 65,480 –4.4 456,210 10.5 15,630 3.0 14,660 2.3 26,460 21.9 37,470 169.2 .. .. 7,080 38.6 11,410 137.7 10,160 13.3 3,787,800 s 14.0 w 477,730 53.9 2,179,990 13.7 1,448,600 25.7 731,390 –4.3 2,657,720 19.3 .. .. 215,350 –46.5 645,520 24.6 151,830 29.4 428,050 37.6 .. .. 1,130,080 3.2 303,960 –3.1
Other greenhouse gas emissions
% of total Industrial Agricultural 2005
2005
25.9 8.0 .. 0.0 0.0 11.1 .. 95.7 32.2 0.0 .. 7.3 3.5 0.0 0.0 .. 8.4 8.1 2.8 0.0 0.0 0.7 .. 0.0 0.0 4.1 9.0 20.0 .. 41.6 0.0 37.1 5.5 0.0 0.3 0.1 0.0 .. 0.0 3.7 0.0 5.2 w 0.2 3.3 2.4 5.1 2.8 2.2 15.6 1.9 3.4 0.5 0.6 10.7 12.8
69.6 76.2 .. 92.1 99.0 81.5 .. 0.8 58.0 88.2 .. 82.7 85.7 89.1 96.2 .. 76.8 78.2 94.9 99.4 84.3 87.9 .. 88.8 91.7 94.2 88.0 78.8 .. 54.2 90.5 52.2 74.7 99.6 98.3 77.8 94.9 .. 98.9 65.1 97.1 82.6 w 89.6 84.8 86.7 81.0 85.7 90.0 77.5 80.8 92.0 93.4 78.5 75.3 76.4
Total thousand metric tons of carbon dioxide equivalent 2005
% changeb 1990– 2005
2,220 48.0 56,600 192.1 .. .. 1,530 –32.3 10 .. 840 147.1 .. .. 1,300 225.0 710 7,000.0 210 –63.8 .. .. 2,600 79.3 15,050 239.0 0 .. 0 .. .. .. 1,620 63.6 3,310 335.5 0 .. 120 50.0 0 .. 940 –40.5 .. .. 0 .. 0 .. 30 .. 1,480 –47.9 250 .. .. .. 1,390 2,216.7 480 118.2 14,030 138.6 108,420 18.8 20 .. 760 .. 2,300 72.9 10 .. .. .. 10 .. 0 .. 20 .. 601,890 s 126.9 w 2,730 96.4 221,040 244.3 139,690 428.9 81,350 115.3 223,770 241.2 .. .. 67,550 163.6 14,700 38.4 4,300 –15.7 10,130 16.3 .. .. 378,120 89.4 135,750 236.8
a. Calculated using the least squares method, which accounts for ups and downs of all data points in the period (see Statistical methods). b. Calculated as the change in emission since 1990, which is the baseline for Kyoto Protocol requirements. c. Includes Kosovo and Montenegro.
168
2009 World Development Indicators
About the data
ENVIRONMENT
Trends in greenhouse gas emissions
3.9
Definitions
Greenhouse gases—which include carbon dioxide,
compared. A kilogram of methane is 21 times as
• Carbon dioxide emissions are emissions from
methane, nitrous oxide, hydrofluorocarbons, per-
effective at trapping heat in the earth’s atmosphere
the burning of fossil fuels and the manufacture of
fluorocarbons, and sulfur hexafluoride—contribute
as a kilogram of carbon dioxide within 100 years.
cement and include carbon dioxide produced during
Nitrous oxide emissions are mainly from fossil fuel
consumption of solid, liquid, and gas fuels and gas
Carbon dioxide emissions, largely a by-product of
combustion, fertilizers, rainforest fires, and animal
flaring. • Methane emissions are emissions from
energy production and use (see table 3.7), account
waste. Nitrous oxide is a powerful greenhouse gas,
human activities such as agriculture and from indus-
for the largest share of greenhouse gases. Anthro-
with an estimated atmospheric lifetime of 114 years,
trial methane production. • Industrial methane emis-
pogenic carbon dioxide emissions result primarily
compared with 12 years for methane. The per kilo-
sions are emissions from the handling, transmission,
from fossil fuel combustion and cement manufactur-
gram global warming potential of nitrous oxide is
and combustion of fossil fuels and biofuels. • Agri-
ing. Burning oil releases more carbon dioxide than
nearly 310 times that of carbon dioxide within 100
cultural methane emissions are emissions from
burning natural gas, and burning coal releases even
years.
animals, animal waste, rice production, agricultural
to climate change.
more for the same level of energy use. Cement manu-
Other greenhouse gases covered under the Kyoto
waste burning (nonenergy, on-site), and savannah
facturing releases about half a metric ton of carbon
Protocol are hydrofluorocarbons, perfluorocarbons,
burning. • Nitrous oxide emissions are emissions
dioxide for each metric ton of cement produced.
and sulfur hexafluoride. Although emissions of these
from agricultural biomass burning, industrial activi-
Methane emissions result largely from agricultural
artificial gases are small, they are more powerful
ties, and livestock management. • Industrial nitrous
activities, industrial production landfills and waste-
greenhouse gases than carbon dioxide, with much
oxide emissions are emissions produced during the
water treatment, and other sources such as tropi-
higher atmospheric lifetimes and high global warm-
manufacturing of adipic acid and nitric acid. • Agri-
cal forest and other vegetation fires. The emissions
ing potential.
cultural nitrous oxide emissions are emissions pro-
are usually expressed in carbon dioxide equivalents
For a discussion of carbon dioxide sources and
duced through fertilizer use (synthetic and animal
using the global warming potential, which allows
the methodology behind emissions calculation, see
manure), animal waste management, agricultural
the effective contributions of different gases to be
About the data for table 3.8.
waste burning (nonenergy, on-site), and savannah burning. • Other greenhouse gas emissions are byproduct emissions of hydrofluorocarbons, perfluoro-
The 10 largest contributors to methane emissions account for about 62 percent of emissions
3.9a
carbons, and sulfur hexafluoride.
Methane emissions, 2005 (million metric tons of carbon dioxide equivalent) 1,000
750
500
250
0 China
United States
India
Russian Federation
Brazil
Indonesia
Mexico
Australia Pakistan
Canada
Source: Table 3.9.
The 10 largest contributors to nitrous oxide emissions account for about 56 percent of emissions
3.9b
Nitrous oxide emissions, 2005 (million metric tons of carbon dioxide equivalent) 600
Data sources
400
Data on carbon dioxide emissions are from the Carbon Dioxide Information Analysis Center, Envi-
200
ronmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States. Data on 0
methane, nitrous oxide, and other greenhouse China United States India
Source: Table 3.9.
Brazil
Australia Argentina Pakistan
France
Mexico
Indonesia
gases emissions are compiled by the International Energy Agency.
2009 World Development Indicators
169
3.10
Sources of electricity Electricity production
Sources of electricitya
% of total billion kilowatt hours 1990
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
.. 3.2 16.1 0.8 50.7 10.4 154.3 49.3 23.2 7.7 39.5 70.3 0.0 2.1 14.6 0.9 222.8 42.1 .. .. .. 2.7 481.9 .. .. 18.4 621.2 28.9 36.4 5.7 0.5 3.5 2.0 9.2 15.0 62.3 26.0 3.7 6.3 42.3 2.2 0.1 17.4 1.2 54.4 417.2 1.0 .. 13.7 547.7 5.7 34.8 2.3 .. .. 0.6
Coal
Gas
Oil
Hydropower
Nuclear power
2006
1990
2006
1990
2006
1990
2006
1990
2006
1990
2006
.. 5.1 35.2 3.0 115.0 5.9 251.3 60.7 23.6 24.3 31.8 84.3 0.1 5.3 13.3 1.0 419.3 45.5 .. .. 1.2 4.0 612.5 .. .. 57.6 2,864.2 38.6 54.3 7.9 0.5 8.7 5.5 12.3 16.5 83.7 45.7 14.2 15.4 115.4 5.6 0.3 9.7 3.3 82.3 569.2 1.7 .. 7.3 629.4 8.4 60.2 7.9 .. .. 0.6
.. 0.0 0.0 0.0 1.3 0.0 77.1 14.2 0.0 0.0 0.0 28.2 0.0 0.0 71.8 88.1 2.0 50.3 .. .. .. 0.0 17.1 .. .. 34.3 71.3 98.3 10.1 0.0 0.0 0.0 0.0 6.8 0.0 76.4 90.7 1.2 0.0 0.0 0.0 0.0 85.8 0.0 18.5 8.5 0.0 .. 0.0 58.7 0.0 72.4 0.0 .. .. 0.0
.. 0.0 0.0 0.0 1.8 0.0 79.2 13.7 0.0 0.0 0.0 10.9 0.0 0.0 54.9 99.4 2.4 42.2 .. .. 0.0 0.0 17.1 .. .. 17.1 80.4 68.9 7.5 0.0 0.0 0.0 0.0 18.3 0.0 60.4 53.9 13.4 0.0 0.0 0.0 0.0 90.2 0.0 20.6 4.6 0.0 .. 0.0 48.0 0.0 53.6 13.6 .. .. 0.0
.. 0.0 93.7 0.0 39.2 16.4 10.6 15.7 0.0 84.3 58.1 7.7 0.0 37.6 0.0 0.0 0.0 7.6 .. .. .. 0.0 2.0 .. .. 1.3 0.4 0.0 12.4 0.0 0.0 0.0 0.0 20.2 0.2 0.6 2.7 0.0 0.0 39.6 0.0 0.0 5.9 0.0 8.6 0.7 16.4 .. 15.6 7.4 0.0 0.3 0.0 .. .. 0.0
.. 0.0 97.2 0.0 50.2 24.8 12.2 17.6 63.5 87.6 95.0 27.3 0.0 39.3 0.0 0.0 4.4 4.7 .. .. 0.0 0.0 5.5 .. .. 19.9 0.5 30.8 12.4 0.0 17.9 0.0 72.7 16.7 0.0 3.9 20.6 9.1 9.6 72.1 0.0 0.0 8.0 0.0 15.0 3.9 15.3 .. 26.7 12.1 0.0 17.6 0.0 .. .. 0.0
.. 10.9 5.4 13.8 9.8 68.6 2.7 3.8 97.0 4.3 41.8 1.9 100.0 5.3 7.3 11.9 2.2 2.9 .. .. .. 1.5 3.4 .. .. 7.6 7.9 1.7 1.0 0.4 0.6 2.5 33.3 31.6 91.5 0.9 3.4 88.6 21.5 36.9 6.9 100.0 8.3 11.6 3.1 2.1 11.2 .. 29.2 1.9 0.0 22.3 9.0 .. .. 20.6
.. 1.8 2.2 9.9 7.0 0.0 0.9 2.7 25.9 6.7 4.6 1.6 100.0 16.7 1.2 0.6 3.0 0.8 .. .. 95.7 5.9 1.5 .. .. 1.6 1.8 0.3 0.2 0.3 0.0 6.1 0.0 15.9 96.7 0.3 3.5 67.3 44.1 16.1 44.2 99.3 0.3 0.3 0.6 1.2 29.5 .. 0.3 1.5 33.3 16.0 25.3 .. .. 52.5
.. 89.1 0.8 86.2 35.2 15.0 9.2 63.9 3.0 11.4 0.1 0.4 0.0 55.3 20.9 0.0 92.8 4.5 .. .. .. 98.5 61.6 .. .. 55.3 20.4 0.0 75.6 99.6 99.4 97.5 66.7 41.3 0.6 1.9 0.1 9.4 78.5 23.5 73.5 0.0 0.0 88.4 20.0 12.9 72.1 .. 55.2 3.2 100.0 5.1 76.0 .. .. 76.5
.. 98.2 0.6 90.1 33.0 30.7 6.2 57.4 10.7 5.7 0.1 0.4 0.0 40.8 43.9 0.0 83.2 9.3 .. .. 4.1 94.1 58.0 .. .. 59.5 15.2 0.0 78.7 99.7 82.1 75.9 27.3 48.8 0.6 3.0 0.1 10.0 46.3 11.2 35.1 0.0 0.1 99.7 14.0 9.8 54.8 .. 72.9 3.2 66.7 9.7 48.3 .. .. 47.5
.. 0.0 0.0 0.0 14.3 0.0 0.0 0.0 0.0 0.0 0.0 60.8 0.0 0.0 0.0 0.0 1.0 34.8 .. .. .. 0.0 15.1 .. .. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 35.3 75.3 0.0 .. 0.0 27.8 0.0 0.0 0.0 .. .. 0.0
.. 0.0 0.0 0.0 6.7 44.4 0.0 0.0 0.0 0.0 0.0 55.3 0.0 0.0 0.0 0.0 3.3 42.8 .. .. 0.0 0.0 16.0 .. .. 0.0 1.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 31.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 27.8 79.1 0.0 .. 0.0 26.6 0.0 0.0 0.0 .. .. 0.0
2009 World Development Indicators
Electricity production
3.10
Sources of electricitya
% of total billion kilowatt hours 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
2.3 28.4 289.4 33.3 59.1 24.0 14.2 20.9 213.1 2.5 835.5 3.6 87.4 3.2 27.7 105.4 18.5 15.7 .. 6.6 1.5 .. .. 10.2 28.4 5.8 .. .. 23.0 .. .. .. 124.1 16.2 3.5 9.6 0.5 2.5 1.4 0.9 71.9 32.3 1.4 .. 13.5 121.6 4.5 37.7 2.7 .. 27.2 13.8 25.2 134.4 28.4 ..
Coal
Gas
Oil
Hydropower
Nuclear power
2006
1990
2006
1990
2006
1990
2006
1990
2006
1990
2006
6.0 35.9 744.1 133.1 201.0 31.9 27.7 51.8 307.7 7.5 1,090.5 11.6 71.7 6.5 22.4 402.3 47.6 17.1 .. 4.9 9.3 .. .. 24.0 12.1 7.0 .. .. 91.6 .. .. .. 249.6 3.8 3.6 23.2 14.7 6.2 1.6 2.7 98.4 43.5 3.0 .. 23.1 121.3 13.6 98.4 6.0 .. 53.8 27.4 56.7 160.8 48.6 ..
0.0 30.5 66.2 31.5 0.0 0.0 41.6 50.1 16.8 0.0 14.0 0.0 71.1 0.0 40.1 16.8 0.0 13.1 .. 0.0 0.0 .. .. 0.0 0.0 89.7 .. .. 12.3 .. .. .. 6.3 30.8 92.4 23.0 13.9 1.6 1.5 0.0 38.3 1.9 0.0 .. 0.1 0.1 0.0 0.1 0.0 .. 0.0 0.0 7.7 97.5 32.1 ..
0.0 19.8 68.3 44.1 0.0 0.0 21.3 69.3 16.4 0.0 27.4 0.0 70.3 0.0 40.9 38.0 0.0 3.3 .. 0.0 0.0 .. .. 0.0 0.0 72.9 .. .. 25.3 .. .. .. 12.7 0.0 96.9 58.1 0.0 0.0 5.2 0.0 26.9 12.5 0.0 .. 0.0 0.1 0.0 0.1 0.0 .. 0.0 3.0 27.0 93.6 30.8 ..
0.0 15.7 3.4 2.3 52.5 0.0 27.7 0.0 18.6 0.0 20.0 11.9 10.5 0.0 0.0 9.1 45.7 23.5 .. 26.1 0.0 .. .. 0.0 23.8 0.0 .. .. 22.0 .. .. .. 11.6 42.3 0.0 0.0 0.0 39.3 0.0 0.0 50.9 17.6 0.0 .. 53.7 0.0 81.6 33.6 0.0 .. 0.0 1.7 0.0 0.1 0.0 ..
0.0 36.7 8.3 14.6 73.7 0.0 52.3 17.5 51.4 0.0 23.3 70.4 11.8 0.0 0.0 18.1 27.5 9.6 .. 42.9 0.0 .. .. 40.9 20.4 0.0 .. .. 64.0 .. .. .. 45.5 97.5 0.0 12.8 0.1 40.2 0.0 0.0 57.6 22.6 0.0 .. 57.8 0.4 82.0 36.4 0.0 .. 0.0 9.5 28.8 1.9 25.4 ..
1.7 4.8 3.5 42.7 37.3 89.2 10.0 49.9 48.2 92.4 18.5 87.8 10.0 7.1 3.6 17.9 54.3 0.0 .. 5.4 66.7 .. .. 100.0 14.6 1.8 .. .. 48.4 .. .. .. 56.7 25.4 7.6 64.4 23.6 10.9 3.3 0.1 4.3 0.0 39.8 .. 13.7 0.0 18.4 20.6 14.7 .. 0.0 21.5 46.7 1.2 33.1 ..
56.1 1.5 4.2 29.1 17.2 98.5 9.8 13.1 14.9 96.4 8.5 29.2 7.0 30.5 2.8 5.6 72.5 0.0 .. 0.1 92.5 .. .. 59.1 1.7 3.5 .. .. 3.0 .. .. .. 21.6 0.0 3.1 21.4 0.1 5.8 0.6 0.4 2.1 0.1 72.2 .. 8.8 0.0 18.0 28.6 38.9 .. 0.0 8.4 8.2 1.5 10.8 ..
98.3 0.6 24.8 20.2 10.3 10.8 4.9 0.0 14.8 3.6 10.7 0.3 8.4 76.6 56.3 6.0 0.0 63.5 .. 67.6 33.3 .. .. 0.0 1.5 8.5 .. .. 17.3 .. .. .. 18.9 1.6 0.0 12.7 62.6 48.1 95.2 99.9 0.1 72.3 28.8 .. 32.6 99.6 0.0 44.9 83.2 .. 99.9 75.8 24.0 1.1 32.3 ..
43.2 0.5 15.3 7.2 9.1 1.5 2.6 0.1 12.0 2.2 7.9 0.4 10.8 50.6 56.2 0.9 0.0 87.2 .. 55.2 7.5 .. .. 0.0 3.3 23.6 .. .. 7.7 .. .. .. 12.2 2.0 0.0 6.9 99.9 53.9 94.1 99.6 0.1 53.9 12.5 .. 33.4 98.5 0.0 32.5 59.8 .. 100.0 78.5 17.5 1.3 22.6 ..
0.0 48.3 2.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 24.2 0.0 0.0 0.0 0.0 50.2 0.0 0.0 .. 0.0 0.0 .. .. 0.0 60.0 0.0 .. .. 0.0 .. .. .. 2.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.9 0.0 0.0 .. 0.0 0.0 0.0 0.8 0.0 .. 0.0 0.0 0.0 0.0 0.0 ..
0.0 37.5 2.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 27.8 0.0 0.0 0.0 0.0 37.0 0.0 0.0 .. 0.0 0.0 .. .. 0.0 71.6 0.0 .. .. 0.0 .. .. .. 4.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.5 0.0 0.0 .. 0.0 0.0 0.0 2.3 0.0 .. 0.0 0.0 0.0 0.0 0.0 ..
2009 World Development Indicators
171
ENVIRONMENT
Sources of electricity
3.10
Sources of electricity Electricity production
Sources of electricitya
% of total billion kilowatt hours
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
1990
2006
64.3 1,082.2 .. 69.2 0.9 40.9b .. 15.7 25.5 12.0 .. 165.4 151.2 3.2 1.5 .. 146.0 55.0 11.6 18.1 1.6 44.2 .. 0.2 3.6 5.8 57.5 14.6 .. 298.6 17.1 317.8 3,202.8 7.4 56.3 59.3 8.7 .. 1.7 8.0 9.4 11,845.0 t 261.0 4,017.2 1,705.1 2,312.1 4,278.5 794.1 2,085.5 606.2 187.9 341.7 260.2 7,585.5 1,693.6
62.7 993.9 .. 179.8 2.4 36.5 .. 39.4 31.3 15.1 .. 251.9 299.1 9.4 4.2 .. 143.3 62.1 37.3 16.9 2.8 138.7 .. 0.2 7.0 14.1 176.3 13.7 .. 193.2 66.8 394.5 4,274.3 5.6 49.3 110.4 56.5 .. 5.3 9.4 9.8 18,977.0 t 449.1 7,919.8 4,921.3 2,998.3 8,376.6 3,394.2 1,966.8 1,192.1 515.0 886.2 416.8 10,644.0 2,324.4
Coal
Gas
1990
2006
1990
28.8 14.3 .. 0.0 0.0 69.1b .. 0.0 31.9 31.9 .. 94.3 40.1 0.0 0.0 .. 1.1 0.1 0.0 0.0 0.0 25.0 .. 0.0 0.0 0.0 35.1 0.0 .. 38.2 0.0 65.0 53.1 0.0 7.4 0.0 23.1 .. 0.0 0.5 53.3 37.3 w 9.4 35.3 47.0 26.6 33.7 61.1 27.7 3.8 1.2 56.1 62.2 39.2 33.7
40.3 17.9 .. 0.0 0.0 68.7 .. 0.0 18.3 36.0 .. 93.5 22.8 0.0 0.0 .. 1.1 0.0 0.0 0.0 3.8 18.0 .. 0.0 0.0 0.0 26.5 0.0 .. 33.6 0.0 38.5 49.8 0.0 4.7 0.0 17.2 .. 0.0 0.2 43.0 40.8 w 5.8 48.3 61.2 27.0 45.9 72.1 29.4 5.2 2.6 57.4 57.8 36.6 24.6
35.1 47.3 .. 48.1 2.3 3.2b .. 0.0 7.1 0.0 .. 0.0 1.0 0.0 0.0 .. 0.3 0.6 20.5 9.1 0.0 40.2 .. 0.0 99.0 63.7 17.7 95.2 .. 16.7 96.3 1.6 11.9 0.0 76.4 26.2 0.1 .. 0.0 0.0 0.0 14.6 w 29.0 20.6 10.6 28.0 21.1 3.4 34.3 9.2 36.9 8.5 2.8 10.9 8.6
Oil
Hydropower
2006
1990
2006
18.9 46.1 .. 47.7 1.9 0.3 .. 78.0 6.1 2.5 .. 0.0 30.2 0.0 0.0 .. 0.4 1.3 38.0 2.3 43.8 67.8 .. 0.0 99.4 84.9 45.8 100.0 .. 12.7 98.0 35.8 19.6 0.1 69.6 13.4 37.0 .. 0.0 0.0 0.0 20.1 w 30.2 18.6 11.9 29.5 19.2 6.7 35.5 19.3 60.7 13.5 4.6 20.7 21.2
18.4 11.9 .. 51.9 93.0 4.6b .. 100.0 6.4 4.8 .. 0.0 5.7 0.2 36.8 .. 0.9 0.7 56.0 0.0 4.9 23.5 .. 39.9 0.1 35.5 6.9 0.0 .. 16.1 3.7 10.9 4.1 5.1 4.4 11.5 15.0 .. 100.0 0.3 0.0 10.3 w 7.3 14.4 15.9 13.4 14.0 12.5 13.0 18.9 48.3 5.3 1.9 8.2 9.5
2.6 2.5 .. 52.3 85.1 0.9 .. 22.0 2.3 0.3 .. 0.0 8.0 50.6 67.5 .. 1.2 0.3 51.2 0.0 0.6 6.1 .. 57.5 0.2 14.2 2.5 0.0 .. 0.4 2.0 1.3 1.9 35.1 12.8 14.6 4.1 .. 100.0 0.4 0.2 5.5 w 12.4 6.1 6.0 6.2 6.4 3.3 2.8 12.5 27.8 7.5 3.2 4.7 5.0
Nuclear power
1990
2006
1990
2006
17.7 15.3 .. 0.0 0.0 23.1b .. 0.0 7.4 24.7 .. 0.6 16.8 99.8 63.2 .. 49.7 54.2 23.5 90.9 95.1 11.3 .. 60.1 0.0 0.8 40.2 4.8 .. 3.5 0.0 1.6 8.5 94.2 11.8 62.3 61.8 .. 0.0 99.2 46.7 18.0 w 41.9 22.3 20.7 23.5 23.5 21.5 13.9 63.7 12.4 27.4 15.9 14.9 11.1
29.3 17.4 .. 0.0 9.6 30.1 .. 0.0 14.1 23.8 .. 1.5 8.5 49.4 32.5 .. 43.1 49.8 10.7 97.7 51.7 5.9 .. 41.2 0.0 0.7 25.1 0.0 .. 6.7 0.0 1.2 6.8 64.0 12.8 72.0 41.8 .. 0.0 99.4 56.8 15.9 w 38.8 20.6 16.3 27.6 21.5 15.0 17.4 57.3 7.4 17.4 18.0 11.4 9.1
0.0 10.9 .. 0.0 0.0 0.0 b .. 0.0 47.2 38.7 .. 5.1 35.9 0.0 0.0 .. 46.7 43.0 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 .. 25.5 0.0 20.7 19.1 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 17.0 w 0.1 6.3 4.8 7.4 5.9 0.0 10.8 2.1 0.0 1.9 3.2 23.2 35.6
9.0 15.7 .. 0.0 0.0 0.0 .. 0.0 57.6 36.7 .. 4.7 20.1 0.0 0.0 .. 46.7 44.8 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 .. 46.7 0.0 19.1 19.1 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 14.7 w 0.5 5.1 3.4 7.8 4.8 1.6 14.4 2.7 0.0 2.4 2.8 22.5 33.3
a. Shares may not sum to 100 percent because some sources of generated electricity (such as wind, solar, and geothermal) are not shown. b. Includes Kosovo and Montenegro.
172
2009 World Development Indicators
About the data
3.10
Definitions
Use of energy is important in improving people’s
adjustments are made to compensate for differences
• Electricity production is measured at the termi-
standard of living. But electricity generation also
in definitions. The IEA makes these estimates in con-
nals of all alternator sets in a station. In addition to
can damage the environment. Whether such damage
sultation with national statistical offices, oil compa-
hydropower, coal, oil, gas, and nuclear power gen-
occurs depends largely on how electricity is gener-
nies, electric utilities, and national energy experts. It
eration, it covers generation by geothermal, solar,
ated. For example, burning coal releases twice as
occasionally revises its time series to reflect political
wind, and tide and wave energy as well as that from
much carbon dioxide—a major contributor to global
changes. For example, the IEA has constructed his-
combustible renewables and waste. Production
warming—as does burning an equivalent amount
torical energy statistics for countries of the former
includes the output of electric plants designed to
of natural gas (see About the data for table 3.8).
Soviet Union. In addition, energy statistics for other
produce electricity only, as well as that of combined
Nuclear energy does not generate carbon dioxide
countries have undergone continuous changes in
heat and power plants. • Sources of electricity are
emissions, but it produces other dangerous waste
coverage or methodology in recent years as more
the inputs used to generate electricity: coal, gas, oil,
products. The table provides information on electric-
detailed energy accounts have become available.
hydropower, and nuclear power. • Coal is all coal and
ity production by source.
Breaks in series are therefore unavoidable.
brown coal, both primary (including hard coal and
The International Energy Agency (IEA) compiles
lignite-brown coal) and derived fuels (including pat-
data on energy inputs used to generate electricity.
ent fuel, coke oven coke, gas coke, coke oven gas,
IEA data for countries that are not members of the
and blast furnace gas). Peat is also included in this
Organisation for Economic Co-operation and Devel-
category. • Gas is natural gas but not natural gas
opment (OECD) are based on national energy data
liquids. • Oil is crude oil and petroleum products.
adjusted to conform to annual questionnaires com-
• Hydropower is electricity produced by hydroelectric
pleted by OECD member governments. In addition,
power plants. • Nuclear power is electricity produced
estimates are sometimes made to complete major
by nuclear power plants.
aggregates from which key data are missing, and
3.10a
Sources of electricity generation have shifted since 1990 . . . World 1990
2006
Other 3%
Other 3% Nuclear power 15%
Nuclear power 17% Coal 37%
Oil 10%
Coal 41%
Hydropower 16%
Hydropower 18%
Oil 5%
Gas 15%
Gas 20%
Source: Table 3.10.
3.10b
. . . with developing economies relying more on coal Developing economies 1990 Nuclear power 7%
2006 Other 1%
Coal 34%
Hydropower 23%
Oil 14%
Nuclear power 5%
Other 2%
Hydropower 22%
Data sources Data on electricity production are from the IEA’s
Oil 6% Gas 21%
Coal 46%
Gas 19%
electronic files and its annual publications Energy Statistics and Balances of Non-OECD Countries, Energy Statistics of OECD Countries, and Energy
Source: Table 3.10.
Balances of OECD Countries.
2009 World Development Indicators
173
ENVIRONMENT
Sources of electricity
3.11
Urbanization Urban population
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
174
millions 1990 2007
% of total population 1990 2007
2.3 1.2 13.2 3.9 28.3 2.4 14.6 5.1 3.8 22.4 6.7 9.6 1.8 3.7 1.7 0.6 111.8 5.8 1.2 0.4 1.2 5.0 21.3 1.1 1.3 11.0 311.0 5.7 22.6 10.5 1.3 1.6 5.1 2.6 7.8 7.8 4.4 4.0 5.7 24.0 2.5 0.5 1.1 6.1 3.1 42.0 0.6 0.4 3.0 58.1 5.7 6.0 3.7 1.7 0.3 2.0
18 36 52 37 87 68 85 66 54 20 66 96 35 56 39 42 75 66 14 6 13 41 77 37 21 83 27 100 68 28 54 51 40 54 73 75 85 55 55 44 49 16 71 13 61 74 69 38 55 73 36 59 41 28 28 29
6.4 1.5 21.9 9.5 36.3 1.9 18.6 5.6 4.4 42.3 7.1 10.3 3.7 6.2 1.8 1.1 163.1 5.4 2.8 0.9 3.0 10.4 26.5 1.7 2.8 14.6 556.3 6.9 32.6 20.8 2.3 2.8 9.3 2.5 8.5 7.6 4.7 6.7 8.7 32.2 4.1 1.0 0.9 13.2 3.3 47.6 1.1 0.9 2.3 60.5 11.6 6.8 6.4 3.2 0.5 4.4
2009 World Development Indicators
24 46 65 56 92 64 89 67 52 27 73 97 41 65 47 59 85 71 19 10 21 56 80 38 26 88 42 100 74 33 61 63 48 57 76 74 86 68 65 43 60 20 69 17 63 77 85 56 53 74 49 61 48 34 30 45
average annual % growth 1990–2007
6.0 1.2 3.0 5.2 1.4 –1.3 1.4 0.5 0.8 3.7 0.3 0.4 4.3 3.0 0.3 3.9 2.2 –0.4 4.9 5.1 5.3 4.3 1.3 2.4 4.7 1.7 3.4 1.2 2.2 4.0 3.3 3.4 3.5 –0.1 0.5 –0.2 0.5 3.0 2.5 1.7 2.9 4.0 –1.1 4.6 0.5 0.7 3.4 5.6 –1.5 0.2 4.2 0.8 3.3 3.7 3.3 4.5
Population in urban agglomerations of more than 1 million
Population in largest city
% of total population 1990 2007
% of urban population 1990 2007
% of urban population 1990 2006
% of rural population 1990 2006
11 .. 8 15 39 33 60 27 24 8 16 10 .. 25 .. .. 34 14 .. .. 6 14 40 .. .. 35 13 100 32 15 29 24 16 .. 20 12 26 21 26 22 19 .. .. 4 17 23 .. .. 22 8 12 30 .. 15 .. 16
62 .. 14 40 37 49 25 41 45 29 24 10 .. 29 .. .. 13 21 49 .. 49 19 18 .. 38 42 3 100 22 35 53 47 41 .. 27 16 31 38 28 38 39 .. .. 30 28 22 .. .. 41 6 21 51 22 53 .. 56
.. 97 99 55 86 94 100 100 .. 56 .. .. 32 47 99 60 82 100 23 41 .. 47 100 21 19 91 61 .. 81 53 .. 96 39 99 99 100 100 77 88 68 88 20 96 19 100 .. .. .. 96 100 11 100 87 19 .. 49
.. .. 77 9 45 .. 100 100 .. 18 .. .. 2 15 .. 22 37 96 2 44 2 34 99 5 1 48 43 .. 39 1 .. 92 8 98 95 98 100 57 50 37 59 0 94 2 100 .. .. .. 91 100 3 93 58 10 .. 20
12 .. 10 23 39 37 61 28 22 12 19 17 .. 32 .. .. 38 16 8 .. 10 19 44 .. .. 34 18 100 35 17 36 29 20 .. 19 11 20 22 32 21 21 .. .. 4 21 22 .. .. 25 9 16 29 8 16 .. 21
51 .. 15 42 35 57 24 41 43 32 26 17 .. 26 .. .. 12 22 41 .. 48 18 20 .. 35 39 3 100 23 38 59 46 41 .. 26 16 23 32 29 37 35 .. .. 22 34 21 .. .. 48 6 18 48 16 47 .. 46
Access to improved sanitation facilities
45 98 98 79 92 96 100 100 90 48 91 .. 59 54 99 60 84 100 41 44 62 58 100 40 23 97 74 .. 85 42 19 96 38 99 99 100 100 81 91 85 90 14 96 27 100 .. 37 50 94 100 15 99 90 33 48 29
25 97 87 16 83 81 100 100 70 32 97 .. 11 22 92 30 37 96 6 41 19 42 99 25 4 74 59 .. 58 25 21 95 12 98 95 98 100 74 72 52 80 3 94 8 100 .. 30 55 92 100 6 97 79 12 26 12
Urban population
millions 1990 2007
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 11.8 31.6 2.1 1.7 0.6 1.9 2.5 0.2 1.0 3.3 2.5 1.1 2.8 1.1 9.0 1.8 0.8 0.5 59.4 2.1 1.2 11.7 2.9 10.0 0.4 1.7 10.3 2.9 2.2 1.2 33.3 3.1 1.2 33.0 1.3 0.6 2.1 15.0 29.9 23.4 4.7 2.6
3.4 6.7 329.1 113.6 48.3 19.4 2.7 6.6 40.3 1.4 84.7 4.5 8.9 8.0 14.8 39.4 2.6 1.9 1.7 1.5 3.6 0.5 2.2 4.8 2.3 1.4 5.7 2.5 18.4 3.9 1.3 0.5 81.0 1.6 1.5 17.2 7.7 15.6 0.8 4.7 13.3 3.7 3.2 2.3 70.5 3.6 1.9 58.0 2.4 0.8 3.7 19.9 56.4 23.4 6.2 3.9
% of total population 1990 2007
40 66 26 31 56 70 57 90 67 49 63 72 56 18 58 74 98 38 15 69 83 14 45 76 68 58 24 12 50 23 40 44 71 47 57 48 21 25 28 9 69 85 52 15 35 72 66 31 54 15 49 69 49 61 48 72
47 67 29 50 68 67 61 92 68 53 66 78 58 21 62 81 98 36 30 68 87 25 59 77 67 66 29 18 69 32 41 42 77 42 57 56 36 32 36 17 81 86 56 16 48 77 72 36 72 13 60 71 64 61 59 98
average annual % growth 1990–2007
3.1 –0.1 2.5 4.3 2.7 2.4 1.7 2.6 0.4 1.1 0.5 4.0 –0.2 3.7 1.4 1.3 1.3 0.7 6.0 –1.0 2.1 4.7 4.9 2.2 –0.6 1.2 4.1 5.0 4.2 4.6 2.9 0.8 1.8 –1.5 1.3 2.3 5.8 2.6 3.8 6.0 1.5 1.3 2.2 3.9 4.4 1.0 2.5 3.3 3.7 1.5 3.4 1.7 3.7 0.0 1.6 2.4
Population in urban agglomerations of more than 1 million
Population in largest city
% of total population 1990 2007
% of urban population 1990 2007
.. 19 10 9 23 26 26 43 19 .. 46 27 7 6 15 51 65 .. .. .. 43 .. .. 48 .. .. 8 .. 6 10 .. .. 32 .. .. 16 6 7 .. .. 14 25 18 .. 11 .. .. 16 35 .. 22 27 14 4 37 44
.. 17 12 9 23 22 25 60 17 .. 48 18 8 8 19 48 72 .. .. .. 45 .. 28 55 .. .. 9 .. 5 12 .. .. 34 .. .. 19 7 8 .. .. 12 30 21 .. 14 .. .. 18 38 .. 31 29 14 4 39 67
29 29 6 14 21 32 46 48 9 .. 42 37 12 32 21 33 67 38 .. .. 52 .. 55 45 .. .. 33 .. 12 42 .. .. 26 .. 48 23 27 29 .. 23 10 30 34 36 14 22 .. 22 65 .. 45 39 27 7 54 60
29 25 6 8 16 26 40 49 8 .. 42 30 14 38 22 25 74 43 .. .. 52 .. 47 46 .. .. 30 .. 8 38 .. .. 23 .. 60 18 19 26 .. 19 8 34 38 39 13 22 .. 21 53 .. 51 40 20 7 45 69
ENVIRONMENT
Urbanization
3.11
Access to improved sanitation facilities
% of urban population 1990 2006
% of rural population 1990 2006
68 100 44 73 86 75 .. 100 .. 82 100 .. 97 18 .. .. .. .. .. .. 100 .. 59 97 .. .. 15 50 95 53 33 95 74 .. .. 80 .. 47 73 36 100 .. 59 16 33 .. 97 76 .. 67 88 73 71 .. 97 ..
29 100 4 42 78 .. .. .. .. 83 100 .. 96 44 .. .. .. .. .. .. .. 30 24 96 .. .. 6 46 .. 30 11 94 8 .. .. 25 12 15 8 6 100 88 23 1 22 .. 61 14 .. 41 34 15 46 .. 88 ..
78 100 52 67 .. 80 .. 100 .. 82 100 88 97 19 .. .. .. 94 87 82 100 43 49 97 .. 92 18 51 95 59 44 95 91 85 64 85 53 85 66 45 100 .. 57 27 35 .. 97 90 78 67 89 85 81 .. 99 ..
2009 World Development Indicators
55 100 18 37 .. 69 .. .. .. 84 100 71 98 48 .. .. .. 93 38 71 .. 34 7 96 .. 81 10 62 93 39 10 94 48 73 31 54 19 81 18 24 100 .. 34 3 25 .. .. 40 63 41 42 36 72 .. 98 ..
175
3.11
Urbanization Urban population
millions 1990 2007
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
12.3 108.8 0.4 12.5 3.1 3.8 1.3 3.0 3.0 1.0 2.0 18.3 29.3 2.9 6.9 0.2 7.1 4.9 6.2 1.7 4.8 16.0 0.2 1.2 0.1 4.7 33.2 1.7 2.0 34.7 1.5 50.8 188.0 2.8 8.2 16.6 13.4 1.3 2.6 3.2 3.0 2,252.1 s 219.6 1,361.3 873.3 488.0 1,580.9 460.0 273.7 308.0 115.7 279.2 144.3 671.1 213.0
11.6 103.5 1.8 19.9 5.2 3.8 2.2 4.6 3.0 1.0 3.1 28.8 34.5 3.0 16.4 0.3 7.7 5.5 10.7 1.8 10.1 21.1 0.3 2.7 0.2 6.8 50.4 2.4 4.0 31.6 3.4 54.8 245.5 3.1 9.9 25.6 23.3 2.7 6.7 4.2 4.9 3,260.9 s 410.5 2,049.5 1,430.3 619.3 2,460.1 827.7 283.3 438.8 179.3 443.9 287.1 800.9 236.7
a. Includes Kosovo.
176
2009 World Development Indicators
% of total population 1990 2007
53 73 5 77 39 50 33 100 57 50 30 52 75 17 27 23 83 73 49 32 19 29 21 30 9 58 59 45 11 67 79 89 75 89 40 84 20 68 21 39 29 43 w 25 39 32 69 37 29 63 71 52 25 28 73 71
54 73 18 81 42 52 37 100 56 49 36 60 77 15 43 25 84 73 54 26 25 33 27 41 13 66 68 48 13 68 78 90 81 92 37 93 27 72 30 35 37 50 w 32 48 42 75 44 43 64 78 57 29 36 77 73
average annual % growth 1990–2007
–0.3 –0.3 8.8 2.7 3.1 0.0 2.9 2.4 0.1 –0.1 2.7 2.7 1.0 0.2 5.1 2.8 0.5 0.7 3.2 0.3 4.4 1.6 3.7 4.8 2.9 2.1 2.4 2.2 4.1 –0.5 4.9 0.5 1.6 0.6 1.1 2.5 3.2 4.0 5.7 1.6 2.9 2.2 w 3.7 2.4 2.9 1.4 2.6 3.5 0.2 2.1 2.6 2.7 4.0 1.0 0.6
Population in urban agglomerations of more than 1 million
Population in largest city
% of total population 1990 2007
% of urban population 1990 2007
8 18 .. 30 18 .. .. 99 .. .. 14 25 22 .. 9 .. 17 14 26 .. 5 11 .. 16 .. .. 22 .. 4 12 25 26 41 41 10 34 13 .. 5 9 10 18 w 11 15 13 .. 14 .. 15 32 20 10 .. .. 18
9 18 .. 40 21 11 .. 100 .. .. 13 33 24 .. 12 .. 14 15 31 .. 7 10 .. 22 .. .. 27 .. 5 11 31 26 43 45 8 32 13 .. 9 11 12 20 w 12 18 16 28 17 .. 16 34 21 12 13 .. 18
14 8 56 19 45 .. 40 99 .. .. 47 10 15 .. 34 .. 21 19 25 .. 27 37 .. 52 .. .. 20 .. 38 7 32 15 9 46 25 17 30 .. 25 24 34 17 w 28 14 12 17 16 9 13 24 27 10 26 20 15
17 10 48 22 50 21 39 100 .. .. 35 12 16 .. 29 .. 16 20 25 .. 29 32 .. 53 .. .. 20 .. 36 9 40 16 8 49 22 12 22 .. 30 32 32 16 w 26 12 10 17 14 7 14 22 24 11 25 19 15
Access to improved sanitation facilities
% of urban population 1990 2006
% of rural population 1990 2006
88 93 31 100 52 .. .. 100 100 .. .. 64 100 85 53 .. 100 100 94 .. 29 92 .. 25 93 95 96 .. 27 98 98 .. 100 100 97 90 62 .. 79 49 65 76 w 50 72 64 86 69 65 95 81 83 49 41 100 ..
52 70 29 .. 9 .. .. .. 99 .. .. 45 100 68 26 .. 100 100 69 .. 36 72 .. 8 93 44 69 .. 29 93 95 .. 99 99 91 47 21 .. 14 38 35 34 w 19 33 31 53 30 42 77 35 51 8 20 99 ..
88 93 34 100 54 96a 20 100 100 .. 51 66 100 89 50 64 100 100 96 95 31 95 64 24 92 96 96 .. 29 97 98 .. 100 100 97 .. 88 84 88 55 63 78 w 54 76 71 89 73 75 94 86 88 57 42 100 ..
54 70 20 .. 9 88a 5 .. 99 .. 7 49 100 86 24 46 100 100 88 91 34 96 32 3 92 64 72 .. 34 83 95 .. 99 99 95 .. 56 69 30 51 37 44 w 33 45 43 64 41 59 79 51 62 23 24 99 ..
About the data
ENVIRONMENT
Urbanization
3.11
Definitions
There is no consistent and universally accepted
populous nations were to change their definition of
• Urban population is the midyear population of
standard for distinguishing urban from rural areas, in
urban centers. According to China’s State Statis-
areas defined as urban in each country and reported
part because of the wide variety of situations across
tical Bureau, by the end of 1996 urban residents
to the United Nations (see About the data). • Popula-
countries. Most countries use an urban classification
accounted for about 43 percent of China’s popula-
tion in urban agglomerations of more than 1 million
related to the size or characteristics of settlements.
tion, more than double the 20 percent considered
is the percentage of a country’s population living in
Some define urban areas based on the presence of
urban in 1994. In addition to the continuous migra-
metropolitan areas that in 2005 had a population of
certain infrastructure and services. And other coun-
tion of people from rural to urban areas, one of the
more than 1 million. • Population in largest city is
tries designate urban areas based on administrative
main reasons for this shift was the rapid growth in
the percentage of a country’s urban population living
arrangements.
the hundreds of towns reclassified as cities in recent
in that country’s largest metropolitan area. • Access
years.
to improved sanitation facilities is the percentage
The population of a city or metropolitan area depends on the boundaries chosen. For example, in
Because the estimates in the table are based on
of the urban or rural population with access to at
1990 Beijing, China, contained 2.3 million people in
national definitions of what constitutes a city or met-
least adequate excreta disposal facilities (private or
87 square kilometers of “inner city” and 5.4 million in
ropolitan area, cross-country comparisons should be
shared but not public) that can effectively prevent
158 square kilometers of “core city.” The population
made with caution. To estimate urban populations,
human, animal, and insect contact with excreta.
of “inner city and inner suburban districts” was 6.3
UN ratios of urban to total population were applied
Improved facilities range from simple but protected
million and that of “inner city, inner and outer sub-
to the World Bank’s estimates of total population
pit latrines to flush toilets with a sewerage connec-
urban districts, and inner and outer counties” was
(see table 2.1).
tion. To be effective, facilities must be correctly con-
10.8 million. (Most countries use the last definition.)
The table shows access to improved sanitation
For further discussion of urban-rural issues see box
facilities for both urban and rural populations to
3.1a in About the data for table 3.1.
allow comparison of access. Definitions of access
Estimates of the world’s urban population would change significantly if China, India, and a few other
structed and properly maintained.
and urban areas vary, however, so comparisons between countries can be misleading.
Developing economies had the largest increase in urban population between 1990 and 2007
3.11a
Urban population (millions) 1,600
1990
2007
1,200
800
400
0 Low income
Lower middle income
Upper middle income
High income
Source: Table 3.11.
Latin America and the Caribbean had the same share of urban population as high-income economies in 2007
3.11b Urban
Percent
Rural
100
Data sources Data on urban population and the population in
75
urban agglomerations and in the largest city are from the United Nations Population Division’s
50
World Urbanization Prospects: The 2007 Revision. Data on total population are World Bank
25
estimates. Data on access to sanitation are from the World Health Organization and United Nations
0 East Asia & Pacific Source: Table 3.11.
Europe & Central Asia
Latin America Middle East & Caribbean & North Africa
South Asia
Sub-Saharan Africa
High income
Children’s Fund’s Progress on Drinking Water and Sanitation (2008).
2009 World Development Indicators
177
3.12
Urban housing conditions Census year
Household size
number of people National Urban
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
178
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
2009 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
Households 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 88b .. 67 59 70 .. 61 74 98 .. .. .. 73 64 85 .. 66 88 .. 68b .. 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 .. .. .. .. .. .. .. .. .. .. .. 58b .. 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
Households 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 .. 38b .. .. .. .. ..
.. .. 71 .. 26b .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 57 .. .. .. .. 7 .. .. .. .. 28 .. .. .. .. .. .. .. .. .. .. .. .. .. ..b .. .. .. .. ..
Durable dwelling units
Home ownership
Buildings with durable structure % of total National Urban
Privately owned dwellings % of total National Urban
69 .. 83 .. 72 88 .. .. .. 98b .. 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 98b .. 98b 64 .. .. .. ..
.. .. 87 .. 73 70 .. .. .. 58b 61 69 .. 72 50 .. .. .. 96 58 .. 84 1 .. .. 48b 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 .. .. ..
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
2009 World Development Indicators
.. .. 9 .. .. 15 .. .. .. .. .. .. .. 17 .. .. .. .. .. .. .. .. .. .. .. 3b .. .. .. .. .. 6 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 6b 3 4 .. .. ..
179
ENVIRONMENT
3.12
Urban housing conditions
3.12
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 Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela. RB Vietnam West Bank and Gaza Yemen Zambia Zimbabwe
1992 2002 1991 2004 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 5.5 .. 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
Households 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.
180
2009 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 92b .. .. 34 .. .. .. .. .. .. 93b .. .. .. .. .. .. .. 93 .. .. 98b 99 .. .. 21b .. .. .. .. .. .. .. 77 .. .. .. ..
.. .. 78 .. .. .. .. .. .. .. .. .. .. 92b .. .. .. .. .. .. .. 93 .. .. .. .. .. .. .. .. .. .. .. .. .. .. 89 .. .. .. ..
87 .. 92 43 .. .. 68 .. .. 69 .. .. 78 70 b 86b .. .. 31 .. .. 82b 81 .. .. 74b 71 70 .. 80 b .. .. .. 66 57b .. 78 95 78 88b 94 94
77 .. 73 .. .. .. .. .. .. .. .. .. .. 58b 58b .. .. 24 .. .. 43b 62 .. .. .. 89b .. .. 24b .. .. 69 .. 57b .. .. 86 .. 68b 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
3.12
Definitions
Urbanization can yield important social benefi ts,
There is a strong demand for quantitative indi-
• Census year is the year in which the underlying
improving access to public services and the job mar-
cators that can measure housing conditions on a
data were collected. • Household size is the average
ket. It also leads to significant demands for services.
regular basis to monitor progress. However, data
number of people within a household, calculated by
Inadequate living quarters and demand for housing
deficiencies and lack of rigorous quantitative analy-
dividing total population by the number of households
and shelter are major concerns for policymakers.
sis hamper informed decisionmaking on desirable
in the country and in urban areas. • Overcrowding
The unmet demand for affordable housing, along
policies to improve housing conditions. The data
refers to the number of households living in dwell-
with urban poverty, has led to the emergence of
in the table are from housing and population cen-
ings with two or more people per room as a percent-
slums in many poor countries. Improving the shel-
suses, collected using similar definitions. The table
age of total households in the country and in urban
ter situation requires a better understanding of the
will incorporate household survey data in future edi-
areas. • Durable dwelling units are the number of
mechanisms governing housing markets and the pro-
tions. The table focuses attention on urban areas,
housing units in structures made of durable building
cesses governing housing availability. That requires
where housing conditions are typically most severe.
materials (concrete, stone, cement, brick, asbestos,
good data and adequate policy-oriented analysis so
Not all the compiled indicators are presented in the
zinc, and stucco) expected to maintain their stability
that housing policy can be formulated in a global
table because of space limitations.
for 20 years or longer under local conditions with
comparative perspective and drawn from lessons
normal maintenance and repair, taking into account
learned in other countries. Housing policies and
location and environmental hazards such as floods,
outcomes affect such broad socioeconomic condi-
mudslides, and earthquakes, as a percentage of
tions as the infant mortality rate, performance in
total dwellings. • Home ownership refers to the
school, household saving, productivity levels, capital
number of privately owned dwellings as a percent-
formation, and government budget deficits. A good
age of total dwellings. When the number of private
understanding of housing conditions thus requires
dwellings is not available from the census data, the
an extensive set of indicators within a reasonable
share of households that own their housing unit is
framework.
used. Privately owned and owner-occupied units are included, depending on the definition used in the
3.12a
Selected housing indicators for smaller economies
census data. State- and community-owned units and rented, squatted, and rent-free units are excluded.
Census Household Overcrowding Durable Home Multiunit Vacancy year size dwelling ownership dwellings rate units Households
Antigua and Barbuda Bahamas Bahrain Barbados Belize Cape Verde Cayman Islands Equatorial Guinea Fiji Guam Isle of Man Maldives Marshall Islands Netherlands Antilles New Caledonia Northern Mariana Islands Palau Seychelles Solomon Islands St. Vincent & Grenadines Turks and Caicos Virgin Islands (UK) Western Samoa
2001 1990 2001 1990 2000 1990 1999 1993 1996 2000 2001 2000 1999 2001 1989 1995 2000 1997 1999 1991 1990 1991 1991
number of people
living in overcrowded dwellingsa % of total
3.0 3.8 5.9 3.5 4.6 5.1 3.1 7.5 5.4 4.0 2.4 6.6 7.8 2.9 4.1 4.9 5.7 4.2 6.3 3.9 3.3 3.0 7.3
.. 12 .. 3 .. 28 .. 14 .. 2b 0 .. .. 24b .. 9b 8 15b 51 .. 4 2 ..
Privately Buildings owned with durable dwellings structure % of total % of total
99b 99 94b 100 93 78 100 56b 60 93 .. 93 95 99 77 99 76 97 23 98 96 99 42
65b 55 51 76 63 72 53 75 65 48 68 .. 72 60 53 33 79 78 85 71 66 40 90
• Multiunit dwellings are the number of multiunit dwellings, such as apartments, flats, condominiums, barracks, boardinghouses, orphanages, retirement
Unoccupied dwellings % of total % of total
3b 13 28 9 4 2 38 14 7 29 16 1 12 16 9 27 11 .. 1 7 11 46 47
22 14 6 9 .. .. 19 .. .. 19 .. 15 8 12 13 17 3 0 .. .. .. .. 30
houses, hostels, hotels, and collective dwellings, as a percentage of total dwellings. • Vacancy rate is the percentage of completed dwelling units that are currently unoccupied. It includes all vacant units, whether on the market or not (such as second homes).
Data sources Data on urban housing conditions are from
a. More than two people per room. b. Data are from a previous census. Source: National population and housing censuses.
national population and housing censuses.
2009 World Development Indicators
181
ENVIRONMENT
Urban housing conditions
3.13
Traffic and congestion Motor vehicles
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
182
Passenger Road cars density
Road sector fuel consumption
km. of road per 100 sq. km. of % of total liters land area consumption per capita
per 1,000 people
per kilometer of road
per 1,000 people
2006
2006
2006
2006
2006
.. 97 91 .. .. .. 671 552 61 2 .. 535 .. 49 .. 113 170 360 7 .. 36 11 582 .. 6 157 28 70 59 .. .. 198 .. 366 .. 394 437 115 66 .. .. .. 477 2 542 598 .. 7 71 598 18 522 68 14 1 ..
9 15 27 .. .. .. 17 43 10 1 .. 36 .. 7 .. 7 18 63 7 .. 37 3 13 .. 2 26 11 245 16 .. .. 24 .. 56 .. 31 33 .. 20 .. .. .. 11 4 36 39 .. 3 16 213 9 47 53 4 1 ..
.. 71 58 8 146 .. 542 507 57 1 183 474 13 15 .. 47 136 314 5 1 25 11 561 1 .. 97 18 52 37 .. 8 146 7 323 .. 399 354 78 39 29 24 .. 367 1 470 496 .. 5 56 565 12 409 53 8 .. ..
6 63 5 4 8 25 11 128 68 166 46 499 17 6 43 4 20 37 34 48 22 11 14 4 3 11 36 180 15 7 5 70 25 51 55 163 168 26 15 9 48 3 126 3 23 141 3 33 29 185 25 89 13 10 12 15
.. 27 14 11 19 7 19 20 11 4 5 13 22 20 15 26 22 12 .. .. 8 10 16 .. .. 18 5 8 23 1 22 29 5 21 7 12 20 20 31 16 20 6 14 5 11 15 8 .. 16 15 12 21 20 .. .. 5
2009 World Development Indicators
Transport sector fuel consumption
Fuel price
Particulate matter concentration
liters per capita
$ per liter
Diesel
Gasoline
Super grade gasoline
Diesel
Urban-populationweighted PM10 micrograms per cubic meter
2006
2006
2006
2008
2008
1990
2006
.. 225 179 82 396 71 1,321 964 211 8 171 897 83 149 250 327 308 383 .. .. 32 46 1,537 .. .. 378 76 250 186 3 85 358 24 487 79 650 904 192 310 157 161 11 619 16 898 809 128 .. 139 748 58 674 149 .. .. 16
.. 156 98 48 217 0 418 631 92 9 129 731 29 70 159 114 166 220 .. .. 19 25 452 .. .. 232 48 187 83 0 55 175 19 295 23 399 520 62 180 93 84 10 375 13 499 590 91 .. 44 374 29 269 66 .. .. 19
.. 77 60 31 94 67 781 275 100 2 62 163 50 56 87 199 86 92 .. .. 12 19 1,084 .. .. 149 45 53 90 3 27 164 9 184 47 230 390 119 148 52 69 1 269 2 416 191 32 .. 87 312 29 415 76 .. .. 15
1.05 1.36 0.34 0.53 0.78 1.08 0.74 1.37 0.74 1.17 1.33 1.50 1.03 0.68 1.13 0.88 1.26 1.28 1.38 1.39 0.94 1.14 0.76 1.44 1.30 0.95 0.99 1.95 1.04 1.23 0.81 1.24 1.33 1.27 1.67 1.37 1.54 1.04 0.51 0.49 0.78 2.53 1.18 0.92 1.57 1.52 1.14 0.79 1.09 1.56 0.90 1.23 0.86 1.02 0.00 1.16
0.96 1.31 0.20 0.39 0.58 1.11 0.94 1.43 0.56 0.70 1.06 1.34 1.03 0.53 1.18 1.02 1.03 1.37 1.33 1.23 0.89 1.04 0.90 1.44 1.32 0.95 1.01 1.16 0.73 1.21 0.57 1.10 1.20 1.37 1.51 1.45 1.54 0.94 0.27 0.20 0.81 1.07 1.30 0.89 1.39 1.45 0.90 0.75 1.16 1.56 0.90 1.41 0.82 1.02 0.00 0.89
78 92 115 142 105 453 23 38 226 231 23 30 75 120 36 95 40 111 151 56 86 116 25 62 217 88 114 .. 39 73 135 45 94 44 44 67 30 44 38 223 46 118 45 112 23 18 10 144 208 27 39 67 63 108 119 70
41 44 71 66 73 59 15 33 60 135 6 22 46 94 19 67 23 57 84 29 46 62 17 44 109 48 73 .. 22 47 64 36 36 30 17 21 19 20 25 119 33 56 13 68 18 13 8 86 47 19 34 36 62 70 72 37
Motor vehicles
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
Passenger Road cars density
Road sector fuel consumption
km. of road per 100 sq. km. of % of total liters land area consumption per capita
per 1,000 people
per kilometer of road
per 1,000 people
2006
2006
2006
2006
2006
67 374 12 109 .. .. 447 293 667 188 586 127 139 18 .. 328 422 .. 57 415 .. .. .. 257 513 163 .. .. 272 .. .. 138 222 94 43 59 .. 6 85 .. 486 722 46 5 .. 546 .. 14 103 .. 85 47 34 416 507 ..
31 20 3 62 .. .. 20 115 81 24 63 91 23 10 .. 156 181 .. 10 14 .. .. .. .. 22 25 .. .. 72 .. .. 89 65 31 2 29 .. .. 4 .. 62 32 13 4 .. 27 .. 8 27 .. 15 16 14 35 67 ..
52 292 8 .. 24 30 382 239 595 138 441 88 114 9 .. 240 349 39 .. 357 403 .. 6 232 468 150 .. .. 225 .. .. 104 147 84 28 46 .. 4 42 3 429 609 18 4 17 439 156 10 73 5 50 30 9 351 471 ..
12 172 100 20 10 10 132 85 162 201 316 9 3 11 21 102 32 9 13 108 67 20 10 5 123 52 8 16 28 1 1 99 18 38 3 13 4 4 5 12 372 35 14 1 21 29 11 34 15 4 7 6 67 135 90 289
17 15 6 12 21 32 29 16 21 12 14 22 5 6 2 12 13 9 .. 22 28 .. .. 18 16 12 .. .. 19 .. .. .. 26 7 11 3 4 8 37 3 14 25 14 .. 7 13 9 10 16 .. 27 25 14 13 24 ..
Transport sector fuel consumption
Fuel price
Particulate matter concentration
liters per capita
$ per liter
Diesel
Gasoline
Super grade gasoline
Diesel
Urban-populationweighted PM10 micrograms per cubic meter
2006
2006
2006
2008
2008
1990
2006
127 496 33 117 612 428 1,222 574 779 246 689 332 241 34 16 653 1,441 55 .. 528 384 .. .. 620 475 196 .. .. 588 .. .. .. 514 75 144 16 19 29 316 12 826 1,237 101 .. 64 863 649 56 164 .. 206 145 82 388 683 ..
64 307 23 50 239 161 676 185 480 163 252 167 46 21 9 363 356 0 .. 342 3 .. .. 364 284 109 .. .. 211 .. .. .. 151 55 39 8 16 19 101 8 492 536 63 .. 8 644 61 47 140 .. 165 103 53 198 473 ..
57 176 10 69 331 244 518 357 252 230 406 156 207 13 7 171 1,002 52 .. 191 355 .. .. 227 119 62 .. .. 355 .. .. .. 335 22 127 15 4 9 196 2 300 660 37 .. 52 378 548 8 153 .. 32 32 36 125 186 ..
0.80 1.27 1.09 0.60 0.53 0.03 1.56 1.47 1.57 0.74 1.74 0.61 0.83 1.20 0.76 1.65 0.24 0.80 0.92 1.12 0.76 0.79 0.74 0.14 1.13 1.15 1.55 1.78 0.53 1.30 1.49 0.74 0.74 1.20 1.38 1.29 1.71 0.43 0.78 1.13 1.68 1.09 0.87 0.99 0.59 1.63 0.31 0.84 0.67 0.94 1.17 1.42 0.91 1.43 1.61 0.65
0.80 1.38 0.70 0.46 0.03 0.01 1.64 1.27 1.63 0.84 1.54 0.61 0.72 1.14 0.95 1.33 0.20 0.88 0.76 1.23 0.76 0.93 1.03 0.12 1.22 1.12 1.43 1.67 0.53 1.10 1.06 0.56 0.54 1.04 1.42 0.83 1.37 0.52 0.88 0.82 1.45 0.85 0.82 0.97 1.13 1.63 0.38 0.77 0.68 0.90 0.96 0.99 0.81 1.40 1.47 0.78
45 36 112 137 86 146 25 71 42 59 43 110 43 67 165 51 75 75 91 38 43 86 61 106 53 46 78 75 37 274 147 23 69 97 198 34 111 107 74 67 46 16 48 220 175 24 148 224 58 34 106 98 55 59 51 27
43 19 65 83 51 115 16 31 27 43 30 45 19 36 68 35 97 22 49 16 36 41 40 88 19 21 34 33 23 152 86 18 36 36 110 21 28 58 47 34 34 14 28 132 45 15 108 120 35 21 77 54 23 37 23 21
2009 World Development Indicators
183
ENVIRONMENT
3.13
Traffic and congestion
3.13
Traffic and congestion Motor vehicles
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Passenger Road cars density
Road sector fuel consumption
km. of road per 100 sq. km. of % of total liters land area consumption per capita
per 1,000 people
per kilometer of road
per 1,000 people
2006
2006
2006
2006
180 228 3 .. 14 244 4 141 287 531 .. 151 550 55 .. 84 516 564 48 .. .. .. .. .. .. 95 124 .. 5 128 .. 517 814b 176 .. .. 8 36 .. .. .. .. w .. 46 19 172 38 27 162 155 .. 12 .. 630 604
20 35 .. 20 9 46 2 194 35 28 .. 16 35 11 .. 25 7 59 23 .. .. .. .. .. .. 49 20 .. .. 36 .. 80 31 .. .. .. .. 18 .. .. .. .. w .. 13 10 .. .. 11 30 .. .. 3 .. 41 66
156 188 1 415 10 204 2 105 247 493 .. 103 445 17 .. 40 462 520 19 19 1 54 .. 10 .. 83 84 .. 2 118 228 457 461b,c 151 .. 94 .. 29 19 .. 45 118 w .. 41 14 139 38 18 184 115 33 8 .. 455 418d
83 5 57 10 7 44 16 461 89 190 3 30 132 148 1 21 155 173 21 19 8 35 .. 13 162 12 55 5 17 28 5 171 68 102 18 11 68 80 14 12 25 26 w .. 23 40 .. 15 35 9 18 .. 75 .. 76 123
2006
10 6 .. 18 16 14 .. 7 9 21 .. 11 22 16 12 .. 14 20 24 37 4 17 .. 8 5 17 13 5 .. 5 17 17 23 24 3 25 12 .. 31 4 4 14 w 7 10 8 13 10 6 8 23 20 6 8 19 17
2006
218 338 .. 1,329 47 236 .. 608 379 879 .. 345 866 92 68 .. 948 871 277 238 28 328 .. 35 640 174 199 228 .. 186 2,147 775 2,104 274 71 678 87 .. 119 30 34 301 w 42 146 95 360 125 95 263 329 297 33 66 1,210 801
Transport sector fuel consumption
Fuel price
Particulate matter concentration
liters per capita
$ per liter
Diesel
Gasoline
Super grade gasoline
Diesel
Urban-populationweighted PM10 micrograms per cubic meter
2006
2006
2008
2008
1990
2006
1.11 0.89 1.37 0.16 1.35 1.11 0.91 1.07 1.57 1.18 1.12 0.87 1.23 1.43 0.65 0.86 1.38 1.30 0.85 1.03 1.11 0.87 1.22 0.89 0.36 0.96 1.87 0.22 1.30 0.88 0.37 1.44 0.56 1.18 1.35 0.02 0.80 1.34 0.30 1.70 1.30 1.11 m 1.12 0.91 0.88 1.12 1.03 0.92 1.13 0.87 0.61 1.09 1.14 1.28 1.54
1.22 0.86 1.37 0.09 1.26 1.29 0.91 0.90 1.68 1.26 1.15 0.95 1.28 0.75 0.45 0.93 1.52 1.52 0.53 1.00 1.30 0.64 1.35 0.88 0.24 0.84 1.63 0.20 1.22 0.96 0.52 1.65 0.78 1.17 0.75 0.01 0.77 1.25 0.17 1.61 1.05 1.03 m 1.03 0.90 0.83 1.03 0.95 0.85 1.13 0.83 0.53 0.76 1.06 1.36 1.44
36 41 49 161 97 33a 92 106 41 40 78 34 42 94 296 56 15 37 159 103 57 88 .. 57 142 74 68 177 28 72 266 25 30 237 85 22 123 .. .. 96 35 80 w 143 91 112 53 98 112 63 59 124 134 113 37 33
14 18 26 113 95 15a 50 41 15 30 31 21 32 82 165 33 12 26 75 50 25 71 .. 35 101 30 40 55 12 21 127 15 21 175 55 11 55 .. .. 40 27 50 w 69 56 67 30 58 69 27 35 72 78 53 26 23
138 119 .. 590 38 160 .. 374 227 485 .. 136 708 63 44 .. 414 291 148 0 20 217 .. 18 250 118 133 0 .. 70 1,108 427 548 182 15 120 47 .. 34 16 21 128 w 21 72 53 154 62 55 116 143 131 24 27 468 514
78 230 .. 672 9 68 .. 206 132 372 .. 198 185 27 21 .. 485 548 115 226 6 98 .. 16 357 47 44 217 .. 119 936 352 1,468 76 53 489 38 .. 73 16 13 164 w 20 72 46 178 61 51 139 152 146 9 37 691 257
a. Includes Montenegro. b. Data are from the U.S. Federal Highway Administration. c. Excludes personal passenger vans, passenger minivans, and utility-type vehicles, which are all treated as trucks. d. Data are from the European Commission and the European Road Federation.
184
2009 World Development Indicators
About the data
3.13
Definitions
Traffic congestion in urban areas constrains eco-
be comparable. Another reason is coverage. For
• Motor vehicles include cars, buses, and freight
nomic productivity, damages people’s health, and
example, for the United States the 2005 estimate
vehicles but not two-wheelers. Population fi gures
degrades the quality of life. The particulate air pollu-
for passenger cars from the U.S. Federal Highway
refer to the midyear population in the year for which
tion emitted by motor vehicles—the dust and soot in
Administration excludes personal passenger vans,
data are available. Roads refer to motorways, high-
exhaust—is far more damaging to human health than
passenger minivans, and utility-type vehicles, which
ways, main or national roads, and secondary or
once believed. (For information on particulate matter
are all treated as trucks. Moreover, the data do not
regional roads. A motorway is a road designed and
and other air pollutants, see table 3.14.)
cover vehicle quality or age. Road density is a rough
built for motor traffic that separates the traffic flow-
In recent years ownership of passenger cars has
indicator of accessibility and does not capture road
ing in opposite directions. • Passenger cars are road
increased, and expanded economic activity has led
width, type, or condition. Thus comparisons over time
motor vehicles, other than two-wheelers, intended
to more goods and services transported by road over
and across countries should be made with caution.
for the carriage of passengers and designed to seat
greater distances (see table 5.9). These develop-
Data on fuel prices are compiled by the German
no more than nine people (including the driver).
ments have increased demand for roads and vehicles,
Agency for Technical Cooperation (GTZ), from its global
• Road density is the ratio of the length of the
adding to urban congestion, air pollution, health haz-
network, and other sources, including the Allgemeiner
country’s total road network to the country’s land
ards, and traffic accidents and injuries. Congestion,
Deutscher Automobile Club (for Europe) and the Latin
area. The road network includes all roads in the
the most visible cost of expanding vehicle ownership,
American Energy Organization (for Latin America).
country— motorways, highways, main or national
is reflected in the indicators in the table. Other rel-
Local prices are converted to U.S. dollars using the
roads, secondary or regional roads, and other urban
evant indicators—such as average vehicle speed in
exchange rate in the Financial Times international mon-
and rural roads. • Road sector fuel consumption is
major cities and the cost of congestion, which takes a
etary table on the survey date. When multiple exchange
the average fuel used per capita in the roads sector.
heavy toll on economic productivity—are not included
rates exist, the market, parallel, or black market rate
• Transport sector fuel consumption is the average
because data are incomplete or difficult to compare.
is used. Prices were compiled in mid-November 2008,
volume of fuel consumed per capita in the trans-
The data in the table—except those on fuel prices
when crude oil prices had dropped to $48 a barrel
port sector. • Fuel price is the pump price of super
Brent (from a high of $148 in July).
grade gasoline (usually 95 octane) and of diesel fuel.
and particulate matter—are compiled by the International Road Federation (IRF) through questionnaires
Considerable uncertainty surrounds estimates
Prices are converted from the local currency to U.S.
sent to national organizations. The IRF uses a hier-
of particulate matter concentrations, and caution
dollars (see About the data). • Particulate matter
archy of sources to gather as much information as
should be used in interpreting them. They allow for
concentration is fine suspended particulates of less
possible. Primary sources are national road asso-
cross-country comparisons of the relative risk of par-
than 10 microns in diameter (PM10) that are capable
ciations. If they lack data or do not respond, other
ticulate matter pollution facing urban residents. Major
of penetrating deep into the respiratory tract and
agencies are contacted, including road directorates,
sources of urban outdoor particulate matter pollution
causing significant health damage. Data are urban-
ministries of transport or public works, and central
are traffic and industrial emissions, but nonanthro-
population-weighted PM10 levels in residential areas
statistical offices. As a result, data quality is uneven.
pogenic sources such as dust storms may also be
of cities with more than 100,000 residents. The esti-
Coverage of each indicator may differ across coun-
a substantial contributor for some cities. Country
mates represent the average annual exposure level
tries because of different definitions. Comparability
technology and pollution controls are important deter-
of the average urban resident to outdoor particulate
is also limited when time series data are reported.
minants of particulate matter. Data on particulate
matter.
The IRF took steps to improve the quality of the data
matter for selected cities are in table 3.14. Estimates
in its World Road Statistics 2008. Because this effort
of economic damages from death and illness due to
covers 1999–2006 only, time series data may not
particulate matter pollution are in table 3.16.
Particulate matter concentration has fallen in all income groups, and the higher the income, the lower the concentration
3.13a Data sources
Urban-population-weighted particulate matter (PM10, micrograms per cubic meter) 150
1990
2006
Data on vehicles, road density, and fuel consumption are from the IRF’s electronic files and its annual World Road Statistics, except where noted. Data on fuel prices are from the GTZ’s electronic
100
files. Data on particulate matter concentrations are from Kiran Dev Pandey, David Wheeler, Bart 50
Ostro, Uwe Deichmann, Kirk Hamilton, and Katie Bolt’s “Ambient Particulate Matter Concentrations in Residential and Pollution Hotspot Areas of World
0 Low income Source: Table 3.13.
Lower middle income
Upper middle income
High income
Euro area
Cities: New Estimates Based on the Global Model of Ambient Particulates (GMAPS)” (2006).
2009 World Development Indicators
185
ENVIRONMENT
Traffic and congestion
3.14
Air pollution City
City population
thousands 2007
Argentina Australia
Austria Belgium Brazil Bulgaria Canada
Chile China
Colombia Croatia Cuba Czech Republic Denmark Ecuador Egypt, Arab Rep. Finland France Germany
Ghana Greece Hungary Iceland India
186
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 Bengaluru
2009 World Development Indicators
1,452 3,728 1,532 4,327 2,315 1,743 11,748 18,845 1,185 3,678 5,213 2,146 5,720 1,639 11,106 3,183 4,123 6,461 3,167 8,829 3,662 3,621 2,798 2,931 2,561 1,221 2,350 905 2,817 14,987 4,787 2,794 7,180 2,025 7,243 2,636 3,061 7,772 908 2,174 1,162 1,085 2,514 1,701 11,893 1,115 9,904 3,406 668 1,275 2,121 3,242 1,679 164 5,375 6,787
Particulate matter concentration
Sulfur dioxide
Nitrogen dioxide
Urbanpopulationweighted PM10 micrograms per micrograms per micrograms per cubic meter cubic meter cubic meter 2006 2001 a 2001 a
55 12 12 19 39 25 29 34 63 17 20 12 54 83 90 75 87 124 50 64 71 77 95 71 92 60 79 67 62 74 102 89 126 57 80 98 75 30 32 20 21 19 23 30 149 19 11 21 18 19 33 38 20 18 76 41
.. .. 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 ..
About the data
Indoor and outdoor air pollution place a major burden on world health. More than half the world’s people rely on dung, wood, crop waste, or coal to meet basic energy needs. Cooking and heating with these fuels on open fires or stoves without chimneys lead to indoor air pollution, which is responsible for 1.6 million deaths a year—one every 20 seconds. In many urban areas air pollution exposure is the main environmental threat to health. Long-term exposure to high levels of soot and small particles contributes to a range of health effects, including respiratory diseases, lung cancer, and heart disease. Particulate pollution, alone or with sulfur dioxide, creates an enormous burden of ill health. Sulfur dioxide and nitrogen dioxide emissions lead to deposition of acid rain and other acidic compounds over long distances, which can lead to the leaching of trace minerals and nutrients critical to trees and plants. Sulfur dioxide emissions can damage human health, particularly that of the young and old. Nitrogen dioxide is emitted by bacteria, motor vehicles, industrial activities, nitrogen fertilizers, fuel and biomass combustion, and aerobic decomposition of organic matter in soils and oceans. Where coal is the primary fuel for power plants without effective dust controls, steel mills, industrial boilers, and domestic heating, high levels of urban air pollution are common— especially particulates and sulfur dioxide. Elsewhere the worst emissions are from petroleum product combustion. Sulfur dioxide and nitrogen dioxide concentration data are based on average observed concentrations at urban monitoring sites, which not all cities have. The data on particulate matter are estimated average annual concentrations in residential areas away from air pollution “hotspots,” such as industrial districts and transport corridors. The data are from the World Bank’s Development Research Group and Environment Department estimates of annual ambient concentrations of particulate matter in cities with populations exceeding 100,000 (Pandey and others 2006b). A country’s technology and pollution controls are important determinants of particulate matter concentrations. Pollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Thus these data should be considered only a general indication of air quality, and comparisons should be made with caution. Current World Health Organization (WHO) air quality guidelines are annual mean concentrations of 20 micrograms per cubic meter for particulate matter less than 10 microns in diameter and 40 micrograms for nitrogen dioxide and daily mean concentrations of 20 micrograms per cubic meter for sulfur dioxide.
City
City population
thousands 2007
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
Chennai Delhi Hyderabad Kanpur Kolkata Lucknow Mumbai Nagpur Pune Jakarta Tehran Dublin Milan Rome Turin Osaka-Kobe Tokyo Yokohama Nairobi Pusan Seoul Taegu Kuala Lumpur Mexico City Amsterdam Auckland Oslo Manila Katowice Lódz Warsaw Lisbon Bucharest Moscow Omsk Singapore Bratislava Cape Town Durban Johannesburg Barcelona Madrid Stockholm Zurich Bangkok Ankara Istanbul Kiev Birmingham London Manchester Chicago Los Angeles New York-Newark Caracas
7,163 15,926 6,376 3,162 14,787 2,695 18,978 2,454 4,672 9,125 7,873 1,059 2,945 3,339 1,652 11,294 35,676 3,366 3,010 3,480 9,796 2,460 1,448 19,028 1,031 1,245 802 11,100 2,914 776 1,707 2,812 1,942 10,452 1,135 4,436 456 3,215 2,729 3,435 4,920 5,567 1,264 1,108 6,704 3,716 10,061 2,709 2,285 8,567 2,230 8,990 12,500 19,040 2,985
Particulate matter concentration
Sulfur dioxide
Nitrogen dioxide
Urbanpopulationweighted PM10 micrograms per micrograms per micrograms per cubic meter cubic meter cubic meter 2006 2001 a 2001 a
34 136 37 99 116 99 57 50 42 84 50 16 30 29 43 33 38 29 40 35 37 40 23 48 34 13 18 28 39 38 42 21 16 19 19 41 15 13 25 26 33 29 11 24 76 39 46 26 14 19 15 23 32 20 16
15 24 12 15 49 26 33 6 .. .. 209 20 31 .. .. 19 18 100 .. 60 44 81 24 74 10 3 8 33 83 21 16 8 10 109 20 20 21 21 31 19 11 24 3 11 11 55 120 14 9 25 26 14 9 26 33
17 41 17 14 34 25 39 13 .. .. .. .. 248 .. .. 63 68 13 .. 51 60 62 .. 130 58 20 43 .. 79 43 32 52 71 .. 34 30 27 72 .. 31 43 66 20 39 23 46 .. 51 45 77 49 57 74 79 57
3.14
Definitions
• City population is the number of residents of the city or metropolitan area as defined by national authorities and reported to the United Nations. • Particulate matter concentration is fine suspended particulates of less than 10 microns in diameter (PM10) that are capable of penetrating deep into the respiratory tract and causing significant health damage. Data are urban- population-weighted PM10 levels in residential areas of cities with more than 100,000 residents. The estimates represent the average annual exposure level of the average urban resident to outdoor particulate matter. • Sulfur dioxide is an air pollutant produced when fossil fuels containing sulfur are burned. • Nitrogen dioxide is a poisonous, pungent gas formed when nitric oxide combines with hydrocarbons and sunlight, producing a photochemical reaction. These conditions occur in both natural and anthropogenic activities.
Data sources Data on city population are from the United Nations Population Division. Data on particulate matter concentrations are from Kiran D. Pandey, David Wheeler, Bart Ostro, Uwe Deichman, Kirk Hamilton, and Kathrine Bolt’s “Ambient Particulate Matter Concentration in Residential and Pollution Hotspot Areas of World Cities: New Estimates Based on the Global Model of Ambient Particulates (GMAPS)” (2006). Data on sulfur dioxide and nitrogen dioxide concentrations are from the WHO’s Healthy Cities Air Management Information System and the World Resources Institute.
a. Data are for the most recent year available.
2009 World Development Indicators
187
ENVIRONMENT
Air pollution
3.15
Government commitment EnvironBiodiversity mental assessments, strategies strategies, or or action action plans plans
Participation in treatiesa
Climate changeb
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
188
.. 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
.. .. .. .. .. .. 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 ..
2009 World Development Indicators
Ozone layer
CFC control
Law of the Seac
Biological diversity b
Kyoto Protocol 1997
2005d 2005d 2007 2001 2008e .. 2002 2000 d 2001d 2007e 2002 2002d 1999 2007 2003d 2002 2002 2005d 2001d 2002d 2002d 2002 2008 .. 2002 2002f .. 2001d 2005d 2007 2002 2007 .. 2002 2007e 2002 2002d 2000 2005d 1998 2005d 2002 2005d 2002 2002f .. 2001d 1999d 2002 2003d 2002 1999 2000 d .. 2005d
1992
1985
1987
1982
1992
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
2004d 1999d 1992d 2000 d 1990 1999d 1987d 1987 1996d 1990 d 1986e 1988 1993d 1994 d 1992g 1991d 1990 d 1990 d 1989 1997d 2001d 1989d 1986 1993d 1989d 1990 1989d .. 1990 d 1994 d 1994 d 1991d 1993d 1991e 1992d 1993e 1988 1993d 1990 d 1988 1992 2005d 1996d 1994 d 1986 1987f 1994 d 1990 d 1996d 1988 1989d 1988 1987d 1992d 2002d 2000 d
2004 d 1999d 1992d 2000 d 1990 1999d 1989 1989 1996d 1990 d 1988e 1988 1993d 1994 d 1992g 1991d 1990 d 1990 d 1989 1997d 2001d 1989d 1988 1993d 1994 1990 1991d .. 1993d 1994 d 1994 d 1991d 1993d 1991e 1992d 1993e 1988 1993d 1990 d 1988 1992 2005d 1996d 1994 d 1988 1988f 1994 d 1990 d 1996d 1988 1989 1988 1989d 1992d 2002d 2000 d
.. 2003d 1996 1994 1995 2002d 1994 1995 .. 2001 2006d 1998 1997 1995 1994g 1994 1994 1996 2005 .. .. 1994 2003 .. .. 1997 1996 ..
2002 1994 d 1995 1998 1994 1993e 1993 1994 2000 f 1994 1993 1996 1994 1994 2002d 1995 1994 1996 1993 1997 1995d 1994 1992 1995 1994 1994 1993 .. 1994 1996 1994 1994 1994 1996 1994 1993f 1993 1996 1993 1994 1994 1996d 1994 1994 1994 e 1994 1997 1994 1994 d 1993 1994 1994 1995 1993 1995 1996
1995 2008 1994 1994 1994g 1994 1996 2004 .. .. 1994 .. .. 2005d .. 1996 1996 1998 1994 1996d 1994d 1994 1995 1997 1994 1994 1996
CCD
Stockholm Convention
1973
1994
2001
1985d 2003d 1983d .. 1981 .. 1976 1982d 1998d 1981 1995d 1983 1984 d 1979 2002 1977d 1975 1991d 1989d 1988d 1997 1981d 1975 1980 d 1989d 1975 1981d .. 1981 1976d 1983d 1975 1994 d 2000 d 1990 d 993g 1977 1986d 1975 1978 1987d 1994 d 1992d 1989d 1976d 1978 1989d 1977d 1996d 1976 1975 1992d 1979 1981d 1990 d ..
1995d 2000 d 1996 1997 1997 1997 2000 1997d 1998d 1996 2001d 1997d 1996 1996 2002d 1996 1997 2001d 1996 1997 1997 1997 1995 1996 1996 1997 1997 .. 1999 1997 1999 1998 1997 2000e 1997 2000 d 1995d 1997d 1995 1995 1997d 1996 .. 1997 1995e 1997 1996d 1996 1999 1996 1996 1997 1998d 1997 1995 1996
.. 2004 2006 2006 2005 2003 2004 2002 2004 d 2007 2004 d 2006 2004 2003 .. 2002d 2004 2004 2004 2005 2006 .. 2001 .. 2004 2005 2004 .. .. 2005d 2007 2007 2004 2007 2007 2002 2003 2007 2004 2003 .. 2005d .. 2003 2002e 2004f 2007 2006 2006 2002 2003 2006 .. .. 2008 ..
CITES
EnvironBiodiversity mental assessments, strategies strategies, or or action action plans plans
Participation in treatiesa
Climate changeb
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
.. .. 1994 1993 .. .. .. .. .. .. .. .. .. 1992 .. .. .. .. .. .. .. ..
.. .. .. 1988 1994 1991 .. 1988 1990 .. 2002 1995 .. 1994 .. 1992 1993 1994 1994 1994 .. 1990 .. .. 1994 1990 1992 .. .. 1989 1993 1995 ..
.. .. .. 1991 .. 1988 1989 .. .. 1988 .. .. 1988 .. 1989 .. .. .. .. .. 1991 1992 1994 .. 1991 .. 1993 .. 1988 1989 1991 .. ..
3.15
Ozone layer
CFC control
Law of the Seac
Biological diversity b
Kyoto Protocol
CITES
CCD
Stockholm Convention
1992
1985
1987
1982
1992
1997
1973
1994
2001
1996 1994 1994 1994 1996 .. 1994 1996 1994 1995 1994 1994 1995 1994 1995 1994 1995 2000 1995 1995 1995 1995 2003 1999 1995 1998 1999 1994 1994 1995 1994 1994 1994 1995 1994 1996 1995 1995 1995 1994 1994 1994 1996 1995 1994 1994 1995 1994 1995 1994 1994 1994 1994 1994 1994 ..
1993d 1988d 1991d 1992d 1990 d .. 1988d 1992d 1988 1993d 1988d 1989d 1998d 1988d 1995d 1992 1992d 2000 d 1998d 1995d 1993d 1994 d 1996d 1990d 1995d 1994g 1996d 1991d 1989d 1994 d 1994 d 1992d 1987 1996d 1996d 1995 1994 d 1993d 1993d 1994 d 1988d 1987 1993d 1992d 1988d 1986 1999d 1992d 1989d 1992d 1992d 1989 1991d 1990 d 1988d ..
1993d 1989d 1992d 1992 1990 d .. 1988 1992 1988 1993d 1988 1989d 1998d 1988 1995d 1992 1992d 2000 d 1998d 1995d 1993d 1994 d 1996d 1990 d 1995d 1994g 1996d 1991d 1989d 1994 d 1994 d 1992d 1988 1996d 1996d 1995 1994 d 1993d 1993d 1994 d 1988e 1988 1993d 1992d 1988d 1988 1999d 1992d 1989 1992d 1992d 1993d 1991 1990 d 1988 ..
1994 2002 1995 1994 .. 1994 1996 .. 1995 1994 1996 1995d .. 1994 .. 1996 1994 .. 1998 2004 d 1995 2007 2008 .. 2003d 1994g 2001 .. 1996 1994 1996 1994 1994 2007 1996 2007 1997 1996 1994 1998 1996 1996 2000 .. 1994 1996 1994 1997 1996 1997 1994 .. 1994 1998 1997 ..
1995 1994 1994 1994 1996 .. 1996 1995 1994 1995 1993e 1993 1994 1994 1994f 1994 2002 1996f 1996f 1995 1994 1995 2000 2001 1996 1997d 1996 1994 1994 1995 1996 1992 1993 1995 1993 1995 1995 1995 1997 1993 1994 e 1993 1995 1995 1994 1993 1995 1994 1995 1993 1994 1993 1993 1996 1993 ..
2000 2002d 2008 e 2004 2005d .. 2002 2004 2002 1999d 2002e 2003d .. 2005d 2005d 2002 2005d 2003d 2003d 2002 2006 2000d 2002d 2006 2003 2004 d 2003d 2001d 2002 2002 2005d 2001d 2000 2008e 1999d 2002d 2005d 2003d 2003d 2005d 2002d 2002 1999 2004 2004 d 2008e 2005d 2005d 1999 2002 1999 2002 2003 2002 2002f ..
1985d 1985d 1976 1978d 1976 .. 2002 1979 1979 1997d 1980 1978d 2000 d 1978 .. 1993d 2002 .. 2004 d 1997d .. 2003 2005d 2003d 2001d 2000 d 1975 1982d 1977d 1994 d 1998d 1975 1991d 2001d 1996d 1975 1981d 1997d 1990 d 1975d 1984 1989d 1977d 1975 1974 1976 .. 1976d 1978 1975d 1976 1975 1981 1989 1980 ..
1997 1999d 1996 1998 1997 .. 1997 1996 1997 1997d 1998e 1996 1997 1997 2003d 1999 1997 1997d 1996e 2002d 1996 1995 1998d 1996 2003d 2002d 1997 1996 1997 1995 1996 1996 1995 1999d 1996 1996 1997 1997d 1997 1996 1995e 2000 d 1998 1996 1997 1996 1996d 1997 1996 2000 d 1997 1995 2000 2001d 1996 ..
2005 2008 2006 .. 2006 .. .. .. .. 2007 2002d 2004 .. 2004 2002d 2007 2006 2006 2006 2004 2003 2002 2002d 2005d 2006 2004 .. .. .. 2003 2005 2004 2003 2004 2004 2004 2005 2004 d 2005d 2007 2002e 2004 .. 2006 2004 2002 2005 .. 2003 2003 2004 2005 2004 2008 2004 e ..
2009 World Development Indicators
189
ENVIRONMENT
Government commitment
3.15
Government commitment EnvironBiodiversity mental assessments, strategies strategies, or or action action plans plans
Participation in treatiesa
Climate changeb
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
.. 1994 .. .. 1991
1994 1993 .. 1994
.. 1995 .. ..
1993 .. 1994 ..
.. .. 1991 ..
.. .. 1999 .. 1994 .. 1991 .. 1994 1998 .. 1994 1999 .. 1995 1995 .. .. .. .. .. 1996 1994 1987
.. .. .. .. 1988 .. .. .. 1988 .. .. 1988 .. .. 1994 1995 .. .. .. 1993 .. 1992 .. ..
Ozone layer
CFC control
Law of the Seac
Biological diversity b
Kyoto Protocol
CITES
CCD
1992
1985
1987
1982
1992
1997
1973
1994
2001
1994 1995 1998 1995 1995 2001 1995 1997 1994 1996 .. 1997 1994 1994 1994 1997 1994 1994 1996 1998 1996 1995 1995 1994 1994 2004 1995 1994 1997 1996 1994 1994 1994 1994 1995 1995 .. 1996 1994 1994
1993d 1986e 2001d 1993d 1993d 2001g 2001d 1989d 1993g 1992g 2001d 1990 d 1988d 1989d 1993d 1992d 1986 1987 1989d 1996d 1993d 1989d 1991d 1989d 1989d 1991d 1993d 1988d 1986e 1989d 1987 1986 1989d 1993d 1988d 1994 d .. 1996d 1990 d 1992d
1993d 1988e 2001d 1993d 1993 2001g 2001d 1989d 1993g 1992g 2001d 1990 d 1988 1989d 1993d 1992d 1988 1988 1989d 1998d 1993d 1989 1991 1989d 1989d 1991d 1993d 1988 1988e 1989d 1988 1988 1991d 1993d 1989 1994 d .. 1996d 1990 d 1992d
1996 1997 .. 1996 1994 2001g 1994 1994 1996 1995g 1994 1997 1997 1994 1994 .. 1996 .. .. .. 1994 .. 1994 1994 1994 .. .. 1994 1999 .. 1997d .. 1994 .. .. 2006d .. 1994 1994 1994
1994 1995 1996 2001f 1994 2002 1994f 1995 1994f 1996 .. 1995 1995 1994 1995 1994 1993 1994 1996 1997f 1996 2004 1995e 1996 1993 1997 1996f 1993 1995 2000 1994 .. 1993 1995f 1994 1994 .. 1996 1993 1994
2001 2008 e 2004 d 2005d 2001d 2007 2006d 2006d 2002 2002 .. 2002d 2002 2002d 2004 d .. 2002 2006d 2006d .. 2002d 2002 2004 d 1999 2003d .. 2008e 2002d 2004 2005d 2002 .. 2001 2007e .. 2008 e .. 2004 d 2006d ..
1994d 1992 1980 d 1996d 1977d 2002 1994 d 1986d 1993 2000 d 1985d 1975 1986d 1979d 1982 1997d 1974 1974 2003d .. 1979 1983 1978 1984 d 1974 1996d .. 1991d 1999d 1990 d 1976 1974 1975 1997d 1977 1994 d .. 1997d 1980 d 1981d
1998d 2003d 1998 1997d 1995 .. 1997 1999d 2002d 2001d 2002d 1997 1996 1998d 1995 1996 1995 1996 1997 1997d 1997 2001d 1995e 2000 d 1995 1998 1996 1997 2002d 1998d 1996 2000 1999d 1995 1998d 1998d .. 1997d 1996 1997
2004 .. 2002d .. 2003 2002 2003d 2005 2002 2004 .. 2002 2004 .. 2006 2006 2002 2003 2005 2007 2004 2005 2004 2002d 2004 .. .. 2004 d .. 2002 2005 .. 2004 .. 2005 2002
a. Ratification of the treaty. b. Year the treaty entered into force in the country. c. Convention became effective November 16, 1994. d. Accession. e. Acceptance. f. Approval. g. Succession.
190
2009 World Development Indicators
Stockholm Convention
2004 2006 ..
About the data
3.15
Definitions
National environmental strategies and participation
Environment and Development (the Earth Summit) in
• Environmental strategies or action plans pro-
in international treaties on environmental issues pro-
Rio de Janeiro, which produced Agenda 21—an array
vide a comprehensive analysis of conservation and
vide some evidence of government commitment to
of actions to address environmental challenges:
resource management issues that integrate envi-
sound environmental management. But the signing
• The Framework Convention on Climate Change
ronmental concerns with development. They include
of these treaties does not always imply ratification,
aims to stabilize atmospheric concentrations of
national conservation strategies, environmental
nor does it guarantee that governments will comply
greenhouse gases at levels that will prevent human
action plans, environmental management strategies,
with treaty obligations.
activities from interfering dangerously with the
and sustainable development strategies. The date
global climate.
is the year a country adopted a strategy or action
In many countries efforts to halt environmental degradation have failed, primarily because govern-
• The Vienna Convention for the Protection of the
plan. • Biodiversity assessments, strategies, or
ments have neglected to make this issue a priority, a
Ozone Layer aims to protect human health and the
action plans include biodiversity profiles (see About
reflection of competing claims on scarce resources.
environment by promoting research on the effects
the data). • Participation in treaties covers nine
To address this problem, many countries are prepar-
of changes in the ozone layer and on alternative
international treaties (see About the data). • Climate
ing national environmental strategies—some focus-
substances (such as substitutes for chlorofluoro-
change refers to the Framework Convention on Cli-
ing narrowly on environmental issues, and others
carbon) and technologies, monitoring the ozone
mate Change (signed in 1992). • Ozone layer refers
integrating environmental, economic, and social
layer, and taking measures to control the activities
to the Vienna Convention for the Protection of the
concerns. Among such initiatives are conservation
that produce adverse effects.
Ozone Layer (signed in 1985). • CFC control refers to
strategies and environmental action plans. Some
• The Montreal Protocol for Chlorofl uorocarbon
the Protocol on Substances That Deplete the Ozone
countries have also prepared country environmental
Control requires that countries help protect the
Layer (the Montreal Protocol for Chlorofluorocarbon
profiles and biodiversity strategies and profiles.
earth from excessive ultraviolet radiation by cut-
Control) (signed in 1987). • Law of the Sea refers
National conservation strategies—promoted by
ting chlorofluorocarbon consumption by 20 per-
to the United Nations Convention on the Law of the
the World Conservation Union (IUCN)—provide a
cent over their 1986 level by 1994 and by 50
Sea (signed in 1982). • Biological diversity refers
comprehensive, cross-sectoral analysis of conser-
percent over their 1986 level by 1999, with allow-
to the Convention on Biological Diversity (signed at
vation and resource management issues to help inte-
ances for increases in consumption by developing
the Earth Summit in 1992). • Kyoto Protocol refers
grate environmental concerns with the development
countries.
to the protocol on climate change adopted at the
process. Such strategies discuss current and future
• The United Nations Convention on the Law of the
third conference of the parties to the United Nations
needs, institutional capabilities, prevailing technical
Sea, which became effective in November 1994,
Framework Convention on Climate Change in Decem-
conditions, and the status of natural resources in
establishes a comprehensive legal regime for seas
ber 1997. • CITES is the Convention on International
a country.
and oceans, establishes rules for environmental
Trade in Endangered Species of Wild Fauna and Flora,
National environmental action plans, supported by
standards and enforcement provisions, and devel-
an agreement among governments to ensure that
the World Bank and other development agencies,
ops international rules and national legislation to
the survival of wild animals and plants is not threat-
describe a country’s main environmental concerns,
prevent and control marine pollution.
ened by uncontrolled exploitation. Adopted in 1973,
identify the principal causes of environmental prob-
• The Convention on Biological Diversity promotes
it entered into force in 1975. • CCD is the United
lems, and formulate policies and actions to deal with
conservation of biodiversity through scientifi c
Nations Convention to Combat Desertification, an
them. These plans are a continuing process in which
and technological cooperation among countries,
international convention addressing the problems
governments develop comprehensive environmental
access to financial and genetic resources, and
of land degradation in the world’s drylands. Adopted
policies, recommend specific actions, and outline
transfer of ecologically sound technologies.
in 1994, it entered into force in 1996. • Stockholm
the investment strategies, legislation, and institu-
But 10 years after the Earth Summit in Rio de
Convention is an international legally binding instru-
Janeiro the World Summit on Sustainable Develop-
ment to protect human health and the environment
Biodiversity profiles—prepared by the World Con-
ment in Johannesburg recognized that many of the
from persistent organic pollutants. Adopted in 2001,
servation Monitoring Centre and the IUCN—provide
proposed actions had yet to materialize. To help
it entered into force in 2004.
basic background on species diversity, protected
developing countries comply with their obligations
areas, major ecosystems and habitat types, and
under these agreements, the Global Environment
legislative and administrative support. In an effort
Facility (GEF) was created to focus on global improve-
Data sources
to establish a scientific baseline for measuring prog-
ment in biodiversity, climate change, international
Data on environmental strategies and participation
ress in biodiversity conservation, the United Nations
waters, and ozone layer depletion. The UNEP, United
in international environmental treaties are from
Environment Programme (UNEP) coordinates global
Nations Development Programme, and World Bank
the Secretariat of the United Nations Framework
biodiversity assessments.
manage the GEF according to the policies of its gov-
Convention on Climate Change, the Ozone Secre-
To address global issues, many governments have
erning body of country representatives. The World
tariat of the UNEP, the World Resources Institute,
also signed international treaties and agreements
Bank is responsible for the GEF Trust Fund and chairs
the UNEP, the Center for International Earth Sci-
launched in the wake of the 1972 United Nations
the GEF.
ence Information Network, and the United Nations
tional arrangements required to implement them.
Conference on the Human Environment in Stock-
Treaty Series.
holm and the 1992 United Nations Conference on
2009 World Development Indicators
191
ENVIRONMENT
Government commitment
3.16 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Cote 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
192
Toward a broader measure of savings Gross savings
Consumption of fixed capital
Net national 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
2007
2007
2007
2007
2007
2007
2007
2007
2007
2007
.. 19.2 57.9 31.8 27.2 29.7 22.8 26.2 59.9 32.2 26.9 24.8 .. 30.1 8.9 57.9 17.0 17.8 .. .. 15.9 19.7 23.0 4.5 26.9 28.7 54.4 33.8 19.6 12.1 45.4 19.2 9.6 24.6 .. 27.0 24.0 21.0 26.9 22.4 12.5 .. 21.9 20.9 26.5 19.2 46.3 12.6 17.0 24.9 23.2 9.5 16.8 8.8 24.0 ..
.. 10.9 11.6 14.3 12.4 10.7 15.3 15.1 13.5 7.7 11.8 14.6 8.8 10.1 11.1 12.8 12.6 11.9 8.4 6.6 9.1 9.7 14.9 8.2 10.2 14.3 10.7 13.8 12.1 7.0 13.4 12.4 10.0 13.4 .. 14.4 14.9 12.0 11.7 10.2 11.3 7.8 14.5 7.5 14.8 13.3 14.2 8.7 10.4 14.6 8.9 14.6 10.9 8.6 7.5 9.8
.. 8.3 46.3 17.5 14.8 18.9 7.5 11.1 46.4 24.5 15.1 10.2 .. 20.0 –2.2 45.0 4.4 5.9 .. .. 6.8 10.0 8.1 –3.7 16.6 14.4 43.7 20.1 7.5 5.1 32.0 6.8 –0.4 11.3 .. 12.6 9.1 9.0 15.2 12.2 1.2 .. 7.4 13.4 11.6 5.9 32.1 3.9 6.5 10.4 14.2 –5.1 5.9 0.2 16.5 ..
.. 2.8 4.5 2.3 4.0 2.2 4.8 5.3 2.8 1.8 4.9 5.8 3.6 6.3 .. 6.6 4.4 4.1 4.3 5.1 1.7 2.6 4.8 1.3 1.2 3.4 1.8 3.0 4.8 0.9 2.3 4.1 4.7 4.3 8.2 4.0 7.8 3.5 1.4 4.4 2.8 1.9 4.6 3.7 5.9 5.1 3.1 2.0 2.8 4.4 4.7 2.8 2.8 2.0 2.3 1.5
.. 0.0 29.7 55.6 7.7 0.0 2.9 0.2 52.6 2.9 0.1 0.0 0.0 21.6 0.2 0.2 2.3 0.6 0.0 0.0 0.0 6.4 4.1 0.0 40.7 0.2 4.5 0.0 6.6 3.1 56.5 0.0 7.0 0.7 .. 0.4 2.3 0.0 18.4 13.4 0.0 0.0 0.8 0.0 0.0 0.0 33.3 0.0 0.0 0.2 0.0 0.2 0.6 0.0 0.0 0.0
.. 0.0 0.1 0.0 0.6 1.1 3.8 0.0 0.0 0.0 0.0 0.0 0.0 2.4 0.0 8.2 1.6 1.1 0.0 0.8 0.0 0.1 0.9 0.0 0.0 16.7 1.3 0.0 1.7 2.9 0.0 0.0 0.0 0.0 .. 0.0 0.0 3.5 0.5 0.1 0.0 0.0 0.0 0.4 0.1 0.0 0.0 0.0 0.0 0.0 4.5 0.2 0.0 4.9 0.0 0.0
.. 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 1.1 11.5 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.2 .. 0.1 0.0 0.0 0.0 0.2 0.5 0.9 0.1 5.4 0.0 0.0 0.0 0.6 0.0 0.0 2.3 0.0 0.8 1.7 0.0 0.6
.. 0.2 1.2 0.2 0.5 0.4 0.3 0.1 2.0 0.4 1.4 0.2 0.3 0.5 0.9 0.3 0.2 1.0 0.1 0.2 0.1 0.2 0.4 0.1 0.0 0.4 1.4 0.2 0.3 0.2 0.4 0.2 0.2 0.4 .. 0.6 0.1 0.6 0.5 1.0 0.3 0.4 0.9 0.4 0.2 0.1 0.1 0.4 0.4 0.2 0.4 0.3 0.3 0.2 0.6 0.2
.. 0.2 0.3 1.3 1.6 1.6 0.1 0.3 1.2 0.5 .. 0.2 0.4 1.4 0.1 .. 0.2 1.5 1.3 0.1 0.3 0.8 0.1 0.4 1.1 0.6 1.6 .. 0.1 0.6 0.7 0.3 0.3 0.5 0.1 0.1 0.1 0.1 0.1 1.0 0.2 0.4 0.0 0.3 0.1 0.0 .. 0.7 1.3 0.1 0.1 0.7 0.5 0.3 0.9 0.4
2009 World Development Indicators
.. 10.7 19.4 –37.3 8.3 18.1 5.2 15.7 –6.6 21.8 18.5a 15.7 .. 0.4 .. 42.9a,b 4.5 5.7 .. .. 7.9 5.3 7.4 c –2.9 –24.0 –0.1 36.8 22.9a 3.6 –0.8 –23.4 10.2 –3.2 13.8 .. 15.4 14.4 8.4 –2.9 0.9 3.0 .. 10.2 10.6 17.1 10.9 1.7a 4.2 7.7 14.3 11.5 –3.7 6.5 –4.9 17.3 ..
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
3.16
Gross savings
Consumption of fixed capital
Net national 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
2007
2007
2007
2007
2007
2007
2007
2007
2007
23.8 17.7 38.8 27.2 43.4 .. 25.6 .. 19.8 .. 31.0 8.2 32.5 17.1 .. 29.9 .. 6.7 23.5 15.4 0.4 32.7 –19.3 .. 17.0 21.1 13.4 9.6 38.4 13.6 28.0 19.7 25.7 20.4 42.5 32.8 3.1 .. 40.3 28.2 27.6 .. 14.6 .. .. 38.3 .. 24.5 24.7 39.2 19.6 25.7 31.6 22.0 12.6 ..
10.8 14.2 9.6 10.8 11.6 .. 18.1 13.9 14.6 13.2 14.0 10.4 13.8 8.8 .. 13.7 13.3 9.1 9.3 13.9 12.1 7.3 9.4 12.4 13.5 11.4 8.2 7.6 12.5 9.0 8.9 11.8 12.9 8.8 10.3 10.9 8.9 .. 11.3 8.0 14.6 15.5 9.7 7.7 10.8 15.7 .. 9.1 12.9 10.6 10.3 12.4 9.3 13.3 14.4 ..
13.0 3.5 29.2 16.3 31.8 .. 7.6 .. 5.2 .. 17.0 –2.2 18.7 8.3 .. 16.2 .. –2.4 14.2 1.6 –11.7 25.4 –28.7 .. 3.5 9.6 5.2 2.0 25.9 4.6 19.1 7.9 12.8 11.6 32.3 21.9 –5.8 .. 29.0 20.2 12.9 .. 4.9 .. .. 22.6 .. 15.4 11.8 28.7 9.4 13.3 22.3 8.7 –1.8 ..
3.5 5.4 3.2 1.1 4.9 .. 5.1 6.0 4.2 5.4 3.2 5.6 4.4 6.6 .. 3.9 3.0 5.2 1.3 5.6 2.5 10.0 .. .. 4.8 4.9 3.1 3.5 5.5 3.6 2.8 3.4 5.5 6.6 4.6 5.2 3.8 0.8 7.3 2.4 4.8 6.7 3.0 2.6 0.9 6.5 3.9 2.1 4.4 .. 3.9 2.6 2.2 5.3 5.4 ..
0.0 0.6 2.7 6.9 26.8 .. 0.0 0.2 0.2 0.0 0.0 0.3 28.3 0.0 .. 0.0 32.5 0.1 0.0 0.0 0.0 0.0 0.0 45.1 0.1 0.0 0.0 0.0 10.3 0.0 0.0 0.0 6.9 0.0 2.5 0.0 7.1 .. 0.0 0.0 1.4 1.3 0.0 0.0 25.2 13.4 .. 3.3 0.0 18.0 0.0 1.5 0.4 0.9 0.0 ..
2.0 0.0 0.7 2.0 0.6 .. 0.2 0.0 0.0 1.9 0.0 0.5 2.4 0.1 .. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 17.0 0.0 0.4 0.0 14.0 1.0 0.0 .. 4.2 0.0 0.0 0.2 0.7 0.0 0.0 0.0 .. 0.0 0.0 30.0 0.0 10.5 1.6 0.5 0.1 ..
0.0 0.0 0.9 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.2 .. 0.0 0.0 0.0 0.0 0.6 0.0 1.3 6.6 0.0 0.1 0.2 0.2 0.8 0.0 0.0 0.5 0.0 0.0 0.1 0.0 0.0 0.6 .. 0.0 4.4 0.0 0.0 0.0 2.4 0.1 0.0 0.0 0.9 0.0 0.0 0.0 0.0 0.1 0.1 0.0 ..
0.5 0.4 1.1 0.8 1.3 .. 0.2 0.4 0.2 0.7 0.2 0.9 2.0 0.3 .. 0.4 0.5 1.2 0.3 0.3 0.5 .. 0.6 0.9 0.3 1.2 0.3 0.3 0.8 0.1 0.8 0.4 0.4 1.4 2.2 0.5 0.2 .. 0.3 0.2 0.1 0.2 0.6 0.2 0.7 0.2 .. 0.7 0.3 0.4 0.3 0.3 0.5 0.7 0.2 ..
0.4 0.1 0.7 1.1 0.7 .. 0.0 0.4 0.2 0.3 0.5 0.6 0.3 0.1 .. 0.6 1.4 0.2 1.2 0.0 0.8 0.2 0.4 .. 0.2 0.1 0.2 0.2 0.1 1.6 2.3 .. 0.4 0.6 2.0 0.1 0.2 0.6 0.1 0.1 0.6 0.0 0.1 0.9 0.5 0.0 1.4 1.5 0.2 0.0 0.7 0.6 0.2 0.4 0.3 ..
2009 World Development Indicators
2007
13.6 7.9 26.4 6.7 7.3 .. 12.3c .. 8.9 .. 19.5c 1.1 –9.9 13.1 .. 19.1 .. 1.2 14.0 6.2 –10.6 .. .. .. 7.6 13.1 7.7 4.3 20.2 6.5 1.2 10.9a 10.3 16.0 16.3 25.6 –10.2 .. 31.7 17.9 15.6 .. 6.5 .. .. 15.5 .. 11.0 15.7 .. 12.3 3.1 21.7 11.5 3.0 ..
193
ENVIRONMENT
Toward a broader measure of savings
3.16 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Toward a broader measure of savings Gross savings
Consumption of fixed capital
Net national 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
2007
2007
2007
2007
2007
2007
2007
2007
2007
2007
21.1 31.3 16.2 .. 21.8 .. 9.8 .. 24.0 28.0 .. 14.5 21.9 23.3 13.2 19.8 27.5 .. 19.7 13.9 .. 34.0 .. .. 31.0 23.9 16.0 .. 14.0 23.1 .. 15.7 14.0 13.4 38.6 34.8 35.5 .. .. 26.2 .. 22.7 w 25.4 32.3 41.7 23.2 32.0 48.0 24.0 22.9 33.3 36.2 17.4 20.6 22.3
12.4 12.9 8.0 13.4 9.4 .. 7.9 15.1 13.8 14.2 .. 12.4 14.8 10.3 10.8 10.6 14.7 13.9 10.6 8.9 8.2 11.8 1.9 8.3 14.0 11.8 12.7 11.1 8.3 11.2 .. 14.7 14.8 12.5 9.2 12.3 9.4 .. 10.1 10.7 .. 13.7 w 9.3 11.7 10.7 12.8 11.6 10.7 12.8 12.6 11.3 9.5 11.1 14.5 14.4
8.7 18.4 8.2 .. 12.4 .. 2.0 .. 10.2 13.7 .. 2.1 7.1 13.0 2.4 9.2 12.8 .. 9.1 5.0 .. 22.2 .. .. 16.9 12.0 3.2 .. 5.7 11.9 .. 1.0 –0.8 1.0 29.4 22.5 26.1 .. .. 15.5 .. 8.9 w 16.2 20.6 31.0 10.5 20.4 37.3 11.2 10.3 22.0 26.8 6.3 6.1 7.8
2.1 17.9 0.0 42.1 0.0 .. 0.0 0.0 0.1 0.1 .. 3.1 0.0 0.0 15.7 0.0 0.0 0.0 19.2 0.3 0.5 4.1 .. 0.0 41.9 4.6 0.2 92.6 0.0 3.0 .. 1.5 1.2 0.0 38.5 18.7 11.6 .. 22.5 0.1 .. 3.0 w 9.8 7.1 6.6 7.6 7.2 4.9 9.8 5.4 21.3 2.7 11.7 1.5 0.2
0.1 1.3 0.0 0.0 0.1 .. 0.9 0.0 0.0 0.0 .. 2.2 0.0 0.0 0.1 0.0 0.3 0.0 0.0 0.0 5.6 0.0 0.0 0.6 0.0 0.6 0.1 0.0 0.0 0.0 .. 0.0 0.1 0.0 0.0 0.7 0.1 .. 0.0 19.8 .. 0.4 w 0.9 1.2 1.2 1.3 1.2 1.3 0.7 1.9 0.4 0.6 1.5 0.2 0.0
3.4 3.5 4.6 7.2 4.5 .. 3.9 2.7 3.8 5.5 .. 5.3 3.9 2.6 0.9 6.4 7.2 4.8 2.6 3.2 2.4 4.8 .. 2.5 4.0 6.7 3.7 .. 4.0 4.4 .. 5.0 4.8 2.6 9.4 3.4 2.8 .. .. 2.1 6.9 4.3 w 2.6 3.5 2.6 4.4 3.4 2.1 4.0 4.5 4.7 3.0 3.6 4.6 4.6
0.0 0.0 3.6 0.0 0.0 .. 1.6 0.0 0.4 0.2 .. 0.2 0.0 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 .. 2.6 0.0 0.1 0.0 .. 4.6 0.0 .. 0.0 0.0 0.3 0.0 0.0 0.4 .. 0.0 0.0 .. 0.0 w 0.8 0.1 0.2 0.0 0.1 0.0 0.0 0.0 0.0 0.9 0.5 0.0 0.0
0.5 1.1 0.2 0.7 0.3 .. 0.4 0.3 0.5 0.3 .. 1.3 0.2 0.3 0.2 0.3 0.1 0.1 1.3 1.3 0.2 0.9 0.1 0.6 1.6 0.6 0.3 2.5 0.1 2.2 .. 0.2 0.3 0.2 5.8 0.7 1.2 .. 0.8 0.2 .. 0.4 w 0.7 0.9 1.2 0.6 0.9 1.3 1.0 0.3 1.0 1.0 0.7 0.3 0.2
a. Excludes particulate emissions damage. b. Likely to be overestimated because mineral depletion excludes diamonds. c. World Bank staff estimate.
194
2009 World Development Indicators
0.0 0.2 0.1 1.4 1.2 .. 0.9 0.9 0.0 0.2 .. 0.1 0.4 0.3 0.4 0.1 .. 0.2 0.9 0.4 0.1 0.4 .. 0.2 0.3 0.2 1.1 1.0 .. 0.3 .. 0.0 0.3 1.9 0.7 0.0 0.5 .. .. 0.6 0.1 0.4 w 0.7 0.8 1.1 0.4 0.8 1.3 0.5 0.4 0.6 0.8 0.4 0.3 0.2
9.3 1.4 9.0 .. 15.2 .. 2.1 .. 13.1 18.4 .. 0.4 10.4 14.3 –13.2 15.2 19.6a .. –9.7 6.3 .. 21.4 .. .. –22.8 12.5 5.3 .. 4.9a 10.7 .. 4.3c 2.0 c 1.2 –6.2 5.9 15.2 .. .. –3.0 .. 8.8 w 5.8 14.0 23.5 4.9 13.6 30.6 3.2 6.7 3.4 23.9 –5.0 8.5 11.9
About the data
3.16
Definitions
Adjusted net savings measure the change in value of
future rents from resource extractions. An economic
• Gross savings are the difference between gross
a specified set of assets, excluding capital gains. If
rent represents an excess return to a given factor
national income and public and private consumption,
a country’s net savings are positive and the account-
of production. Natural resources give rise to rents
plus net current transfers. • Consumption of fixed
ing includes a sufficiently broad range of assets,
because they are not produced; in contrast, for pro-
capital is the replacement value of capital used up in
economic theory suggests that the present value
duced goods and services competitive forces will
production. • Net national savings are gross savings
of social welfare is increasing. Conversely, persis-
expand supply until economic profits are driven to
minus consumption of fixed capital. • Education expen-
tently negative adjusted net savings indicate that an
zero. For each type of resource and each country, unit
diture is public current operating expenditures in edu-
economy is on an unsustainable path.
resource rents are derived by taking the difference
cation, including wages and salaries and excluding capi-
The table provides a check on the extent to which
between world prices (to reflect the social oppor-
tal investments in buildings and equipment. • Energy
today’s rents from a number of natural resources
tunity cost of resource extraction) and the average
depletion is the ratio of the value of the stock of energy
and changes in human capital are balanced by
unit extraction or harvest costs (including a “normal”
resources to the remaining reserve lifetime (capped at
net savings, or this generation’s bequest to future
return on capital). Unit rents are then multiplied by
25 years). It covers coal, crude oil, and natural gas.
generations.
the physical quantity extracted or harvested to arrive
• Mineral depletion is the ratio of the value of the stock
Adjusted net savings are derived from standard
at total rent. To estimate the value of the resource,
of mineral resources to the remaining reserve lifetime
national accounting measures of gross savings by
rents are assumed to be constant over the life of the
(capped at 25 years). It covers tin, gold, lead, zinc, iron,
making four adjustments. First, estimates of capital
resource (the El Serafy approach), and the present
copper, nickel, silver, bauxite, and phosphate. • Net for-
consumption of produced assets are deducted to
value of the rent flow is calculated using a 4 percent
est depletion is unit resource rents times the excess of
obtain net savings. Second, current public expen-
social discount rate. For details on the estimation of
roundwood harvest over natural growth. • Carbon diox-
ditures on education are added to net savings (in
natural wealth see World Bank (2006a).
ide damage is estimated at $20 per ton of carbon (the
standard national accounting these expenditures
A positive net depletion figure for forest resources
unit damage in 1995 U.S. dollars) times tons of carbon
are treated as consumption). Third, estimates of
implies that the harvest rate exceeds the rate of
emitted. • Particulate emission damage is the will-
the depletion of a variety of natural resources are
natural growth; this is not the same as deforesta-
ingness to pay to avoid illness and death attributable
deducted to reflect the decline in asset values asso-
tion, which represents a change in land use (see
to particulate emissions.• Adjusted net savings are
ciated with their extraction and harvest. And fourth,
Definitions for table 3.4). In principle, there should
net savings plus education expenditure minus energy
deductions are made for damages from carbon diox-
be an addition to savings in countries where growth
depletion, mineral depletion, net forest depletion, and
ide and particulate emissions.
exceeds harvest, but empirical estimates suggest
carbon dioxide and particulate emissions damage.
The exercise treats public education expenditures
that most of this net growth is in forested areas that
as an addition to savings. However, because of the
cannot currently be exploited economically. Because
Data sources
wide variability in the effectiveness of public edu-
the depletion estimates reflect only timber values,
Data on gross savings are from World Bank
cation expenditures, these figures cannot be con-
they ignore all the external and nontimber benefits
national accounts data files (see table 4.8). Data
strued as the value of investments in human capital.
associated with standing forests.
on consumption of fixed capital are from the United
A current expenditure of $1 on education does not
Pollution damage from emissions of carbon dioxide
Nations Statistics Division’s National Accounts
necessarily yield $1 of human capital. The calcula-
is calculated as the marginal social cost per unit mul-
Statistics: Main Aggregates and Detailed Tables,
tion should also consider private education expen-
tiplied by the increase in the stock of carbon dioxide.
1997, extrapolated to 2007. Data on education
diture, but data are not available for a large number
The unit damage figure represents the present value
expenditure are from the United Nations Statistics
of countries.
of global damage to economic assets and to human
Division’s Statistical Yearbook 1997 and from the
welfare over the time the unit of pollution remains
United Nations Educational, Scientific, and Cultural
While extensive, the accounting of natural resource depletion and pollution costs still has some gaps.
in the atmosphere.
Key estimates missing on the resource side include
Pollution damage from particulate emissions is
the value of fossil water extracted from aquifers, net
estimated by valuing the human health effects from
depletion of fish stocks, and depletion and degrada-
exposure to particulate matter pollution in urban
tion of soils. Important pollutants affecting human
areas. The estimates are calculated as willingness
health and economic assets are excluded because
to pay to avoid illness and death from cardiopulmo-
no internationally comparable data are widely avail-
nary disease and lung cancer in adults and acute
able on damage from ground-level ozone or sulfur
respiratory infections in children that is attributable
oxides.
to particulate emissions.
Estimates of resource depletion are based on the “change in real wealth” method described in Hamilton and Ruta (2008), which estimates depletion as
ENVIRONMENT
Toward a broader measure of savings
For a detailed note on methodology, see www. worldbank.org/data.
Organization Institute for Statistics online database. Missing data are estimated by World Bank staff. Data on energy, mineral, and forest depletion are estimates based on sources and methods in Arundhati Kunte and others’ “Estimating National Wealth: Methodology and Results” (1998). Data on carbon dioxide damage are from Samuel Fankhauser’s Valuing Climate Change: The Economics of the Greenhouse (1995). Data on particulate emission damage are from Kiran D. Pandey and others’ “The Human Costs of Air Pollution: New Estimates for Developing Countries” (2006). The conceptual underpinnings of the savings measure appear in
the ratio between the total value of the resource
Kirk Hamilton and Michael Clemens’ “Genuine Sav-
and the remaining reserve lifetime. The total value
ings Rates in Developing Countries” (1999).
of the resource is the present value of current and
2009 World Development Indicators
195
Text figures, tables, and boxes
Introduction
T
he global economy in 2007
Global output grew 3.8 percent in 2007, receding slightly from 4 percent in 2006. The downturn was greatest in high-income economies, where growth fell from 3 percent to 2.5 percent, affected by the cooling of the housing market, a precursor to the 2008 financial crisis. Low- and middle-income economies, which have grown faster on average, reached a peak of 8.1 percent annual growth in 2007. Their strong performance was led by the economies of East Asia and Pacific and South Asia (figure 4a), dominated by China at 13 percent annual growth and India at 9 percent. After a decade of sustained growth India’s gross national income (GNI) per capita (using the World Bank Atlas method) now places it with China among the lower middle-income economies. Cambodia, Lao PDR, Malaysia, Mongolia, the Philippines, and Vietnam in East Asia and Pacific all grew faster than 6 percent, as did all South Asian economies except Afghanistan and Nepal (figure 4b). Sub-Saharan Africa achieved 6.2 percent growth for the second year in a row, thanks to higher prices for its oil and commodity exports. The commodity boom also helped the Middle East and North Africa and Latin America and the Caribbean achieve their highest growth rate since 2004. Egypt, Iran, Jordan, Libya, Syria, and Tunisia grew more than 6 percent. About half the countries in Latin America and the Caribbean grew more than 6 percent, with Argentina at 9 percent and Venezuela at 8 percent. Similarly, 28 of the 47 Sub-Saharan countries grew 5 percent or more. Growth slowed in Europe and Central Asia, but annual rates remained above 5 percent, except for Moldova.
Economic growth slowed in 2007
4a 2006
GDP growth, by region (%) 12
2007
2008a
Large middle-income economies with economic growth above 10 percent
4b
GDP growth, 2007 (%) 30
9 20 6 10 3
Middle East & North Africa
a. Preliminary estimate as of November 2008. Source: World Development indicators data files.
South Asia
SubSaharan Africa
High income
La tv ia Ca m bo di a M on go lia Su da n
Europe & Latin Central America & Asia Caribbean
Ch in a Ge or gi a Pa na m a Et Sl h ov ak iopi a Re pu bl ic
East Asia & Pacific
Az er ba ija n An go la Ar m en ia
0
0
Source: World Development indicators data files.
2009 World Development Indicators
197
Savings and investment were higher in 2007
High-income economies still dominate manufactured output and exports
The rapid growth of developing economies since 2000 has been marked by large increases in investment (figure 4c). Between 2000 and 2007 investment rates rose from 32 percent of gross domestic product (GDP) to 38 percent in East Asia and Pacific and from 23 percent to 34 percent in South Asia. Investment in China, India, Lao PDR, Mongolia, and Vietnam exceeded 37 percent of GDP during 2005–07. Sub-Saharan Africa saw its investment grow 73 percent, Europe and Central Asia 91 percent, and the Middle East and North Africa 63 percent. At 34 percent, investment growth in Latin America and the Caribbean was not as rapid. Most of the increase was financed by rising savings. Annual gross savings in developing economies grew from 25 percent of GDP to 31 percent between 2000 and 2007. East Asia and Pacific saw the largest increase, from 36 percent of GDP to 47 percent, followed by South Asia, from 25 percent to 34 percent (figure 4d). Countries enjoying a surge in demand for their exports of fuels, other commodities, or manufactures—such as Algeria, Azerbaijan, Botswana, China, Gabon, Iran, and Mongolia—saved more than 40 percent of their GDP during 2005–07.
During their industrial revolutions today’s developed economies transformed themselves from agrarian economies into producers and exporters of manufactured goods. Manufacturing has yet to take off in most developing economies. Value added in manufacturing accounted for as much as 20 percent of GDP in only about a dozen developing countries in 2007, among them China, Indonesia, Lao PDR, Philippines, and Vietnam. China’s share was 32 percent. Some developing economies have been investing in and expanding their services, which in 38 countries accounted for more then 50 percent of GDP, among them Bangladesh, India, Pakistan, and Sri Lanka. Resource extraction remains important for many countries: minerals and petroleum were the leading sources of growth in many Sub-Saharan, and Middle Eastern and North African economies. Services are now the largest sector in high-income economies, accounting for more than 70 percent of GDP. Although their manufacturing share has fallen, they still accounted for 73 percent of global manufactured output in 2006, down from 79 percent in 2000 (figure 4e). They also accounted for the largest share of manufactures exports (figure 4f). East Asia and Pacific made the largest inroads, improving its share of global manufactured output by 4 percentage points, from 9 percent to 13 percent.
Asian countries invested more
4c
High-income economies still produce the largest share of manufactured goods . . .
4e
Share of world manufacturing value added Gross capital formation, by region (index, 2000 = 100)
East Asia & Pacific 9% Europe & Central Asia 3%
2000
250
Latin America & Caribbean 6% Middle East & North Africa 1% South Asia 1% Sub-Saharan Africa 1%
South Asia East Asia & Pacific
200 Europe & Central Asia High income 79%
150
East Asia & Pacific 13%
2006
Europe & Central Asia 4% Latin America & Caribbean 6%
100
Sub-Saharan Africa Middle East & North Africa Latin America & Caribbean
High income 73%
50 2000
2001
2002
2003
2004
2005
2006
Middle East & North Africa 1% South Asia 2% Sub-Saharan Africa 1%
2007
Source: World Development Indicators data files.
Source: World Development Indicators data files.
East Asia and Pacific is the largest saver
4d
. . . And account for the largest share of manufactures exports
4f
Share of world manufactures exports Savings and investment, by region, 2005–07 (% of GDP) 50
Savings
Investment 2000
40
East Asia & Pacific 9% Europe & Central Asia 2% Latin America & Caribbean 4% Middle East & North Africa 1% South Asia 1% Sub-Saharan Africa 1%
30 High income 82%
20
2006
Europe & Central Asia 4% Latin America & Caribbean 4% Middle East & North Africa 1% South Asia 1% Sub-Saharan Africa 1%
10 0 East Asia & Pacific
Europe & Central Asia
Latin America & Caribbean
Middle East & North Africa
Source: World Development Indicators data files.
198
East Asia & Pacific 14%
2009 World Development Indicators
South Asia
SubSaharan Africa
High income
High income 76%
Source: World Development Indicators data files.
Macroeconomic stability and fiscal space Macroeconomic stability is good for economic growth. Despite concerns that strict fiscal and monetary policies were preventing countries from pursuing aggressive antipoverty programs and progressing toward the Millennium Development Goals, most developing economies have reduced their fiscal deficits, controlled inflation, and kept real interest rates low. In periods of rapid growth monetary policy is crucial in maintaining macroeconomic stability. But in a deep recession it has limitations because interest rates cannot go below zero. The current financial crisis has renewed interest in fiscal policy to stimulate economic growth. Discretionary fiscal spending is possible when a country has “fiscal space,” the difference between current government spending and the maximum spending a government can undertake without jeopardizing its fiscal solvency—that is, without jeopardizing its current and future ability to service its debt. Indicators that assess fiscal space include the ratios of public debt to GDP, external debt to GDP, and fiscal balance to GDP. The ratio of the current account balance to GDP and of private capital flows excluding foreign direct investment to GDP are also relevant to determining the extent of fiscal space. Fiscal space can be created without issuing new debt, through improved efficiency and public expenditure, increased revenue mobilization, and additional grant aid. But some Twelve developing economies had a cash deficit greater than 3 percent of GDP
4g
countries may also be able to undertake fiscal stimulus programs by taking on additional debt within the available fiscal space. Among large economies with data for 2005–07, 34 had a cash surplus and 16 had a cash deficit greater than 2 percent and 12 developing economies had a deficit greater than 3 percent (figure 4g). In 2007, five developing economies had a public debt to GDP ratio greater than 60 percent (figure 4h). Maintaining fiscal solvency is one aspect of fiscal policy. A second is macroeconomic stability. The ability to increase public spending while maintaining macroeconomic stability has been referred to as having “macroeconomic space.” High inflation limits the possibility of fiscal expansion. Rapid growth, supply constraints, and rising demand for commodities by developed and developing economies boosted prices globally, and inflation rose modestly in many countries (figure 4i), even as the highest rates were being brought under control. Some countries responded by raising interest rates between 2005 and 2007, but real interest rates continued to fall in many others (figure 4j). Now, faced with a crisis that began in the United States in 2007 and spread to other economies in 2008, developing economies will face difficult policy choices as they continue to grow while maintaining a stable— and sustainable—fiscal stance. Modest inflationary pressure affected 74 countries
4i
Change in inflation from 2000–05 to 2005–07 (percentage points) 10 In 74 countries inflation increased
Cash deficit, 2005–07 (% of GDP) 0
0 –10 –10 –20 –20 –30 Jamaica Lebanon Ghana
Sri Burkina Lanka Faso
Mali
Egypt, Jordan Pakistan Kenya Arab Rep.
Lao Madagascar PDR
Source: World Development Indicators data files.
–30
In 58 countries inflation decreased
Source: International Monetary Fund and World Development Indicators data files.
Five developing economies had a public debt to GDP ratio greater than 60 percent over 2005–07
4h
Real interest rates declined in 66 countries
4j
Change in real interest rate, 2002 to 2007 (percentage points) 40 33 countries had higher real interest rates in 2007 than in 2002 20
Public debt to GDP ratio, 2005–07 (%) 150
100
0 –20
50
–40 –60
0 Jamaica
Cuba
Bolivia
Source: World Development Indicators data files.
Sri Lanka
Jordan
–80
66 countries had lower real interest rates in 2007 than in 2002
Source: International Monetary Fund and World Development Indicators data files.
2009 World Development Indicators
199
Growth in GDP and investment Quarterly data for selected major economies in each of the six developing regions show that the 2008 global crisis has affected countries differently (figure 4k–4p). In Brazil GDP rose slightly in 2008 over 2007. In China and India GDP growth has declined but remains above 7 percent. The GDP growth rate fell significantly in the Russian Federation in the fourth quarter of 2008, to 1.1 percent. South Africa and Egypt saw their GDP growth rates decline rapidly in the second half of 2008. However, overall GDP growth remained positive in all six countries.
4k
Brazil Year on year change in GDP and investment (%)
Year on year change in GDP and investment (%)
40
40
30
30 Investment
20
10
In the current global crisis growth in the industrial sector, particularly the manufacturing sector, has fallen sharply. The large developing economies shown here, with the exception of China, saw their industrial production fall into the negative range by the end of 2008 (figures 4q–4v). Even China saw a large decline in its industrial output due to falling demand.
10
0 Q1-07
Q3-07
Q1-08
Q3-08
Q1-07
Q3-07
Q1-08
Q4-08
Source: Haver Analytics and China, National Bureau of Statistics.
4q
Brazil
4r
China
Year on year change in industrial production (%) 20
Year on year change in industrial production (%) 20
10
10
0
0
–10
–10
Jan-08 Jun-08 Dec-08
Source: Haver Analytics.
Countries need macroeconomic space to pursue monetary policies to stimulate their economies. But many countries are still experiencing double-digit inflation despite cooling economies (figure 4w–4bb). Egypt has a high inflation rate. But China’s lending rate and inflation rate are both below 10 percent.
GDP
0
–20 Jan-07 Jun-07
Lending and inflation rates
Investment
20
GDP
Source: Haver Analytics.
Growth in industrial production
4l
China
–20 Feb-07
Jan-08 Jun-08
Dec-08
Source: Haver Analytics.
4w
Brazil
4x
China
Lending and inflation rates (%)
Lending and inflation rates (%)
30
30
20
20 Lending rate
10
10
Lending rate
Inflation rate Inflation rate
0 Jan-07 Jun-07
Jan-08 Jun-08 Dec-08
With monetary policy limited in scope during the current crisis, countries are looking to fiscal policy to lift them out of the economic crisis (figures 4cc–4hh). China seems to have the fiscal space to afford a large stimulus package. Brazil and India have a more constrained fiscal environment, limiting the actions they can take without affecting fiscal solvency. For the Russian Federation the falling price of oil and the depreciation of the ruble may also limit the scope for fiscal policy.
4cc
Brazil Central government debt (% of GDP) 60
Domestic debt Foreign debt
40
20
20
4dd Domestic debt Foreign debt
0 Q1-07
Q3-07
Q1-08
Source: Banco Central do Brasil.
2009 World Development Indicators
China Central government debt (% of GDP) 60
40
0
200
Jan-08 Jun-08 Dec-08
Source: World Development Indicators data files.
Source: Haver Analytics.
Central government debt
0 Jan-07 Jun-07
Q4-08
2000 2001 2002 2003 2004 2005 2006 Source: Haver Analytics.
4m
Arab Republic of Egypt
4n
India
Year on year change in GDP and investment (%)
Year on year change in GDP and investment (%)
40 30 Investment
20
4o
Russian Federation
4p
South Africa Year on year change in GDP and investment (%)
40
Year on year change in GDP and investment (%) 40
30
30
30
20
20
40
Investment
20 Investment
Investment
10
10
10
10 GDP
GDP
GDP
0
0 Q3-07 Q4-07 Q1-08 Q2-08 Q3-08
Source: Haver Analytics.
0 Q1-07
Q3-07
Q1-08
Q4-08
Source: Haver Analytics.
Arab Republic of Egypt
4s
Q3-07
Q1-08
Q4-08
Source: Haver Analytics.
4t
India
GDP
0 Q1-07
Q1-07
Q3-07
Q1-08
Q4-08
Source: Haver Analytics.
4u
Russian Federation
4v
South Africa
Year on year change in industrial production (%) 20
Year on year change in industrial production (%) 20
Year on year change in industrial production (%) 20
Year on year change in industrial production (%) 20
10
10
10
10
0
0
0
0
–10
–10
–10
–10
–20 Jan-07 Jun-07
Jan-08 Jun-08 Dec-08
Source: Haver Analytics.
–20 Jan-07 Jun-07
Jan-08 Jun-08 Dec-08
Source: Haver Analytics.
Arab Republic of Egypt
4y
–20 Jan-07 Jun-07
Jan-08 Jun-08 Dec-08
Source: Haver Analytics.
4z
India
–20 Jan-07 Jun-07
Jan-08 Jun-08 Dec-08
Source: Haver Analytics.
Russian Federation
4aa
4bb
South Africa
Lending and inflation rates (%)
Lending and inflation rates (%)
Lending and inflation rates (%)
Lending and inflation rates (%)
30
30
30
30
Inflation rate
20
20
20
20
10
10
10
Jan-08 Jun-08 Dec-08
Source: Haver Analytics.
Arab Republic of Egypt Central government debt (% of GDP) 60
0 Jan-07 Jun-07
Jan-08 Jun-08 Dec-08
Source: Haver Analytics.
4ee
Domestic debt Foreign debt
Lending rate
10 Inflation rate
Inflation rate
Lending rate
0 Jan-07 Jun-07
Jan-08 Jun-08 Dec-08
Source: Haver Analytics.
4ff
India Central government debt (% of GDP) 60
0 Jan-07 Jun-07
Domestic debt Foreign debt
Russian Federation Central government debt (% of GDP) 60
0 Jan-07 Jun-07
4gg
Domestic debt Foreign debt
40
40
20
20
20
20
0 Q3-07
Q1-08
Source: Haver Analytics.
Q4-08
Q1-07
Q3-07
Q1-08
Source: Haver Analytics.
Q3-08
0 Q1-07
4hh
South Africa Central government debt (% of GDP) 60
40
Q1-07
Jan-08 Jun-08 Dec-08
Source: Haver Analytics.
40
0
Lending rate
Inflation rate
Lending rate
Domestic debt Foreign debt
0 Q3-07
Q1-08
Source: Haver Analytics.
Q4-08
Q1-07
Q3-07
Q1-08
Q4-08
Source: Haver Analytics.
2009 World Development Indicators
201
Tables
4.a Recent economic performance of selected developing countries
Algeria Angola Argentina Armenia Azerbaijan Bangladesh Belarus Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burundi Cameroon Chile China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Ethiopia Gabon Ghana Guatemala Honduras India Indonesia Iran, Islamic Rep. Jamaica Jordan Kazakhstan Kenya Latvia Lebanon Lesotho
202
Gross domestic product
Exports of goods and services
Imports of goods and services
average annual % growth 2007 2008a
average annual % growth 2007 2008a
average annual % growth 2007 2008a
% growth 2007 2008a
7.6 .. 20.6 13.0 14.0 16.0 0.0 4.8 16.6 13.6 20.7 9.9 .. 6.2 14.3 13.9 18.4 9.1 .. 4.5 1.3 5.8 6.7 6.5 28.8 8.1 3.8 22.0 8.9 7.0 8.0 7.7 8.9 4.9 .. 6.5 25.5 12.7 16.9 8.9 13.3
7.5 7.1 14.2 4.2 14.4 6.8 12.1 7.4 6.0 11.7 4.1 7.8 9.5 2.0 4.9 5.2 2.9 17.0 –7.9 9.6 2.7 4.0 5.7 4.7 12.6 4.4 16.8 5.2 14.8 6.1 7.0 4.3 11.5 20.5 .. 6.0 15.5 4.7 13.3 4.9 6.2
3.1 21.1 8.7 13.8 25.0 6.4 8.2 4.6 6.8 5.3 5.4 6.2 3.6 3.5 5.1 11.9 8.2 6.8 –1.6 7.3 1.7 5.6 8.5 2.6 7.1 4.7 11.1 5.6 6.3 5.7 6.3 9.0 6.3 7.8 .. 6.0 8.9 7.0 10.3 2.0 4.9
3.0 14.8 6.0 6.8 10.8 6.2 10.0 6.1 6.0 2.4 5.5 6.0 4.5 3.9 3.5 9.0 3.5 8.2 7.6 2.9 2.8 2.2 5.3 3.1 7.2 3.8 11.6 2.1 7.2 4.6 4.0 7.0 6.1 5.0 –1.3 5.5 3.2 3.6 –4.6 5.5 3.9
2009 World Development Indicators
–0.6 .. 9.0 –3.5 43.3 13.0 7.3 3.3 12.6 8.8 6.6 5.2 .. –12.1 7.8 19.9 12.2 9.8 .. 8.4 –9.9 5.7 7.6 –1.7 23.3 3.9 10.2 4.2 2.6 10.8 3.6 7.5 8.0 2.8 .. 0.8 9.0 6.0 12.5 6.6 14.6
4.4 .. .. –13.8 6.0 8.7 1.2 9.4 13.1 –20.8 2.9 2.8 34.0 4.7 1.8 7.8 7.9 –0.2 6.2 3.2 7.4 12.9 –10.9 1.5 28.8 4.0 0.6 –0.9 4.7 8.1 11.0 9.7 9.5 –40.0 .. –3.5 8.0 –4.2 –1.8 32.6 –21.8
5.6 .. .. 9.6 20.9 14.5 16.8 16.3 17.2 19.6 23.0 4.8 4.3 5.1 13.4 3.7 3.2 12.1 –0.6 7.0 0.3 18.0 12.7 5.4 26.3 10.8 10.7 10.9 8.9 12.5 17.6 4.7 10.0 –13.0 .. 11.1 3.2 8.2 –13.7 7.5 7.7
GDP deflator
9.0 32.5 10.0 8.5 20.8 8.0 20.6 14.9 7.4 12.2 7.5 10.3 24.5 1.7 8.7 7.2 8.7 19.7 33.3 13.0 5.0 6.6 10.6 11.9 12.3 6.3 25.9 14.2 18.1 8.5 9.8 9.5 18.3 25.0 21.1 14.9 20.0 27.0 15.2 7.5 8.3
Current account balance
Gross international reserves
% of GDP 2007 2008a
months of import coverage 2008a
.. 15.3 2.7 –6.4 28.9 1.3 –6.8 13.7 –12.7 19.8 0.1 –22.0 –11.9 –2.6 4.4 12.0 –2.9 0.0 –28.5 –6.0 –0.7 –8.7 –6.1 3.6 0.3 –5.5 –4.3 .. –14.2 –5.0 –10.0 .. 2.4 .. .. –17.5 –7.0 –4.6 –23.9 –8.4 13.2
20.2 19.5 0.9 –12.8 29.5 0.9 –7.6 11.9 –15.5 –1.9 –1.8 –24.3 –13.0 1.3 –2.6 8.9 –3.2 –12.3 6.5 –8.0 –0.5 –10.0 –9.6 2.8 0.5 –6.1 –10.5 17.1 19.9 –5.5 –13.3 –2.6 .. 10.3 –13.8 –14.0 6.7 –7.1 –13.2 –17.4 –3.7
$ millions 2008a
138,945 .. .. 1,405 18,038 5,788 3,061 7,722 4,497 9,200 207,467 17,923 154 3,991 23,162 1,950,000 23,671 83 5,507 4,216 .. 14,327 2,644 4,473 34,603 2,515 906 1,954 1,896 4,564 2,532 254,613 51,639 63,450 1,600 8,344 19,401 2,928 5,248 17,062 982
31.0 .. .. 3.0 28.9 2.8 0.9 16.6 5.4 21.3 10.2 5.4 3.9 6.5 4.8 19.1 6.9 0.2 22.2 2.9 .. 4.9 1.8 2.4 6.6
2.7 .. 5.1 2.1 3.5 3.8 9.2 4.9 7.4 2.7 4.8 4.7 3.0 7.1 12.6 6.6
ECONOMY
Lithuania Macedonia, FYR Malawi Malaysia Mauritius Mexico Moldova Mongolia Montenegro Morocco Nicaragua Nigeria Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Romania Russian Federation Senegal Serbia Seychelles South Africa Sri Lanka Sudan Swaziland Syrian Arab Republic Thailand Tunisia Togo Turkey Uganda Ukraine Uruguay Uzbekistan Venezuela, RB Vietnam Zambia Zimbabwe
Gross domestic product
Exports of goods and services
Imports of goods and services
average annual % growth 2007 2008a
average annual % growth 2007 2008a
average annual % growth 2007 2008a
% growth 2007 2008a
9.1 15.2 –4.2 5.4 5.9 7.0 13.4 .. 10.3 15.0 16.3 .. –2.8 19.3 –1.4 10.8 21.3 –4.5 15.0 12.0 27.3 8.7 23.9 43.6 10.4 .. –4.4 3.0 8.4 3.5 6.1 .. 10.7 16.7 19.9 12.5 36.8 33.6 30.8 16.2 29.4
8.6 5.1 7.4 5.2 7.0 4.7 15.8 12.3 7.2 3.8 9.2 5.1 7.9 1.9 2.4 10.2 2.0 2.8 3.3 10.8 13.5 5.2 6.8 7.3 8.9 14.0 7.0 9.0 3.5 3.2 2.4 1.3 7.6 6.9 21.8 8.5 24.0 14.0 8.2 9.4 ..
8.8 5.0 7.9 6.3 4.7 3.2 3.0 10.2 10.3 2.7 3.9 5.9 6.0 11.5 6.2 6.8 8.9 7.2 6.6 6.0 8.1 4.8 7.5 6.3 5.1 6.8 10.2 3.5 6.6 4.8 6.3 1.9 4.6 7.9 7.6 7.4 9.5 8.4 8.5 6.0 ..
3.2 5.3 8.7 5.1 5.3 1.3 7.2 8.9 7.5 5.8 3.5 6.2 6.0 9.0 6.9 5.8 9.0 4.0 4.8 7.8 7.3 2.7 6.0 0.1 3.1 6.3 8.3 2.0 5.1 2.6 5.0 0.8 1.0 9.5 2.1 10.6 8.0 4.9 6.2 5.8 ..
4.7 11.8 –1.1 4.2 4.7 6.2 9.5 .. 10.3 5.2 9.7 .. 2.3 15.0 –13.4 9.6 6.2 5.6 8.5 12.0 6.4 –1.8 16.3 30.6 8.3 .. 33.6 –1.9 2.5 7.1 8.5 .. 7.3 12.2 3.2 10.2 32.4 –5.6 21.0 21.2 ..
13.1 6.4 –5.4 9.2 5.4 –3.5 –7.8 19.6 6.1 –8.0 3.9 .. –8.9 14.4 13.6 32.4 10.1 2.5 10.8 13.8 5.7 14.1 12.1 16.2 3.0 5.2 23.0 6.4 –2.4 5.5 1.1 5.6 .. 7.3 –0.4 12.0 15.8 0.0 5.6 –5.9 ..
11.6 20.0 –4.6 3.5 2.9 1.3 –6.1 50.3 12.3 5.6 11.4 .. –2.1 16.5 13.9 39.3 24.1 3.6 11.4 9.4 19.4 13.5 12.0 19.2 .. 4.0 0.3 3.5 2.5 7.5 6.0 8.9 .. 28.1 6.7 20.0 20.0 2.7 9.2 13.1 ..
GDP deflator
12.1 6.6 8.6 1.3 7.6 7.4 9.7 22.4 9.0 3.0 16.8 15.8 13.4 9.3 11.4 7.1 5.8 8.5 3.5 13.5 15.0 7.4 11.1 29.6 10.5 23.5 15.8 9.7 12.3 4.5 5.0 4.8 10.1 6.3 28.2 6.5 17.0 38.0 21.7 11.0 ..
Current account balance
Gross international reserves
% of GDP 2007 2008a
months of import coverage 2008a
–13.7 .. .. 15.5 –6.0 –0.5 –15.8 .. .. –0.2 –18.3 13.3 –5.8 –7.3 .. 1.0 1.4 4.4 –4.4 –13.9 5.9 .. .. –36.2 –7.3 –4.2 –7.1 –2.3 .. 6.4 –2.6 .. –5.7 –6.3 –3.7 –0.8 .. 8.8 –10.2 –4.4 ..
–12.6 –13.2 –17.9 12.8 –6.3 –1.7 –15.4 –9.6 –31.3 –4.6 –24.4 6.2 –8.3 –9.8 2.8 –2.0 –1.6 1.5 –5.5 –12.9 7.6 –12.4 –17.4 –32.1 –8.1 –7.5 –8.3 –1.3 0.0 –0.1 –4.6 –7.0 –5.5 –9.1 –6.7 –2.7 17.9 12.0 –10.2 –8.9 ..
$ millions 2008a
6,441 2,115 209 91,400 2,570 85,421 1,672 637 468 25,350 1,206 52,800 9,385 2,602 2,090 2,864 37,297 36,659 62,180 37,972 614,638 1,610 14,383 51 34,089 1,753 .. 685 5,516 111,008 8,769 .. 72,946 2,673 31,543 6,329 2,684 43,127 22,420 976 ..
2.4 3.2 1.3 7.4 4.9 2.9
4.2 2.1 .. 7.0 2.5 12.0 2.2 3.5 4.4 3.7 10.0 5.4 3.8 5.7 16.5 3.2 5.7 0.7 3.0 1.5 .. 2.9 3.4 6.6
4.2 3.7 4.3 6.4 3.9 8.6 3.6 9.3 4.0 2.3 ..
a. Data are preliminary estimates. Source: World Development Indicators data files.
2009 World Development Indicators
203
4.1 Afghanistan Albania Algeria Angolaa Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benina Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile Chinaa Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep.a Costa Rica Côte d’Ivoirea Croatia Cubaa Czech Republic Denmark Dominican Republica Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabona Gambia, The Georgia Germany Ghanaa Greece Guatemalaa Guinea Guinea-Bissau Haiti
204
Growth of output Gross domestic product
Agriculture
average annual % growth 1990–2000 2000–07
average annual % growth 1990–2000 2000–07
.. 3.5 1.9 1.6 4.3 –1.9 3.6 2.4 –6.3 4.8 –1.7 2.1 4.8 4.0 .. 6.0 2.7 –1.8 5.5 –2.9 7.0 1.7 3.1 2.0 2.2 6.6 10.6 3.6 2.8 –4.9 1.0 5.3 3.2 0.6 4.2 1.1 2.7 6.0 1.9 4.4 4.8 5.7 0.4 3.8 2.6 1.9 2.3 3.0 –7.1 1.8 4.3 2.2 4.2 4.4 1.2 –1.5
2009 World Development Indicators
10.7 5.3 4.5 12.9 4.7 12.7 3.2 2.0 17.6 5.7 8.3 2.0 3.8 3.6 5.3 5.3 3.3 5.7 5.8 2.7 9.9 3.5 2.7 0.0 12.2 4.5 10.3 5.2 4.9 5.0 4.1 5.4 0.3 4.8 3.4 4.6 1.8 4.8 5.0 4.3 2.8 1.4 8.1 7.5 3.0 1.8 2.0 4.9 8.3 1.0 5.5 4.3 3.6 2.8 0.4 0.2
.. 4.3 3.6 –1.4 3.5 0.5 3.1 1.6 –2.1 2.9 –4.0 2.7 5.8 2.9 .. –1.2 3.6 3.0 5.9 –1.9 3.7 5.5 1.1 3.8 4.9 2.2 4.1 .. –2.6 1.4 0.7 4.1 3.5 –2.1 .. 0.0 4.6 3.9 –1.7 3.1 1.2 1.5 –6.2 2.6 –1.1 2.0 2.0 3.3 –11.0 0.1 3.4 0.5 2.8 4.3 3.9 ..
.. 1.4 6.3 14.3 3.7 7.8 0.2 –0.4 6.1 3.1 6.4 –1.7 4.6 3.4 5.0 –1.5 4.0 –4.1 6.2 –1.5 5.5 3.4 2.3 0.7 3.3 5.9 4.2 –2.2 3.0 1.2 .. 4.1 1.2 1.3 .. 0.4 0.6 4.7 4.8 3.3 3.2 9.3 –3.0 6.2 1.3 –0.3 1.4 2.6 1.6 –0.4 2.8 –2.6 3.0 4.0 4.8 ..
Industry
Manufacturing
average annual % growth 1990–2000 2000–07
.. –0.5 1.8 4.4 3.8 –7.8 2.7 2.7 –2.1 7.3 –1.8 1.8 4.1 4.1 .. 5.8 2.4 –5.0 5.9 –4.3 14.3 –0.9 3.2 0.7 0.6 5.6 13.7 .. 1.5 –8.0 1.7 6.2 6.3 –1.1 .. 0.2 2.5 7.0 2.6 5.1 5.1 15.0 –2.4 4.1 4.1 1.0 1.6 1.0 –8.1 –0.1 2.7 1.0 4.3 4.9 –3.1 ..
.. 3.6 3.9 14.0 6.2 15.9 2.5 3.0 23.0 7.9 11.5 1.3 3.8 4.4 7.1 4.6 3.1 5.9 7.3 –6.2 14.2 –0.4 1.5 0.8 33.2 3.6 11.6 –2.8 5.0 9.6 .. 5.9 –1.0 5.9 .. 6.4 0.1 2.2 5.4 4.5 2.3 0.8 9.8 9.0 4.5 1.4 1.2 7.3 12.5 1.5 5.2 4.8 2.9 3.7 4.0 ..
average annual % growth 1990–2000 2000–07
.. .. –2.1 –0.3 2.7 –4.3 1.8 2.7 –15.7 7.2 –0.7 3.1 5.8 3.8 .. 4.4 2.0 .. 5.9 –8.7 18.6 1.4 .. –0.2 .. 4.4 12.9 .. –2.5 –8.7 –2.4 6.8 5.5 –3.5 .. 4.3 2.5 4.9 1.5 6.4 5.2 10.6 7.3 3.9 6.4 .. 3.0 0.9 .. 0.2 –4.5 .. 2.8 4.0 –2.0 ..
.. –0.2 2.3 20.2 5.8 6.5 1.2 2.3 11.0 7.7 11.3 0.7 2.7 4.1 8.1 2.8 3.2 6.6 6.3 .. 13.8 5.8 0.1 1.1 .. 4.0 10.9 –3.3 5.5 6.3 .. 5.8 –2.8 5.5 .. 8.2 –0.4 2.3 5.4 4.0 2.4 –4.9 10.5 6.4 4.7 1.2 3.4 4.2 10.4 1.6 .. 4.0 2.8 2.6 4.0 ..
Services
average annual % growth 1990–2000 2000–07
.. 6.9 1.8 –2.2 4.5 6.4 4.2 2.3 –2.3 4.5 –0.4 1.9 4.2 4.3 .. 7.8 3.8 –5.2 3.9 –2.8 7.1 0.2 3.0 0.2 0.8 6.9 10.2 .. 4.1 –13.0 –0.7 4.7 2.0 1.3 .. 1.2 2.7 6.0 2.4 4.1 4.0 5.7 3.3 5.2 2.5 2.2 3.1 3.7 –0.3 2.9 5.6 2.6 4.7 3.6 –0.6 ..
.. 7.2 5.0 9.1 3.6 13.7 3.7 1.8 15.1 5.9 6.3 2.2 3.2 2.6 3.9 5.9 3.4 6.3 5.5 10.4 10.2 6.2 3.2 –1.6 8.4 4.7 10.6 4.9 4.7 11.5 .. 5.6 0.4 4.7 .. 4.3 2.0 6.2 2.7 4.8 2.9 0.1 7.7 8.4 2.0 2.0 2.7 6.0 9.5 1.1 7.6 4.1 4.1 1.4 0.3 ..
Honduras Hungary India Indonesiaa Iran, Islamic Rep. Iraq Ireland Israela Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwaita Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysiaa Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmara Namibia Nepal Netherlands New Zealand Nicaragua Nigera Nigeria Norway Omana Pakistan Panama Papua New Guinea Paraguaya Peru Philippinesa Poland Portugal Puerto Ricoa
Gross domestic product
Agriculture
average annual % growth 1990–2000 2000–07
average annual % growth 1990–2000 2000–07
3.2 1.5 5.9 4.2 3.1 .. 7.4 5.4 1.5 1.8 1.1 5.0 –4.1 2.2 .. 5.8 4.9 –4.1 6.5 –1.5 6.1 5.1 4.1 .. –2.7 –0.8 2.0 3.7 7.0 4.1 2.9 5.2 3.1 –9.6 1.0 2.4 6.1 6.9 4.0 4.9 3.2 3.2 3.7 2.4 2.5 3.9 4.5 3.8 4.7 3.8 2.2 4.7 3.3 4.7 2.8 4.2
5.3 4.0 7.8 5.1 5.9 –11.4 5.5 3.2 1.0 1.0 1.7 6.3 10.0 4.4 .. 4.7 9.2 4.1 6.7 9.0 3.3 3.8 –2.7 3.7 8.0 2.7 3.2 3.3 5.4 5.4 5.1 4.0 2.6 6.5 7.5 5.0 8.1 9.2 4.8 3.4 1.6 3.4 3.4 3.9 6.6 2.4 4.7 5.6 6.0 2.3 3.3 5.4 5.1 4.1 0.9 ..
2.2 –2.4 3.2 2.0 3.2 .. 0.8 .. 2.1 –0.3 –1.3 –3.0 –8.0 1.9 .. 1.6 1.0 1.5 4.8 –5.2 1.9 2.4 .. .. –0.8 0.2 1.9 8.6 0.3 2.6 –0.2 –0.5 1.5 –11.2 2.5 –0.4 5.2 5.7 3.8 2.5 1.8 2.9 4.7 3.0 .. 2.6 5.0 4.4 3.1 4.5 3.3 5.5 1.7 0.5 –0.4 ..
3.9 4.8 3.1 3.2 5.9 .. –4.3 .. –0.2 –1.4 –1.7 7.5 5.0 3.2 .. 0.3 .. 2.0 3.1 2.9 0.5 –5.5 .. .. 1.9 1.3 1.9 0.2 3.8 4.8 0.6 0.6 2.0 –1.8 4.6 5.4 7.5 .. 1.4 3.3 1.1 2.5 3.0 .. 7.0 3.7 2.2 3.4 4.1 1.4 5.4 3.6 3.8 3.9 0.5 ..
Industry
Manufacturing
average annual % growth 1990–2000 2000–07
3.6 3.6 6.1 5.2 2.6 .. 12.7 .. 1.0 –1.0 –0.3 5.2 0.6 1.2 .. 6.0 0.3 –10.3 11.1 –8.3 –1.8 5.3 .. .. 3.2 –2.3 2.4 2.0 8.6 6.4 3.4 5.5 3.8 –13.6 –2.5 3.2 12.3 10.5 2.4 7.1 1.7 2.4 5.5 2.0 .. 3.8 3.9 4.1 6.0 5.4 0.6 5.4 3.5 7.1 3.2 ..
4.3 3.8 8.6 4.1 6.9 .. 4.9 .. 0.3 1.7 1.7 8.4 11.1 4.7 .. 6.3 .. –0.4 12.8 8.4 4.0 5.8 .. .. 10.3 2.0 2.7 4.6 4.6 4.5 4.2 1.4 1.7 0.8 7.9 4.4 11.2 .. 5.9 2.7 0.6 2.8 4.4 .. 3.8 0.6 –0.5 7.9 4.0 3.2 1.7 6.3 4.0 4.7 –0.4 ..
average annual % growth 1990–2000 2000–07
4.0 7.9 6.7 6.7 5.1 .. .. .. 1.4 –2.2 .. 5.6 2.7 1.3 .. 7.3 –0.1 –7.5 11.7 –7.3 –0.8 6.6 .. .. 5.5 –5.3 2.0 0.5 9.5 –1.4 5.8 5.3 4.3 –7.1 –9.7 2.6 10.2 7.9 2.6 8.9 2.6 2.2 5.3 2.6 .. 1.5 6.0 3.8 2.7 4.6 1.4 3.8 3.0 9.9 2.6 ..
5.6 5.6 8.0 5.1 9.9 .. .. .. –1.2 –0.2 1.9 10.2 8.6 4.2 .. 7.4 .. –2.4 10.1 6.3 2.8 6.7 .. .. 9.9 1.1 3.8 2.8 5.8 5.1 –1.4 0.2 1.7 4.8 8.1 3.3 11.0 .. 3.5 0.9 0.7 2.6 5.5 .. .. 3.7 9.3 9.9 0.6 3.2 1.2 6.2 4.5 6.9 –0.1 ..
ECONOMY
Growth of output
4.1 Services
average annual % growth 1990–2000 2000–07
3.8 1.3 7.7 4.0 3.8 .. 7.5 .. 1.6 2.3 2.0 5.0 0.3 3.2 .. 5.6 3.5 –4.9 6.6 2.7 3.7 7.1 .. .. 5.5 0.5 2.3 1.6 7.3 3.0 4.9 6.4 2.9 0.7 0.7 3.1 5.0 7.2 4.5 6.2 3.6 3.6 5.0 1.9 .. 3.9 5.0 4.4 4.5 –0.6 2.5 4.0 4.0 5.1 2.4 ..
2009 World Development Indicators
6.3 4.0 9.3 6.8 5.3 .. 6.1 .. 1.4 0.4 1.6 5.8 10.9 4.2 .. 3.7 .. 8.3 7.1 9.5 2.6 3.7 .. .. 7.4 3.2 3.6 4.0 6.6 6.5 6.9 6.0 3.0 11.5 8.4 5.1 6.7 .. 5.4 3.4 2.1 3.9 3.5 .. 14.3 3.1 7.5 6.0 6.5 2.7 3.0 5.3 6.5 3.5 1.6 ..
205
4.1 Romania Russian Federation Rwandaa Saudi Arabiaa Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lankaa Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzaniab Thailanda Timor-Lestea Togoa Trinidad and Tobago Tunisiaa Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnama West Bank and Gaza Yemen, Rep.a Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Growth of output Gross domestic product
Agriculture
average annual % growth 1990–2000 2000–07
average annual % growth 1990–2000 2000–07
–0.6 –4.7 –0.2 2.1 3.0 –4.7 –5.0 7.6 2.2 2.7 .. 2.1 2.7 5.3 5.5 3.4 2.2 1.0 5.1 –10.4 2.9 4.2 .. 3.5 3.2 4.7 3.9 –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 3.4 3.9 6.2 2.2 3.9 8.5 –0.8 3.2 3.8 5.5 2.5 2.7 2.1
6.1 6.6 5.8 4.1 4.5 5.6 11.2 5.8 6.0 4.3 .. 4.3 3.4 5.3 7.1 2.6 3.0 1.8 4.5 8.8 6.7 5.3 0.9 2.6 8.8 4.8 5.9 .. 7.1 7.6 7.7 2.6 2.6 3.3 6.2 4.6 7.8 –0.9 4.0 5.1 –5.7 3.2 w 5.6 6.2 8.0 4.3 6.2 9.0 6.1 3.6 4.5 7.3 5.1 2.4 1.7
–1.9 –4.9 2.5 1.6 2.4 .. –13.0 –2.4 0.4 0.4 .. 1.0 3.1 1.8 7.4 0.9 –0.8 –0.9 6.0 –6.8 3.2 1.0 .. 4.0 2.7 2.3 1.3 –5.7 3.7 –5.6 13.2 –0.3 3.7 2.8 0.5 1.2 4.3 .. 5.6 4.2 4.3 2.0 w 3.3 2.3 2.9 0.8 2.5 3.5 –1.8 2.1 2.9 3.3 3.2 1.3 1.6
a. Components are at producer prices. b. Covers mainland Tanzania only.
206
2008 World Development Indicators
7.7 3.9 4.0 1.4 0.7 .. .. 2.9 1.4 –1.8 .. 0.4 –1.6 1.8 1.8 1.2 2.6 –1.1 3.6 8.8 4.9 2.6 .. 2.8 –10.8 2.9 1.5 .. 1.3 2.6 3.6 1.4 3.2 6.1 6.8 3.8 3.8 .. .. 2.0 –8.5 2.5 w 3.5 3.6 3.8 3.2 3.6 4.0 3.3 3.5 4.4 3.1 3.0 0.7 –0.6
Industry
average annual % growth 1990–2000 2000–07
–1.2 –7.1 –3.8 2.2 3.8 .. –4.5 7.8 3.8 1.6 .. 1.1 2.3 6.9 8.5 3.2 4.3 0.3 9.2 –11.4 3.1 5.7 .. 1.8 3.2 4.6 4.7 –3.4 12.1 –12.6 3.0 1.5 3.7 1.1 –3.4 1.2 11.9 .. 8.2 –4.2 0.4 2.4 w 4.5 4.7 7.9 1.6 4.7 11.0 –2.6 3.1 4.1 6.0 2.0 1.9 1.1
6.5 5.8 8.2 4.5 4.1 .. .. 5.5 7.0 5.3 .. 3.4 2.8 5.0 10.2 1.7 4.3 1.7 2.8 9.8 9.6 6.6 .. 8.1 12.3 3.3 6.9 .. 10.3 6.8 6.0 0.1 1.3 3.7 4.2 2.4 10.3 .. .. 9.4 –10.0 3.0 w 7.3 7.1 9.1 4.3 7.2 10.1 6.5 3.3 3.6 8.3 5.2 1.7 1.6
Manufacturing
average annual % growth 1990–2000 2000–07
.. .. –5.8 5.6 3.1 .. 6.1 7.0 9.3 1.8 .. 1.6 .. 8.1 7.5 2.8 .. .. .. –12.6 2.7 6.9 .. 1.8 4.9 5.5 4.7 .. 14.1 –11.2 11.9 1.3 .. –0.1 0.7 4.5 11.2 .. 5.7 0.8 0.4 .. w 4.7 6.3 8.4 3.7 6.2 10.9 .. 2.9 4.2 6.4 2.1 .. 1.2
.. .. 6.7 6.1 1.9 .. .. 7.0 10.7 5.3 .. 3.2 1.1 4.0 3.5 1.6 .. .. 16.2 9.5 8.0 6.8 .. 7.5 8.7 3.2 6.5 .. 6.6 11.1 8.1 –0.4 2.1 5.3 1.9 2.4 11.9 .. .. 5.3 –12.0 3.3 w 7.8 7.1 9.1 4.2 7.2 9.7 .. 3.3 6.0 8.1 3.2 2.1 1.1
Services
average annual % growth 1990–2000 2000–07
0.9 –1.7 –0.9 2.2 3.0 .. –2.9 7.8 5.3 3.3 .. 2.7 2.7 5.7 1.9 3.9 1.8 1.2 1.5 –10.8 2.7 3.7 .. 3.9 3.2 5.3 4.0 –5.4 8.2 –8.1 7.2 3.3 3.4 3.7 0.4 –0.1 7.5 .. 5.0 2.5 2.9 3.1 w 3.6 4.3 6.3 3.0 4.2 8.0 0.9 3.5 3.4 6.9 2.5 2.9 2.5
4.8 7.2 6.7 4.1 6.1 .. .. 6.0 5.4 4.2 .. 5.0 3.7 6.3 10.3 4.1 2.5 1.4 7.9 8.3 6.2 4.7 .. –0.7 5.2 6.0 6.1 .. 9.8 7.2 9.6 3.4 2.9 2.3 7.0 6.3 7.4 .. .. 6.5 –10.0 3.0 w 5.8 6.2 8.4 4.4 6.2 9.3 6.1 3.6 5.1 8.6 5.1 2.5 1.9
About the data
ECONOMY
Growth of output
4.1
Definitions
An economy’s growth is measured by the change in
Rebasing national accounts
• Gross domestic product (GDP) at purchaser prices
the volume of its output or in the real incomes of
When countries rebase their national accounts, they
is the sum of gross value added by all resident pro-
its residents. The 1993 United Nations System of
update the weights assigned to various components
ducers in the economy plus any product taxes (less
National Accounts (1993 SNA) offers three plausible
to better reflect current patterns of production or
subsidies) not included in the valuation of output. It
indicators for calculating growth: the volume of gross
uses of output. The new base year should represent
is calculated without deducting for depreciation of
domestic product (GDP), real gross domestic income,
normal operation of the economy—it should be a year
fabricated capital assets or for depletion and degra-
and real gross national income. The volume of GDP
without major shocks or distortions. Some developing
dation of natural resources. Value added is the net
is the sum of value added, measured at constant
countries have not rebased their national accounts
output of an industry after adding up all outputs and
prices, by households, government, and industries
for many years. Using an old base year can be mis-
subtracting intermediate inputs. The industrial origin
operating in the economy.
leading because implicit price and volume weights
of value added is determined by the International
become progressively less relevant and useful.
Standard Industrial Classifi cation (ISIC) revision
Each industry’s contribution to growth in the economy’s output is measured by growth in the industry’s
To obtain comparable series of constant price data,
3. • Agriculture is the sum of gross output less
value added. In principle, value added in constant
the World Bank rescales GDP and value added by
the value of intermediate input used in production
prices can be estimated by measuring the quantity
industrial origin to a common reference year. This
for industries classified in ISIC divisions 1–5 and
of goods and services produced in a period, valu-
year’s World Development Indicators continues to
includes forestry and fishing. • Industry is the sum
ing them at an agreed set of base year prices, and
use 2000 as the reference year. Because rescaling
of gross output less the value of intermediate input
subtracting the cost of intermediate inputs, also in
changes the implicit weights used in forming regional
used in production for industries classified in ISIC
constant prices. This double-deflation method, rec-
and income group aggregates, aggregate growth rates
divisions 10–45, which cover mining, manufactur-
ommended by the 1993 SNA and its predecessors,
in this year’s edition are not comparable with those
ing (also reported separately), construction, electric-
requires detailed information on the structure of
from earlier editions with different base years.
ity, water, and gas. • Manufacturing is the sum of
prices of inputs and outputs.
Rescaling may result in a discrepancy between
gross output less the value of intermediate input
In many industries, however, value added is
the rescaled GDP and the sum of the rescaled com-
used in production for industries classified in ISIC
extrapolated from the base year using single volume
ponents. Because allocating the discrepancy would
divisions 15–37. • Services correspond to ISIC divi-
indexes of outputs or, less commonly, inputs. Par-
cause distortions in the growth rates, the discrep-
sions 50–99. This sector is derived as a residual
ticularly in the services industries, including most of
ancy is left unallocated. As a result, the weighted
(from GDP less agriculture and industry) and may not
government, value added in constant prices is often
average of the growth rates of the components gen-
properly reflect the sum of services output, including
imputed from labor inputs, such as real wages or
erally will not equal the GDP growth rate.
banking and financial services. For some countries it includes product taxes (minus subsidies) and may
number of employees. In the absence of well defined measures of output, measuring the growth of ser-
Computing growth rates
vices remains difficult.
Growth rates of GDP and its components are calcu-
also include statistical discrepancies.
Moreover, technical progress can lead to improve-
lated using the least squares method and constant
ments in production processes and in the quality of
price data in the local currency. Constant price U.S.
goods and services that, if not properly accounted
dollar series are used to calculate regional and
for, can distort measures of value added and thus
income group growth rates. Local currency series are
of growth. When inputs are used to estimate output,
converted to constant U.S. dollars using an exchange
as for nonmarket services, unmeasured technical
rate in the common reference year. The growth rates
progress leads to underestimates of the volume of
in the table are average annual compound growth
Data on national accounts for most developing
output. Similarly, unmeasured improvements in qual-
rates. Methods of computing growth rates and the
countries are collected from national statistical
ity lead to underestimates of the value of output and
alternative conversion factor are described in Sta-
organizations and central banks by visiting and
value added. The result can be underestimates of
tistical methods.
resident World Bank missions. Data for highincome economies are from Organisation for
growth and productivity improvement and overestimates of inflation.
Data sources
Changes in the System of National Accounts
Economic Co-operation and Development (OECD)
Informal economic activities pose a particular
World Development Indicators adopted the termi-
data files. The World Bank rescales constant price
measurement problem, especially in developing
nology of the 1993 SNA in 2001. Although many
data to a common reference year. The complete
countries, where much economic activity is unre-
countries continue to compile their national accounts
national accounts time series is available on the
corded. A complete picture of the economy requires
according to the SNA version 3 (referred to as the
World Development Indicators 2009 CD-ROM.
estimating household outputs produced for home
1968 SNA), more and more are adopting the 1993
The United Nations Statistics Division publishes
use, sales in informal markets, barter exchanges,
SNA. Some low-income countries still use concepts
detailed national accounts for UN member coun-
and illicit or deliberately unreported activities. The
from the even older 1953 SNA guidelines, including
tries in National Accounts Statistics: Main Aggre-
consistency and completeness of such estimates
valuations such as factor cost, in describing major
gates and Detailed Tables and publishes updates
depend on the skill and methods of the compiling
economic aggregates. Countries that use the 1993
in the Monthly Bulletin of Statistics.
statisticians.
SNA are identified in Primary data documentation.
2008 World Development Indicators
207
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 Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile Chinaa Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep.a Costa Rica Côte d’Ivoirea Croatia Cubaa Czech Republic Denmark Dominican Republica Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabona Gambia, The Georgia Germany Ghanaa Greece Guatemalaa Guinea Guinea-Bissau Haiti
208
Industry
% of GDP
Manufacturing
% of GDP
Services
% of GDP
% of GDP
1995
2007
1995
2007
1995
2007
1995
2007
1995
2007
.. 2,424 41,764 5,040 258,032 1,468 361,306 239,561 3,052 37,940 13,973 284,321 2,009 6,715 1,867 4,774 768,951 13,107 2,380 1,000 3,239 8,733 590,517 1,122 1,446 71,349 728,007 144,230 92,503 5,643 2,116 11,722 11,000 18,808 .. 55,257 181,985 12,585 20,206 60,159 9,500 578 4,343 7,606 130,605 1,569,983 4,959 382 2,694 2,522,792 6,457 131,718 14,657 3,694 254 2,908
8,399 10,831 135,285 61,403 262,451 9,204 820,974 373,192 31,248 68,415 44,773 452,754 5,428 13,120 15,144 12,311 1,313,361 39,549 6,767 974 8,350 20,686 1,329,885 1,712 7,085 163,913 3,205,507 207,169 207,786 8,953 7,646 26,267 19,796 51,278 .. 174,998 311,580 36,686 44,490 130,476 20,373 1,375 20,901 19,395 244,661 2,589,839 11,568 644 10,175 3,317,365 15,147 313,354 33,855 4,564 357 6,715
.. 56 10 7 6 42 3 3 27 26 17 2 34 17 21 4 6 14 35 48 50 24 3 46 36 9 20 0 15 57 10 14 25 11 6 5 3 13 .. 17 14 21 6 57 4 3 8 30 52 1 39 9 24 19 55 25
36 21 8 9 9 20 2 2 6 19 9 1 32 13 10 2 6 6 33 35 32 19 .. 54 23 4 11 0 9 42 5 9 24 7 .. 3 1 12 7 14 12 24 3 46 3 2 5 29 11 1 34 4 11 17 64 ..
.. 22 50 66 28 32 29 30 34 25 37 28 15 33 26 51 28 35 21 19 15 31 31 21 14 35 47 15 32 17 45 30 21 34 45 38 25 33 .. 32 30 17 33 10 33 25 52 13 16 32 24 21 20 29 12 32
24 20 61 70 34 44 29 31 73 28 42 24 13 36 22 49 29 32 22 20 27 31 .. 14 44 47 49 8 35 28 60 29 25 32 .. 39 26 28 37 36 29 19 30 13 32 21 60 15 24 30 26 23 28 45 12 ..
.. 14 11 4 18 25 15 19 13 15 31 20 9 19 11 5 19 24 15 9 10 22 18 10 11 18 34 8 16 9 8 22 15 24 38 24 17 18 .. 17 23 9 21 5 25 .. 5 6 17 23 9 .. 14 4 8 20
15 .. 5 5 21 17 11 20 6 18 32 17 8 15 13 3 18 17 14 9 19 17 .. 8 6 14 32 3 18 6 6 21 18 21 .. 27 14 13 10 16 22 5 18 5 24 12 4 5 12 23 8 13 18 4 8 ..
.. 22 39 26 66 26 68 67 39 49 46 70 51 50 54 45 67 50 43 33 36 45 66 33 51 55 33 85 53 26 45 57 55 55 49 57 71 55 .. 51 56 62 61 33 63 72 40 57 32 67 37 70 56 52 33 44
39 59 31 21 57 36 69 67 21 52 48 75 54 51 69 49 66 61 44 45 41 50 .. 32 32 49 40 92 56 29 35 63 51 61 .. 59 73 60 56 50 59 56 67 40 65 77 35 56 65 69 41 73 61 38 24 ..
2009 World Development Indicators
Gross domestic product
Agriculture
$ millions
Honduras Hungary India Indonesiaa Iran, Islamic Rep. Iraq Ireland Israela Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwaita Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysiaa Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar a Namibia Nepal Netherlands New Zealand Nicaragua Nigera 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
1995
2007
1995
2007
1995
2007
1995
2007
1995
2007
3,911 44,656 356,299 202,132 90,829 10,114 67,090 95,907 1,126,042 5,813 5,247,609 6,727 20,374 9,046 .. 517,118 27,192 1,661 1,764 5,236 11,719 1,009 135 25,541 7,507 4,449 3,160 1,397 88,832 2,466 1,415 3,820 286,698 1,753 1,227 32,986 2,247 .. 3,503 4,401 418,969 62,049 3,191 1,881 28,109 148,920 13,803 60,636 7,906 4,636 8,066 53,674 74,120 139,062 112,960 42,647
12,234 138,429 1,176,890 432,817 286,058 .. 259,018 163,957 2,101,637 11,430 4,384,255 15,833 104,853 24,190 .. 969,795 112,116 3,745 4,108 27,155 24,352 1,600 735 58,333 38,332 7,674 7,382 3,563 186,719 6,863 2,644 6,786 1,022,815 4,396 3,930 75,119 7,790 .. 7,015 10,315 765,818 135,667 5,726 4,170 165,469 388,413 35,729 142,893 19,485 6,259 12,222 107,297 144,062 422,090 222,758 ..
22 7 26 17 18 9 7 .. 3 9 2 4 13 31 .. 6 0 44 56 9 7 16 82 .. 12 13 27 30 13 50 37 10 6 33 41 15 35 60 12 42 3 7 23 40 .. 3 3 26 8 35 21 9 22 8 6 1
13 4 18 14 10 .. 2 .. 2 6 1 3 6 26 .. 3 .. 34 42 3 6 12 54 .. 5 12 26 34 10 37 13 5 4 12 23 14 28 .. 11 34 2 .. 19 .. 33 1 .. 21 7 35 22 7 14 4 3 ..
31 32 28 42 34 75 38 .. 30 37 34 29 32 16 .. 42 55 20 19 30 27 36 5 .. 34 30 9 20 41 19 25 32 28 32 29 34 15 10 28 23 27 27 27 17 .. 34 46 24 18 34 23 31 32 35 28 44
28 30 30 47 44 .. 35 .. 27 35 30 29 41 18 .. 39 .. 19 32 22 24 47 19 .. 33 30 17 20 48 24 47 28 36 15 41 27 26 .. 30 17 24 .. 30 .. 39 43 .. 27 17 45 20 37 32 31 24 ..
18 24 18 24 12 1 30 .. 22 16 23 15 15 10 .. 28 4 9 14 21 15 15 3 .. 21 23 8 16 26 8 8 23 21 26 12 19 8 7 13 10 17 19 19 6 .. 13 5 16 9 8 16 17 23 21 18 42
20 22 16 27 11 .. 23 .. 18 15 21 19 12 11 .. 28 .. 11 21 11 11 19 13 .. 19 19 16 14 28 3 5 20 19 14 4 15 15 .. 11 8 13 .. 19 .. 3 10 .. 19 7 6 13 16 22 18 15 ..
48 61 46 41 47 16 55 .. 66 54 64 67 55 53 .. 52 45 37 25 61 66 48 13 .. 55 57 64 50 46 32 37 58 66 35 30 51 51 30 60 35 69 66 49 43 .. 63 51 50 74 31 56 60 46 57 66 55
59 66 52 39 45 .. 63 .. 71 59 68 67 53 56 .. 58 .. 47 26 75 70 41 27 .. 61 59 56 45 42 39 41 67 60 73 36 59 47 .. 59 49 74 .. 51 .. 28 56 .. 53 77 20 58 56 54 65 73 ..
2009 World Development Indicators
209
4.2
Structure of output Gross domestic product
Agriculture
$ millions
Romania Russian Federation Rwandaa Saudi Arabiaa Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lankaa Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzaniab Thailanda Timor-Lestea Togoa Trinidad and Tobago Tunisiaa Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnama West Bank and Gaza Yemen, Rep.a Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
% of GDP
1995
2007
35,477 395,529 1,293 142,458 4,879 19,681 871 84,291 19,579 20,814 .. 151,113 596,751 13,030 13,830 1,699 253,706 315,940 11,397 1,232 5,255 167,896 .. 1,309 5,329 18,031 244,946 2,482 5,756 48,214 42,807 1,141,045 7,342,300 18,348 13,350 74,889 20,736 3,220 4,236 3,478 7,111 29,669,867 t 301,247 4,878,804 2,149,301 2,731,355 5,181,211 1,312,340 998,317 1,751,109 315,655 476,196 327,582 24,484,804 7,274,362
165,976 1,290,082 3,339 381,683 11,165 40,122 1,664 161,347 74,972 47,182 .. 283,007 1,436,891 32,346 46,228 2,894 454,310 424,367 37,745 3,712 16,181 245,351 395 2,499 20,886 35,020 655,881 12,933 11,771 141,177 163,296 2,772,024 13,751,400 23,136 22,308 228,071 68,643 4,016 22,523 11,363 3,418 54,583,788 t 801,382 13,490,034 6,896,111 6,594,607 14,296,294 4,365,487 3,156,118 3,615,910 850,182 1,443,539 847,438 40,309,714 12,277,625
1995
21 7 44 6 21 .. 43 0 6 4 .. 4 5 23 39 12 3 2 32 38 47 10 .. 38 2 11 11 17 49 15 3 2 2 9 32 6 27 .. 20 18 15 4w 32 13 20 7 14 19 11 7 16 26 18 2 3
a. Components are at producer prices. b. Covers mainland Tanzania only.
210
2008 World Development Indicators
Industry
Manufacturing
% of GDP 2007
9 5 40 3 14 13 45 0 3 2 .. 3 3 12 28 7 2 1 18 21 45 11 .. 44 0 10 9 .. 24 8 2 1 1 10 23 4 20 .. .. 22 19 3w 25 9 13 6 10 12 7 6 11 18 15 1 2
1995
43 37 16 49 24 .. 39 35 38 35 .. 35 29 27 11 45 31 30 20 39 14 41 .. 22 47 29 23 63 14 43 52 32 26 29 28 41 29 .. 32 36 29 30 w 23 35 39 31 34 44 33 29 34 27 29 30 29
Services
% of GDP 2007
36 38 14 65 23 28 24 31 36 34 .. 31 30 30 31 49 29 28 35 28 17 44 .. 24 59 30 28 .. 26 37 59 23 22 32 31 58 42 .. .. 38 24 28 w 30 37 41 33 37 47 34 33 40 29 32 26 27
1995
29 .. 10 10 17 .. 9 27 27 26 .. 21 .. 16 5 39 .. .. 15 28 7 30 .. 10 9 19 .. 40 7 35 10 22 19 20 12 15 15 .. 14 11 22 20 w 13 23 25 20 22 31 .. 19 15 17 16 20 22
% of GDP 2007
22 19 6 10 14 .. .. 25 22 23 .. 18 16 19 6 44 .. .. 12 20 7 35 .. 10 6 17 19 .. 8 23 12 14 14 23 12 16 21 .. .. 11 14 18 w 16 19 24 19 18 30 19 18 12 17 14 17 18
1995
36 56 40 45 55 .. 18 65 56 60 .. 61 66 50 51 43 67 68 48 22 38 50 .. 40 51 59 66 20 36 42 45 66 72 62 40 53 44 .. 48 46 56 65 w 45 52 41 62 52 36 56 64 50 46 53 68 68
2007
55 57 46 32 62 59 31 69 61 63 .. 66 67 58 41 43 70 71 47 51 37 45 .. 32 41 60 63 .. 50 55 39 76 77 58 46 38 38 .. .. 40 57 69 w 46 53 46 61 53 41 60 61 49 53 53 72 71
About the data
ECONOMY
Structure of output
4.2
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 fi rms.
is the sum of gross value added by all resident pro-
the economy. Value added is the value of the gross
But in most developing countries such surveys are
ducers in the economy plus any product taxes (less
output of producers less the value of intermediate
infrequent, so earlier survey results must be extrapo-
subsidies) not included in the valuation of output.
goods and services consumed in production, before
lated using an appropriate indicator. The choice of
It is calculated without deducting for depreciation
taking account of the consumption of fixed capital
sampling unit, which may be the enterprise (where
of fabricated assets or for depletion and degrada-
in the production process. The United Nations Sys-
responses may be based on financial records) or
tion of natural resources. Value added is the net
tem of National Accounts calls for estimates of value
the establishment (where production units may be
output of an industry after adding up all outputs and
added to be valued at either basic prices (excluding
recorded separately), also affects the quality of
subtracting intermediate inputs. The industrial origin
net taxes on products) or producer prices (including
the data. Moreover, much industrial production is
of value added is determined by the International
net taxes on products paid by producers but excluding
organized in unincorporated or owner-operated ven-
Standard Industrial Classifi cation (ISIC) revision
sales or value added taxes). Both valuations exclude
tures that are not captured by surveys aimed at the
3. • Agriculture is the sum of gross output less
transport charges that are invoiced separately by pro-
formal sector. Even in large industries, where regu-
the value of intermediate input used in production
ducers. Total GDP shown in the table and elsewhere
lar surveys are more likely, evasion of excise and
for industries classified in ISIC divisions 1–5 and
in this volume is measured at purchaser prices. Value
other taxes and nondisclosure of income lower the
includes forestry and fishing. • Industry is the sum
added by industry is normally measured at basic
estimates of value added. Such problems become
of gross output less the value of intermediate input
prices. When value added is measured at producer
more acute as countries move from state control
used in production for industries classified in ISIC
prices, this is noted in Primary data documentation.
of industry to private enterprise, because new firms
divisions 10–45, which cover mining, manufactur-
While GDP estimates based on the production
enter business and growing numbers of established
ing (also reported separately), construction, electric-
approach are generally more reliable than estimates
firms fail to report. In accordance with the System
ity, water, and gas. • Manufacturing is the sum of
compiled from the income or expenditure side, dif-
of National Accounts, output should include all such
gross output less the value of intermediate input
ferent countries use different definitions, methods,
unreported activity as well as the value of illegal
used in production for industries classified in ISIC
and reporting standards. World Bank staff review the
activities and other unrecorded, informal, or small-
divisions 15–37. • Services correspond to ISIC divi-
quality of national accounts data and sometimes
scale operations. Data on these activities need to be
sions 50–99. This sector is derived as a residual
make adjustments to improve consistency with
collected using techniques other than conventional
(from GDP less agriculture and industry) and may not
international guidelines. Nevertheless, significant
surveys of firms.
properly reflect the sum of services output, including
discrepancies remain between international stan-
In industries dominated by large organizations and
banking and financial services. For some countries
dards and actual practice. Many statistical offices,
enterprises, such as public utilities, data on output,
it includes product taxes (minus subsidies) and may
especially those in developing countries, face severe
employment, and wages are usually readily available
also include statistical discrepancies.
limitations in the resources, time, training, and bud-
and reasonably reliable. But in the services industry
gets required to produce reliable and comprehensive
the many self-employed workers and one-person busi-
series of national accounts statistics.
nesses are sometimes difficult to locate, and they have little incentive to respond to surveys, let alone
Data problems in measuring output
to report their full earnings. Compounding these prob-
Among the difficulties faced by compilers of national
lems are the many forms of economic activity that
accounts is the extent of unreported economic activ-
go unrecorded, including the work that women and
ity in the informal or secondary economy. In develop-
children do for little or no pay. For further discussion
ing countries a large share of agricultural output is
of the problems of using national accounts data, see
either not exchanged (because it is consumed within
Srinivasan (1994) and Heston (1994).
the household) or not exchanged for money.
Data sources Data on national accounts for most developing countries are collected from national statistical
Dollar conversion
organizations and central banks by visiting and
indirectly, using a combination of methods involv-
To produce national accounts aggregates that are
resident World Bank missions. Data for high-
ing estimates of inputs, yields, and area under cul-
measured in the same standard monetary units,
income economies are from Organisation for Eco-
tivation. This approach sometimes leads to crude
the value of output must be converted to a single
nomic Co-operation and Development (OECD) data
approximations that can differ from the true values
common currency. The World Bank conventionally
files. The complete national accounts time series
over time and across crops for reasons other than
uses the U.S. dollar and applies the average official
is available on the World Development Indicators
climate conditions or farming techniques. Similarly,
exchange rate reported by the International Monetary
2009 CD-ROM. The United Nations Statistics Divi-
agricultural inputs that cannot easily be allocated to
Fund for the year shown. An alternative conversion
sion publishes detailed national accounts for UN
specific outputs are frequently “netted out” using
factor is applied if the official exchange rate is judged
member countries in National Accounts Statistics:
equally crude and ad hoc approximations. For further
to diverge by an exceptionally large margin from the
Main Aggregates and Detailed Tables and publishes
discussion of the measurement of agricultural pro-
rate effectively applied to transactions in foreign cur-
updates in the Monthly Bulletin of Statistics.
duction, see About the data for table 3.3.
rencies and traded products.
Agricultural production often must be estimated
2008 World Development Indicators
211
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 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
212
Structure of manufacturing Manufacturing value added
Food, beverages, and tobacco
Textiles and clothing
Machinery and transport equipment
Chemicals
Other manufacturinga
$ millions
% of total 1995 2005
% of total 1995 2005
% of total 1995 2005
% of total 1995 2005
% of total 1995 2005
1995
2007
.. 405 4,366 202 44,502 356 50,044 41,681 352 5,586 3,909 51,721 174 1,123 213 242 124,976 2,015 336 83 296 1,758 100,393 108 159 10,594 245,002 10,524 13,506 510 172 2,339 1,655 3,666 .. 12,124 26,924 2,286 2,830 9,829 2,026 47 804 344 28,814 .. 224 20 523 516,542 602 .. 2,069 142 19 558
1,053 1,830 6,393 3,074 51,305 1,380 81,096 58,005 1,604 11,755 12,194 59,893 322 1,498 1,614 381 199,714 5,395 775 64 1,447 3,328 .. 129 398 21,488 1,341,337 5,034 33,565 543 456 4,900 3,471 8,832 .. 42,681 31,100 4,852 4,063 19,520 4,209 72 3,193 923 43,121 283,186 470 28 1,080 595,045 1,199 31,426 6,203 178 26 ..
2009 World Development Indicators
.. .. .. .. 30 .. 20 11 .. 28 .. 13 .. 36 .. 44 21 23 .. .. 20 .. 13 .. .. .. 4 .. .. .. .. .. .. .. .. 12 20 .. 26 19 .. 55 .. 51 10 13 .. 65 .. .. .. 25 .. .. .. ..
.. 20 .. .. .. .. 17 10 28 .. .. 13 .. .. .. 23 18 17 .. .. .. .. .. .. .. 16 4 .. 27 .. .. .. .. .. .. 10 14 .. 30 .. .. 43 13 48 7 14 .. .. 41 9 32 .. .. .. .. ..
.. .. .. .. 6 .. 6 5 .. 44 .. 6 .. 5 .. 1 6 12 .. .. 3 .. 4 .. .. .. 2 .. .. .. .. .. .. .. .. 6 2 .. 7 13 .. 12 .. 20 3 5 .. .. .. .. .. 15 .. .. .. ..
.. .. .. .. .. .. 1 2 1 .. .. 4 .. .. .. 0 4 15 .. .. .. .. .. .. .. 2 2 .. 9 .. .. .. .. .. .. 4 2 .. 4 .. .. 12 6 10 2 4 .. .. 2 2 6 .. .. .. .. ..
.. .. .. .. 10 .. 11 27 .. 4 .. 22 .. 1 .. 15 23 20 .. .. .. .. 23 .. .. .. 2 .. .. .. .. .. .. .. .. 24 25 .. 4 12 .. 1 .. 2 27 28 .. .. .. .. .. 13 .. .. .. ..
.. .. .. .. .. .. 5 31 11 .. .. 21 .. .. .. .. 22 18 .. .. .. .. .. .. .. 3 3 .. 7 .. .. .. .. .. .. 29 18 .. 3 .. .. 2 14 1 35 29 .. .. 5 42 0 .. .. .. .. ..
.. .. .. .. 9 .. .. 2 .. 11 .. 8 .. 3 .. 5 13 15 .. .. .. .. 10 .. .. .. .. .. .. .. .. .. .. .. .. 4 1 .. 4 18 .. 19 .. 4 4 12 .. .. .. .. .. 10 .. .. .. ..
.. .. .. .. .. .. 7 6 7 .. .. 20 .. .. .. .. 12 7 .. .. .. .. .. .. .. 8 .. .. 13 .. .. .. .. .. .. 3 2 .. 5 .. .. 8 5 5 4 12 .. .. 13 10 12 .. .. .. .. ..
.. .. .. .. 54 .. 63 56 .. 13 .. 51 .. 55 .. 55 38 30 .. .. 80 .. 50 .. .. .. 93 .. .. .. .. .. .. .. .. 55 52 .. 59 38 .. 13 .. 23 57 41 .. 35 .. .. .. 38 .. .. .. ..
.. 80 .. .. .. .. 69 52 53 .. .. 42 .. .. .. 77 44 44 .. .. .. .. .. .. .. 72 93 .. 44 .. .. .. .. .. .. 54 64 .. 58 .. .. 35 63 35 52 41 .. .. 39 38 49 .. .. .. .. ..
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
Structure of manufacturing
4.3
Manufacturing value added
Food, beverages, and tobacco
Textiles and clothing
Machinery and transport equipment
Chemicals
Other manufacturinga
$ millions
% of total 1995 2005
% of total 1995 2005
% of total 1995 2005
% of total 1995 2005
% of total 1995 2005
1995
2007
607 8,839 57,917 48,781 10,918 67 18,096 .. 225,514 865 1,077,348 866 2,976 757 .. 128,839 1,032 142 245 965 1,577 129 4 .. 1,390 873 233 195 23,432 174 107 765 54,546 400 143 6,056 166 .. 403 393 65,999 10,645 533 120 .. 17,018 643 8,864 694 372 1,280 8,105 17,043 25,885 18,249 17,867
2,218 25,977 175,691 116,894 29,832 .. 44,801 .. 299,459 1,631 933,818 2,665 12,049 2,355 .. 240,325 .. 367 867 2,589 2,284 272 95 .. 6,548 1,244 1,062 442 52,223 195 84 1,198 182,916 517 158 10,019 1,080 .. 728 740 79,146 .. 946 .. 3,760 34,306 .. 25,654 1,290 361 1,570 15,600 31,718 64,821 23,509 ..
.. 19 .. .. 15 12 15 13 9 .. 11 30 .. .. .. 8 .. .. .. 39 .. .. .. .. .. 35 .. .. .. .. .. 25 26 .. 23 .. .. .. .. 35 18 29 .. .. .. 17 15 .. 54 .. .. 28 29 18 13 ..
.. 13 9 23 9 .. 16 11 9 .. 11 23 .. 29 .. 7 .. 14 .. 20 .. .. .. .. 20 .. 40 .. 8 .. .. 30 .. 50 .. 36 .. .. .. .. 15 25 .. .. .. 19 7 .. .. .. .. 31 23 22 14 ..
.. 3 .. .. 12 7 0 5 14 .. 4 7 .. .. .. 10 .. .. .. 9 .. .. .. .. .. .. .. .. .. .. .. .. 4 .. 62 .. .. .. .. 34 3 .. .. .. .. 2 8 .. 7 .. .. 9 1 8 22 ..
.. 4 9 13 4 .. 0 2 11 .. 2 11 .. 2 .. 5 .. 5 .. 8 .. .. .. .. 11 .. 27 .. 3 .. .. .. .. .. .. 14 .. .. .. .. 1 .. .. .. .. 1 1 .. .. .. .. 14 1 4 3 ..
.. 10 .. .. 18 2 24 15 27 .. 37 5 .. .. .. 39 .. .. .. 18 .. .. .. .. .. .. .. .. .. .. .. .. 22 .. 1 .. .. .. .. 2 15 .. .. .. .. 24 3 .. .. .. .. 7 3 0 18 ..
.. 41 20 18 25 .. 7 16 26 .. 41 6 .. .. .. 47 .. 5 .. 10 .. .. .. .. 11 .. 1 .. 37 .. .. .. .. 0 .. 8 .. .. .. .. 14 .. .. .. .. 23 3 .. .. .. .. 2 6 19 15 ..
.. 13 .. .. 15 .. 13 6 8 .. 10 15 .. .. .. 8 .. .. .. 4 .. .. .. .. .. .. .. .. .. .. .. .. 15 .. 1 .. .. .. .. .. 16 .. .. .. .. 9 6 .. 7 .. .. 9 2 3 6 ..
.. 10 16 9 15 .. 27 10 7 .. 11 16 .. 5 .. 8 .. 1 .. 4 .. .. .. .. 6 .. 2 .. 12 .. .. .. .. .. .. 14 .. .. .. .. 10 .. .. .. .. 9 13 .. .. .. .. 11 2 7 2 ..
.. 55 .. .. 40 80 49 61 41 .. 39 44 .. .. .. 34 .. .. .. 30 .. .. .. .. .. 65 .. .. .. .. .. 75 33 .. 12 .. .. .. .. 29 48 71 .. .. .. 48 68 .. 32 .. .. 48 66 81 41 ..
2009 World Development Indicators
.. 33 46 36 47 .. 50 61 46 .. 35 43 .. 64 .. 33 .. 74 .. 58 .. .. .. .. 52 .. 30 .. 40 .. .. 70 .. 50 .. 27 .. .. .. .. 59 75 .. .. .. 47 76 .. .. .. .. 42 68 47 66 ..
213
4.3 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzaniab Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro Area
Structure of manufacturing Manufacturing value added
Food, beverages, and tobacco
Textiles and clothing
Machinery and transport equipment
Chemicals
Other manufacturinga
$ millions
% of total 1995 2005
% of total 1995 2005
% of total 1995 2005
% of total 1995 2005
% of total 1995 2005
1995
2007
9,387 .. 132 13,714 730 .. 75 20,799 4,704 4,573 .. 29,274 .. 1,836 640 557 .. .. 1,574 331 349 50,194 .. 130 439 3,419 .. 948 359 14,922 4,452 219,282 1,289,100 3,614 1,376 10,668 3,109 .. 599 344 1,370 5,486,528 t 35,085 1,002,230 514,247 499,190 1,037,197 390,709 .. 290,974 39,388 75,044 45,959 4,467,711 1,343,206
32,925 210,692 203 36,349 1,424 .. .. 38,275 10,923 9,677 .. 45,674 175,881 5,985 2,679 1,095 .. .. 5,145 659 819 85,451 .. 214 1,157 6,009 109,200 .. 851 29,003 19,995 269,610 1,700,000 5,269 2,541 21,941 14,673 .. .. 1,187 324 7,998,553 t 90,319 2,355,279 1,409,173 1,100,621 2,434,141 1,160,250 .. 580,099 86,834 221,220 82,642 5,544,426 1,691,606
a. Includes unallocated data. b. Covers mainland Tanzania only.
214
2008 World Development Indicators
28 .. .. .. .. .. .. 4 11 .. .. 15 16 .. .. .. 7 .. .. .. .. 21 .. .. .. .. 15 .. .. .. .. 13 12 .. .. .. .. .. .. .. 30
15 15 .. .. .. .. .. 3 7 .. .. 17 15 .. .. .. 7 .. .. .. .. .. .. .. 13 .. .. .. .. .. .. 15 14 40 .. .. .. .. 55 .. ..
7 .. .. .. .. .. .. 1 4 .. .. 5 7 .. .. .. 1 .. .. .. .. 9 .. .. .. .. 17 .. .. .. .. 5 4 .. .. .. .. .. .. .. 7
16 2 .. .. .. .. .. 1 5 .. .. 1 5 .. .. .. 1 .. .. .. .. .. .. .. 1 .. .. .. .. .. .. 3 2 10 .. .. .. .. .. .. ..
10 .. .. .. .. .. .. 60 14 .. .. 19 23 .. .. .. 33 .. .. .. .. 29 .. .. .. .. 16 .. .. .. .. 28 34 .. .. .. .. .. .. .. 29
19 8 .. .. .. .. .. 47 23 .. .. .. 22 .. .. .. 30 .. .. .. .. .. .. .. 2 .. .. .. .. .. .. 27 29 1 .. .. .. .. .. .. ..
7 .. .. .. .. .. .. 9 9 .. .. 10 10 .. .. .. 3 .. .. .. .. 6 .. .. .. .. 10 .. .. .. .. 11 12 .. .. .. .. .. .. .. 6
6 8 .. .. .. .. .. 24 2 .. .. 7 8 .. .. .. 12 .. .. .. .. .. .. .. 35 .. .. .. .. .. .. 11 14 9 .. .. .. .. .. .. ..
48 .. .. .. .. .. .. 26 63 .. .. 50 43 .. .. .. 56 .. .. .. .. 35 .. .. .. .. 42 .. .. .. .. 42 38 .. .. .. .. .. .. .. 29
44 67 .. .. .. .. .. 50 63 .. .. 76 49 .. .. .. 51 .. .. .. .. .. .. .. 50 .. .. .. .. .. .. 45 40 39 .. .. .. .. 45 .. ..
About the data
ECONOMY
Structure of manufacturing
4.3
Definitions
The data on the distribution of manufacturing value
accord with revision 3. Concordances matching ISIC
• Manufacturing value added is the sum of gross
added by industry are provided by the United Nations
categories to national classification systems and to
output less the value of intermediate inputs used in
Industrial Development Organization (UNIDO). UNIDO
related systems such as the Standard International
production for industries classified in ISIC major divi-
obtains the data from a variety of national and inter-
Trade Classification are available.
sion 3. • Food, beverages, and tobacco correspond
national sources, including the United Nations Sta-
In establishing classifi cations systems compil-
to ISIC divisions 15 and 16. • Textiles and clothing
tistics Division, the World Bank, the Organisation for
ers must define both the types of activities to be
correspond to ISIC divisions 17–19. • Machinery and
Economic Co-operation and Development, and the
described and the units whose activities are to
transport equipment correspond to ISIC divisions
International Monetary Fund. To improve comparabil-
be reported. There are many possibilities, and the
29, 30, 32, 34, and 35. • Chemicals correspond to
ity over time and across countries, UNIDO supple-
choices affect how the statistics can be interpreted
ISIC division 24. • Other manufacturing, a residual,
ments these data with information from industrial
and how useful they are in analyzing economic
covers wood and related products (ISIC division 20),
censuses, statistics from national and international
behavior. The ISIC emphasizes commonalities in the
paper and related products (ISIC divisions 21 and
organizations, unpublished data that it collects in the
production process and is explicitly not intended to
22), petroleum and related products (ISIC division
field, and estimates by the UNIDO Secretariat. Nev-
measure outputs (for which there is a newly devel-
23), basic metals and mineral products (ISIC divi-
ertheless, coverage may be incomplete, particularly
oped Central Product Classification). Nevertheless,
sion 27), fabricated metal products and professional
for the informal sector. When direct information on
the ISIC views an activity as defined by “a process
goods (ISIC division 28), and other industries (ISIC
inputs and outputs is not available, estimates may
resulting in a homogeneous set of products” (UN
divisions 25, 26, 31, 33, 36, and 37).
be used, which may result in errors in industry totals.
1990 [ISIC, series M, no. 4, rev. 3], p. 9).
Moreover, countries use different reference periods
Firms typically use multiple processes to produce
(calendar or fiscal year) and valuation methods (basic
a product. For example, an automobile manufacturer
or producer prices) to estimate value added. (See
engages in forging, welding, and painting as well as
also About the data for table 4.2.)
advertising, accounting, and other service activities.
The data on manufacturing value added in U.S. dol-
Collecting data at such a detailed level is not practical,
lars are from the World Bank’s national accounts files
nor is it useful to record production data at the highest
and may differ from those UNIDO uses to calculate
level of a large, multiplant, multiproduct firm. The ISIC
shares of value added by industry, in part because
has therefore adopted as the definition of an estab-
of differences in exchange rates. Thus value added
lishment “an enterprise or part of an enterprise which
in a particular industry estimated by applying the
independently engages in one, or predominantly one,
shares to total manufacturing value added will not
kind of economic activity at or from one location . . .
match those from UNIDO sources. Classification of
for which data are available . . .” (UN 1990, p. 25).
manufacturing industries in the table accords with
By design, this definition matches the reporting unit
the United Nations International Standard Industrial
required for the production accounts of the United
Classification (ISIC) revision 3 for the first time. Pre-
Nations System of National Accounts. The ISIC sys-
vious editions of World Development Indicators used
tem is described in the United Nations’ International
revision 2, first published in 1948. Revision 3 was
Standard Industrial Classification of All Economic Activi-
completed in 1989, and many countries now use it.
ties, Third Revision (1990). The discussion of the ISIC
But revision 2 is still widely used for compiling cross-
draws on Jacob Ryten’s “Fifty Years of ISIC: Historical
country data. UNIDO has converted these data to
Origins and Future Perspectives” (1998).
4.3a
Manufacturing continues to show strong growth in East Asia through 2007 Value added in manufacturing (index, 1990 = 100) 500 East Asia & Pacific 400 South Asia
300 Middle East & North Africa Latin America & Caribbean
200
Data sources Sub-Saharan Africa
100
Data on manufacturing value added are from the World Bank’s national accounts files. Data used
0
to calculate shares of industry value added are 1990
1995
2000
2005
2007
provided to the World Bank in electronic files
Manufacturing continues to be the dominant sector in East Asia and Pacific, growing an average of about
by UNIDO. The most recent published source is
10 percent a year between 1990 and 2007.
UNIDO’s International Yearbook of Industrial Sta-
Source: World Development Indicators data files.
tistics 2008.
2008 World Development Indicators
215
4.4 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China† Hong Kong, 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
216
Structure of merchandise exports Merchandise exports
Food
Agricultural raw materials
Fuels
Ores and metals
Manufactures
$ millions
% of total 1995 2007
% of total 1995 2007
% of total 1995 2007
% of total 1995 2007
% of total 1995 2007
1995
2007
156 202 10,258 3,642 20,967 271 53,111 57,738 635 3,501 4,803 178,265a 420 1,100 152 2,142 46,506 5,355 276 105 855 1,651 192,197 171 243 16,024 148,780 173,871 10,056 1,563 1,172 3,453 3,806 4,517 1,600 21,335 50,906 3,780 4,307 3,450 1,652 86 1,840 422 40,490 301,162 2,713 16 151 523,461 1,724 11,054 2,155 702 24 110 113,047
480 1,072 60,163 39,900 55,933 1,157 141,317 162,920 10,500 12,453 24,339 430,779 650 4,490 4,166 5,117 160,649 18,466 607 62 4,100 3,604 418,974 195 3,450 68,296 1,217,776 349,386 29,991 2,650 6,100 9,353 8,500 12,360 3,701 122,421 103,453 7,237 13,785 16,201 3,980 15 10,996 1,284 89,705 553,398 6,150 13 1,240 1,326,411 4,214 23,809 6,926 1,100 95 522 246,377
2009 World Development Indicators
.. 11 1 .. 50 11 21 4 4 10 .. 10a 20 19 .. .. 29 18 23 60 .. 27 8 4 .. 24 8 3 31 .. 1 63 63 11 .. 6 24 11 52 10 57 .. 16 73 2 14 0 60 29 5 41 30 65 7 89 37 3
.. 5 0 .. 50 8 13 6 8 6 7 8 26 15 5 3 26 9 .. 35 .. 12 8 1 .. 15 3 3 15 .. .. 30 39 10 11 4 17 .. 27 8 35 .. 8 61 2 11 1 82 24 4 47 20 37 .. .. .. 1
.. 9 0 .. 4 5 9 3 8 3 .. 1a 64 9 .. .. 5 3 63 3 .. 28 9 20 .. 14 2 0 5 .. 8 5 20 5 .. 4 3 0 3 6 1 .. 10 13 8 1 13 1 3 1 10 4 4 1 11 0 2
.. 3 0 .. 1 3 3 2 1 2 2 1 64 2 8 0 4 2 .. 4 .. 16 4 41 .. 6 0 1 4 .. .. 2 9 4 0 1 3 .. 4 2 1 .. 6 20 5 1 7 6 2 1 5 2 4 .. .. .. 1
.. 3 95 .. 10 1 18 1 66 0 .. 3a 7 13 .. .. 1 7 0 0 .. 29 9 1 .. 0 4 0 27 .. 88 1 10 8 .. 4 3 0 35 37 0 .. 7 3 2 2 83 0 19 1 3 7 2 0 0 0 1
.. 7 98 .. 11 1 23 3 81 1 35 7 0 48 8 0 8 13 .. 4 .. 62 22 0 .. 1 2 2 36 .. .. 1 33 13 0 3 10 .. 60 52 5 .. 12 0 5 4 86 .. 4 2 1 12 5 .. .. .. 6
.. 12 1 .. 2 26 20 3 1 0 .. 4a 0 31 .. .. 10 10 0 1 .. 8 7 30 .. 47 2 1 1 .. 0 1 0 2 .. 3 1 0 0 6 3 .. 3 0 3 3 2 1 8 3 6 8 0 65 0 0 1
.. 14 1 .. 4 31 29 3 3 0 1 4 1 26 18 23 12 18 .. 2 .. 5 9 17 .. 65 2 4 2 .. .. 1 0 5 2 2 2 .. 1 3 4 .. 3 3 5 3 3 0 20 3 2 11 4 .. .. .. 3
.. 65 3 .. 34 54 24 88 20 85 .. 76a 10 17 .. .. 53 60 6 2 .. 8 62 45 .. 12 84 94 34 .. 3 25 7 74 .. 82 60 8 8 40 39 .. 64 11 83 79 2 36 41 85 9 50 28 24 0 62 93
.. 70 1 .. 31 56 19 82 6 91 53 78 9 7 61 73 47 55 .. 21 .. 3 53 36 .. 10 93 68 39 .. .. 63 18 68 24 90 66 .. 8 19 55 .. 64 13 81 79 4 12 45 83 11 52 50 .. .. .. 89
4.4
Merchandise exports
Food
Agricultural raw materials
Fuels
Ores and metals
Manufactures
$ millions
% of total 1995 2007
% of total 1995 2007
% of total 1995 2007
% of total 1995 2007
% of total 1995 2007
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
ECONOMY
Structure of merchandise exports
1,769 12,865 30,630 45,417 18,360 496 44,705 19,046 233,766 1,427 443,116 1,769 5,250 1,878 959 125,058 12,785 409 311 1,305 816 160 820 8,975 2,705 1,204 507 405 73,914 441 488 1,538 79,542 745 473 6,881 168 860 1,409 345 203,171 13,645 466 288 12,342 41,992 6,068 8,029 625 2,654 919 5,575 17,502 22,895 22,783 ..
2007
5,594 94,618 145,325 118,014 86,000 41,600 121,024 54,065 491,507 1,942 712,769 5,700 47,755 4,080 1,690 371,489 62,376 1,135 923 8,311 3,574 805 184 45,400 17,161 3,356 1,190 710 176,211 1,480 1,510 2,231 271,990 1,342 1,889 14,656 2,700 6,257 2,919 888 551,250 26,974 1,202 733 65,500 136,377 24,722 17,838 1,164 4,671 2,785 27,956 50,466 138,806 51,455 ..
87 21 19 11 4 .. 19 5 7 22 0 25 10 56 .. 2 0 23 .. 14 20 .. .. 0 18 18 69 90 10 19 .. 29 8 72 2 31 66 .. .. 7 20 44 74 19 2 8 5 12 74 13 44 29 13 10 7 ..
52 6 9 15 4 0 10 3 6 16 1 15 4 43 .. 1 .. 17 .. 13 .. .. .. .. 17 14 31 86 9 7 25 31 5 57 2 19 11 .. 24 .. 13 52 81 14 0 5 2 12 83 .. 80 14 6 9 9 ..
3 2 1 7 1 .. 1 2 1 0 1 2 3 7 .. 1 0 13 .. 23 1 .. .. 0 8 5 6 2 6 61 .. 1 1 2 28 3 16 .. .. 1 4 19 3 1 2 2 0 4 0 20 36 2 1 3 5 ..
2 1 2 6 0 0 1 1 1 0 1 0 1 12 .. 1 .. 5 .. 16 .. .. .. .. 3 1 3 4 2 14 0 1 0 1 11 2 3 .. 1 .. 3 10 1 3 0 1 0 1 1 .. 6 1 1 1 2 ..
0 3 2 25 86 .. 0 0 1 1 1 0 25 6 .. 2 95 11 .. 2 0 .. .. 95 11 0 1 0 7 0 .. 0 10 1 0 2 2 .. .. 0 7 2 1 0 96 47 79 1 3 38 0 5 2 8 3 ..
5 3 16 26 83 100 1 0 4 15 1 1 66 4 .. 7 .. 12 .. 4 .. .. .. .. 13 5 5 0 14 0 .. 0 16 0 9 4 15 .. 0 .. 9 4 1 2 98 64 89 6 1 .. 0 9 2 4 4 ..
0 5 4 6 1 .. 1 1 1 51 1 24 24 3 .. 1 0 12 .. 1 8 .. .. 0 5 18 7 0 1 0 .. 0 3 3 60 12 2 .. .. 0 3 5 1 80 0 9 2 0 1 25 0 42 4 7 2 ..
7 2 8 11 2 0 1 1 2 64 2 7 15 3 .. 2 .. 4 .. 4 .. .. .. .. 2 5 3 0 2 0 69 1 3 9 61 10 64 .. 35 .. 3 5 2 63 0 8 1 1 4 .. 1 49 5 5 4 ..
9 68 73 51 9 .. 71 89 89 26 95 49 38 28 .. 92 5 41 .. 58 69 .. .. 5 58 58 14 7 75 2 .. 70 77 23 10 51 13 .. .. 91 62 28 20 1 1 27 14 83 20 4 19 14 41 71 83 ..
2009 World Development Indicators
29 81 64 42 10 0 84 76 84 5 90 76 13 37 .. 89 .. 35 .. 60 .. .. .. .. 64 76 57 11 71 3 0 67 72 32 5 65 6 .. 39 .. 60 25 10 6 1 18 7 79 11 .. 14 12 51 80 74 ..
217
4.4
Structure of merchandise exports Merchandise exports
Food
Agricultural raw materials
Fuels
Ores and metals
Manufactures
$ millions
% of total 1995 2007
% of total 1995 2007
% of total 1995 2007
% of total 1995 2007
% of total 1995 2007
1995
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singaporeb Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
2007
7,910 40,286 81,095 355,175 54 177 50,040 233,174 993 1,698 .. 8,825 42 244 118,268 299,272 8,580 58,171 8,316 30,054 .. .. 69,788 27,853c 97,849 241,018 3,798 7,740 555 8,879 866 2,450 80,440 169,084 81,641 172,060 3,563 11,700 750 1,468 682 2,022 56,439 153,103 .. .. 378 690 2,455 15,100 5,475 15,029 21,637 107,215 1,880 8,920 460 1,623 13,128 49,248 28,364 173,000 237,953 437,807 584,743 1,162,479 2,106 4,485 3,430 8,029 18,457 69,165 5,449 48,387 .. .. 1,945 7,310 1,040 4,619 2,118 2,060 5,172,481 t 13,952,366 t 62,712 230,764 884,363 3,939,704 413,659 2,196,661 470,822 1,741,884 947,082 4,170,475 354,784 1,784,975 183,585 878,671 223,927 753,753 62,002 307,393 46,647 185,551 76,554 262,784 4,225,020 9,784,163 1,742,200 4,158,254
7 2 57 1 9 28 .. 4 6 4 .. 8c 15 21 44 .. 2 3 .. .. 65 19 .. 19 8 10 20 1 86 19 8 8 11 44 .. 3 30 .. .. 3 43 9w 20 14 14 15 14 11 9 20 6 17 17 8 10
4 2 45 1 37 19 .. 2 4 3 .. 7 13 22 6 21 4 3 17 .. 35 12 .. 16 3 10 8 .. 62 13 1 5 8 53 .. 0 19 .. 5 7 16 7w 14 9 8 11 10 7 6 17 5 10 11 6 8
3 3 16 0 7 4 .. 1 4 2 .. 4c 2 4 46 .. 7 1 .. .. 23 5 .. 42 0 1 1 13 4 1 0 1 4 15 .. 0 3 .. .. 1 7 3w 4 3 3 4 4 4 3 3 1 2 6 2 2
2 3 5 0 3 2 .. 0 1 2 .. 2 1 2 2 7 4 0 2 .. 7 5 .. 9 0 0 0 .. 8 1 0 1 2 11 .. 0 4 .. 0 1 11 2w 3 2 2 2 2 2 2 2 0 2 3 2 1
8 43 0 88 22 2 .. 7 4 1 .. 9c 2 0 0 .. 2 0 .. .. 0 1 .. 0 48 8 1 77 0 4 9 6 2 1 .. 76 18 .. .. 3 1 6w 36 11 11 12 11 6 25 15 72 1 37 6 2
8 61 0 90 19 3 .. 14 5 2 .. 11 5 0 90 1 5 2 40 .. 1 4 .. 0 66 16 5 .. 1 5 62 10 4 4 .. 93 24 .. 93 1 1 10 w 44 18 15 21 18 7 35 14 75 14 39 8 4
3 10 12 1 12 15 .. 2 4 3 .. 8c 2 1 0 .. 3 3 .. .. 0 1 .. 32 0 2 3 1 1 8 55 3 3 1 .. 7 0 .. .. 87 12 3w 3 5 3 6 5 2 8 8 3 3 8 3 3
Note: Components may not sum to 100 percent because of unclassified trade. Exports of gold are excluded. a. Includes Luxembourg. b. Includes re-exports. c. Refers to the South African Customs Union (Botswana, Lesotho, Namibia, South Africa, and Swaziland).
218
2008 World Development Indicators
5 8 46 0 4 10 .. 2 3 6 .. 29 3 4 0 1 4 3 1 .. 13 2 .. 13 3 1 3 .. 2 6 1 4 4 1 .. 2 1 .. 0 78 19 4w 2 6 4 8 6 3 7 10 2 7 14 4 3
78 26 14 11 48 49 .. 84 82 90 .. 43c 78 73 6 .. 79 93 .. .. 10 73 .. 7 43 79 74 8 4 66 28 82 77 39 .. 14 44 .. .. 7 37 75 w 35 64 66 62 63 73 47 54 18 76 27 78 80
80 17 5 9 36 66 .. 76 87 88 .. 51 75 70 0 70 77 91 32 .. 17 76 .. 62 28 70 81 .. 21 74 3 74 77 30 .. 5 51 .. 1 13 48 72 w 35 61 68 55 60 77 45 54 16 66 30 75 78
About the data
ECONOMY
Structure of merchandise exports
4.4
Definitions
Data on merchandise trade are from customs
c. In some compilations categories b and c are
• Merchandise exports are the f.o.b. value of goods
reports of goods moving into or out of an economy
classified as re-exports. Because of differences in
provided to the rest of the world. • Food corresponds
or from reports of financial transactions related to
reporting practices, data on exports may not be fully
to the commodities in SITC sections 0 (food and live
merchandise trade recorded in the balance of pay-
comparable across economies.
animals), 1 (beverages and tobacco), and 4 (animal
ments. Because of differences in timing and defi -
The data on total exports of goods (merchandise)
and vegetable oils and fats) and SITC division 22
nitions, trade flow estimates from customs reports
are from the World Trade Organization (WTO), which
(oil seeds, oil nuts, and oil kernels). • Agricultural
and balance of payments may differ. Several inter-
obtains data from national statistical offices and the
raw materials correspond to SITC section 2 (crude
national agencies process trade data, each correct-
IMF’s International Financial Statistics, supplemented
materials except fuels), excluding divisions 22, 27
ing unreported or misreported data, leading to other
by the Comtrade database and publications or data-
(crude fertilizers and minerals excluding coal, petro-
differences.
bases of regional organizations, specialized agen-
leum, and precious stones), and 28 (metalliferous
The most detailed source of data on international
cies, economic groups, and private sources (such as
ores and scrap). • Fuels correspond to SITC section
trade in goods is the United Nations Statistics Divi-
Eurostat, the Food and Agriculture Organization, and
3 (mineral fuels). • Ores and metals correspond to
sion’s Commodity Trade (Comtrade) database. The
country reports of the Economist Intelligence Unit).
the commodities in SITC divisions 27, 28, and 68
International Monetary Fund (IMF) also collects
Country websites and email contact have improved
(nonferrous metals). • Manufactures correspond to
customs-based data on trade in goods. Exports are
collection of up-to-date statistics, reducing the pro-
the commodities in SITC sections 5 (chemicals), 6
recorded as the cost of the goods delivered to the
portion of estimates. The WTO database now covers
(basic manufactures), 7 (machinery and transport
frontier of the exporting country for shipment—the
most major traders in Africa, Asia, and Latin America,
equipment), and 8 (miscellaneous manufactured
free on board (f.o.b.) value. Many countries report
which together with high-income countries account
goods), excluding division 68.
trade data in U.S. dollars. When countries report in
for nearly 95 percent of world trade. Reliability of
local currency, the United Nations Statistics Division
data for countries in Europe and Central Asia has
applies the average official exchange rate to the U.S.
also improved.
dollar for the period shown.
Export shares by major commodity group are from
Countries may report trade according to the gen-
Comtrade. The values of total exports reported
eral or special system of trade (see Primary data
here have not been fully reconciled with the esti-
documentation). Under the general system exports
mates from the national accounts or the balance
comprise outward-moving goods that are (a) goods
of payments.
wholly or partly produced in the country; (b) foreign
The classification of commodity groups is based
goods, neither transformed nor declared for domestic
on the Standard International Trade Classification
consumption in the country, that move outward from
(SITC) revision 3. Previous editions contained data
customs storage; and (c) goods previously included
based on the SITC revision 1. Data for earlier years in
as imports for domestic consumption but subse-
previous editions may differ because of this change
quently exported without transformation. Under the
in methodology. Concordance tables are available to
special system exports comprise categories a and
convert data reported in one system to another.
Developing economies’ share of world merchandise exports continues to expand 1995 ($5.2 billion)
4.4a
2007 ($13.9 billion)
Data sources East Asia & Pacific 13%
East Asia & Pacific 7% Europe & Central Asia 4% Latin America & Caribbean 4% Sub-Saharan Africa 1% Middle East & N. Africa 1% South Asia 1% High income 82%
Europe & Central Asia 6%
High income 71%
Data on merchandise exports are from the WTO. Data on shares of exports by major commodity group are from Comtrade. The WTO publishes
Latin America & Caribbean 5%
data on world trade in its Annual Report. The IMF
Sub-Saharan Africa 2% Middle East & N. Africa 2% South Asia 1%
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
Developing economies’ share of world merchandise exports increased 11 percentage points from 1995
structure of exports in its Handbook of Statistics.
to 2007. East Asia and Pacific was the biggest gainer, capturing an additional 5 percentage points. Every
Tariff line records of exports are compiled in the
region except South Asia increased its share in world trade.
United Nations Statistics Division’s Comtrade
Source: World Development Indicators data files and World Trade Organization.
database.
2008 World Development Indicators
219
4.5
Structure of merchandise imports Merchandise imports
Food
Agricultural raw materials
Fuels
Ores and metals
Manufactures
$ millions
% of total 1995 2007
% of total 1995 2007
% of total 1995 2007
% of total 1995 2007
% of total 1995 2007
1995
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China† Hong Kong, 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
220
387 714 10,100 1,468 20,122 674 61,283 66,237 668 6,694 5,564 164,934a 746 1,424 1,082 1,911 54,137 5,660 455 234 1,187 1,199 168,426 175 365 15,900 132,084 196,072 13,853 871 670 4,036 2,931 7,352 2,825 25,085 45,939 5,170 4,152 11,760 3,329 454 2,546 1,145 29,470 289,391 882 182 392 463,872 1,906 25,898 3,292 819 133 653 103,558
2007
2,950 4,196 27,631 11,400 44,780 3,282 165,334 162,351 5,712 18,595 28,674 413,163 1,500 3,444 9,772 4,035 126,581 29,983 1,650 319 5,500 3,760 389,600 230 1,500 47,114 955,950 370,132 32,897 3,700 2,900 12,955 6,160 25,830 10,083 117,900 99,621 13,817 13,565 27,064 8,677 515 15,516 5,395 81,491 615,229 2,250 315 5,217 1,058,580 8,043 76,149 13,578 1,190 140 1,682 219,649
2009 World Development Indicators
.. 34 29 .. 5 31 5 6 39 17 .. 11a 36 10 .. .. 11 8 21 21 .. 17 6 16 24 7 7 5 9 .. 21 10 21 12 .. 7 12 .. 8 28 15 .. 14 14 6 11 19 36 36 10 8 16 12 31 44 .. 5
.. 16 20 .. 4 17 5 6 16 17 7 8 30 10 16 13 5 6 .. 12 .. 18 6 17 .. 7 4 3 9 .. .. 8 17 8 12 5 12 .. 8 19 17 .. 8 7 5 8 17 31 16 7 15 11 11 .. .. .. 3
.. 1 3 .. 2 0 2 3 1 3 .. 2a 4 2 .. .. 3 3 2 2 .. 3 2 10 1 2 5 2 3 .. 1 1 1 2 .. 3 3 .. 3 7 2 .. 3 2 4 3 1 1 0 3 1 2 2 1 0 .. 4
.. 1 2 .. 1 1 1 2 1 7 1 1 4 1 1 1 1 1 .. 1 .. 2 1 27 .. 1 4 1 1 .. .. 1 1 1 0 1 2 .. 1 4 2 .. 3 1 3 1 0 2 1 1 1 1 1 .. .. .. 2
.. 3 1 .. 4 27 5 4 5 8 .. 6a 0 5 .. .. 12 34 14 11 .. 2 4 9 18 9 4 2 3 .. 20 9 19 12 .. 8 3 .. 6 1 9 .. 11 11 9 7 3 14 39 6 6 7 12 19 16 .. 7
.. 15 1 .. 6 16 13 10 3 13 35 11 20 8 14 16 19 5 .. 28 .. 31 9 17 .. 26 12 3 3 .. .. 12 30 15 0 8 6 .. 21 15 18 .. 14 13 14 14 4 17 18 11 2 15 18 .. .. .. 20
.. 1 2 .. 3 0 1 4 2 2 .. 5a 1 3 .. .. 3 4 1 1 .. 6 3 2 1 2 4 2 2 .. 1 2 1 3 .. 4 2 .. 2 3 2 .. 1 1 6 3 1 0 0 4 3 3 1 1 0 .. 6
.. 3 2 .. 3 3 1 5 2 3 4 5 1 1 4 2 5 9 .. 1 .. 3 3 2 .. 4 12 2 3 .. .. 2 1 3 1 4 2 .. 1 4 1 .. 1 1 10 3 1 1 1 5 2 4 1 .. .. .. 9
.. 61 65 .. 86 39 86 82 53 69 .. 71a 59 81 .. .. 71 48 62 64 .. 72 83 64 57 79 79 87 78 .. 58 77 57 67 .. 77 73 .. 82 61 72 .. 71 72 74 76 76 46 24 70 75 71 73 47 40 .. 74
.. 65 75 .. 85 59 76 75 78 60 48 74 45 79 64 67 64 62 .. 58 .. 46 77 37 .. 62
68 90 83 .. .. 74 49 73 50 81 77 .. 67 42 62 .. 66 77 66 74 78 49 60 67 81 68 68 .. .. .. 65
4.5
Merchandise imports
Food
Agricultural raw materials
Fuels
Ores and metals
Manufactures
$ millions
% of total 1995 2007
% of total 1995 2007
% of total 1995 2007
% of total 1995 2007
% of total 1995 2007
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
ECONOMY
Structure of merchandise imports
1,879 15,465 34,707 40,630 13,882 665 32,340 29,578 205,990 2,818 335,882 3,697 3,807 2,991 1,380 135,119 7,790 522 589 1,815 7,278 1,107 510 5,392 3,650 1,719 628 475 77,691 772 431 1,976 75,858 840 415 10,023 704 1,348 1,616 1,333 185,232 13,957 975 374 8,222 32,968 4,379 11,515 2,510 1,452 3,144 7,584 28,341 29,050 32,610 ..
2007
8,556 95,041 216,622 92,381 46,000 32,000 82,472 59,039 504,454 5,899 621,091 13,511 32,756 8,989 3,460 356,846 23,642 2,417 1,065 15,285 12,251 1,730 499 7,750 24,207 5,228 2,590 1,450 146,982 2,255 1,510 3,895 296,275 3,690 2,117 31,695 3,300 3,250 3,420 2,904 491,583 30,890 3,579 970 29,500 80,281 16,100 32,590 6,872 2,909 7,280 20,180 57,985 162,674 78,138 ..
13 6 4 9 21 .. 8 7 11 14 16 21 10 10 .. 5 16 18 .. 10 19 .. .. .. 13 17 16 14 5 20 .. 17 6 8 14 20 22 .. .. 12 14 7 18 32 18 7 20 18 11 .. 19 14 8 10 14 ..
15 4 3 11 2 .. 9 6 8 13 9 15 7 11 .. 4 .. 15 .. 10 .. .. .. .. 9 12 15 11 6 15 25 19 6 12 12 12 18 .. 15 .. 9 8 16 24 18 7 10 9 10 .. 7 10 7 6 12 ..
1 3 4 6 2 .. 1 2 6 2 6 2 2 2 .. 5 1 3 .. 2 2 .. .. .. 4 3 2 1 1 1 .. 3 2 3 1 6 3 .. .. 6 2 1 1 1 1 3 1 5 1 .. 0 2 2 3 4 ..
1 1 2 4 1 .. 1 1 2 1 2 1 1 2 .. 2 .. 2 .. 3 .. .. .. .. 2 1 1 1 1 1 1 2 1 2 0 3 1 .. 1 .. 1 1 0 5 1 2 1 5 0 .. 0 2 1 2 2 ..
12 12 24 7 2 .. 3 6 7 13 16 13 25 15 .. 14 1 36 .. 21 8 .. .. .. 19 12 14 11 2 16 .. 7 2 46 19 14 10 .. .. 13 8 5 18 13 1 3 2 16 14 .. 7 9 9 9 8 ..
20 9 33 30 4 .. 9 16 12 34 28 22 11 21 .. 27 .. 31 .. 11 .. .. .. .. 16 19 17 14 9 22 27 18 7 21 27 20 16 .. 10 .. 14 14 23 17 3 4 3 26 18 .. 13 19 17 10 14 ..
1 4 7 5 3 .. 2 2 5 1 7 2 5 2 .. 6 2 3 .. 1 2 .. .. .. 4 3 1 1 3 1 .. 1 2 2 1 4 1 .. .. 5 4 3 1 3 2 7 2 3 1 .. 1 1 3 3 2 ..
1 3 6 4 0 .. 2 2 6 0 9 3 2 2 .. 8 .. 2 .. 2 .. .. .. .. 2 6 0 1 5 1 0 1 3 1 1 4 0 .. 1 .. 4 3 0 1 3 10 5 4 1 .. 1 1 2 4 3 ..
74 75 53 73 70 .. 76 82 67 68 53 61 58 71 .. 67 81 41 .. 66 63 .. .. .. 58 54 65 73 84 63 .. 72 80 42 65 56 62 .. .. 63 72 82 63 51 77 80 68 57 73 .. 74 75 58 74 72 ..
2009 World Development Indicators
64 76 46 53 16 .. 73 74 65 49 51 57 79 62 .. 58 .. 50 .. 70 .. .. .. .. 70 62 67 74 75 62 47 58 76 64 60 61 47 .. 73 .. 61 73 60 53 76 77 81 55 68 .. 78 65 48 75 65 ..
221
4.5
Structure of merchandise imports Merchandise imports
Food
Agricultural raw materials
Fuels
Ores and metals
Manufactures
$ millions
% of total 1995 2007
% of total 1995 2007
% of total 1995 2007
% of total 1995 2007
% of total 1995 2007
1995
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
2007
10,278 69,863 60,945 223,421 236 737 28,091 90,157 1,412 4,452 .. 18,350 133 445 124,507 263,155 8,770 60,218 9,492 31,534 .. .. 90,990 30,546b 113,537 372,569 5,306 11,300 1,218 8,775 1,008 2,650 65,036 151,269 80,152 161,232 4,709 14,500 810 2,455 1,675 5,337 70,786 140,795 .. .. 594 1,440 1,714 7,450 7,902 18,980 35,709 170,057 1,365 4,460 1,056 3,466 15,484 60,670 23,778 132,000 267,250 619,575 770,852 2,020,403 2,867 5,726 2,750 4,848 12,649 46,097 8,155 60,830 .. .. 1,582 6,500 700 3,971 2,660 2,420 5,228,953 t 14,144,532 t 74,778 257,014 948,310 3,651,462 452,835 1,955,705 495,317 1,691,013 1,023,140 3,908,450 366,057 1,476,968 200,774 939,184 241,363 734,326 77,167 238,291 60,322 286,538 78,377 242,243 4,205,548 10,242,758 1,644,739 4,068,352
8 18 19 16 25 14 .. 5 9 8 .. 7b 14 16 24 .. 7 6 .. .. 10 4 .. 18 16 13 7 24 16 8 15 10 5 10 .. 14 5 .. .. 10 6 9w 14 8 9 8 8 6 11 8 22 8 11 9 11
6 13 14 13 25 6 .. 3 5 6 .. 5 9 12 5 21 7 5 13 .. 12 4 .. 15 8 10 3 .. 12 7 6 9 4 9 .. 7 6 .. 25 5 11 6w 13 6 6 6 7 5 7 7 12 4 10 6 8
2 1 3 1 2 4 .. 1 3 5 .. 2b 3 2 2 .. 2 2 .. .. 1 4 .. 2 1 4 6 0 3 2 0 2 2 4 .. 4 2 .. .. 2 2 3w 3 3 4 3 3 4 3 2 4 4 2 3 3
1 1 2 1 1 2 .. 0 1 3 .. 1 1 1 0 1 2 1 3 .. 1 2 .. 1 1 2 3 .. 1 1 1 1 1 3 .. 1 4 .. 1 0 4 1w 3 2 3 1 2 3 2 1 2 2 1 1 2
21 3 12 0 30 14 .. 8 13 7 .. 8b 8 6 14 .. 6 3 .. .. 1 7 .. 30 1 7 13 3 2 48 4 4 8 10 .. 1 10 .. .. 13 9 7w 12 7 8 7 7 5 14 5 6 21 10 7 7
11 1 9 0 27 17 .. 20 11 9 .. 19 15 13 0 16 11 7 27 .. 30 18 .. 27 33 13 14 .. 19 26 1 10 18 22 .. 0 15 .. 22 12 18 15 w 17 14 17 11 14 14 12 11 14 32 18 15 12
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).
222
2008 World Development Indicators
4 3 3 3 1 7 .. 2 6 4 .. 2b 4 1 0 .. 4 3 .. .. 4 3 .. 1 6 3 6 2 2 3 6 3 3 1 .. 4 2 .. .. 2 2 4w 2 4 4 3 3 4 4 2 3 6 2 4 4
3 2 3 5 1 6 .. 2 3 7 .. 3 4 3 0 1 5 4 3 .. 1 5 .. 2 7 3 8 .. 1 4 4 4 3 1 .. 1 4 .. 1 5 6 5w 3 5 7 4 5 8 4 3 2 6 2 4 5
63 44 64 75 42 60 .. 83 70 74 .. 78b 71 75 59 .. 80 85 .. .. 84 80 .. 49 77 73 69 71 78 38 75 80 79 74 .. 76 76 .. .. 72 78 75 w 66 75 72 77 74 78 61 78 64 55 73 75 72
77 77 73 81 45 69 .. 71 79 74 .. 65 70 69 93 61 72 82 52 .. 55 69 .. 55 50 71 63 .. 63 62 62 72 70 65 .. 64 66 .. 51 76 54 69 w 62 68 63 72 68 67 70 72 50 48 64 70 69
About the data
ECONOMY
Structure of merchandise imports
4.5
Definitions
Data on imports of goods are derived from the
domestic consumption from bonded warehouses
• Merchandise imports are the c.i.f. value of goods
same sources as data on exports. In principle, world
and free trade zones. Goods transported through a
purchased from the rest of the world valued in U.S.
exports and imports should be identical. Similarly,
country en route to another are excluded.
dollars. • Food corresponds to the commodities in
exports from an economy should equal the sum of
The data on total imports of goods (merchandise)
SITC sections 0 (food and live animals), 1 (beverages
imports by the rest of the world from that economy.
in the table come from the World Trade Organization
and tobacco), and 4 (animal and vegetable oils and
But differences in timing and definitions result in dis-
(WTO). For further discussion of the WTO’s sources
fats) and SITC division 22 (oil seeds, oil nuts, and oil
crepancies in reported values at all levels. For further
and methodology, see About the data for table 4.4.
kernels). • Agricultural raw materials correspond to
discussion of indicators of merchandise trade, see
The import shares by major commodity group are
SITC section 2 (crude materials except fuels), exclud-
About the data for tables 4.4 and 6.2.
from the United Nations Statistics Division’s Com-
ing divisions 22, 27 (crude fertilizers and minerals
The value of imports is generally recorded as the
modity Trade (Comtrade) database. The values of
excluding coal, petroleum, and precious stones), and
cost of the goods when purchased by the importer
total imports reported here have not been fully rec-
28 (metalliferous ores and scrap). • Fuels correspond
plus the cost of transport and insurance to the fron-
onciled with the estimates of imports of goods and
to SITC section 3 (mineral fuels). • Ores and met-
tier of the importing country—the cost, insurance,
services from the national accounts (shown in table
als correspond to the commodities in SITC divisions
and freight (c.i.f.) value, corresponding to the landed
4.8) or those from the balance of payments (table
27, 28, and 68 (nonferrous metals). • Manufactures
cost at the point of entry of foreign goods into the
4.15).
correspond to the commodities in SITC sections 5
country. A few countries, including Australia, Canada,
The classification of commodity groups is based
(chemicals), 6 (basic manufactures), 7 (machinery
and the United States, collect import data on a free
on the Standard International Trade Classification
and transport equipment), and 8 (miscellaneous
on board (f.o.b.) basis and adjust them for freight and
(SITC) revision 3. Previous editions contained data
manufactured goods), excluding division 68.
insurance costs. Many countries report trade data in
based on the SITC revision 1. Data for earlier years in
U.S. dollars. When countries report in local currency,
previous editions may differ because of this change
the United Nations Statistics Division applies the
in methodology. Concordance tables are available to
average official exchange rate to the U.S. dollar for
convert data reported in one system to another.
the period shown. Countries may report trade according to the general or special system of trade (see Primary data documentation). Under the general system imports include goods imported for domestic consumption and imports into bonded warehouses and free trade zones. Under the special system imports comprise goods imported for domestic consumption (including transformation and repair) and withdrawals for
4.5a
Top 10 developing economy exporters of merchandise goods in 2007 Merchandise exports ($ billions)
1995
2007
1,500
Data sources Data on merchandise imports are from the WTO.
1,200
Data on shares of imports by major commodity group are from Comtrade. The WTO publishes data
900
on world trade in its Annual Report. The International Monetary Fund publishes estimates of total
600
imports of goods in its International Financial Statistics and Direction of Trade Statistics, as does the
300
United Nations Statistics Division in its Monthly Bulletin of Statistics. And the United Nations Con-
0 China
Russian Mexico Federation
Malaysia
Brazil
Thailand
India
Poland
Indonesia
Turkey
ference on Trade and Development publishes data on the structure of imports in its Handbook
China continues to dominate merchandise exports among developing economies. Even when developed
of Statistics. Tariff line records of imports are com-
economies are included, China ranks as the third leading merchandise exporter.
piled in the United Nations Statistics Division’s
Source: World Development Indicators data files and World Trade Organization.
Comtrade database.
2008 World Development Indicators
223
4.6
Structure of service exports Commercial service exports
Transport
$ millions 1995
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
224
.. 94 .. 113 3,676 27 16,076 31,692 166 469 466 33,619a 159 174 457 236 6,005 1,431 38 4 103 242 25,425 0 23 3,249 18,430 33,790 1,641 .. 61 957 426 2,223 .. 6,638 15,171 1,894 687 8,262 342 49 868 310 7,334 83,108 191 38 188 73,576 139 9,528 628 17 2 98
Travel
% of total
Insurance and financial services
% of total
Computer, information, communications, and other commercial services
% of total
% of total
2007
1995
2007
1995
2007
1995
2007
1995
2007
.. 1,399 .. 311 10,175 571 39,727 55,210 1,172 685 3,235 76,875 196 453 1,361 922 22,555 6,333 .. 7 1,510 476 61,386 .. .. 8,677 121,654 83,563 3,559 .. 303 3,598 787 12,524 .. 17,144 61,608 4,740 1,087 19,660 1,454 .. 4,336 1,185 20,154 144,680 120 114 976 210,820 1,614 42,984 1,599 44 6 152
.. 19.1 .. 31.8 27.4 53.4 29.3 11.8 45.9 15.0 64.8 29.4 a 25.8 44.8 3.8 16.2 43.3 34.5 17.3 46.2 30.5 48.3 20.7 34.1 4.5 36.8 18.2 32.5 34.4 .. 52.2 14.0 28.9 31.8 .. 22.0 44.6 2.2 46.8 38.8 28.3 70.4 43.0 76.9 28.1 24.6 46.4 21.7 48.2 27.0 58.7 3.9 8.6 75.3 18.2 5.1
.. 8.2 .. 5.4 16.5 23.3 18.2 21.7 51.6 11.7 72.5 32.3 14.9 13.3 12.2 9.0 18.0 30.0 .. 21.2 13.9 38.7 18.5 .. .. 59.1 25.7 30.9 31.0 .. 4.0 9.5 28.7 12.3 .. 29.4 47.1 7.4 31.9 35.3 25.2 .. 41.4 61.9 13.8 23.1 22.0 17.4 52.4 24.4 19.4 54.2 11.6 13.6 22.9 ..
.. 69.3 .. 0.7 60.5 5.2 50.6 42.4 42.3 5.3 5.0 17.4 a 53.2 31.5 54.1 68.5 16.2 33.0 47.8 32.4 51.7 14.8 31.1 33.9 49.8 28.0 47.4 16.8 40.0 .. 22.4 71.2 20.9 60.7 .. 43.4 24.3 82.9 37.1 32.5 25.0 3.1 41.1 5.3 22.4 33.2 9.0 73.4 25.0 24.5 7.9 43.4 33.9 5.1 14.0 91.9
.. 71.6 .. 72.4 42.4 53.4 56.4 34.0 15.2 11.1 10.0 14.2 59.2 57.2 53.6 59.2 22.0 49.4 .. 20.2 75.1 37.2 25.4 .. .. 16.4 30.6 16.0 46.9 .. 18.0 56.4 13.2 73.7 .. 38.7 15.6 86.1 57.4 47.3 58.2 .. 23.9 14.9 14.0 37.4 7.7 65.3 39.3 17.1 56.3 36.2 65.9 0.4 16.6 91.8
.. 1.4 .. 9.2 0.2 6.7 5.4 3.9 0.1 0.1 0.5 14.8a 6.9 9.8 2.6 7.8 16.9 7.6 .. 0.5 .. 7.2 11.4 19.6 1.7 7.4 10.1 9.2 6.5 .. 0.0 -0.2 12.3 1.3 .. 1.1 .. 0.1 0.0 1.0 7.8 1.0 0.4 1.5 2.0 5.3 3.3 0.3 .. 5.0 3.0 0.3 4.0 1.4 .. 0.6
.. 2.5 .. .. 0.1 3.3 3.7 5.1 0.7 4.6 0.4 6.1 2.5 12.9 3.4 3.7 7.2 1.3 .. 0.0 0.9 5.1 9.1 .. .. 3.0 0.9 13.3 1.9 .. 31.4 0.3 12.8 0.7 .. 1.8 .. 0.9 .. 0.9 2.7 .. 3.3 4.9 1.6 2.1 24.1 0.3 2.3 8.3 0.8 1.3 1.9 0.6 19.5 ..
.. 10.2 .. 59.0 11.9 41.3 14.8 41.9 11.7 79.6 29.7 38.4 a 14.1 13.9 39.5 7.5 23.6 32.5 34.8 21.0 17.7 29.7 36.8 12.5 43.9 27.8 24.4 41.5 19.1 .. 25.4 14.9 37.9 6.2 .. 33.5 31.0 14.9 16.0 27.8 39.0 26.5 15.5 16.4 47.5 36.9 41.3 4.7 26.9 43.5 30.3 52.4 53.6 18.2 81.8 2.4
.. 17.6 .. 22.2 41.0 20.0 21.7 39.2 32.6 72.6 17.1 47.4 23.5 16.6 30.8 28.1 52.8 19.3 .. 58.6 10.1 19.0 46.9 .. .. 21.5 42.7 39.8 20.2 .. 46.6 33.8 58.1 13.3 .. 30.1 37.4 5.5 10.7 16.5 13.8 .. 31.4 18.3 70.7 37.3 46.2 17.0 6.0 50.1 23.6 8.3 20.6 85.4 41.0 8.2
2009 World Development Indicators
Commercial service exports
Transport
$ millions 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
221 5,086 6,763 5,342 533 .. 4,799 7,906 61,173 1,568 63,966 1,689 535 1,183 .. 22,133 1,124 39 68 718 .. 30 .. 20 482 151 219 24 11,438 68 19 773 9,585 143 47 2,020 242 353 301 592 44,646 4,401 94 12 608 13,458 13 1,432 1,298 321 566 1,042 9,323 10,637 8,161 ..
Travel
% of total
Insurance and financial services
% of total
ECONOMY
Structure of service exports
4.6 Computer, information, communications, and other commercial services
% of total
% of total
2007
1995
2007
1995
2007
1995
2007
1995
2007
732 16,642 89,746 12,065 .. 353 88,994 21,091 110,468 2,665 127,060 3,298 3,242 2,177 .. 61,536 8,572 654 278 3,633 12,516 68 156 99 3,980 581 420 .. 28,184 291 .. 2,194 17,726 631 483 11,490 404 321 580 340 94,212 9,178 332 84 1,333 40,357 1,163 2,221 4,854 285 774 3,209 8,448 28,694 22,906 ..
25.6 8.0 28.0 1.1 25.9 .. 22.2 25.5 17.7 16.0 35.2 24.8 65.7 59.4 .. 41.9 83.6 39.6 22.8 91.9 .. 7.0 .. 62.7 59.6 32.0 29.8 27.6 21.6 32.5 9.1 25.8 12.1 29.5 31.7 20.3 24.8 6.5 .. 9.3 40.4 34.7 17.7 3.3 16.4 63.3 .. 58.0 60.4 10.8 13.3 32.5 2.9 28.6 18.6 ..
5.6 19.4 10.2 18.3 .. 57.8 3.9 21.3 16.1 16.8 33.1 19.3 53.5 51.7 .. 54.9 34.1 21.3 .. 50.8 4.6 1.0 13.0 25.4 59.0 32.0 28.2 .. 25.0 13.4 .. 19.6 11.3 46.3 44.4 15.8 31.8 35.1 20.7 10.9 29.2 21.7 12.8 10.5 82.8 47.3 33.1 48.2 53.7 10.9 16.6 19.7 15.6 32.2 25.7 ..
36.3 57.6 38.2 97.9 12.6 .. 46.1 37.9 47.0 68.2 5.0 39.1 22.7 35.7 .. 23.3 10.7 11.9 76.0 2.8 .. 90.9 .. 12.0 16.0 13.6 26.3 72.4 34.7 37.3 57.9 55.6 64.5 39.8 43.6 64.2 .. 42.7 92.4 30.0 14.7 52.7 52.5 57.8 2.8 16.6 .. 7.7 23.8 7.8 24.3 41.1 12.2 21.7 59.2 ..
76.0 28.5 11.9 44.3 .. 40.8 6.9 14.5 38.6 71.5 7.4 70.1 31.3 41.8 .. 9.4 2.6 52.9 .. 18.5 39.9 63.0 84.3 74.6 29.0 22.2 43.7 .. 45.8 60.1 .. 59.4 72.8 26.0 46.6 62.5 40.4 26.8 74.9 58.9 14.2 58.9 76.8 43.1 16.1 10.5 55.5 12.4 24.4 1.3 13.1 60.4 58.4 36.9 44.4 ..
2.0 3.2 2.5 .. 8.8 .. 17.9 0.2 6.6 1.1 0.9 0.2 0.0 1.4 .. 0.4 5.7 0.6 0.6 2.4 .. 1.4 .. .. 0.9 3.6 2.2 0.3 0.1 5.1 .. 0.0 6.7 11.6 5.3 1.4 .. 0.0 1.5 .. 1.2 0.1 2.5 0.0 0.6 3.7 .. 1.0 6.1 1.2 5.0 7.2 0.7 8.3 4.5 ..
3.1 1.7 4.2 2.5 .. 0.4 24.9 0.1 4.9 3.0 5.9 .. 3.4 0.3 .. 7.3 1.4 1.3 .. 7.7 3.1 -0.1 .. .. 0.6 1.9 0.1 .. 1.6 2.4 .. 1.9 11.3 1.0 2.0 0.6 1.6 .. 0.9 0.9 2.1 1.2 1.2 4.4 0.3 3.2 0.7 4.6 8.1 5.4 4.3 10.0 1.2 1.2 2.1 ..
36.1 31.3 31.4 2.1 52.7 .. 31.7 36.5 28.8 14.7 58.8 36.1 11.6 3.4 .. 34.5 .. 48.4 0.6 3.0 .. 0.7 .. 25.3 23.5 50.7 41.6 .. 43.7 25.2 33.0 18.5 16.7 19.1 19.5 14.2 75.2 50.9 6.2 60.7 43.7 12.6 27.4 38.9 80.2 16.4 .. 33.4 9.6 80.2 57.4 19.3 84.2 41.4 17.7 ..
15.2 50.4 73.7 34.9 .. 1.0 64.2 64.1 40.3 8.7 53.6 10.6 11.8 6.2 .. 28.5 61.9 24.5 .. 23.0 52.4 36.2 2.8 .. 11.4 43.9 28.1 .. 27.6 24.1 .. 19.0 4.6 26.7 7.0 21.1 26.2 38.1 3.5 29.3 54.5 18.2 9.2 42.0 0.8 39.0 10.7 34.7 13.8 82.4 65.9 9.8 24.8 29.6 27.9 ..
2009 World Development Indicators
225
4.6
Structure of service exports Commercial service exports
Transport
$ millions 1995
% of total 2007
Romania 1,476 10,398 Russian Federation 10,567 39,119 Rwanda 11 126 Saudi Arabia 3,475 7,901 Senegal 364 712 Serbia .. 3,140 Sierra Leone 71 42 Singapore 25,404 69,712 Slovak Republic 2,378 7,022 Slovenia 2,016 5,643 Somalia .. .. South Africa 4,414 13,242 Spain 40,019 128,340 Sri Lanka 800 1,691 Sudan 82 342 Swaziland 150 447 Sweden 15,336 63,054 Switzerland 25,179 64,947 Syrian Arab Republic 1,632 2,649 Tajikistan .. 116 Tanzania 566 1,675 Thailand 14,652 30,124 Timor-Leste .. .. Togo 64 175 Trinidad and Tobago 331 883 Tunisia 2,401 4,757 Turkey 14,475 28,253 Turkmenistan 79 .. Uganda 104 483 Ukraine 2,846 13,651 United Arab Emirates .. .. United Kingdom 77,549 277,647 United States 198,501 472,679 Uruguay 1,309 1,735 Uzbekistan .. .. Venezuela, RB 1,529 1,552 Vietnam 2,243 6,030 West Bank and Gaza .. .. Yemen, Rep. 141 562 Zambia 112 279 Zimbabwe 353 .. World 1,211,160 t 3,355,922 t Low income 11,661 33,841 Middle income 182,607 640,179 Lower middle income 90,944 380,479 Upper middle income 91,769 265,566 Low & middle income 194,194 675,065 East Asia & Pacific 62,745 211,292 Europe & Central Asia 48,999 168,014 Latin America & Carib. 37,663 93,750 Middle East & N. Africa .. .. South Asia 10,333 95,745 Sub-Saharan Africa 12,142 38,309 High income 1,014,833 2,679,427 Euro Area 422,602 1,083,420 a. Includes Luxembourg.
226
2008 World Development Indicators
Travel
Insurance and financial services
% of total
Computer, information, communications, and other commercial services
% of total
% of total
1995
2007
1995
2007
1995
2007
1995
2007
31.9 35.8 60.6 .. 15.4 .. 13.7 32.7 25.9 25.1 .. 24.2 15.8 41.9 0.9 18.2 32.2 15.1 14.5 .. 0.3 16.8 .. 33.9 58.6 24.9 11.8 79.9 17.9 75.6 .. 20.7 22.7 30.5 .. 38.2 .. .. 21.9 64.3 26.4 26.9 w 27.3 24.8 21.4 27.5 24.9 17.4 37.2 24.0 .. 31.8 26.2 27.5 25.6
24.4 30.2 28.5 .. 15.9 23.1 36.7 33.3 32.1 30.6 .. 13.6 16.8 49.6 3.1 2.0 17.6 8.4 8.2 53.9 19.8 21.1 .. 38.6 24.4 30.2 21.9 .. 3.1 44.8 .. 11.8 16.3 35.1 .. 26.0 .. .. 5.5 34.5 .. 23.7 w 26.4 23.3 23.5 23.1 24.4 23.3 32.9 18.2 .. 19.3 32.5 23.5 23.2
40.0 40.8 21.9 .. 46.1 .. 80.5 30.0 26.2 53.8 .. 48.2 63.4 28.2 9.7 32.2 22.6 37.6 77.1 .. 88.6 54.8 .. 19.9 23.4 63.7 34.2 9.3 75.1 6.7 .. 26.4 37.7 46.7 .. 55.5 .. .. 35.3 25.9 50.6 32.5 w 15.9 45.2 47.6 43.0 43.8 49.2 32.6 51.3 .. 29.7 31.3 29.3 31.5
14.1 24.6 51.8 .. 35.1 27.6 52.0 12.5 28.9 39.3 .. 63.8 45.1 22.8 76.6 7.1 19.0 18.8 76.4 2.8 61.9 55.3 .. 11.8 51.3 54.1 65.4 .. 73.6 33.7 .. 13.6 25.2 46.6 .. 52.6 .. .. 75.6 49.5 .. 26.6 w 19.8 45.0 41.7 47.5 44.1 40.6 32.9 55.4 .. 13.7 46.3 21.7 25.1
5.4 0.6 1.1 .. 0.6 .. 0.3 8.5 4.9 0.6 .. 9.9 3.9 3.4 3.7 0.0 2.4 27.8 .. .. 0.0 0.7 .. 1.8 9.2 1.5 1.5 0.9 .. 2.7 .. 17.5 4.2 1.5 .. 0.1 .. .. .. .. 0.3 5.9 w .. 5.9 6.3 5.5 5.7 7.1 2.3 6.9 .. 2.1 5.8 6.0 5.6
16.6 4.0 0.7 .. 2.6 1.8 2.0 11.6 4.8 1.4 .. 8.2 5.9 3.3 7.9 6.9 4.4 39.1 2.4 10.7 1.6 1.0 .. 3.8 15.3 2.5 3.7 .. 7.5 3.0 .. 29.9 14.5 4.6 .. 0.1 .. .. .. 6.5 .. 8.4 w 2.6 3.8 1.7 5.5 3.7 1.3 3.8 7.1 .. 4.2 5.5 9.6 5.9
22.7 22.8 17.6 .. 37.9 .. 5.6 28.9 43.0 20.6 .. 17.7 16.9 26.5 85.8 49.6 42.7 19.5 8.4 .. 11.1 27.7 .. 44.3 8.8 9.8 52.4 10.0 7.0 15.0 .. 35.4 35.5 21.3 .. 6.1 .. .. 42.8 9.8 22.7 36.2 w 55.4 26.6 28.3 25.0 27.9 30.6 28.1 17.9 .. 36.4 40.1 38.5 37.6
44.9 41.2 19.0 .. 46.4 47.6 9.3 42.7 34.3 28.7 .. 14.4 32.2 24.3 12.4 84.0 58.9 33.8 12.9 32.6 16.6 22.5 .. 45.8 9.0 13.1 9.0 .. 15.8 18.5 .. 44.7 43.9 13.7 .. 21.2 .. .. 18.9 9.5 .. 42.1 w 51.3 28.0 33.1 24.0 27.9 34.8 30.5 19.3 .. 62.8 16.6 46.0 45.8
About the data
ECONOMY
Structure of service exports
4.6
Definitions
Balance of payments statistics, the main source of
affiliates. Another important dimension of service
• Commercial service exports are total service
information on international trade in services, have
trade not captured by conventional balance of pay-
exports minus exports of government services not
many weaknesses. Some large economies—such
ments statistics is establishment trade—sales in
included elsewhere. • Transport covers all transport
as the former Soviet Union—did not report data on
the host country by foreign affiliates. By contrast,
services (sea, air, land, internal waterway, space,
trade in services until recently. Disaggregation of
cross-border intrafirm transactions in merchandise
and pipeline) performed by residents of one economy
important components may be limited and varies
may be reported as exports or imports in the balance
for those of another and involving the carriage of
considerably across countries. There are inconsis-
of payments.
passengers, movement of goods (freight), rental of
tencies in the methods used to report items. And the
The data on exports of services in the table and
carriers with crew, and related support and auxiliary
recording of major flows as net items is common (for
on imports of services in table 4.7, unlike those in
services. Excluded are freight insurance, which is
example, insurance transactions are often recorded
editions before 2000, include only commercial ser-
included in insurance services; goods procured in
as premiums less claims). These factors contribute
vices and exclude the category “government services
ports by nonresident carriers and repairs of trans-
to a downward bias in the value of the service trade
not included elsewhere.” The data are compiled by
port equipment, which are included in goods; repairs
reported in the balance of payments.
the IMF based on returns from national sources.
of harbors, railway facilities, and airfield facilities,
Efforts are being made to improve the coverage,
Data on total trade in goods and services from the
which are included in construction services; and
quality, and consistency of these data. Eurostat and
IMF’s Balance of Payments database are shown in
rental of carriers without crew, which is included
the Organisation for Economic Co-operation and
table 4.15.
in other services. • Travel covers goods and ser-
Development, for example, are working together
International transactions in services are defined
vices acquired from an economy by travelers in that
to improve the collection of statistics on trade in
by the IMF’s Balance of Payments Manual (1993) as
economy for their own use during visits of less than
services in member countries. In addition, the Inter-
the economic output of intangible commodities that
one year for business or personal purposes. • Insur-
national Monetary Fund (IMF) has implemented
may be produced, transferred, and consumed at the
ance and financial services cover freight insurance
the new classifi cation of trade in services intro-
same time. Definitions may vary among reporting
on goods exported and other direct insurance such
duced in the fifth edition of its Balance of Payments
economies. Travel services include the goods and
as life insurance; financial intermediation services
Manual (1993).
services consumed by travelers, such as meals,
such as commissions, foreign exchange transac-
lodging, and transport (within the economy visited),
tions, and brokerage services; and auxiliary services
including car rental.
such as financial market operational and regulatory
Still, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm
services. • Computer, information, communica-
service transactions, which are usually not captured
tions, and other commercial services cover such
in the balance of payments, have increased in recent
activities as international telecommunications and
years. An example is transnational corporations’ use
postal and courier services; computer data; news-
of mainframe computers around the clock for data
related service transactions between residents and
processing, exploiting time zone differences between
nonresidents; construction services; royalties and
their home country and the host countries of their
license fees; miscellaneous business, professional, and technical services; and personal, cultural, and
4.6a
Top 10 developing economy exporters of commercial services in 2007 Commercial service exports ($ billions)
1995
recreational services.
2007
150
120
90
60
30
0 China
India
Russian Thailand Federation
Poland
Turkey
Malaysia
Brazil
Egypt, Arab. Rep
Mexico
Data sources Data on exports of commercial services are from
The top 10 developing economy exporters of commercial services accounted for almost 63 percent of
the IMF, which publishes balance of payments
developing economy commercial service exports and 13 percent of world commercial service exports.
data in its International Financial Statistics and
Source: International Monetary Fund balance of payments data files.
Balance of Payments Statistics Yearbook.
2008 World Development Indicators
227
4.7
Structure of service imports Commercial service imports
Transport
$ millions 1995
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
228
.. 98 .. 1,665 6,992 52 16,979 27,552 297 1,192 276 32,511a 235 321 262 440 13,161 1,278 116 62 181 485 32,985 114 174 3,524 24,635 24,962 2,813 .. 690 895 1,235 1,327 .. 4,860 13,945 957 1,141 4,511 488 45 420 337 9,418 64,523 832 47 249 128,865 331 4,003 672 252 27 236
Travel
% of total
Insurance and financial services
% of total
Computer, information, communications, and other commercial services
% of total
% of total
2007
1995
2007
1995
2007
1995
2007
1995
2007
.. 1,391 .. 11,610 10,522 772 38,540 38,909 3,324 2,673 1,999 72,383 342 810 550 970 34,776 4,812 .. 168 859 1,413 79,824 .. .. 9,718 129,255 41,234 6,170 .. 3,523 1,799 2,223 3,858 .. 14,308 53,889 1,692 2,481 13,088 1,706 .. 3,022 1,740 21,745 129,464 1,020 77 872 257,096 1,808 19,783 2,005 248 42 476
.. 61.4 .. 18.2 30.1 82.6 36.9 11.9 31.1 65.0 35.9 24.1a 59.2 65.9 51.5 42.6 44.1 41.5 56.0 49.4 46.4 35.4 24.1 43.7 55.0 54.0 38.7 22.2 42.4 .. 18.6 41.4 50.5 29.5 .. 16.5 45.1 61.1 42.4 35.1 55.1 1.6 52.9 63.4 22.8 32.9 17.7 59.6 27.0 18.4 61.3 29.9 41.4 58.4 53.1 77.6
.. 12.8 .. 21.6 28.6 46.8 33.9 30.7 16.5 78.6 44.9 28.2 63.4 35.6 44.1 41.1 24.7 33.9 .. 32.0 59.4 49.7 23.5 .. .. 54.3 33.5 31.5 42.5 .. 15.0 34.8 57.3 22.6 .. 25.3 43.4 65.5 53.5 46.0 42.6 .. 42.0 64.7 24.2 29.1 31.4 47.2 58.2 23.6 47.3 54.0 52.2 38.9 53.5 83.9
.. 6.7 .. 4.5 46.9 6.2 30.4 39.5 49.1 19.6 31.5 27.7a 14.7 15.0 30.9 33.0 25.8 15.3 19.6 41.0 4.6 21.7 31.1 38.0 14.9 19.9 15.0 54.0 31.2 .. 7.5 36.1 15.4 31.8 .. 33.7 30.8 18.1 20.6 28.3 14.9 6.9 21.5 7.5 24.2 25.4 16.5 30.4 62.8 46.8 6.2 33.1 21.0 8.4 14.1 14.7
.. 66.3 .. 1.8 37.3 38.1 37.0 27.2 7.9 5.8 30.3 23.9 10.0 30.8 35.4 28.7 23.6 38.0 .. 61.7 14.3 22.5 31.2 .. .. 18.1 23.0 38.1 24.9 .. 4.8 34.9 17.8 25.5 .. 25.5 21.8 19.3 20.3 18.7 35.5 .. 22.2 6.2 18.3 28.4 26.9 9.6 20.2 32.3 30.9 17.3 29.8 11.7 30.9 11.7
.. 22.1 .. 2.7 7.1 10.3 7.2 5.6 0.8 5.6 3.6 10.2a 10.4 9.3 9.5 8.1 9.6 9.1 4.8 5.9 4.3 7.2 11.3 7.9 1.5 4.1 17.3 6.2 11.9 .. 7.3 4.6 11.0 3.4 .. 5.2 .. 10.2 5.9 4.6 11.0 0.3 4.7 7.4 5.0 6.1 8.6 5.8 8.4 1.6 6.5 4.5 8.7 7.2 4.7 1.7
.. 4.7 .. 4.9 4.4 7.0 3.3 6.6 5.7 8.4 2.9 4.8 9.9 13.8 8.0 4.3 6.1 4.9 .. 1.9 5.5 6.7 11.3 .. .. 8.7 8.7 7.1 8.1 .. 5.2 7.3 9.3 4.0 .. 6.9 .. 7.9 5.8 10.4 8.7 .. 2.9 4.1 2.2 4.6 6.5 8.1 14.1 4.2 4.6 8.5 10.6 6.2 0.4 1.5
.. 9.8 .. 74.6 15.9 0.9 25.6 43.0 19.0 9.7 29.0 38.0a 15.7 9.9 8.1 16.3 20.6 43.2 19.6 3.8 44.7 35.7 33.5 10.4 28.6 21.9 29.0 17.6 14.5 .. 66.6 17.9 23.2 35.3 .. 44.7 24.1 10.6 31.1 32.0 19.0 93.1 20.9 21.7 48.0 35.6 57.2 4.2 1.8 33.2 26.0 32.5 28.9 26.0 28.1 5.9
.. 16.1 .. 71.7 29.7 8.1 25.8 35.6 69.9 7.2 21.9 43.2 16.7 19.9 12.6 25.8 45.6 23.2 .. 4.3 20.8 21.0 34.1 .. .. 18.9 34.8 23.3 24.5 .. 75.0 23.0 24.9 47.9 .. 42.3 34.8 7.3 20.4 24.9 13.2 .. 33.0 25.1 55.3 37.9 35.2 35.1 7.6 39.8 17.3 20.2 7.5 43.2 15.1 3.0
2009 World Development Indicators
Commercial service imports
Transport
$ 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
Travel
% of total
Insurance and financial services
% of total
ECONOMY
Structure of service imports
4.7 Computer, information, communications, and other commercial services
% of total
% of total
1995
2007
1995
2007
1995
2007
1995
2007
1995
2007
326 3,765 10,062 13,230 2,192 .. 11,252 8,131 54,613 1,073 121,547 1,385 776 900 .. 25,394 3,826 193 119 225 .. 58 .. 510 457 300 277 151 14,821 412 197 630 9,021 193 87 1,350 350 233 538 305 43,618 4,571 207 120 4,398 13,052 985 2,431 1,049 642 676 1,781 6,906 7,008 6,339 ..
1,027 15,034 77,200 24,022 .. 5,030 94,472 17,587 118,261 2,205 148,685 3,317 11,370 1,268 .. 82,523 10,431 577 76 2,678 9,970 88 214 2,438 3,282 548 462 .. 27,784 672 .. 1,538 22,990 593 514 4,527 819 635 505 716 83,769 8,940 525 327 11,837 38,941 4,996 8,409 2,050 1,151 442 4,133 7,245 23,696 13,686 ..
60.4 12.8 56.7 36.7 43.0 .. 15.9 44.9 24.5 .. 29.6 52.3 38.4 46.4 .. 38.0 39.4 27.1 43.3 68.2 .. 74.9 .. 60.4 63.9 49.6 55.6 66.8 37.8 59.6 61.5 39.9 38.0 51.6 69.6 48.1 32.7 11.0 36.5 36.3 28.9 41.2 39.1 74.4 22.4 38.2 41.8 67.0 71.0 25.2 66.4 50.8 29.7 25.2 26.8 ..
60.9 20.3 40.0 39.6 .. 51.3 3.0 33.1 23.2 .. 33.0 54.1 18.6 63.8 .. 36.2 38.1 58.4 .. 29.3 17.2 78.5 57.3 51.8 47.7 42.3 48.5 .. 39.5 60.3 .. 37.4 12.0 41.3 49.5 48.8 36.0 48.6 47.7 40.1 24.9 33.1 54.3 69.4 28.5 33.8 34.5 38.7 59.1 24.2 59.1 45.7 52.7 23.9 32.3 ..
17.5 39.8 9.9 16.4 11.0 .. 18.1 26.1 27.2 13.8 30.2 30.7 36.4 21.4 .. 25.0 58.8 3.4 25.0 10.8 .. 22.6 .. 15.0 23.3 8.8 21.1 26.0 15.6 11.9 11.6 25.2 35.1 29.2 22.3 22.4 .. 7.7 16.7 44.7 26.8 27.5 19.3 11.1 20.6 32.4 4.8 18.4 11.5 9.1 19.7 16.7 6.1 5.9 33.1 ..
29.8 19.6 11.7 20.4 .. 7.8 9.2 18.5 23.1 13.5 17.8 26.6 9.2 20.7 .. 25.3 58.7 15.6 .. 34.6 31.2 18.5 9.8 36.4 34.8 12.9 15.9 .. 18.9 17.9 .. 23.5 36.4 35.9 36.5 19.4 22.0 5.8 26.2 38.2 22.8 34.3 23.0 8.5 20.6 36.1 14.9 18.9 15.0 4.8 24.6 24.4 22.3 32.7 28.7 ..
2.5 4.9 5.6 3.4 9.9 .. 1.4 3.0 9.7 9.2 2.4 6.1 1.8 10.4 .. 1.5 1.7 4.3 4.0 7.0 .. 0.2 .. .. 1.1 20.7 3.7 0.1 .. 1.4 1.4 4.6 12.5 9.3 1.8 3.5 2.2 0.5 9.5 3.0 3.0 5.2 3.3 2.6 2.5 5.6 4.6 4.3 8.8 2.8 12.4 10.2 1.6 13.6 8.9 ..
1.5 3.6 6.3 4.3 .. 19.4 17.2 2.2 4.0 10.5 5.2 8.1 3.9 -6.6 .. 2.2 1.9 3.2 .. 4.0 3.0 .. 2.3 7.8 3.2 4.4 1.0 .. 3.2 5.0 .. 5.5 48.7 2.1 3.9 2.5 2.9 .. 6.3 4.3 3.2 4.1 10.8 3.1 0.0 3.5 7.7 3.2 14.3 10.3 12.0 8.1 6.7 3.9 4.1 ..
19.7 42.5 27.9 43.4 36.1 .. 64.6 26.0 38.6 77.1 37.8 10.9 25.2 21.8 .. 35.5 0.1 65.3 27.7 14.0 .. 2.4 .. 24.7 11.7 20.9 19.6 7.2 46.5 27.1 25.4 30.3 14.4 9.9 8.1 25.9 65.1 80.8 37.3 15.9 41.3 26.1 38.3 12.0 54.4 23.7 48.8 10.3 8.7 63.0 1.4 22.3 62.6 55.3 31.1 ..
7.8 56.5 42.1 35.7 .. 21.4 70.6 46.2 49.7 76.0 44.0 11.1 68.3 22.1 .. 36.3 1.3 22.8 .. 32.0 48.6 3.1 30.6 3.9 14.3 40.4 34.7 .. 38.3 16.7 .. 33.6 2.9 20.7 10.1 29.2 39.2 45.6 19.8 17.4 49.2 28.6 11.9 19.0 50.8 26.7 42.9 39.2 11.7 60.7 4.3 21.8 18.3 39.5 35.0 ..
2009 World Development Indicators
229
4.7
Structure of service imports Commercial service imports
Transport
$ millions 1995
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
230
% of total 2007
1,801 10,047 20,205 57,810 58 259 8,670 30,798 405 805 .. 3,456 79 86 20,728 72,214 1,800 6,449 1,429 4,186 .. .. 5,756 16,258 22,354 98,431 1,169 2,569 150 2,873 206 494 17,112 47,813 14,899 33,209 1,358 2,437 .. 590 729 1,424 18,629 38,173 .. .. 148 295 223 471 1,245 2,662 4,654 14,160 403 .. 563 1,159 1,334 11,055 .. .. 62,524 197,188 129,227 341,673 814 1,208 .. .. 4,654 7,243 2,304 6,924 .. .. 604 2,069 282 886 645 .. 1,218,748 t 3,075,521 t 22,663 59,403 214,348 676,675 107,442 386,677 106,960 291,319 236,387 735,826 82,593 239,360 43,870 165,739 52,171 116,734 19,235 52,758 15,377 92,697 24,584 73,653 981,892 2,345,145 421,783 1,001,166
a. Includes Luxembourg.
2008 World Development Indicators
Travel
Insurance and financial services
% of total
Computer, information, communications, and other commercial services
% of total
% of total
1995
2007
1995
2007
1995
2007
1995
2007
33.5 16.4 72.8 25.3 57.1 .. 17.4 44.8 17.0 30.6 .. 39.9 31.1 58.1 27.3 15.7 28.4 35.2 57.2 .. 29.8 41.8 .. 70.8 .. 45.3 30.3 40.4 38.2 34.0 .. 27.1 32.3 46.2 .. 30.7 .. .. 35.6 78.9 56.0 31.2 w 49.0 38.7 41.8 36.0 39.2 38.0 29.3 41.3 44.9 58.6 39.6 29.1 24.9
32.6 16.2 33.2 28.9 58.5 28.7 56.3 34.5 28.5 24.0 .. 46.9 25.1 62.8 45.6 11.7 15.9 21.3 51.5 24.1 34.1 47.6 .. 78.6 .. 54.8 46.0 .. 53.8 35.3 .. 19.1 28.0 51.4 .. 51.3 .. .. 42.7 46.5 .. 28.4 w 47.4 33.7 40.9 28.2 34.4 38.8 31.2 25.9 46.4 45.4 43.8 27.0 25.8
38.7 57.4 17.1 .. 17.7 .. 62.5 22.5 17.8 40.2 .. 32.1 20.3 15.9 28.7 20.7 31.8 49.8 36.7 .. 49.4 22.9 .. 12.5 31.0 20.1 19.6 18.2 14.3 15.7 .. 39.9 35.8 29.0 .. 36.8 .. .. 12.5 9.2 18.7 30.9 w 18.0 23.0 16.3 28.7 22.7 15.5 26.4 31.2 21.4 13.4 24.1 33.1 31.8
15.3 38.5 26.7 .. 6.7 30.2 16.5 16.4 23.8 26.3 .. 24.2 20.0 15.3 51.4 10.4 29.2 30.9 22.2 1.1 45.3 13.5 .. 1.8 38.2 16.4 23.0 .. 9.7 32.3 .. 36.3 23.7 19.8 .. 19.2 .. .. 8.9 6.3 .. 25.7 w 15.7 25.8 21.8 28.9 25.4 20.6 29.7 30.5 18.8 13.1 22.2 25.8 26.0
5.3 0.4 0.8 2.8 7.0 .. 3.8 10.1 4.9 1.8 .. 14.1 7.4 5.4 0.3 4.3 1.4 1.1 5.6 .. 2.7 5.2 .. 4.4 7.9 6.5 8.4 6.9 4.2 7.3 .. 4.4 5.9 4.5 .. 2.6 .. .. 7.1 0.0 2.9 6.2 w 4.8 9.9 10.4 9.3 9.6 12.1 7.1 10.1 .. 5.3 8.8 5.4 5.4
17.8 4.0 0.7 3.2 9.8 2.5 9.9 6.7 9.6 2.6 .. 5.1 8.1 6.0 0.3 12.2 2.8 8.1 15.2 4.7 5.4 5.0 .. 11.4 6.5 8.3 15.3 .. 6.9 9.5 .. 8.1 18.1 5.3 .. 9.8 .. .. 7.9 8.7 .. 9.7 w .. 13.8 7.5 18.5 13.4 6.7 8.0 27.7 9.9 6.1 4.0 8.9 5.0
22.4 25.9 10.1 71.9 18.2 .. 16.3 22.6 60.2 27.4 .. 13.8 41.2 20.5 43.7 59.2 38.4 13.9 6.1 .. 18.0 30.2 .. 12.3 61.0 28.1 41.7 34.6 43.3 42.9 .. 28.7 26.0 20.2 .. 29.9 .. .. 44.8 11.9 22.5 32.1 w 29.0 29.4 31.5 27.6 29.4 36.7 37.7 17.8 28.2 22.6 28.1 32.7 37.9
34.3 41.3 39.4 67.8 25.0 38.6 17.3 42.4 38.1 47.1 .. 23.9 46.8 15.9 2.7 65.7 52.1 39.7 11.2 70.1 15.2 33.9 .. 8.2 55.3 20.5 15.6 .. 29.6 22.9 .. 36.5 30.3 23.5 .. 19.6 .. .. 40.5 38.6 .. 36.3 w 31.4 26.8 29.8 24.6 27.0 34.0 31.1 16.1 24.9 35.3 30.2 38.5 43.2
About the data
ECONOMY
Structure of service imports
4.7
Definitions
Trade in services differs from trade in goods because
• Commercial service imports are total service
services are produced and consumed at the same
imports minus imports of government services not
time. Thus services to a traveler may be consumed
included elsewhere. • Transport covers all transport
in the producing country (for example, use of a hotel
services (sea, air, land, internal waterway, space,
room) but are classified as imports of the traveler’s
and pipeline) performed by residents of one economy
country. In other cases services may be supplied
for those of another and involving the carriage of
from a remote location; for example, insurance
passengers, movement of goods (freight), rental of
services may be supplied from one location and
carriers with crew, and related support and auxiliary
consumed in another. For further discussion of the
services. Excluded are freight insurance, which is
problems of measuring trade in services, see About
included in insurance services; goods procured in
the data for table 4.6.
ports by nonresident carriers and repairs of trans-
The data on imports of services in the table and on
port equipment, which are included in goods; repairs
exports of services in table 4.6, unlike those in edi-
of harbors, railway facilities, and airfield facilities,
tions before 2000, include only commercial services
which are included in construction services; and
and exclude the category “government services not
rental of carriers without crew, which is included
included elsewhere.” The data are compiled by the
in other services. • Travel covers goods and ser-
International Monetary Fund (IMF) based on returns
vices acquired from an economy by travelers in that
from national sources.
economy for their own use during visits of less than
International transactions in services are defined
one year for business or personal purposes. • Insur-
by the IMF’s Balance of Payments Manual (1993) as
ance and financial services cover freight insurance
the economic output of intangible commodities that
on goods imported and other direct insurance such
may be produced, transferred, and consumed at the
as life insurance; financial intermediation services
same time. Definitions may vary among reporting
such as commissions, foreign exchange transac-
economies.
tions, and brokerage services; and auxiliary services
Travel services include the goods and services
such as financial market operational and regulatory
consumed by travelers, such as meals, lodging, and
services. • Computer, information, communica-
transport (within the economy visited), including car
tions, and other commercial services cover such
rental.
activities as international telecommunications, and postal and courier services; computer data; newsrelated service transactions between residents and nonresidents; construction services; royalties and license fees; miscellaneous business, professional, and technical services; and personal, cultural, and
4.7a
The mix of commercial service imports by developing economies is changing 1995 ($236 million)
Other 29%
Insurance and financial 10%
Transport 39%
recreational services.
2007 ($736 million)
Other 27%
Transport 34%
Travel 23% Insurance and financial 13% Travel 25%
Data sources Between 1995 and 2007 developing economies’ commercial service imports more than doubled. Insur-
Data on imports of commercial services are from
ance and financial services and travel services are displacing transport and other services as the most
the IMF, which publishes balance of payments
important services imported.
data in its International Financial Statistics and
Source: International Monetary Fund balance of payments data files.
Balance of Payments Statistics Yearbook.
2008 World Development Indicators
231
4.8 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
232
Structure of demand Household final consumption expenditure
General government final consumption expenditure
Gross capital formation
Exports of goods and services
Imports of goods and services
Gross savings
% of GDP 1995 2007
% of GDP 1995 2007
% of GDP 1995 2007
% of GDP 1995 2007
% of GDP 1995 2007
% of GDP 1995 2007
.. 12 26 82 10 24 18 35 28 11 50 68 20 23 20 51 7 45 14 13 31 24 37 20 22 29 23 143 15 28 65 38 42 39 13 51 38 31 26 23 22 22 68 10 36 23 59 49 26 24 24 17 19 21 12 9
.. 35 29 68 10 62 20 35 42 17 54 63 33 27 71 38 9 46 27 27 47 18 34 28 34 27 21 148 21 24 64 40 34 49 16 55 34 34 28 28 38 83 76 16 29 22 36 73 42 23 33 27 25 25 35 27
.. 87 55 51 69 109 59 57 77 83 59 54 82 76 .. 34 62 71 63 89 95 72 57 79 91 61 42 62 65 81 49 71 66 64 71 51 51 79 68 74 87 94 54 80 52 57 41 90 102 58 76 75 86 74 95 87
110 88 31 54 59 72 56 54 26 77 54 52 78 63 90 29 61 69 75 91 83 73 56 96 60 55 33 60 63 80 29 67 77 56 .. 48 50 79 66 72 96 86 56 84 52 57 36 77 70 57 79 71 88 84 70 91
2009 World Development Indicators
.. 14 17 40 13 11 18 20 13 5 21 22 11 14 .. 29 21 15 25 19 6 9 21 15 7 10 14 8 15 5 13 14 11 29 24 21 25 5 13 11 9 44 26 8 23 24 12 14 11 20 12 15 6 8 6 7
9 9 12 ..a 13 11 18 18 11 6 19 22 15 14 22 20 20 16 22 29 3 9 19 3 6 10 14 8 17 11 14 13 8 20 .. 20 26 7 11 11 7 31 17 11 21 23 9 16 22 18 14 17 8 6 16 9
.. 21 31 35 18 18 24 23 24 19 25 20 20 15 20 25 18 16 24 6 15 13 19 14 13 26 42 34 26 9 37 18 16 18 7 33 20 19 22 20 20 23 28 18 18 19 23 20 4 22 20 19 15 21 22 24
25 30 33 14 24 37 27 21 21 24 33 22 20 15 23 41 18 37 18 17 21 17 23 9 19 21 43 21 24 20 27 25 9 33 .. 27 23 22 24 21 20 11 38 25 22 22 26 23 35 18 34 26 21 13 17 29
12 28 47 71 25 19 21 59 72 20 62 89 13 42 39 47 14 63 12 11 65 22 36 15 49 47 42 207 17 28 73 49 47 48 .. 80 52 35 34 30 26 6 74 13 45 27 65 33 32 47 33 22 26 37 43 14
56 54 23 39 20 39 22 52 30 27 68 87 26 34 74 37 12 85 27 48 73 21 34 22 34 33 32 196 21 39 43 54 41 56 .. 75 51 41 34 35 50 34 85 32 40 28 36 50 58 40 60 35 42 39 46 43
.. 21 26 30 16 –7 18 21 14 21 21 25 8 11 10 36 16 12 18 4 5 14 18 6 5 25 43 30 18 1 –3 15 12 11 .. 29 22 18 17 22 15 4 24 21 22 19 33 6 –7 20 18 18 11 14 5 10
24 20 57 25 27 31 22 26 50 35 27 25 11 30 9 55 17 18 6 1 15 20 24 4 23 25 55 35 19 12 36 19 9 24 .. 25 24 20 26 23 12 10 20 21 27 19 41 12 17 25 23 9 17 9 23 ..
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
Structure of demand
4.8
Household final consumption expenditure
General government final consumption expenditure
Gross capital formation
Exports of goods and services
Imports of goods and services
Gross savings
% of GDP 1995 2007
% of GDP 1995 2007
% of GDP 1995 2007
% of GDP 1995 2007
% of GDP 1995 2007
% of GDP 1995 2007
44 45 11 26 22 .. 76 29 26 51 9 52 39 33 .. 29 52 29 23 43 11 19 9 29 50 33 24 30 94 21 37 58 30 49 48 27 16 1 49 25 59 29 19 17 44 38 44 17 101 61 59 13 36 23 29 72
48 45 12 28 13 .. 64 37 22 61 8 73 44 39 .. 30 42 42 37 45 62 103 72 22 61 43 32 48 98 36 45 64 28 58 49 34 41 2 56 35 54 28 35 24 42 32 36 19 98 44 71 18 44 21 35 97
64 66 64 62 46 .. 54 56 58 70 55 65 68 70 .. 52 43 75 .. 63 101 104 .. 59 67 70 90 79 48 83 77 63 67 57 56 68 90 87 54 75 49 58 83 86 .. 50 51 72 52 44 76 71 74 60 65 ..
77 66 54 63 45 .. 46 56 59 72 57 91 47 74 .. 55 30 101 68 65 92 98 116 .. 66 78 84 84 46 76 61 69 65 95 48 58 76 .. 50 81 47 60 90 75 .. 42 39 75 60 47 74 61 75 60 65 ..
9 11 11 8 16 .. 16 28 18 11 15 24 14 15 .. 11 32 20 .. 24 15 24 .. 22 22 19 7 21 12 10 11 13 10 27 13 17 8 ..a 30 9 24 17 11 14 .. 22 25 12 15 17 10 10 11 20 18 ..
16 10 10 8 11 .. 16 25 20 16 18 23 10 17 .. 15 15 18 8 18 15 26 15 .. 17 18 5 12 12 11 20 14 10 19 13 18 12 .. 25 9 25 19 12 12 .. 20 18 9 11 11 11 9 10 19 20 ..
32 23 27 32 29 .. 18 25 20 29 28 33 23 22 .. 38 15 18 .. 14 36 56 .. 12 22 21 11 17 44 23 20 29 20 25 32 21 27 14 22 25 21 23 22 7 .. 22 15 19 30 22 26 25 22 19 23 ..
33 22 39 25 33 .. 27 20 21 30 24 27 36 20 .. 29 20 26 40 37 18 28 20 .. 30 23 27 26 22 23 26 27 26 38 40 33 19 .. 30 28 20 23 32 23 .. 23 19 23 23 20 18 23 15 24 22 ..
51 80 21 29 32 .. 80 43 29 45 16 58 49 26 .. 46 65 45 37 44 25 55 33 .. 55 55 30 24 110 27 58 62 28 46 64 36 39 .. 50 13 75 29 33 15 40 46 63 14 80 90 51 29 43 41 33 ..
78 79 24 25 22 .. 69 45 30 63 15 99 43 37 .. 45 30 90 53 65 50 107 84 .. 67 75 47 45 90 37 65 71 30 98 66 45 46 .. 54 31 67 31 67 25 30 30 38 21 75 68 54 22 42 44 40 ..
27 19 27 28 37 .. 23 13 22 24 30 29 18 16 .. 36 .. 9 15 14 –3 27 .. .. 13 14 1 –4 34 14 17 26 19 19 35 17 –6 14 32 23 27 18 –1 –4 .. 26 .. 21 30 36 18 25 19 20 23 ..
2009 World Development Indicators
23 16 39 26 43 .. 23 .. 20 15 28 9 29 17 .. 30 .. 7 23 15 0 41 40 .. 16 21 13 10 38 13 29 20 25 22 42 32 3 .. 40 29 28 15 14 .. .. 38 .. 25 23 35 20 24 34 21 12 ..
233
4.8 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzaniab Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Structure of demand Household final consumption expenditure
General government final consumption expenditure
Gross capital formation
Exports of goods and services
Imports of goods and services
Gross savings
% of GDP 1995 2007
% of GDP 1995 2007
% of GDP 1995 2007
% of GDP 1995 2007
% of GDP 1995 2007
% of GDP 1995 2007
68 52 97 47 80 73 88 41 52 60 .. 63 60 73 85 82 50 60 66 62 86 55 .. 77 53 63 78 44 85 55 48 63 68 73 51 69 74 98 71 72 65 61 w 75 60 55 64 61 47 63 66 63 67 69 61 57
69 49 86 28 78 81 83 38 56 52 .. 62 57 68 65 73 47 59 68 114 73 53 .. 81 55 63 71 46 79 60 45 63 70 74 54 54 66 96 .. 59 72 61 w 74 55 48 61 55 41 60 62 58 59 66 62 57
14 19 10 24 13 23 14 8 22 19 .. 18 18 11 5 15 27 12 13 16 12 10 .. 12 12 16 7 8 11 21 16 20 15 12 22 7 8 18 14 15 18 17 w 11 14 13 15 14 13 16 15 15 10 15 17 20
13 18 11 23 10 18 10 10 17 18 .. 20 18 15 15 15 26 11 12 9 16 13 .. 19 12 14 13 13 13 19 10 22 16 11 17 12 6 33 .. 10 27 17 w 9 14 12 15 14 13 16 14 13 10 16 18 20
24 25 13 20 14 12 6 34 24 24 .. 18 22 26 14 16 17 23 27 29 20 42 .. 16 21 25 18 49 12 27 30 17 18 15 27 18 27 35 22 16 20 22 w 20 27 33 20 26 40 21 20 25 25 18 21 21
30 25 21 22 33 23 13 23 28 31 .. 21 31 27 24 13 20 22 20 22 17 27 .. 18 13 25 22 23 22 27 21 19 20 15 19 28 42 26 .. 24 17 22 w 25 29 35 23 29 38 25 22 28 35 22 21 22
28 29 5 38 31 17 19 .. 58 50 .. 23 22 36 5 60 40 36 31 66 24 42 .. 32 54 45 14 75 12 47 69 28 11 19 28 27 33 16 51 36 38 21 w 24 23 23 23 23 29 26 18 26 12 28 21 29
31 30 10 65 24 29 21 231 86 70 .. 32 26 29 20 80 52 52 41 21 22 73 .. 40 58 54 22 65 17 45 91 26 11 29 40 31 77 14 .. 42 57 28 w 32 33 37 30 33 48 34 24 36 21 34 27 41
33 26 26 28 37 24 26 .. 56 52 .. 22 22 46 10 74 33 31 38 72 42 49 .. 37 39 49 17 75 21 50 63 29 12 19 28 22 42 68 58 40 41 21 w 29 24 24 23 24 29 27 19 29 15 30 20 28
44 22 28 38 44 51 28 202 87 71 .. 35 33 40 24 81 45 45 41 66 28 66 .. 57 37 57 27 48 31 51 68 29 17 30 30 25 90 68 .. 36 73 29 w 38 31 33 28 31 39 36 23 34 25 37 28 39
19 28 12 20 8 6 –3 52 27 23 .. 17 22 20 4 7 20 30 23 23 0 34 .. 11 26 20 15 50 8 23 .. 16 16 14 27 21 19 11 20 5 17 21 w 17 26 32 19 25 38 21 18 25 25 14 21 21
21 31 16 .. 22 8 10 .. 23 27 .. 14 21 23 12 20 28 35 19 14 11 33 .. 7 30 23 16 34 14 23 .. 15 14 13 39 35 35 13 .. 23 0 22 w 26 32 42 23 32 48 23 22 33 36 17 20 22
a. Data for general government final consumption expenditure are not available separately; they are included in household final consumption expenditure. b. Covers mainland Tanzania only.
234
2008 World Development Indicators
About the data
ECONOMY
Structure of demand
4.8
Definitions
Gross domestic product (GDP) from the expenditure
capital outlays on defense establishments that may
• Household final consumption expenditure is the
side is made up of household final consumption
be used by the general public, such as schools, air-
market value of all goods and services, including
expenditure, general government final consumption
fields, and hospitals, and intangibles such as com-
durable products (such as cars and computers),
expenditure, gross capital formation (private and
puter software and mineral exploration outlays. Data
purchased by households. It excludes purchases
public investment in fixed assets, changes in inven-
on capital formation may be estimated from direct
of dwellings but includes imputed rent for owner-
tories, and net acquisitions of valuables), and net
surveys of enterprises and administrative records
occupied dwellings. It also includes government fees
exports (exports minus imports) of goods and ser-
or based on the commodity flow method using data
for permits and licenses. Expenditures of nonprofit
vices. Such expenditures are recorded in purchaser
from production, trade, and construction activities.
institutions serving households are included, even
prices and include net taxes on products.
The quality of data on government fixed capital forma-
when reported separately. Household consumption
Because policymakers have tended to focus on
tion depends on the quality of government account-
expenditure may include any statistical discrepancy
fostering the growth of output, and because data
ing systems (which tend to be weak in developing
in the use of resources relative to the supply of
on production are easier to collect than data on
countries). Measures of fixed capital formation by
resources. • General government fi nal consump-
spending, many countries generate their primary
households and corporations—particularly capital
tion expenditure is all government current expendi-
estimate of GDP using the production approach.
outlays by small, unincorporated enterprises—are
tures for purchases of goods and services (including
Moreover, many countries do not estimate all the
usually unreliable.
compensation of employees). It also includes most
components of national expenditures but instead
Estimates of changes in inventories are rarely
expenditures on national defense and security but
derive some of the main aggregates indirectly using
complete but usually include the most important
excludes military expenditures with potentially wider
GDP (based on the production approach) as the
activities or commodities. In some countries these
public use that are part of government capital forma-
control total. Household final consumption expen-
estimates are derived as a composite residual along
tion. • Gross capital formation is outlays on addi-
diture (private consumption in the 1968 System of
with household fi nal consumption expenditure.
tions to fixed assets of the economy, net changes in
National Accounts, or SNA) is often estimated as
According to national accounts conventions, adjust-
inventories, and net acquisitions of valuables. Fixed
a residual, by subtracting all other known expendi-
ments should be made for appreciation of the value
assets include land improvements (fences, ditches,
tures from GDP. The resulting aggregate may incor-
of inventory holdings due to price changes, but this
drains); plant, machinery, and equipment purchases;
porate fairly large discrepancies. When household
is not always done. In highly inflationary economies
and construction (roads, railways, schools, buildings,
consumption is calculated separately, many of the
this element can be substantial.
and so on). Inventories are goods held to meet tem-
estimates are based on household surveys, which
Data on exports and imports are compiled from
porary or unexpected fluctuations in production or
tend to be one-year studies with limited coverage.
customs reports and balance of payments data.
sales, and “work in progress.” • Exports and imports
Thus the estimates quickly become outdated and
Although the data from the payments side provide
of goods and services are the value of all goods
must be supplemented by estimates using price- and
reasonably reliable records of cross-border transac-
and other market services provided to or received
quantity-based statistical procedures. Complicating
tions, they may not adhere strictly to the appropriate
from the rest of the world. They include the value of
the issue, in many developing countries the distinc-
definitions of valuation and timing used in the bal-
merchandise, freight, insurance, transport, travel,
tion between cash outlays for personal business
ance of payments or correspond to the change-of-
royalties, license fees, and other services (com-
and those for household use may be blurred. World
ownership criterion. This issue has assumed greater
munication, construction, financial, information,
Development Indicators includes in household con-
significance with the increasing globalization of inter-
business, personal, government services, and so
sumption the expenditures of nonprofit institutions
national business. Neither customs nor balance of
on). They exclude compensation of employees and
serving households.
payments data usually capture the illegal transac-
investment income (factor services in the 1968 SNA)
General government final consumption expenditure
tions that occur in many countries. Goods carried
and transfer payments. • Gross savings are gross
(general government consumption in the 1968 SNA)
by travelers across borders in legal but unreported
national income less total consumption, plus net
includes expenditures on goods and services for
shuttle trade may further distort trade statistics.
transfers.
individual consumption as well as those on services
Gross savings represent the difference between
for collective consumption. Defense expenditures,
disposable income and consumption and replace
including those on capital outlays (with certain excep-
gross domestic savings, a concept used by the World
tions), are treated as current spending.
Bank and included in World Development Indicators Data sources
Gross capital formation (gross domestic investment
editions before 2006. The change was made to con-
in the 1968 SNA) consists of outlays on additions
form to SNA concepts and definitions. For further
Data on national accounts indicators for most
to the economy’s fixed assets plus net changes in
discussion of the problems in compiling national
developing countries are collected from national
the level of inventories. It is generally obtained from
accounts, see Srinivasan (1994), Heston (1994),
statistical organizations and central banks by vis-
industry reports of acquisitions and distinguishes only
and Ruggles (1994). For an analysis of the reliability
iting and resident World Bank missions. Data for
the broad categories of capital formation. The 1993
of foreign trade and national income statistics, see
high-income economies are from Organisation for
SNA recognizes a third category of capital forma-
Morgenstern (1963).
Economic Co-operation and Development (OECD)
tion: net acquisitions of valuables. Included in gross
data files.
capital formation under the 1993 SNA guidelines are
2008 World Development Indicators
235
4.9
Growth of consumption and investment Household final consumption expenditure
General government final consumption expenditure
average annual % growth Total Per capita 1990–2000 2000–07 1990–2000 2000–07
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 China Hong Kong, China Colombia Congo, Dem. Rep.a Congo, Rep.a Costa Ricaa Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republica Ecuador a Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabona Gambia, The Georgia Germany Ghana Greece Guatemalaa Guinea Guinea-Bissau Haiti
236
.. 4.3 –0.1 .. 2.8 –0.5 3.2 1.9 –1.7 2.6 –0.5 1.8 2.6 3.6 .. 2.5 3.7 –3.7 5.7 –4.9 6.0 3.1 2.6 .. 1.5 7.3 8.9 3.8 2.2 –4.5 –1.8 5.1 4.1 2.7 .. 3.0 2.2 5.3 2.1 3.7 5.3 –5.0 0.6 3.6 1.7 1.6 –0.3 3.6 6.1 1.9 4.1 2.1 4.2 5.2 2.6 ..
.. 4.3 5.5 .. 3.8 8.6 3.9 1.4 12.4 4.1 11.2 1.4 2.3 2.8 1.7 4.2 2.6 6.1 4.5 .. 8.9 4.5 3.4 –0.9 2.2 5.6 7.8 3.1 4.4 .. .. 3.9 –1.2 4.8 .. 3.8 3.0 3.8 5.9 3.8 3.5 1.6 10.0 9.5 3.5 2.4 4.3 1.8 9.2 0.2 5.8 4.4 4.0 3.7 6.9 ..
2009 World Development Indicators
.. 5.2 –1.9 .. 1.5 1.1 2.0 1.5 –2.7 0.5 –0.3 1.5 –0.8 1.3 .. 0.1 2.2 –3.0 2.7 .. 3.4 0.5 1.6 .. –1.8 5.6 7.8 2.0 0.4 –7.2 .. 2.5 1.2 3.4 .. 3.0 1.8 3.4 0.3 1.8 3.3 –6.7 2.1 0.4 1.4 1.2 –2.8 –0.1 7.5 1.6 1.4 1.4 1.8 2.0 –0.4 ..
.. 3.8 3.9 .. 2.8 9.0 2.5 0.9 11.4 2.2 11.7 0.8 –0.9 0.8 .. 2.9 1.2 6.8 1.2 .. 7.0 2.2 2.4 –2.5 –1.3 4.5 7.2 2.6 2.9 .. .. 2.0 –2.9 4.8 .. 3.7 2.6 2.2 4.7 2.0 2.0 –2.3 10.3 6.7 3.1 1.7 2.6 –1.4 10.3 0.2 3.5 4.0 1.5 1.8 3.7 ..
average annual % growth 1990–2000 2000–07
.. 2.4 3.6 .. 2.2 –1.5 2.9 2.5 –1.7 4.7 –1.9 1.4 4.4 3.6 .. 6.5 1.0 –8.4 2.9 –2.6 7.2 0.7 0.3 .. –8.3 3.7 9.7 3.7 10.5 –17.4 –4.4 2.0 0.8 1.3 .. –0.9 2.4 5.2 –1.5 4.4 2.8 22.6 5.7 9.0 0.6 1.4 3.7 –2.2 12.0 1.9 4.8 2.1 5.1 –0.5 1.9 ..
.. 2.2 4.3 .. 2.3 11.8 3.2 1.5 14.2 10.2 2.2 1.5 8.3 3.4 4.5 3.9 3.0 3.6 8.7 .. 1.9 2.8 2.8 –1.3 5.7 4.6 9.1 0.9 4.5 .. .. 1.4 2.7 1.1 .. 2.2 1.5 5.1 2.9 2.8 1.3 1.2 1.8 0.4 1.6 1.7 0.4 4.2 7.3 0.4 –1.0 2.3 0.4 –0.9 –0.8 ..
Gross capital formation
Goods and services
average annual % growth average annual Exports Imports % growth 1990–2000 2000–07 1990–2000 2000–07 1990–2000 2000–07
.. 25.8 –0.6 .. 7.4 –1.9 5.1 2.4 41.7 9.2 –7.5 2.7 12.2 8.5 .. 6.7 4.2 –5.0 3.1 –0.5 10.3 0.4 4.6 .. 4.0 9.3 11.5 4.8 2.0 –0.7 10.4 5.1 8.1 5.4 .. 4.6 5.7 10.4 –0.6 5.8 7.1 19.1 0.5 6.5 2.2 1.8 3.0 1.9 –12.5 1.1 4.3 4.1 6.1 0.1 –6.5 ..
.. 5.5 9.4 .. 11.2 24.5 7.4 0.8 26.6 8.6 15.2 3.6 7.7 0.2 6.4 –0.3 2.5 16.5 9.0 .. 13.5 3.9 6.5 1.2 1.5 8.8 13.4 2.1 13.3 .. .. 8.8 –0.8 12.2 .. 4.7 3.9 1.2 8.1 5.7 2.5 –1.0 14.0 9.3 4.2 2.7 7.3 10.7 18.4 –0.2 14.4 7.6 4.1 –5.6 –0.4 ..
.. 18.9 3.2 .. 8.7 –18.4 7.7 5.5 5.7 13.1 –4.8 4.7 1.8 4.5 .. 4.7 5.9 3.9 4.4 –1.2 21.7 3.2 8.7 .. 2.3 9.4 12.9 7.8 5.3 –0.5 3.0 10.9 1.9 5.9 .. 8.7 5.1 9.1 5.3 3.5 13.4 –2.5 11.0 7.1 10.3 6.9 2.1 0.1 12.2 6.0 10.1 7.6 6.1 0.3 15.4 ..
.. 11.0 3.3 .. 7.5 13.2 2.0 6.1 22.9 11.8 7.8 3.3 2.7 10.2 9.8 4.5 9.5 9.5 10.9 .. 16.9 0.7 0.9 –1.5 41.9 6.7 24.4 10.2 5.2 7.7 .. 7.8 4.5 6.2 .. 11.9 4.2 4.2 7.1 16.7 4.9 –6.3 9.0 12.8 5.1 2.5 –1.6 –0.4 6.4 7.2 3.9 3.0 2.3 1.3 4.5 ..
.. 15.7 –1.0 .. 15.6 –12.7 7.6 5.0 14.1 9.7 –8.7 4.5 2.1 6.0 .. 3.8 11.6 2.7 1.9 –1.6 14.8 5.1 7.1 .. –1.8 11.7 14.3 8.4 9.0 –2.4 2.0 9.2 8.2 4.6 .. 12.0 6.1 9.4 2.8 3.0 11.6 7.5 12.0 5.8 6.5 5.7 0.1 0.1 11.2 5.8 10.4 7.4 9.2 –1.1 –0.4 ..
.. 14.2 7.9 .. 8.1 11.3 8.8 4.9 24.0 9.5 9.6 3.3 1.8 6.2 2.4 1.8 6.0 12.7 7.2 .. 15.4 4.0 4.2 –1.8 6.2 11.5 18.6 9.0 10.8 20.3 .. 6.6 4.3 7.9 .. 10.6 6.8 1.2 9.9 13.5 5.0 –3.7 12.2 12.9 6.1 4.4 4.5 0.9 8.4 5.4 7.2 4.8 3.4 –1.6 0.7 ..
Household final consumption expenditure
General government final consumption expenditure
average annual % growth Total Per capita 1990–2000 2000–07 1990–2000 2000–07
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 –0.1 4.8 6.6 3.2 .. 4.6 4.8 1.5 .. 1.5 4.9 –8.1 3.6 .. 4.9 4.5 –6.5 .. –3.9 1.8 1.5 .. .. 5.2 2.2 2.2 5.4 5.3 3.0 .. 5.1 3.9 9.9 .. 1.8 5.7 3.9 4.8 .. 3.1 3.2 6.1 1.8 .. 3.5 5.4 4.9 6.4 2.5 2.6 4.0 3.7 5.2 3.0 ..
5.4 4.5 6.0 4.1 7.4 .. 4.7 3.2 0.9 .. 1.3 6.7 10.3 4.1 .. 2.9 5.9 12.6 3.2 11.0 3.5 12.0 .. .. 10.1 4.4 3.6 4.2 7.5 0.9 7.4 5.3 3.8 10.1 .. 4.6 7.2 .. 2.1 .. 0.6 4.8 3.2 .. .. 4.1 1.3 5.0 6.7 10.1 2.4 4.6 5.1 3.4 1.4 ..
0.6 0.1 2.9 5.0 1.6 .. 3.7 2.2 1.5 .. 1.3 1.1 –7.0 0.6 .. 3.9 0.6 –7.4 .. –2.7 0.0 –0.2 .. .. 6.0 1.7 –0.8 3.4 2.6 0.3 .. 3.9 2.2 10.7 .. 0.1 2.6 .. 1.9 .. 2.4 2.0 3.9 .. .. 2.9 2.6 2.3 4.2 –0.2 0.2 2.3 1.5 5.1 2.7 ..
3.4 4.7 4.5 2.7 5.8 .. 2.7 1.3 0.2 .. 1.1 4.1 9.7 1.4 .. 2.4 2.9 11.6 1.5 11.7 2.3 11.1 .. .. 10.6 4.1 0.8 1.6 5.5 –2.1 4.4 4.4 2.7 11.5 .. 3.5 4.8 .. 0.7 .. 0.2 3.3 1.9 .. .. 3.4 0.6 2.5 4.8 7.6 0.5 3.3 3.0 3.5 0.8 ..
average annual % growth 1990–2000 2000–07
2.0 0.9 6.6 0.1 1.6 .. 6.1 2.7 –0.2 .. 2.9 4.7 –7.1 6.9 .. 4.7 –2.4 –8.8 .. 1.8 10.2 9.0 .. .. 1.6 –0.4 0.0 –4.4 4.8 3.2 .. 4.8 1.8 –12.4 .. 3.9 3.2 .. 3.3 .. 2.0 2.5 –1.5 0.8 .. 2.7 2.4 0.7 1.7 2.5 2.5 5.2 3.8 3.7 2.9 ..
5.8 2.3 3.4 7.9 3.6 .. 6.0 1.0 2.0 .. 1.9 2.9 8.1 2.3 .. 4.9 6.6 –0.1 3.9 2.8 1.1 2.0 .. .. 4.3 –0.3 7.6 5.2 8.4 22.2 3.1 4.5 0.1 7.0 .. 3.1 –9.3 .. 2.0 .. 2.9 3.8 2.7 .. .. 2.2 6.1 8.5 4.0 –0.3 2.5 4.5 2.2 3.3 1.6 ..
Gross capital formation
ECONOMY
Growth of consumption and investment
4.9
Goods and services
average annual % growth average annual Exports Imports % growth 1990–2000 2000–07 1990–2000 2000–07 1990–2000 2000–07
6.9 9.6 6.9 –0.6 –0.1 .. 10.2 1.8 1.6 .. –0.8 0.3 –18.3 6.1 .. 3.4 1.0 –3.9 .. –3.7 –7.1 2.8 .. .. 11.1 3.6 3.3 –8.4 5.3 0.4 .. 4.7 4.7 –15.5 .. 2.5 8.6 15.3 6.9 .. 4.4 5.9 11.3 4.0 .. 6.0 4.0 1.8 10.4 1.9 0.7 7.4 4.1 10.6 5.8 ..
6.6 0.8 15.1 5.7 8.3 .. 5.6 1.7 1.6 .. 0.1 8.7 21.3 7.7 .. 3.3 13.7 –2.4 15.2 16.5 0.1 –4.4 .. .. 13.1 3.4 17.0 26.2 2.7 6.2 23.8 5.1 0.6 11.9 .. 8.7 3.1 .. 8.9 .. 0.5 6.4 2.2 .. .. 6.5 17.0 6.3 6.7 0.7 1.8 8.6 0.5 4.8 –1.7 ..
1.6 9.9 12.3 5.9 1.2 .. 15.7 10.9 5.9 .. 4.1 2.6 –2.6 1.0 .. 16.0 –1.6 –1.6 .. 4.3 15.5 9.7 .. .. 4.9 4.2 3.8 4.0 12.0 9.9 –1.3 5.4 14.6 0.7 .. 5.9 13.1 10.0 3.8 .. 7.3 5.2 9.3 3.1 .. 5.5 6.2 1.7 –0.4 5.1 3.1 8.5 7.8 11.3 5.3 1.6
6.9 11.2 15.7 8.3 5.0 .. 4.9 5.6 1.8 .. 7.7 8.9 7.4 7.7 .. 12.3 5.4 4.6 –11.5 9.4 9.9 21.1 .. .. 11.7 3.6 3.9 –10.2 6.8 6.3 11.5 2.7 5.7 13.0 .. 7.6 15.9 .. 6.4 .. 4.6 3.2 8.8 .. .. 1.0 7.0 10.1 5.8 –0.4 7.4 8.8 7.1 10.3 4.1 ..
3.8 11.4 14.4 5.7 –6.8 .. 14.5 7.5 4.4 .. 4.2 1.5 –11.2 9.4 .. 10.0 0.8 –8.2 .. 7.6 –1.0 2.1 .. .. 7.5 7.5 4.1 –1.1 10.3 3.5 0.6 5.2 12.3 5.6 .. 5.1 7.6 5.8 5.4 .. 7.6 6.2 12.2 –2.1 .. 5.8 5.9 2.5 1.2 3.4 2.9 9.0 7.8 16.7 7.3 4.5
2009 World Development Indicators
7.4 10.0 19.4 9.8 13.2 .. 4.4 3.4 2.8 .. 4.3 8.4 7.4 8.2 .. 10.0 9.4 16.0 –11.5 13.8 4.2 15.0 .. .. 14.5 4.2 7.3 2.7 7.8 3.9 14.1 2.9 6.0 15.4 .. 8.3 4.4 .. 4.7 .. 4.2 7.5 5.5 .. .. 5.6 12.8 9.5 6.2 4.5 5.0 8.6 4.4 8.6 2.8 ..
237
4.9
Growth of consumption and investment Household final consumption expenditure
General government final consumption expenditure
average annual % growth Total Per capita 1990–2000 2000–07 1990–2000 2000–07
Romaniaa Russian Federation Rwandaa Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lankaa Sudan Swazilanda Sweden Switzerland Syrian Arab Republic Tajikistan Tanzaniab Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States 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 Euro area
1.3 –0.9 0.4 .. 2.6 .. –4.4 .. 5.6 3.9 .. 2.9 2.4 5.3 3.7 7.3 1.4 1.1 3.0 –11.8 4.9 3.7 .. 5.0 0.7 4.3 3.8 .. 6.7 –6.9 7.1 2.9 3.6 5.0 .. 0.6 5.4 5.3 3.2 2.4 0.0 3.0 w 3.5 4.1 5.3 3.1 4.0 7.4 1.3 3.6 2.9 4.6 3.2 2.8 1.9
8.0 10.1 .. .. 4.2 6.2 .. .. 5.0 3.0 .. 5.6 3.6 .. 6.4 5.7 2.3 1.3 7.6 12.0 2.8 4.8 .. 0.5 13.3 5.1 6.3 .. 7.2 13.7 12.9 2.7 3.0 2.2 .. 8.1 7.5 –1.5 .. 0.1 –3.8 2.9 w 4.9 5.6 6.3 4.9 5.5 7.0 7.4 3.8 5.2 5.7 4.9 2.4 1.5
1.7 –0.7 .. .. –0.2 .. .. .. 5.4 4.0 .. 0.6 2.0 4.4 1.1 4.0 1.0 0.5 0.3 –13.1 2.0 2.5 .. 1.7 0.1 2.6 1.9 .. 3.3 –6.4 1.1 2.6 2.4 4.3 .. –1.5 3.9 1.2 –0.7 –0.2 –1.9 1.5 w 1.0 2.7 3.8 2.0 2.4 6.1 1.1 1.9 0.7 2.6 0.4 2.0 1.6
8.5 10.6 .. .. 1.5 6.4 .. .. 4.9 2.8 .. 4.4 1.9 .. 4.2 4.3 1.9 0.6 4.7 10.7 0.2 4.1 .. –2.4 12.9 4.1 5.0 .. 3.8 14.6 7.5 2.2 2.1 2.1 .. 6.2 6.1 –4.8 .. –1.7 –4.5 1.7 w 2.6 4.5 5.2 4.1 4.2 6.1 7.4 2.4 3.3 4.0 2.4 1.7 0.9
average annual % growth 1990–2000 2000–07
0.8 –2.2 –2.6 .. 0.9 .. 10.4 .. 1.3 2.2 .. 0.3 2.7 10.5 5.5 7.1 0.6 0.5 2.0 –12.6 –7.0 5.1 .. 0.0 0.3 4.1 4.6 .. 7.1 –4.1 6.8 1.0 0.7 2.3 .. 3.7 3.2 12.7 1.7 –8.1 –2.2 1.7 w 0.1 3.5 6.6 1.3 3.4 8.1 0.2 2.1 3.5 5.9 0.4 1.5 1.5
Gross capital formation
average annual % growth average annual Exports Imports % growth 1990–2000 2000–07 1990–2000 2000–07 1990–2000 2000–07
6.8 2.1 .. .. 0.2 0.1 .. .. 3.2 3.2 .. 5.2 5.1 .. 8.1 0.2 0.8 1.2 6.9 0.8 16.9 4.9 .. 1.3 4.3 4.3 3.6 .. 4.1 2.5 0.8 2.8 2.5 –1.3 .. 7.1 7.5 1.3 .. 24.9 –3.0 2.6 w 6.6 4.9 6.7 3.0 4.9 8.5 3.1 2.7 3.5 4.2 4.9 2.2 1.8
a. Household final consumption expenditure includes statistical discrepancy. b. Covers mainland Tanzania only.
238
2008 World Development Indicators
Goods and services
–5.1 –19.1 0.4 .. 3.5 .. –5.6 .. 8.1 10.4 .. 5.0 3.2 6.9 22.0 –4.7 1.8 0.7 3.3 –17.6 –1.6 –4.0 .. –0.1 12.5 3.6 4.7 1.9 8.9 –18.5 5.5 4.7 7.5 6.3 –2.5 11.0 19.8 9.2 11.4 13.3 –2.5 3.3 w 5.2 2.8 5.9 –0.3 2.9 8.1 –8.4 5.4 1.2 6.5 4.6 3.4 2.2
9.1 11.7 .. .. 11.8 17.4 .. .. 7.1 7.2 .. 9.1 5.4 .. 13.1 –3.0 4.3 1.1 3.0 9.3 7.3 7.6 .. 5.9 4.2 1.3 11.2 .. 11.3 8.8 5.5 3.8 2.5 5.6 7.0 10.8 12.5 –3.0 .. 6.6 –10.6 3.7 w 8.7 9.8 12.0 6.3 9.8 12.0 10.5 4.7 7.2 13.9 8.0 2.1 2.0
8.1 0.8 –6.4 .. 4.1 .. –11.2 .. 8.8 1.7 .. 5.6 10.5 7.5 11.6 6.4 8.5 4.1 12.0 –5.3 9.3 9.5 .. 1.2 6.9 5.1 11.1 –6.1 14.7 –3.6 5.5 6.6 7.3 6.0 2.5 1.0 19.2 8.7 16.6 6.7 10.5 6.9 w 7.1 7.4 8.0 6.9 7.4 10.9 2.6 8.5 4.0 10.0 .. 6.8 6.8
10.8 9.0 .. .. 3.0 12.1 .. .. 11.4 9.2 .. 3.8 3.7 .. 13.0 2.8 5.9 3.9 6.6 10.0 12.0 6.9 .. 6.0 5.8 4.1 7.6 13.9 12.9 3.8 12.2 4.1 3.4 8.3 6.7 –0.9 19.5 –3.1 .. 21.9 –7.5 7.1 w 11.8 12.0 16.3 7.2 12.0 17.3 8.8 6.2 7.6 14.5 .. 5.1 4.7
6.0 –6.1 6.1 .. 2.0 .. –0.2 .. 12.1 5.2 .. 7.1 9.4 8.6 8.4 6.2 6.3 4.3 4.4 –6.0 3.9 4.5 .. 1.1 9.9 3.8 10.8 0.6 10.0 –6.6 6.4 6.8 9.8 9.9 –0.4 8.2 19.5 7.5 8.3 15.5 9.4 7.0 w 6.9 6.6 6.9 6.3 6.6 10.2 0.0 10.8 0.0 11.2 6.0 7.1 6.2
11.9 19.6 .. .. 6.4 13.2 .. .. 10.1 8.8 .. 10.3 6.9 .. 13.6 4.0 4.8 3.8 13.1 11.2 5.7 7.6 .. 3.1 9.5 2.4 12.3 12.3 9.6 7.1 13.6 5.1 5.2 5.2 8.3 14.0 20.2 –2.3 .. 15.6 –3.3 6.6 w 11.9 11.8 13.9 9.6 11.8 14.1 12.9 7.1 10.0 17.4 8.4 5.3 4.7
About the data
ECONOMY
Growth of consumption and investment
4.9
Definitions
Measures of growth in consumption and capital for-
the change in government employment. Neither
• Household final consumption expenditure is the
mation are subject to two kinds of inaccuracy. The
technique captures improvements in productivity
market value of all goods and services, including
first stems from the difficulty of measuring expendi-
or changes in the quality of government services.
durable products (such as cars and computers),
tures at current price levels, as described in About
Deflators for household consumption are usually cal-
purchased by households. It excludes purchases of
the data for table 4.8. The second arises in deflat-
culated on the basis of the consumer price index.
dwellings but includes imputed rent for owner-occu-
ing current price data to measure volume growth,
Many countries estimate household consumption
pied dwellings. It also includes government fees for
where results depend on the relevance and reliability
as a residual that includes statistical discrepancies
permits and licenses. Expenditures of nonprofit insti-
of the price indexes and weights used. Measuring
associated with the estimation of other expenditure
tutions serving households are included, even when
price changes is more difficult for investment goods
items, including changes in inventories; thus these
reported separately. Household consumption expen-
than for consumption goods because of the one-time
estimates lack detailed breakdowns of household
diture may include any statistical discrepancy in the
nature of many investments and because the rate
consumption expenditures.
use of resources relative to the supply of resources.
of technological progress in capital goods makes
• Household fi nal consumption expenditure per
capturing change in quality diffi cult. (An example
capita is household final consumption expenditure
is computers—prices have fallen as quality has
divided by midyear population. • General government
improved.) Several countries estimate capital forma-
final consumption expenditure is all government cur-
tion from the supply side, identifying capital goods
rent expenditures for goods and services (including
entering an economy directly from detailed produc-
compensation of employees). It also includes most
tion and international trade statistics. This means
expenditures on national defense and security but
that the price indexes used in deflating production
excludes military expenditures with potentially wider
and international trade, reflecting delivered or offered
public use that are part of government capital forma-
prices, will determine the deflator for capital forma-
tion. • Gross capital formation is outlays on addi-
tion expenditures on the demand side.
tions to fixed assets of the economy, net changes in
Growth rates of household final consumption expen-
inventories, and net acquisitions of valuables. Fixed
diture, household final consumption expenditure per
assets include land improvements (fences, ditches,
capita, general government final consumption expen-
drains); plant, machinery, and equipment purchases;
diture, gross capital formation, and exports and
and construction (roads, railways, schools, buildings,
imports of goods and services are estimated using
and so on). Inventories are goods held to meet tem-
constant price data. (Consumption, capital forma-
porary or unexpected fluctuations in production or
tion, and exports and imports of goods and services
sales, and “work in progress.” • Exports and imports
as shares of GDP are shown in table 4.8.)
of goods and services are the value of all goods
To obtain government consumption in constant
and other market services provided to or received
prices, countries may defl ate current values by
from the rest of the world. They include the value of
applying a wage (price) index or extrapolate from
merchandise, freight, insurance, transport, travel, royalties, license fees, and other services (commu-
4.9a
GDP per capita is still lagging in some regions
nication, construction, financial, information, business, personal, government services, and so on).
GDP per capita (2000 $) 5,000
They exclude compensation of employees and investLatin America & Caribbean
ment income (factor services in the 1968 System of 4,000
National Accounts) and transfer payments. Europe & Central Asia
3,000
Middle East & North Africa
2,000
Data sources
East Asia & Pacific
1,000
South Asia Sub-Saharan Africa
0 1990
1995
2000
2005
Data on national accounts indicators for most developing countries are collected from national
2007
statistical organizations and central banks by vis-
Although GDP per capita more than tripled in East Asia and Pacific between 1990 and 2007, it is still
iting and resident World Bank missions. Data for
less than GDP per capita in Latin America and Caribbean, Europe and Central Asia, and Middle East
high-income economies are from Organisation for
and North Africa.
Economic Co-operation and Development (OECD)
Source: World Development Indicators data files.
data files.
2008 World Development Indicators
239
4.10
Central government finances Revenuea
Expense
Cash surplus or deficit
Net incurrence of liabilities
Debt and interest payments
% of GDP 1995 2007
% of GDP 1995 2007
Domestic 1995 2007
1995
2007
Total debt % of GDP 2007
.. 25.6 24.2 .. .. .. .. 41.7 19.8 .. 28.7 45.6 .. .. .. 30.4 32.9 39.4 .. 23.6 .. 10.6 24.2 .. .. .. .. .. .. 8.2 .. 21.3 .. 42.5 .. 32.6 38.2 .. 26.3 28.1 .. .. .. .. 49.9 47.6 .. .. 15.4 38.6 .. 42.6 7.6 12.1 .. ..
.. –8.9 –1.3 .. .. .. .. –4.2 –3.1 .. –2.7 –3.8 .. .. .. 4.9 –2.7 –5.1 .. –4.7 .. 0.2 –4.3 .. .. .. .. .. .. 0.0 .. –2.1 .. –1.3 .. –0.9 1.5 .. 0.1 3.4 .. .. .. .. –7.5 –4.1 .. .. –4.3 –8.3 .. –9.1 –0.5 –4.3 .. ..
.. 7.4 –7.4 .. .. .. .. .. .. .. 2.2 .. .. .. .. 0.2 .. 7.4 .. 3.1 .. –0.3 4.9 .. .. .. 1.6 .. .. 0.0 .. .. –1.2 –2.7 .. –0.5 .. .. .. .. .. .. .. .. 8.9 .. .. .. 2.2 .. .. .. .. –0.1 .. ..
.. 2.1 8.6 .. .. .. .. .. .. .. 0.4 .. .. .. .. –0.4 .. –0.8 .. 4.0 .. 0.3 0.0 .. .. .. .. .. .. 0.2 .. –0.8 3.8 0.8 .. –0.4 .. .. .. .. .. .. .. .. 0.2 .. .. .. 2.4 .. .. .. 0.4 4.5 .. ..
2.1 1.0 –1.2 .. 1.5 1.8 .. .. .. 0.5 3.2 .. 2.5 –0.1 0.3 .. .. –0.8 4.3 .. 2.1 .. 0.2 0.2 .. –0.3 –0.1 .. –1.0 .. .. .. 1.2 –0.5 .. 0.8 .. 2.5 .. 0.5 –1.0 .. .. .. –0.8 .. .. .. 0.2 0.1 2.3 .. 1.2 .. .. ..
9.3 .. .. .. .. .. 21.0 59.2 .. .. 9.0 84.1 .. .. .. .. .. .. .. .. .. .. 45.1 .. .. .. .. .. 49.1 .. .. .. 107.7 .. .. 25.0 23.9 .. .. .. 40.5 .. 4.2 .. 37.5 66.7 .. .. 22.7 40.8 .. 113.7 22.2 .. .. ..
% of GDP % of GDP 1995 2007
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 Cote 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
240
.. 21.2 30.2 .. .. .. .. 37.1 18.0 .. 30.0 41.5 .. .. .. 40.5 26.9 35.5 .. 19.3 .. 11.8 20.3 .. .. .. 5.4 .. .. 5.3 .. 20.3 20.1 43.1 .. 33.2 39.1 .. 30.9 34.8 .. .. .. .. 40.6 43.3 .. 23.7 12.2 29.9 17.0 35.1 8.4 11.2 .. ..
7.4 23.6 40.1 .. 18.1 21.0 27.5 37.1 .. 10.3 38.7 40.8 17.2 23.3 40.3 .. .. 37.2 13.0 .. 9.8 .. 21.0 8.3 .. 27.5 10.3 .. 24.0 .. 39.9 24.7 19.2 41.0 .. 31.0 40.4 18.1 .. 27.1 19.2 .. 32.7 .. 38.9 41.8 .. .. 24.0 28.5 25.5 38.9 12.5 .. .. ..
2009 World Development Indicators
17.1 21.9 18.6 .. 18.3 16.6 25.6 38.4 .. 10.1 35.0 41.1 13.9 21.8 37.5 .. .. 32.0 12.8 .. 8.6 .. 19.1 9.7 .. 17.3 11.4 .. 25.5 .. 24.8 21.7 20.5 39.7 .. 33.6 36.3 17.2 .. 29.3 17.2 .. 27.5 .. 34.0 44.5 .. .. 22.9 29.0 29.1 41.7 13.5 .. .. ..
–1.7 –3.0 6.1 .. –0.5 –0.6 1.7 –0.9 .. –1.3 0.4 0.3 0.3 1.2 1.0 .. .. 3.5 –6.1 .. –1.7 .. 1.8 –0.5 .. 8.8 –1.4 .. –1.8 .. 9.6 1.7 –0.8 –1.3 .. –1.7 4.8 –1.8 .. –4.6 0.8 .. 3.2 .. 5.5 –2.3 .. .. 0.8 –0.4 –7.6 –3.7 –2.0 .. .. ..
0.3 1.9 –3.6 .. 0.5 0.3 .. .. .. 2.4 0.3 .. –2.7 –0.2 0.3 .. .. –0.6 0.1 .. –0.3 .. –0.9 1.3 .. –1.1 1.2 .. 1.5 .. .. .. –0.1 0.7 .. 1.8 .. 0.1 .. 7.3 0.4 .. .. .. –0.4 .. .. .. –0.1 0.2 5.0 .. 1.9 .. .. ..
Foreign
Interest % of revenue 2007
0.1 15.5 2.1 .. 26.5 1.5 3.5 7.3 .. 20.7 0.9 9.2 1.3 8.0 1.2 .. .. 2.8 3.1 .. 1.5 .. 6.1 8.0 .. 2.2 4.3 .. 26.5 .. 6.5 12.6 8.9 4.8 .. 3.0 4.5 8.4 .. 18.7 10.8 .. 0.2 .. 3.2 5.9 .. .. 2.3 6.0 9.6 11.1 10.7 .. .. ..
Revenuea
Expense
Cash surplus or deficit
Net incurrence of liabilities
Debt and interest payments
Domestic 1995 2007
1995
2007
Total debt % of GDP 2007
.. 3.9 5.1 –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 ..
.. –0.7 0.0 –0.4 0.1 .. .. .. .. .. .. 6.1 2.8 –1.3 .. –0.1 .. .. .. 1.5 .. 5.8 .. .. .. .. .. .. –0.8 .. .. –0.6 5.5 2.7 .. .. .. .. .. 2.5 .. .. 3.4 .. .. .. 0.0 .. .. –0.7 .. 3.9 –0.7 .. 4.1 ..
1.1 4.5 0.2 –0.4 0.0 .. .. .. .. .. .. –3.0 –0.4 0.1 .. –0.1 .. 1.3 3.8 0.4 2.7 .. .. .. 1.8 .. 2.2 .. .. 3.5 .. 2.1 .. 0.2 2.8 0.1 .. 0.0 .. 0.3 .. 2.8 .. 2.5 .. 1.6 .. .. .. .. –0.3 –2.0 0.9 1.7 2.5 ..
.. 69.4 53.7 28.8 .. .. 27.4 .. 104.8 128.7 .. 77.5 5.3 .. .. .. .. .. .. .. .. .. .. .. 19.3 .. .. .. .. .. .. 40.4 .. 23.3 46.9 .. .. .. .. 43.0 44.0 38.6 .. .. .. 46.1 .. .. .. .. .. 27.2 77.7 47.4 70.9 ..
% of GDP % of GDP 1995 2007
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
.. 42.7 12.3 17.7 24.2 .. 33.6 .. 40.4 .. 20.7 28.2 14.0 21.6 .. 17.8 36.8 16.7 .. 25.8 .. 46.1 .. .. .. .. .. .. 24.4 .. .. 21.6 15.3 28.4 .. .. .. 6.4 31.7 10.5 41.5 .. 12.8 .. .. .. 27.8 17.2 26.1 22.7 .. 17.4 17.7 .. 35.3 ..
22.1 38.1 13.6 18.4 37.2 .. 33.3 40.2 37.9 64.4 .. 32.3 15.9 18.9 .. 26.6 48.3 21.0 13.6 28.1 21.5 63.0 .. .. 29.3 .. 11.8 .. .. 16.2 .. 20.9 .. 34.3 40.5 34.8 .. 8.0 .. 11.9 41.3 36.8 19.5 13.9 .. 50.3 .. 14.4 .. .. 20.3 20.0 15.8 32.7 39.4 ..
% of GDP 1995 2007
.. 49.6 14.4 9.7 15.8 .. 37.5 .. 48.0 33.3 .. 26.1 18.7 25.9 .. 14.3 46.4 25.6 .. 28.3 .. 31.8 .. .. .. .. .. .. 17.2 .. .. 19.9 15.0 38.4 .. .. .. .. 35.7 .. 50.8 .. 14.2 .. .. .. 32.4 19.1 22.0 24.5 .. 17.4 15.9 .. 37.8 ..
22.5 42.9 15.3 16.9 20.5 .. 32.2 42.1 39.8 63.1 .. 36.6 14.1 19.7 .. 20.1 32.0 18.4 10.8 27.8 32.2 47.4 .. .. 29.7 .. 11.2 .. .. 15.2 .. 21.3 .. 32.5 25.0 29.2 .. 3.4 .. 15.1 40.8 32.7 19.0 12.0 .. 31.7 .. 16.2 .. .. 16.8 17.0 17.2 34.5 41.9 ..
% of GDP 1995 2007
.. –4.7 –2.2 3.0 1.1 .. –2.2 .. –7.5 .. .. 0.9 –1.8 –5.1 .. 2.4 –13.6 –10.8 .. –2.7 .. 4.7 .. .. .. .. .. .. 2.4 .. .. –1.3 –0.6 –6.3 .. .. .. .. –5.0 .. –9.2 .. 0.6 .. .. .. –8.9 –5.3 1.5 –0.5 .. –1.3 –0.8 .. –3.0 ..
–1.1 –4.9 –1.4 –1.1 10.6 .. 0.4 0.3 –1.8 –28.9 .. –5.1 1.2 –3.0 .. 4.6 16.3 –1.5 –3.0 0.9 –11.5 9.2 .. .. –0.9 .. –2.7 .. .. –5.6 .. –2.3 .. –0.3 7.7 2.5 .. –1.8 .. –1.0 0.3 3.1 0.4 –1.0 .. 18.0 .. –4.1 .. .. 1.8 2.0 –1.5 –2.0 –2.6 ..
4.10
1.3 –0.8 2.8 0.0 –0.6 .. .. .. .. .. .. 3.1 1.0 2.1 .. –2.6 .. 0.1 0.1 –0.2 3.9 .. .. .. –0.7 .. 0.7 .. .. –1.0 .. –0.6 .. 0.1 2.6 –2.9 .. 1.8 .. 1.2 .. –1.7 .. –2.0 .. –1.3 .. .. .. .. 0.9 2.1 1.2 1.9 –0.2 ..
Foreign
2009 World Development Indicators
Interest % of revenue 2007
2.6 10.2 23.9 14.8 0.8 .. 2.8 10.4 12.4 20.1 .. 8.1 1.5 10.9 .. 5.6 0.1 2.6 3.1 1.1 56.0 4.0 .. .. 2.2 .. 7.0 .. .. 1.7 .. 14.8 .. 3.2 1.0 5.6 .. .. .. 6.0 4.4 3.4 6.5 1.8 .. 1.6 .. 29.2 .. .. 4.0 8.7 26.5 5.9 7.0 ..
241
ECONOMY
Central government finances
4.10
Central government finances Revenuea
Expense
Cash surplus or deficit
Net incurrence of liabilities
Debt and interest payments
% of GDP % of GDP 1995 2007
Romania Russian Federation Rwandab Saudi Arabia Senegalb Serbiab Sierra Leoneb Singaporeb Slovak Republic Sloveniab Somalia South Africa Spain Sri Lankab Sudanb Swazilandb Sweden Switzerlandb Syrian Arab Republicb Tajikistanb Tanzania Thailand Timor-Leste 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 Euro area
.. .. 10.6 .. 15.2 .. 9.4 26.7 .. 35.8 .. .. 32.0 20.4 7.2 .. 35.0 22.6 22.9 9.3 .. .. .. .. 27.2 30.0 .. .. 10.6 .. 10.1 37.0 .. 27.6 .. 16.9 .. .. 17.3 20.0 26.7 .. w .. 16.4 11.9 .. .. 8.4 .. 21.2 28.8 13.1 .. .. 34.8
25.8 31.6 .. .. .. .. 12.3 21.6 29.1 38.6 .. 32.0 27.8 15.8 .. .. .. 18.6 .. 13.5 .. 19.6 .. 17.0 33.4 30.0 25.5 .. 12.7 34.7 .. 38.1 19.6 26.9 .. 28.3 .. .. .. 17.7 .. 26.8 w .. 18.2 16.2 .. 17.9 11.6 30.1 .. 33.0 13.5 .. 27.0 35.0
% of GDP 1995 2007
% of GDP 1995 2007
Domestic 1995 2007
1995
2007
.. .. 15.0 .. .. .. .. 12.4 .. 34.3 .. .. 37.1 26.0 6.8 .. 44.1 25.7 .. 11.4 .. .. .. .. 25.3 28.4 .. .. .. .. 9.3 36.9 .. 27.1 .. 18.5 .. .. 19.1 21.4 32.1 .. w .. .. .. .. .. .. .. 23.4 .. 15.3 .. .. 42.4
.. .. –5.6 .. .. .. .. 19.8 .. –0.1 .. .. –5.8 –7.6 –0.4 .. –9.3 –0.6 .. –3.3 .. .. .. .. –0.1 –2.5 .. .. .. .. 0.5 0.3 .. –1.2 .. –2.3 .. .. –3.9 –3.1 –5.4 .. w .. .. .. .. .. .. .. –1.5 .. –2.7 .. .. –7.5
.. .. 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 .. .. ..
.. .. .. .. .. .. .. 0.0 .. 0.3 .. .. .. 3.2 .. .. –1.2 .. .. 2.3 .. .. .. .. 2.6 2.9 .. .. .. .. .. 0.0 .. 1.1 .. 0.1 .. .. .. 16.2 1.6 .. m .. .. .. .. .. .. .. .. .. 1.1 .. .. ..
1.2 –0.8 .. .. .. .. .. .. 1.7 –0.4 .. –0.2 .. 2.8 .. .. .. .. .. .. .. –1.0 .. 0.7 –0.3 –1.0 –0.3 .. 1.5 0.4 .. 0.0 1.7 4.4 .. 3.3 .. .. .. .. .. .. m .. 0.0 0.1 0.4 .. .. 0.4 1.2 0.1 0.4 .. .. ..
26.8 23.2 .. .. .. .. 23.7 13.6 30.6 38.6 .. 30.1 25.1 20.1 .. .. .. 18.4 .. 13.7 .. 17.7 .. 17.5 28.4 29.0 24.2 .. 16.5 35.0 .. 40.8 21.6 26.9 .. 25.1 .. .. .. 23.0 .. 27.4 w .. 18.6 16.1 .. 18.4 12.2 27.4 .. 25.2 15.1 .. 28.0 35.9
–2.4 6.2 .. .. .. .. –2.5 12.5 –1.8 –0.7 .. 1.7 2.6 –6.5 .. .. .. 0.6 .. –6.6 .. 0.1 .. –0.8 1.8 –2.2 1.4 .. –1.9 –0.6 .. –2.7 –2.1 –1.6 .. 2.2 .. .. .. –0.8 .. –0.8 w .. –1.5 –1.1 .. –1.5 –1.1 1.8 .. 2.4 –1.8 .. –1.0 –0.7
a. Excludes grants. b. Data were reported on a cash basis and have been adjusted to the accrual framework.
242
2008 World Development Indicators
2.0 0.5 .. .. .. .. .. 13.6 –1.2 1.5 .. 0.3 .. 4.2 .. .. .. –1.1 .. .. .. 2.7 .. –0.5 –0.9 0.3 1.3 .. 1.6 0.5 .. 3.5 0.7 –0.4 .. 1.2 .. .. .. .. .. .. m .. 1.0 1.1 0.3 .. 2.1 0.3 1.7 –0.2 2.6 .. .. ..
Foreign
Total debt % of GDP 2007
.. 7.2 .. .. .. .. .. 86.7 31.3 .. .. .. 35.4 85.0 .. .. 47.4 25.4 .. .. .. 26.2 .. .. .. 50.9 43.8 .. .. 12.4 .. 48.4 47.3 59.3 .. .. .. .. .. .. .. .. m .. .. .. .. .. .. .. .. .. 53.7 .. 44.0 44.0
Interest % of revenue 2007
2.6 1.4 .. .. .. .. 21.0 0.1 5.2 3.7 .. 8.3 4.4 30.7 .. .. .. 4.6 .. 5.1 .. 5.6 .. 5.6 6.1 8.8 22.8 .. 7.8 1.5 .. 5.7 11.6 14.2 .. 10.4 .. .. .. 7.2 .. 5.6 m .. 5.6 5.6 4.8 7.1 .. 2.2 8.7 8.1 22.3 .. 5.2 5.9
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.
• Revenue is cash receipts from taxes, social con-
and role of central governments relative to national
Government excludes public corporations and quasi
tributions, and other revenues such as fines, fees,
economies. The tables are based on the concepts
corporations (such as the central bank).
rent, and income from property or sales. Grants, usu-
and recommendations of the second edition of the
Units of government at many levels meet this defini-
ally considered revenue, are excluded. • Expense is
International Monetary Fund’s (IMF) Government
tion, from local administrative units to the national
cash payments for government operating activities in
Finance Statistics Manual 2001. Before 2005 World
government, but inadequate statistical coverage pre-
providing goods and services. It includes compensa-
Development Indicators reported data derived on the
cludes presenting subnational data. Although data
tion of employees, interest and subsidies, grants,
basis of the 1986 manual’s cash-based method. The
for general government under the 2001 manual are
social benefi ts, and other expenses such as rent
2001 manual, harmonized with the 1993 System of
available for a few countries, only data for the cen-
and dividends. • Cash surplus or deficit is revenue
National Accounts, recommends an accrual account-
tral government are shown to minimize disparities.
(including grants) minus expense, minus net acquisi-
ing method, focusing on all economic events affect-
Still, different accounting concepts of central govern-
tion of nonfinancial assets. In editions before 2005
ing assets, liabilities, revenues, and expenses, not
ment make cross-country comparisons potentially
nonfinancial assets were included under revenue
only those represented by cash transactions. It takes
misleading.
and expenditure in gross terms. This cash surplus
all stocks into account, so that stock data at the
Central government can refer to consolidated or bud-
or deficit is close to the earlier overall budget balance
end of an accounting period equal stock data at the
getary accounting. For most countries central govern-
(still missing is lending minus repayments, which are
beginning of the period plus flows over the period.
ment finance data have been consolidated into one
included as a financing item under net acquisition
The 1986 manual considered only the debt stock
account, but for others only budgetary central gov-
of financial assets). • Net incurrence of liabilities
data. Further, the new manual no longer distinguishes
ernment accounts are available. Countries reporting
is domestic financing (obtained from residents) and
between current and capital revenue or expenditures,
budgetary data are noted in Primary data documenta-
foreign financing (obtained from nonresidents), or
and it introduces the concepts of nonfinancial and
tion. Because budgetary accounts may not include
the means by which a government provides financial
financial assets. Most countries still follow the 1986
all central government units (such as social security
resources to cover a budget deficit or allocates finan-
manual, however. The IMF has reclassified histori-
funds), they usually provide an incomplete picture.
cial resources arising from a budget surplus. The net
cal Government Finance Statistics Yearbook data to
Data on government revenue and expense are col-
incurrence of liabilities should be offset by the net
conform to the 2001 manual’s format. Because of
lected by the IMF through questionnaires to member
acquisition of financial assets (a third financing item).
reporting differences, the reclassified data under-
countries and by the Organisation for Economic Co-
The difference between the cash surplus or deficit
state both revenue and expense.
operation and Development. Despite IMF efforts to stan-
and the three financing items is the net change in
dardize data collection, statistics are often incomplete,
the stock of cash. • Total debt is the entire stock of
untimely, and not comparable across countries.
direct government fixed-term contractual obligations
The 2001 manual describes government’s economic functions as the provision of goods and services on a nonmarket basis for collective or individual
Government finance statistics are reported in local
to others outstanding on a particular date. It includes
consumption, and the redistribution of income and
currency. The indicators here are shown as percent-
domestic and foreign liabilities such as currency and
wealth through transfer payments. Government
ages of GDP. Many countries report government finance
money deposits, securities other than shares, and
activities are financed mainly by taxation and other
data by fiscal year; see Primary data documentation for
loans. It is the gross amount of government liabili-
income transfers, though other financing such as
information on fiscal year end by country.
ties reduced by the amount of equity and financial derivatives held by the government. Because debt is a stock rather than a flow, it is measured as of
Fifteen developing economies had a government expenditure to GDP ratio of 30 percent or higher
4.10a
a given date, usually the last day of the fiscal year. • Interest payments are interest payments on gov-
Central government expense, 2007 (% of GDP) 75
ernment debt—including long-term bonds, long-term loans, and other debt instruments —to domestic and foreign residents.
50
Data sources 25
Data on central government finances are from the IMF’s Government Finance Statistics Yearbook ia Le ba no n M ol do va Lit hu an ia So ut h Af ric a
Bu lga r
Po la nd
rd an Be la ru s Uk ra in e
Jo
Le so th o C ro an at d ia He rz eg ov in a Bo sn ia
Ja m ai ca M al di ve s Se yc he lle s
0
2008 and 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-
Source: International Monetary Fund, Government Finance Statistics data files, and World Development Indicators data files.
tions of concepts, definitions, and data sources.
2008 World Development Indicators
243
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
244
Central government expenses Goods and services
Compensation of employees
Interest payments
Subsidies and other transfers
Other expense
% of expense 1995 2007
% of expense 1995 2007
% of expense 1995 2007
% of expense 1995 2007
% of expense 1995 2007
.. 18 6 .. .. .. .. 5 49 .. 39 3 .. .. .. 32 5 18 .. 20 .. 17 8 .. .. .. .. .. .. 37 .. 12 .. 35 .. 7 8 .. 6 18 .. .. .. .. 8 8 .. .. 52 4 .. 10 15 17 .. ..
2009 World Development Indicators
67 12 5 .. 5 37 11 6 .. 13 14 2 31 14 24 .. .. 14 21 .. 41 .. 8 27 .. 11 27 .. 5 .. 18 12 34 10 .. 6 9 17 .. 8 16 .. 14 .. 10 6 .. .. 38 5 15 11 13 .. .. ..
.. 14 39 .. .. .. .. 8 10 .. 5 7 .. .. .. 30 8 7 .. 30 .. 40 10 .. .. .. .. .. .. 58 .. 38 .. 27 .. 9 13 .. 49 22 .. .. .. .. 9 23 .. .. 11 5 .. 22 50 34 .. ..
28 30 28 .. 12 22 10 13 .. 25 11 7 40 22 28 .. .. 18 39 .. 33 .. 12 53 .. 21 5 .. 19 .. 18 42 33 26 .. 9 13 31 .. 24 40 .. 22 .. 10 22 .. .. 16 5 38 24 24 .. .. ..
.. 9 13 .. .. .. .. 9 0 .. 1 18 .. .. .. 2 45 37 .. 6 .. 26 18 .. .. .. .. .. .. 1 .. 20 .. 3 .. 3 13 .. 26 26 .. .. .. .. 7 6 .. .. 10 6 .. 27 12 28 .. ..
0 17 5 .. 26 2 4 7 .. 22 1 9 2 10 1 .. .. 3 4 .. 2 .. 7 9 .. 4 4 .. 25 .. 11 14 9 5 .. 3 5 9 .. 18 12 .. 0 .. 4 6 .. .. 3 6 11 10 10 .. .. ..
.. 59 34 .. .. .. .. 77 41 .. 55 71 .. .. .. 36 45 38 .. 14 .. 14 64 .. .. .. .. .. .. 2 .. 26 .. 32 .. 75 64 .. .. 6 .. .. .. .. 68 59 .. .. 26 67 .. 36 18 9 .. ..
5 42 32 .. 50 34 70 70 .. 29 68 79 8 47 43 .. .. 58 35 .. 19 .. 67 .. .. 57 60 .. 45 .. 53 18 18 53 .. 71 70 30 .. 39 22 .. 43 .. 71 62 .. .. 35 82 37 45 25 .. .. ..
.. 0 8 .. .. .. .. 3 0 .. 0 3 .. .. .. 2 1 2 .. 10 .. .. .. .. .. .. .. .. .. .. .. 4 .. 3 .. 5 4 .. .. .. .. .. .. .. 11 6 .. .. .. 20 .. 5 6 1 .. ..
0 0 29 .. 7 5 6 5 .. 11 5 3 20 7 4 .. .. 6 0 .. 5 .. 6 11 .. 11 4 .. 7 .. 0 15 7 6 .. 12 4 13 .. 11 11 .. 4 .. 7 6 .. .. 9 3 0 4 27 .. .. ..
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
ECONOMY
Central government expenses
4.11
Goods and services
Compensation of employees
Interest payments
Subsidies and other transfers
Other expense
% of expense 1995 2007
% of expense 1995 2007
% of expense 1995 2007
% of expense 1995 2007
% of expense 1995 2007
.. 8 14 21 21 .. 5 .. 4 22 .. 7 .. 15 .. 16 33 32 .. 20 .. 32 .. .. .. .. .. .. 23 .. .. 12 9 10 .. .. .. .. 28 .. 5 .. 14 .. .. .. 55 .. 16 19 .. 20 15 .. 7 ..
17 9 12 8 10 .. 12 27 4 47 .. 5 23 15 .. 7 15 25 37 12 3 37 .. .. 13 .. 14 .. .. 38 .. 12 .. 18 37 9 .. .. .. .. 8 30 13 30 .. 11 .. 31 .. .. 11 19 18 8 7 ..
.. 10 10 20 56 .. 15 .. 14 24 .. 67 .. 28 .. 15 31 36 .. 20 .. 45 .. .. .. .. .. .. 34 .. .. 45 19 8 .. .. .. .. 53 .. 8 .. 25 .. .. .. 30 .. 45 36 .. 19 34 .. 30 ..
49 13 7 13 37 .. 24 25 16 20 .. 26 8 44 .. 12 24 27 38 20 27 33 .. .. 18 .. 46 .. .. 33 .. 35 .. 15 24 46 .. .. .. .. 8 25 36 30 .. 17 .. 4 .. .. 52 18 31 12 27 ..
.. 20 27 16 0 .. 14 .. 24 32 .. 11 3 46 .. 3 5 5 .. 3 .. 5 .. .. .. .. .. .. 17 .. .. 12 19 11 .. .. .. .. 1 .. 9 .. 17 .. .. .. 7 28 8 20 .. 19 33 .. 10 ..
3 9 21 16 1 .. 3 11 12 21 .. 8 2 11 .. 7 0 3 5 1 40 5 .. .. 2 .. 10 .. .. 2 .. 15 .. 4 2 7 .. .. .. 7 4 4 8 3 .. 2 .. 26 .. .. 5 10 24 6 7 ..
.. 57 33 41 .. .. 33 .. 54 1 .. 12 58 .. .. 63 24 27 .. 56 .. 8 .. .. .. .. .. .. 27 .. .. 28 .. 71 .. .. .. .. .. .. 77 .. 29 .. .. .. 8 2 30 26 .. 33 15 .. 43 ..
16 61 36 63 32 .. 37 30 64 1 .. 26 64 9 .. 58 40 34 18 66 22 11 .. .. 62 .. 14 .. .. 16 .. 33 .. 56 36 29 .. .. .. .. 79 38 38 9 .. 67 .. 31 .. .. 25 50 19 69 49 ..
.. 13 0 2 .. .. 1 .. 6 21 .. 4 .. 2 .. 3 7 .. .. 0 .. 3 .. .. .. .. .. .. 1 .. .. 2 .. 1 .. .. .. .. 4 .. 3 .. 14 .. .. .. 0 .. 1 1 .. 8 .. .. 11 ..
2009 World Development Indicators
16 10 1 0 18 .. 1 9 6 11 .. 35 3 2 .. 15 20 11 3 0 8 6 .. .. 9 .. 16 .. .. 11 .. 5 .. 7 1 10 .. .. .. .. 3 7 6 28 .. 6 .. 8 .. .. 8 3 8 7 2 ..
245
4.11 Romania Russian Federation Rwandaa Saudi Arabia Senegala Serbia Sierra Leonea Singaporea Slovak Republic Sloveniaa Somalia South Africa Spain Sri Lankaa Sudana Swazilanda Sweden Switzerlanda Syrian Arab Republica Tajikistana Tanzania Thailand Timor-Leste 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 Euro area
Central government expenses Goods and services
Compensation of employees
Interest payments
Subsidies and other transfers
Other expense
% of expense 1995 2007
% of expense 1995 2007
% of expense 1995 2007
% of expense 1995 2007
% of expense 1995 2007
.. .. 52 .. .. .. .. 38 .. 19 .. .. 5 23 44 .. 10 24 .. 47 .. .. .. .. 20 7 .. .. .. .. 50 22 .. 13 .. 6 .. .. 8 32 16 .. m .. .. .. 17 .. .. .. 13 8 .. .. 8 5
15 14 .. .. .. .. 28 44 9 12 .. 10 4 11 .. .. .. 8 .. 29 .. 27 .. 25 16 6 7 .. 30 12 .. 19 15 16 .. 6 .. .. .. 32 .. 13 m .. 13 13 12 16 .. 14 14 6 13 .. 9 7
.. .. 36 .. .. .. .. 39 .. 21 .. .. 14 20 38 .. 5 6 .. 8 .. .. .. .. 36 37 .. .. .. .. 37 7 .. 17 .. 22 .. .. 67 35 34 .. m .. .. .. 25 .. .. .. 26 47 .. .. 14 11
23 17 .. .. .. .. 26 32 13 19 .. 14 9 30 .. .. .. 7 .. 9 .. 37 .. 31 22 38 24 .. 12 14 .. 15 13 23 .. 16 .. .. .. 30 .. 24 m .. 24 28 20 28 .. 18 23 28 25 .. 13 13
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.
246
2008 World Development Indicators
.. .. 12 .. .. .. .. 8 .. 3 .. .. 11 22 8 .. 13 4 .. 12 .. .. .. .. 20 13 .. .. .. .. .. 9 .. 6 .. 27 .. .. 16 16 31 .. m .. .. .. 14 .. .. .. 20 13 27 .. 9 10
3 2 .. .. .. .. 19 0 5 4 .. 9 5 25 .. .. .. 5 .. 5 .. 6 .. 6 7 9 24 .. 8 1 .. 5 10 14 .. 12 .. .. .. 7 .. 6m .. 6 6 5 7 .. 2 10 8 22 .. 5 6
.. .. 5 .. .. .. .. 15 .. 55 .. .. 42 24 10 .. 71 66 .. 33 .. .. .. .. 24 36 .. .. .. .. .. 54 .. 64 .. 61 .. .. 8 19 19 .. m .. .. .. .. .. .. .. .. .. 24 .. 63 61
50 61 .. .. .. .. 9 24 68 62 .. 59 78 23 .. .. .. 75 .. 27 .. 30 .. 27 36 37 37 .. 49 67 .. 53 60 48 .. 64 .. .. .. 24 .. 40 m .. 37 34 57 35 .. 57 38 32 29 .. 62 68
.. .. .. .. .. .. .. .. .. 3 .. .. 2 10 .. .. 1 0 .. .. .. .. .. .. 1 7 .. .. .. .. .. 9 .. 0 .. 2 .. .. 0 0 .. .. m .. .. .. .. .. .. .. .. .. .. .. 3 3
13 15 .. .. .. .. 18 .. 5 3 .. 8 5 10 .. .. .. 5 .. 30 .. 3 .. 12 19 10 1 .. 0 6 .. 10 2 .. .. 3 .. .. .. 7 .. 6m .. 7 7 8 6 .. 6 9 10 8 .. 6 5
About the data
ECONOMY
Central government expenses
4.11
Definitions
The term expense has replaced expenditure in the
to households are shown as subsidies and other
• Goods and services are all government payments
table since the 2005 edition of World Development
transfers, and other expenses. The economic clas-
in exchange for goods and services used for the
Indicators in accordance with use in the International
sification can be problematic. For example, the dis-
production of market and nonmarket goods and ser-
Monetary Fund’s (IMF) Government Finance Statis-
tinction between current and capital expense may
vices. Own-account capital formation is excluded.
tics Manual 2001. Government expenses include all
be arbitrary, and subsidies to public corporations or
• Compensation of employees is all payments in
nonrepayable payments, whether current or capital,
banks may be disguised as capital financing. Subsi-
cash, as well as in kind (such as food and hous-
requited or unrequited. The concept of total central
dies may also be hidden in special contractual pric-
ing), to employees in return for services rendered,
government expense as presented in the IMF’s Gov-
ing for goods and services. For further discussion of
and government contributions to social insurance
ernment Finance Statistics Yearbook is comparable
government finance statistics, see About the data for
schemes such as social security and pensions that
to the concept used in the 1993 System of National
tables 4.10 and 4.12.
provide benefits to employees. • Interest payments
Accounts.
are payments made to nonresidents, to residents,
Expenses can be measured either by function
and to other general government units for the use of
(health, defense, education) or by economic type
borrowed money. (Repayment of principal is shown
(interest payments, wages and salaries, purchases
as a financing item, and commission charges are
of goods and services). Functional data are often
shown as purchases of services.) • Subsidies and
incomplete, and coverage varies by country because
other transfers include all unrequited, nonrepayable
functional responsibilities stretch across levels of
transfers on current account to private and public
government for which no data are available. Defense
enterprises; grants to foreign governments, inter-
expenses, usually the central government’s respon-
national organizations, and other government units;
sibility, are shown in table 5.7. For more information
and social security, social assistance benefits, and
on education expenses, see table 2.10; for more on
employer social benefits in cash and in kind. • Other
health expenses, see table 2.15.
expense is spending on dividends, rent, and other
The classification of expenses by economic type in
miscellaneous expenses, including provision for con-
the table shows whether the government produces
sumption of fixed capital.
goods and services and distributes them, purchases the goods and services from a third party and distributes them, or transfers cash to households to make the purchases directly. When the government produces and provides goods and services, the cost is reflected in compensation of employees, use of goods and services, and consumption of fixed capital. Purchases from a third party and cash transfers
Interest payments are a large part of government expenses for some developing economies
4.11a
Central government interest payments as a share of total expense, 2007 (%) 40
30
20
Data sources 10
Data on central government expenses are from the IMF’s Government Finance Statistics Yearbook r do lva
ua
y
ca
El
Sa
ug Ur
s
Ri
iu Co
st
a
rit
ll e
au M
he
Re
yc Se
ab Ar
2008 and data files. Each country’s accounts are reported using the system of common defi -
Eg
yp
t,
s
p.
ca
a
ai
sh
di
m Ja
In
y
de la ng
Ba
es
ke Tu r
a pp ili Ph
m lo Co
in
bi
ka
an
an
st
iL Sr
ki Pa
Le
ba
no
na
0
Interest payments accounted for more than 12 percent of total expenses in 2007 for 15 countries.
nitions and classifications in the IMF’s Government Finance Statistics Manual 2001. See these sources for complete and authoritative explana-
a. Data are for 2005. Source: International Monetary Fund, Government Finance Statistics data files, and World Development Indicators data files.
tions of concepts, definitions, and data sources.
2008 World Development Indicators
247
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 Ecuador a Egypt, Arab Rep.a El Salvador Eritrea Estonia Ethiopiaa Finland France Gabon Gambia, Thea Georgiaa Germany Ghanaa Greece Guatemalaa Guineaa Guinea-Bissau Haiti
248
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 2007
% of revenue 1995 2007
% of revenue 1995 2007
% of revenue 1995 2007
% of revenue 1995 2007
% of revenue 1995 2007
.. 8 65 .. .. .. .. 26 31 .. 16 36 .. .. .. 21 14 17 .. 14 .. 17 50 .. .. .. 9 .. .. 21 .. 11 15 11 .. 15 34 .. 50 17 .. .. .. .. 16 17 .. 14 7 16 15 17 19 8 .. ..
4 15 7 .. 19 18 66 26 .. 17 6 37 19 10 3 .. .. 16 15 .. 10 .. 55 14 .. 40 25 .. 17 .. 5 16 12 9 .. 19 44 20 .. 28 24 .. 11 .. 21 25 .. .. 12 18 19 19 28 .. .. ..
2009 World Development Indicators
.. 39 10 .. .. .. .. 24 34 .. 33 23 .. .. .. 4 24 28 .. 30 .. 25 17 .. .. .. 61 .. .. 12 .. 32 14 42 .. 32 40 .. 26 13 .. .. .. .. 31 25 .. 32 48 20 31 32 46 4 .. ..
4 49 63 .. 29 42 23 24 .. 28 34 25 36 43 49 .. .. 46 35 .. 40 .. 16 23 .. 34 57 .. 26 .. 6 38 15 45 .. 27 40 53 .. 19 42 .. 41 .. 32 23 .. .. 56 23 34 29 55 .. .. ..
.. 14 18 .. .. .. .. 0 33 .. 6 .. .. .. .. 15 2 8 .. 20 .. 28 2 .. .. .. 7 .. .. 21 .. 15 58 9 .. 4 .. .. 11 10 .. .. .. .. 0 0 .. 42 10 .. 24 0 23 62 .. ..
8 8 4 .. 16 4 2 0 .. 27 17 .. 24 3 0 .. .. 1 13 .. 22 .. 1 19 .. 1 5 .. 6 .. 3 5 41 1 .. 0 .. 14 .. 5 5 .. 0 .. 0 0 .. .. 1 .. 18 0 9 .. .. ..
.. 1 1 .. .. .. .. 2 2 .. 11 2 .. .. .. 0 4 3 .. 1 .. 3 .. .. .. .. 0 .. .. 5 .. 1 3 1 .. 1 7 .. 1 10 .. .. .. .. 1 3 .. 0 .. 0 .. 3 3 2 .. ..
0 1 1 .. 14 10 0 4 .. 4 5 1 6 9 2 .. .. 0 2 .. 0 .. .. 4 .. 2 1 .. 8 .. 1 2 10 1 .. 1 2 4 .. 3 1 .. 0 .. 2 4 .. .. 1 .. .. 3 1 .. .. ..
.. 15 .. .. .. .. .. 40 23 .. 31 36 .. .. .. .. 31 21 .. 5 .. 2 22 .. .. .. .. .. .. 1 .. 28 5 33 .. 40 5 .. .. 10 .. .. .. .. 34 47 .. 0 13 58 .. 31 2 1 .. ..
0 18 .. .. 17 13 .. 40 .. .. 30 35 .. 7 33 .. .. 22 .. .. .. .. 21 6 .. 5 .. .. 4 .. 1 30 7 33 .. 45 3 1 .. .. 10 .. 34 .. 31 43 .. .. 17 55 .. 36 2 .. .. ..
.. 22 5 .. .. .. .. 8 0 .. 3 3 .. .. .. 59 26 23 .. 30 .. 25 10 .. .. .. 22 .. .. 41 .. 12 5 4 .. 8 14 .. 12 41 .. .. .. .. 17 8 .. 7 22 6 9 16 6 23 .. ..
83 10 26 .. 5 14 9 7 .. 24 8 2 15 28 12 .. .. 15 35 .. 28 .. 7 34 .. 17 12 .. 39 .. 84 9 15 11 .. 8 10 9 .. 44 18 .. .. .. 14 6 .. .. 13 4 30 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 2007
% of revenue 1995 2007
% of revenue 1995 2007
% of revenue 1995 2007
% of revenue 1995 2007
% of revenue 1995 2007
.. 18 23 46 12 .. 37 .. 32 .. 35 10 11 35 .. 31 1 26 .. 7 .. 15 .. .. .. .. .. .. 37 .. .. 12 27 6 .. .. .. 20 27 10 26 .. 9 .. .. .. 21 18 20 40 .. 15 33 .. 23 ..
21 21 41 28 12 .. 37 32 35 9 .. 12 35 37 .. 31 1 9 18 13 13 17 .. .. 21 .. 9 .. .. 18 .. 16 .. 3 15 27 .. 25 .. 13 27 57 23 12 .. 32 .. 25 .. .. 10 34 41 16 23 ..
.. 28 28 33 5 .. 35 .. 21 .. 14 23 28 40 .. 32 0 56 .. 41 .. 12 .. .. .. .. .. .. 26 .. .. 25 54 38 .. .. .. 26 32 33 24 .. 52 .. .. .. 1 27 17 8 .. 46 26 .. 32 ..
42 33 29 32 2 .. 34 29 21 18 .. 40 30 43 .. 25 .. 50 36 39 35 14 .. .. 37 .. 18 .. .. 38 .. 47 .. 49 20 31 .. 31 .. 36 28 26 50 18 .. 24 .. 30 .. .. 38 36 28 39 32 ..
.. 8 24 4 9 .. 0 .. .. .. 1 22 3 14 .. 7 2 5 .. 3 .. 49 .. .. .. .. .. .. 12 .. .. 34 4 5 .. .. .. 12 28 26 .. .. 7 .. .. .. 3 24 11 27 .. 10 29 .. 0 ..
5 0 15 3 5 .. 0 1 .. 5 .. 9 7 11 .. 3 1 12 9 1 7 57 .. .. 0 .. 35 .. .. 9 .. 14 .. 5 5 7 .. 2 .. 16 1 3 4 26 .. 0 .. 10 .. .. 7 2 20 0 0 ..
.. 1 0 1 1 .. 2 .. 5 .. 5 9 5 1 .. 10 0 1 .. 0 .. 1 .. .. .. .. .. .. 5 .. .. 6 2 1 .. .. .. .. 2 4 2 .. 0 .. .. .. 2 7 3 2 .. 8 4 .. 2 ..
1 2 0 4 1 .. 6 5 5 13 .. 14 0 1 .. 8 0 .. 1 0 12 0 .. .. 0 .. 9 .. .. 8 .. 7 .. 0 20 6 .. .. .. 4 3 0 0 3 .. 1 .. 1 .. .. 1 6 6 1 2 ..
.. 33 0 6 6 .. 17 .. 35 .. 26 .. 48 0 .. 8 .. .. .. 35 .. .. .. .. .. .. .. .. 1 .. .. 6 14 38 .. .. .. .. .. .. 40 .. 11 .. .. .. .. .. 16 0 .. 10 .. .. 29 ..
11 35 0 3 12 .. 18 16 35 5 .. 0 .. 0 .. 15 .. .. .. 29 1 .. .. .. 30 .. .. .. .. .. .. 5 .. 27 9 13 .. .. .. .. 34 0 19 .. .. 18 .. .. .. .. 16 8 .. 36 32 ..
.. 12 25 9 66 .. 9 .. 6 .. 18 36 6 10 .. 12 97 11 .. 13 .. 24 .. .. .. .. .. .. 19 .. .. 16 16 2 .. .. .. 42 11 27 8 .. 31 .. .. .. 74 24 34 23 .. 11 8 .. 14 ..
2009 World Development Indicators
20 9 15 30 69 .. 4 17 4 51 .. 24 29 9 .. 18 98 29 36 18 32 12 .. .. 11 .. 29 .. .. 27 .. 12 .. 16 31 15 .. 42 .. 30 8 15 22 41 .. 25 .. 33 .. .. 28 14 11 8 .. ..
249
ECONOMY
Central government revenues
4.12 Romania Russian Federation Rwandaa Saudi Arabia Senegala Serbiaa Sierra Leonea Singaporea Slovak Republic Sloveniaa Somalia South Africa Spain Sri Lankaa Sudana Swazilanda Sweden Switzerlanda Syrian Arab Republica Tajikistana Tanzania Thailand Timor-Leste 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 Euro area
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 2007
% of revenue 1995 2007
% of revenue 1995 2007
% of revenue 1995 2007
% of revenue 1995 2007
% of revenue 1995 2007
.. .. 11 .. 17 .. 15 26 .. 13 .. .. 28 12 17 .. 12 11 23 6 .. .. .. .. 50 16 .. .. 10 .. .. 39 .. 10 .. 38 .. .. 17 27 36 .. m .. .. 19 .. .. 35 .. .. 17 15 .. 26 26
14 6 .. .. .. .. 16 28 12 18 .. 52 33 18 .. .. .. 19 .. 3 .. 37 .. 19 60 27 23 .. 19 13 .. 37 57 13 .. 21 .. .. .. 33 .. 19 m .. 16 17 16 15 26 13 17 13 18 .. 28 25
.. .. 25 .. 19 .. 34 20 .. 33 .. .. 21 49 41 .. 31 21 37 63 .. .. .. .. 26 20 .. .. 45 .. 15 31 .. 32 .. 33 .. .. 10 22 22 .. m .. .. 34 .. .. 26 .. .. 13 31 .. 24 24
30 24 .. .. .. .. 9 23 36 32 .. 32 16 48 .. .. .. 32 .. 54 .. 40 .. 46 13 32 42 .. 30 29 .. 28 3 50 .. 25 .. .. .. 36 .. 32 m .. 38 39 37 35 35 40 38 32 29 .. 26 28
.. .. 23 .. 36 .. 39 1 .. 9 .. .. 0 17 27 .. 1 1 13 12 .. .. .. .. 6 28 .. .. 7 .. .. .. .. 4 .. 9 .. .. 18 36 17 .. m .. .. 14 .. .. 12 .. .. 18 24 .. .. 0
1 23 .. .. .. .. 27 0 0 0 .. 4 0 14 .. .. .. 1 .. 11 .. 6 .. 20 5 6 1 .. 21 4 .. .. 1 5 .. 5 .. .. .. 8 .. 5m .. 5 5 4 7 6 1 5 6 15 .. 0 0
Note: Components may not sum to 100 percent because of missing data or adjustment to tax revenue. a. Data were reported on a cash basis and have been adjusted to the accrual framework.
250
2008 World Development Indicators
.. .. 3 .. 2 .. 0 15 .. 0 .. .. 0 4 1 .. 7 2 8 0 .. .. .. .. 1 4 .. .. 2 .. .. 6 .. 10 .. 0 .. .. 3 0 3 .. m .. .. .. .. .. .. .. .. 4 4 .. 3 2
0 0 .. .. .. .. .. 15 0 3 .. 3 0 5 .. .. .. 2 .. 1 .. 0 .. 3 9 5 6 .. 0 0 .. 7 1 1 .. 4 .. .. .. 0 .. 2m .. 1 1 1 2 .. 0 2 5 2 .. 2 3
.. .. 2 .. .. .. .. .. .. 42 .. .. 40 1 .. .. 37 49 0 13 .. .. .. .. 2 15 .. .. .. .. 1 19 .. 31 .. 4 .. .. .. 0 2 .. m .. .. .. .. .. .. .. .. .. .. .. 35 38
40 19 .. .. .. .. .. .. 40 38 .. 2 46 1 .. .. .. 36 .. 12 .. 5 .. .. 4 18 .. .. .. 37 .. 21 35 21 .. 2 .. .. .. .. .. .. m .. 13 10 22 .. .. 30 8 .. 0 .. 34 36
.. .. 36 .. 26 .. 12 38 .. 3 .. .. .. 18 14 .. 13 17 19 5 .. .. .. .. 15 17 .. .. 37 .. 84 5 .. 8 .. 19 .. .. 51 15 19 .. m .. .. 16 .. .. 20 .. .. 36 25 .. 9 8
14 28 .. .. .. .. 48 33 12 9 .. 7 5 13 .. .. .. 9 .. 18 .. 12 .. 12 9 11 28 .. 30 16 .. 6 3 10 .. 43 .. .. .. 23 .. 14 m .. 15 16 14 16 22 15 20 26 27 .. 9 6
About the data
4.12
Definitions
The International Monetary Fund (IMF) classifi es
Direct taxes tend to be progressive, whereas indirect
• Taxes on income, profits, and capital gains are
government revenues as taxes, grants, and property
taxes are proportional.
levied on the actual or presumptive net income
income. Taxes are classified by the base on which
Social security taxes do not reflect compulsory pay-
of individuals, on the profi ts of corporations and
the tax is levied, grants by the source, and property
ments made by employers to provident funds or other
enterprises, and on capital gains, whether real-
income by type (for example, interest, dividends,
agencies with a like purpose. Similarly, expenditures
ized or not, on land, securities, and other assets.
or rent). The most important source of revenue is
from such funds are not refl ected in government
Intragovernmental payments are eliminated in con-
taxes. Grants are unrequited, nonrepayable, non-
expenses (see table 4.11). For further discussion of
solidation. • Taxes on goods and services include
compulsory receipts from other government units
taxes and tax policies, see About the data for table
general sales and turnover or value added taxes,
and foreign governments or from international orga-
5.6. For further discussion of government revenues
selective excises on goods, selective taxes on ser-
nizations. Transactions are generally recorded on an
and expenditures, see About the data for tables 4.10
vices, taxes on the use of goods or property, taxes
accrual basis.
and 4.11.
on extraction and production of minerals, and prof-
The IMF’s Government Finance Statistics Manual
its of fiscal monopolies. • Taxes on international
2001 describes taxes as compulsory, unrequited
trade include import duties, export duties, profits
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. However, the dis-
contributions include social security contributions by
tinctions are not always clear. Taxes levied on the
employees, employers, and self-employed individu-
income and profits of individuals and corporations
als, and other contributions whose source cannot
are classified as direct taxes, and taxes and duties
be determined. They also include actual or imputed
levied on goods and services are classified as indi-
contributions to social insurance schemes operated
rect taxes. This distinction may be a useful simplifica-
by governments. • Grants and other revenue include
tion, but it has no particular analytical significance
grants from other foreign governments, international
except with respect to the capacity to fix tax rates.
organizations, and other government units; interest; dividends; rent; requited, nonrepayable receipts for
4.12a
Rich economies rely more on direct taxes
public purposes (such as fines, administrative fees, and entrepreneurial income from government owner-
Taxes on income and capital gains as a share of central government revenue, 2007 (%)
ship of property); and voluntary, unrequited, nonre70 United States
payable receipts other than grants.
60 South Africa
50 India
40
30
Norway
Data sources
20
Data on central government revenues are from 10
the IMF’s Government Finance Statistics Yearbook 2008 and data files. Each country’s accounts
0 100
are reported using the system of common defini-
10,000
1,000
100,000
tions and classifications in the IMF’s Government GNI per capita ($, log scale) Low income
Middle income
Finance Statistics Manual 2001. The IMF receives High income
additional information from the Organisation for
High-income economies tend to tax income and property, whereas low-income economies tend to rely on
Economic Co-operation and Development on the
indirect taxes on international trade and goods and services. But there are exceptions in all groups.
tax revenues of some of its members. See the IMF sources for complete and authoritative explana-
Note: Data are for the most recent year for 2005–07. Source: International Monetary Fund, Government Finance Statistics data files, and World Development Indicators data files.
tions of concepts, definitions, and data sources.
2008 World Development Indicators
251
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 Cote 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
252
Monetary indicators Money and quasi money
Claims on private sector
Claims on governments and other public entities
annual % growth 1995 2007
Annual growth % of M2 1995 2007
Annual growth % of M2 1995 2007
.. 51.8 9.6 4,105.6 –2.8 64.3 8.5 .. 25.4 12.1 158.4 .. –1.8 7.7 22.0 12.3 44.3 40.5 22.3 –8.0 43.6 –6.2 4.8 4.3 48.8 24.3 29.5 10.6 28.2 357.6 –0.1 4.7 18.1 40.4 .. 29.3 6.2 16.6 6.8 9.9 13.5 21.0 27.5 9.0 .. .. 10.1 14.2 40.2 .. 43.2 .. 15.6 11.3 43.0 27.1
46.0 13.7 22.8 38.6 24.5 42.3 29.9 .. 73.2 13.6 34.7 .. 19.6 26.2 32.6 31.2 18.6 31.3 23.8 13.8 61.8 14.9 –25.3 –3.6 9.8 18.2 16.7 18.8 11.9 50.7 7.1 18.5 23.6 18.2 .. 12.8 12.1 22.1 18.4 19.1 17.8 12.1 13.6 –46.8 .. .. 6.9 6.7 49.7 .. 42.8 .. 11.3 33.4 24.9 11.1
2009 World Development Indicators
.. 1.8 1.0 471.4 –1.1 70.3 12.5 .. 6.1 25.0 61.4 .. 2.2 13.7 23.9 –1.7 40.5 22.1 2.9 –7.1 12.5 0.3 3.8 3.9 6.4 34.9 22.5 9.8 34.3 59.6 6.3 –1.4 13.3 30.5 .. 15.8 2.6 14.4 15.1 12.1 22.6 27.8 28.9 13.4 .. .. 11.9 –5.0 –11.1 .. 10.2 .. 36.1 12.1 –6.7 15.7
20.1 14.2 3.9 33.7 15.2 39.7 23.6 .. 56.4 10.5 46.6 .. 14.3 6.6 24.1 12.7 21.8 44.0 0.9 4.5 40.2 3.0 –2.5 3.0 3.5 27.1 13.4 4.8 30.8 17.6 1.0 53.7 10.5 14.2 .. 16.3 38.4 21.8 13.1 5.7 9.5 2.4 52.5 14.7 .. .. 20.7 4.8 79.0 .. 20.1 .. 18.2 19.8 7.5 2.7
.. –8.3 –10.0 119.5 7.8 7.2 0.4 .. –32.7 4.8 44.7 .. 6.0 1.1 –0.4 10.0 14.6 –7.2 –7.3 0.2 1.2 –2.2 0.2 –7.9 –18.6 –2.0 0.8 –2.4 2.9 –7.9 2.0 5.6 0.3 –2.4 .. 2.1 –1.5 –1.7 –74.8 0.6 –0.9 20.5 –9.3 –3.5 .. .. 5.8 15.2 73.8 .. 28.1 .. –7.1 8.4 –20.4 0.1
–7.8 3.3 –19.9 0.7 –1.1 –4.7 5.9 .. 2.8 4.3 –31.8 .. –17.6 –4.5 0.8 –26.9 4.6 –6.5 –11.1 –1.4 –13.1 –17.1 –0.4 3.5 –28.6 1.6 3.7 –3.8 –6.9 5.1 –4.2 –7.0 3.7 –0.8 .. –2.6 –2.2 9.4 –3.3 2.2 3.1 11.3 0.9 5.4 .. .. –47.2 –5.4 –1.4 .. 10.9 .. –1.4 18.1 –0.3 –3.6
Interest rate
Deposit 1995 2007
.. 15.3 16.6 125.9 11.9 63.2 6.1 2.2 .. 6.0 100.8 4.0 3.5 18.9 51.9 9.8 52.2 35.9 3.5 .. 8.7 5.5 5.3 5.5 5.5 13.7 11.0 5.6 32.3 60.0 5.5 23.9 3.5 5.5 .. 7.0 3.9 14.9 43.3 10.9 14.4 .. 8.7 11.5 3.2 4.5 5.5 12.5 31.0 3.9 28.7 15.8 7.9 17.5 3.5 4.2
.. 5.7 1.8 6.8 8.0 6.3 4.7 .. 11.6 9.2 8.3 .. 3.5 3.5 3.6 8.6 10.6 3.7 3.5 .. 1.9 4.3 2.1 4.3 4.3 5.6 4.1 2.4 8.0 .. 4.3 6.4 3.5 2.3 .. 1.3 .. 7.0 5.0 6.1 .. .. 4.4 3.6 1.0 2.9 4.3 12.9 9.5 .. 8.9 2.2 4.8 14.4 3.5 1.5
% Lending 1995 2007
.. 19.7 18.4 206.3 17.9 111.9 10.7 6.4 .. 14.0 175.0 8.4 16.8 51.0 73.5 14.4 78.2 79.4 16.8 15.3 18.7 16.0 8.7 16.0 16.0 18.2 12.1 8.8 42.7 293.9 16.0 36.7 16.8 20.2 .. 12.8 10.3 30.7 55.7 16.5 19.1 .. 19.0 15.1 7.8 8.1 16.0 25.0 58.2 10.9 .. 23.1 21.2 21.5 32.9 24.8
18.1 14.1 8.0 17.7 11.1 17.5 10.0 .. 19.1 16.0 8.6 8.6 .. 12.9 7.2 16.2 43.7 10.0 .. 16.8 16.4 15.0 6.1 15.0 15.0 8.7 7.5 6.8 15.4 .. 15.0 12.8 .. 9.3 .. 5.8 .. 15.8 12.1 12.5 .. .. 6.5 7.0 3.7 6.6 15.0 27.9 20.4 .. .. .. 12.8 .. .. 47.0
Real 1995
2007
.. 8.9 –7.9 –84.7 14.2 –18.9 9.1 6.1 .. 6.2 –63.9 7.1 13.0 35.5 76.3 5.2 65.5 10.1 16.5 –0.7 6.4 6.0 6.2 5.2 6.6 7.0 –1.5 4.4 20.1 –30.5 12.2 11.9 16.8 14.1 .. –3.6 9.0 16.0 45.7 4.6 7.8 .. –9.3 2.1 2.9 6.7 14.5 20.3 10.6 8.9 .. 12.1 11.5 14.7 –8.2 –2.4
7.5 10.6 0.5 9.9 –2.7 12.8 5.1 .. 4.1 8.6 –3.1 6.8 .. 5.1 1.1 4.0 38.1 2.0 .. 6.7 11.2 12.7 10.3 12.8 12.3 3.6 0.0 3.7 10.1 .. 24.9 3.2 .. 5.1 .. 2.1 .. 9.6 7.1 –0.1 .. .. –2.9 –4.1 3.0 4.9 9.3 21.0 9.8 .. .. .. 6.3 .. .. 22.9
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 1995 2007
Annual growth % of M2 1995 2007
Annual growth % of M2 1995 2007
28.9 20.9 11.0 27.5 30.1 .. .. 21.7 .. 28.0 4.1 5.7 108.2 29.0 .. 15.6 9.4 14.8 16.4 –21.4 16.4 9.8 29.5 9.6 28.9 1.8 16.2 56.2 18.5 7.3 –5.1 18.6 31.9 65.3 32.6 7.0 47.7 36.5 22.6 15.4 .. 9.3 35.1 3.8 19.4 3.8 7.7 13.8 8.4 13.7 0.5 29.3 23.9 35.6 .. ..
19.3 9.5 22.3 19.3 30.6 37.1 .. –4.4 .. 12.6 0.8 12.4 25.9 20.4 .. 0.3 19.3 33.2 38.7 13.5 12.4 16.4 42.4 38.0 21.7 30.7 20.9 36.6 10.5 13.7 .. 15.4 13.8 39.8 56.7 16.1 26.2 30.0 10.2 18.6 .. 9.8 18.5 24.7 64.2 .. 34.7 19.5 17.4 27.8 29.5 23.0 5.4 13.0 .. ..
18.0 4.9 6.0 25.9 9.8 .. .. 18.3 .. 18.0 1.3 9.6 –72.5 26.7 .. 21.6 10.9 0.1 18.1 –23.8 13.1 –2.3 –6.0 3.1 12.7 –138.9 9.4 2.8 29.2 18.9 –42.5 8.7 –2.9 34.6 14.4 6.9 21.8 13.4 30.5 18.1 .. 15.8 30.3 –22.8 22.3 9.5 9.3 10.8 14.5 0.2 4.9 31.1 27.9 19.1 .. ..
27.5 19.6 13.1 13.3 37.1 3.0 .. 7.5 .. 15.7 –0.1 9.4 73.2 10.8 .. 18.1 31.2 29.2 8.4 59.2 4.9 8.2 16.6 2.9 48.8 26.7 8.0 9.9 8.1 10.2 .. 14.6 12.0 37.8 57.5 17.3 8.1 4.2 15.7 14.6 .. 18.7 27.4 11.5 79.2 .. 29.9 10.0 18.9 14.4 28.0 19.7 2.1 22.3 .. ..
–7.5 20.2 3.4 –2.3 17.3 .. .. –0.5 .. 6.1 2.5 –3.8 24.7 6.6 .. –1.2 –2.0 62.6 –9.7 6.5 5.1 –18.7 37.2 3.6 –2.4 –229.7 –10.3 –10.4 –0.7 –11.6 –28.9 3.0 27.6 19.1 –31.8 5.1 –12.5 19.7 1.7 3.3 .. –3.9 –21.5 10.2 –9.1 –1.9 –2.3 8.7 –4.3 5.0 0.1 –8.1 3.0 3.1 .. ..
0.4 1.9 1.5 0.5 –0.5 –44.3 .. –1.8 .. –1.6 –0.4 5.3 –20.0 0.8 .. –3.7 5.4 –8.8 –1.8 –2.1 0.4 –52.0 67.9 –38.9 2.3 9.0 1.0 1.7 –1.4 –2.2 .. –0.4 1.8 –5.6 –17.0 0.2 3.8 28.9 –10.0 4.8 .. –2.7 –4.8 –14.7 4.7 .. –11.2 7.1 –6.6 –11.3 –7.7 –9.0 0.1 –2.5 .. ..
4.13
Interest rate
Deposit 1995 2007
12.0 24.4 .. 16.7 .. .. 0.4 14.1 6.4 23.2 0.9 7.7 .. 13.6 .. 8.8 6.5 36.7 14.0 14.8 16.3 13.3 6.4 5.5 20.1 24.1 18.5 37.3 5.9 3.5 9.0 12.2 39.8 25.4 74.6 7.3 38.8 9.8 10.8 9.6 4.4 8.5 11.1 3.5 13.5 5.0 6.5 .. 7.2 7.3 21.2 9.6 8.4 26.8 8.4 ..
7.8 6.8 .. 8.0 11.6 11.4 0.0 3.5 .. 7.1 0.8 5.4 .. 5.2 .. 5.2 5.4 5.4 5.0 6.1 8.0 6.5 3.8 2.5 5.4 4.9 16.5 6.0 3.2 3.5 8.0 11.8 3.2 15.0 13.5 3.7 11.9 12.0 7.5 2.3 3.9 7.8 6.1 3.5 10.3 4.9 4.1 5.3 4.8 1.1 5.0 3.2 3.7 2.2 .. ..
% Lending 1995 2007
27.0 32.6 15.5 18.9 .. .. 6.6 20.2 13.2 43.6 3.5 10.7 .. 28.8 .. 9.0 8.4 65.0 25.7 34.6 24.7 16.4 15.6 7.0 27.1 46.0 37.5 47.3 8.7 16.8 20.3 20.8 59.4 36.7 134.4 11.3 24.4 16.5 18.5 12.9 7.2 12.1 19.9 16.8 20.2 7.6 9.4 .. 11.1 13.1 33.9 36.2 14.7 33.5 13.8 ..
16.6 9.1 13.0 13.9 12.0 19.7 2.7 6.3 6.3 17.2 1.9 8.7 .. 13.3 .. 6.6 8.5 25.3 28.5 10.9 10.3 14.1 15.1 6.0 6.9 10.2 45.0 27.7 6.4 .. 23.5 21.9 7.6 18.8 17.5 11.5 19.5 17.0 12.9 8.0 4.6 12.8 13.0 .. 16.9 6.7 7.3 11.8 8.3 9.8 25.0 22.9 8.7 5.5 .. ..
Real 1995
2007
1.7 4.6 5.9 8.3 .. .. 3.4 –0.2 7.9 17.5 4.0 8.6 .. 15.8 .. 1.5 3.4 21.9 5.0 5.5 12.8 15.3 8.5 .. –13.2 24.6 –5.3 –16.9 4.9 14.5 17.0 16.1 15.6 7.7 46.9 3.1 18.0 –2.4 12.1 4.7 5.0 9.9 5.7 15.5 –22.9 4.4 7.5 .. 10.6 –2.3 17.9 20.5 6.6 –5.2 10.0 ..
9.0 3.2 7.8 2.2 –7.0 .. 0.1 6.5 4.0 –7.9 2.5 2.6 .. 8.3 .. 5.3 –7.7 10.4 24.2 –2.1 5.1 7.5 –0.8 0.6 –1.6 4.9 31.5 18.9 1.2 .. 26.7 13.9 2.7 2.7 4.6 10.4 12.6 –2.2 12.0 0.3 3.4 7.5 3.5 .. 11.3 5.0 –0.3 3.5 6.2 7.2 13.4 20.4 5.7 3.9 .. ..
2009 World Development Indicators
253
ECONOMY
Monetary indicators
4.13 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republica Sloveniaa Somalia South Africa Spaina Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe
Monetary indicators Money and quasi money
Claims on private sector
Claims on governments and other public entities
annual % growth 1995 2007
Annual growth % of M2 1995 2007
Annual growth % of M2 1995 2007
69.6 112.6 69.5 3.4 7.4 33.0 19.6 8.5 18.4 30.4 .. 16.0 .. 35.8 72.7 3.9 3.1 4.6 9.2 .. 33.0 17.7 .. 22.3 4.0 6.6 104.2 449.5 13.9 115.5 10.2 20.3 6.9 36.9 .. 36.6 25.8 .. 50.7 55.5 25.5
33.9 44.2 18.0 20.1 13.1 42.5 22.6 13.4 11.1 8.4 .. 20.2 .. 16.5 10.3 21.5 11.4 3.3 12.4 169.9 20.5 2.5 43.9 16.8 10.8 12.4 15.2 .. 22.0 50.8 41.7 15.9 12.1 5.0 .. 28.7 49.1 5.6 17.0 25.3 64,472.5
23.1 50.3 46.2 41.7 32.7 14.5 3.4 15.4 1.2 6.9 88.5 36.2 1.6 8.5 19.7 12.8 3.4 14.8 31.6 40.5 .. .. 18.9 26.2 .. .. 75.4 15.9 10.6 8.0 1.3 21.5 –1.1 31.1 4.0 10.1 3.9 4.4 .. 253.7 –3.9 16.1 40.3 4.3 .. –11.0 17.6 15.5 9.0 12.0 10.4 9.8 66.9 16.2 76.3 .. 9.6 10.7 7.7 68.6 10.7 36.3 19.6 19.9 6.0 7.1 35.2 7.6 .. .. 15.3 39.6 18.9 44.5 .. 2.9 6.0 7.1 34.2 20.5 25.5 67,582.1
11.6 5.0 73.6 –17.7 –41.0 –13.8 1.4 –23.3 1.0 5.0 34.1 –1.0 –101.6 –4.5 –8.1 3.0 –4.8 –1.4 5.0 –2.3 .. .. –4.1 –0.9 .. .. 5.4 1.4 389.1 4.2 –14.8 –50.9 –4.0 0.3 0.2 –0.1 6.1 –18.8 .. –13.8 16.3 –0.7 –4.2 0.4 .. –135.6 14.9 0.8 0.6 8.8 –1.2 1.2 30.1 3.9 –573.1 .. –41.2 –13.4 95.4 1.3 –4.3 0.7 1.0 –0.3 0.2 1.4 1.0 –10.6 .. .. 32.8 –10.7 0.7 –1.0 .. 2.4 13.3 12.6 185.8 –8.7 –0.3 11,566.1
a. As members of the European Monetary Union, these countries share a single currency, the euro.
254
2008 World Development Indicators
Interest rate
Deposit 1995 2007
44.7 102.0 11.1 6.2 3.5 19.1 7.0 3.5 9.0 15.4 .. 13.5 7.7 12.1 .. 9.4 6.2 1.3 4.0 23.9 24.6 11.6 .. 3.5 6.9 .. 76.0 .. 7.6 70.3 .. 4.1 .. 57.7 .. 24.7 8.5 .. 23.8 30.2 25.9
6.7 5.1 6.8 4.8 3.5 4.1 10.0 0.5 3.7 3.6 .. 9.2 .. 9.1 .. 7.1 0.8 2.1 8.3 8.4 8.7 2.9 0.8 3.5 5.9 .. 22.6 .. 9.3 8.1 .. .. .. 2.4 .. 10.7 7.5 3.0 13.0 9.2 121.5
% Lending 1995 2007
50.7 320.3 18.5 .. 16.8 78.0 28.8 6.4 16.8 23.4 .. 17.9 10.1 18.0 .. 17.1 11.1 5.5 9.0 75.5 42.8 13.3 .. 17.5 15.2 .. .. .. 20.2 122.7 .. 6.7 8.8 93.1 .. 39.7 20.1 .. 31.5 45.5 34.7
13.4 10.0 15.8 .. .. 11.1 25.0 5.3 8.0 5.9 .. 13.2 .. 17.1 .. 13.2 3.3 3.2 10.2 22.9 16.0 7.1 15.1 .. 11.8 .. .. .. 19.1 13.9 .. 5.5 8.1 8.9 .. 17.1 11.2 7.7 18.0 18.9 579.0
Real 1995
2007
11.4 72.3 6.9 .. 17.8 23.0 –3.6 4.0 7.1 –4.0 .. 6.9 4.9 8.0 .. –1.5 7.2 4.7 2.2 6.2 12.6 7.3 .. 13.8 10.7 .. .. .. 9.9 –56.8 .. 3.9 6.7 36.9 .. –7.9 10.5 .. –3.2 5.4 23.0
2.3 –3.1 6.4 .. .. 4.1 13.3 1.2 6.8 1.7 .. 3.9 .. 2.7 .. 3.9 2.4 1.7 6.5 –3.9 9.5 3.7 2.5 .. 7.6 .. .. .. 11.4 –6.5 .. 2.4 5.3 0.4 .. 2.7 2.7 2.3 2.6 8.7 –0.7
About the data
4.13
Definitions
Money and the financial accounts that record the
reporting period. The valuation of financial deriva-
• Money and quasi money are the sum of currency
supply of money lie at the heart of a country’s
tives and the net liabilities of the banking system
outside banks, demand deposits other than those of
financial system. There are several commonly used
can also be difficult. The quality of commercial bank
the central government, and the time, savings, and
defi nitions of the money supply. The narrowest,
reporting also may be adversely affected by delays in
foreign currency deposits of resident sectors other
M1, encompasses currency held by the public and
reports from bank branches, especially in countries
than the central government. This definition of the
demand deposits with banks. M2 includes M1 plus
where branch accounts are not computerized. Thus
money supply, often called M2, corresponds to lines
time and savings deposits with banks that require
the data in the balance sheets of commercial banks
34 and 35 in the IMF’s International Financial Statis-
prior notice for withdrawal. M3 includes M2 as well
may be based on preliminary estimates subject to
tics (IFS). The change in money supply is measured
as various money market instruments, such as cer-
constant revision. This problem is likely to be even
as the difference in end-of-year totals relative to M2
tificates of deposit issued by banks, bank deposits
more serious for nonbank financial intermediaries.
in the preceding year. • Claims on private sector
denominated in foreign currency, and deposits with
Many interest rates coexist in an economy, reflect-
(IFS line 32 d) include gross credit from the financial
fi nancial institutions other than banks. However
ing competitive conditions, the terms governing loans
system to individuals, enterprises, nonfinancial pub-
defined, money is a liability of the banking system,
and deposits, and differences in the position and
lic entities not included under net domestic credit,
distinguished from other bank liabilities by the spe-
status of creditors and debtors. In some economies
and financial institutions not included elsewhere.
cial role it plays as a medium of exchange, a unit of
interest rates are set by regulation or administra-
• Claims on governments and other public enti-
account, and a store of value.
tive fiat. In economies with imperfect markets, or
ties (IFS line 32 an + 32 b + 32 bx + 32 c) usually
The banking system’s assets include its net for-
where reported nominal rates are not indicative of
comprise direct credit for specific purposes, such
eign assets and net domestic credit. Net domestic
effective rates, it may be difficult to obtain data on
as financing the government budget deficit; loans
credit includes credit extended to the private sector
interest rates that reflect actual market transactions.
to state enterprises; advances against future credit
and general government and credit extended to the
Deposit and lending rates are collected by the Inter-
authorizations; and purchases of treasury bills and
nonfinancial public sector in the form of investments
national Monetary Fund (IMF) as representative inter-
bonds, net of deposits by the public sector. Public
in short- and long-term government securities and
est rates offered by banks to resident customers.
sector deposits with the banking system also include
loans to state enterprises; liabilities to the public
The terms and conditions attached to these rates
sinking funds for the service of debt and temporary
and private sectors in the form of deposits with the
differ by country, however, limiting their comparabil-
deposits of government revenues. • Deposit interest
banking system are netted out. Net domestic credit
ity. Real interest rates are calculated by adjusting
rate is the rate paid by commercial or similar banks
also includes credit to banking and nonbank financial
nominal rates by an estimate of the inflation rate in
for demand, time, or savings deposits. • Lending
institutions.
the economy. A negative real interest rate indicates
interest rate is the rate charged by banks on loans to
Domestic credit is the main vehicle through which
a loss in the purchasing power of the principal. The
prime customers. • Real interest rate is the lending
changes in the money supply are regulated, with cen-
real interest rates in the table are calculated as
interest rate adjusted for inflation as measured by
tral bank lending to the government often playing the
(i – P) / (1 + P), where i is the nominal lending inter-
the GDP deflator.
most important role. The central bank can regulate
est rate and P is the inflation rate (as measured by
lending to the private sector in several ways—for
the GDP deflator).
example, by adjusting the cost of the refinancing 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
Data on monetary and financial statistics are
ceilings on the credit provided by banks to the pri-
published by the IMF in its monthly International
vate sector.
Financial Statistics and annual International Finan-
Monetary accounts are derived from the balance
cial Statistics Yearbook. The IMF collects data on
sheets of financial institutions—the central bank,
the financial systems of its member countries. The
commercial banks, and nonbank financial interme-
World Bank receives data from the IMF in elec-
diaries. Although these balance sheets are usually
tronic files that may contain more recent revisions
reliable, they are subject to errors of classification,
than the published sources. The discussion of
valuation, and timing and to differences in account-
monetary indicators draws from an IMF publication
ing practices. For example, whether interest income
by Marcello Caiola, A Manual for Country Econo-
is recorded on an accrual or a cash basis can make
mists (1995). Also see the IMF’s Monetary and
a substantial difference, as can the treatment of non-
Financial Statistics Manual (2000) for guidelines
performing assets. Valuation errors typically arise
for the presentation of monetary and financial sta-
for foreign exchange transactions, particularly in
tistics. Data on real interest rates are derived from
countries with flexible exchange rates or in countries
World Bank data on the GDP deflator.
that have undergone currency devaluation during the
2008 World Development Indicators
255
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
256
Exchange rates and prices Official exchange rate
Purchasing power parity (PPP) conversion factor
local currency units to $ 2007 2008a
local currency units to international $ 1995 2007
49.96 90.43 69.29 76.71 3.10 342.08 1.20 0.73 0.86 68.88 2,146.08 0.73 479.27 7.85 1.43 6.14 1.95 1.43 479.27 1,081.87 4,056.17 479.27 1.07 479.27 479.27 522.46 7.61 7.80 2,078.29 516.75 479.27 516.62 479.27 5.37 .. 20.29 5.44 33.26 1.00 5.73 1.00 15.38 11.43 8.95 0.73 0.73 479.27 24.88 1.67 0.73 0.94 0.73 7.67 3,644.33 479.27 36.86
50.52 .. 96.84 24.4 70.91 15.3 75.13 0.0 3.41 0.9 307.83 116.6 1.49 1.3 0.73 0.9 0.81 0.2 68.89 19.2 2,189.82 3.5 0.73 0.9 481.53 187.5 7.02 1.6 1.44 0.6 7.84 1.4 2.39 0.7 1.46 0.0 481.53 189.6 1,234.53 126.6 4,091.00 1,142.8 481.53 241.2 1.24 1.2 481.53 272.0 481.53 135.5 649.32 263.8 6.84 3.4 7.75 7.9 2,273.16 423.8 560.84 0.0 481.53 150.9 549.80 103.1 481.53 261.9 5.38 3.1 .. .. 19.48 11.1 5.58 8.5 35.44 6.7 1.00 0.4 5.30 1.2 1.00 0.4 15.38 1.9 11.66 4.8 9.32 2.1 0.73 1.0 0.73 1.0 481.53 188.0 21.64 3.9 1.66 0.4 0.73 1.0 1.07 0.1 0.73 0.6 7.71 2.9 4,478.00 666.8 481.53 114.9 39.82 6.0
2009 World Development Indicators
16.1 43.7 35.8 51.6 1.6 183.8 1.4 0.9 0.4 24.0 913.5 0.9 219.7 2.6 0.7 3.0 1.4 0.7 195.3 363.9 1,345.7 251.4 1.2 264.8 213.6 371.8 3.6 5.5 1,143.3 267.8 277.1 280.5 291.4 3.9 .. 14.2 8.6 18.7 0.4 1.8 0.5 7.0 8.7 2.8 1.0 0.9 274.8 7.7 0.8 0.9 0.5 0.7 4.2 1,857.1 211.4 22.3
Ratio of PPP conversion factor to market exchange rate
2007
0.3 0.5 0.5 0.7 0.5 0.5 1.1 1.2 0.5 0.3 0.4 1.2 0.5 0.3 0.5 0.5 0.7 0.5 0.4 0.3 0.3 0.5 1.1 0.6 0.4 0.7 0.5 0.7 0.6 0.5 0.6 0.5 0.6 0.7 .. 0.7 1.6 0.6 0.4 0.3 0.5 0.5 0.8 0.3 1.3 1.2 0.6 0.3 0.5 1.2 0.5 1.0 0.6 0.4 0.4 0.6
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 2007 1990–2000 2000–07 1990–2000 2000–07 1990–2000 2000–07
.. .. 82.3 .. .. 122.6 133.3 106.7 .. .. .. 110.8 .. 81.8 .. .. .. 133.9 .. 68.7 95.3 114.6 132.3 115.1 126.7 95.2 99.1 .. 115.8 31.8 .. 95.3 117.9 112.9 .. 136.8 109.7 100.9 138.5 .. .. .. .. 99.6 106.2 109.0 105.6 59.6 109.7 108.9 115.5 116.8 .. .. .. ..
.. 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 3.7 9.7 211.8 103.3 3.7 13.4 4.4 6.3 1.5 4.5 7.1 7.9 7.9 4.5 22.3 964.9 9.0 15.9 9.2 86.0 3.0 12.8 1.6 9.4 4.3 8.7 6.2 7.9 53.6 6.4 2.0 1.3 7.0 4.2 356.7 1.7 26.7 9.2 10.4 5.5 32.5 22.8
11.8 3.7 8.8 56.1 12.3 4.3 3.7 1.7 8.9 4.4 27.6 2.0 2.9 6.8 3.7 7.4 8.5 5.0 2.2 8.4 3.8 2.2 2.0 2.0 8.2 7.0 3.8 –2.3 7.0 31.0 5.7 10.0 3.0 3.7 2.6 2.2 2.2 17.4 9.6 7.2 3.5 18.6 5.2 6.6 0.9 2.1 5.0 11.9 6.9 1.1 19.5 3.3 4.7 18.1 1.6 17.4
.. 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
.. 2.9 2.6 54.4 10.6 3.5 2.9 1.9 6.8 6.4 22.2 2.0 2.7 4.0 .. 8.4 7.7 5.7 2.4 7.4 3.5 2.1 2.2 2.4 2.0 2.7 1.8 –0.5 5.9 29.5 2.8 11.1 2.9 2.5 .. 2.1 1.9 17.3 7.5 6.2 3.6 .. 3.7 8.3 1.2 1.9 1.1 9.7 6.7 1.6 17.0 3.3 7.1 .. 1.5 18.9
.. .. .. .. 0.1 .. 1.1 0.3 .. .. 267.8 1.2 .. .. .. .. 204.9 85.7 .. .. .. .. 2.7 6.0 .. 7.0 .. 0.6 16.4 .. .. 14.1 .. 74.2 .. 8.2 1.1 .. .. 6.1 .. .. 8.1 .. 1.0 .. .. .. .. 0.4 .. 3.6 .. .. .. ..
.. 4.8 3.5 .. 18.0 0.9 3.5 2.4 .. .. 26.6 2.3 .. .. .. .. 11.6 5.5 .. .. .. .. 1.2 4.4 .. 5.8 .. 0.5 5.4 .. .. 12.2 .. 2.5 .. 2.3 2.3 .. 8.7 9.5 4.2 .. 2.7 .. 1.9 1.9 .. .. 6.6 2.6 .. 4.0 .. .. .. ..
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
Official exchange rate
Purchasing power parity (PPP) conversion factor
local currency units to $ 2007 2008a
local currency units to international $ 1995 2007
18.90 183.63 41.35 9,141.00 9,281.15 1,254.57 0.73 4.11 0.73 68.95 117.75 0.71 122.55 67.32 .. 929.26 0.28 37.32 9,603.16 0.51 1,507.50 7.05 61.27 1.26 2.52 44.73 1,873.88 139.96 3.44 479.27 258.59 31.31 10.93 12.14 1,170.97 8.19 25.84 5.56 7.05 66.42 0.73 1.36 18.45 479.27 125.81 5.86 0.39 60.74 1.00 2.97 5,032.72 3.13 46.15 2.77 0.73 1.00
18.90 3.0 196.78 61.6 48.64 11.1 11,836.00 1,031.8 9,896.38 567.5 1,203.00 .. 0.73 0.8 3.87 3.1 0.73 0.8 73.88 16.8 91.32 174.3 0.71 0.4 120.58 17.5 78.04 15.8 .. .. 1,373.84 690.0 0.28 0.1 39.38 3.5 8,640.71 309.7 0.52 0.2 1,507.50 837.2 9.97 2.3 62.57 0.6 1.30 .. 2.57 1.2 45.73 17.9 1,841.20 287.6 140.60 3.9 3.55 1.4 481.53 226.8 235.99 62.4 32.10 10.5 13.37 2.9 10.40 1.2 1,228.87 158.7 8.10 4.9 24.13 3.9 5.65 40.2 9.97 2.5 78.07 15.4 0.73 0.9 1.80 1.5 19.81 3.5 481.53 209.8 117.73 15.5 7.01 9.2 0.39 0.2 79.09 10.1 1.00 0.5 2.66 0.7 4,837.00 949.3 3.11 1.2 48.09 14.1 2.97 1.2 0.73 0.6 1.00 ..
8.6 134.8 15.3 4,724.6 3,412.4 558.7 1.0 3.6 0.9 47.8 120.1 0.4 76.4 31.3 .. 749.9 0.2 13.3 3,096.8 0.4 886.4 3.6 33.5 0.8 1.6 18.5 754.2 47.1 1.8 246.2 118.0 15.4 7.5 5.5 544.9 4.9 11.7 249.7 4.4 24.7 0.9 1.5 7.3 224.4 71.4 9.0 0.2 21.5 0.5 1.4 2,267.1 1.5 22.2 1.9 0.7 ..
Ratio of PPP conversion factor to market exchange rate
2007
0.5 0.7 0.4 0.5 0.4 .. 1.3 0.9 1.2 0.7 1.0 0.6 0.6 0.4 .. 0.8 0.8 0.4 0.3 0.7 0.6 0.5 0.5 0.7 0.6 0.4 0.4 0.3 0.5 0.5 0.4 0.5 0.7 0.5 0.5 0.6 0.5 .. 0.7 0.4 1.2 1.2 0.4 0.5 0.6 1.5 0.6 0.4 0.5 0.5 0.5 0.5 0.5 0.7 0.9 ..
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 2007 1990–2000 2000–07 1990–2000 2000–07 1990–2000 2000–07
.. 142.5 .. .. 143.1 .. 133.1 79.4 112.3 .. 66.6 .. .. .. .. .. .. .. .. .. .. 128.8 .. .. .. 100.4 .. 65.0 102.1 .. .. .. .. 101.9 .. 92.6 .. .. .. .. 113.5 137.8 88.6 .. 130.6 112.2 .. 95.6 .. 96.5 97.6 .. 112.3 114.1 113.5 ..
19.8 19.6 8.1 15.8 27.7 .. 3.7 11.0 3.8 23.0 0.1 3.2 204.7 16.6 .. 5.7 1.5 110.6 27.0 48.0 17.9 7.7 51.8 .. 75.0 79.3 19.1 33.6 4.0 7.0 8.7 6.4 19.0 119.6 57.8 4.0 34.1 25.5 10.4 8.0 2.1 1.7 42.4 6.0 29.5 2.7 0.1 11.1 3.6 7.6 11.5 26.7 8.3 24.7 5.2 3.0
6.2 5.1 4.3 10.1 17.4 .. 2.9 1.1 2.6 11.2 –1.2 3.0 14.3 5.1 .. 1.7 8.7 6.3 9.6 6.9 2.1 5.4 10.1 21.0 2.9 2.8 11.6 21.2 3.8 3.6 11.3 5.4 8.6 11.5 14.1 1.3 7.9 21.1 4.8 5.6 2.2 2.7 7.7 2.0 17.9 4.2 5.7 6.7 1.7 7.2 10.8 3.6 5.1 2.4 3.0 ..
22.8 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 10.6 18.7 33.8 3.6 5.2 6.1 6.9 19.4 19.2 35.7 3.8 31.8 25.9 .. 8.7 2.4 1.7 .. 6.1 32.5 2.2 .. 9.7 1.1 9.3 13.1 27.3 7.7 25.3 4.5 ..
7.8 5.5 4.4 9.3 14.3 .. 3.5 1.5 2.3 10.7 –0.2 3.3 7.5 9.4 .. 3.1 2.3 4.6 9.5 5.1 .. 7.8 .. –3.0 1.6 1.9 10.8 13.2 2.1 1.7 7.5 5.9 4.5 11.0 6.8 1.8 11.8 23.6 4.7 5.0 2.0 2.6 7.5 1.8 13.5 1.6 1.3 6.0 1.5 6.1 8.7 2.0 5.2 2.3 2.9 ..
.. 16.8 7.4 15.4 28.4 .. 1.6 8.1 2.9 .. –0.9 .. 16.3 .. .. 3.7 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 5.6 19.8 .. ..
2009 World Development Indicators
.. 3.2 4.9 9.4 10.7 .. 0.3 4.6 2.7 .. 0.3 9.0 11.5 .. .. 2.1 2.5 7.9 .. 6.6 .. .. .. .. 4.3 0.7 .. .. 4.6 .. .. .. 6.2 .. .. .. .. .. .. .. 2.5 2.8 .. .. .. 6.2 .. 7.0 2.7 .. 11.8 2.3 8.2 2.7 2.6 ..
257
ECONOMY
4.14
Exchange rates and prices
4.14 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republicb Sloveniab Somalia South Africa Spainb Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe
Exchange rates and prices Official exchange rate
Purchasing power parity (PPP) conversion factor
local currency units to $ 2007 2008a
local currency units to international $ 1995 2007
2.44 2.90 0.1 25.58 28.13 1.5 546.96 551.35 129.3 3.75 3.75 1.8 479.27 481.53 252.0 58.45 64.71 2.6 2,985.19 2,990.63 387.6 1.51 1.48 1.3 24.69c 22.65c 13.0 0.73 0.73 0.4 .. .. .. 7.05 9.97 2.3 0.73 0.73 0.7 110.62 111.37 18.3 2.02 2.03 0.3 7.05 9.97 2.2 6.76 8.02 9.4 1.20 1.15 2.0 11.23 11.23 12.8 3.44 3.42 0.0 1,245.04 1,273.83 154.8 34.52 35.08 15.1 1.00 1.00 .. 479.27 481.53 238.6 6.33 6.26 2.9 1.28 1.34 0.5 1.30 1.54 0.0 .. .. .. 1,723.49 1,959.26 472.8 5.05 7.58 0.3 3.67 3.67 1.7 0.50 0.64 0.6 1.00 1.00 1.0 23.47 24.35 5.7 .. .. 11.2 2.15 2.15 0.1 16,105.13 16,600.00 3,170.2 .. .. 198.95 200.03 22.0 4,002.52 4,882.97 393.5 9,675.78 30,000.00 ..
1.5 15.8 216.5 2.6 258.5 31.0 1,251.2 1.1 17.1 0.6 .. 4.3 0.7 42.1 1.2 3.7 9.1 1.7 21.0 1.1 412.4 16.3 0.5 230.9 4.2 0.6 0.9 3,950.3 640.1 2.2 2.7 0.6 1.0 14.5 432.7 1.5 5,167.5 .. 85.7 2,808.8 ..
Ratio of PPP conversion factor to market exchange rate
2007
0.6 0.6 0.4 0.7 0.5 0.5 0.4 0.7 0.7 0.9 .. 0.6 1.0 0.4 0.6 0.5 1.4 1.4 0.4 0.3 0.3 0.5 0.5 0.5 0.7 0.5 0.7 0.4 0.4 0.4 0.7 1.3 1.0 0.6 0.3 0.7 0.3 .. 0.4 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 2007 1990–2000 2000–07 1990–2000 2000–07 1990–2000 2000–07
140.5 172.7 .. 78.5 .. .. 73.8 95.8 158.1 .. .. 94.9 117.3 .. .. .. 98.9 98.0 .. .. .. .. .. 113.5 115.5 82.2 .. .. 90.0 112.6 .. 107.9 88.8 79.6 .. 81.2 .. .. .. 151.5 ..
98.0 161.5 14.3 1.6 6.0 .. 31.9 1.3 11.1 29.3 .. 9.9 3.9 9.1 65.5 10.5 2.2 1.1 7.9 235.0 21.6 4.2 .. 7.0 5.4 4.4 74.7 408.0 12.0 271.0 2.2 2.9 2.0 31.1 245.8 45.3 15.2 5.7 22.4 52.1 26.7
18.0 16.7 9.7 8.1 2.2 18.8 8.8 0.9 4.0 4.4 .. 6.7 4.1 10.0 9.6 7.3 1.5 0.8 6.2 20.4 9.0 2.9 2.6 0.8 6.4 2.7 18.7 .. 4.8 14.0 7.7 2.7 2.6 9.4 26.5 26.8 6.6 3.4 13.5 18.3 232.0
100.5 99.1 16.2 1.0 5.4 50.2 29.3 1.7 8.4 12.0 .. 8.7 3.8 9.9 71.9 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
13.8 12.9 7.8 0.9 1.7 18.0 8.1 0.8 5.4 4.5 .. 4.9 3.2 9.9 7.8 6.5 1.4 0.9 5.1 12.7 4.9 2.7 4.4 2.2 5.5 3.0 20.6 .. 5.0 8.5 .. 2.8 2.7 9.9 .. 20.2 5.8 3.8 12.9 17.6 497.7
93.8 99.8 .. 1.3 .. .. .. –1.0 9.5 9.1 .. 7.7 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
17.5 16.9 .. 1.9 .. .. .. 3.4 5.2 4.0 .. 6.0 3.0 11.1 .. .. 2.6 0.8 2.2 .. .. 5.3 .. .. 2.0 3.7 8.7 .. .. 12.2 .. 1.3 4.2 15.3 .. 27.6 .. .. .. .. ..
Note: The differences in the growth rates of the GDP deflator and consumer and wholesale price indexes are due mainly to differences in data availability for each of the indexes during the period. a. Average for December or latest monthly data available. b. As members of the euro area, these countries share a single currency, the euro. c. Koruny.
258
2008 World Development Indicators
About the data
4.14
Definitions
In a market-based economy, household, producer,
for currencies of selected countries and the euro
• Official exchange rate is the exchange rate deter-
and government choices about resource allocation
area. For most high-income countries weights are
mined by national authorities or the rate determined
are influenced by relative prices, including the real
derived from industrial country trade in manufac-
in the legally sanctioned exchange market. It is cal-
exchange rate, real wages, real interest rates, and
tured goods. Data are compiled from the nominal
culated as an annual average based on monthly
other prices in the economy. Relative prices also
effective exchange rate index and a cost indicator
averages (local currency units relative to the U.S.
largely reflect these agents’ choices. Thus relative
of relative normalized unit labor costs in manufactur-
dollar). • Purchasing power parity (PPP) conversion
prices convey vital information about the interaction
ing. For selected other countries the nominal effec-
factor is the number of units of a country’s currency
of economic agents in an economy and with the rest
tive exchange rate index is based on manufactured
required to buy the same amount of goods and ser-
of the world.
goods and primary products trade with partner or
vices in the domestic market that a U.S. dollar would
The exchange rate is the price of one currency
competitor countries. For these countries the real
buy in the United States. • Ratio of PPP conver-
in terms of another. Offi cial exchange rates and
effective exchange rate index is the nominal index
sion factor to market exchange rate is the result
exchange rate arrangements are established by
adjusted for relative changes in consumer prices; an
obtained by dividing the PPP conversion factor by the
governments. Other exchange rates recognized by
increase represents an appreciation of the local cur-
market exchange rate. • Real effective exchange
governments include market rates, which are deter-
rency. Because of conceptual and data limitations,
rate is the nominal effective exchange rate (a mea-
mined largely by legal market forces, and for coun-
changes in real effective exchange rates should be
sure of the value of a currency against a weighted
tries with multiple exchange arrangements, principal
interpreted with caution.
average of several foreign currencies) divided by
rates, secondary rates, and tertiary rates. (Also see
Inflation is measured by the rate of increase in a
a price deflator or index of costs. • GDP implicit
Statistical methods for alternative conversion factors
price index, but actual price change can be nega-
deflator measures the average annual rate of price
in the World Bank Atlas method of calculating gross
tive. The index used depends on the prices being
change in the economy as a whole for the periods
national income [GNI] per capita in U.S. dollars.)
examined. The GDP deflator reflects price changes
shown. • Consumer price index reflects changes
Official or market exchange rates are often used
for total GDP. The most general measure of the over-
in the cost to the average consumer of acquiring a
to convert economic statistics in local currencies to
all price level, it accounts for changes in government
basket of goods and services that may be fixed or
a common currency in order to make comparisons
consumption, capital formation (including inventory
may change at specified intervals, such as yearly.
across countries. Since market rates reflect at best
appreciation), international trade, and the main com-
The Laspeyres formula is generally used. • Whole-
the relative prices of tradable goods, the volume of
ponent, household final consumption expenditure.
sale price index refers to a mix of agricultural and
goods and services that a U.S. dollar buys in the
The GDP deflator is usually derived implicitly as the
industrial goods at various stages of production and
United States may not correspond to what a U.S.
ratio of current to constant price GDP—or a Paasche
distribution, including import duties. The Laspeyres
dollar converted to another country’s currency at
index. It is defective as a general measure of inflation
formula is generally used.
the official exchange rate would buy in that country,
for policy use because of long lags in deriving esti-
particularly when nontradable goods and services
mates and because it is often an annual measure.
account for a significant share of a country’s output.
Consumer price indexes are produced more fre-
An alternative exchange rate—the purchasing power
quently and so are more current. They are also con-
parity (PPP) conversion factor—is preferred because
structed explicitly, based on surveys of the cost of
it reflects differences in price levels for both tradable
a defined basket of consumer goods and services.
and nontradable goods and services and therefore
Nevertheless, consumer price indexes should be
provides a more meaningful comparison of real out-
interpreted with caution. The definition of a house-
put. See table 1.1 for further discussion.
hold, the basket of goods, and the geographic (urban
The ratio of the PPP conversion factor to the official
or rural) and income group coverage of consumer
exchange rate—the national price level or compara-
price surveys can vary widely by country. In addi-
tive price level—measures differences in the price
tion, weights are derived from household expendi-
level at the gross domestic product (GDP) level. The
ture surveys, which, for budgetary reasons, tend to
price level index tends to be lower in poorer coun-
be conducted infrequently in developing countries,
tries and to rise with income. The market exchange
impairing comparability over time. Although useful for
rate (or alternative conversion factor) is the official
measuring consumer price inflation within a country,
exchange rate adjusted for some countries by World
consumer price indexes are of less value in compar-
Bank staff to reflect actual price changes. National
ing countries.
price levels vary systematically, rising with GNI per
Wholesale price indexes are based on the prices at
capita. The real effective exchange rate is a nominal
the first commercial transaction of commodities that
effective exchange rate index adjusted for relative
are important in a country’s output or consumption.
Data on official and real effective exchange rates
movements in national price or cost indicators of
Prices are farm-gate for agricultural commodities and
and consumer and wholesale price indexes are
the home country, selected countries, and the euro
ex-factory for industrial goods. Preference is given to
from the International Monetary Fund’s Interna-
area. A nominal effective exchange rate index is the
indexes with the broadest coverage of the economy.
tional Financial Statistics. PPP conversion factors
ratio (expressed on the base 2000 = 100) of an
The least-squares method is used to calculate
and GDP deflators are from the World Bank’s data
index of a currency’s period-average exchange rate
growth rates of the GDP implicit deflator, consumer
to a weighted geometric average of exchange rates
price index, and wholesale price index.
Data sources
files.
2008 World Development Indicators
259
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 1995 2007
$ millions 1995 2007
$ millions 1995 2007
$ millions 1995 2007
$ millions Exports
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 Cote 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
260
Imports
1995
2007
1995
.. 304 .. 3,836 24,987 300 69,710 89,906 785 4,431 5,269 190,686b 614 1,234 .. 2,421 52,641 6,776 272 129 969 2,040 219,501 179 190 19,358 147,240 .. 12,294 .. 1,374 4,451 4,337 6,972 .. 28,202 65,655 5,731 5,196 13,260 2,040 135 2,573 768 47,973 362,717 2,945 177 575 600,347 1,582 15,523 2,823 700 30 192 128,369
.. 2,201 .. 44,707 66,085 1,777 182,872 217,883 22,517 14,091 27,583 402,821 953 4,959 5,608 6,092 184,544 24,911 .. 84 5,637 4,952 495,016 .. .. 76,429 1,342,206 429,542 34,213 .. 6,127 12,895 9,419 25,148 .. 139,971 162,058 11,972 16,038 44,398 5,527 .. 15,471 2,658 110,338 691,775 5,610 233 3,182 1,571,251 6,004 67,071 8,721 1,252 .. 729 277,811
.. 836 .. 3,519 26,066 726 74,841 92,055 1,290 7,589 5,752 178,798b 895 1,574 .. 2,050 63,293 6,502 483 259 1,375 1,608 200,991 244 411 18,301 135,282 .. 16,012 .. 1,346 4,717 3,806 9,106 .. 30,044 57,860 6,137 5,708 17,140 3,623 498 2,860 1,446 37,705 333,746 1,723 232 1,413 586,662 2,120 24,711 3,728 1,011 89 802 124,171
2009 World Development Indicators
2007
.. 4,292 .. 26,305 53,353 3,589 199,457 199,334 9,424 19,553 30,421 394,601 1,398 4,078 10,514 4,417 157,871 33,467 .. 435 6,327 5,531 468,502 .. .. 53,938 1,034,729 406,913 37,416 .. 6,386 14,092 8,376 29,481 .. 131,275 154,725 15,370 15,638 53,697 9,842 .. 17,828 6,920 99,954 731,671 2,400 322 5,917 1,334,445 10,060 101,311 14,511 1,514 .. 2,321 251,157
.. .. 44 217 .. .. –767 –8,778 –4,636 –5,929 40 278 –14,036 –40,817 –1,597 –5,166 –6 –5,079 68 –968 –51 –411 6,808b 5,816 –8 –30 –207 –173 .. 400 –32 –346 –11,105 –29,242 –432 –624 –29 .. –13 –6 –57 –360 –412 –385 –22,721 –12,988 –23 .. –7 .. –2,714 –18,265 –11,774 25,688 .. 5,693 –1,596 –7,894 .. .. –695 –1,885 –226 –850 –787 –810 –53 –1,545 .. .. –104 –11,041 –4,549 176 –769 –2,079 –930 –2,048 –405 1,388 –67 –579 8 .. 3 –1,574 –19 40 –4,440 1,560 –8,964 39,305 –665 –958 –5 –40 127 39 –2,814 57,894 –129 –139 –1,684 –12,469 –159 –770 –85 –63 –21 .. –31 7 4,188 10,132
.. .. .. 477 1,043 –12 .. .. .. 156 –222 –295 597 318 –5,118 168 945 –218 –109 –280 –19,277 –1,702 –1,352 –5,448 111 1,005 –401 2,265 7,287 –824 76 189 –458 –4,463b –6,819 14,232b 121 259 –167 244 1,091 –303 .. 2,576 .. –39 1,105 300 3,621 4,029 –18,136 132 464 –26 255 .. 15 153 241 10 277 544 –186 69 416 90 –117 –888 –4,328 63 .. –25 191 .. –38 307 2,974 –1,350 1,435 38,668 1,618 .. –2,576 .. 799 5,231 –4,516 .. .. .. 42 –38 –625 134 470 –358 –237 –379 –492 802 1,431 –1,385 .. .. .. 572 –887 –1,374 –1,391 –5,130 1,855 992 3,409 –183 442 3,246 –1,000 4,031 8,322 –254 1,389 3,776 –262 324 .. –32 126 159 –158 736 3,395 39 –597 –1,897 5,231 –9,167 –30,658 10,840 –42 –269 515 52 76 –8 197 689 –514 –38,768 –41,771 –27,897 523 2,043 –144 8,008 2,122 –2,864 491 4,863 –572 179 –131 –216 46 .. –35 553 1,505 –87 –2,912 –3,807 5,474
.. .. .. –831 265 2,162 .. 4,164 114,972 9,402 213 11,197 7,122 15,979 46,149 –590 111 1,659 –57,682 14,952 26,908 12,031 23,369 18,194 9,019 121 4,273 857 2,376 5,277 –3,060 377 4,179 7,216 24,120 16,485 –217 198 1,209 1,800 1,005 5,314 –1,930 80 4,525 2,434 4,695 9,790 1,460 51,477 180,334 –8,716 1,635 17,545 .. 347 1,029 –116 216 177 –506 192 2,140 –547 15 2,932 12,639 16,369 41,082 .. 238 92 .. 147 964 7,200 14,860 16,843 371,833 80,288 1,546,365 25,746 55,424 152,693 –5,866 8,452 20,951 .. 157 181 –2,181 64 2,184 –1,578 1,060 4,115 –146 529 2,519 –4,447 1,896 13,675 .. .. .. –3,232 14,613 34,907 2,379 11,652 34,318 –2,068 373 2,562 1,598 1,788 3,521 412 17,122 32,214 –1,119 940 2,304 .. 40 25 –3,772 583 3,269 –827 815 833 10,048 10,657 8,380 –31,249 58,510 115,487 1,983 153 1,238 –53 106 143 –2,006 199 1,361 252,929 121,816 135,932 –2,151 804 2,269 –44,587 16,119 3,648 –1,697 783 4,315 –456 87 97 .. 20 113 –80 199 453 32,979 95,559 281,658
Goods and services
4.15
Net income
Net current transfers
Current account balance
Total reservesa
$ millions 1995 2007
$ millions 1995 2007
$ millions 1995 2007
$ millions 1995 2007
$ millions Exports
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
Imports
1995
2007
1995
2007
1,635 19,765 38,013 52,923 18,953 .. 49,439 27,478 295,618 3,394 493,991 3,479 5,975 3,526 .. 147,761 14,215 448 408 2,088 .. 199 .. 7,513 3,191 1,302 749 470 83,369 529 504 2,349 89,321 884 508 9,044 411 1,307 1,734 1,029 241,517 17,882 662 321 12,342 56,058 6,078 10,214 7,610 2,992 4,802 6,622 26,795 35,716 32,260 ..
6,344 111,005 198,971 130,492 .. 30,887 204,512 70,901 614,384 4,928 807,207 9,110 51,906 6,822 .. 442,016 73,317 2,021 1,201 11,898 16,603 881 542 47,069 21,187 2,998 1,332 .. 204,674 1,864 .. 4,436 289,612 2,018 2,031 27,311 2,871 4,870 3,521 1,436 558,952 36,591 2,685 599 66,768 177,889 25,886 21,879 14,263 3,580 6,317 31,298 57,960 173,399 74,902 ..
1,852 19,916 48,225 54,461 15,113 .. 42,169 35,287 250,319 3,729 419,556 4,903 6,102 5,922 .. 155,104 12,615 726 748 2,193 .. 1,046 .. 5,755 3,902 1,773 987 660 86,851 991 510 2,454 82,168 1,006 521 11,243 1,055 2,020 2,100 1,624 216,558 17,248 1,150 457 12,841 46,848 5,035 14,185 7,768 1,905 5,200 9,597 33,317 33,825 39,545 ..
9,594 107,499 230,232 109,571 .. 24,198 178,698 73,631 619,592 8,050 723,705 15,500 44,887 9,840 .. 433,181 33,707 3,218 1,141 17,828 21,912 1,715 1,742 20,368 26,429 4,258 2,042 .. 167,061 2,150 .. 5,202 305,775 4,306 1,880 34,732 3,667 2,890 3,615 3,655 491,853 38,111 4,673 1,077 46,066 116,784 19,339 37,525 14,627 2,692 6,471 23,870 65,094 184,234 89,803 ..
–226 –1,701 –3,734 –5,874 –478 .. –7,325 –2,654 –15,644 –371 44,285 –279 –146 –219 .. –1,303 4,881 –35 –6 19 .. 314 .. 133 –13 –30 –167 –44 –4,144 –41 –48 –19 –12,689 –18 –25 –1,318 –140 –110 139 9 7,247 –3,957 –372 –47 –2,878 –1,919 –374 –1,939 –466 –488 110 –2,482 3,662 –1,995 21 ..
–598 –10,702 –4,264 –15,524 .. –3,546 –36,851 –25 –26,918 –662 138,502 835 –12,193 –192 .. 769 12,937 –52 –50 –937 377 420 –150 1,971 –1,614 –3 –80 .. –3,995 –269 .. 239 –13,684 414 –145 –405 –592 –1,290 –158 137 4,813 –9,164 –135 1 –16,746 2,001 –959 –3,735 –1,311 –538 –93 –8,418 –542 –16,253 –10,135 ..
243 2,622 –201 203 452 –1,650 8,382 26,109 –5,563 981 4,968 –6,431 –4 .. 3,358 .. –462 .. 1,776 –1,658 1,721 5,673 7,278 –4,790 –4,579 –18,905 25,076 607 2,040 –99 –7,676 –11,514 111,044 1,444 2,779 –259 59 –2,160 –213 1,037 2,108 –1,578 .. .. .. –19 –3,649 –8,665 –1,465 –5,076 5,016 79 1,020 –235 110 98 –237 71 382 –16 .. 2,886 .. 210 625 –323 .. 1,139 .. –220 –219 1,672 109 1,163 –614 213 1,239 –288 129 236 –276 157 .. –78 –1,017 –4,687 –8,644 219 325 –284 76 .. 22 101 119 –22 3,960 24,323 –1,576 56 1,179 –85 77 215 39 2,330 7,703 –1,186 339 592 –445 562 123 –261 403 1,000 176 230 2,088 –356 –6,434 –12,326 25,773 255 449 –3,068 138 1,075 –722 31 163 –152 799 18,016 –2,578 –2,059 –2,647 5,233 –1,469 –3,670 –801 2,562 11,086 –3,349 153 253 –471 75 291 674 195 373 –92 832 2,495 –4,625 880 13,977 –1,980 958 8,493 854 7,132 3,618 –132 .. .. ..
–1,225 270 2,546 –6,743 12,017 24,052 –9,415 22,865 276,578 10,365 14,908 56,936 .. .. .. 2,681 8,347 19,655 –12,695 8,770 925 4,523 8,123 28,519 –51,032 60,690 94,109 –1,744 681 1,879 210,490 192,620 973,297 –2,776 2,279 7,925 –7,333 1,660 17,641 –1,102 384 3,355 .. .. .. 5,954 32,804 262,533 47,471 4,543 18,776 –228 134 1,177 107 99 708 –6,485 602 5,761 –2,046 8,100 20,599 212 457 658 –211 28 119 28,454 7,415 83,260 –5,692 829 7,721 –24 275 2,264 –554 109 847 .. 115 227 28,931 24,699 101,995 –231 323 1,087 .. 90 207 –408 887 1,832 –5,525 17,046 87,208 –695 257 1,334 222 158 1,396 –122 3,874 24,714 –795 195 1,524 813 651 1,383 747 221 896 6 646 1,565 59,586 47,162 26,928 –10,235 4,410 17,247 –1,048 142 1,103 –314 95 593 21,972 1,709 51,907 60,459 22,976 60,840 1,918 1,943 9,524 –8,295 2,528 15,798 –1,422 781 1,935 640 267 2,106 126 1,106 2,462 1,505 8,653 27,786 6,301 7,781 33,740 –18,595 14,957 65,725 –21,418 22,063 11,512 .. .. ..
2009 World Development Indicators
261
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 1995 2007
$ millions 1995 2007
$ millions 1995 2007
$ millions 1995 2007
$ millions Exports 1995
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Imports 2007
9,404 50,794 92,987 393,817 75 363 53,450 242,046 1,506 2,398 .. 11,945 128 335 157,658 372,964 10,969 64,869 10,377 32,772 .. .. 34,402 89,746 133,910 385,984 4,617 9,452 681 9,264 1,020 2,199 95,525 234,049 123,320 266,864 5,757 13,169 .. 1,706 1,265 3,941 70,292 180,382 .. .. 465 970 2,799 10,569 7,979 20,057 36,581 144,209 1,774 .. 664 2,185 17,090 64,001 .. .. 322,114 723,781 794,397 1,645,726 3,507 6,825 .. .. 20,753 70,838 9,498 54,591 .. .. 2,160 7,774 1,222 4,872 2,344 .. 6,392,988 t 17,070,490 t 72,352 270,815 1,087,015 4,680,736 512,935 2,687,262 572,897 2,040,686 1,159,335 4,959,944 397,583 1,997,264 238,763 1,079,371 272,861 863,832 .. .. 58,893 244,054 89,262 322,767 5,228,660 12,191,150 2,097,739 5,031,654
1995
11,306 82,809 374 34,286 1,821 .. 260 144,520 10,658 10,749 .. 33,375 135,000 5,982 1,238 1,274 81,142 108,916 5,541 .. 2,139 82,246 .. 671 2,110 8,811 40,113 1,796 1,490 18,280 .. 327,000 890,784 3,568 .. 16,905 12,334 .. 2,471 1,338 2,515 6,233,515 t 99,243 1,127,240 543,345 584,277 1,223,867 413,802 248,679 288,143 103,655 78,652 99,763 5,008,580 1,974,212
2007
74,858 282,673 909 113,396 4,032 21,432 490 326,455 65,245 33,663 .. 98,502 479,096 12,773 10,661 2,523 200,184 221,114 11,879 3,707 6,334 162,904 .. 1,517 6,265 20,826 176,999 .. 4,161 72,153 .. 824,103 2,345,982 6,803 .. 52,987 65,845 .. 9,337 4,524 .. 16,620,157 t 308,995 4,202,021 2,265,062 1,943,357 4,509,716 1,634,141 1,099,718 827,588 303,535 300,537 301,623 12,186,397 4,839,351
a. International reserves including gold valued at London gold price. b. Includes Luxembourg.
262
2008 World Development Indicators
–241 –5,788 369 6,716 –1,774 –23,136 2,624 39,974 –3,372 –31,396 157 –3,506 6,963 76,241 18,024 477,950 7 –15 350 413 57 –147 99 553 2,800 238 –27,282 –33,808 –5,318 95,080 10,399 37,592 –124 –63 195 837 –244 –861 272 1,660 .. –829 .. 3,970 .. –6,346 .. 14,214 –30 –104 43 78 –118 –181 35 217 2,130 –5,752 –894 –1,696 14,373 39,062 68,816162,957 –14 –3,288 93 –438 390 –4,103 3,863 18,973 201 –1,000 95 –402 –75 –2,293 1,821 1,066 .. .. .. .. .. .. .. .. –2,875 –8,923 –645 –2,953 –2,493 –20,631 4,464 32,919 –5,402 –43,243 4,525 –9,000 –1,967 –145,355 40,531 19,029 –137 –358 732 2,311 –770 –1,368 2,112 3,654 –3 –2,253 60 382 –500 –3,268 163 1,378 81 64 144 194 –30 –66 298 774 –6,473 9,534 –2,970 –4,983 4,940 38,416 25,870 31,033 10,708 7,600 –4,409 –9,405 20,703 43,946 68,620 75,172 –560 –935 607 565 263 920 .. .. .. –51 .. 1,557 .. –495 39 204 –110 –79 395 617 –590 –1,856 270 2,886 –2,114 –5,661 487 3,938 –13,582 15,755 36,939 87,472 .. .. .. .. .. .. .. 230 –34 –38 118 244 –122 –340 130 438 –390 –760 –4 50 294 3,594 379 6,745 –716 –1,754 774 1,619 –774 –904 1,689 8,032 –3,204 –7,143 4,398 2,236 –2,338 –37,697 13,891 76,496 17 .. 5 .. 0 .. 1,168 .. –96 –279 639 1,509 –281 –745 459 2,560 –434 –659 472 3,539 –1,152 –5,272 1,069 32,484 .. .. .. .. .. .. 7,778 77,239 3,393 48,667 –11,943 –27,111 –13,436 –78,765 49,144 57,275 20,899 81,751 –38,073 –112,705 –113,561 –731,209 175,996277,549 –227 –342 76 134 –213 –186 1,813 4,121 .. .. .. .. .. .. .. .. –1,943 2,565 109 –415 2,014 20,001 10,715 33,759 –384 –2,168 1,200 6,430 –2,020 –6,992 1,324 23,479 .. .. .. .. .. .. .. .. –561 –1,152 1,056 1,387 184 –1,328 638 7,757 –249 –1,383 182 530 –182 –505 223 1,090 –294 .. 40 .. –425 .. 888 ..
About the data
4.15
Definitions
The balance of payments records an economy’s trans-
system, external debt records, information provided
• Exports and imports of goods and services are all
actions with the rest of the world. Balance of payments
by enterprises, surveys to estimate service transac-
transactions between residents of an economy and
accounts are divided into two groups: the current
tions, and foreign exchange records. Differences in
the rest of the world involving a change in ownership
account, which records transactions in goods, ser-
collection methods—such as in timing, definitions
of general merchandise, goods sent for processing
vices, income, and current transfers, and the capital
of residence and ownership, and the exchange rate
and repairs, nonmonetary gold, and services. • Net
and financial account, which records capital transfers,
used to value transactions—contribute to net errors
income is receipts and payments of employee com-
acquisition or disposal of nonproduced, nonfinancial
and omissions. In addition, smuggling and other ille-
pensation for nonresident workers, and investment
assets, and transactions in financial assets and liabili-
gal or quasi-legal transactions may be unrecorded or
income (receipts and payments on direct investment,
ties. The table presents data from the current account
misrecorded. For further discussion of issues relat-
portfolio investment, and other investments and
plus gross international reserves.
ing to the recording of data on trade in goods and
receipts on reserve assets). Income derived from
services, see About the data for tables 4.4–4.7.
the use of intangible assets is recorded under busi-
The balance of payments is a double-entry accounting system that shows all flows of goods and services
The concepts and definitions underlying the data in
ness services. • Net current transfers are recorded
into and out of an economy; all transfers that are the
the table are based on the fifth edition of the Inter-
in the balance of payments whenever an economy
counterpart of real resources or financial claims pro-
national Monetary Fund’s (IMF) Balance of Payments
provides or receives goods, services, income, or
vided to or by the rest of the world without a quid pro
Manual (1993). That edition redefined as capital trans-
financial items without a quid pro quo. All transfers
quo, such as donations and grants; and all changes
fers some transactions previously included in the cur-
not considered to be capital are current. • Current
in residents’ claims on and liabilities to nonresidents
rent account, such as debt forgiveness, migrants’ cap-
account balance is the sum of net exports of goods
that arise from economic transactions. All transac-
ital transfers, and foreign aid to acquire capital goods.
and services, net income, and net current transfers.
tions are recorded twice—once as a credit and once
Thus the current account balance now reflects more
• Total reserves are holdings of monetary gold,
as a debit. In principle the net balance should be
accurately net current transfer receipts in addition to
special drawing rights, reserves of IMF members
zero, but in practice the accounts often do not bal-
transactions in goods, services (previously nonfac-
held by the IMF, and holdings of foreign exchange
ance, requiring inclusion of a balancing item, net
tor services), and income (previously factor income).
under the control of monetary authorities. The gold
errors and omissions.
Many countries maintain their data collection systems
component of these reserves is valued at year-end
Discrepancies may arise in the balance of pay-
according to the fourth edition of the Balance of Pay-
(December 31) London prices ($386.75 an ounce in
ments because there is no single source for balance
ments Manual (1977). Where necessary, the IMF con-
1995 and $696.70 an ounce in 2007).
of payments data and therefore no way to ensure
verts such reported data to conform to the fifth edition
that the data are fully consistent. Sources include
(see Primary data documentation). Values are in U.S.
customs data, monetary accounts of the banking
dollars converted at market exchange rates.
4.15a
Top 15 economies with the largest reserves in 2007 Total reserves ($ billions) 2006
2007
Share of world total (%) 2007
China
1,081
1,546
21.8
43.1
17.0
Japan
895
973
13.7
8.7
14.9
Russian Federation
304
478
6.8
57.3
15.9
Taiwan, China
275
282
4.0
2.5
12.8
United States
221
278
3.9
25.5
1.1 8.8a
Annual change (%) 2006–07
Months of imports 2007
Data sources Data on the balance of payments are published in the IMF’s Balance of Payments Statistics Yearbook
India
178
277
3.9
55.3
Korea, Rep.
239
263
3.7
9.8
7.0
86
180
2.5
110.1
10.9
Singapore
136
163
2.3
19.6
5.2
cover a longer period than the published sources.
Hong Kong, China
133
153
2.2
14.6
3.6
More information about the design and compila-
Germany
112
136
1.9
21.8
1.0
tion of the balance of payments can be found in
France
98
115
1.6
17.6
1.5
the IMF’s Balance of Payments Manual, fifth edition
Algeria
81
115
1.6
41.1
..
(1993), Balance of Payments Textbook (1996), and
Malaysia
83
102
1.4
23.1
6.7
Balance of Payments Compilation Guide (1995).
Italy
76
94
1.3
24.2
1.5
The IMF’s International Financial Statistics and
Brazil
and International Financial Statistics. The World Bank exchanges data with the IMF through electronic files that in most cases are more timely and
Balance of Payments databases are available on a. Data are for 2006. Source: International Monetary Fund, International Financial Statistics data files.
CD-ROM.
2006 World Development Indicators
263
ECONOMY
Balance of payments current account
STATES AND MARKETS
Introduction
I
nformation and communication technology for development
Rapid advances in information and communication technology (ICT) have connected people, businesses, and governments around the world, enabling knowledge sharing across cultures and countries. ICTs used in e-government projects can reduce corruption, and some ICTs, such as broadband, can contribute to economic growth (box 5a). Good government policies and regulations are creating competitive ICT markets, increasing access to ICT services for people everywhere. Recognizing the need to analyze ICT’s impact on development, many statistical offi ces in developing countries are beginning to conduct household and business surveys to improve their ICT policy and analysis. Information on ICT infrastructure, access, use, quality, affordability, applications, and trade are included in tables 5.10 and 5.11. The well known success of mobile telephony worldwide has been achieved through high demand, low-cost technologies, and market liberalization. Research on the diffusion of advanced telecommunications services in developing economies finds that the rate of adoption depends on an appropriate business environment—which depends in turn on the regulatory and policy environment. Many countries that have created a competitive market environment for ICTs have more people using ICT services. Competition lowers prices for ICT services and expands markets. Prices for ICT services, such as mobile cellular phone tariffs, have been falling rapidly. But the services are still unaffordable for many people in low-income economies, leaving them yet to realize the potential of ICT for economic and social development. ICT services range from telecommunication infrastructure (voice, data, and media services) to information applications tailored to Improving governance and contributing to growth 5a specifi c sectors and functions (such E-government projects increase revenues and improve governance as services in banking and fi nance, Successful e-government projects have reduced transaction costs land management, education, and and processing time and increased government revenue. The e-Customs System in Ghana (GCNet) increased revenues 49 perhealth), to electronic government cent in the first 18 months of operation and reduced clearance (e-government), and to the production times to two days from three weeks. And a land registration system has cut bribes $18.3 million a year in the Indian state of Karnataka, of equipment. In developing economies innovative use of ICT services is changing people’s lives and providing new opportunities. For example, banking services and job search text messaging services can be delivered through mobile phones and portable devices. Farmers and fishers also use these technologies to track prices and market demand.
where an overwhelming proportion of supervisors now sense that the abuse of discretionary power in providing services to citizens has narrowed. Broadband increases productivity and contributes to growth
Although the benefits of broadband are not yet available to most people in developing economies, access to information and communication technology, especially broadband, supports the growth of firms by lowering costs and raising productivity. Analysis of broadband access in developed economies suggests a robust and noticeable growth dividend. For developing economies the growth benefit of broadband is about the same as that for developed economies— about a 1.4 percentage point increase in per capita GDP for each 10 percent increase in broadband coverage. Source: World Bank forthcoming.
2009 World Development Indicators
265
Mobile phones have captured the market in developing economies At the end of 2007 there were about 1.1 billion fixed telephone lines and 3.3 billion mobile phone subscribers worldwide. Developing economies increased their share of mobile phone subscribers from about 30 percent of the world total in 2000 to more than 50 percent in 2004 and to about 70 percent in 2007 (figure 5b). But access to mobile phones is still low in many countries, including Burundi, Central African Republic, Eritrea, Ethiopia, and Papua New Guinea, with fewer than 5 mobile phone subscribers per 100 people. Mobile phone service in developing economies has overtaken fixed-line service. Wireless technology can be deployed more quickly than fixed-line telephone systems and requires less upfront investment in infrastructure. This translates to lower prices and stronger customer demand. Liberalization started later for fixed-line markets (previously dominated by state-owned monopolies), while mobile phone markets were generally opened to one or more new entrants from the start. And even where the opening of mobile phone markets was delayed, once they were opened competition often led to an immediate growth in mobile subscribers (figure 5c). Countries that have taken decisive steps to establish independent regulators and foster competition have seen greater improvements in sector performance. In some cases the announcement of Seventy percent of mobile phone subscribers are in developing economies, 2000–07
a plan to issue a new license has been enough to trigger growth, encouraging the existing mobile phone operator to improve service, reduce prices, and increase market penetration before the new entrant starts operations. The demand for always-on, high-capacity Internet services is increasing. Advanced Internet service—beyond what can be achieved through dial-up connections—has become more important as the demand for data and value-added services grows. Broadband allows for large volumes of data to be transmitted and for cheaper voice communications (say, by routing calls over the Internet). Broadband also enables voice, data, and media services to be transmitted over the same network. This convergence of services can be very good for economic and social development—increasing productivity, lowering transaction costs, facilitating trade, and boosting retail sales and tax revenues. Where broadband has been introduced in rural areas of developing economies, villagers and farmers have gained better access to training, job opportunities, and market prices of crops. But in 2007 broadband reached just 2 percent of the population on average in developing economies, concentrated in urban areas. Why? Because of the relatively high cost of computers and, in rural areas, the limited access to electricity. Internet use (narrowband and broadband) in developing economies is only about one-fifth that in developed economies (figure 5d). Competition can spur growth in mobile phone service
5b
Mobile cellular phone subscribers per 100 people 60
Developing economies Developed economies
Mobile cellular phone subscribers (billions) 4
5c
Tunisia
45
Belarus
3 Honduras
30
Tonga
2 15
Afghanistan
1
Kenya
0 0
–3 2000
2001
2002
2003
2004
2005
2006
–2
–1
0
1
2
3
2007
Source: International Telecommunication Union, World Telecommunication/ICT Indicators database.
Note: Year 0 is the year of entry of a second mobile operator. Source: International Telecommunication Union, World Telecommunication/ICT Indicators database.
Internet use in developing economies is growing, but still lags behind use in developed economies
5d
Internet users per 100 people 80
Developing economies
Developed economies
2004
2006
60
40
20
0 1995
1996
1997
1998
1999
2000
2001
Source: International Telecommunication Union, World Telecommunication/ICT Indicators database.
266
2009 World Development Indicators
2002
2003
2005
2007
Broadband access is also limited in rural areas of some developed economies, such as the United States, where there is a renewed commitment to improve access to rural areas as part of an economic stimulus package. The goal is to create jobs, bring broadband to every community, and improve the U.S. ranking for per capita broadband access (with about 24 broadband subscribers per 100 people in 2007, the U.S. is below the top tier of developed economies; figure 5e). Although the capacity of broadband service is measured by the advertised speed available to consumers, speed may be constrained by bandwidth availability (effective rate of data transfer), which is increasing faster in developed economies, with their robust infrastructure, than in developing economies. In high-income economies, average per capita international bandwidth increased from 586 bits per second (bps) in 2000 to 18,240 bps in 2007. Among developing regions Europe and Central Asia and Latin America and the Caribbean have the greatest capacity. Over 2000–07 bandwidth per capita increased from 12 bps to 1,114 bps in Europe and Central Asia and from 8 bps to 1,126 bps in Latin America and the Caribbean. With improved fiber-optic connectivity some countries in South Asia are seeing a rise in international bandwidth, yet South Asia and Sub- Saharan Africa are still well behind other regions in international bandwidth per capita (figure 5f). Broadband access in developed and developing economies
5e
To unleash ICT’s potential impact on growth, services must be affordable to more people The price of ICT access continues to fall with technological advances, market growth, and greater competition, a trend that is especially important in allowing people in developing economies to take full advantage of ICT services. In recent years steep price reductions have contributed to the rapid expansion of mobile phone use in many economies (figures 5b and 5g). Prepaid services allow mobile customers to pay in small amounts instead of committing to fixed monthly subscriptions. Prepaid cards give even low-income consumers access to mobile communications, increasing penetration in poor and rural areas. Pricing for Internet access has also been falling in many countries, including many Sub-Saharan countries (figure 5h). Still, the average price in Sub-Saharan Africa as a whole continues to be well above the world average and, as measured against income, is not affordable for most people. In 2006 the Internet access tariff for Sub-Saharan Africa was about 62 percent of average monthly per capita income, far more than the roughly 12 percent in South Asia, and less than the 9 percent average for all other developing regions. In high-income economies Internet service costs less than 1 percent of the average monthly income. Prices for mobile phone services have declined in many countries
5g
Mobile cellular tariff, average annual change, 2004–06 (%) Subscribers per 100 people 40 Developed economies
0 –10 –20
30
–30 20
–40
Developing economies
–50 Benin
10 0 Liechtenstein
Sweden
Denmark
Netherlands
Monaco
Lithuania Hungary Romania
Poland
Slovak Republic
Source: International Telecommunication Union, World Telecommunication/ICT Indicators database.
International bandwidth has increased rapidly in Europe and Central Asia and Latin America and the Caribbean Bits per second per capita, by region 1,200
5f
Latin America & Caribbean
China
Armenia
Chile
Moldova
Chad Uzbekistan
Sri Lanka
Note: Tariff is based on the prepaid price for 25 calls per month spread over the same mobile network, other mobile networks, and mobile to fi xed calls and during peak, off-peak, and weekend times (Organisation for Economic Co-operation and Development low user definition). It also includes 30 text messages per month. Countries that have experienced significant reductions in mobile phone service prices do not necessarily have the lowest prices. Source: International Telecommunication Union, World Telecommunication/ICT Indicators database.
Internet service prices have fallen in some Sub-Saharan African countries, 2005–07 Internet access tariff ($ per month) 100
5h 2005
2006
2007
75
900 Europe & Central Asia
World average, 2006 ($22)
50
600 25 Middle East & North Africa Sub-Saharan Africa
300
East Asia & Pacific South Asia
0 2000
2001
2002
2003
2004
2005
2006
2007
Source: International Telecommunication Union, World Telecommunication/ICT Indicators database and World Development Indicators data files.
0 Sub-Saharan Tanzania Africa average
Burkina Faso
South Africa
Mauritania
Nigeria
Ethiopia
Note: Tariff is based on the cheapest available tariff for accessing the Internet for 20 hours a month (10 hours peak and 10 hours off-peak). The basket does not include the telephone line rental but does include any usage charges. Source: International Telecommunication Union, World Telecommunication/ICT Indicators database.
2009 World Development Indicators
267
Developing economies benefit from ICT exports Although ICT exports do not necessarily reflect high rates of ICT use, they indicate the importance of a country’s ICT sector and its international competitiveness. As barriers to ICT trade are removed, opportunities for developing economies to benefit from such exports will likely grow. Some developing economies have already become key exporters of ICT goods. China leads in dollar values of ICT export goods in 2006, with $299 billion. For many countries in East Asia and Pacific ICT export goods make up a large share of total goods exports (figure 5i). The share is 56 percent for the Philippines, 46 percent for Singapore, 45 percent for Malaysia, 42 percent for Hong Kong, China, and 31 percent for China. Trade in ICT services includes communications services (telecommunications, business network services, teleconferencing, support services, and postal services) and computer and information services (databases, data processing, software design and development, maintenance and repair, and news agency services). India’s software exports jumped from about $1 billion in 1995 to $22 billion in 2006, generating employment for about 1.6 million people. India leads all other developing economies in exports of communication, computer, and information services as a share of total service exports, at 42 percent in 2006 (table 5j). East Asia & Pacific leads in share of information and communication technology goods exports
5i
ICT applications are transforming how information is shared and transactions are made Governments are becoming increasingly important users of ICT, particularly for e-government—using Internet technology as a platform for exchanging information, providing services, and transacting with citizens, businesses, and other arms of government. That makes them major actors in fostering ICT uptake and setting information technology standards. E-government initiatives can make public administration more efficient, improve delivery of public services, and increase government accountability and transparency. They can also reduce transaction costs and processing times and increase government revenues. Some e-government projects have also improved governance, so vital for development. A secure, reliable business-enabling environment is a key element of e-commerce. Privacy and security concerns about the transmission of personal or financial information over the Internet are major issues for both consumers and firms, perhaps explaining why they can be reluctant to use the Internet to make transactions. The number of secure servers indicates how many companies are conducting encrypted transactions over the Internet. Developing economies have only a fraction of the world’s secure servers—about 4 percent (figure 5k). Developing economies have only about 4 percent of the world’s secure servers, 2008
Information and communication technology goods exports as a share of total goods exports, by region (%) 40
5k
Developing economies 3.9%
East Asia & Pacific
30 20 High income Latin America & Caribbean High income 96.1%
10 Sub-Saharan Africa
South Asia
Europe & Central Asia
0 2000
2001
2002
2003
2004
2005
2006
Source: United Nations Statistics Division Commodity Trade (Comtrade) database.
Source: Netcraft (www.netcraft.com).
India leads developing economies in information and communications technology service export shares, 2007
5j
ICT service exports as a share of total service exports (%) Top five developing economies Country
Top five developed economies Country
Share (percent)
India
Share (percent)
41.6a
Kuwait
48.8
Niger
38.8a
Ireland
30.1
Guyana
21.5
Israel
28.5
Yemen, Rep.
18.9a
Canada
11.1
Romania
16.3
Finland
8.4
a. Data are for 2006. Source: International Monetary Fund, Balance of Payments Statistics Yearbook database.
268
2009 World Development Indicators
Progress in measuring ICT Improving ICT indicators to analyze the impact of ICT on development was highlighted at the World Summits on the Information Society, held in Geneva in 2003 and Tunis in 2005. Attending were 50 heads of state, prime ministers, and vice presidents—and 80 ministers and vice ministers from 180 countries. The challenge has been taken up by the Partnership on Measuring ICT for Development, with country statistical offices and ICT agencies (box 5l). The partnership was launched in 2004 with the following objectives to improve ICT measures: • Continue to raise awareness among policymakers about the importance of statistical indicators for monitoring ICT policies and carrying out impact analysis. • Expand the core list of indicators to other areas of interest such as ICT in education, government, and health, building on the original core ICT list of access and use by individuals, households and businesses, and production and trade in ICT goods and services. • Conduct regional workshops to exchange national experiences and discuss methodologies, definitions, survey vehicles, and data collection efforts. • Assist statistical agencies in developing economies in ICT data collection and dissemination, including national databases to store and analyze survey results. • Develop a global database of ICT indicators and make it available on the World Wide Web.
In May 2008 the partnership published the Global Information Society: A Statistical View, with information on more than 40 core ICT indicators covering ICT infrastructure; access and use of ICT by households, individuals, and businesses; ICT in education; and ICT sector activity and trade in ICT goods (see http://measuring-ict.unctad.org for a complete list). Partnership on Measuring ICT for Development
5l
Members of the Partnership on Measuring ICT for Development include a wide range of organizations: • Eurostat. • International Telecommunication Union. • Organisation for Economic Co-operation and Development. • United Nations Conference on Trade and Development. • United Nations Economic Commission for Africa. • United Nations Economic Commission for Asia and the Pacific. • United Nations Economic Commission for Latin America and the Caribbean. • United Nations Economic Commission for Western Asia. • United Nations Educational, Scientific, and Cultural Organization’s Institute for Statistics. • World Bank.
2009 World Development Indicators
269
Tables
5.1
Private sector in the economy Domestic credit to private sector
Investment commitments in infrastructure projects with private participationa
$ millions Telecommunications
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Cote 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
270
Energy
2000–05
2006–07
2000–05
466.1 569.2 3,422.5 278.7 5,836.8 317.1 .. .. 355.6 1,294.3 735.4 .. 116.9 520.5 0.0 104.0 41,053.8 2,179.1 41.9 53.6 148.1 394.4 .. 0.0 11.0 3,561.6 8,548.0 .. 1,570.9 473.4 61.8 .. 134.9 1,205.7 100.8 8,508.0 .. 393.0 357.8 3,471.9 1,110.6 40.0 334.7 .. .. .. 26.6 6.6 173.8 .. 156.5 .. 560.1 50.6 21.9 18.0
795.4 267.0 1,263.0 448.0 2,134.2 77.0 .. .. 601.6 2,461.8 881.3 .. 222.0 181.0 901.5 46.0 12,667.3 1,116.0 378.8 0.0 217.0 212.4 .. 12.0 79.9 1,930.7 0.0 .. 2,704.0 394.0 100.0 .. 266.7 2,401.0 0.0 488.0 .. 77.1 439.7 6,509.0 583.0 0.0 132.4 .. .. .. 131.4 0.0 484.2 .. 635.0 .. 780.1 66.0 39.5 306.0
1.6 790.6 962.0 45.0 3,826.9 74.0 .. .. 375.2 501.5 .. .. 590.0 884.4 277.9 .. 25,489.2 3,094.1 .. .. 108.1 91.8 .. .. 0.0 1,393.2 10,493.2 .. 351.6 .. .. 80.0 0.0 7.1 .. 3,865.3 .. 1,306.6 302.0 678.0 85.0 .. .. .. .. .. 0.0 .. 40.0 .. 590.0 .. 110.0 .. .. 5.5
2009 World Development Indicators
2006–07
.. .. 2,320.0 9.4 2,320.7 57.0 .. .. .. .. .. .. .. 117.3 800.0 .. 11,470.5 909.3 .. .. 648.7 440.0 .. .. .. 458.8 2,765.2 .. 639.0 .. .. 80.0 .. .. .. .. .. 0.0 129.0 469.0 0.0 .. .. .. .. .. 0.0 0.0 557.3 .. 100.0 .. 226.8 .. .. ..
Transport 2000–05
.. 308.0 120.9 .. 203.6 63.0 .. .. .. 0.0 .. .. .. 16.6 .. .. 4,206.4 2.1 .. .. 125.3 0.0 .. .. .. 4,821.2 13,796.8 .. 1,005.4 .. .. 465.2 176.4 451.0 0.0 106.7 .. 898.9 685.0 821.5 .. .. 298.4 .. .. .. 177.4 .. .. .. 10.0 .. .. .. .. ..
2006–07
.. .. 161.0 53.0 1,065.7 10.0 .. .. .. 0.0 .. .. .. .. .. .. 1,966.2 531.6 .. .. 200.0 .. .. .. .. 538.0 11,455.2 .. 2,142.4 .. .. 373.0 .. 492.0 .. .. .. 250.0 1,166.0 730.0 .. .. .. .. .. .. .. .. 228.0 .. .. .. .. .. .. ..
Water and sanitation 2000–05
.. 8.0 510.0 .. 791.6 0.0 .. .. 0.0 .. .. .. .. .. .. .. 1,215.3 152.0 .. .. .. .. .. .. .. 1,495.2 3,477.0 .. 314.3 .. 0.0 .. .. 298.7 600.0 263.7 .. .. 500.0 .. .. .. 115.0 .. .. .. .. .. .. .. 0.0 .. .. .. .. ..
2006–07
.. 0.0 230.0 .. .. .. .. .. .. .. .. .. .. .. .. .. 639.6 .. .. .. .. 0.0 .. .. .. 3.1 2,616.6 .. 305.0 .. .. .. .. .. .. 0.0 .. .. .. .. .. .. .. .. .. .. .. .. 435.0 .. .. .. .. .. .. ..
Businesses registered
% of GDP
New
Total
2007
2007
2007
.. 2,176 10,662 .. 16,400 3,822 89,960 3,484 4,945 5,328 .. 28,016 .. 1,625 314 7,301 490,542 49,328 639 .. .. .. 207,000 .. .. 25,124 .. 80,935 28,801 .. 237 10,567 .. 11,055 .. 16,395 28,811 .. 3,196 9,595 1,802 .. .. .. 10,424 137,481 .. .. 5,260 66,747 .. .. 4,251 .. .. 9
.. 16,110 105,128 .. 218,700 56,461 641,538 76,374 69,309 67,459 .. 354,489 .. 24,649 23,634 .. 5,668,003 315,037 .. .. .. .. 2,500,000 .. .. 223,345 .. 524,445 497,778 .. .. 102,311 .. 200,955 .. 244,417 200,060 20,808 37,434 367,559 .. .. .. .. 120,294 1,267,419 .. .. 59,641 573,985 .. .. .. .. .. 300
4.0 29.6 13.3 10.2 14.5 13.6 127.5 114.2 15.3 37.3 25.1 92.3 20.0 37.0 54.7 20.1 49.8 66.8 16.8 23.6 18.2 9.2 136.4 6.7 2.9 88.5 111.0 139.6 32.7 3.9 2.5 44.3 16.1 72.1 .. 47.7 202.4 33.4 25.5 50.6 42.8 25.6 96.1 23.8 82.0 105.2 12.0 16.2 28.3 105.5 17.8 91.5 35.2 5.1 6.1 11.3
Domestic credit to private sector
Investment commitments 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
2000–05
2006–07
135.0 5,172.8 20,642.0 6,557.2 695.0 984.0 .. .. .. 700.3 .. 1,589.0 1,153.7 1,434.0 .. .. .. 11.5 87.7 700.0 138.1 88.4 70.3 .. 993.0 706.6 12.6 36.3 3,756.9 82.6 92.1 413.0 18,191.4 46.1 22.1 6,139.5 123.0 .. 35.0 109.3 .. .. 218.5 85.5 6,950.7 .. 1,005.0 6,595.1 211.4 .. 199.0 2,233.4 4,616.4 16,800.1 .. ..
319.9 1,523.3 14,849.4 5,192.7 221.0 3,790.0 .. .. .. 166.7 .. 394.3 1,473.2 1,496.0 .. .. .. 40.9 10.0 283.1 0.0 10.3 27.5 .. 326.2 371.0 119.6 67.5 1,130.0 87.0 50.1 26.1 5,642.2 197.3 0.0 1,466.6 81.2 .. 8.5 26.0 .. .. 171.2 110.0 5,296.1 .. 306.0 5,213.4 182.5 150.0 319.7 1,117.0 1,929.4 4,970.7 .. ..
Energy 2000–05
358.8 851.6 8,285.8 1,763.5 650.0 .. .. .. .. 201.0 .. .. 300.0 .. .. .. .. .. 1,250.0 71.1 .. 0.0 .. .. 446.3 .. 0.0 0.0 6,637.6 365.9 .. 0.0 6,749.3 127.2 .. 1,049.0 1,205.8 .. 1.0 39.0 .. .. 126.3 .. 1,920.0 .. 1,364.3 524.6 429.8 .. .. 2,498.9 3,428.4 2,341.5 .. ..
2006–07
.. 1,707.0 16,537.1 1,116.1 .. 590.0 .. .. .. 78.0 .. 420.0 .. 116.7 .. .. .. .. 800.0 .. .. .. .. .. 96.0 391.0 .. .. 203.0 .. .. .. 1,081.0 434.0 .. .. .. 556.1 .. .. .. .. 95.0 .. 280.0 .. 600.0 1,494.3 495.7 .. .. 399.7 3,370.8 11.2 .. ..
Transport 2000–05
120.0 3,297.5 4,281.3 159.2 .. .. .. .. .. 565.0 .. 0.0 231.0 .. .. .. .. .. 0.0 .. 153.0 .. .. .. .. .. 48.5 .. 4,461.4 55.4 .. .. 2,970.4 0.0 .. 200.0 334.6 .. .. .. .. .. 104.0 .. 2,355.4 .. 473.8 71.0 51.4 .. .. 522.5 1,060.5 1,672.0 .. ..
2006–07
.. 1,588.0 13,164.7 3,495.9 .. .. .. .. .. .. .. 675.0 0.0 404.0 .. .. .. .. .. 135.0 .. .. .. .. .. .. 0.0 .. 954.0 .. .. .. 8,808.4 .. .. 140.0 .. .. .. .. .. .. .. .. 262.1 .. .. 801.0 .. .. .. 2,290.8 550.0 .. .. ..
Water and sanitation 2000–05
2006–07
207.9 0.0 112.9 44.8 .. .. .. .. .. .. .. 169.0 100.0 .. .. .. .. 0.0 .. .. 0.0 .. .. .. .. .. .. .. 6,502.2 .. .. .. 523.7 .. .. .. .. .. 0.0 .. .. .. .. 3.4 .. .. .. .. .. .. .. 152.0 0.0 64.3 .. ..
.. 0.0 142.3 20.2 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 0.0 .. .. .. 303.8 .. .. .. .. .. .. .. .. .. .. .. .. .. 173.0 .. .. .. .. .. 503.9 0.8 .. ..
STATES AND MARKETS
Private sector in the economy
5.1 Businesses registered
% of GDP
New
Total
2007
2007
2007
.. 28,153 20,000 18,960 .. .. 18,704 18,814 77,587 2,023 145,151 2,361 .. 7,371 .. .. .. 3,987 .. 12,017 1,030 .. .. .. 6,578 .. 1,234 420 43,279 .. .. .. 306,400 6,806 .. 24,811 .. .. .. .. 116,000 74,247 2,070 .. .. 18,082 6,362 4,840 .. .. .. .. 18,189 26,388 30,934 ..
.. 273,549 732,000 271,255 .. .. 180,891 162,910 638,987 54,116 2,572,088 .. .. 125,102 .. .. .. .. .. .. .. .. .. .. 67,095 .. 19,305 5,595 .. .. .. .. 4,290,000 73,532 .. .. .. .. .. .. 1,030,000 474,212 .. .. .. 132,788 38,864 .. .. .. .. .. .. 523,584 423,719 ..
53.1 61.5 47.3 25.4 49.2 .. 199.6 90.1 101.8 31.1 171.6 99.0 58.9 27.2 .. 107.8 69.6 15.3 6.8 93.9 75.6 10.5 10.0 7.2 61.2 38.0 10.1 10.6 105.3 18.8 .. 83.5 22.0 36.9 45.5 69.9 13.9 5.6 65.3 36.3 195.0 148.1 39.1 9.6 25.4 .. 32.0 29.4 92.0 21.4 20.0 21.0 28.8 39.7 169.0 ..
2009 World Development Indicators
271
5.1
Private sector in the economy Investment commitments in infrastructure projects with private participationa
$ millions Telecommunications 2000–05
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
2006–07
Energy 2000–05
3,693.9 2,230.7 1,240.8 22,049.4 11,757.4 1,726.0 72.3 124.4 1.6 .. .. .. 593.1 779.0 93.3 563.5 2,864.1 .. 48.8 66.3 .. .. .. .. 2,709.9 581.7 3,384.6 .. .. .. 13.4 0.0 .. 10,519.5 2,574.0 1,251.3 .. .. .. 766.1 723.1 270.8 747.7 1,184.3 .. 27.7 3.8 .. .. .. .. .. .. .. 583.0 104.3 .. 8.5 11.0 16.0 515.3 485.5 348.0 5,602.7 2,180.0 4,693.3 0.0 0.0 .. 0.0 0.0 657.7 .. 190.0 .. 751.0 2,419.0 30.0 12,788.6 4,206.7 5,854.8 20.0 48.1 .. 387.6 500.6 113.9 3,162.9 2,211.0 160.0 .. .. .. .. .. .. .. .. .. 114.2 60.9 330.0 285.6 362.1 .. 3,337.0 1,683.0 39.5 430.0 1,326.7 2,360.6 279.8 0.0 150.0 376.8 292.1 .. 208.3 379.0 3.0 72.0 20.0 .. .. s .. s .. s 21,743.0 23,001.5 10,746.1 230,758.0 116,028.5 102,036.5 79,126.8 54,904.0 40,467.3 151,631.2 61,124.5 61,569.2 252,501.0 139,029.9 112,782.6 29,854.1 12,191.0 30,741.7 68,767.4 38,203.5 16,942.6 80,867.3 31,561.1 44,627.8 18,430.6 16,459.3 3,519.0 29,926.2 24,086.6 9,623.3 24,655.4 16,528.5 7,328.3 .. .. .. .. .. ..
2006–07
Transport 2000–05
2,645.0 .. 14,011.2 109.4 .. .. .. .. .. 55.4 .. .. 1.2 .. .. .. 1,272.0 .. .. .. .. .. 9.9 504.7 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 28.4 27.7 .. 939.0 .. .. .. .. 39.0 .. .. .. 328.7 3,118.6 .. .. 822.6 .. .. .. .. .. .. .. .. .. .. 251.1 .. .. .. 34.0 287.0 20.0 .. .. 15.8 .. .. 15.6 .. .. .. s .. s 5,150.7 3,295.8 66,150.7 49,769.0 31,883.7 25,282.6 34,267.0 24,486.5 71,301.4 53,064.8 9,746.8 20,562.2 20,240.6 5,955.1 17,659.6 16,958.3 3,814.8 1,475.4 18,031.4 4,352.3 1,808.2 3,761.6 .. .. .. ..
2006–07
116.8 144.0 .. .. .. .. .. .. 42.0 .. .. 3,483.0 .. .. 30.0 .. .. .. 37.0 .. 134.0 .. .. .. .. 840.0 2,598.0 .. 404.0 .. .. .. .. .. .. .. 400.0 .. .. .. .. .. s 2,605.1 58,924.6 37,718.9 21,205.7 61,529.7 17,055.1 4,255.4 18,600.4 2,883.0 13,965.7 4,770.1 .. ..
Domestic credit to private sector
Water and sanitation
% of GDP
New
Total
2000–05
2006–07
2007
2007
2007
1,116.0 935.4 .. .. 0.0 0.0 .. .. .. .. .. 31.3 .. .. .. .. .. .. .. .. 8.5 287.7 .. .. 120.0 .. .. .. 0.0 100.0 .. .. .. 368.0 0.0 15.0 174.0 .. .. 0.0 .. .. s 185.9 20,141.7 5,883.5 14,258.2 20,327.6 10,485.7 2,774.4 6,232.5 679.0 112.9 43.2 .. ..
0.0 396.2 .. .. 0.0 .. .. .. 13.6 .. .. 0.0 .. .. 120.7 .. .. .. .. .. .. 18.8 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. s 0.0 5,735.9 4,392.4 1,343.5 5,735.9 3,159.5 832.0 1,251.5 230.0 142.3 120.7 .. ..
35.8 39.0 12.2 40.4 23.0 34.2 5.3 99.9 42.4 79.0 .. 164.3 182.7 33.3 12.6 25.4 123.7 177.6 16.2 28.9 15.5 92.4 25.4 21.3 33.8 64.3 29.1 .. 10.6 58.8 64.3 190.0 210.1 23.7 .. 23.6 93.3 7.5 7.9 12.0 26.6 135.6 w 31.2 60.4 74.7 45.5 59.0 97.5 39.5 36.6 42.1 44.7 70.4 163.2 121.6
a. Data refer to total for the period shown. Includes infrastructure projects with private sector participation that reached financial closure in 1990–2007.
272
2009 World Development Indicators
Businesses registered
103,733 489,955 .. .. 23 10,876 .. 25,904 16,025 4,957 .. 41,356 145,593 4,529 .. .. 27,994 18,284 216 794 3,933 25,184 .. .. .. 6,675 93,634 .. 8,906 41,809 .. 449,700 676,830 .. 10,264 .. .. .. 50 5,300 ..
870,195 3,267,325 455 .. 1,000 83,499 .. 133,235 135,330 47,312 .. 553,425 2,435,689 .. .. .. 326,052 162,326 2,268 .. 59,163 297,084 .. .. .. 63,584 764,240 .. 89,503 528,864 .. 2,546,200 5,156,000 .. 56,465 .. 52,506 .. .. .. ..
About the data
STATES AND MARKETS
Private sector in the economy
5.1
Definitions
Private sector development and investment—tapping
and small-scale operators—may be omitted because
• Investment commitments in infrastructure proj-
private sector initiative and investment for socially
they are not publicly reported. The database is a joint
ects with private participation refers to infrastruc-
useful purposes—are critical for poverty reduction.
product of the World Bank’s Finance, Economics, and
ture projects in telecommunications, energy (electric-
In parallel with public sector efforts, private invest-
Urban Development Department and the Public-
ity and natural gas transmission and distribution),
ment, especially in competitive markets, has tre-
Private Infrastructure Advisory Facility. Geographic
transport, and water and sanitation that have reached
mendous potential to contribute to growth. Private
and income aggregates are calculated by the World
financial closure and directly or indirectly serve the
markets are the engine of productivity growth, creat-
Bank’s Development Data Group. For more informa-
public. Incinerators, movable assets, standalone
ing productive jobs and higher incomes. And with gov-
tion, see http://ppi.worldbank.org/.
solid waste projects, and small projects such as
ernment playing a complementary role of regulation,
Credit is an important link in money transmission;
windmills are excluded. Included are operation and
funding, and service provision, private initiative and
it finances production, consumption, and capital for-
management contracts, operation and management
investment can help provide the basic services and
mation, which in turn affect economic activity. The
contracts with major capital expenditure, greenfield
conditions that empower poor people—by improving
data on domestic credit to the private sector are
projects (new facilities built and operated by a private
health, education, and infrastructure.
taken from the banking survey of the International
entity or a public-private joint venture), and dives-
Investment in infrastructure projects with private
Monetary Fund’s (IMF) International Financial Statis-
titures. Investment commitments are the sum of
participation has made important contributions to
tics or, when unavailable, from its monetary survey.
investments in facilities and investments in govern-
easing fiscal constraints, improving the efficiency
The monetary survey includes monetary authorities
ment assets. Investments in facilities are resources
of infrastructure services, and extending delivery
(the central bank), deposit money banks, and other
the project company commits to invest during the
to poor people. Developing countries have been in
banking institutions, such as finance companies,
contract period in new facilities or in expansion and
the forefront, pioneering better approaches to infra-
development banks, and savings and loan institu-
modernization of existing facilities. Investments in
structure services and reaping the benefits of greater
tions. Credit to the private sector may sometimes
government assets are the resources the project
competition and customer focus.
include credit to state-owned or partially state-owned
company spends on acquiring government assets
enterprises.
such as state-owned enterprises, rights to provide
The data on investment in infrastructure projects with private participation refer to all investment (pub-
Entrepreneurship is essential to the dynamism of
services in a specific area, or use of specific radio
lic and private) in projects in which a private com-
the modern market economy, and a greater entry rate
spectrums. • Domestic credit to private sector is
pany assumes operating risk during the operating
of new businesses can foster competition and eco-
financial resources provided to the private sector—
period or development and operating risk during the
nomic growth. The table includes data on business
such as through loans, purchases of nonequity
contract period. Investment refers to commitments
registrations from the 2008 World Bank Group Entre-
securities, and trade credits and other accounts
not disbursements. Foreign state-owned companies
preneurship Survey, which includes entrepreneurial
receivable—that establish a claim for repayment.
are considered private entities for the purposes of
activity in more than 100 countries for 2000–08.
For some countries these claims include credit to
this measure.
Survey data are used to analyze firm creation, its
public enterprises. • New businesses registered
Investments are classified into two types: invest-
relationship to economic growth and poverty reduc-
are the number of limited liability firms registered
ments in physical assets—the resources a com-
tion, and the impact of regulatory and institutional
in the calendar year. • Total businesses registered
pany commits to invest in expanding and modern-
reforms. The 2008 survey improves on earlier sur-
are the year-end stock of total registered limited
izing facilities—and payments to the government to
veys’ methodology and country coverage for better
liability firms.
acquire state-owned enterprises or rights to provide
cross-country comparability. Data on total and newly
services in a specific area or to use part of the radio
registered businesses were collected directly from
spectrum.
national registrars of companies. For cross-country
The data are from the World Bank’s Private Partici-
comparability, only limited liability corporations
pation in Infrastructure (PPI) Project database, which
that operate in the formal sector are included. For
tracks infrastructure projects with private participa-
additional information on sources, methodology,
tion in developing countries. It provides information
calculation of entrepreneurship rates, and data limi-
on more than 4,100 infrastructure projects in 141
tations see http://econ.worldbank.org/research/
Data sources
developing economies from 1984 to 2007. The data-
entrepreneurship.
Data on investment commitments in infra-
base contains more than 30 fields per project record,
structure projects with private participation are
including country, financial closure year, infrastruc-
from the World Bank’s PPI Project database
ture services provided, type of private participation,
(http://ppi.worldbank.org). Data on domestic
investment, technology, capacity, project location,
credit are from the IMF’s International Financial
contract duration, private sponsors, bidding process,
Statistics. Data on business registration and are
and development bank support. Data on the projects
from the World Bank’s Entrepreneurship Survey
are compiled from publicly available information. The
and
database aims to be as comprehensive as possible,
research/entrepreneurship).
database
(http://econ.worldbank.org/
but some projects—particularly those involving local
2009 World Development Indicators
273
5.2
Business environment: enterprise surveys Survey year
Regulations and tax
Time dealing with officials
Average number of times % of management meeting with tax officials time
Afghanistan Albania Algeriaa Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarusa 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.a El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgiaa Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
274
2007 2007 2006 2006 2005
2005 2007 2008 2004 2006 2005 2006 2003 2007 2006 2006 2007 2006
2006 2003 2006 2006 2009 2005 2007 2005 2005 2006 2007 2006 2002 2005 2006
2009 2006 2008 2005 2007 2005 2006 2006 2006
.. 18.7 25.1 7.1 14.1 3.0 .. .. 5.2 3.2 13.6 .. 6.5 13.5 4.3 5.0 7.2 17.4 9.5 5.7 5.6 12.8 .. .. .. 9.0 18.3 .. 14.3 6.3 5.9 9.6 .. 10.9 .. 2.1 .. 8.8 17.3 .. 9.2 .. 2.3 3.8 .. .. 3.0 7.3 2.1 4.5 4.0 3.7 9.2 2.7 2.9 ..
2009 World Development Indicators
.. 5.5 3.4 5.2 4.6 2.9 .. .. 1.3 1.4 2.1 .. 6.3 3.5 1.9 2.4 .. 4.1 2.5 2.1 2.3 6.4 .. .. .. 5.4 14.4 .. 2.5 10.0 2.9 0.7 .. 1.7 .. 1.7 .. 2.7 2.6 3.8 4.1 .. 2.2 1.8 .. .. 22.6 3.2 1.7 1.3 4.6 1.7 3.9 3.6 4.4 ..
Permits Corruption and licenses
Crime
Time required to obtain operating license
Informal payments to public officials
Losses due to theft, robbery, vandalism, and arson
days
% of firms
% of sales
.. 21.2 19.3 24.1 175.8 .. .. .. .. 6.1 38.2 .. 39.9 30.0 .. 13.7 .. 48.2 5.8 27.3 .. 15.6 .. .. .. 67.7 11.8 .. 28.2 17.8 .. .. .. 26.5 .. .. .. .. 19.9 81.5 35.4 .. .. 11.4 .. .. 12.7 9.1 11.8 .. 6.4 .. 75.4 13.0 30.4 ..
.. 57.7 64.7 46.8 18.7 24.6 .. .. 37.8 85.1 13.5 .. 57.7 32.4 24.1 27.6 .. 16.1 87.0 56.5 61.2 77.6 .. .. .. 8.2 72.6 .. 8.2 83.8 48.3 33.8 .. 14.5 .. 25.5 .. 26.3 21.5 7.3 34.3 .. 16.2 12.4 .. .. 24.1 52.4 4.1 .. 38.8 21.6 15.7 84.8 62.7 ..
.. 3.3 6.3 2.4 3.7 0.0 .. .. 0.2 1.2 1.8 .. 0.3 3.3 0.4 3.2 0.4 1.3 1.8 4.9 0.4 3.8 .. .. .. 1.3 0.1 .. 2.9 6.5 17.3 0.4 .. 0.9 .. 0.4 .. 0.7 3.0 .. 5.6 .. 0.4 1.4 .. .. 3.7 8.7 7.6 0.5 3.7 0.0 5.2 8.3 3.3 ..
Informality
Gender
Finance
Firms that Firms with Firms using do not banks to female report all finance sales for tax participation purposes in ownership investment
Infrastructure Innovation
Trade
Average Intertime to nationally recognized clear direct exports quality Value lost due to electrical certification through customs ownership outages
% of firms
% of firms
% of firms
% of sales
.. .. .. 67.8 49.1 26.2 .. .. 38.7 .. 20.0 .. 39.6 51.4 29.2 65.3 82.8 39.7 58.8 42.7 .. 38.7 .. .. .. 27.9 49.5 .. 38.7 65.4 86.8 68.3 .. 33.3 .. 51.1 .. 73.6 37.6 34.8 42.3 .. 24.7 51.6 .. .. 62.7 88.1 36.0 .. 59.2 53.2 44.2 95.4 68.2 ..
.. 10.8 15.0 23.4 30.3 12.5 .. .. 14.4 16.1 52.9 .. .. 41.1 25.2 40.9 .. 39.2 23.3 34.8 .. 35.3 .. .. .. 27.8 .. .. 43.0 21.2 27.5 34.7 .. 33.5 .. 21.8 .. .. 32.7 20.9 39.6 .. 34.1 30.9 .. .. 26.9 21.3 40.8 20.3 44.0 24.4 28.4 25.4 19.9 ..
.. 12.4 8.9 2.1 6.9 35.0 .. .. 0.6 24.7 35.8 .. 20.8 22.2 17.5 11.3 22.9 40.5 23.1 12.3 11.3 19.5 .. .. .. 29.1 9.8 .. 30.6 3.3 6.3 9.3 .. 60.0 .. 11.4 .. 3.6 24.0 9.5 17.3 .. 17.8 11.0 .. .. 5.9 7.6 38.2 44.3 16.0 16.1 12.8 0.9 0.7 ..
.. 13.7 4.0 3.7 1.4 2.5 .. .. 5.9 10.6 0.8 .. 6.5 4.4 2.4 1.4 1.6 1.2 3.9 10.7 2.4 3.9 .. .. .. 1.8 1.3 .. 2.3 5.6 15.7 1.9 .. 0.8 .. 1.6 .. 15.2 2.7 4.7 2.9 .. 1.1 0.9 .. .. 1.8 11.8 1.4 .. 6.0 .. 4.5 14.0 5.3 ..
% of firms
.. 24.6 5.0 5.1 26.9 5.7 .. .. 10.3 7.8 13.9 .. 2.7 13.8 14.5 12.7 19.1 22.3 7.4 7.1 .. 16.4 .. .. .. 22.0 35.9 .. 5.9 4.3 23.8 10.5 .. 16.5 .. 12.5 .. 9.6 18.2 20.0 11.0 .. 13.2 4.2 .. .. 22.3 22.2 16.0 .. 6.8 11.7 8.0 5.2 8.4 ..
days
.. 1.9 14.1 16.5 5.5 5.0 .. .. 1.6 8.4 2.6 .. 6.3 15.3 2.0 1.4 8.2 2.7 2.8 0.0 1.7 4.3 .. .. .. 5.8 6.7 .. 7.1 3.6 .. 3.5 .. 1.3 .. 3.6 .. 11.4 7.0 6.4 2.6 .. 1.8 4.3 .. .. 3.9 5.0 3.8 4.7 7.8 5.5 4.5 4.3 5.6 ..
Workforce
Firms offering formal training % of firms
.. 19.9 17.3 19.4 52.2 35.9 .. .. 16.3 16.2 44.4 .. 35.3 53.9 47.2 37.7 67.1 36.5 43.1 22.1 48.4 42.4 .. .. .. 46.9 84.8 .. 39.5 11.4 38.5 46.4 .. 28.0 .. 60.3 .. 53.3 61.6 21.2 49.6 .. 64.9 38.2 .. .. 33.7 25.6 14.5 35.4 33.0 20.0 28.1 21.1 12.4 ..
Survey year
Regulations and tax
Time dealing with officials
Average number of times % of management meeting with tax officials time
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenyaa Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Malia Mauritania Mauritius Mexico Moldova Mongolia Moroccoa Mozambiquea Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeriaa Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
2006 2005 2006 2003
2005
2005 2006 2005 2007 2005 2005 2005 2005 2006 2009 2009 2005 2005 2005 2006 2002 2007 2006 2005 2006 2005 2004 2007 2007 2006
2006 2006 2007
2002 2006 2006 2006 2003 2005 2005
4.6 4.0 6.7 4.0 .. .. 2.3 .. .. 6.3 .. 6.7 3.1 5.1 .. 3.2 .. 6.1 4.5 2.9 12.0 5.7 8.5 .. 5.1 8.2 20.8 5.8 7.3 2.4 5.8 9.6 20.5 3.6 6.0 11.4 3.3 .. 2.9 .. .. .. 9.3 11.5 6.1 .. .. 8.7 10.3 .. 7.9 13.5 6.9 3.0 3.3 ..
2.4 2.5 3.1 2.0 .. .. 1.3 .. .. 2.2 .. 2.2 4.0 9.0 .. 2.4 .. 3.5 3.8 2.2 4.7 3.1 7.1 .. 4.2 2.7 2.7 8.9 5.2 2.3 1.9 2.1 2.3 2.7 7.3 5.1 2.7 .. 1.6 .. .. .. 2.5 4.3 3.7 .. 5.2 4.2 2.7 .. 2.2 2.5 3.9 2.7 1.7 ..
Permits Corruption and licenses
Crime
Time required to obtain operating license
Informal payments to public officials
Losses due to theft, robbery, vandalism, and arson
days
% of firms
% of sales
31.6 .. .. 18.6 .. .. .. .. .. .. .. 6.4 .. 23.4 .. .. .. 43.9 .. .. .. 10.9 16.9 .. 55.5 .. .. 17.4 .. 41.0 10.7 .. 11.9 .. .. 3.4 34.3 .. 9.6 .. .. .. 19.7 10.9 12.8 .. 11.8 35.2 41.2 .. 37.8 81.1 25.0 .. .. ..
16.7 32.1 47.5 44.2 .. .. 8.3 .. .. 17.7 .. 18.1 45.1 79.2 .. 14.1 .. 66.3 31.2 31.3 51.2 12.9 52.9 .. 44.6 26.0 24.5 35.7 .. 28.9 82.1 17.5 22.6 36.0 .. 13.4 14.7 .. 11.4 .. .. .. 17.2 69.7 40.9 .. 33.2 57.0 25.4 .. 84.8 11.3 44.7 23.7 14.5 ..
6.1 0.1 0.1 0.2 .. .. 0.3 .. .. 1.1 .. 1.3 0.3 3.9 .. 0.0 .. 0.7 1.5 0.5 0.5 6.7 7.9 .. 0.4 0.3 1.9 2.3 0.3 3.7 5.6 0.1 3.4 0.1 0.6 0.4 5.1 .. 3.0 .. .. .. 3.8 6.1 4.1 .. .. 0.1 2.7 .. 3.1 2.4 0.9 0.4 0.2 ..
Informality
Gender
Finance
Firms that Firms with Firms using do not banks to female report all finance sales for tax participation purposes in ownership investment
5.2
Infrastructure Innovation
Trade
Average Intertime to nationally recognized clear direct exports quality Value lost due to electrical certification through customs ownership outages
% of firms
% of firms
% of firms
% of sales
36.0 40.0 59.2 44.0 .. .. 28.8 .. .. 28.8 .. 13.0 23.2 45.9 .. 43.7 .. 43.2 14.9 26.3 67.5 .. 90.0 .. 39.0 52.2 21.0 55.3 .. 39.7 82.5 26.3 57.7 40.2 80.4 .. 73.5 .. 45.5 .. .. .. 60.4 29.7 68.0 .. 42.5 .. 54.2 .. 42.8 27.2 57.9 43.9 37.3 ..
39.9 40.1 9.1 .. .. .. 41.6 .. .. 32.2 .. 13.1 36.1 37.1 .. 19.1 .. 27.3 .. 42.3 27.9 22.5 27.6 .. 25.5 17.5 .. 15.8 .. 18.4 17.3 .. 24.8 27.5 .. 13.1 24.4 .. 33.4 .. .. .. 41.4 10.0 20.0 .. .. .. 37.1 .. 44.8 32.8 .. 33.6 50.8 ..
8.5 22.3 19.4 13.9 .. .. 20.8 .. .. 10.6 .. 8.6 15.4 22.9 .. 11.5 .. 7.9 13.8 15.1 26.8 26.7 11.5 .. 15.6 9.0 13.0 20.6 23.8 7.0 3.2 36.3 2.6 17.7 32.8 12.3 11.3 .. 8.1 .. .. .. 13.0 14.6 2.7 .. 6.5 3.6 19.2 .. 8.2 30.9 5.5 20.7 9.5 ..
3.8 1.4 6.6 3.3 .. .. 1.5 .. .. 11.8 .. 1.7 2.2 6.4 .. .. .. 4.1 4.3 1.4 6.0 6.0 3.7 .. 1.2 1.8 6.6 22.6 1.8 1.8 1.6 2.9 2.4 2.7 1.5 1.3 2.5 .. 0.7 .. .. .. 8.7 2.5 8.9 .. 4.2 4.9 2.4 .. 2.5 3.2 5.9 1.6 .. ..
% of firms
16.5 23.1 22.5 22.1 .. .. 17.2 .. .. 16.4 .. 15.5 9.9 9.8 .. 17.6 .. 11.9 3.3 9.3 20.9 31.9 4.7 .. 15.1 11.0 6.6 17.2 31.4 8.6 5.9 28.4 20.3 6.9 20.5 17.3 18.7 .. 17.6 .. .. .. 18.7 4.8 8.5 .. 10.8 17.0 14.7 .. 7.1 14.6 15.8 13.9 12.7 ..
STATES AND MARKETS
Business environment: enterprise surveys
days
6.0 4.5 15.6 4.1 .. .. 2.6 .. .. 4.3 .. 3.8 6.8 5.6 .. 7.2 .. 4.1 2.0 2.0 7.4 8.0 .. .. 1.8 2.4 3.5 3.5 2.5 4.8 3.9 4.4 5.4 2.6 3.5 1.8 10.0 .. 1.5 .. .. .. 5.0 7.4 7.5 .. 4.2 9.7 5.7 .. 5.5 5.6 6.6 3.3 7.2 ..
2009 World Development Indicators
Workforce
Firms offering formal training % of firms
33.3 39.9 15.9 23.8 .. .. 73.2 .. .. 53.5 .. 23.9 30.7 40.7 .. 39.5 .. 47.0 28.2 51.7 67.8 52.7 29.4 .. 52.6 37.4 48.5 51.6 42.0 22.5 25.5 62.1 24.6 32.5 46.2 24.7 22.5 .. 44.5 .. .. .. 28.9 34.4 25.7 .. 20.9 11.1 43.9 .. 46.9 57.7 21.7 48.4 31.9 ..
275
5.2
Business environment: enterprise surveys Survey year
Regulations and tax
Time dealing with officials
Average number of times % of management meeting with tax officials time
Romania Russian Federation Rwanda Saudi Arabia Senegala Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistana Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukrainea United Arab Emirates United Kingdom United States Uruguay Uzbekistana Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambiaa Zimbabwe
2005 2005 2006 2007 2005 2009 2005 2005 2003 2005 2004 2006
2003 2008 2006 2006
2005 2006 2008
2006 2008 2006 2005 2006 2007
1.1 6.3 5.9 .. 2.9 8.1 7.9 .. 3.0 3.7 .. 9.2 0.8 3.5 .. 4.4 .. .. 10.3 11.7 4.0 0.4 .. .. .. .. 10.8 .. 5.2 11.3 .. .. .. 7.0 11.1 33.6 3.1 5.7 .. 4.6 ..
1.8 2.5 4.0 .. 1.8 4.1 2.6 .. 1.8 1.4 .. 3.3 1.5 5.1 .. 1.9 .. .. 6.0 2.7 3.3 1.1 .. .. .. .. 2.2 .. 2.9 3.8 .. .. .. 2.2 1.7 3.4 2.2 5.2 .. 2.9 ..
Permits Corruption and licenses
Crime
2009 World Development Indicators
Gender
Finance
Firms that Firms with Firms using do not banks to female report all finance sales for tax participation purposes in ownership investment
Infrastructure Innovation
Trade
Average Intertime to nationally recognized clear direct exports quality Value lost due to electrical certification through customs ownership outages
Time required to obtain operating license
Informal payments to public officials
Losses due to theft, robbery, vandalism, and arson
days
% of firms
% of sales
% of firms
% of firms
% of firms
% of sales
.. .. 6.5 .. 21.4 .. 12.0 .. .. .. .. 6.4 .. 49.5 .. 24.0 .. .. .. 22.6 15.9 32.1 .. .. .. .. .. .. 9.3 31.0 .. .. .. 133.8 9.1 41.6 .. 21.3 .. 47.3 ..
33.1 59.9 20.0 .. 18.1 31.8 18.7 .. 34.3 11.2 .. 2.1 4.4 16.3 .. 40.6 .. .. .. 40.5 49.5 .. .. .. .. .. 45.7 .. 51.7 22.9 .. .. .. 7.3 56.2 .. 67.2 13.3 .. 14.8 ..
0.2 0.5 7.1 .. 4.1 0.6 4.2 .. 0.4 0.2 .. 0.5 0.2 0.5 .. 3.4 .. .. .. 4.5 3.9 0.1 .. .. .. .. 0.2 .. 4.1 3.6 .. .. .. 2.1 18.3 6.8 0.1 7.5 .. 3.3 ..
26.9 40.3 28.9 .. 21.6 33.3 77.3 .. 22.0 35.6 .. 15.9 18.3 42.0 .. 74.6 .. .. 79.9 .. 71.0 .. .. .. .. .. 63.1 .. 74.5 .. .. .. .. 45.5 .. .. 70.3 25.7 .. .. ..
27.7 28.6 41.0 .. 26.3 25.0 10.0 .. 18.2 34.5 .. .. 34.1 .. .. 28.6 .. .. .. 34.4 30.9 .. .. .. .. .. 8.9 .. 34.7 47.1 .. .. .. 41.6 39.8 .. 27.4 18.0 .. 37.4 ..
23.2 10.2 15.9 .. 19.8 16.7 12.5 .. 13.2 29.6 .. 24.2 32.2 16.2 .. 7.7 .. .. 2.9 21.4 6.8 74.4 .. .. .. .. 7.5 .. 7.7 32.1 .. .. .. 6.9 8.2 35.7 29.2 4.2 .. 10.1 ..
2.1 2.0 8.7 .. 5.0 2.4 7.8 .. 1.2 1.1 .. 0.4 3.0 .. .. 2.5 .. .. 8.6 15.1 9.6 1.5 .. .. .. .. 2.2 .. 10.2 4.4 .. .. .. 0.9 5.4 4.4 .. 4.6 .. 3.6 ..
a. Representative sample of the nonagricultural economy, excluding financial and public services.
276
Informality
% of firms
16.8 9.3 10.8 .. 6.1 11.7 17.3 .. 10.0 20.2 .. 42.4 21.3 .. .. 22.1 .. .. 7.4 16.7 14.7 39.0 .. .. .. .. 12.6 .. 15.5 13.0 .. .. .. 6.8 1.3 12.5 11.4 18.2 .. 17.2 ..
days
2.4 8.2 6.7 .. 8.9 3.2 .. .. 5.8 2.9 .. 4.5 4.9 7.6 .. 4.0 .. .. 6.3 20.4 5.7 1.5 .. .. .. .. 4.5 .. 4.7 3.5 .. .. .. 2.8 5.1 14.1 4.9 6.0 .. 3.1 ..
Workforce
Firms offering formal training % of firms
32.7 37.3 27.6 .. 16.3 47.5 25.0 .. 79.4 69.9 .. 64.0 51.3 32.6 .. 51.0 .. .. 21.0 21.1 36.5 75.3 .. .. .. .. 25.5 .. 35.0 25.1 .. .. .. 24.6 9.6 42.3 44.0 26.5 .. 25.4 ..
About the data
STATES AND MARKETS
Business environment: enterprise surveys
5.2
Definitions
The World Bank Group’s Enterprise Survey gath-
with access to modern and efficient infrastructure—
• Survey year is the year in which the underlying
ers firm-level data on the business environment
telecommunications, electricity, and transport—can
data were collected. • Time dealing with officials
to assess constraints to private sector growth and
be more productive. Firm-level innovation and use of
is the time senior management spends dealing with
enterprise performance. Standardized surveys are
modern technology may help firms compete.
the requirements of government regulation. • Aver-
conducted all over the world, and data are available
Delays in clearing customs can be costly, deterring
age number of times meeting with tax offi cials
on almost 85,000 firms in 106 countries. The survey
firms from engaging in trade or making them uncom-
is the average number of visits with tax officials.
covers 11 dimensions of the business environment,
petitive globally. Ill-considered labor regulations dis-
• Time required to obtain operating license is the
including corruption, crime, informality, regulation,
courage firms from creating jobs, and while employed
average wait to obtain an operating license from
and finance. For some countries, firm-level panel data
workers may benefit, unemployed, low-skilled, and
the day applied for to the day granted. • Informal
are available, making it possible to track changes in
informally employed workers will not. A trained labor
payments to public offi cials are the percentage
the business environment over time.
force enables firms to thrive, compete, innovate, and
of firms expected to make informal payments to
adopt new technology.
public offi cials to “get things done” for customs,
Firms evaluating investment options, governments interested in improving business conditions, and
Most of the data in the table are from the World
taxes, licenses, regulations, services, and the like.
economists seeking to explain economic perfor-
Bank Financial and Private Sector Development
• Losses due to theft, robbery, vandalism, and
mance have all grappled with defining and measuring
Group’s Enterprise Surveys. Data for 27 countries in
arson are the estimated losses from those causes
the business environment. The firm-level data from
Europe and Central Asia and 2 comparator countries
that occurred on establishments’ premises as a per-
Enterprise Surveys provide a useful tool for bench-
in Asia (Republic of Korea and Vietnam) are based on
centage of annual sales. • Firms that do not report
marking performance and monitoring progress.
the joint European Bank for Reconstruction and Devel-
all sales for tax purposes are the percentage of firms
opment (EBRD)–World Bank Business Environment
that expressed that a typical firm reports less than
and Enterprise Performance Surveys (BEEPS).
100 percent of sales for tax purposes; such firms are
Most countries can improve regulation and taxation without compromising broader social interests. Excessive regulation may harm business perfor-
All BEEPS economies plus the Latin American and
termed “informal firms.” • Firms with female partici-
mance and growth. For example, time spent with
Caribbean countries, the North African countries for
pation in ownership are the percentage of firms with
tax officials is a burden firms may face in paying
2007, the Sub-Saharan African countries for 2006
a woman among the principal owners. • Firms using
taxes. The business environment suffers when gov-
and 2007 (except Burkina Faso, Cameroon, and Cape
banks to finance investment are the percentage of
ernments increase uncertainty and risks or impose
Verde), Jordan, and Bangladesh for 2007 draw a sam-
firms using banks to finance investments. • Value
unnecessary costs and unsound regulation and taxa-
ple from the universe of registered nonagricultural
lost due to electrical outages is the percentage of
tion. Time to obtain licenses and permits and the
businesses excluding the financial and public sectors.
sales lost due to power outages. • Internationally
associated red tape constrain firm operations.
Economies with samples that are representative of
recognized quality certifi cation ownership is the
In some countries doing business requires infor-
the economy are footnoted. Samples for most of the
percentage of firms that have earned a quality certi-
mal payments to “get things done” in customs, taxes,
remaining economies were drawn from the manufac-
fication recognized by the International Organization
licenses, regulations, services, and the like. Such cor-
turing sector. Typical Enterprise Survey sample sizes
for Standardization (ISO). • Average time to clear
ruption harms the business environment by distorting
range from 100 to 1,800, depending on the size of
direct exports through customs is the average num-
policymaking, undermining government credibility, and
the economy. Samples are selected by simple random
ber of days to clear direct exports through customs.
diverting public resources. Crime, theft, and disorder
sampling or stratified random sampling. BEEPS 2005
• Firms offering formal training are the percentage
also impose costs on businesses and society.
use a simple random sample method based on GDP
of firms offering formal training programs for their
In many developing countries informal businesses
contributions, so samples are self-weighted. BEEPS
permanent, full-time employees.
operate without licenses. These firms have less
2008 economies, Latin American and Caribbean, North
access to financial and public services and can
African, and Sub-Saharan African countries (except
engage in fewer types of contracts and investments,
Burkina Faso, Cameroon, and Cape Verde), Bangladesh,
constraining growth.
and Jordan use stratified random sampling. Stratified
Equal opportunities for men and women contribute to development. Female participation in firm ownership is a measure of women’s integration as decisionmakers.
random sampling allows indicators to be computed by sector, firm size, and geographic region. At the sector level the strata are composed of selected manufacturing industries and one service
Financial markets connect firms to lenders and
sector (retail), plus the rest of the economy, a resid-
investors, allowing firms to grow their businesses:
ual stratum. Firm size is stratified into small (5–19
creditworthy firms can obtain credit from financial
employees), medium (20–100 employees), and large
intermediaries at competitive prices. But too often
(more than 100 employees). Geographic stratifica-
Data sources
market imperfections and government-induced distor-
tion is defined by country. Economy wide indicators
Data on the business environment are from the
tions limit access to credit and thus restrain growth.
can be computed with more precision under stratified
World Bank Group’s Enterprise Surveys website
The reliability and availability of infrastructure
random sampling than under simple random sampling
(www.enterprisesurveys.org).
benefit households and support development. Firms
when individual observations are properly weighted.
2009 World Development Indicators
277
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
278
Registering property
Number of procedures June 2008
Time required days June 2008
Cost % of per capita income June 2008
Number of procedures June 2008
Time required days June 2008
4 6 14 8 15 9 2 8 6 7 8 3 7 15 12 10 18 4 5 11 9 13 1 10 19 9 14 5 9 13 10 12 10 8 .. 8 4 8 14 6 8 13 5 7 3 5 9 8 3 9 9 15 11 13 17 13
9 8 24 68 32 18 2 28 16 73 31 4 31 50 60 78 152 49 16 43 85 37 5 14 75 27 40 11 36 155 37 60 40 40 .. 15 6 19 65 7 17 84 7 16 14 7 58 27 3 18 34 19 26 41 233 195
59.5 25.8 10.8 196.8 9.0 3.6 0.8 5.1 3.2 25.7 7.8 5.2 196.0 112.4 30.8 2.3 8.2 2.0 62.3 215.0 151.7 137.1 0.5 232.3 175.0 7.5 8.4 2.0 14.1 435.4 106.4 20.5 135.1 11.5 .. 9.6 0.0 19.4 38.5 18.3 49.6 102.2 1.7 29.8 1.0 1.0 20.3 254.9 4.0 5.6 32.7 10.2 50.6 135.7 257.7 159.6
9 6 14 7 5 3 5 3 4 8 4 7 4 7 7 4 14 8 6 5 7 5 6 5 6 6 4 5 9 8 7 6 6 5 .. 4 6 7 9 7 5 12 3 13 3 9 8 5 2 4 5 11 5 6 9 5
250 42 51 334 51 4 5 32 11 245 21 132 120 92 128 11 42 19 136 94 56 93 17 75 44 31 29 54 23 57 116 21 62 174 .. 123 42 60 16 72 31 101 51 43 14 113 60 371 3 40 34 22 30 104 211 405
2009 World Development Indicators
Dealing with construction permits
Employing workers
Enforcing contracts
Rigidity of Time Number of required employment index procedures to build a to build a warehouse 0–100 (least Number of to most rigid) procedures warehouse days June June June June 2008 2008 2008 2008
13 24 22 12 28 19 16 13 31 14 17 14 15 17 16 24 18 24 15 20 23 15 14 21 9 18 37 15 13 14 14 23 21 19 .. 36 6 17 19 28 34 .. 14 12 18 13 16 17 12 12 18 15 22 32 15 11
340 331 240 328 338 116 221 194 207 231 210 169 410 249 296 167 411 139 214 384 709 426 75 239 181 155 336 119 114 322 169 191 628 410 .. 180 69 214 155 249 155 .. 118 128 38 137 210 146 113 100 220 169 215 255 167 1,179
27 35 48 66 35 31 3 33 3 35 27 20 40 79 46 20 46 29 21 30 45 46 4 61 46 24 27 0 24 74 69 28 38 50 .. 28 10 28 51 27 24 20 58 34 48 56 52 27 7 44 37 51 28 44 66 21
47 39 47 46 36 49 28 25 39 41 28 25 42 40 38 29 45 39 37 44 44 43 36 43 41 36 34 24 34 43 44 40 33 38 .. 27 34 34 39 42 30 39 36 39 32 30 38 32 36 30 36 39 31 50 41 35
Protecting investors
Closing a business
Time required days June 2008
Disclosure index 0–10 (least to most disclosure) June 2008
Time to resolve insolvency years June 2008
1,642 390 630 1,011 590 285 395 397 237 1,442 225 505 825 591 595 987 616 564 446 832 401 800 570 660 743 480 406 211 1,346 645 560 877 770 561 .. 820 380 460 588 1,010 786 405 425 690 235 331 1,070 434 285 394 487 819 1,459 276 1,140 508
0 8 6 5 6 5 8 3 7 6 5 8 6 1 3 7 6 10 6 4 5 6 8 6 6 7 10 10 8 3 6 2 6 1 .. 2 7 5 1 8 5 4 8 4 6 10 6 2 8 5 7 1 3 6 6 2
.. .. 2.5 6.2 2.8 1.9 1.0 1.1 2.7 4.0 5.8 0.9 4.0 1.8 3.3 1.7 4.0 3.3 4.0 .. .. 3.2 0.8 4.8 .. 4.5 1.7 1.1 3.0 5.2 3.0 3.5 2.2 3.1 .. 6.5 1.1 3.5 5.3 4.2 4.0 .. 3.0 3.0 0.9 1.9 5.0 3.0 3.3 1.2 1.9 2.0 3.0 3.8 .. 5.7
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 June 2008
Time required days June 2008
Cost % of per capita income June 2008
Number of procedures June 2008
Time required days June 2008
13 4 13 11 8 11 4 5 6 6 8 10 8 12 .. 10 13 4 8 5 5 7 8 .. 7 7 5 10 9 11 9 5 9 9 7 6 10 .. 10 7 6 1 6 11 8 6 7 11 7 8 7 10 15 10 6 7
20 5 30 76 47 77 13 34 10 8 23 14 21 30 .. 17 35 15 103 16 11 40 27 .. 26 9 7 39 13 26 19 6 28 15 13 12 26 .. 66 31 10 1 39 19 31 10 14 24 13 56 35 65 52 31 6 7
52.6 8.4 70.1 77.9 4.6 150.7 0.3 4.4 18.5 7.9 7.5 60.4 5.2 39.7 .. 16.9 1.3 7.4 14.1 2.3 87.5 37.8 100.2 .. 2.7 3.8 11.0 125.9 14.7 121.5 33.9 5.0 12.5 8.9 4.0 10.2 22.9 .. 22.1 60.2 5.9 0.4 121.0 170.1 90.1 2.1 3.6 12.6 19.6 23.6 67.9 25.7 29.8 18.8 2.9 0.8
7 4 6 6 9 5 5 7 8 5 6 8 5 8 .. 7 8 7 9 7 8 6 13 .. 2 6 7 6 5 5 4 4 5 6 5 8 8 .. 9 3 2 2 8 4 14 1 2 6 7 4 6 5 8 6 5 8
23 17 45 39 36 8 38 144 27 54 14 22 40 64 .. 11 55 8 135 50 25 101 50 .. 3 66 74 88 144 29 49 210 74 48 11 47 42 .. 23 5 5 2 124 35 82 3 16 50 44 72 46 33 33 197 42 194
Dealing with construction permits
Employing workers
Enforcing contracts
Rigidity of Time Number of required employment index procedures to build a to build a warehouse 0–100 (least Number of to most rigid) procedures warehouse days June June June June 2008 2008 2008 2008
17 31 20 18 19 14 11 20 14 10 15 18 38 10 .. 13 25 13 24 25 20 15 25 .. 17 21 16 21 25 14 25 18 12 30 21 19 17 .. 12 15 18 7 17 17 18 14 16 12 21 24 13 21 24 30 21 22
125 204 224 176 670 215 185 235 257 156 187 122 231 100 .. 34 104 159 172 187 211 601 321 .. 162 198 178 213 261 208 201 107 138 292 215 163 381 .. 139 424 230 65 219 265 350 252 242 223 131 217 291 210 203 308 328 209
53 30 30 40 40 38 17 24 38 4 17 30 23 17 .. 45 13 38 34 43 25 21 31 .. 48 47 63 25 10 38 45 23 48 41 34 63 49 .. 20 42 42 7 27 70 7 47 24 43 66 10 59 48 35 37 48 25
45 33 46 39 39 51 20 35 41 35 30 39 38 44 .. 35 50 39 42 27 37 41 41 .. 30 38 38 42 30 39 46 37 38 31 32 40 30 .. 33 39 25 30 35 39 39 33 51 47 31 43 38 41 37 38 34 39
STATES AND MARKETS
Business environment: Doing Business indicators
5.3 Protecting investors
Closing a business
Time required days June 2008
Disclosure index 0–10 (least to most disclosure) June 2008
Time to resolve insolvency years June 2008
900 335 1,420 570 520 520 515 890 1,210 655 316 689 230 465 .. 230 566 177 443 279 721 695 1,280 .. 210 385 871 432 600 860 370 750 415 365 314 615 730 .. 270 735 514 216 540 545 457 310 598 976 686 591 591 468 842 830 577 620
1 2 7 9 5 4 10 7 7 4 7 5 7 3 .. 7 7 9 0 5 9 2 4 .. 5 5 5 4 10 6 5 6 8 7 5 6 5 .. 5 6 4 10 4 6 5 7 8 6 1 5 6 8 2 7 6 7
3.8 2.0 10.0 5.5 4.5 .. 0.4 4.0 1.8 1.1 0.6 4.3 3.3 4.5 .. 1.5 4.2 4.0 .. 3.0 4.0 2.6 3.0 .. 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 1.1 1.3 2.2 5.0 2.0 0.9 4.0 2.8 2.5 3.0 3.9 3.1 5.7 3.0 2.0 3.8
2009 World Development Indicators
279
5.3
Business environment: Doing Business indicators Starting a business
Number of procedures June 2008
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
280
6 8 8 7 4 11 7 4 6 5 .. 6 10 4 10 13 3 6 8 13 12 8 10 13 9 10 6 .. 18 10 8 6 6 11 7 16 11 11 7 6 10 9u 10 9 9 9 9 9 8 10 9 7 10 7 7
Time required days June 2008
10 29 14 12 8 23 17 4 16 19 .. 22 47 38 39 61 15 20 17 49 29 33 83 53 43 11 6 .. 25 27 17 13 6 44 15 141 50 49 13 18 96 38 u 49 42 35 53 44 44 23 70 27 33 46 21 17
2009 World Development Indicators
Registering property
Cost % of per capita income June 2008
3.6 2.6 108.9 14.9 72.7 7.6 56.2 0.7 3.3 0.1 .. 6.0 14.9 7.1 50.8 35.1 0.6 2.1 18.2 27.6 41.5 4.9 6.6 251.3 0.9 7.9 14.9 .. 100.7 5.5 13.4 0.8 0.7 43.5 10.3 26.8 16.8 69.1 93.0 28.6 432.7 47.1 u 110.1 34.9 45.9 19.1 60.3 38.0 9.3 43.9 60.9 31.9 111.4 7.2 5.9
Number of procedures June 2008
8 6 4 2 6 6 7 3 3 6 .. 6 4 8 6 11 1 4 4 6 9 2 .. 5 8 4 6 .. 13 10 3 2 4 8 12 8 4 7 6 6 4 6u 7 6 6 6 6 5 6 7 7 6 7 5 6
Time required days June 2008
83 52 315 2 124 111 86 9 17 391 .. 24 18 83 9 46 2 16 19 37 73 2 .. 295 162 39 6 .. 227 93 6 21 12 66 78 47 57 63 19 39 30 72 u 108 67 69 64 81 113 59 66 37 106 97 47 69
Dealing with construction permits
Employing workers
Enforcing contracts
Rigidity of Time Number of required employment index procedures to build a to build a warehouse 0–100 (least Number of to most rigid) procedures warehouse days June June June June 2008 2008 2008 2008
17 54 14 18 16 20 25 11 13 15 .. 17 11 21 19 13 8 14 26 32 21 11 22 15 20 20 25 .. 16 30 21 19 19 30 26 11 13 21 13 17 19 18 u 18 19 19 20 19 19 24 17 20 16 17 17 14
243 704 210 125 220 279 283 38 287 208 .. 174 233 214 271 93 116 154 128 351 308 156 208 277 261 84 188 .. 143 471 125 144 40 234 260 395 194 199 107 254 1,426 222 u 304 213 209 219 243 183 265 233 215 245 273 160 190
62 44 38 13 61 39 51 0 36 59 .. 42 56 27 36 13 44 17 34 51 63 18 34 57 7 49 38 .. 3 45 13 14 0 31 34 79 24 31 33 34 33 33 u 39 33 33 32 35 20 37 33 39 26 41 29 44
31 37 24 44 44 36 40 21 30 32 .. 30 39 40 53 40 30 32 55 34 38 35 51 41 42 39 35 .. 38 30 50 30 32 40 42 29 34 44 37 35 38 38 u 40 39 39 38 39 37 37 39 43 44 39 35 31
Time required days June 2008
512 281 310 635 780 635 515 150 565 1,350 .. 600 515 1,318 810 972 508 417 872 295 462 479 1,800 588 1,340 565 420 .. 535 354 607 404 300 720 195 510 295 700 520 471 410 613 u 626 652 669 627 643 591 385 714 716 1,053 662 522 591
Protecting investors
Closing a business
Disclosure index 0–10 (least to most disclosure) June 2008
Time to resolve insolvency years June 2008
9 6 2 8 6 7 3 10 3 3 .. 8 5 4 0 0 6 0 6 4 3 10 3 6 4 0 9 .. 2 1 4 10 7 3 4 3 6 6 6 3 8 5u 5 5 5 5 5 5 6 4 6 4 5 6 6
3.3 3.8 .. 1.5 3.0 2.7 2.6 0.8 4.0 2.0 .. 2.0 1.0 1.7 .. 2.0 2.0 3.0 4.1 3.0 3.0 2.7 .. 3.0 .. 1.3 3.3 .. 2.2 2.9 5.1 1.0 1.5 2.1 4.0 4.0 5.0 .. 3.0 2.7 3.3 3.0 u 3.6 3.2 3.5 2.9 3.3 3.1 3.2 3.2 3.5 5.0 3.4 2.1 1.4
About the data
STATES AND MARKETS
Business environment: Doing Business indicators
5.3
Definitions
The Doing Business project encompasses two
• Number of procedures for starting a business is
in macroeconomic terms but also by other factors that
types of data: data from readings of laws and regu-
the number of procedures required to start a busi-
shape daily economic activity such as laws, regula-
lations and data on time and motion indicators that
ness, including interactions to obtain necessary per-
tions, and institutional arrangements. The Doing Busi-
measure efficiency in achieving a regulatory goal.
mits and licenses and to complete all inscriptions,
ness indicators measure business regulation, gauge
Within the time and motion indicators cost estimates
verifications, and notifications to start operations for
regulatory outcomes, and measure the extent of legal
are recorded from official fee schedules where appli-
businesses with specific characteristics of owner-
protection of property, the flexibility of employment
cable. The data from surveys are subjected to numer-
ship, size, and type of production. • Time required
regulation, and the tax burden on businesses.
ous tests for robustness, which lead to revision or
for starting a business is the number of calendar
expansion of the information collected.
days to complete the procedures for legally operating
The economic health of a country is measured not only
The table presents a subset of Doing Business indicators covering 7 of the 10 sets of indicators:
The Doing Business methodology has limitations
a business using the fastest procedure, independent
starting a business, registering property, dealing with
that should be considered when interpreting the
of cost. • Cost for starting a business is normalized
construction permits, employing workers, enforcing
data. First, the data collected refer to businesses
as a percentage of gross national income (GNI) per
contracts, protecting investors, and closing a busi-
in the economy’s largest city and may not represent
capita. • Number of procedures for registering prop-
ness. Table 5.5 includes Doing Business measures
regulations in other locations of the economy. To
erty is the number of procedures required for a busi-
of getting credit, and table 5.6 presents data on
address this limitation, subnational indicators are
ness to legally transfer property. • Time required for
paying taxes.
being collected for six economies, and data collec-
registering property is the number of calendar days
The fundamental premise of the Doing Business
tion is under way in six more. These subnational stud-
for a business to legally transfer property. • Number
project is that economic activity requires good rules
ies point to significant differences in the speed of
of procedures for dealing with licenses to build a
and regulations that are efficient, accessible to all
reform and the ease of doing business across cities
warehouse is the number of interactions of a com-
who need to use them, and simple to implement.
in the same economy. Second, the data often focus
pany’s employees or managers with external parties,
Thus some Doing Business indicators give a higher
on a specifi c business form—generally a limited
including government staff, public inspectors, nota-
score for more regulation, such as stricter disclosure
liability company of a specified size—and may not
ries, land registry and cadastre staff, and technical
requirements in related-party transactions, and oth-
represent regulation for other types of businesses
experts apart from architects and engineers. • Time
ers give a higher score for simplified regulations,
such as sole proprietorships. Third, transactions
required for dealing with construction permits to
such as a one-stop shop for completing business
described in a standardized business case refer to
build a warehouse is the number of calendar days
startup formalities.
a specific set of issues and may not represent the
to complete the required procedures for building a
In constructing the indicators, it is assumed that
full set of issues a business encounters. Fourth, the
warehouse using the fastest procedure, independent
entrepreneurs know about all regulations and comply
time measures involve an element of judgment by the
of cost. • Rigidity of employment index, a measure
with them; in practice, entrepreneurs may not be
expert respondents. When sources indicate different
of employment regulation, is the average of three
aware of all required procedures or may avoid legally
estimates, the Doing Business time indicators repre-
subindexes: a difficulty of hiring index, a rigidity of
required procedures altogether. But where regula-
sent the median values of several responses given
hours index, and a difficulty of firing index. Higher
tion is particularly onerous, levels of informality are
under the assumptions of the standardized case.
values indicate more rigid regulations. • Number of
higher, which comes at a cost: firms in the informal
Fifth, the methodology assumes that a business has
procedures for enforcing contracts is the number
sector usually grow more slowly, have less access
full information on what is required and does not
of independent actions, mandated by law or court
to credit, and employ fewer workers—and those
waste time when completing procedures.
regulation, that demand interaction between the par-
workers remain outside the protections of labor law.
ties to a contract or between them and the judge or
The indicators in the table can help policymakers
court officer. • Time required for enforcing contracts
understand the business environment in a country
is the number of calendar days from the time of the
and—along with information from other sources such
filing of a lawsuit in court to the final determination
as the World Bank’s Enterprise Surveys—provide
and payment. • Extent of disclosure index measures
insights into potential areas of reform.
the degree to which investors are protected through
Doing Business data are collected with a stan-
disclosure of ownership and financial information.
dardized survey that uses a simple business case
Higher values indicate more disclosure. • Time to
to ensure comparability across economies and over
resolve insolvency is the number of years from time
time—with assumptions about the legal form of the
of filing for insolvency in court until resolution of dis-
business, its size, its location, and nature of its
tressed assets and payment of creditors.
operation. Surveys in 181 countries are administered through more than 6,700 local experts, including
Data sources
lawyers, business consultants, accountants, freight
Data on the business environment are from
forwarders, government officials, and other profes-
the World Bank’s Doing Business project (www.
sionals who routinely administer or advise on legal
doingbusiness.org).
and regulatory requirements.
2009 World Development Indicators
281
5.4
Stock markets Market capitalization
$ millions 2000
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Cote 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
282
.. .. .. .. 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 .. .. ..
% of GDP
Market liquidity
Turnover ratio
Value of shares traded % of GDP
Value of shares traded % of market capitalization
Listed domestic companies
number
2008
2000
2007
2000
2007
2000
2008
2000
2008
.. .. .. .. 52,309 105 1,298,429 228,707 .. 6,671 .. 386,362 .. 2,263 .. 3,556 589,384 8,858 .. .. .. .. 2,186,550 .. .. 132,428 2,793,613 1,162,566 87,032 .. .. 2,035 7,071 26,790 .. 48,850 277,746 .. 4,562 85,885 6,743 .. 1,951 .. 369,168 2,771,217 .. .. 1,389 2,105,506 3,394 264,942 .. .. .. ..
.. .. .. .. 58.4 0.1 92.0 15.4 0.1 2.5 .. 78.7 .. 20.7 .. 15.8 35.1 4.9 .. .. .. .. 116.1 .. .. 80.3 48.5 368.6 10.2 .. .. 18.3 11.4 14.9 .. 19.4 67.3 0.8 4.4 28.8 15.5 .. 32.8 .. 241.0 108.9 .. .. 0.8 66.8 10.1 88.3 1.2 .. .. ..
.. .. .. .. 33.0 1.1 158.2 61.3 .. 9.9 .. 85.3 .. 17.2 .. 47.8 104.3 55.1 .. .. .. .. 164.4 .. .. 129.9 194.2 561.2 49.1 .. .. 7.7 42.2 128.7 .. 42.0 89.1 .. 9.6 106.8 33.1 .. 28.9 .. 150.9 107.0 .. .. 13.7 63.5 15.7 84.6 .. .. .. ..
.. .. .. .. 2.1 0.0 55.9 4.8 .. 1.6 .. 16.4 .. 0.8 .. 0.8 15.7 0.5 .. .. .. .. 87.6 .. .. 8.1 60.2 223.4 0.4 .. .. 0.7 0.3 1.0 .. 11.6 57.2 .. 0.1 11.1 0.2 .. 5.8 .. 169.6 81.6 .. .. 0.1 56.3 0.2 75.7 0.0 .. .. ..
.. .. .. .. 3.1 0.1 161.1 32.5 .. 7.0 .. 56.5 .. 0.0 .. 0.9 44.5 13.9 .. .. .. .. 123.7 .. .. 27.1 243.1 442.6 5.0 .. .. 0.2 0.8 8.0 .. 24.0 77.7 .. 0.7 40.7 0.9 .. 10.0 .. 222.1 132.0 .. .. 0.4 101.4 0.7 48.4 .. .. .. ..
.. .. .. .. 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 .. .. ..
.. .. .. .. 19.3 6.1 110.5 57.8 .. 137.3 .. 65.3 .. 0.0 .. 3.1 74.3 10.8 .. .. .. .. 84.7 .. .. 21.2 121.3 89.1 13.2 .. .. 3.1 4.1 7.4 .. 70.4 99.1 .. 3.6 58.6 3.7 .. 23.2 .. 182.0 131.5 .. .. 4.4 179.7 5.2 64.0 .. .. .. ..
.. .. .. .. 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 .. .. ..
.. .. .. .. 107 29 1,913 96 .. 290 .. 153 .. 37 .. 19 432 334 .. .. .. .. 3,881 .. .. 235 1,604 1,029 96 .. .. 12 38 376 .. 28 201 .. 38 373 51 .. 18 .. 134 707 .. .. 161 658 35 292 .. .. .. ..
2009 World Development Indicators
S&P/Global Equity Indices
% change 2007
2008
.. .. .. .. 0.7 .. .. .. .. 126.4a .. .. .. .. .. 37.2a 74.7 39.0a .. .. .. .. .. .. .. 22.6 66.6 .. 12.7a .. .. .. 115.6a 68.1a .. 49.7 .. .. 3.8 a 52.2 .. .. –15.5a .. .. .. .. .. .. .. 21.6a .. .. .. .. ..
.. .. .. .. –56.2 .. .. .. .. 4.3a .. .. .. .. .. –38.4 a –57.2 –70.2a .. .. .. .. .. .. .. –41.2 –52.7 .. .. .. .. .. –16.9a –59.3a .. –45.9 .. .. –8.8a –55.8 .. .. –65.5a .. .. .. .. .. .. .. –10.4a .. .. .. .. ..
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
Market liquidity
Turnover ratio
Listed domestic companies
Value of shares traded % of GDP
Value of shares traded % of market capitalization
number
2000
2008
2000
2007
2000
2007
2000
2008
2000
2008
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 .. .. 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 ..
.. 18,579 645,478 98,761 45,574 .. 144,026 134,463 1,072,692 7,513 4,453,475 35,847 31,075 10,917 .. 494,631 107,168 121 .. 1,609 9,641 .. .. .. 3,625 2,715 .. 587 187,066 .. .. 3,443 232,581 .. 612 65,748 .. .. 619 4,909 956,469 47,454 .. .. 49,803 357,420 14,914 23,491 6,568 6,632 409 55,625 52,101 90,233 132,258 ..
8.8 25.1 32.2 16.3 7.3 .. 85.0 51.8 70.0 44.6 67.6 58.4 7.3 10.1 .. 33.5 55.1 0.3 .. 7.2 9.4 .. .. .. 13.9 0.2 .. .. 124.7 .. 97.2 29.8 21.5 30.4 3.4 29.4 .. .. 9.1 14.4 166.3 37.1 .. .. 9.2 38.6 17.4 8.9 24.0 49.3 3.5 19.8 34.2 18.3 53.9 ..
.. 34.4 154.6 48.9 15.9 .. 55.6 144.2 51.0 107.9 101.6 260.3 39.5 55.3 .. 115.9 167.7 3.2 .. 11.5 44.6 .. .. .. 26.4 35.4 .. 18.6 174.4 .. .. 83.5 38.9 .. 15.6 100.5 .. .. 10.0 47.6 124.9 35.0 .. .. 52.2 92.0 45.2 49.2 31.9 118.9 4.4 98.8 71.7 49.1 59.4 ..
.. 25.3 110.8 8.7 1.1 .. 15.0 18.9 70.9 0.9 57.7 4.9 0.5 0.4 .. 208.7 11.2 1.7 .. 2.9 0.7 .. .. .. 1.8 3.3 .. .. 62.4 .. .. 1.7 7.8 1.9 0.7 3.0 .. .. 0.6 0.6 175.9 21.2 .. .. 0.6 35.7 2.8 44.6 1.3 0.0 0.1 2.9 10.8 8.5 48.3 ..
.. 34.3 94.1 26.1 2.9 .. 52.7 69.2 110.1 3.1 148.2 110.1 8.5 5.4 .. 203.5 107.7 3.7 .. 0.5 4.1 .. .. .. 2.7 6.6 .. 0.5 80.3 .. .. 5.4 11.3 2.3 1.4 35.0 .. .. 0.3 2.2 235.5 16.0 .. .. 10.1 121.5 9.3 70.3 0.6 0.4 0.0 6.8 20.3 20.0 64.9 ..
.. 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 ..
.. 93.0 85.2 71.3 19.7 .. 88.9 60.2 220.4 3.6 141.6 72.7 22.2 11.8 .. 181.2 83.2 131.2 .. 1.8 6.9 .. .. .. 59.9 26.5 .. 3.5 31.4 .. .. 8.9 34.3 .. 14.7 31.4 .. .. 2.8 6.9 207.8 46.9 .. .. 29.3 147.8 44.2 116.0 4.0 0.5 0.5 6.3 22.2 45.7 122.2 ..
46 60 5,937 290 304 .. 76 654 291 46 2,561 163 23 57 .. 1,308 77 80 .. 64 12 .. .. .. 54 1 .. .. 795 .. 40 40 179 36 410 53 .. .. 13 110 234 142 .. .. 195 191 131 762 29 7 56 230 228 225 109 ..
.. 41 4,921 396 329 .. 57 630 301 39 3,844 262 74 53 .. 1,798 202 10 .. 35 11 .. .. .. 41 38 .. 9 977 .. .. 41 125 .. 384 77 .. .. 7 144 226 154 .. .. 213 195 127 653 31 15 55 199 244 349 47 ..
STATES AND MARKETS
Stock markets
5.4 S&P/Global Equity Indices
% change 2007
2008
.. 13.1 78.6 49.3 .. .. .. 34.3 .. 0.3a –5.2b 32.6a –47.0a 11.8a .. 27.7 39.9a .. .. 1.9a 40.5a .. .. .. 14.3a .. .. .. 44.6 .. .. 94.0a 12.8 .. .. 45.3 .. .. 39.4a .. .. .. .. .. 108.3a .. 67.0a 41.7a –15.7a .. .. 66.4 36.0 23.2 .. ..
.. –62.5 –64.1 –61.1 .. .. .. –33.1 .. –38.2a –39.7b .. –47.0a –40.3a .. –55.6 .. .. .. –58.7a –25.3a .. .. .. –73.0a .. .. .. –43.7 .. .. –49.2a –45.1 .. .. –17.0 .. .. –9.9a .. .. .. .. .. .. .. .. .. .. .. .. –41.1 –53.7 –57.8 .. ..
2009 World Development Indicators
283
5.4
Stock markets Market capitalization
$ millions 2000
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
% of GDP 2008
2000
1,069 19,923 2.9 38,922 397,183 15.0 .. .. .. 67,171 246,337 35.6 .. .. .. 734 23,934 4.6 .. .. .. 152,827 353,489 164.8 1,217 5,079 6.0 2,547 11,772 12.8 .. .. .. 204,952 491,282 154.2 504,219 1,800,097 86.8 1,074 4,326 6.6 .. .. .. 73 203 4.9 328,339 612,497 133.7 792,316 1,274,516 317.0 .. .. .. .. .. .. 233 541 2.6 29,489 102,594 24.0 .. .. .. .. .. .. 4,330 12,157 53.1 2,828 6,374 14.5 69,659 117,930 26.1 .. .. .. 35 116 0.6 1,881 24,358 6.0 5,727 97,852 8.1 2,576,992 3,858,505 177.6 15,104,037 19,947,284 154.7 161 159 0.8 32 715 0.2 8,128 8,251 6.9 .. 9,589 .. 765 2,475 18.6 .. .. .. 236 2,346 7.3 2,432 5,333 32.9 32,187,756 s ..e s 102.4 w 18,702 110,935 7.9 1,964,730 6,475,916 36.5 895,510 4,062,921 35.0 1,069,220 2,412,995 37.9 1,983,431 6,586,851 35.3 780,487 3,243,723 47.1 150,122 721,582 17.5 620,263 1,168,004 31.6 57,110 203,494 19.9 157,695 679,965 26.1 217,754 570,083 89.9 30,204,325 49,648,498 116.9 5,433,547 10,468,592 87.0
Market liquidity
Turnover ratio
Value of shares traded % of GDP
Value of shares traded % of market capitalization
2007
2000
2007
2000
2008
27.1 116.5 .. 135.0 .. 59.7 .. 219.1 9.3 61.4 .. 294.5 125.3 23.4 .. 7.0 134.8 300.3 .. .. 3.8 79.9 .. .. 74.7 15.3 43.7 .. 1.2 79.2 84.8 139.2 145.1 0.7 4.2 4.5 28.5 111.1 .. 20.6 70.3 121.3 w 40.5 117.0 144.9 88.5 113.9 165.1 77.3 71.4 56.1 133.4 149.0 123.8 85.3
0.6 7.8 .. 9.2 .. 0.1 .. 98.7 4.4 2.3 .. 58.3 169.8 0.9 .. 0.0 158.8 243.7 .. .. 0.4 19.0 .. .. 1.7 3.2 67.1 .. 0.0 0.9 0.2 126.5 326.3 0.0 0.1 0.6 .. 4.6 .. 0.2 3.8 152.4 w 14.7 33.8 50.6 18.3 33.0 49.8 24.9 8.4 5.1 90.2 32.3 178.2 80.4
4.9 58.5 .. 178.1 .. 6.4 .. 238.1 0.0 5.8 .. 150.4 206.1 2.9 .. 0.0 213.3 418.9 .. .. 0.1 44.1 .. .. 1.7 1.9 46.1 .. 0.1 1.4 69.2 372.5 309.9 0.1 0.4 0.4 18.3 52.2 .. 0.6 9.7 186.6 24.8 94.3 146.7 40.6 91.3 191.2 38.7 24.3 18.8 84.8 60.9 217.8 124.0
23.1 36.9 .. 27.1 .. 0.0 .. 52.1 129.8 20.7 .. 33.9 210.7 11.0 .. 9.8 111.2 82.0 .. .. 2.4 53.2 .. .. 3.1 23.3 206.2 .. .. 19.6 3.9 66.6 200.8 0.5 .. 8.9 .. 10.0 .. 20.8 10.8 122.3 w .. 78.5 112.7 48.6 82.8 125.0 92.4 27.2 12.4 167.9 22.2 130.5 90.6
11.3 75.0 .. 137.8 .. 14.6 .. 122.0 0.4 6.9 .. 60.6 189.7 17.2 .. 0.0 147.4 143.0 .. .. 2.1 78.2 .. .. 2.6 25.5 118.5 .. 5.2 3.7 89.8 270.1 216.5 12.0 5.9 1.3 28.8 31.3 .. 4.1 5.1 ..e w 69.3 78.2 96.2 61.0 77.8 112.0 68.8 47.0 28.7 89.3 29.1 180.5 162.8
Listed domestic companies
number 2000
2008
1,824 5,555 314 249 .. .. 127 75 .. .. 1,771 6 .. .. 472 418 120 493 84 38 .. .. 425 616 3,498 1,019 234 239 .. .. 6 6 272 292 257 252 .. .. .. .. 7 4 476 381 .. .. .. .. 37 27 49 44 284 315 .. .. 5 2 251 139 96 54 2,588 1,904 5,130 7,524 8 16 114 5 60 85 171 .. 35 24 .. .. 15 9 81 69 ..e s 47,877 s 1,534 1,575 20,998 15,300 9,227 11,241 6,073 9,757 22,573 16,834 3,868 3,190 3,882 7,588 1,302 1,762 772 1,676 6,098 7,269 912 1,088 25,304 29,505 5,711 5,028
S&P/Global Equity Indices
% change 2007
2008
32.8a 21.9 .. 35.6 .. .. .. .. 57.4a 95.0a .. 15.5 .. –10.6a .. .. .. .. .. .. .. 39.4 .. .. –2.8 15.6a 74.8a .. .. 112.2a 52.1a 5.6c 3.5d .. .. 79.0 10.7a .. .. .. –83.8a
–72.2a –73.4 .. .. .. .. .. .. –36.0a –66.9a .. –41.7 .. .. .. .. .. .. .. .. .. –50.5 .. .. –9.9 –3.1a –62.4 a .. .. –82.2a .. –31.3c –38.5d .. .. .. –68.2a .. .. .. ..
a. Refers to the S&P Frontier BMI index. b. Refers to the Nikkei 225 index. c. Refers to the FT 100 index. d. Refers to the S&P 500 index. e. Aggregates not presented because data for high-income economies are not available for 2008.
284
2009 World Development Indicators
About the data
STATES AND MARKETS
Stock markets
5.4
Definitions
The development of an economy’s financial markets
size of the stock market in U.S. dollars and as a
• Market capitalization (also known as market
is closely related to its overall development. Well
percentage of GDP. The number of listed domestic
value) is the share price times the number of shares
functioning financial systems provide good and eas-
companies is another measure of market size. Mar-
outstanding. • Market liquidity is the total value
ily accessible information. That lowers transaction
ket size is positively correlated with the ability to
of shares traded during the period divided by gross
costs, which in turn improves resource allocation and
mobilize capital and diversify risk.
domestic product (GDP). This indicator complements
boosts economic growth. Both banking systems and
Market liquidity, the ability to easily buy and sell
the market capitalization ratio by showing whether
stock markets enhance growth, the main factor in
securities, is measured by dividing the total value
market size is matched by trading. • Turnover ratio
poverty reduction. At low levels of economic develop-
of shares traded by GDP. The turnover ratio—the
is the total value of shares traded during the period
ment commercial banks tend to dominate the finan-
value of shares traded as a percentage of market
divided by the average market capitalization for the
cial system, while at higher levels domestic stock
capitalization—is also a measure of liquidity as well
period. Average market capitalization is calculated as
markets tend to become more active and efficient
as of transaction costs. (High turnover indicates low
the average of the end-of-period values for the cur-
relative to domestic banks.
transaction costs.) The turnover ratio complements
rent period and the previous period. • Listed domes-
Open economies with sound macroeconomic poli-
the ratio of value traded to GDP, because the turn-
tic companies are the domestically incorporated
cies, good legal systems, and shareholder protection
over ratio is related to the size of the market and the
companies listed on the country’s stock exchanges
attract capital and therefore have larger financial mar-
value traded ratio to the size of the economy. A small,
at the end of the year. This indicator does not include
kets. Recent research on stock market development
liquid market will have a high turnover ratio but a low
investment companies, mutual funds, or other col-
shows that modern communications technology and
value of shares traded ratio. Liquidity is an impor-
lective investment vehicles. • S&P/Global Equity
increased financial integration have resulted in more
tant attribute of stock markets because, in theory,
Indices measure the U.S. dollar price change in the
cross-border capital flows, a stronger presence of
liquid markets improve the allocation of capital and
stock markets.
financial firms around the world, and the migration of
enhance prospects for long-term economic growth.
stock exchange activities to international exchanges.
A more comprehensive measure of liquidity would
Many firms in emerging markets now cross-list on
include trading costs and the time and uncertainty
international exchanges, which provides them with
in finding a counterpart in settling trades.
lower cost capital and more liquidity-traded shares.
The S&P/EMDB, the source for all the data in the
However, this also means that exchanges in emerg-
table, provides regular updates on 58 emerging
ing markets may not have enough financial activity
stock markets and 35 frontier markets. Standard
to sustain them, putting pressure on them to rethink
& Poor’s maintains a series of indexes for investors
their operations.
interested in investing in stock markets in developing
The stock market indicators in the table are from
countries. The S&P/IFCI index, Standard & Poor’s
Standard & Poor’s Emerging Markets Data Base. The
leading emerging markets index, is designed to be
indicators include measures of size (market capital-
sufficiently investable to support index tracking port-
ization, number of listed domestic companies) and
folios in emerging market stocks that are legally and
liquidity (value of shares traded as a percentage of
practically open to foreign portfolio investment. The
gross domestic product, value of shares traded as a
S&P/Frontier BMI measures the performance of 35
percentage of market capitalization). The comparabil-
small and illiquid markets. The individual country indi-
ity of such indicators across countries may be limited
ces include all publicly listed equities representing
by conceptual and statistical weaknesses, such as
an aggregate of at least 80 percent or more of mar-
inaccurate reporting and differences in accounting
ket capitalization in each market. These indexes are
standards. The percentage change in stock market
widely used benchmarks for international portfolio
prices in U.S. dollars are from Standard & Poor’s
management. See www.standardandpoors.com for
Global Equity Indices (S&P IFCI) and Standard &
further information on the indexes. Data sources
Poor’s Frontier Broad Market Index (BMI) and is an
Because markets included in Standard & Poor’s
important measure of overall performance. Regula-
emerging markets category vary widely in level of
Data on stock markets are from Standard & Poor’s
tory and institutional factors that can affect investor
development, it is best to look at the entire category
Global Stock Markets Factbook 2008, which draws
confidence, such as entry and exit restrictions, the
to identify the most significant market trends. And
on the Emerging Markets Data Base, supple-
existence of a securities and exchange commission,
it is useful to remember that stock market trends
mented by other data from Standard & Poor’s.
and the quality of laws to protect investors, may influ-
may be distorted by currency conversions, espe-
The firm collects data through an annual survey
ence the functioning of stock markets but are not
cially when a currency has registered a significant
of the world’s stock exchanges, supplemented by
included in the table.
devaluation.
information provided by its network of correspon-
Stock market size can be measured in various
About the data is based on Demirgüç-Kunt and
ways, and each may produce a different ranking of
Levine (1996), Beck and Levine (2001), and Claes-
countries. Market capitalization shows the overall
sens, Klingebiel, and Schmukler (2002).
dents and by Reuters. Data on GDP are from the World Bank’s national accounts data files.
2009 World Development Indicators
285
5.5
Financial access, stability, and efficiency Getting credit
Strength of legal rights index 0–10 (weak to strong)
Depth of % of adult population credit Private Public information credit bureau credit registry index coverage coverage 0–6 (low to high)
June 2008
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
286
Bank capital to asset ratio
1 9 3 4 4 7 9 7 8 8 2 7 3 1 5 7 3 8 3 2 9 3 6 3 3 4 6 10 5 3 3 5 3 6 .. 6 9 3 3 3 5 2 6 4 7 7 3 5 6 7 7 3 7 3 3 2
2009 World Development Indicators
June 2008
0 4 2 4 6 5 5 6 5 2 5 4 1 6 5 4 5 6 1 1 0 2 6 2 1 5 4 5 5 0 2 5 1 3 .. 5 4 6 5 5 6 0 5 2 5 4 2 0 6 6 0 4 5 0 1 2
June 2008
0.0 8.3 0.2 2.7 31.2 2.6 0.0 1.3 3.1 0.9 2.4 57.7 10.5 11.9 0.0 0.0 20.2 30.7 1.9 0.3 0.0 4.9 0.0 1.2 0.6 28.1 58.8 0.0 0.0 0.0 6.9 5.9 2.9 0.0 .. 4.6 0.0 33.9 37.7 2.2 18.4 0.0 0.0 0.1 0.0 28.3 20.7 0.0 0.0 0.7 0.0 0.0 16.1 0.0 1.0 0.7
June 2008
0.0 0.0 0.0 0.0 100.0 24.4 100.0 40.9 0.0 0.0 0.0 0.0 0.0 29.7 69.2 52.9 62.2 5.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 34.5 0.0 69.9 42.5 0.0 0.0 51.6 0.0 71.8 .. 65.2 5.0 35.0 46.8 4.7 83.0 0.0 20.6 0.0 14.8 0.0 0.0 0.0 4.5 98.4 0.0 39.0 19.7 0.0 0.0 0.0
Ratio of 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
2007
2007
2007
2007
2007
.. 5.8 .. .. 13.1 22.5 4.6 6.5 14.2 6.5 15.9 4.3 .. 9.6 13.1 .. 9.9 7.7 .. .. .. .. 5.5 .. .. 6.7 5.5 12.0 11.4 .. .. 10.7 .. 12.5 .. 6.0 6.1 9.5 8.5 5.1 11.8 .. 8.6 .. 9.2 5.5 7.0 .. 20.4 4.3 11.8 6.6 9.2 .. .. ..
.. 3.4 .. .. 2.7 2.4 0.2 2.1 7.2 14.0 0.7 1.2 .. 5.6 3.0 .. 3.0 2.1 .. .. .. .. 0.7 .. .. 0.8 6.7 0.9 3.2 .. .. 1.2 .. 4.8 .. 2.6 0.6 4.0 2.9 24.7 2.1 .. 0.5 .. 0.3 2.7 7.6 .. 2.6 3.4 6.4 4.5 5.8 .. .. ..
–1.6 62.1 –3.5 1.9 28.5 12.1 141.8 125.8 18.2 58.2 27.2 113.4 8.9 53.5 54.7 –16.4 95.9 59.2 12.4 38.5 12.9 5.8 165.1 17.4 1.1 90.0 132.0 125.3 41.6 5.6 –10.1 47.4 20.7 82.9 .. 52.9 205.1 54.0 18.7 89.5 46.3 124.5 95.1 47.2 85.6 122.0 2.9 23.9 31.6 124.9 32.9 108.6 41.4 16.0 12.1 22.8
.. 8.4 6.3 10.9 3.1 11.3 5.4 .. 7.6 6.8 0.3 .. .. 9.3 3.6 7.6 33.1 6.3 .. .. 14.6 10.8 4.0 10.8 10.8 3.1 3.3 4.3 7.4 .. 10.8 6.4 .. 7.0 .. 4.5 .. 8.9 7.1 6.4 .. .. 2.1 3.4 .. .. 10.8 15.0 10.9 .. .. .. 8.1 .. .. 45.5
.. 8.2 7.0 .. .. 11.4 .. .. 8.5 .. .. 4.8 .. 6.8 .. .. 32.2 6.2 .. 8.2 .. .. 2.0 .. .. .. .. 4.8 .. .. .. .. .. .. .. 2.2 .. .. .. 5.7 .. .. .. 6.9 .. .. .. .. .. .. .. .. .. .. .. 32.7
Getting credit
Strength of legal rights index 0–10 (weak to strong) June 2008
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
6 7 8 3 5 3 8 9 3 8 7 4 5 10 .. 7 4 7 4 9 3 8 4 .. 5 7 2 8 10 3 3 5 4 8 6 3 2 .. 8 5 6 9 3 3 8 7 4 6 6 5 3 7 3 8 3 8
Bank capital to asset ratio
Depth of % of adult population credit Private Public information credit bureau credit registry index coverage coverage 0–6 (low to high) June 2008
6 5 4 4 3 0 5 5 5 0 6 2 6 4 .. 6 4 5 0 4 5 0 1 .. 6 4 0 0 6 1 1 3 6 0 3 2 4 .. 5 2 5 5 5 1 0 4 2 4 6 0 6 6 3 4 4 5
June 2008
11.3 0.0 0.0 26.1 21.7 0.0 0.0 0.0 11.8 0.0 0.0 1.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 3.7 6.8 0.0 0.3 .. 8.9 6.5 0.1 0.0 52.9 4.1 0.2 20.6 0.0 0.0 22.7 2.4 1.9 .. 0.0 0.0 0.0 0.0 13.4 0.9 0.1 0.0 23.4 4.9 0.0 0.0 9.7 23.7 0.0 0.0 76.4 0.0
June 2008
60.5 10.0 10.5 0.0 0.0 0.0 100.0 91.0 74.9 0.0 76.2 0.0 25.6 2.1 .. 90.4 31.2 3.7 0.0 0.0 0.0 0.0 0.0 .. 7.2 0.0 0.0 0.0 .. 0.0 0.0 0.0 70.8 0.0 0.0 0.0 0.0 .. 59.6 0.2 81.0 100.0 100.0 0.0 0.0 100.0 0.0 1.5 43.7 0.0 48.6 33.2 5.4 50.0 11.3 61.4
Ratio of bank nonperforming loans to total gross loans
Domestic credit provided by banking sector
STATES AND MARKETS
Financial access, stability, and efficiency
5.5 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
2007
2007
2007
2007
2007
8.4 8.3 6.4 10.0 .. .. 4.5 6.2 7.7 8.7 5.0 6.7 15.2 12.4 .. 9.0 12.0 .. .. 7.9 8.1 14.6 .. .. 7.4 .. .. .. 7.5 .. .. .. 14.4 17.3 .. 6.9 6.4 .. 7.9 .. 3.3 .. 8.8 .. 16.3 5.0 13.2 10.2 13.7 .. 11.6 8.8 11.7 7.4 6.2 ..
6.6 2.4 2.5 9.3 .. .. 0.7 1.7 4.8 2.6 1.5 4.1 2.7 22.7 .. 0.7 3.2 .. .. 0.4 10.1 3.0 .. .. 1.0 9.1 10.1 .. 6.6 .. .. .. 2.5 3.7 .. 7.9 2.6 .. 2.8 .. 0.8 .. 8.0 .. 8.4 0.6 3.2 8.4 1.3 .. 1.3 1.3 5.8 3.1 0.8 ..
51.0 74.4 64.2 40.5 50.5 .. 195.7 74.8 129.7 63.9 294.1 123.5 41.0 37.6 .. 110.2 72.5 14.2 6.8 94.8 186.9 –18.4 160.6 –66.3 61.1 35.5 9.9 16.1 113.4 14.9 .. 110.5 37.7 40.2 30.1 90.1 10.4 28.1 63.7 49.3 204.4 144.4 73.5 7.1 20.2 .. 28.7 45.7 89.3 22.8 19.5 16.2 46.0 46.6 172.3 ..
8.8 2.3 .. 5.9 0.4 8.4 2.6 2.8 .. 10.1 1.1 3.2 .. 8.2 .. 1.4 3.1 19.9 23.5 4.8 2.3 7.7 11.3 3.5 1.5 5.4 28.5 21.7 3.2 .. 15.5 10.1 4.4 3.8 4.1 .. 7.7 5.0 5.3 5.8 0.7 5.0 7.0 .. 6.7 1.8 3.1 6.5 3.5 8.7 20.0 19.6 5.0 3.3 .. ..
.. 1.4 .. .. .. –1.3 .. 1.9 2.3 4.6 1.3 .. .. 6.8 .. .. 5.5 20.4 9.7 6.7 5.0 6.3 .. .. 2.6 .. 33.2 13.8 3.0 .. 13.1 .. 0.4 5.7 .. .. 4.4 .. 4.3 4.4 .. 5.3 .. .. 10.1 .. .. 2.8 .. 5.1 .. .. 5.3 1.3 .. ..
2009 World Development Indicators
287
5.5
Financial access, stability, and efficiency Getting credit
Strength of legal rights index 0–10 (weak to strong)
Depth of % of adult population credit Private Public information credit bureau credit registry index coverage coverage 0–6 (low to high)
June 2008
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
288
Bank capital to asset ratio
8 3 2 4 3 7 4 10 9 6 .. 9 6 4 5 6 5 8 1 2 8 4 1 3 8 3 4 .. 7 9 4 9 8 5 3 3 7 0 2 9 8 5.3 u 4.4 5.2 4.7 5.9 4.9 5.4 6.3 5.2 2.6 4.8 4.5 6.5 5.9
2009 World Development Indicators
June 2008
5 4 2 6 1 5 0 4 4 2 .. 6 5 5 0 5 4 5 0 0 0 5 0 1 4 5 5 .. 0 3 5 6 6 6 3 0 4 3 0 0 0 2.9 u 1.0 3.2 3.0 3.4 2.4 1.7 4.1 3.5 2.3 2.1 1.4 4.2 4.2
June 2008
4.5 0.0 0.3 0.0 4.4 0.0 0.0 0.0 1.4 2.7 .. 0.0 45.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.6 0.0 14.9 12.7 .. 0.0 0.0 6.5 0.0 0.0 15.4 2.3 0.0 13.4 7.8 0.1 0.0 0.0 5.6 u 1.2 7.6 7.4 7.9 5.4 8.7 4.9 9.9 4.8 0.7 2.5 6.0 17.3
June 2008
24.7 10.0 0.0 14.1 0.0 91.9 0.0 48.3 39.9 0.0 .. 64.8 8.1 8.7 0.0 43.5 100.0 22.5 0.0 0.0 0.0 31.8 0.0 0.0 37.6 0.0 26.3 .. 0.0 3.0 7.7 100.0 100.0 98.0 2.2 0.0 0.0 0.0 0.0 0.1 0.0 20.9 u 0.2 19.1 14.4 25.9 12.6 4.2 18.2 34.3 0.4 2.6 5.0 45.7 36.0
Ratio of 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 2007
%
%
% of GDP
Lending rate minus deposit rate percentage points
2007
2007
2007
2007
7.3 13.3 9.2 9.9 10.4 17.1 17.7 9.3 10.6 8.4 .. 7.9 7.0 .. .. 22.9 4.0 4.9 .. .. .. 9.5 .. .. .. 7.7 13.0 .. 10.3 12.5 12.6 8.9 10.3 10.5 .. 8.3 .. .. .. .. .. 8.9 m .. 9.6 9.5 9.9 9.7 .. 13.0 10.2 .. 6.5 .. 6.5 6.4
9.7 2.5 27.2 2.1 18.6 3.8 31.7 1.8 2.5 2.5 .. 1.4 0.7 9.6 .. 4.0 0.5 0.3 .. .. .. 7.9 .. .. .. 17.3 3.6 .. 4.1 13.2 6.3 0.9 1.4 1.1 .. 1.2 .. .. .. 10.8 .. 2.7 m .. 3.0 3.9 2.5 3.5 .. 3.1 2.3 .. 8.4 .. 1.4 1.9
35.7 25.7 8.7 17.5 24.8 30.8 9.8 80.7 51.5 82.0 .. 198.1 192.5 45.0 0.2 6.8 131.7 189.7 38.8 15.4 14.1 104.9 –29.9 22.0 26.2 71.5 48.7 .. 5.2 61.7 66.6 190.5 240.6 24.8 .. 22.7 96.2 8.3 9.9 16.6 93.1 162.5 w 36.9 75.0 90.5 58.7 73.2 116.3 38.8 59.9 47.8 61.5 80.6 194.8 138.7
6.6 4.9 9.1 .. .. 7.1 15.0 4.8 4.3 2.3 .. 4.0 .. 8.0 .. 6.1 2.5 1.0 1.8 14.4 7.3 4.2 14.3 .. 5.9 .. .. .. 9.8 5.8 .. .. .. 6.6 .. 6.4 3.7 4.8 5.0 9.7 457.5 6.5 m 10.3 6.6 7.3 6.4 7.1 5.9 6.3 7.0 4.3 6.7 10.0 4.0 ..
6.2 .. 8.6 .. .. 6.7 6.6 3.0 .. 2.0 .. 4.1 .. 0.5 .. 4.1 1.6 1.0 .. .. 2.6 3.6 .. .. 4.8 .. .. .. 10.1 .. .. 0.0 3.6 1.8 .. .. 7.0 .. 2.1 6.9 330.2
About the data
STATES AND MARKETS
Financial access, stability, and efficiency
5.5
Definitions
Financial sector development has positive impacts
The size and mobility of international capital
• Strength of legal rights index measures the degree
on economic growth and poverty. The size of the
flows make it increasingly important to monitor the
to which collateral and bankruptcy laws protect the
sector determines the resources mobilized for invest-
strength of financial systems. Robust financial sys-
rights of borrowers and lenders and thus facilitate
ment. Access to finance can expand opportunities for
tems can increase economic activity and welfare,
lending. Higher values indicate that the laws are bet-
all with higher levels of access and use of banking
but instability in the financial system can disrupt
ter designed to expand access to credit. • Depth of
services associated with lower financing obstacles
financial activity and impose widespread costs on
credit information index measures rules affecting the
for people and businesses. A stable financial sys-
the economy. The ratio of bank capital to assets,
scope, accessibility, and quality of information avail-
tem that promotes efficient savings and investment
a measure of bank solvency and resiliency, shows
able through public or private credit registries. Higher
is also crucial for a thriving democracy and market
the extent to which banks can deal with unexpected
values indicate the availability of more credit informa-
economy. The banking system is the largest sector
losses. Capital includes tier 1 capital (paid-up shares
tion. • Public credit registry coverage is the number
in the financial system in most countries, so most
and common stock), a common feature in all coun-
of individuals and firms listed in a public credit reg-
indicators in the table cover the banking system.
tries’ banking systems, and total regulatory capital,
istry with current information on repayment history,
There are several aspects of access to financial
which includes several types of subordinated debt
unpaid debts, or credit outstanding as a percentage
services: availability, cost, and quality of services.
instruments that need not be repaid if the funds
of the adult population. • Private credit bureau cov-
The development and growth of credit markets
are required to maintain minimum capital levels
erage is the number of individuals or firms listed by
depend on access to timely, reliable, and accurate
(tier 2 and tier 3 capital). Total assets include all
a private credit bureau with current information on
data on borrowers’ credit experiences. For secured
nonfinancial and financial assets. Data are from
repayment history, unpaid debts, or credit outstand-
transactions, such as mortgages or vehicle loans,
internally consistent financial statements.
ing as a percentage of the adult population. • Bank
rapid access to information in property registries is
The ratio of bank nonperforming loans to total gross
capital to asset ratio is the ratio of bank capital and
also vital, and for small business loans corporate
loans, a measure of bank health and efficiency, helps
reserves to total assets. Capital and reserves include
registry data are needed. Access to credit can be
to identify problems with asset quality in the loan port-
funds contributed by owners, retained earnings, gen-
improved by increasing information about potential
folio. A high ratio may signal deterioration of the credit
eral and special reserves, provisions, and valuation
borrowers’ creditworthiness and making it easy to
portfolio. International guidelines recommend that
adjustments. • Ratio of bank nonperforming loans to
create and enforce collateral agreements. Lenders
loans be classified as nonperforming when payments
total gross loans is the value of nonperforming loans
look at a borrower’s credit history and collateral.
of principal and interest are 90 days or more past
divided by the total value of the loan portfolio (includ-
Where credit registries and effective collateral laws
due or when future payments are not expected to be
ing nonperforming loans before the deduction of loan
are absent—as in many developing countries—
received in full. See the International Monetary Fund’s
loss provisions). The amount recorded as nonperform-
banks make fewer loans. Indicators that cover finan-
(IMF) Global Financial Stability Report for details.
ing should be the gross value of the loan as recorded
cial access, or getting credit, include the strength
Domestic credit by the banking sector as a share of
on the balance sheet, not just the amount overdue.
of legal rights index (ranges from 0, weak, to 10,
GDP is a measure of banking sector depth and finan-
• Domestic credit provided by banking sector is all
strong), depth of credit information index (ranges
cial sector development in terms of size. In a few coun-
credit to various sectors on a gross basis, except to
from 0, low, to 6, high), public registry coverage, and
tries governments may hold international reserves
the central government, which is net. The banking
private bureau coverage.
as deposits in the banking system rather than in the
sector includes monetary authorities, deposit money
The strength of legal rights index is based on eight
central bank. Since the claims on the central govern-
banks, and other banking institutions for which data
aspects related to legal rights in collateral law and
ment are a net item (claims on the central govern-
are available. • Interest rate spread is the interest
two aspects in bankruptcy law. The methodology for
ment minus central government deposits), this net
rate charged by banks on loans to prime customers
the index in this edition includes three improvements.
figure may be negative, resulting in a negative figure of
minus the interest rate paid by commercial or similar
First, a standardized case scenario with assumptions
domestic credit provided by the banking sector.
banks for demand, time, or savings deposits. • Risk
was introduced to bring the indicator in line with
The interest rate spread—the margin between
premium on lending is the interest rate charged by
other Doing Business indicators. Second, the indica-
the cost of mobilizing liabilities and the earnings on
banks on loans to prime private sector customers
tor focuses on revolving movable collateral, such as
assets—is a measure of financial sector efficiency in
minus the “risk-free” treasury bill interest rate at
accounts receivable and inventory, rather than tangi-
intermediation. A narrow interest rate spread means
which short-term government securities are issued
ble movable collateral, such as equipment. Third, the
low transaction costs, which lowers the cost of funds
or traded in the market.
indicator no longer considers whether management
for investment, crucial to economic growth. Data sources
remains in place during reorganization, thus better
The risk premium on lending is the spread between
accommodating economies that adopt reorganization
the lending rate to the private sector and the “risk-
Data on getting credit are from the World Bank’s
procedures similar to U.S. Chapter 11 reorganization
free” government rate. Spreads are expressed as
Doing Business project (www.doingbusiness.org).
or redressement procedures in civil law systems. The
annual averages. A small spread indicates that the
Data on bank capital and nonperforming loans are
depth of credit information index assesses six fea-
market considers its best corporate customers to be
from the IMF’s Global Financial Stability Report.
tures of the public registry or the private credit bureau
low risk. A negative rate indicates that the market
Data on credit and interest rates are from the
(or both). For more information on these indexes, see
considers its best corporate clients to be lower risk
IMF’s International Financial Statistics.
www.doingbusiness.org/MethodologySurveys/.
than the government.
2009 World Development Indicators
289
5.6
Tax policies Tax revenue collected by central government
% of GDP
Afghanistanc Albaniac Algeriac Angola Argentina Armeniac Australia Austria Azerbaijanc Bangladeshc Belarusc Belgium Beninc Bolivia Bosnia and Herzegovina Botswanac Brazilc Bulgariac Burkina Faso Burundic Cambodia Cameroonc Canadac Central African Republicc Chad Chile Chinac Hong Kong, China Colombia Congo, Dem. Rep.c Congo, Rep. Costa Ricac Côte d’Ivoirec Croatiac Cuba Czech Republicc Denmark Dominican Republicc Ecuadorc Egypt, Arab Rep.c El Salvador Eritrea Estonia Ethiopiac Finland France Gabon Gambia, Thec Georgiac Germany Ghanac Greece Guatemalac Guineac Guinea-Bissau Haiti
290
Taxes payable by businesses
Number of payments
Time to prepare, file, and pay taxes hours
Total tax rate % of profi t
%
June 2008
June 2008
2006–08b
2006–08b
2006–08b
.. 20 .. .. 35 .. 45 50 35 .. .. 50 35 13 15 25 28 24 .. .. 20 .. 29 .. .. 40 45 17 22 50 .. 15 10 45 .. 15 59 30 25 20 .. .. 21 35 32 .. .. .. 12 45 25 40 31 .. .. ..
.. 2,413 .. .. 38,339 .. 104,167 69,473 13,793 .. .. 47,048 .. .. .. 19,967 13,462 5,414 .. .. 36,973 .. 100,970 .. .. 9,722 175,695 15,484 45,564 4,631 4,681 14,575 5,386 4,014 .. 53,482 63,598 26,209 62,800 .. 13,600 .. .. .. 84,457 .. .. .. .. 340,553 10,435 102,650 38,714 .. .. ..
.. 10 .. .. 35 .. 30 25 22 .. .. 33 38 25 30 15 15 10 .. .. 20 .. 38 .. .. 35 25 18 33 40 .. 30 35 .. .. 21 25 30 25 20 25 .. 21 30 26 33 .. .. 20 15 22 25 31 .. .. ..
2000
2007
June 2008
.. 16.1 36.9 .. 9.8 .. 23.7 19.6 12.7 7.6 16.6 27.4 15.5 13.2 .. .. 11.3 18.3 .. 13.6 8.2 11.2 15 .. .. 16.7 6.8 .. 11.7 3.5 9.2 12.1 14.6 26.2 .. 15.4 31 .. .. 13.4 10.7 .. 15.9 10.2 24.6 23.2 .. .. 7.7 11.9 17.2 23.3 10.1 11.1 .. ..
5.8 .. 29.6 .. .. 16 25.1 20.1 .. 8 24 25.8 16.3 17 22.3 .. .. 24.6 12 .. 8.2 .. 15.2 .. .. 21.5 9.4 .. 13.6 .. 6.2 15.2 15.5 23.4 .. 15.1 35.5 16.6 .. 15.4 14 .. 17.2 .. 21.8 21.8 .. .. 17.7 11.8 22.6 19.9 11.9 .. .. ..
8 44 34 31 9 50 12 22 23 21 112 11 55 41 51 19 11 17 45 32 27 41 9 54 54 10 9 4 31 32 61 43 66 17 .. 12 9 9 8 29 53 18 10 20 20 11 26 50 30 16 33 10 39 56 46 42
2009 World Development Indicators
Highest marginal tax ratea
275 244 451 272 453 958 107 170 376 302 1,188 156 270 1,080 428 140 2,600 616 270 140 137 1,400 119 504 122 316 504 80 256 308 606 282 270 196 .. 930 135 480 600 711 320 216 81 198 269 132 272 376 387 196 224 224 344 416 208 160
36.4 50.5 74.2 53.2 108.1 36.6 50.3 54.5 41.1 39.5 117.5 58.1 73.2 78.1 44.1 17.1 69.4 34.9 44.6 278.7 22.6 51.4 45.4 203.8 60.5 25.9 79.9 24.2 78.4 229.8 65.5 55.7 45.4 32.5 .. 48.6 29.9 35.7 34.9 46.1 34.9 84.5 48.6 31.1 47.8 65.4 44.7 292.4 38.6 50.5 32.7 47.4 36.5 49.9 45.9 40.1
Individual On income over $
Corporate %
Tax revenue collected by central government
% of GDP
Honduras Hungary Indiac Indonesiac Iran, Islamic Rep.c Iraq Ireland Israel Italy Jamaicac Japan Jordanc Kazakhstanc Kenyac Korea, Dem. Rep. Korea, Rep.c Kuwait Kyrgyz Republicc Lao PDR Latviac Lebanon Lesothoc Liberia Libya Lithuania Macedonia, FYRc Madagascar Malawi Malaysiac Mali Mauritania Mauritiusc Mexicoc Moldovac Mongolia Moroccoc Mozambique Myanmar Namibiac Nepalc Netherlands New Zealand Nicaraguac Niger Nigeria Norway Omanc Pakistanc Panamac Papua New Guineac Paraguayc Peruc Philippinesc Poland Portugal Puerto Rico
2000
2007
.. 21.9 9 11.6 6.3 .. 26.1 29 23.2 24.7 .. 19 10.2 16.8 .. 16.1 1.3 11.7 .. 14.2 12.2 32.7 .. .. 14.6 .. 11.3 .. 13.8 13.2 .. 18.2 11.7 14.7 .. 19.9 .. 3 30 8.7 22.3 29.5 13.8 .. .. 27.4 7.2 10.1 10.2 19 .. 12.2 13.7 16 21.5 ..
16.4 21.5 11.6 .. 7.3 .. 25.6 28.4 23.3 28.9 .. 26.7 12.3 18 .. 17.9 0.9 16.6 10.5 16.6 15.2 56.8 .. .. 18.1 .. 11.4 .. .. 15.5 .. 17.6 .. 20.6 25.3 25.2 .. 4.7 .. 9.8 23.9 31.4 18 11.7 .. 28.9 .. 9.8 .. .. 11.5 15.6 14 18.4 22.6 ..
Taxes payable by businesses
STATES AND MARKETS
Tax policies
5.6
Highest marginal tax ratea
Number of payments
Time to prepare, file, and pay taxes hours
Total tax rate % of profi t
%
June 2008
June 2008
June 2008
2006–08b
2006–08b
2006–08b
224 330 271 266 344 312 76 230 334 414 355 101 271 417 .. 250 118 202 560 279 180 324 158 .. 166 75 238 292 145 270 696 161 549 234 204 358 230 .. 375 408 180 70 240 270 938 87 62 560 482 194 328 424 195 418 328 218
49.3 57.5 71.5 37.3 44.2 24.7 28.8 33.9 73.3 51.3 55.4 31.1 36.4 50.9 .. 33.7 14.4 61.4 33.7 33.0 36.0 18.0 35.8 .. 46.4 18.4 42.8 31.4 34.5 51.4 98.7 22.2 51.5 42.1 30.3 44.6 34.3 .. 25.3 34.1 39.1 35.6 63.2 42.3 32.2 41.6 21.6 28.9 50.6 41.7 35.0 41.2 50.8 40.2 43.6 64.7
25 36 30 35 35 .. 42 49 43 .. 40 .. 20 .. .. 35 0 .. .. 25 .. .. .. .. 27 24 .. .. 28 .. .. 15 28 18 .. .. 32 .. 35 .. 52 39 30 .. .. .. 0 35 27 .. 8 30 32 40 42 33
26,455 .. 6,344 22,173 9,183 .. 43,591 111,695 102,166 .. 157,895 .. .. .. .. 69,980 .. .. .. .. .. .. .. .. .. 17,283 .. .. 72,254 .. .. .. 9,496 2,423 .. .. 38,814 .. 28,694 .. 72,631 40,463 26,455 .. .. 120,148 .. 11,490 30,000 .. .. 62,100 12,077 35,052 85,202 50,000
25 16 30 30 25 .. 13 27 28 .. 30 .. 30 .. .. 25 .. .. .. 15 .. .. .. .. 15 15 .. .. 26 .. .. 15 28 0 .. .. 32 .. 35 .. 26 30 30 .. .. 28 .. 37 30 .. 10 30 35 19 25 19
47 14 60 51 22 13 9 33 15 72 13 26 9 41 .. 14 14 75 34 7 19 21 32 .. 15 40 25 19 12 58 38 7 27 53 42 28 37 .. 37 34 9 8 64 42 35 4 14 47 59 33 35 9 47 40 8 16
Individual On income over $
2009 World Development Indicators
Corporate %
291
5.6
Tax policies Tax revenue collected by central government
% of GDP
Romania Russian Federation Rwandac Saudi Arabia Senegalc Serbiac Sierra Leonec Singaporec Slovak Republic Sloveniac Somalia South Africa Spain Sri Lankac Sudanc Swazilandc Sweden Switzerlandc Syrian Arab Republicc Tajikistanc Tanzania Thailand Timor-Leste Togoc Trinidad and Tobagoc Tunisiac Turkeyc Turkmenistan Ugandac Ukrainec United Arab Emiratesc United Kingdom United States Uruguayc Uzbekistan Venezuela, RBc Vietnam West Bank and Gaza Yemen, Rep.c Zambiac Zimbabwec World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
2000
2007
11.7 13.6 .. .. 16.1 .. 10.2 15.4 .. 20.6 .. 24 16.2 14.5 6.4 .. 20.6 11.1 17.4 7.7 .. .. .. .. 22.1 21.3 .. .. 10.4 14.1 1.7 28.9 12.7 16.7 .. 13.3 .. .. 9.4 18.6 .. 15.7 w .. 11.7 8.9 .. 11.6 7.7 14.8 11.4 15.1 9.3 .. 16.5 19.1
12 16.7 .. .. .. .. .. 14.4 14.1 21 .. 29.1 13.9 14.2 .. .. .. 10.5 .. .. .. 16.2 .. 16.4 29.1 21.2 18.5 .. 12.3 16.7 .. 28 12.2 18.7 .. 15.5 .. .. .. 17.2 .. 16.8 w .. 14.1 11.6 .. 14.0 10.1 17.3 .. 16.4 11.3 .. 17.0 18.5
Taxes payable by businesses
Highest marginal tax ratea
Number of payments
Time to prepare, file, and pay taxes hours
Total tax rate % of profi t
%
June 2008
June 2008
June 2008
2006–08b
2006–08b
2006–08b
113 22 34 14 59 66 28 5 31 22 .. 9 8 62 42 33 2 24 20 54 48 23 15 53 40 22 15 .. 32 99 14 8 10 53 106 70 32 27 44 37 52 31 u 41 34 34 34 36 28 50 35 27 32 38 16 14
202 448 160 79 666 279 399 84 325 260 .. 200 234 256 180 104 122 63 336 224 172 264 640 270 114 228 223 .. 222 848 12 105 187 336 196 864 1,050 154 248 132 256 300 u 314 352 346 361 339 270 384 428 295 293 312 181 201
48.0 48.7 33.7 14.5 46.0 34.0 233.5 27.9 47.4 36.7 .. 34.2 60.2 63.7 31.6 36.6 54.5 28.9 43.5 85.5 45.1 37.8 28.3 48.2 33.1 59.1 45.5 .. 34.5 58.4 14.4 35.3 42.3 58.5 90.6 56.6 40.1 16.8 47.8 16.1 63.7 49.3 u 68.4 44.1 42.9 45.8 52.3 39.9 48.5 48.6 42.2 40.4 66.9 40.3 48.2
16 13 .. 0 .. 15 .. 20 19 41 .. 40 27 35 .. 33 32 .. .. .. 30 37 .. .. 25 .. 35 .. 30 15 0 40 35 25 25 34 40 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
.. .. .. .. .. .. .. 222,222 .. 19,582 .. 52,688 72,752 4,419 .. 10,760 77,239 .. .. .. .. 118,624 .. .. .. .. 36,752 .. 2,899 .. .. 50,435 326,450 3,970 .. 93,767 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
16 24 .. .. .. 10 .. 18 19 22 .. 28 30 35 .. 30 28 .. .. .. 30 30 .. .. 25 .. 20 .. 30 25 .. 28 35 25 10 34 28 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
Individual On income over $
Corporate %
a. Data are from PriceWaterhouseCooper’s Worldwide Tax Summaries online. b. Data are for the most recent year available. c. Data on central government taxes were reported on a cash basis and have been adjusted to the accrual framework of the International Monetary Fund’s Government Finance Statistics Manual 2001.
292
2009 World Development Indicators
About the data
STATES AND MARKETS
Tax policies
5.6
Definitions
Taxes are the main source of revenue for most gov-
they have certain levels of start-up capital, employ-
• Tax revenue collected by central government
ernments. The sources of tax revenue and their rel-
ees, and turnover. For details about the assump-
is compulsory transfers to the central government
ative contributions are determined by government
tions, see the World Bank’s Doing Business 2009.
for public purposes. Certain compulsory transfers
policy choices about where and how to impose taxes
A potentially important influence on both domestic
such as fines, penalties, and most social security
and by changes in the structure of the economy. Tax
and international investors is a tax system’s progres-
contributions are excluded. Refunds and corrections
policy may refl ect concerns about distributional
sivity, as reflected in the highest marginal tax rate
of erroneously collected tax revenue are treated as
effects, economic efficiency (including corrections
levied at the national level on individual and corpo-
negative revenue. The analytic framework of the
for externalities), and the practical problems of
rate income. Data for individual marginal tax rates
International Monetary Fund’s (IMF) Government
administering a tax system. There is no ideal level
generally refer to employment income. In some coun-
Finance Statistics Manual 2001 (GFSM 2001) is
of taxation. But taxes influence incentives and thus
tries the highest marginal tax rate is also the basic
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
The level of taxation is typically measured by tax
ent corporate tax rates may be levied, depending on
GFSM 2001 accrual framework. These countries are
revenue as a share of gross domestic product (GDP).
the type of business (mining, banking, insurance,
footnoted in the table. • Number of tax payments
Comparing levels of taxation across countries pro-
agriculture, manufacturing), ownership (domestic or
by businesses is the total number of taxes paid by
vides a quick overview of the fiscal obligations and
foreign), volume of sales, and whether surtaxes or
businesses during one year. When electronic filing is
incentives facing the private sector. The table shows
exemptions are included. The corporate tax rates in
available, the tax is counted as paid once a year even
only central government data, which may significantly
the table are mainly general rates applied to domes-
if payments are more frequent. • Time to prepare,
understate the total tax burden, particularly in coun-
tic companies. For more detailed information, see
file, and pay taxes is the time, in hours per year, it
tries where provincial and municipal governments are
the country’s laws, regulations, and tax treaties and
takes to prepare, file, and pay (or withhold) three
large or have considerable tax authority.
PricewaterhouseCoopers’s Worldwide Tax Summaries
major types of taxes: the corporate income tax, the
Online (www.pwc.com).
value-added or sales tax, and labor taxes, includ-
Low ratios of tax revenue to GDP may reflect weak administration and large-scale tax avoidance or eva-
ing payroll taxes and social security contributions.
sion. Low ratios may also reflect a sizable parallel
• Total tax rate is the total amount of taxes pay-
economy with unrecorded and undisclosed incomes.
able by businesses (except for consumption taxes)
Tax revenue ratios tend to rise with income, with
after accounting for deductions and exemptions as
higher income countries relying on taxes to finance
a percentage of profi t. For further details on the
a much broader range of social services and social
method used for assessing the total tax payable, see
security than lower income countries are able to.
the World Bank’s Doing Business 2009. • Highest
The indicators covering taxes payable by busi-
marginal tax rate is the highest rate shown on the
nesses measure all taxes and contributions that
national schedule of tax rates applied to the annual
are government mandated (at any level—federal,
taxable income of individuals and corporations. Also
state, or local), apply to standardized businesses,
presented are the income levels for individuals above
and have an impact in their income statements. The
which the highest marginal tax rates levied at the
taxes covered go beyond the definition of a tax for
national level apply.
government national accounts (compulsory, unrequited payments to general government) and also measure any imposts that affect business accounts. The main differences are in labor contributions and value-added taxes. The indicators account for government-mandated contributions paid by the employer to a requited private pension fund or workers insurance fund but exclude value-added taxes
Data sources
because they do not affect the accounting profits of
Data on central government tax revenue are from
the business—that is, they are not reflected in the
print and electronic editions of the IMF’s Govern-
income statement.
ment Finance Statistics Yearbook. Data on taxes
To make the data comparable across countries,
payable by businesses are from Doing Business
several assumptions are made about businesses.
2009 (www.doingbusiness.org). Data on individ-
The main assumptions are that they are limited liabil-
ual and corporate tax rates are from Pricewater-
ity companies, they operate in the country’s most
houseCoopers’s Worldwide Tax Summaries Online
populous city, they are domestically owned, they per-
(www.pwc.com).
form general industrial or commercial activities, and
2009 World Development Indicators
293
5.7
Military expenditures and arms transfers Military expenditures
% of central government expenditure
% of GDP
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
294
Armed forces personnel
thousands
Arms transfers
1990 $ millions
% of labor force
Exports
Imports
2000
2007
2000
2007
2000
2007
2000
2007
2000
2007
2000
2007
.. 1.2 3.4 2.4 1.3 3.6 1.9 1.0 2.3 1.4 1.3 1.4 0.6 1.7 3.6 3.0 1.6 2.5 1.2 6.0 2.2 1.3 1.1 1.0 1.9 3.7 1.8a .. 3.6 1.0 1.4 .. .. 3.6 .. 2.0 1.5 0.9 1.7 3.2 0.9 36.4 1.4 7.6 1.3 2.5 1.8 0.8 0.6 1.5 1.0 4.3 0.8 1.5 4.4 ..
1.5 1.8 2.9 3.7 0.7 3.0 2.0 1.0 3.0 1.1 1.6 1.1 1.1 1.2 1.3 2.6 1.6 2.0 1.4 4.8 0.9 1.4 1.3 1.1 1.0 3.4 2.0a .. 3.1 1.7 1.1 .. 1.5 2.0 .. 1.5 1.3 0.5 2.8 2.5 0.6 .. 1.9 1.9 1.3 2.3 1.1 0.6 7.5 1.3 0.7 3.5 0.5 .. 4.0 ..
.. 5.4 16.5 .. 6.2 .. 7.5 2.5 13.8 14.9 5.3 3.2 4.7 6.5 .. .. 7.1 7.8 .. 30.3 16.8 12.0 6.0 .. .. 17.7 19.6a .. 19.0 11.4 5.9 .. .. 7.8 .. 6.1 4.2 .. .. 12.3 4.3 .. 4.7 18.0 3.7 5.7 .. .. 5.3 4.7 3.3 9.8 7.5 11.8 .. ..
8.7 .. 15.7 .. .. 18.1 7.8 2.5 .. 11.7 4.7 2.6 7.6 5.7 3.5 .. .. 6.3 9.6 .. 12.8 .. 7.0 .. .. 19.5 17.9a .. 12.3 .. 5.2 .. 7.1 5.0 .. 4.5 3.7 3.5 .. 8.4 3.3 .. 7.0 .. 3.7 5.3 .. .. 32.7 4.4 2.6 8.3 3.6 .. .. ..
400 68 305 118 102 42 52 41 87 137 91 39 7 70 76 10 673 114 11 46 360 22 69 5 35 117 3,910 .. 247 93 15 15 15 101 85 63 22 40 58 679 29 200 8 353 35 389 7 1 33 221 8 163 53 19 9 5
51 15 334 117 107 42 55 35 82 221 183 39 8 83 9 11 721 75 11 51 191 23 64 3 35 103 2,885 .. 411 143 12 10 19 21 76 27 30 65 58 866 33 202 7 138 32 353 7 1 33 244 14 161 35 19 9 0
6.3 5.2 2.8 1.9 0.6 3.1 0.5 1.0 2.4 0.2 1.9 0.9 0.3 2.0 4.1 1.6 0.8 3.5 0.2 1.4 6.1 0.4 0.4 0.3 1.0 1.9 0.5 .. 1.3 0.5 1.2 0.9 0.2 5.0 1.6 1.2 0.8 1.1 1.2 3.4 1.2 14.1 1.1 1.2 1.4 1.5 1.3 0.1 1.4 0.5 0.1 3.4 1.7 0.5 1.8 0.2
0.6 1.0 2.4 1.6 0.6 2.8 0.5 0.8 1.9 0.3 3.8 0.8 0.2 1.9 0.5 1.6 0.7 2.2 0.2 1.2 2.6 0.3 0.3 0.2 0.8 1.5 0.4 .. 1.8 0.6 0.8 0.5 0.3 1.1 1.5 0.5 1.0 1.5 1.0 3.6 1.2 10.4 1.0 0.4 1.2 1.3 1.1 0.1 1.4 0.6 0.1 3.1 0.7 0.4 1.4 0.0
.. .. .. 1 2 .. 43 21 .. .. 293 22 .. .. 4 .. 26 2 .. .. .. .. 109 .. .. 1 228 .. .. .. .. .. .. 2 .. 78 20 .. .. 38 .. 0 .. .. 9 1,033 .. .. 22 1,622 .. 2 .. .. .. ..
.. .. .. .. .. .. 1 86 .. .. 35 10 .. .. .. .. 24 7 .. .. .. .. 343 .. .. .. 355 .. .. .. .. .. .. .. .. 13 5 .. .. .. .. .. .. .. 24 2,690 .. .. .. 3,395 .. 23 .. .. .. ..
33 3 428 157 224 2 366 25 3 205 41 39 6 19 25 50 126 7 .. 1 .. 1 560 .. 15 177 1,874 .. 62 41 0 .. 32 70 .. 16 64 13 12 826 16 4 27 125 518 58 .. .. 6 135 1 651 1 19 .. ..
37 5 700 4 41 .. 685 335 27 17 254 171 3 5 .. .. 175 38 4 .. 36 0 623 9 3 615 1,424 .. 38 17 1 .. .. 14 .. 15 201 2 45 418 .. 271 30 .. 110 63 21 .. 4 85 13 2,089 .. .. .. ..
2009 World Development Indicators
Military expenditures
% of GDP
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
Armed forces personnel
% of central government expenditure
thousands
STATES AND MARKETS
Military expenditures and arms transfers
5.7
Arms transfers
1990 $ millions
% of labor force
Exports
Imports
2000
2007
2000
2007
2000
2007
2000
2007
2000
2007
2000
2007
0.5 1.7 3.1 1.0 3.8 .. 0.7 7.9 2.0 0.5 1.0 6.2 0.8 1.3 .. 2.5 7.1 2.9 2.0 0.9 5.5 3.6 .. 3.1 1.4 1.9 1.2 0.7 1.6 2.4 3.5 0.2 0.5 0.4 2.2 2.3 1.3 2.3 2.7 1.0 1.6 1.2 0.8 1.1 0.8 1.7 10.6 4.0 1.0 0.9 1.1 1.7 1.1 1.9 2.0 ..
0.6 1.1 2.5 1.2 3.0 .. 0.5 8.3 1.8 0.7 0.9 6.9 1.2 1.8 .. 2.7 4.3 3.3 .. 1.8 5.8 2.5 0.8 1.1 1.2 2.1 1.1 1.2 2.1 2.3 3.1 0.2 0.4 0.4 1.2 3.2 0.9 .. 3.4 1.5 1.5 1.1 0.7 1.0 0.6 1.4 11.3 3.5 .. 0.5 0.8 1.1 0.8 2.0 1.7 ..
.. 4.1 19.5 5.7 22.5 .. 2.6 17.6 5.2 1.5 .. 23.1 5.7 7.8 .. 14.4 18.9 18.0 .. 3.2 17.7 7.8 .. .. 5.2 .. 11.5 .. 10.5 20.7 .. 1.0 3.4 1.4 .. 12.0 .. .. 8.6 .. 4.0 3.5 4.7 .. .. 5.3 40.4 23.4 4.6 2.9 .. 9.7 6.2 5.7 5.1 ..
2.8 2.6 16.5 .. 14.7 .. 1.6 19.8 4.5 1.1 .. 19.0 8.5 9.3 .. 13.3 13.5 18.1 .. 6.6 17.9 5.3 .. .. 3.9 .. 10.0 .. .. 15.3 .. 0.8 .. 1.3 5.0 11.0 .. .. .. 12.6 3.6 3.2 3.5 10.6 .. 4.5 .. 21.7 .. .. 4.5 6.7 4.7 5.8 4.2 ..
14 58 2,372 492 753 479 12 181 503 3 249 149 99 27 1,244 688 20 14 129 9 77 2 15 77 17 24 29 6 116 15 21 2 208 13 16 241 6 429 9 90 57 9 16 11 107 27 48 900 12 4 35 193 149 239 91 ..
20 37 2,576 582 563 362 10 185 436 3 242 111 81 29 1,295 692 23 21 129 17 76 2 2 76 24 19 22 7 134 12 21 2 286 9 16 246 11 513 15 131 41 9 14 10 162 19 47 921 12 3 26 198 147 142 91 ..
0.6 1.4 0.6 0.5 3.6 7.8 0.7 7.3 2.1 0.3 0.4 11.6 1.3 0.2 11.2 3.0 1.8 0.7 5.4 0.8 5.8 0.3 1.3 4.1 1.0 2.8 0.4 0.1 1.2 0.6 2.0 0.3 0.5 0.7 1.7 2.4 0.1 1.7 1.5 1.0 0.7 0.5 0.9 0.3 0.3 1.1 5.4 2.2 0.9 0.2 1.4 1.7 0.5 1.4 1.7 ..
0.8 0.9 0.6 0.5 2.0 5.0 0.5 6.5 1.7 0.3 0.4 6.8 1.0 0.2 10.4 2.8 1.7 0.9 4.5 1.4 5.1 0.2 0.1 3.4 1.5 2.1 0.2 0.1 1.2 0.4 1.6 0.3 0.6 0.6 1.4 2.2 0.1 1.8 2.2 1.1 0.5 0.4 0.6 0.2 0.4 0.8 4.9 1.6 0.8 0.1 0.8 1.4 0.4 0.8 1.6 ..
.. .. 16 16 0 .. .. 316 192 .. .. .. 16 .. 13 8 99 .. .. .. 45 .. .. 11 3 0 .. 1 8 .. .. .. .. 3 .. .. .. .. .. .. 259 1 .. .. .. 3 .. 3 .. .. .. 4 .. 43 .. ..
.. 6 14 8 10 .. .. 238 562 .. .. 13 12 .. .. 214 .. .. .. .. .. .. .. 9 .. .. .. .. .. .. .. .. .. 4 .. .. .. .. .. .. 1,355 .. .. .. .. 14 1 9 .. .. .. .. 4 135 30 ..
.. 14 826 170 413 .. 0 364 241 5 431 130 144 .. 19 1,266 245 .. 7 3 4 6 8 145 5 11 .. .. 40 7 31 .. 227 .. .. 123 0 3 18 11 142 45 .. .. 42 263 120 .. 0 .. 6 24 9 159 2 ..
.. 192 1,318 475 297 244 13 891 176 1 519 83 21 25 9 1,807 117 1 4 51 3 1 .. 3 4 0 .. .. 550 7 .. 4 11 .. .. 44 .. 20 72 5 210 70 .. 0 15 483 4 .. .. .. 1 172 28 985 2 ..
2009 World Development Indicators
295
5.7
Military expenditures and arms transfers Military expenditures
% of GDP 2000
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
2.5 3.7 3.5 10.6 1.3 5.4 3.7 4.7 1.7 1.1 .. 1.6 1.2 4.5 4.7 1.8 2.0 1.1 5.4 1.2 1.5 1.4 .. .. .. 1.7 3.7 2.9 2.5 3.6 3.4 2.4 3.1 1.5 0.8 1.2 .. .. 5.0 1.8 0.0 2.3 w 2.4 2.0 2.2 1.9 2.1 1.7 3.1 1.4 3.5 3.1 1.8 2.3 1.8
2007
1.8 3.6 1.7 9.3 1.7 2.4 1.8 4.3 1.7 1.5 .. 1.4 1.2 2.9 4.3 .. 1.3 0.8 3.9 .. 1.0 1.4 .. 1.6 .. 1.4 2.1 .. 1.7 2.9 1.9 2.5 4.2 1.3 .. 1.1 .. .. 4.7 1.8 0.0 2.5 w 1.8 2.0 2.1 1.9 2.0 1.8 2.7 1.3 3.0 2.6 1.5 2.6 1.6
Armed forces personnel
% of central government expenditure
thousands
2000
2007
2000
2007
8.9 19.3 .. .. 10.4 .. 12.8 28.7 .. 2.9 .. 5.6 3.9 19.7 53.0 .. 5.5 4.2 .. 13.4 .. .. .. .. .. 6.2 .. .. 16.0 13.5 45.7 6.7 15.6 5.0 .. 5.4 .. .. 23.9 10.3 .. 10.2 w .. 15.3 17.3 .. 15.3 18.4 12.6 6.5 13.1 19.9 .. 10.0 4.8
6.8 15.4 .. .. .. .. .. 31.5 5.4 4.0 .. 4.7 4.7 14.6 .. .. .. 4.4 .. .. .. 7.8 .. 9.8 .. 4.9 8.6 .. 12.4 8.3 .. 6.2 19.4 4.8 .. 5.2 .. .. .. 7.6 .. 11.2 w .. 14.2 15.6 .. 14.2 16.8 11.0 .. 13.5 16.9 .. 10.6 4.6
283 1,427 76 217 15 136 4 169 41 14 50 72 242 204 120 3 88 28 425 7 35 417 .. 8 8 47 828 15 51 420 66 213 1,455 25 79 79 524 .. 136 23 62 29,353 s 5,806 17,507 12,481 5,026 23,313 7,794 4,220 2,084 3,379 4,114 1,724 6,040 1,820
153 1,476 35 238 19 24 11 167 17 12 .. 62 222 213 127 .. 18 23 401 17 28 420 1 10 4 48 612 22 47 215 51 160 1,555 26 87 115 495 56 138 16 51 27,254 s 5,359 16,185 11,528 4,657 21,544 6,815 3,394 2,408 3,289 4,113 1,525 5,710 1,690
Arms transfers
% of labor force 2000
2.4 2.0 2.1 3.2 0.4 .. 0.2 8.2 1.6 1.4 1.8 0.5 1.3 2.6 1.2 0.8 1.9 0.7 8.8 0.4 0.2 1.2 .. 0.4 1.3 1.5 3.5 0.8 0.5 1.8 3.5 0.7 1.0 1.6 0.8 0.8 1.4 .. 3.4 0.6 1.2 1.0 w 1.3 0.9 0.8 1.4 1.0 0.8 2.1 0.9 4.0 0.8 0.7 1.2 1.3
2007
1990 $ millions Exports 2000
2007
1.6 3 16 1.9 4,190 4,588 0.8 .. .. 2.7 .. 36 0.4 .. .. 0.8 7 5 0.5 .. .. 6.8 10 3 0.6 92 7 1.2 .. .. .. .. .. 0.4 18 80 1.0 46 529 2.4 .. .. 1.1 .. .. .. .. .. 0.4 308 413 0.5 104 211 6.3 .. 3 0.7 .. .. 0.1 .. .. 1.1 .. .. 0.2 .. .. 0.4 .. .. 0.6 .. .. 1.3 .. .. 2.5 15 33 1.0 .. .. 0.3 .. .. 0.9 280 109 1.9 .. 3 0.5 1,356 1,151 1.0 7,505 7,454 1.6 1 .. 0.7 73 4 0.9 .. 1 1.1 .. .. 6.8 .. .. 2.6 .. .. 0.4 .. .. 3 .. 0.9 0.9 w 18,266 s 24,192 s 1.0 17 9 0.8 5,135 5,459 0.7 549 481 1.2 4,586 4,978 0.8 5,152 5,459 0.6 241 359 1.6 4,869 4,973 0.9 4 25 3.1 0 22 0.7 19 23 0.5 19 80 1.1 13,114 18,733 1.1 3,204 8,681
Imports 2000
2007
23 70 .. 4 14 3 81 72 .. 15 1 .. 13 .. 612 707 2 4 1 2 1 .. 16 855 332 385 226 1 146 49 1 .. 210 85 14 126 439 30 .. 13 .. 9 93 9 .. .. .. .. 10 .. 11 18 1,042 944 .. .. 6 5 0 .. 309 1,040 808 698 268 587 4 33 6 .. 89 887 5 1 .. 2 158 57 27 3 2 20 18,066 s 23,493 s 724 148 8,181 10,718 5,846 5,396 2,335 5,322 8,905 10,866 2,211 2,532 1,397 2,163 966 1,978 2,522 1,824 1,257 1,373 552 996 9,161 12,627 2,146 3,641
Note: For some countries data are partial or uncertain or based on rough estimates; see SIPRI (2008). a. Estimates differ from official statistics of the government of China, which has published the following estimates: military expenditure as 1.2 percent of GDP in 2000 and 1.4 percent in 2006 and 7.6 percent of central government expenditure in 2000 and 7.4 percent in 2006 (see National Bureau of Statistics of China, www.stats.gov.cn).
296
2009 World Development Indicators
About the data
STATES AND MARKETS
Military expenditures and arms transfers
5.7
Definitions
Although national defense is an important function of
completeness of data, data on military expenditures
• Military expenditures are SIPRI data derived from
government and security from external threats that
are not strictly comparable across countries. More
the NATO definition, which includes all current and
contributes to economic development, high levels of
information on SIPRI’s military expenditure project
capital expenditures on the armed forces, including
military expenditures for defense or civil conflicts bur-
can be found at www.sipri.org/contents/milap/.
peacekeeping forces; defense ministries and other
den the economy and may impede growth. Data on
Data on armed forces refer to military personnel on
government agencies engaged in defense projects;
military expenditures as a share of gross domestic
active duty, including paramilitary forces. Because
paramilitary forces, if judged to be trained and
product (GDP) are a rough indicator of the portion of
data exclude personnel not on active duty, they
equipped for military operations; and military space
national resources used for military activities and of
underestimate the share of the labor force working
activities. Such expenditures include military and civil
the burden on the national economy. As an “input”
for the defense establishment. Governments rarely
personnel, including retirement pensions and social
measure military expenditures are not directly related
report the size of their armed forces, so such data
services for military personnel; operation and main-
to the “output” of military activities, capabilities, or
typically come from intelligence sources.
tenance; procurement; military research and develop-
security. Comparisons of military spending between
SIPRI’s Arms Transfers Project collects data on
ment; and military aid (in the military expenditures
countries should take into account the many fac-
arms transfers from open sources. Since publicly
of the donor country). Excluded are civil defense and
tors that influence perceptions of vulnerability and
available information is inadequate for tracking all
current expenditures for previous military activities,
risk, including historical and cultural traditions, the
weapons and other military equipment, SIPRI covers
such as for veterans benefits, demobilization, and
length of borders that need defending, the quality of
only what it terms major conventional weapons. Data
weapons conversion and destruction. This definition
relations with neighbors, and the role of the armed
cover the supply of weapons through sales, aid, gifts,
cannot be applied for all countries, however, since
forces in the body politic.
and manufacturing licenses; therefore the term arms
that would require more detailed information than is
Data on military spending reported by governments
transfers rather than arms trade is used. SIPRI data
available about military budgets and off-budget mili-
are not compiled using standard definitions. They are
also cover weapons supplied to or from rebel forces
tary expenditures (for example, whether military bud-
often incomplete and unreliable. Even in countries
in an armed conflict as well as arms deliveries for
gets cover civil defense, reserves and auxiliary forces,
where the parliament vigilantly reviews budgets and
which neither the supplier nor the recipient can be
police and paramilitary forces, and military pensions).
spending, military expenditures and arms transfers
identified with acceptable certainty; these data are
• Armed forces personnel are active duty military per-
rarely receive close scrutiny or full, public disclosure
available in SIPRI’s database.
sonnel, including paramilitary forces if the training,
(see Ball 1984 and Happe and Wakeman-Linn 1994).
SIPRI’s estimates of arms transfers are designed
organization, equipment, and control suggest they
Therefore, SIPRI has adopted a definition of military
as a trend-measuring device in which similar weap-
may be used to support or replace regular military
expenditure derived from the North Atlantic Treaty
ons have similar values, reflecting both the value and
forces. Reserve forces, which are not fully staffed or
Organization (NATO) definition (see Definitions). The
quality of weapons transferred. SIPRI cautions that
operational in peace time, are not included. The data
data on military expenditures as a share of GDP and
the estimated values do not reflect financial value
also exclude civilians in the defense establishment
as a share of central government expenditure are
(payments for weapons transferred) because reliable
and so are not consistent with the data on military
estimated by the Stockholm International Peace
data on the value of the transfer are not available,
expenditures on personnel. • Arms transfers cover
Research Institute (SIPRI). Central government
and even when values are known, the transfer usually
the supply of military weapons through sales, aid,
expenditures are from the International Monetary
includes more than the actual conventional weapons,
gifts, and manufacturing licenses. Weapons must be
Fund (IMF). Therefore the data in the table may
such as spares, support systems, and training, and
transferred voluntarily by the supplier, have a military
differ from comparable data published by national
details of the financial arrangements (such as credit
purpose, and be destined for the armed forces, para-
governments.
and loan conditions and discounts) are usually not
military forces, or intelligence agencies of another
known.
country. The trends shown in the table are based on
SIPRI’s primary source of military expenditure data is official data provided by national governments.
Given these measurement issues, SIPRI’s method
actual deliveries only. Data cover major conventional
These data are derived from national budget docu-
of estimating the transfer of military resources
weapons such as aircraft, armored vehicles, artil-
ments, defense white papers, and other public docu-
includes an evaluation of the technical parameters
lery, radar systems, missiles, and ships designed for
ments from official government agencies, including
of the weapons. Weapons for which a price is not
military use. Excluded are transfers of other military
governments’ responses to questionnaires sent by
known are compared with the same weapons for
equipment such as small arms and light weapons,
SIPRI, the United Nations, or the Organization for
which actual acquisition prices are available (core
trucks, small artillery, ammunition, support equip-
Security and Co-operation in Europe. Secondary
weapons) or for the closest match. These weapons
ment, technology transfers, and other services.
sources include international statistics, such as
are assigned a value in an index that reflects their
those of NATO and the IMF’s Government Finance
military resource value in relation to the core weap-
Statistics Yearbook. Other secondary sources include
ons. These matches are based on such characteris-
Data on military expenditures are from SIPRI’s
country reports of the Economist Intelligence Unit,
tics as size, performance, and type of electronics,
Yearbook 2008: Armaments, Disarmament, and
country reports by IMF staff, and specialist journals
and adjustments are made for secondhand weapons.
International Security. Data on armed forces per-
and newspapers.
More information on SIPRI’s Arms Transfers Project
sonnel are from the International Institute for Stra-
is available at www.sipri.org/contents/armstrad/.
tegic Studies’ The Military Balance 2009. Data on
In the many cases where SIPRI cannot make
Data sources
independent estimates, it uses the national data
arms transfers are from SIPRI’s Arms Transfer
provided. Because of the differences in defi ni-
Project (www.sipri.org/contents/armstrad/).
tions and the difficulty in verifying the accuracy and
2009 World Development Indicators
297
5.8
Public policies and institutions IDA Resource Allocation Index
Economic management
Structural policies
1–6 (low to high)
1–6 (low to high)
1–6 (low to high)
Afghanistan Angola Armenia Azerbaijan Bangladesh Benin Bhutan Bolivia Bosnia and Herzegovina Burkina Faso Burundi Cambodia Cameroon Cape Verde Central African Republic Chad Comoros Congo, Dem. Rep. Congo, Rep. Côte d’Ivoire Djibouti Dominica Eritrea Ethiopia Gambia, The Georgia Ghana Grenada Guinea Guinea-Bissau Guyana Haiti Honduras India Kenya Kiribati Kyrgyz Republic Lao PDR
Macroeconomic management
Fiscal policy
Debt policy
Average
2007
2007
2007
2007
2007
2.5 2.7 4.4 3.8 3.5 3.6 3.9 3.7 3.7 3.7 3.0 3.2 3.2 4.2 2.5 2.6 2.4 2.8 2.7 2.6 3.1 3.9 2.4 3.4 3.2 4.3 4.0 3.7 3.0 2.6 3.4 2.9 3.8 3.9 3.6 3.1 3.7 3.1
3.5 3.0 5.5 4.5 4.0 4.5 4.5 4.0 4.5 4.5 3.5 4.5 4.0 4.5 3.5 3.0 2.5 3.5 3.0 3.0 3.5 4.0 2.0 3.0 4.0 4.5 4.0 3.5 3.0 2.0 3.5 3.5 4.0 4.5 4.5 2.5 4.5 4.5
3.0 3.0 5.0 4.5 3.5 4.0 4.5 4.0 3.5 4.5 3.5 3.0 4.0 4.5 3.0 2.5 1.5 3.5 2.0 2.5 2.5 4.5 2.0 4.0 3.5 4.5 4.0 2.5 3.5 2.5 3.5 3.5 3.5 3.5 4.0 2.0 4.0 3.5
3.0 3.0 6.0 5.0 4.5 3.5 4.5 4.5 4.0 4.0 2.5 3.5 3.0 4.5 2.0 2.5 2.0 2.5 2.5 1.5 2.5 3.0 2.5 3.5 2.5 5.0 4.0 3.0 2.5 1.5 4.0 2.5 4.0 4.5 4.0 5.0 4.0 3.5
3.2 3.0 5.5 4.7 4.0 4.0 4.5 4.2 4.0 4.3 3.2 3.7 3.7 4.5 2.8 2.7 2.0 3.2 2.5 2.3 2.8 3.8 2.2 3.5 3.3 4.7 4.0 3.0 3.0 2.0 3.7 3.2 3.8 4.2 4.2 3.2 4.2 3.8
Trade
Financial sector
Business regulatory environment
Average
2007
2007
2007
2007
2.5 4.0 4.5 4.0 3.5 4.0 3.0 5.0 3.5 4.0 3.5 3.5 3.5 4.0 3.5 3.0 3.0 4.0 3.5 3.5 4.0 4.0 1.5 3.0 4.0 5.5 4.0 4.0 4.0 4.0 4.0 4.0 5.0 4.0 4.0 3.0 5.0 3.5
2.0 2.5 3.5 3.0 3.0 3.5 3.0 3.5 4.0 3.0 3.0 2.5 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 4.0 3.5 3.0 3.0 3.5 3.0 3.5 4.0 3.5 3.0 3.5 2.0
2.5 2.0 4.0 3.5 3.5 3.5 3.5 2.5 4.0 3.0 2.5 3.5 3.0 3.5 2.0 2.5 2.5 3.0 2.5 3.0 3.5 4.5 2.0 3.5 3.5 5.0 4.0 4.5 3.0 2.5 3.0 2.5 4.5 3.5 4.0 3.0 3.5 3.0
2.3 2.8 4.0 3.5 3.3 3.7 3.2 3.7 3.8 3.3 3.0 3.2 3.2 3.8 2.7 2.8 2.7 3.0 2.8 3.2 3.7 4.2 1.8 3.2 3.5 4.7 4.0 4.0 3.3 3.2 3.5 3.2 4.3 3.8 3.8 3.0 4.0 2.8
About the data The International Development Association (IDA) is the
exercise even though they are IDA eligible. Albania and
extent, based on its per capita gross national income.
part of the World Bank Group that helps the poorest
Indonesia are no longer included in the table because
This ensures that good performers receive a higher
countries reduce poverty by providing concessional
they have graduated from IDA. Country assessments
IDA allocation in per capita terms. The IRAI is a key
loans and grants for programs aimed at boosting
have been carried out annually since the mid-1970s
element in the country performance rating.
economic growth and improving living conditions.
by World Bank staff. Over time the criteria have been
The CPIA exercise is intended to capture the quality
IDA funding helps these countries deal with the com-
revised from a largely macroeconomic focus to include
of a country’s policies and institutional arrangements,
plex challenges they face in meeting the Millennium
governance aspects and a broader coverage of social
focusing on key elements that are within the country’s
Development Goals.
and structural dimensions. Country performance is
control, rather than on outcomes (such as economic
The World Bank’s IDA Resource Allocation Index
assessed against a set of 16 criteria grouped into four
growth rates) that are influenced by events beyond
(IRAI), presented in the table, is based on the results of
clusters: economic management, structural policies,
the country’s control. More specifically, the CPIA
the annual Country Policy and Institutional Assessment
policies for social inclusion and equity, and public sec-
measures the extent to which a country’s policy and
(CPIA) exercise, which covers the IDA-eligible coun-
tor management and institutions. IDA resources are
institutional framework supports sustainable growth
tries. The table does not include Liberia, Myanmar,
allocated to a country on per capita terms based on
and poverty reduction and, consequently, the effective
and Somalia because they were not rated in the 2007
its IDA country performance rating and, to a limited
use of development assistance.
298
2009 World Development Indicators
IDA Resource Allocation Index
STATES AND MARKETS
5.8
Public policies and institutions Economic management
Structural policies
1–6 (low to high)
1–6 (low to high)
1–6 (low to high)
Lesotho Madagascar Malawi Maldives Mali Mauritania Moldova Mongolia Mozambique Nepal Nicaragua Niger Nigeria Pakistan Papua New Guinea Rwanda Samoa São Tome and Principe Senegal Sierra Leone Solomon Islands Sri Lanka St. Lucia St. Vincent & Grenadines Sudan Tajikistan Tanzania Timor-Leste Togo Tonga Uganda Uzbekistan Vanuatu Vietnam Yemen, Rep. Zambia Zimbabwe
Macroeconomic management
Fiscal policy
Debt policy
Average
2007
2007
2007
2007
2007
3.5 3.7 3.4 3.6 3.7 3.4 3.8 3.4 3.6 3.4 3.8 3.3 3.4 3.6 3.3 3.7 3.9 3.0 3.7 3.1 2.7 3.5 4.0 3.8 2.5 3.2 3.9 2.7 2.5 3.0 3.9 3.1 3.3 3.8 3.2 3.5 1.7
4.0 4.0 3.5 3.0 4.5 3.5 4.0 3.5 4.0 4.5 4.0 4.0 4.0 3.5 4.5 4.0 4.0 3.0 4.5 4.0 3.5 2.5 4.5 4.0 3.5 4.0 4.5 2.5 2.5 3.0 4.5 3.5 4.0 4.5 3.5 4.0 1.0
4.0 3.0 3.5 2.5 4.0 3.0 4.0 3.0 4.0 3.5 4.0 3.5 4.5 3.5 3.5 4.0 3.5 3.0 4.0 3.5 3.0 3.0 3.5 3.5 3.0 4.0 4.5 3.0 2.5 2.5 4.5 3.5 3.0 4.5 3.0 3.5 1.0
4.0 4.0 3.0 3.0 4.5 4.0 4.0 3.0 4.5 3.5 4.5 3.5 4.5 4.5 4.5 3.5 4.0 2.5 4.0 3.5 2.5 3.5 4.0 3.5 1.5 3.0 4.0 3.5 1.5 3.0 4.5 4.0 4.0 4.0 4.0 3.5 1.0
4.0 3.7 3.3 2.8 4.3 3.5 4.0 3.2 4.2 3.8 4.2 3.7 4.3 3.8 4.2 3.8 3.8 2.8 4.2 3.7 3.0 3.0 4.0 3.7 2.7 3.7 4.3 3.0 2.2 2.8 4.5 3.7 3.7 4.3 3.5 3.7 1.0
Trade
Financial sector
Business regulatory environment
Average
2007
2007
2007
2007
3.5 4.0 4.0 4.0 4.0 4.5 4.5 4.5 4.5 4.0 4.5 4.0 3.0 4.0 4.5 3.5 4.5 4.0 4.0 3.5 3.0 3.5 4.0 4.0 2.5 4.0 4.0 3.5 4.0 3.5 4.0 2.5 3.5 3.5 4.5 4.0 2.0
3.5 3.5 3.0 4.0 3.0 2.5 3.5 3.0 3.5 3.0 3.5 3.0 3.5 4.5 3.0 3.5 4.0 2.5 3.5 3.0 3.0 4.0 4.0 4.0 2.5 3.0 3.5 2.5 2.5 3.0 3.5 2.5 3.0 3.0 2.5 3.5 2.5
3.0 4.0 3.5 4.0 3.5 3.5 3.5 3.5 3.0 3.0 3.5 3.0 3.0 4.0 3.0 3.5 3.5 3.0 4.0 2.5 2.5 4.0 4.5 4.5 3.0 3.5 3.5 1.5 3.0 3.0 4.0 3.0 3.5 3.5 3.5 3.5 1.5
3.3 3.8 3.5 4.0 3.5 3.5 3.8 3.7 3.7 3.3 3.8 3.3 3.2 4.2 3.5 3.5 4.0 3.2 3.8 3.0 2.8 3.8 4.2 4.2 2.7 3.5 3.7 2.5 3.2 3.2 3.8 2.7 3.3 3.3 3.5 3.7 2.0
All criteria within each cluster receive equal weight,
on relevant publicly available indicators. In interpreting
information. To ensure that scores are consistent
and each cluster has a 25 percent weight in the over-
the assessment scores, it should be noted that the
across countries, the process involves two key phases.
all score, which is obtained by averaging the average
criteria are designed in a developmentally neutral man-
In the benchmarking phase a small representative sam-
scores of the four clusters. For each of the 16 criteria
ner. Accordingly, higher scores can be attained by a
ple of countries drawn from all regions is rated. Country
countries are rated on a scale of 1 (low) to 6 (high).
country that, given its stage of development, has a
teams prepare proposals that are reviewed first at the
The scores depend on the level of performance in
policy and institutional framework that more strongly
regional level and then in a Bankwide review process.
a given year assessed against the criteria, rather
fosters growth and poverty reduction.
A similar process is followed to assess the perfor-
than on changes in performance compared with the
The country teams that prepare the ratings are very
mance of the remaining countries, using the benchmark
previous year. All 16 CPIA criteria contain a detailed
familiar with the country, and their assessments are
countries’ scores as guideposts. The final ratings are
description of each rating level. In assessing country
based on country diagnostic studies prepared by the
determined following a Bankwide review. The overall
performance, World Bank staff evaluate the country’s
World Bank or other development organizations and
numerical IRAI score and the separate criteria scores
performance on each of the criteria and assign a rat-
on their own professional judgment. An early consul-
were first publicly disclosed in June 2006.
ing. The ratings reflect a variety of indicators, observa-
tation is conducted with country authorities to make
tions, and judgments based on country knowledge and
sure that the assessments are informed by up-to-date
See IDA’s website at www.worldbank.org/ida for more information.
2009 World Development Indicators
299
5.8 Afghanistan Angola Armenia Azerbaijan Bangladesh Benin Bhutan Bolivia Bosnia and Herzegovina Burkina Faso Burundi Cambodia Cameroon Cape Verde Central African Republic Chad Comoros Congo, Dem. Rep. Congo, Rep. Côte d’Ivoire Djibouti Dominica Eritrea Ethiopia Gambia, The Georgia Ghana Grenada Guinea Guinea-Bissau Guyana Haiti Honduras India Kenya Kiribati Kyrgyz Republic Lao PDR
Public policies and institutions Policies for social inclusion and equity
Public sector management and institutions
1–6 (low to high)
1–6 (low to high)
Policies and institutions for Social protection environmental and labor sustainability Average
Gender equality
Equity of public resource use
Building human resources
2007
2007
2007
2007
2007
2.0 3.0 4.5 4.0 4.0 3.5 4.0 4.0 4.5 3.5 4.0 4.0 3.0 4.5 2.5 2.5 3.0 3.0 3.0 2.5 2.5 3.5 3.5 3.0 3.5 4.5 4.0 5.0 3.5 2.5 4.0 3.0 4.0 3.5 3.0 3.0 4.5 3.5
2.5 2.5 4.5 4.0 3.5 3.0 4.0 4.0 3.0 4.0 3.5 3.0 3.0 4.5 2.0 3.0 3.0 3.0 2.5 1.5 3.0 3.5 3.0 4.5 3.0 4.5 4.0 3.5 3.0 3.0 3.5 3.0 4.0 4.0 3.0 3.0 3.5 3.5
3.0 2.5 4.0 3.5 4.0 3.5 4.5 4.0 3.5 3.5 3.0 3.5 3.5 4.5 2.0 2.5 3.0 3.0 3.0 2.5 3.5 4.0 3.5 4.0 3.5 4.0 4.5 4.0 3.0 2.5 3.5 2.5 4.0 4.0 3.5 2.5 3.5 3.0
2.0 2.5 4.5 4.0 3.5 3.0 3.5 3.5 3.5 3.5 3.0 3.0 3.0 4.5 2.0 2.5 2.5 3.0 2.5 2.5 3.0 3.5 3.0 3.5 2.5 4.0 3.5 3.5 3.0 2.5 3.0 2.5 3.5 3.5 3.0 3.0 3.5 2.5
2.0 3.0 3.5 3.0 3.0 3.5 4.5 3.5 3.5 3.5 3.0 3.0 3.0 3.5 2.5 2.5 2.0 2.5 2.5 2.5 3.0 3.5 2.0 3.5 3.0 3.0 3.5 4.0 2.5 2.5 3.0 2.5 3.0 3.5 3.5 3.0 3.0 3.5
2007
2.3 2.7 4.2 3.7 3.6 3.3 4.1 3.8 3.6 3.6 3.3 3.3 3.1 4.3 2.2 2.6 2.7 2.9 2.7 2.3 3.0 3.6 3.0 3.7 3.1 4.0 3.9 4.0 3.0 2.6 3.4 2.7 3.7 3.7 3.2 2.9 3.6 3.2
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
2007
2007
2007
2007
2007
1.5 2.0 3.5 3.0 3.0 3.0 3.5 2.5 3.0 3.5 2.5 2.5 2.5 4.0 2.0 2.0 2.5 2.0 2.5 2.0 2.5 4.0 2.5 3.0 3.5 3.5 3.5 3.5 2.0 2.5 3.0 2.0 3.5 3.5 2.5 3.5 2.5 3.0
3.0 2.5 4.0 4.0 3.0 3.5 3.5 3.5 3.5 4.0 3.0 3.0 3.5 4.0 2.0 2.0 1.5 2.5 2.5 2.0 3.0 3.5 2.5 4.0 3.0 4.0 4.0 4.0 3.0 2.5 3.5 3.0 4.0 4.0 3.5 3.0 3.0 3.0
2.5 2.5 3.5 3.5 3.0 3.5 4.0 4.0 4.0 3.5 3.0 3.0 3.5 3.5 2.5 2.5 2.5 2.5 3.0 4.0 3.5 4.0 3.5 4.0 3.5 4.5 4.5 3.5 3.0 3.0 3.5 2.5 4.0 4.0 4.0 3.0 3.5 2.5
2.0 2.5 4.0 3.0 3.0 3.0 4.0 3.0 3.0 3.5 2.5 2.5 3.0 4.0 2.5 2.5 2.0 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 3.0
2.0 2.5 3.5 2.5 3.0 3.5 4.0 3.5 3.0 3.0 2.0 2.5 2.5 4.5 2.5 2.0 2.5 2.0 2.5 2.0 2.5 4.0 2.0 2.5 2.0 3.0 4.0 4.0 2.5 2.5 3.0 2.0 3.0 3.5 3.0 3.5 2.5 2.0
2007
2.2 2.4 3.7 3.2 3.0 3.3 3.8 3.3 3.3 3.5 2.6 2.7 3.0 4.0 2.3 2.2 2.2 2.3 2.6 2.4 2.8 3.8 2.7 3.3 3.0 3.7 3.9 3.7 2.7 2.6 3.1 2.4 3.5 3.7 3.3 3.2 2.9 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
300
2009 World Development Indicators
Lesotho Madagascar Malawi Maldives Mali Mauritania Moldova Mongolia Mozambique Nepal Nicaragua Niger Nigeria Pakistan Papua New Guinea Rwanda Samoa São Tome and Principe Senegal Sierra Leone Solomon Islands Sri Lanka St. Lucia St. Vincent & Grenadines Sudan Tajikistan Tanzania Timor-Leste Togo Tonga Uganda Uzbekistan Vanuatu Vietnam Yemen, Rep. Zambia Zimbabwe
5.8
Policies for social inclusion and equity
Public sector management and institutions
1–6 (low to high)
1–6 (low to high)
Policies and institutions for Social protection environmental and labor sustainability Average
Gender equality
Equity of public resource use
Building human resources
2007
2007
2007
2007
2007
4.0 3.5 3.5 4.0 3.5 4.0 5.0 3.5 3.5 3.5 3.5 2.5 3.0 2.0 2.5 3.5 3.5 3.0 3.5 3.0 3.0 4.0 3.5 4.0 2.0 3.5 4.0 3.5 3.0 2.5 3.5 4.0 3.5 4.5 2.0 3.5 2.5
3.0 4.0 3.5 4.0 3.5 3.5 3.5 3.5 3.5 3.5 4.0 3.5 3.5 3.5 3.5 4.5 4.0 3.0 3.5 3.0 2.5 3.5 4.0 3.5 2.5 3.5 4.0 3.0 2.0 3.5 4.5 3.5 3.5 4.5 3.5 3.5 1.5
3.5 3.5 3.0 4.0 3.5 3.5 4.0 3.5 3.5 4.0 3.5 3.0 3.0 3.5 2.5 4.5 4.0 3.0 3.5 3.5 3.0 4.0 4.0 4.0 2.5 3.0 4.0 2.5 3.0 4.0 4.0 4.0 2.5 4.0 3.0 3.5 1.5
3.0 3.5 3.5 3.5 3.5 3.0 3.5 3.5 3.0 3.0 3.5 3.0 3.5 3.0 3.0 3.5 3.5 2.5 3.0 3.0 2.5 3.5 4.0 3.5 2.5 3.5 3.5 2.0 2.5 3.0 3.5 3.5 2.0 3.5 3.5 3.0 1.0
3.5 4.0 3.5 4.0 3.5 3.5 3.5 3.0 3.0 3.0 3.5 3.0 3.0 3.5 2.0 3.0 4.0 2.5 3.5 2.0 2.0 3.5 3.5 3.5 2.5 2.5 3.5 2.5 2.5 3.0 4.0 3.5 3.0 3.5 3.0 3.5 2.5
2007
3.4 3.7 3.4 3.9 3.5 3.5 3.9 3.4 3.3 3.4 3.6 3.0 3.2 3.1 2.7 3.8 3.8 2.8 3.4 2.9 2.6 3.7 3.8 3.7 2.4 3.2 3.8 2.7 2.6 3.2 3.9 3.7 2.9 4.0 3.0 3.4 1.8
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
2007
2007
2007
2007
2007
3.5 3.5 3.5 4.0 3.5 3.0 3.5 3.0 3.0 3.0 3.0 3.0 2.5 3.0 2.0 3.0 4.0 2.5 3.5 2.5 2.5 3.0 4.0 4.0 2.0 2.5 3.5 1.5 2.5 3.5 3.5 2.5 3.0 3.5 2.5 3.0 1.0
3.0 3.5 3.0 3.0 3.5 2.5 4.0 4.0 3.5 3.5 4.0 3.5 3.0 3.5 3.5 4.0 3.5 3.0 3.5 3.5 2.5 4.0 3.5 3.5 2.0 3.0 4.0 3.0 2.0 2.5 4.0 3.0 3.5 4.0 3.0 3.5 2.0
4.0 3.5 4.0 4.0 4.0 3.5 3.5 3.5 4.0 3.5 4.0 3.5 3.0 3.5 3.5 3.5 4.0 3.5 4.0 2.5 2.5 3.5 4.0 4.0 3.0 3.0 4.0 3.0 2.5 3.0 3.0 3.0 3.5 3.5 3.0 3.5 3.5
3.0 3.5 3.5 4.0 3.0 3.0 3.0 3.5 3.0 3.0 3.0 3.0 3.0 3.5 2.5 3.5 4.0 3.0 3.5 3.0 2.0 3.0 3.5 3.5 2.5 2.5 3.5 2.5 2.0 3.0 3.0 2.5 2.5 3.5 3.0 3.0 1.5
3.5 3.5 3.0 2.5 3.5 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 2.5 3.0 3.5 4.0 3.5 3.0 2.5 3.0 3.0 4.5 4.0 2.0 2.0 3.5 3.0 2.0 2.5 3.0 1.5 3.0 3.0 3.0 3.0 1.0
2007
3.4 3.5 3.4 3.5 3.5 3.0 3.4 3.4 3.3 3.2 3.4 3.2 2.9 3.2 2.9 3.5 3.9 3.1 3.5 2.8 2.5 3.3 3.9 3.8 2.3 2.6 3.7 2.6 2.2 2.9 3.3 2.5 3.1 3.5 2.9 3.2 1.8
and institutions for environmental sustainability
accurate accounting and fiscal reporting, including
to which public employees within the executive are
assess the extent to which environmental policies
timely and audited public accounts. • Effi ciency
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 are the accountability
sector management and institutions cluster: Prop-
tax structure, but also revenue from all sources as
of the executive to oversight institutions and of pub-
erty rights and rule-based governance assess the
actually collected. • Quality of public administration
lic employees for their performance, access of civil
extent to which private economic activity is facili-
assesses the extent to which civilian central govern-
society to information on public affairs, and state
tated by an effective legal system and rule-based
ment staff is structured to design and implement
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
the extent to which there is a comprehensive and
executive can be held accountable for its use of
credible budget linked to policy priorities, effective
funds and for the results of its actions by the elector-
fi nancial management systems, and timely and
ate, the legislature, and the judiciary and the extent
Data sources Data on public policies and institutions are from the World Bank Group’s CPIA database available at www.worldbank.org/ida.
2009 World Development Indicators
301
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
302
Total road network km
Paved roads %
Passengers carried million passengerkm
2000–06a
2000–06a
2000–06a
42,150 18,000 108,302 51,429 231,374 7,504 812,972 107,262 59,141 239,226 94,797 152,256 19,000 62,479 21,846 25,798 1,751,868 40,231 92,495 12,322 38,257 51,346 1,408,900 24,307 40,000 79,604 3,456,999 1,983 164,278 153,497 17,289 35,983 80,000 28,788 60,856 128,512 72,361 12,600 43,670 92,370 10,029 4,010 57,016 39,477 78,941 951,500 9,170 3,742 20,329 644,480 57,614 117,533 14,095 44,348 3,455 4,160
29.3 39.0 70.2 10.4 30.0 89.0 .. 100.0 49.4 9.5 88.6 78.2 9.5 7.0 52.3 32.6 5.5 98.4 4.2 10.4 6.3 8.4 39.9 .. 0.8 20.2 70.7 100.0 .. 1.8 5.0 25.2 8.1 89.0 49.0 100.0 100.0 49.4 14.8 81.0 19.8 21.8 22.7 12.7 65.3 100.0 10.2 19.3 38.6 100.0 14.9 91.8 34.5 9.8 27.9 24.3
2009 World Development Indicators
.. 197 .. 166,045 .. 2,344 290,280 69,000 10,892 .. 9,343 130,868 .. .. .. .. .. 13,688 .. .. 201 .. 493,814 .. .. .. 1,013,085 .. 157 .. .. .. .. 3,537 5,121 90,055 70,635 .. 11,410 .. .. .. 3,190 219,113 70,900 724,000 .. 16 5,200 1,062,700 .. .. .. .. .. ..
Railways
Goods hauled million ton-km 2000–06a
.. 2,200 .. 4,709 .. 432 168,630 26,411 7,536 .. 15,779 51,572 .. .. 300 .. .. 11,843 .. .. 3 .. 184,774 .. .. .. 975,420 .. 39,726 .. .. .. .. 10,502 2,133 46,600 11,058 .. 5,453 .. .. .. 7,641 2,456 26,400 197,000 .. .. 570 251,372 .. 18,360 .. .. .. ..
Passengers carried million Rail lines total route- passengerkm km 2000–07a
2000–07a
.. .. 423 51 3,572 821 .. .. 35,753 .. 711 27 9,639 1,309 5,818 9,051 2,122 1,109 2,855 4,164 5,494 9,366 3,374 9,932 .. .. .. .. 1,103 88 .. .. 29,487 .. 4,027 2,424 .. .. .. .. 650 45 974 370 57,042 2,858 .. .. .. .. 6,008 732 63,637 689,616 .. .. 2,137 .. 3,641 79 795 211 .. .. 639 10 2,722 1,611 .. .. 9,491 6,855 2,133 5,724 .. .. .. .. 5,195 40,837 .. .. .. .. 962 273 .. .. 5,899 3,778 29,488 83,299 810 76 .. .. 1,513 809 33,897 74,740 977 85 2,551 1,954 .. .. .. .. .. .. .. ..
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
2000–07a
2007
2007
2007
2007
.. 4 44 5 79 6 354 151 13 11 6 158 .. 24 .. 7 650 12 2 .. 4 12 1,189 .. .. 101 1,754 130 186 .. .. 37 .. 22 12 76 14 .. 45 51 22 .. 9 38 119 825 9 .. 5 1,127 .. 138 .. .. .. ..
.. 213 2,813 277 7,037 606 48,729 9,141 1,441 1,243 344 3,641 .. 1,745 .. 228 45,287 855 78 .. 308 453 52,104 .. .. 7,191 183,613 21,796 11,631 .. .. 1,017 .. 2,288 857 4,870 582 .. 2,631 5,829 2,537 .. 651 2,290 8,289 61,551 535 .. 272 106,102 .. 10,206 .. .. .. ..
.. 0 17 73 133 7 2,348 454 12 89 1 740 .. 9 .. 0 1,478 3 0 .. 2 26 1,430 .. .. 1,295 11,190 8,326 1,070 .. .. 10 .. 3 31 33 1 .. 6 207 22 .. 1 160 490 6,425 70 .. 3 8,529 .. 72 .. .. .. ..
.. 53 1,429 .. 12,871 678 46,036 18,996 10,374 817 47,933 8,149 .. .. 1,138 .. 232,297 5,242 .. .. 92 1,055 353,227 .. .. 3,957 2,211,246 .. 7,751 331 234 .. 675 3,574 .. 16,972 2,030 .. .. 3,917 .. .. 8,153 .. 10,434 40,635 2,202 .. 7,379 91,013 242 835 .. .. .. ..
.. .. .. .. 1,874 .. 6,229 .. .. 978 .. 10,258 .. .. .. .. 6,454 .. .. .. .. .. 4,605 .. .. 2,681 104,559 23,998 1,897 .. .. 977 710 175 .. .. 775 884 671 5,311 .. .. .. .. 1,564 4,928 .. .. .. 16,713 .. 1,820 853 .. .. ..
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
Total road network km
Paved roads %
Passengers carried million passengerkm
2000–06a
2000–06a
2000–06a
13,600 159,600 3,316,452 391,009 172,927 45,550 96,602 17,719 487,700 22,056 1,196,999 7,694 91,563 63,265 25,554 102,062 5,749 18,500 29,811 69,675 6,970 5,940 10,600 83,200 79,987 13,182 49,827 15,451 93,109 18,709 11,066 2,021 356,945 12,838 49,250 57,625 30,400 27,000 42,237 17,280 126,100 93,576 18,669 18,550 193,200 92,864 34,965 260,420 11,643 19,600 29,500 78,986 200,037 423,997 82,900 25,645
20.4 43.9 47.4 55.4 72.8 84.3 100.0 100.0 100.0 73.3 79.3 100.0 91.4 14.1 2.8 88.6 85.0 91.1 13.4 100.0 .. 18.3 6.2 57.2 28.3 .. 11.6 45.0 79.8 18.0 26.8 100.0 50.0 85.5 3.5 61.9 18.7 11.9 12.8 56.9 90.0 65.6 11.4 20.5 15.0 77.5 27.7 65.4 34.6 3.5 50.8 13.9 9.9 69.7 86.0 95.0
.. 13,300 .. .. .. .. .. .. 97,560 .. 947,562 .. 100,865 .. .. 97,854 .. 5,874 .. 2,780 .. .. .. .. 43,167 842 .. .. .. .. .. .. 436,999 1,640 557 .. .. .. 47 .. .. .. .. .. .. 58,247 .. 263,788 .. .. .. .. .. 247,388 .. ..
Railways
Goods hauled million ton-km 2000–06a
.. 9,090 .. .. .. .. 15,900 .. 192,700 .. 327,632 .. 53,816 22 .. 12,545 .. 1,336 .. 2,729 .. .. .. .. 18,134 4,100 .. .. .. .. .. .. 209,392 1,577 242 1,256 .. .. 591 .. 77,100 .. .. .. .. 14,966 .. 129,249 .. .. .. .. .. 136,490 23,187 10
Passengers carried million Rail lines total route- passengerkm km 2000–07a
2000–07a
.. .. 7,730 6,953 63,327 694,764 .. 25,535 7,265 12,549 .. .. 1,919 2,007 958 1,831 16,668 47,113 .. .. 20,050 252,579 293 .. 14,205 13,613 1,917 226 .. .. 3,399 31,596 .. .. .. .. .. .. 2,269 983 .. .. .. .. .. .. .. .. 1,766 409 947 109 .. .. 710 26 1,667 2,193 733 196 .. .. .. .. 26,662 84 .. .. 1,810 1,289 1,907 3,659 .. .. .. .. .. .. .. .. 2,776 15,546 .. .. .. .. .. .. 3,528 174 .. .. .. .. 7,791 25,621 .. .. .. .. .. .. .. 119 491 144 19,419 17,081 2,838 3,610 .. ..
Ports
STATES AND MARKETS
Transport services
5.9 Air
Goods hauled million ton-km
Port container traffic thousand TEU
Registered carrier departures worldwide thousands
Passengers carried thousands
Air freight million ton-km
2000–07a
2007
2007
2007
2007
.. 8,537 480,993 4,698 20,542 .. 129 1,175 22,340 .. 23,145 517 191,189 1,399 .. 10,927 .. .. .. 16,735 .. .. .. .. 14,373 961 .. 38 1,355 189 .. .. 75,600 .. 9,219 5,837 .. .. .. .. 4,331 4,078 .. .. 77 .. .. 5,907 .. .. .. 1,159 1 43,548 2,585 ..
553 .. 7,372 4,481 1,723 .. 1,175 1,957 10,435 2,017 19,008 .. .. .. .. 16,640 750 .. .. .. 948 .. .. .. .. .. .. .. 14,873 .. .. .. 3,071 .. .. .. .. .. .. .. 11,287 2,029 .. .. .. .. 2,877 1,936 4,070 .. .. 1,178 3,835 .. 1,138 1,695
.. 46 569 358 138 .. 350 47 432 21 657 29 19 32 2 243 21 5 10 29 11 .. .. 13 11 2 37 6 185 .. 2 12 310 4 6 69 11 29 7 7 260 219 .. .. 17 .. .. 51 33 22 10 62 65 89 138 ..
.. 2,592 51,897 30,406 13,916 .. 50,738 4,663 37,831 1,527 99,842 2,246 1,295 2,857 105 36,655 2,628 275 328 1,410 969 .. .. 1,204 424 209 616 155 21,326 .. 155 1,278 20,953 274 381 4,624 443 1,663 431 528 28,857 12,546 .. .. 1,363 .. .. 5,439 2,029 919 459 5,273 8,818 4,270 10,320 ..
2009 World Development Indicators
.. 20 968 485 95 .. 131 1,133 1,550 15 8,435 277 17 298 2 9,040 239 1 3 13 74 .. .. 0 1 0 24 1 2,662 .. 0 203 482 1 5 45 6 3 0 8 5,006 868 .. .. 10 .. .. 314 36 23 0 162 286 83 323 ..
303
5.9
Transport services Roads
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Total road network km
Paved roads %
Passengers carried million passengerkm
2000–06a
2000–06a
2000–06a
198,817 933,000 14,008 221,372 13,576 38,799 11,300 3,262 43,761 38,562 22,100 364,131 666,292 97,286 11,900 3,594 697,794 71,298 38,923 27,767 78,891 180,053 .. 7,520 8,320 19,232 426,951 24,000 70,746 169,323 4,030 398,351 6,544,257 77,732 81,600 96,155 222,179 5,147 71,300 91,440 97,267
Railways
Goods hauled million ton-km 2000–06a
Passengers carried million Rail lines total route- passengerkm km 2000–07a
2000–07a
Ports
Goods hauled million ton-km 2000–07a
30.2 7,985 51,531 10,646 7,417 13,471 80.9 .. 25,200 84,158 173,411 2,090,337 19.0 .. .. .. .. .. 21.5 .. .. 1,412 345 1,630 29.3 .. .. 906 88 384 62.7 3,865 452 3,819 762 4,234 8.0 .. .. .. .. .. 100.0 .. .. .. .. .. 87.0 32,214 22,114 3,629 2,148 9,331 100.0 850 12,112 1,228 812 3,603 11.8 .. .. .. .. .. 17.3 .. 434 24,487 14,856 108,513 99.0 397,117 132,868 14,832 21,225 11,064 81.0 21,067 .. 1,200 4,682 135 36.3 .. .. 5,478 40 766 30.0 .. .. .. .. .. 58.2 106,583 40,123 9,821 6,467 11,500 100.0 93,480 16,337 3,619 15,771 16,736 100.0 589 .. 2,043 744 2,552 .. 414 25,604 .. .. 14,529 433b 728b 8.6 .. .. 4,460b 98.5 .. .. 4,044 9,195 4,037 .. .. .. .. .. .. 31.6 .. .. .. .. .. 51.1 .. .. .. .. .. 65.8 .. 16,611 2,218 1,407 2,197 .. 187,592 177,399 8,697 5,553 9,680 81.2 .. .. .. .. .. 23.0 .. .. 259 .. 218 97.4 51,820 23,895 21,891 53,230 240,810 100.0 .. .. .. .. .. 100.0 736,000 163,000 16,208 48,511 22,110 65.3 7,814,575 2,116,532 191,771 .. 2,820,061c 10.0 .. .. 3,003 12 331 87.3 .. 1,200 4,005 2,339 19,281 33.6 .. .. 336 .. 81 .. .. .. 3,147 4,659 3,881 100.0 .. .. .. .. .. 8.7 .. .. .. .. .. 22.0 .. .. 1,273 183 .. 19.0 .. .. .. .. .. 35.9 m .. m .. m .. s .. m .. m 12.1 .. .. .. .. .. 47.7 .. .. .. 1,295 6,608 39.0 .. .. .. 1,025 3,046 41.8 .. .. .. 2,309 13,471 23.1 .. .. .. .. .. 11.4 .. .. .. 2,893 1,902 .. 10,062 15,477 187,104 1,975 13,471 22.0 .. .. .. .. .. 81.0 .. .. .. 2,533 2,552 56.9 .. .. .. 25,621 5,907 11.9 .. .. .. .. .. 87.0 .. 46,600 .. 6,467 11,064 100.0 97,840 39,474 124,917 9,051 9,883
Air
Port container traffic thousand TEU
Registered carrier departures worldwide thousands
Passengers carried thousands
Air freight million ton-km
2007
2007
2007
2007
1,411 2,657 .. 4,209 .. .. .. 27,932 .. .. .. 3,734 11,148 3,382 .. .. 1,439 .. .. .. .. 6,200 .. .. .. .. 4,488 .. .. 979 13,160 9,383 41,625 .. .. 1,332 3,937 .. .. .. .. 465,594 s .. 193,415 142,654 50,761 200,266 137,886 .. 27,286 .. 13,668 .. 265,328 72,409
51 3,004 468 33,188 .. .. 151 17,141 0 539 20 1,042 0 19 85 19,566 24 2,679 20 861 .. .. 153 12,870 658 60,665 21 3,101 9 598 .. .. .. .. 139 12,298 19 1,371 8 581 5 251 130 21,192 .. .. .. .. 15 1,086 22 2,055 197 22,895 16 1,851 0 55 30 1,736 .. .. 1,045 101,623 9,816d 744,302d 9 569 22 1,940 141 5,495 60 7,194 .. .. 14 1,073 6 62 7 255 24,654 s 2,058,936 s 422 30,709 6,368 554,986 3,711 359,286 2,658 195,701 6,791 585,695 2,699 277,535 970 76,387 1,684 112,436 378 33,077 638 59,108 421 27,153 17,864 1,473,241 3,871 335,641
6 1,224 .. 1,230 0 4 10 7,981 45 2 .. 939 1,204 325 46 .. .. 1,105 18 2 1 2,455 .. .. 47 19 466 11 34 18 .. 6,154 40,618d 4 66 2 258 .. 41 0 8 124,628 s 1,320 26,769 17,503 9,266 28,088 17,475 1,916 4,726 699 1,378 1,895 96,540 24,098
a. Data are for the latest year available in the period shown. b. Includes Tazara railway. c. Refers to class 1 railways only. d. Covers only carriers designated by the U.S. Department of Transportation as major and national air carriers.
304
2009 World Development Indicators
About the data
STATES AND MARKETS
Transport services
5.9
Definitions
Transport infrastructure—highways, railways, ports
some indication of economic growth in a country.
• Total road network covers motorways, highways,
and waterways, and airports and air traffic control
But when traffic is merely transshipment, much of
main or national roads, secondary or regional roads,
systems—and the services that fl ow from it are
the economic benefit goes to the terminal operator
and all other roads in a country. • Paved roads are
crucial to the activities of households, producers,
and ancillary services for ships and containers rather
roads surfaced with crushed stone (macadam) and
and governments. Because performance indicators
than to the country more broadly. In transshipment
hydrocarbon binder or bituminized agents, with con-
vary widely by transport mode and focus (whether
centers empty containers may account for as much
crete, or with cobblestones. • Passengers carried
physical infrastructure or the services flowing from
as 40 percent of traffic.
by road are the number of passengers transported
that infrastructure), highly specialized and carefully
The air transport data represent the total (interna-
by road times kilometers traveled. • Goods hauled
specified indicators are required. The table provides
tional and domestic) scheduled traffic carried by the
by road are the volume of goods transported by road
selected indicators of the size, extent, and produc-
air carriers registered in a country. Countries submit
vehicles, measured in millions of metric tons times
tivity of roads, railways, and air transport systems
air transport data to ICAO on the basis of standard
kilometers traveled. • Rail lines are the length of rail-
and of the volume of traffic in these modes as well
instructions and definitions issued by ICAO. In many
way route available for train service, irrespective of
as in ports.
cases, however, the data include estimates by ICAO
the number of parallel tracks. • Passengers carried
Data for transport sectors are not always inter-
for nonreporting carriers. Where possible, these esti-
by railway are the number of passengers transported
nationally comparable. Unlike for demographic sta-
mates are based on previous submissions supple-
by rail times kilometers traveled. • Goods hauled
tistics, national income accounts, and international
mented by information published by the air carriers,
by railway are the volume of goods transported by
trade data, the collection of infrastructure data has
such as flight schedules.
railway, measured in metric tons times kilometers
not been “internationalized.” But data on roads are
The data cover the air traffic carried on scheduled
traveled. • Port container traffic measures the flow
collected by the International Road Federation (IRF),
services, but changes in air transport regulations
of containers from land to sea transport modes and
and data on air transport by the International Civil
in Europe have made it more diffi cult to classify
vice versa in twenty-foot-equivalent units (TEUs), a
Aviation Organization (ICAO).
traffic as scheduled or nonscheduled. Thus recent
standard-size container. Data cover coastal shipping
National road associations are the primary source
increases shown for some European countries may
as well as international journeys. Transshipment traf-
of IRF data. In countries where a national road asso-
be due to changes in the classification of air traffic
fic is counted as two lifts at the intermediate port
ciation is lacking or does not respond, other agencies
rather than actual growth. For countries with few air
(once to off-load and again as an outbound lift) and
are contacted, such as road directorates, ministries
carriers or only one, the addition or discontinuation
includes empty units. • Registered carrier depar-
of transport or public works, or central statistical
of a home-based air carrier may cause significant
tures worldwide are domestic takeoffs and takeoffs
offices. As a result, definitions and data collection
changes in air traffic.
abroad of air carriers registered in the country. • Pas-
methods and quality differ, and the compiled data
sengers carried by air include both domestic and
are of uneven quality. Moreover, the quality of trans-
international passengers of air carriers registered
port service (reliability, transit time, and condition of
in the country. • Air freight is the volume of freight,
goods delivered) is rarely measured, though it may be
express, and diplomatic bags carried on each flight
as important as quantity in assessing an economy’s
stage (operation of an aircraft from takeoff to its next
transport system.
landing), measured in metric tons times kilometers
Unlike the road sector, where numerous qualified
traveled.
motor vehicle operators can operate anywhere on the road network, railways are a restricted transport system with vehicles confined to a fixed guideway. Considering the cost and service characteristics, railways generally are best suited to carry—and can effectively compete for—bulk commodities and con-
Data sources
tainerized freight for distances of 500–5,000 kilo-
Data on roads are from the IRF’s World Road
meters, and passengers for distances of 50–1,000
Statistics, supplemented by World Bank staff
kilometers. Below these limits road transport
estimates. Data on railways are from a database
tends to be more competitive, while above these
maintained by the World Bank’s Transport and
limits air transport for passengers and freight and
Urban Development Department, Transport Divi-
sea transport for freight tend to be more competi-
sion, based on data from the International Union
tive. The railways indicators in the table focus on
of Railways. Data on port container traffic are from
scale and output measures: total route-kilometers,
Containerisation International’s Containerisation
passenger- kilometers, and goods (freight) hauled in
International Yearbook. Data on air transport are
ton-kilometers.
from the ICAO’s Civil Aviation Statistics of the World
Measures of port container traffi c, much of it
and ICAO staff estimates.
commodities of medium to high value added, give
2009 World Development Indicators
305
5.10
Power and communications Electric power
Telephones Affordability and efficiency Access and use
Transmission Consumption and distribution losses per capita % of output kWh 2006
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
306
.. 961 870 153 2,620 1,612 11,332 8,090 2,514 146 3,322 8,684 69 485 2,385 1,419 2,060 4,311 .. .. 88 186 16,753 .. .. 3,207 2,041 5,883 968 96 155 1,801 182 3,636 1,231 6,509 6,864 1,309 759 1,382 721 49 5,883 38 17,177 7,813 1,083 .. 1,549 7,174 304 5,372 529 .. .. 37
2009 World Development Indicators
Quality
Population Inter national covered by a voice traffic mobile cellular per 100 people networka Fixed Mobile cellular minutes per a a person lines subscriptions %
$ per month TelecomMobile cellular Residential Mobile cellular munications and fixed-line a revenue subscribers fi xed-line prepaid per employeea tariff a tariff a % of GDP
2006
2007
2007
2007
2007
2008
2008
2007
2007
.. 52 18 14 13 13 7 6 13 6 12 5 .. 14 17 15 17 11 .. .. 8 15 8 .. .. 12 6 12 19 4 64 10 19 16 16 6 3 11 45 11 13 .. 11 10 4 6 18 .. 14 5 16 8 12 .. .. 38
.. 9 9 1 24 20 46 41 15 1 38 44 1 7 28 7 21 30 1 0 0 1 55 0 0 21 28 60 18 0 0 32 1 42 9 23 52 9 14 15 16 1 37 1 33 56 2 4 13 65 2 54 10 1 0 1
.. 72 81 29 102 62 101 119 53 22 72 101 21 34 65 61 63 129 11 3 18 24 61 3 9 84 42 155 77 11 34 34 37 113 2 123 114 57 76 40 90 2 148 2 115 90 88 47 59 118 32 110 76 21 17 26
.. 125 18 .. 3 128 .. .. .. 6 .. .. 11 80 241 93 .. 31 11 .. 10 4 .. .. .. 40 9 1,387 106 4 .. 119 .. 208 31 74 307 .. 90 42 515 7 .. 3 .. 243 74 .. .. .. 1 182 .. .. .. ..
.. 97 82 40 94 88 99 99 99 90 93 100 80 46 99 99 91 100 61 82 87 58 98 19 24 100 97 100 83 50 53 87 59 100 77 100 114 90 84 94 95 2 100 10 99 99 79 85 96 100 68 100 76 80 65 32
.. 4.3 4.6 20.2 4.8 5.1 27.5 28.7 2.4 1.3 .. 36.4 7.5 22.7 9.5 16.9 29.1 9.2 10.3 .. 8.0 14.8 32.8 10.6 .. 27.0 3.7 11.3 7.6 .. .. 4.6 22.8 16.4 13.2 30.9 28.5 14.4 1.1 3.0 10.4 .. 13.7 1.5 19.3 30.9 .. 4.0 7.3 28.8 4.7 26.7 8.7 3.4 .. ..
.. 22.7 8.2 11.8 12.5 8.4 26.5 24.3 15.2 1.3 11.8 21.9 15.5 5.9 9.9 8.3 37.0 18.6 16.9 12.2 5.0 17.8 19.2 12.6 13.2 13.7 3.6 2.6 9.6 11.0 .. 4.5 14.8 18.7 22.7 18.6 5.8 9.1 9.0 4.7 10.5 .. 13.6 3.1 14.1 35.7 14.9 6.0 8.5 10.1 5.9 25.1 4.5 3.5 21.9 4.5
5.1 6.0 2.7 2.0 3.1 3.0 3.6 2.1 2.6 .. 2.1 3.0 1.1 6.8 5.7 3.0 4.7 5.9 4.0 .. .. 3.1 2.7 1.1 .. .. 2.9 3.5 3.9 7.6 .. 2.2 5.5 5.3 .. 3.7 2.6 0.5 4.1 3.8 5.7 2.0 4.8 2.2 2.5 2.2 2.0 .. 6.5 2.6 .. 3.7 .. .. .. ..
861 710 285 586 1,929 173 310 747 413 .. 280 690 1,539 376 657 1,074 358 522 440 .. .. 1,050 424 293 .. 1,311 1,310 813 .. 3,628 .. 470 1,442 778 58 796 512 .. 512 538 1,657 105 707 142 584 695 .. 481 355 703 1,261 802 .. .. .. ..
Electric power
5.10
Telephones Affordability and efficiency Access and use
Transmission Consumption and distribution losses per capita % of output kWh 2006
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
642 3,882 503 530 2,290 .. 6,488 6,889 5,755 2,453 8,220 1,904 4,293 145 797 8,063 16,311 2,015 .. 2,876 2,141 .. .. 3,688 3,233 3,495 .. .. 3,388 .. .. .. 2,003 1,516 1,298 685 461 93 1,546 80 7,055 9,646 426 .. 116 24,296 4,456 480 1,506 .. 900 899 578 3,585 4,799 ..
Quality
Population Inter national covered by a voice traffic mobile cellular per 100 people networka Fixed Mobile cellular minutes per a a person lines subscriptions %
2006
2007
2007
26 11 25 11 20 6 8 3 6 13 5 13 9 17 16 4 11 24 .. 17 16 .. .. 7 9 24 .. .. 1 .. .. .. 16 40 12 19 14 27 22 21 5 7 22 .. 27 8 16 22 17 .. 5 9 12 9 8 ..
12 32 4 8 34 .. 48 43 46 14 40 10 21 1 5 46 20 9 2 28 17 3 0 14 24 23 1 1 16 1 1 29 19 28 6 8 0 1 7 2 45 41 4 0 1 42 10 3 15 1 6 10 4 27 40 27
59 110 21 36 42 .. 114 124 152 100 84 83 80 30 0 90 104 41 25 97 31 23 15 73 146 96 11 8 88 21 42 74 63 49 30 65 15 0 38 12 118 101 38 6 27 110 96 39 90 5 77 55 59 109 127 86
2007
33 120 .. .. 9 .. .. 364 .. .. 46 32 47 3 .. 29 .. 30 7 67 279 18 .. 66 54 125 1 .. .. 2 5 125 185 149 5 22 13 .. .. 6 .. 310 65 .. .. 193 37 10 66 .. 35 99 .. .. 178 ..
$ per month TelecomMobile cellular Residential Mobile cellular munications and fixed-line a revenue subscribers fi xed-line prepaid per employeea tariff a tariff a % of GDP
2007
2008
2008
2007
2007
90 99 61 90 95 .. 99 100 100 95 100 99 81 77 0 90 100 24 55 99 100 55 .. 71 100 100 23 93 93 22 51 99 100 98 41 98 44 10 95 10 100 98 70 45 60 .. 96 90 81 .. .. 92 99 99 99 100
.. 30.2 3.5 4.5 0.2 .. 42.2 .. 27.4 10.8 18.3 8.3 .. 11.6 .. 6.4 9.3 .. 3.9 11.9 10.9 12.5 .. .. 15.0 8.7 18.3 3.3 5.1 9.9 12.9 5.5 22.3 3.1 .. 27.4 17.7 .. 14.5 3.4 31.2 34.4 5.1 13.6 10.3 37.6 32.6 3.6 9.1 4.0 7.2 15.4 14.2 28.0 25.7 ..
10.8 16.1 1.6 5.3 3.8 .. 18.7 9.3 17.1 7.0 32.2 4.5 11.4 13.4 .. 14.6 7.9 6.4 3.0 7.3 22.2 12.6 .. 6.1 8.7 13.2 12.4 12.0 5.9 10.0 9.9 4.4 15.0 8.9 5.4 22.2 10.1 .. 11.5 2.9 17.7 23.1 13.8 13.8 12.1 9.7 5.5 1.9 5.1 12.8 5.7 8.0 5.7 12.5 26.4 ..
6.6 4.2 2.0 .. 1.4 .. 2.4 4.1 3.2 .. 3.1 8.3 2.9 6.1 .. 5.0 .. 4.8 1.7 4.0 8.0 0.6 8.2 .. 3.1 6.8 3.9 3.3 .. 6.0 7.5 3.6 2.8 10.1 3.9 4.8 1.2 0.6 .. 1.0 .. 3.0 .. 2.2 3.1 1.4 2.7 2.7 3.5 .. 4.8 2.9 4.4 3.7 4.5 ..
391 1,009 .. .. 913 941 .. .. 1,228 .. 1,334 1,026 308 1,782 .. 637 372 311 748 697 .. 1,111 .. .. .. 1,065 394 .. 571 1,490 1,272 492 789 294 190 .. 980 81 435 565 .. 598 .. 328 .. .. 858 50 229 .. 799 624 .. 566 1,365 ..
2009 World Development Indicators
307
STATES AND MARKETS
Power and communications
5.10
Power and communications Electric power
Telephones Affordability and efficiency Access and use
Transmission Consumption and distribution losses per capita % of output kWh 2006
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
2,402 6,122 .. 7,080 150 4,040 .. 8,520 5,136 7,124 .. 4,810 6,206 400 95 .. 15,231 8,360 1,466 2,241 59 2,080 .. 97 5,006 1,221 2,053 2,123 .. 3,400 14,567 6,185 13,564 2,042 1,694 3,174 598 .. 190 730 900 2,751 w 309 1,651 1,269 3,242 1,380 1,669 3,835 1,808 1,418 453 531 9,675 6,956
2006
10 11 .. 7 26 16 .. 5 4 6 .. 9 9 15 15 .. 8 7 24 16 21 8 .. 46 6 13 14 14 .. 12 7 8 6 30 9 22 11 .. 23 6 7 9w 16 12 11 12 12 7 12 16 16 24 11 6 6
Quality
Population Inter national covered by a voice traffic mobile cellular per 100 people networka Fixed Mobile cellular minutes per a a person lines subscriptions % 2007
20 31 0 17 2 41 .. 41 21 42 1 10 45 14 1 4 60 65 17 5 0 11 0 2 23 12 25 9 1 28 32 55 54 29 7 18 34 9 4 1 3 20 w 4 17 15 23 14 23 26 18 17 3 2 50 53
2007
106 115 7 117 29 115 13 129 112 96 7 88 108 40 21 33 113 109 31 35 21 124 7 18 113 77 84 7 14 119 177 118 85 90 22 87 28 28 14 22 9 51 w 22 48 39 84 42 44 95 67 51 23 23 100 117
2007
41 .. 11 216 26 144 .. 1,531 97 92 .. .. .. 34 7 .. .. .. 79 .. 0 14 18 5 .. 73 30 .. 7 57 .. .. .. 127 12 .. .. .. .. .. 21 .. w .. .. .. .. .. 9 .. .. 32 .. .. .. ..
$ per month TelecomMobile cellular Residential Mobile cellular munications and fixed-line a revenue subscribers fi xed-line prepaid per employeea tariff a tariff a % of GDP
2007
2008
2008
2007
98 95 90 98 85 92 70 100 100 100 .. 100 99 90 60 90 98 100 96 .. 65 38 69 85 100 100 98 14 80 100 100 100 100 100 75 90 70 95 68 50 75 80 w 54 83 80 95 76 93 92 91 93 61 56 99 99
12.2 11.7 7.3 9.2 17.4 4.9 .. 7.1 24.5 20.5 .. 22.4 30.8 4.8 4.4 4.8 22.8 29.0 1.2 .. 10.9 5.8 .. 13.1 19.7 3.0 .. .. 12.6 4.2 5.0 27.3 17.2 13.0 .. 7.0 2.3 .. 0.8 27.7 .. 10.9 m 9.0 8.7 5.4 11.9 8.7 4.5 9.0 10.4 3.0 3.5 11.6 27.3 28.7
11.9 8.6 10.0 8.8 8.4 4.9 70.9 4.0 16.1 12.4 .. 12.3 33.3 2.4 4.8 12.1 7.5 35.5 9.1 23.3 11.1 3.9 .. 18.0 7.9 7.2 12.7 17.2 10.4 8.2 4.1 20.5 15.3 13.8 1.8 24.7 4.2 9.6 4.9 12.3 3.4 10.1 m 10.1 8.9 8.4 12.3 9.1 5.0 9.4 9.6 7.2 1.9 11.8 16.1 18.7
3.5 2.6 3.2 3.0 9.9 5.0 .. 2.9 3.4 3.2 .. 7.5 4.2 2.5 3.7 12.7 2.7 3.2 3.0 2.9 .. 4.0 8.0 7.4 2.6 4.3 2.5 0.7 3.2 5.7 2.7 3.7 3.1 3.7 2.5 3.8 4.7 0.8 .. 2.5 .. 3.2 w 3.3 3.2 3.1 3.3 3.3 3.0 2.9 3.8 3.1 2.1 4.7 3.1 2.8
2007
617 439 1,040 933 1,859 787 .. .. 748 587 .. 1,145 809 755 1,557 .. 905 549 409 114 .. 2,808 645 1,059 .. 915 1,782 72 255 .. 852 .. 389 661 117 .. .. 880 .. .. 381 664 m 301 595 624 566 624 546 532 530 691 660 499 747 725
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.
308
2009 World Development Indicators
About the data
5.10
Definitions
The quality of an economy’s infrastructure, includ-
Operators have traditionally been the main source
• Electric power consumption per capita measures
ing power and communications, is an important ele-
of telecommunications data, so information on sub-
the production of power plants and combined heat
ment in investment decisions for both domestic and
scribers has been widely available for most coun-
and power plants less transmission, distribution,
foreign investors. Government effort alone is not
tries. This gives a general idea of access, but a
and transformation losses and own use by heat and
enough to meet the need for investments in modern
more precise measure is the penetration rate—the
power plants divided by midyear population. • Elec-
infrastructure; public-private partnerships, especially
share of households with access to telecommunica-
tric power transmission and distribution losses are
those involving local providers and financiers, are
tions. During the past few years more information
losses in transmission between sources of supply
critical for lowering costs and delivering value for
on information and communication technology use
and points of distribution and in distribution to con-
money. In telecommunications, competition in the
has become available from household and business
sumers, including pilferage. • Fixed telephone lines
marketplace, along with sound regulation, is lower-
surveys. Also important are data on actual use of
are telephone lines connecting a subscriber to the
ing costs, improving quality, and easing access to
telecommunications equipment. Ideally, statistics
telephone exchange equipment. • Mobile cellular
services around the globe.
on telecommunications (and other information and
telephone subscriptions are subscriptions to a pub-
An economy’s production and consumption of elec-
communications technologies) should be compiled
lic mobile telephone service using cellular technol-
tricity are basic indicators of its size and level of
for all three measures: subscription and possession,
ogy, which provide access to the public switched
development. Although a few countries export elec-
access, and use. The quality of data varies among
telephone network. Post-paid and prepaid subscrip-
tric power, most production is for domestic consump-
reporting countries as a result of differences in regu-
tions are included. • International voice traffic is
tion. Expanding the supply of electricity to meet the
lations covering data provision and availability.
the sum of international incoming and outgoing tele-
growing demand of increasingly urbanized and indus-
Globally, there have been huge improvements in
phone traffic (in minutes) divided by total population.
trialized economies without incurring unacceptable
access to telecommunications, driven mainly by
• Population covered by mobile cellular network is
social, economic, and environmental costs is one of
mobile telephony. By 2007 worldwide mobile cel-
the percentage of people that live in areas served by
the great challenges facing developing countries.
lular phone subscribers numbered 3.3 billion, far
a mobile cellular signal regardless of whether they
Data on electric power production and consump-
outpacing the 1.1 billion fixed-line subscribers. By
use it. • Residential fixed-line tariff is the monthly
tion are collected from national energy agencies by
2006 approximately 99 percent of the population
subscription charge plus the cost of 30 three-minute
the International Energy Agency (IEA) and adjusted
in high-income countries and about 77 percent of
local calls (15 peak and 15 off-peak). • Mobile cel-
by the IEA to meet international definitions (for data
the population in developing countries were covered
lular prepaid tariff is based on the Organisation for
on electricity production, see table 3.10). Electricity
by a mobile cellular network (within areas served by
Economic Co-operation and Development’s low-user
consumption is equivalent to production less power
a mobile cellular signal). Indeed, in many develop-
definition, which includes the cost of monthly mobile
plants’ own use and transmission, distribution, and
ing countries, especially in Sub-Saharan Africa, the
use for 25 outgoing calls per month spread over the
transformation losses less exports plus imports. It
number of mobile phones has overtaken the number
same mobile network, other mobile networks, and
includes consumption by auxiliary stations, losses
of fixed-line phones.
mobile to fixed-line calls and during peak, off-peak,
in transformers that are considered integral parts
Although access is the key to delivering telecom-
and weekend times as well as 30 text messages
of those stations, and electricity produced by pump-
munications services to people, if the service is not
per month. • Telecommunications revenue is the
ing installations. Where data are available, it covers
affordable to most people, then goals of universal
revenue from the provision of telecommunications
electricity generated by primary sources of energy—
usage will not be met. Two indicators of telecom-
services such as fixed-line, mobile, and data divided
coal, oil, gas, nuclear, hydro, geothermal, wind, tide
munications affordability are presented in the table:
by GDP. • Mobile cellular and fixed-line subscribers
and wave, and combustible renewables. Neither pro-
fixed-line telephone service tariff and prepaid mobile
per employee are telephone subscribers (fixed-line
duction nor consumption data capture the reliability
cellular service tariff. Telecommunications efficiency
plus mobile) divided by the total number of telecom-
of supplies, including breakdowns, load factors, and
is measured by total telecommunications revenue
munications employees.
frequency of outages.
divided by GDP and by mobile cellular and fixed-line
Over the past decade new financing and technol-
telephone subscribers per employee.
ogy, along with privatization and liberalization, have spurred dramatic growth in telecommunications
Data sources
in many countries. With the rapid development of
Data on electricity consumption and losses are
mobile telephony and the global expansion of the
from the IEA’s Energy Statistics and Balances of
Internet, information and communication technolo-
Non-OECD Countries 2008, the IEA’s Energy Sta-
gies are increasingly recognized as essential tools of
tistics of OECD Countries 2008, and the United
development, contributing to global integration and
Nations Statistics Division’s Energy Statistics Year-
enhancing public sector effectiveness, efficiency,
book. Data on telecommunications are from the
and transparency. The table presents telecommuni-
International Telecommunication Union’s World
cations indicators covering access and use, quality,
Telecommunication Development Report data-
and affordability and efficiency.
base and World Bank estimates.
2009 World Development Indicators
309
STATES AND MARKETS
Power and communications
5.11
The information age Daily Households newspapers with televisionb
Personal computers and the Internet Access and use
per 100 people
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
310
per 1,000 people
%
2000–07a
2006
2007
2007
2007
.. 24 .. 2 36 8 155 311 16 .. 81 165 0 .. .. 41 36 79 .. .. .. .. 175 .. .. 51 74 222 23 .. .. 65 .. .. 65 183 353 39 99 .. 38 .. 191 5 431 164 .. .. 4 267 .. .. .. .. .. ..
62 90 91 9 95 91 99c 98 99 48 97 99 13 63 96 9 91 92 12 15 43 25 99 5 4 97 89 100 84 4 27 94 35 94 70 83 96 78 87 96c 83 18 .. 5 87 97 58 12 89 94 26c 100 50 10 31 27
.. 3.8 1.1 0.7 9.0 31.9 .. 60.7 2.4 2.2 0.8 41.7 0.7 2.4 6.4 4.8 16.1 8.9 0.6 0.8 0.4 1.1 94.3 0.3 0.2 14.1 5.7 68.6 8.0 0.0 0.5 23.1 1.7 .. 3.6 27.4 54.9 3.5 13.0 4.9 5.2 0.8 52.2 0.7 50.0 65.2 3.6 3.3 5.4 65.6 0.6 9.4 2.1 0.5 0.2 5.2
.. 14.9 10.3 2.9 25.9 5.7 68.1 67.4 10.8 0.3 29.0 65.9 1.7 10.5 28.0 5.3 35.2 30.9 0.6 0.7 0.5 2.0 72.8 0.3 0.6 31.1 16.1 57.2 27.5 0.4 1.9 33.6 1.6 44.7 11.6 48.3 80.7 17.2 13.2 14.0 11.1 2.5 63.7 0.4 78.8 51.2 6.2 5.9 8.2 72.3 3.8 32.9 10.1 0.5 2.2 10.4
.. 0.31 0.85 0.07 6.58 0.07 22.98 19.51 0.07 0.00 0.12 25.55 0.02 0.36 2.24 0.19 3.54 8.21 0.01 0.00 0.06 0.00 27.52 0.00 0.00 7.90 5.04 27.42 2.74 0.00 0.00 2.83 0.05 8.73 0.02 12.72 35.87 1.58 2.39 0.63 1.31 0.00 20.70 0.00 30.57 25.20 0.15 0.02 1.06 23.82 0.07 9.09 0.21 0.00 0.00 0.00
2009 World Development Indicators
Personal Internet computersb usersb
Quality Affordability Fixed International Fixed broadband Internet broadband Internet bandwidthb Internet access subscribersb bits per tariffb second per per 100 capita people $ per month 2007
.. 216 89 17 2,320 .. 5,472 20,288 701 4 264 24,945 17 42 530 43 1,041 4,909 15 1 17 11 16,193 0 1 4,086 280 15,892 971 0 0 820 16 3,380 19 7,075 34,506 154 324 143 18 2 11,925 3 17,221 29,466 150 36 745 25,654 21 4,537 187 0 1 17
2008
.. 31 17 164 38 39 28 61 85 54 .. 31 105 34 15 30 47 16 1,861 .. 91 184 20 1,396 .. 53 19 25 36 .. .. 17 47 21 1,630 29 30 28 40 8 18 .. 39 644 38 38 .. 384 48 38 64 25 34 800 .. ..
Information and communications technology trade
Application Information and comSecure Goods munications Exports Imports Internet technology % of total % of total servers expenditures goods per million goods % of GDP imports people exports
Services Exports % of total service exports
December 2008
2007
2007
2007
2007
.. 5 1 1 18 5 993 481 2 0 2 251 0 4 7 2 24 26 0 0 1 0 907 0 .. 35 1 287 11 0 0 99 1 92 0 151 1,041 13 10 1 10 .. 280 0 686 172 4 2 6 549 1 61 8 0 .. 1
.. .. 2.5 .. 6.0 .. 6.6 5.6 .. 8.0 .. 5.8 .. 5.8 .. .. 5.8 7.7 .. .. .. 5.0 6.4 .. .. 4.2 7.9 4.7 4.4 .. .. 3.9 .. .. .. 7.1 5.8 .. 6.1 5.8 .. .. .. .. 5.2 5.7 .. .. .. 6.2 .. 5.4 .. .. .. ..
.. 1.0 0.0 .. 0.6 0.6 1.8 6.3 0.1 .. 0.8 3.7 0.0 0.1 0.5 0.2 3.2 1.8 .. 0.5 .. 0.0 4.7 .. .. 0.1 30.9 42.1 0.3 .. .. 29.4 0.4 5.3 1.9 14.2 7.1 .. 0.3 .. 0.6 .. 14.2 0.3 18.9 8.0 0.1 0.2 0.4 9.6 0.0 3.3 0.5 .. .. ..
.. 3.5 6.9 .. 13.1 5.8 12.8 8.2 6.1 .. 3.0 4.8 3.3 4.9 3.8 5.5 14.5 6.0 .. 2.5 .. 3.2 10.1 .. .. 9.0 28.6 41.8 13.3 .. .. 25.3 4.2 7.5 2.9 15.0 11.9 .. 7.7 .. 8.4 .. 11.1 7.1 14.4 9.8 6.6 4.6 7.1 12.3 6.3 6.0 9.1 .. .. ..
.. 3.6 .. .. 7.9 14.6 4.6 6.3 3.1 5.7 6.8 8.7 5.4 12.5 .. 6.8 1.8 4.4 .. 0.0 3.1 13.0 11.1 .. .. 2.7 4.5 1.6 7.9 .. .. 16.4 11.0 4.2 .. 8.0 .. 3.7 6.2 4.2 9.6 .. 6.1 6.3 8.4 4.1 .. .. 1.5 7.8 0.0 1.6 14.8 .. .. 4.9
Daily Households newspapers with televisionb
Personal computers and the Internet Access and use
per 100 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
Personal Internet computersb usersb
Quality Affordability Fixed International Fixed broadband Internet broadband Internet bandwidthb Internet access subscribersb bits per tariffb second per per 100 capita people $ per month
per 1,000 people
%
2000–07a
2006
2007
2007
2007
.. 217 71 .. .. .. 182 .. 137 .. 551 .. .. .. .. .. .. 1 3 154 54 .. .. .. 108 89 .. .. 109 .. .. 77 93 .. 20 12 3 .. 28 .. 307 182 .. 0 .. 516 .. 50 65 9 .. .. 79 114 .. ..
61 101 53 65 .. .. 119 92 98 70 99 c 96 .. 32c .. 100 95 .. 30 .. 95 13 .. 50 98 98 18 5 95 15 25 96c 98 74 33 78 9 3 41 13 99 99 60 7 26 97 79 .. 87 10 79 73 63 89 99 97
2.0 25.6 3.3 2.0 10.6 .. 58.2 .. 36.7 6.8 .. 6.7 .. 1.4 .. 57.6 23.7 1.9 1.8 32.7 10.4 0.3 .. 2.2 18.3 36.8 0.5 0.2 23.1 0.8 4.6 17.6 14.4 11.1 13.9 3.6 1.4 0.9 24.0 0.5 91.2 52.6 4.0 0.1 0.8 62.9 7.1 .. 4.6 6.4 7.8 10.3 7.3 16.9 17.2 0.8
6.0 51.9 7.2 5.8 32.4 .. 56.1 27.9 53.9 56.1 69.0 19.7 12.3 8.0 0.0 75.9 33.8 14.3 1.7 55.0 38.3 3.5 0.5 4.3 49.2 27.3 0.6 1.0 55.7 0.8 1.0 27.0 22.7 18.4 12.3 21.4 0.9 0.1 4.9 1.4 84.2 69.2 2.8 0.3 6.8 84.8 13.1 10.8 22.3 1.8 8.7 27.4 6.0 44.0 40.1 25.4
0.00 14.21 0.28 0.11 .. .. 18.46 21.29 18.29 3.47 22.14 1.50 1.75 0.05 0.00 30.36 0.99 0.06 0.06 6.42 4.88 0.00 .. 0.16 15.04 4.93 0.01 0.01 3.81 0.03 0.13 4.88 4.32 1.24 0.28 1.55 0.00 0.00 0.01 0.04 33.62 20.17 0.34 0.00 0.00 30.57 0.78 0.03 4.31 0.00 0.84 2.04 0.56 8.99 14.37 3.02
2007
244 4,773 32 53 153 .. 15,229 2,003 10,302 19,151 3,734 164 129 9 .. 1,027 871 114 32 3,537 227 2 .. 50 4,656 17 8 5 998 17 70 226 178 931 116 814 3 2 27 5 78,159 4,544 144 2 5 26,904 142 44 15,977 1 163 2,704 114 2,748 4,790 511
STATES AND MARKETS
5.11
The information age
Information and communications technology trade
Application Information and comSecure Goods munications Exports Imports Internet technology % of total % of total servers expenditures goods per million goods % of GDP imports people exports
2008
December 2008
.. 25 6 22 43 .. 38 .. 26 30 32 31 .. 168 .. 20 46 .. 268 26 23 49 .. .. 16 15 120 900 20 58 62 51 37 23 .. 20 100 .. 46 23 38 31 30 58 690 57 31 18 15 144 35 36 23 27 30 ..
6 83 1 1 0 .. 679 273 93 32 472 9 2 1 .. 696 65 1 0 98 13 0 .. 0 83 12 0 0 27 1 2 60 16 7 9 1 0 0 9 1 1,108 980 7 0 1 851 12 1 87 1 6 10 5 85 115 54
Services Exports % of total service exports
2007
2007
2007
2007
11.2 5.9 5.6 3.9 3.5 .. 5.9 6.5 5.8 6.6 7.2 9.3 .. 8.2 .. 7.1 4.5 .. .. .. .. .. .. .. .. .. .. .. 6.8 .. .. .. 4.0 .. .. 8.3 .. .. .. .. 6.6 5.7 .. .. 3.4 4.4 .. 5.6 5.9 .. .. 3.9 5.7 6.0 5.7 ..
0.3 26.1 1.3 5.3 0.1 .. 22.4 10.9 3.7 0.2 19.3 4.8 0.1 1.0 .. 27.2 .. 0.8 .. 3.4 .. .. .. .. 4.8 0.4 0.5 0.4 41.5 0.2 .. 4.7 19.6 2.6 0.1 5.7 0.1 .. 0.5 .. 18.9 2.3 0.2 0.4 0.0 1.8 0.8 0.5 0.0 .. 0.4 0.1 29.1 5.6 9.0 ..
6.9 19.9 8.3 5.4 1.9 .. 24.1 11.4 7.0 3.6 13.7 7.0 5.2 5.6 .. 16.5 .. 5.1 .. 6.9 .. .. .. .. 6.4 4.4 4.7 3.8 36.0 4.2 2.1 6.1 14.9 4.3 5.9 6.7 5.1 .. 7.3 .. 19.8 9.7 7.3 4.4 6.9 9.7 3.8 7.2 6.7 .. 28.6 8.0 20.6 9.6 9.3 ..
11.5 6.8 41.6 11.9 .. 1.9 30.1 28.5 3.5 6.8 1.2 0.0 2.5 4.1 .. 1.4 48.4 1.9 .. 4.9 2.2 .. .. 2.5 3.1 14.1 0.5 .. 4.9 .. .. 2.8 2.3 15.4 3.7 3.3 5.0 .. 2.7 .. 11.0 5.4 8.2 32.8 .. 4.2 .. 6.8 4.6 2.2 2.2 2.6 7.0 4.0 4.8 ..
2009 World Development Indicators
311
5.11
The information age Daily Households newspapers with televisionb
Personal computers and the Internet Access and use
per 100 people
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
per 1,000 people
%
2000–07a
2006
2007
90 98 2 99 41 80 .. 98 78 97 8 59 96 32 16 18 94 86 105 79 7c 92 .. 14 88 93 112 .. 10 97 86 98 95 92 99 90 89 93 43 .. 32 89 m 16 89 79 92 63 53 96 84 94 42 18 98 97
19.2 13.3 0.3 14.8 2.1 24.4 .. 74.3 51.4 42.5 0.9 8.5 39.3 3.7 11.2 3.7 88.1 91.8 9.0 1.3 0.9 7.0 .. 3.0 13.2 7.5 6.0 7.2 1.7 4.5 33.0 80.2 80.5 13.6 3.1 9.3 9.6 5.6 2.8 1.1 6.5 15.3 w 1.5 5.6 4.6 12.4 5.3 5.6 10.8 11.3 6.3 3.3 1.8 67.4 55.5
70 92 .. .. 9 .. .. 361 126 173 .. 30 144 26 .. 24 481 420 .. .. 2 .. .. 2 149 23 .. 9 .. 131 .. 290 193 .. .. 93 .. 10 4 5 .. 105 w .. 71 72 65 66 74 109 64 .. 68 .. 261 201
Personal Internet computersb usersb 2007
23.9 21.1 1.1 26.4 6.6 20.3 0.2 65.7 55.9 52.6 1.1 8.3 51.3 3.9 9.1 3.7 79.7 76.3 17.4 7.2 1.0 21.0 0.1 5.0 16.0 16.8 16.5 1.4 2.5 21.5 51.8 71.7 73.5 29.1 4.5 20.8 21.0 9.6 1.4 4.2 10.1 21.8 w 5.2 15.2 12.4 26.6 13.1 14.6 21.4 26.9 17.1 6.6 4.4 65.7 59.2
Quality Affordability Fixed International Fixed broadband Internet broadband Internet bandwidthb Internet access subscribersb bits per tariffb second per per 100 capita people $ per month 2007
2007
2008
9.05 2,945 23 2.81 573 14 0.03 16 92 2.58 510 40 0.31 137 29 4.41 2,861 9 .. .. .. 19.51 22,783 22 8.75 5,555 28 17.09 6,720 27 0.00 0 .. 0.79 71 26 17.98 11,008 29 0.32 118 21 0.11 345 29 0.00 1 1,877 35.85 49,828 32 31.52 29,417 32 0.03 53 51 0.00 0 .. 0.00 3 68 1.43 346 18 0.00 9 .. 0.00 4 106 2.66 675 13 1.12 303 13 6.16 1,381 .. .. 16 .. 0.01 11 170 1.72 206 21 8.70 2,785 22 25.58 39,650 29 24.27 11,277 15 4.96 903 24 0.03 9 .. 3.12 628 31 1.52 148 17 1.50 324 .. .. 28 226 0.02 3 91 0.11 4 .. 6.03 w 3,297 w 31.4 m 26 102.4 0.02 2.72 389 28.0 2.33 199 30.5 4.28 1,185 26.0 2.35 318 36.3 3.70 247 21.7 4.39 1,114 21.8 3.64 1,126 34.0 .. 186 23.0 0.24 31 21.0 0.03 36 100.1 22.84 18,242 30.2 21.60 32,560 30.5
Information and communications technology trade
Application Information and comSecure Goods munications Exports Imports Internet technology % of total % of total servers expenditures goods per million goods % of GDP imports people exports December 2008
16 7 0 8 1 2 1 390 58 171 0 37 171 3 0 5 775 982 0 .. 0 10 .. 1 46 11 57 .. 0 4 126 908 1,174 43 0 7 1 1 0 0 1 112 w 0 7 2 26 5 2 24 18 1 1 3 663 321
Services Exports % of total service exports
2007
2007
2007
2007
5.3 4.1 .. 4.7 10.9 .. .. 6.5 6.0 4.7 .. 9.7 5.5 6.0 .. .. 6.4 8.0 .. .. .. 6.1 .. .. .. 6.0 5.5 .. .. 7.1 5.1 6.7 7.5 6.0 .. 3.9 6.1 .. .. .. 3.5 6.5 w .. 5.9 6.5 5.2 5.9 7.3 5.0 4.9 4.5 5.7 .. 6.7 5.9
3.1 0.5 0.8 0.3 0.5 .. .. 45.6 13.2 3.0 .. 1.8 4.0 1.7 0.0 0.0 11.2 3.7 0.1 .. 0.4 24.2 .. 0.1 0.2 4.2 2.0 .. 6.9 1.5 4.3 20.5 16.3 0.1 .. 0.0 5.1 .. 0.1 0.1 0.3 15.4 w 1.4 16.9 20.6 13.5 16.1 30.9 1.8 11.4 .. 1.2 1.1 15.2 9.4
7.6 10.1 8.0 7.8 4.0 .. .. 38.3 10.3 5.3 .. 11.3 7.9 4.9 7.5 3.8 12.2 7.4 2.5 .. 6.2 20.0 .. 4.2 5.9 5.9 4.0 .. 10.0 3.3 8.6 13.6 14.6 6.5 .. 12.1 7.6 .. 3.7 5.1 2.0 15.2 w 6.7 18.0 20.2 16.2 17.5 28.1 7.0 15.9 .. 8.1 8.2 14.6 10.9
16.3 6.0 1.9 .. 18.0 .. 0.2 3.1 6.6 5.2 .. 3.9 5.4 10.6 5.4 1.4 13.1 .. 5.8 12.6 2.5 .. .. 6.9 .. 1.2 1.8 .. 7.6 3.6 .. 7.8 4.3 8.8 .. 11.1 .. .. 18.9 8.5 .. 6.7 w .. 4.9 5.0 4.8 4.9 5.2 5.0 4.7 .. 39.0 .. 7.0 8.2
a. Data are for the most recent year available. b. 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. c. Data are for 2007.
312
2009 World Development Indicators
5.11
About the data
The digital and information revolution has changed the
generally derive their estimates by multiplying sub-
notebooks and excluding terminals connected to
way the world learns, communicates, does business,
scriber counts reported by Internet service providers
mainframe and minicomputers intended primarily
and treats illnesses. New information and communi-
by a multiplier. This method may undercount actual
for shared use and devices such as smart phones
cations technologies (ICT) offer vast opportunities for
users, particularly in developing countries, where
and personal digital assistants. • Internet users are
progress in all walks of life in all countries—opportu-
many commercial subscribers rent out computers
people with access to the worldwide network. • Fixed
nities for economic growth, improved health, better
connected to the Internet or prepaid cards are used
broadband Internet subscribers are the number of
service delivery, learning through distance education,
to access the Internet.
broadband subscribers with a digital subscriber
and social and cultural advances.
Broadband refers to technologies that provide
line, cable modem, or other high-speed technology.
The table presents indicators of the penetration of
Internet speeds of at least 256 kilobits a second of
• International Internet bandwidth is the contracted
the information economy, quality, and secure Internet
upstream and downstream capacity and includes digi-
capacity of international connections between coun-
servers), and some of the economics of the informa-
tal subscriber lines, cable modems, satellite broad-
tries for transmitting Internet traffic. • Fixed broad-
tion age.
band Internet, fiber-to-home Internet access, ethernet
band Internet access tariff is the lowest sampled
Comparable statistics on access, use, quality,
local access networks, and wireless area networks.
cost per 100 kilobits a second per month and are
and affordability of ICT are needed to formulate
Bandwidth refers to the range of frequencies available
calculated from low- and high-speed monthly service
growth-enabling policies for the sector and to moni-
for signals. The higher the bandwidth, the more infor-
charges. Monthly charges do not include installation
tor and evaluate the sector’s impact on development.
mation that can be transmitted at one time. Reporting
fees or modem rentals. • Secure Internet servers
Although basic access data are available for many
countries may have different definitions of broadband,
are servers using encryption technology in Internet
countries, in most developing countries little is known
so data are not strictly comparable.
transactions. • Information and communications
about who uses ICT; what they are used for (school,
The number of secure Internet servers, from the
technology expenditures include computer hard-
work, business, research, government); and how they
Netcraft Secure Server Survey, indicates how many
ware (computers, storage devices, printers, and other
affect people and businesses. The global Partner-
companies conduct encrypted transactions over the
peripherals); computer software (operating systems,
ship on Measuring ICT for Development is helping to
Internet. The survey examines the use of encrypted
programming tools, utilities, applications, and inter-
set standards, harmonize information and commu-
transactions through extensive automated explora-
nal software development); computer services (infor-
nications technology statistics, and build statistical
tion, tallying the number of Web sites using a secure
mation technology consulting, computer and network
capacity in developing countries. For more informa-
socket layer (SSL). Some countries, such as the
systems integration, Web hosting, data processing
tion see www.itu.int/ITU-D/ict/partnership/.
Republic of Korea, use application layers to establish
services, and other services); and communications
the encryption channel, which is SSL equivalent.
services (voice and data communications services)
Data on daily newspapers in circulation are from United Nations Educational, Scientific, and Cultural
According to the World Information Technology and
and wired and wireless communications equipment.
Organization (UNESCO) Institute for Statistics surveys
Services Alliance’s (WITSA) Digital Planet 2008, the
• Information and communication technology
on circulation, online newspapers, journalists, com-
global marketplace for information and communica-
goods exports and imports include telecommunica-
munity newspapers, and news agencies.
tions technologies was expected to be about $3.4 tril-
tions, audio and video, computer and related equip-
Estimates of households with television are derived
lion in 2007 and to rise to almost $3.8 trillion in
ment; electronic components; and other information
from household surveys. Some countries report only
2008. The data on information and communications
and communication technology goods. Software is
the number of households with a color television set,
technology expenditures cover the world’s 75 largest
excluded. • Information and communication technol-
and so the true number may be higher than reported.
buyers among countries and regions.
ogy service exports include computer and communi-
Estimates of personal computers are from an
Information and communication technology goods
cations services (telecommunications and postal and
annual International Telecommunication Union (ITU)
exports and imports are defi ned by the Working
courier services) and information services (computer
questionnaire sent to member states, supplemented
Party on Indicators for the Information Society and
data and news-related service transactions).
by other sources. Many governments lack the capac-
are reported in the Organisation for Economic Co-
ity to survey all places where personal computers
operation and Development’s Guide to Measuring the
are used (homes, schools, businesses, government
Information Society. Information and communication
offices, libraries, Internet cafes) so most estimates
technology service exports data are based on the
are derived from the number of personal computers
International Monetary Fund’s (IMF) Balance of Pay-
sold each year. Annual shipment data can also be mul-
ments Statistics Yearbook classification.
Institute for Statistics. Data on personal computers and the Internet are from the ITU’s World Telecommunication Development Report database. Data on secure Internet servers are from Netcraft (www.
tiplied by an estimated average useful lifespan before replacement to approximate the number of personal
Data sources Data on newspapers are compiled by the UNESCO
Definitions
computers. There is no precise method for determin-
• Daily newspapers are newspapers issued at least
ing replacement rates, but in general personal com-
four times a week that report mainly on events in the
puters are replaced every three to five years.
24-hour period before going to press. The indicator
Data on Internet users and related indicators are
is average circulation (or copies printed) per 1,000
based on nationally reported data. Some countries
people. • Households with television are the percent-
derive these data from surveys, but since survey
age of households with a television set. • Personal
questions and definitions differ, the estimates may
computers are self-contained computers designed
not be strictly comparable. Countries without surveys
for use by a single individual, including laptops and
netcraft.com/) and official government sources. Data on information and communication technology goods trade are from the United Nations Statistics Division’s Commodity Trade (Comtrade) database. Data on information and communication technology expenditures are from WITSA’s Digital Planet 2008 and Global Insight, Inc. Data on information and communication technology service exports are from the IMF’s Balance of Payments Statistics database.
2009 World Development Indicators
313
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
314
per million people
per million people
2000–06c
2000–06c
.. .. 170 .. 895 .. 4,053 3,657 .. .. .. 3,252 .. 120 .. .. 461 1,344 22 .. 17 26 3,922 .. .. 833 926 2,090 127 .. 32 122 68 1,148 .. 2,578 5,277 .. 51 .. 47 .. 2,622 20 7,681 3,353 .. 28 .. 3,386 .. 1,790 31 .. .. ..
.. .. 35 .. 366 .. 904 1,462 .. .. .. 1,447 .. 6 .. .. 394 488 16 .. 13 .. 1,467 .. .. 302 .. 416 97 .. 35 .. .. 538 .. 1,555 1,990 .. .. .. .. .. 579 10 .. 1,746 .. 17 .. 1,144 .. 795 11 .. .. ..
2009 World Development Indicators
2005
.. .. 350 .. 3,058 180 15,957 4,566 116 193 490 6,841 .. .. .. .. 9,889 764 .. .. .. 131 25,836 .. .. 1,559 41,596 .. 400 .. .. 105 .. 953 261 3,169 5,040 .. .. 1,658 .. .. 439 88 4,811 30,309 .. .. 145 44,145 81 4,291 .. .. .. ..
High-technology exports
Royalty and license fees
Patent applications fileda,b
Trademark applications fileda
% of GDP
$ millions
% of manufactured exports
2000–06c
2007
2007
2007
2007
2007
2007
2007
2007
.. .. 0.07 .. 0.49 0.21 1.78 2.46 0.22 .. 0.68 1.85 .. 0.28 .. 0.39 0.82 0.48 0.17 .. 0.05 .. 1.97 .. .. 0.67 1.42 0.74 0.17 0.48 .. 0.37 .. 0.87 0.51 1.54 2.44 .. 0.06 0.19 .. .. 1.15 0.20 3.43 2.12 .. .. 0.18 2.52 .. 0.50 0.03 .. .. ..
.. 8 11 .. 1,144 9 3,541 14,566 15 .. 346 25,178 0 15 76 16 9,295 618 .. 1 .. 3 29,593 0 .. 401 336,988 2,370 338 .. .. 2,088 460 769 248 15,410 11,247 .. 73 5 42 .. 840 4 15,565 80,465 71 0 39 155,922 5 1,005 119 .. .. ..
.. 12 2 .. 7 2 14 11 4 .. 3 7 0 5 3 0 12 6 .. 4 .. 3 14 0 .. 7 30 19 3 .. .. 45 32 9 35 14 17 .. 7 0 4 .. 12 3 21 19 32 2 7 14 1 8 3 .. .. ..
.. 5 .. 12 85 .. 686 757 0 0 7 1,640 0 2 .. 0 319 10 .. 0 0 0 3,635 .. .. 61 343 259 17 .. .. 0 0 40 .. 35 .. 0 0 122 1 .. 10 0 1,216 8,827 .. .. 11 7,249 0 52 9 0 .. 3
.. 8 .. 1 1,036 .. 2,821 1,471 5 8 69 1,867 2 16 .. 11 2,259 73 .. .. 10 6 7,552 .. .. 434 8,192 1,357 188 .. .. 53 10 214 .. 653 .. 32 45 241 24 .. 40 2 1,326 4,603 .. .. 5 9,698 .. 600 79 0 .. 0
.. .. 84 .. 0 192 2,717 2,271 281 22 1,188 454 .. .. 55 .. 3,810 211 .. .. .. .. 4,998 .. .. 291 153,060 160 121 .. .. .. .. 344 74 716 1,660 0 0 516 .. .. 44 .. 1,804 14,722 .. .. 83 47,853 .. 772 9 .. .. ..
.. .. 765 .. 0 1 24,626 378 6 288 337 163 .. .. 162 .. 20,264 28 .. .. .. .. 35,133 .. .. 2,924 92,101 13,606 1,860 .. .. .. .. 93 210 192 197 0 794 1,589 .. .. 19 .. 211 2,387 .. .. 79 13,139 .. 3,889 99 .. .. ..
.. 186 2,235 .. 55,252 883 40,001 7,844 751 .. 3,666 22,964 d .. 1,873 320 .. 76,827 6,868 .. .. 467 .. 21,101 .. .. 30,847 669,276 7,902 14,118 .. .. 5,872 .. 1,486 454 9,156 4,444 .. 6,078 .. .. .. 1,537 .. 3,504 70,432 .. .. 554 72,788 .. 6,416 5,955 .. .. ..
.. 809 1,415 .. 18,465 594 11,176 820 1,025 .. 1,409 1,695d .. 4,208 870 .. 17,842 674 .. .. 1,847 .. 26,657 .. .. 13,473 56,840 15,627 9,876 .. .. 5,882 .. 946 602 1,006 662 .. 6,527 .. .. .. 443 .. 629 3,151 .. .. 605 3,377 .. 889 5,048 .. .. ..
$ millions Receipts
Payments
Residents
Nonresidents
Residents
Nonresidents
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
2000–06c
2000–06c
.. 1,745 111 199 .. .. 2,882 .. 1,407 .. 5,546 .. 783 .. .. 4,162 74 .. .. 1,758 .. 10 .. 361 2,358 547 43 .. 503 .. .. .. 464 .. .. .. .. 18 .. 59 2,524 4,207 .. 8 .. 4,668 .. 80 87 .. 71 .. .. 1,562 2,007 ..
.. 491 86 .. .. .. 740 .. .. .. 561 .. 83 .. .. 583 94 .. .. 648 .. 11 .. 493 411 83 6 .. 63 .. .. .. 260 .. .. .. .. 139 .. 137 1,863 768 .. 10 .. .. .. 41 206 .. 122 .. .. 227 277 ..
2005
.. 2,614 14,608 205 2,635 .. 2,120 6,309 24,645 .. 55,471 275 96 226 .. 16,396 233 .. .. 134 234 .. .. .. 406 .. .. .. 615 .. .. .. 3,902 89 .. 443 .. .. .. .. 13,885 2,983 .. .. 362 3,644 111 492 .. .. .. 133 178 6,844 2,910 ..
High-technology exports
% of GDP
$ millions
% of manufactured exports
2000–06c
2007
2007
8 19,349 4,944 5,225 375 0 28,720 3,088 27,817 2 121,425 38 1,470 81 .. 110,633 .. 8 .. 353 .. .. .. .. 1,214 21 7 2 64,584 3 .. 112 33,314 14 7 858 3 .. 83 .. 74,369 628 5 4 62 4,391 8 188 0 .. 24 69 13,792 4,177 3,285 ..
1 25 5 11 6 0 28 8 7 2 19 1 23 5 .. 33 .. 2 .. 7 .. .. .. .. 11 1 1 2 52 7 .. 8 17 5 8 9 2 .. 5 .. 26 10 4 14 8 18 0 1 0 .. 6 2 54 4 9 ..
0.05 1.00 0.69 0.05 0.59 .. 1.31 4.53 1.10 0.07 3.40 0.34 0.28 .. .. 3.23 0.18 0.20 .. 0.69 .. 0.06 .. .. 0.80 24.77 0.16 .. 0.60 .. .. 0.38 0.50 .. 0.26 0.66 0.50 0.16 .. .. 1.69 1.17 0.05 .. .. 1.49 .. 0.44 0.25 .. 0.09 0.15 0.14 0.56 0.83 ..
Royalty and license fees
Patent applications fileda,b
$ millions
STATES AND MARKETS
5.12
Science and technology
Trademark applications fileda
Receipts
Payments
Residents
Nonresidents
Residents
Nonresidents
2007
2007
2007
2007
2007
2007
25 1,596 949 1,052 .. 29 24,669 946 1,680 60 16,678 0 68 24 .. 5,075 0 12 .. 40 .. .. .. 0 22 9 9 .. 1,195 1 .. 6 503 7 .. 36 2 .. 2 .. 3,662 553 .. 0 174 622 .. 107 47 .. 6 90 364 1,575 451 ..
.. 689 4,521 282 .. .. 847 257 9,255 21 333,498 .. 1,433 38 .. 128,701 .. 155 .. 114 .. .. .. .. 62 .. 4 .. 531 .. .. .. 629 333 103 178 .. .. .. .. 2,079 1,892 .. .. .. 1,223 .. 0 .. .. .. 28 231 2,392 250 ..
.. 102 19,984 4,324 .. .. 78 7,239 870 132 62,793 .. 124 33 .. 43,768 .. 3 .. 37 .. .. .. .. 20 .. 40 .. 4,269 .. .. .. 15,970 14 110 732 .. .. .. .. 367 5,952 .. .. .. 5,431 .. 0 .. .. .. 1,331 3,034 361 31 ..
2,369 3,615 73,308 36,644 .. .. 1,905 3,293 50,604 594 118,130 .. .. 1,451 .. 112,157 .. 191 .. 1,398 .. .. .. .. 2,218 .. 445 222 12,289 .. .. .. 54,610 1,262 339 5,637 553 .. .. .. .. 9,665 1,195 .. .. 3,326 .. 9,033 3,530 76 .. 12,778 8,398 13,951 15,288 ..
5,034 631 12,361 16,005 .. .. 1,116 7,285 4,490 1,114 12,796 .. .. 1,187 .. 20,131 .. 424 .. 479 .. .. .. .. 431 .. 432 582 13,605 .. .. .. 28,606 717 277 1,365 943 .. .. .. .. 9,945 4,780 .. .. 3,286 .. 4,952 6,079 536 .. 8,867 6,335 1,100 959 ..
.. 841 112 31 .. 0 1,110 784 1,050 15 23,229 0 0 23 .. 1,920 0 2 .. 13 .. 20 .. .. 0 3 2 .. 36 0 .. 0 120 6 .. 4 0 .. .. .. 4,322 141 0 0 .. 700 .. 37 0 .. 206 2 5 108 105 ..
2009 World Development Indicators
315
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 Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
per million people
per million people
2000–06c
2000–06c
2005
209 574 .. .. .. .. .. 549 424 1,476 .. 109 919 77 .. .. .. 2,317 .. .. .. 211 .. .. .. 41 65 .. .. .. .. .. .. 51 .. 2 .. .. .. .. .. .. w .. .. .. 304 .. .. 330 283 .. 86 .. .. 1,237
887 14,412 .. 575 83 849 .. 3,609 919 1,035 .. 2,392 18,336 136 .. .. 10,012 8,749 77 .. 107 1,249 .. .. .. 571 7,815 .. 93 2,105 229 45,572 205,320 204 157 534 221 .. .. .. .. 708,086 s 2,103 123,683 67,280 56,403 125,786 44,064 36,442 20,045 6,243 15,429 3,563 582,300 158,066
952 3,255 .. .. .. .. .. 5,713 2,186 2,924 .. 361 2,639 141 .. .. 6,139 3,436 .. .. .. 292 .. .. .. 1,450 577 .. .. .. .. 3,033 4,651 373 .. 86 115 .. .. .. .. 1,173 w .. 510 336 1,107 .. 926 2,014 429 .. 111 .. 3,890 2,767
High-technology exports
% of GDP
$ millions
% of manufactured exports
2000–06c
2007
2007
0.46 1,178 1.08 4,144 .. 1 .. 121 0.09 22 1.65 176 .. .. 2.39 105,549 0.49 2,517 1.63 1,246 .. .. 0.92 1,859 1.21 9,916 0.19 99 0.28 0 .. 0 3.82 20,369 2.93 33,655 .. 29 0.10 .. .. 5 0.26 30,925 .. .. .. 0 0.12 60 1.03 565 0.76 328 .. .. 0.19 24 1.03 1,314 .. 23 1.80 63,066 2.61 228,655 0.26 41 .. .. 0.23 80 0.19 1,273 .. .. .. 1 0.03 9 .. 48 2.30 w 1,807,189 s .. .. 0.94 466,128 1.00 339,542 0.73 126,586 0.92 391,161 1.42 .. 0.86 16,092 0.61 48,714 .. .. 0.68 .. .. 2,717 2.48 1,312,001 2.01 440,779
4 7 16 1 4 4 .. 46 5 5 .. 6 5 2 1 0 16 22 1 .. 1 27 .. 0 2 5 0 .. 11 4 1 19 28 3 .. 3 6 .. 1 2 3 18 w 7 19 23 14 19 31 6 12 4 5 8 18 14
Royalty and license fees
Patent applications fileda,b
$ millions
Trademark applications fileda
Receipts
Payments
Residents
Nonresidents
Residents
Nonresidents
2007
2007
2007
2007
2007
2007
41 242 827 59 10,988 396 2,806 27,505 11,934 31,502 0 1 .. .. .. 0 0 128 642 .. 0 5 .. .. .. .. .. 395 121 2,102 .. 2 .. .. .. 716 9,905 696 9,255 5,383 149 124 239 106 2,889 18 169 331 15 1,493 .. .. .. .. .. 53 1,596 0 5,781 17,106 536 3,402 3,267 265 55,909 0 0 151 279 3,382 .. .. .. .. .. 0 121 .. .. .. 4,753 1,810 2,527 398 10,510 .. .. 1,692 342 11,723 0 20 124 133 .. 1 1 26 0 170 0 5 .. .. .. 54 2,287 877 511 20,140 .. .. .. .. .. 0 7 .. .. .. .. .. 0 551 .. 15 10 56 282 .. .. 647 1,810 211 59,028 .. .. .. .. 146 1 5 6 1 .. 53 577 3,474 2,416 19,888 .. .. .. .. .. 15,108 10,121 17,375 7,624 28,976 82,614 25,047 241,347 214,807 256,429 0 7 .. .. 3,804 .. .. 324 198 1,382 0 276 .. .. .. .. .. 180 1,767 12,884 .. .. .. .. .. 149 9 .. .. .. 0 1 .. .. .. .. .. .. .. .. 165,115 s 164,279 s 1,012,033 s575,469 s 1,381,943 s 68 362 485 202 10,852 2,270 26,188 189,511 128,440 408,371 964 13,441 155,262 99,433 90,521 1,306 12,747 34,249 29,007 317,850 2,337 26,550 189,996 128,642 419,223 469 13,100 153,937 92,623 32,534 693 6,369 34,441 13,121 157,537 880 4,710 861 20,264 199,614 141 296 600 2,354 7,872 44 115 151 279 12,432 110 1,960 6 1 17,106 162,778 137,730 822,037 446,827 962,720 27,601 54,216 81,901 21,591 312,991
883 10,372 .. .. .. 2,030 .. 11,170 1,031 428 .. 12,811 1,924 2,835 .. .. 800 4,670 .. 612 .. 13,415 .. .. .. .. 4,020 380 .. 3,858 .. 4,999 33,065 8,991 680 .. 5,134 .. .. .. .. 405,931 s 7,204 199,853 66,626 133,227 207,057 27,686 29,834 128,807 2,780 7,919 12,811 198,874 21,892
a. Original information was provided by the World Intellectual Property Organization (WIPO). The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data. b. Excludes applications filed under the auspices of the European Patent Office (140,763 by nonresidents) and the Eurasian Patent Organization (2,293 by nonresidents). c. Data are for the most recent year available. d. Includes Luxembourg and the Netherlands.
316
2009 World Development Indicators
About the data
5.12
STATES AND MARKETS
Science and technology Definitions
Technological innovation, often fueled by government-
many low-technology products, the product approach
• Researchers in R&D are professionals engaged in
led research and development (R&D), has been the
is more appropriate than the sectoral approach for
conceiving of or creating new knowledge, products, pro-
driving force for industrial growth. The best opportuni-
analyzing international trade. This method takes only
cesses, methods, and systems and in managing the
ties to improve living standards, including new ways
R&D intensity into account, but other characteristics
projects concerned. Postgraduate doctoral students
of reducing poverty, will come from science and tech-
of high technology are also important, such as know-
(ISCED97 level 6) engaged in R&D are considered
nology. Countries able to access, generate, and apply
how, scientific and technical personnel, and technol-
researchers. • Technicians in R&D and equivalent
scientific knowledge will have a competitive edge. And
ogy embodied in patents. Considering these character-
staff are people whose main tasks require technical
there is greater appreciation of the need for high-
istics would yield a different list (see Hatzichronoglou
knowledge and experience in engineering, physical
quality scientific input into public policy issues such
1997). Moreover, the R&D for high-technology exports
and life sciences (technicians), and social sciences
as regional and global environmental concerns.
may not have occurred in the reporting country.
and humanities (equivalent staff). They engage in R&D
Science and technology cover a range of issues
A patent is an exclusive right granted for an inven-
by performing scientific and technical tasks involving
too broad and complex to be quantified by a single
tion (a product or process that provides a new way
the application of concepts and operational methods,
set of indicators, but those in the table shed light on
of doing something or a new technical solution to a
normally under researcher supervision. • Scientific
countries’ technology base.
problem). The invention must be of practical use and
and technical journal articles are published articles in
The United Nations Educational, Scientific, and Cul-
display a characteristic unknown in the body of exist-
physics, biology, chemistry, mathematics, clinical med-
tural Organization (UNESCO) Institute for Statistics col-
ing knowledge in its technical field. A patent grants
icine, biomedical research, engineering and technol-
lects data on researchers, technicians, and expenditure
protection for a specified period, generally 20 years.
ogy, and earth and space sciences. • Expenditures for
on R&D from around the world, through questionnaires
Most countries have systems to protect patentable
R&D are current and capital expenditures on creative
and surveys and from other international sources. R&D
inventions. The Patent Cooperation Treaty provides a
work undertaken to increase the stock of knowledge,
covers basic research, applied research, and experi-
system for filing patent applications. It consists of an
including on humanity, culture, and society, and the
mental development. Data on researchers and techni-
international phase followed by a national or regional
use of knowledge to devise new applications. • High-
cians are normally calculated as full-time equivalents.
phase. An applicant files an international application
technology exports are products with high R&D inten-
Scientific and technical article counts are from a set
and designates the countries in which patent protection
sity, such as in aerospace, computers, pharmaceuti-
of journals classified and covered by the Institute for
is sought (since 2004 all eligible countries are automat-
cals, scientific instruments, and electrical machinery.
Scientific Information’s Science Citation Index (SCI) and
ically designated in every application under the treaty).
• Royalty and license fees are payments and receipts
Social Sciences Citation Index (SSCI). Counts are based
The application is searched and published, and, option-
between residents and nonresidents for authorized
on fractional assignments; for example, an article with
ally, an international preliminary examination is con-
use of intangible, nonproduced, nonfinancial assets
two authors from different countries is counted as one-
ducted. In the national (or regional) phase the applicant
and proprietary rights (such as patents, copyrights,
half of an article for each country (see Definitions for
requests national processing of the application, pays
trademarks, and industrial processes) and for the use,
fields covered). The SCI and SSCI databases cover the
additional fees, and initiates the national search and
through licensing, of produced originals of prototypes
core set of scientific journals but may exclude some
granting procedure. International applications under the
(such as films and manuscripts). • Patent applications
of regional or local importance. They may also reflect
treaty provide for a national patent grant only—there is
filed are worldwide patent applications filed through
some bias toward English-language journals.
no international patent. The national phase filing repre-
the Patent Cooperation Treaty procedure or with a
R&D expenditures include all expenditures for R&D
sents the applicant’s seeking of patent protection for a
national patent office. • Trademark applications filed
performed within a country, including capital expendi-
given territory, whereas international filings, while they
are applications to register a trademark with a national
tures and current costs (annual wages and salaries
represent a legal right, do not accurately reflect where
or regional trademark office.
and all associated costs of researchers, technicians,
patent protection is eventually sought. Resident filings
and supporting staff and other current costs, includ-
are those from residents of the country or region con-
Data sources
ing noncapital purchases of materials, supplies, and
cerned. Nonresident filings are from applicants outside
Data on R&D are provided by the UNESCO Insti-
R&D equipment such as utilities, books, journals,
the country or region. For regional offices such as the
tute for Statistics. Data on scientific and technical
reference materials, subscriptions to libraries and
European Patent Office, applications from residents of
journal articles are from the U.S. National Science
scientific societies, and materials for laboratories).
any member state of the regional patent convention are
Board’s Science and Engineering Indicators 2008.
The method for determining a country’s high-tech-
considered a resident filing. Some offices (notably the
Data on high-technology exports are from the
nology exports was developed by the Organisation for
U.S. Patent and Trademark Office) use the residence
United Nations Statistics Division’s Commodity
Economic Co-operation and Development in collabora-
of the inventor rather than the applicant to classify resi-
Trade (Comtrade) database. Data on royalty and
tion with Eurostat. Termed the “product approach” to
dent and nonresident filings. A trademark protects its
license fees are from the International Monetary
distinguish it from a “sectoral approach,” the method
owner by ensuring exclusive right to use it to identify
Fund’s Balance of Payments Statistics Yearbook.
is based on R&D intensity (R&D expenditure divided
goods or services or to authorize another to use it in
Data on patents and trademarks are from the
by total sales) for groups of products from Germany,
return for payment. The period of protection varies, but
World Intellectual Property Organization’s WIPO
Italy, Japan, the Netherlands, Sweden, and the United
a trademark can be renewed indefinitely. Trademarks
Patent Report: Statistics on Worldwide Patent Activ-
States. Because industrial sectors specializing in a
help consumers identify a product or service whose
ity (2008) and www.wipo.int.
few high-technology products may also produce
nature and quality meet their needs.
2009 World Development Indicators
317 Text figures, tables, and boxes
Introduction
I
n a more integrated world the financial crisis reaches more economies faster
Although high-income economies remain the principal source and destination of international trade and investment, globalization has allowed more developing countries to participate in the growth of the global economy. They now account for almost 30 percent of world trade, and their share has been increasing. Developing economies attracted 20 times more foreign direct investment in nominal terms in 2007 than in 1990 and raised 40 times more net portfolio equity. The 12 largest developing economies, which produce 70 percent of developing country output, accounted for 67 percent of developing country exports in 2007. They also received 69 percent of the net private financial inflows to developing economies. The financial crisis that originated in high-income economies has spread rapidly to developing economies through the same channels that connect them to the global economy: trade, investment, aid, and the movement of people. Although developing economies have previously encountered financial and economic crises, the current one is larger and may last longer. And because the world is more integrated, the crisis will affect more economies and more people. Even before the current crisis many developing economies’ finances suffered from hikes in commodity prices in the first half of 2008, while net exporters of commodities gained. Since then, the prices of primary commodities have declined rapidly. While the net effect is difficult to measure, some developing economies have benefited from improved terms of trade, but price volatility undermines investment in the commodity-producing and exporting sectors— and reduces government revenue. Export revenues constitute about a third of developing country GDP. But global demand is declining, as economies in recession import less and less. Economies that benefited from growing exports in the past decade will be hurt as export revenues decline. Weak prospects for international capital markets, foreign investments, and aid flows pose an immediate danger for developing economies. Equity prices plunged during the financial crisis, and developing country asset values declined. Credit conditions have tightened, and crossborder lending has become more expensive. Foreign direct investment is likely to decline as businesses around the world suffer from shrinking profits and growing pressure to raise cash. Lower financial flows affect countries differently, depending on their integration with capital markets and their dependence on foreign capital. In the past, as donor economies entered recession, their aid to developing economies fell. Countries dependent on official flows are likely to face fiscal difficulties if donors fail to keep their aid commitments. High-income economies are the primary destination for migrant workers. The financial crisis has distressed labor markets in many high-income economies, and millions of workers— including many migrants—are losing their jobs. This may diminish workers’ remittances, an important source of foreign capital in many developing economies. Data in this section provide snapshots of the world’s integration and a framework for measuring it, to show how developing economies are likely to be affected by the recent financial crisis. Figures 6y–6vv illustrate just how quickly the financial crisis has spread.
2009 World Development Indicators
319
Global trade slows The importance of trade to low- and middle-income economies can be seen in the ratio of trade (imports plus exports) to GDP, which has risen rapidly, from 47 percent in 1990 to 70 percent in 2007 for low-income economies and 39 percent to 64 percent for middle-income economies, surpassing the share of high-income economies (figure 6a). Increased exports drove many developing economies’ GDP growth in the past few years. Developing economies’ share of world trade increased from 18 percent in 1990 to 28 percent in 2007. The 12 largest developing economies— China, India, Russian Federation, Brazil, Mexico, Turkey, Indonesia, Islamic Republic of Iran, Poland, Argentina, Thailand, and South Africa—accounted for 67 percent of developing economies’ exports in 2007, a share that has increased over time (figure 6b). China alone accounted for 27 percent. Low-income economies’ share of global exports in 2007 was a mere 1.8 percent, but export revenues constituted 33 percent of their GDP. Although trade between developing economies increased in the last decade, trade with high-income economies still accounts for the largest portion of developing economies’ total merchandise exports. In 2007 about 70 percent of The importance of trade to developing economies has increased
6a
middle-income economies’ merchandise exports went to high-income economies (figure 6c). Low-income economies exported 67 percent of their goods to high-income markets. And some of the exports to other developing economies are primary goods, which are in turn used for manufactured goods destined for high-income markets. Since the onset of the financial crisis, the output of highincome economies has fallen and with it global trade. In the third quarter of 2008 the volume of imports by Group of Seven industrial economies declined 1.4 percent over the same quarter in 2007. The sharpest reductions were in Italy (7.1 percent), the United Kingdom (5.2 percent), the United States (3.6 percent), and Japan (1.3 percent) (figure 6d). Developing economies’ exports fell sharply starting in the last quarter of 2008. Merchandise exports in January 2009 fell 17 percent over exports in January 2008 for China, 31 percent for Mexico, and 43 percent for the Russian Federation. Imports by large developing economies from other developing economies have also declined, and the ripple effect is likely to hurt the low-income economies whose main exports are primary commodities such as fuels, metals, minerals, and agricultural raw materials. Most developing economy exports were directed to high-income economies in 2007 Middle-income economies
Trade as a percentage of GDP, by income group
6c
Low-income economies
80 Low-income economies Middle-income economies
60
Exports to middle-income economies 27%
High-income economies
40
Exports to middle-income economies 25% Exports to high-income economies 70%
20 0 1990
1995
2000
2005
2007
Exports to low-income economies 3%
Exports to high-income economies 67% Exports to low-income economies 8%
Source: World Bank staff calculations based on data from IMF’s Direction of Trade database.
Source: World Development Indicators data files.
High-income economies and a few large middle-income economies account for a majority of world exports
6b
Merchandise imports of Group of Seven industrial economies have declined, reflecting slowing demand for imports 6d Percentage change over same quarter of previous year, seasonally adjusted
Exports of goods and services (2000 $ billions)
15
15 Low-income economies
12
10
Germany
Other middle-income economies The largest 12 developing economies
9
5 United States
0
6
Japan
United Kingdom
–5
3
Italy High-income economies
0 1990
1995
Source: World Development Indicators data files.
320
2009 World Development Indicators
2000
2005
2007
–10 2006 Q3
2006 Q4
2007 Q1
2007 Q2
2007 Q3
2007 Q4
2008 Q1
Source: Organisation for Economic Co-operation and Development.
2008 Q2
2008 Q3
Primary commodity prices have been volatile
Private financial flows— greater access for some
Commodity prices rose rapidly in early 2008 before collapsing in the second half of the year (figure 6e). Oil prices rose 48 percent between December 2007 and July 2008 and then plunged 69 percent by December 2008. Prices of nonenergy commodities increased an average of 32 percent then dropped 39 percent over the same period. Food, fertilizers, and metals and minerals have been among the most volatile. The price hikes in early 2008 threatened to impoverish around 200 million people. They also weakened the fiscal positions of developing economies that import large quantities of food and fuel (table 6f), as governments spent more on subsidies and safety nets to offset higher costs. The sharp price decline in the second half of 2008 eased the pressure on net importers of fuel and other commodities, but hurt the export revenues of economies that export mostly oil. Net importers of food and fuel may temporarily benefi t from lower prices, but producers of export commodities are likely to suffer. Low prices and uncertain longterm prospects may diminish further investment in primary commodities.
Developing economies now have greater access to international capital markets and attract more foreign direct investment (FDI). In nominal terms private capital flows to developing economies increased from $208 billion in 2003 to $961 billion in 2007, but 70 percent of that went to the 12 largest economies. FDI was the source of 55 percent of private financial flows to developing economies in 2007. The 12 largest economies, with greater access to international capital markets, also received large amounts of portfolio equity investment, at 18 percent of total private flows in 2007 (figure 6g). For developing economies with limited or no access to international capital markets, borrowing from private creditors was the second largest source of private flows, at 25 percent in 2007 (figure 6h). Larger middle-income economies were directly hit by falling equity prices. Sovereign and corporate bond spreads widened, indicating more costly borrowing terms. Cross-border lending by private creditors also slowed, restricting credit to developing economies, especially to the least creditworthy borrowers.
Primary commodity prices have been volatile over the past year
6e
World Bank commodity price indices, current prices, 2000 = 100 500
Large middle-income economies received increasing amount of portfolio equity flows in recent years
6g
Private financial net inflows to largest 12 developing economies ($ billions) 800
Energy
400
Commercial banks and other lending Nonenergy commodities
600
300 Food
Bonds
400
200 Raw materials
100 0 Jan-03 Jan-04 Source: World Bank.
Jan-05
Jan-06
Jan-07
Jan-08
Jan-09
For some economies food imports were equivalent to more than 7 percent of household consumption, 2005–07 average Merchandise imports
Share of PPP household consumption (percent)
Share of PPP GDP (percent)
Value ($ millions)
Namibia
3,003
30.1
11.3
1.9
Botswana
3,419
14.3
9.4
2.2
Gambia, The
270
14.1
9.4
2.4
11,852
45.9
9.1
10.5
Mauritius
3,560
26.7
8.5
4.6
Senegal
3,694
19.0
8.0
5.2 3.5
Swaziland
2,461
47.5
7.6
Jamaica
5,430
32.8
7.3
9.9
Gabon
1,815
9.6
7.0
0.3
PPP is purchasing power parity. Source: World Development Indicators data files.
Foreign direct investment
2004
2005
2006
2007
Source: Global Development Finance data files.
Fuel imports
Economy
0 2003
6f
Food imports
Share of PPP GDP (percent)
Jordan
Portfolio equity
200
Other developing economies borrowed increasing amounts from private creditors
6h
Private financial net inflows to other developing economies ($ billions) 300 Commercial banks and other lending
Bonds
200
Portfolio equity
100
0 2003
Foreign direct investment
2004
2005
2006
2007
Source: Global Development Finance data files.
2009 World Development Indicators
321
Foreign direct investment— largest source of private financing
Foreign direct investment— stable source of financing?
In 2007 high-income economies received about 75 percent of global FDI inflows. The 12 largest developing economies received more than 66 percent of the remainder (figure 6i), with China alone receiving nearly 26 percent. Low-income economies received a mere 1.5 percent of global FDI. Despite accounting for a small part of global flows, FDI is the largest source of private financing for many developing economies, especially important for low-income economies with limited or no access to international capital markets. Between 2000 and 2007 FDI net inflows to low-income economies more than doubled, from 1.7 percent of GDP to 4.2 percent of GDP (figure 6j). The increase was due partly to increased investments in oil, mineral, and other primary commodity production sectors driven by high commodity prices and partly to an increase in infrastructure projects (many through public-private partnerships). In 2007 FDI net inflows were more than 20 percent of GDP for nine small economies— Republic of Congo, Seychelles, St. Kitts and Nevis, St. Lucia, Montenegro, São Tomé and Principe, Djibouti, Grenada, and Bulgaria.
FDI is considered a fairly stable source of external financing, but in past financial crises it declined, sometimes sharply. During the East Asian financial crisis net inflows of FDI fell 105 percent between 1997 and 1998 to Indonesia and 58 percent to Malaysia in parallel with falling output (figure 6k). For Thailand and the Republic of Korea FDI net inflows remained resilient for two to three years (figure 6l). The current financial crisis originated in high-income economies, so the effect on FDI flows to developing economies may be more drastic than in past crises originating in developing economies. Businesses around the world are lowering their capital spending in response to tighter credit and weaker global demand. And reinvested earnings, which have accounted for a rising share of FDI net inflows, have started to plunge as profits weakened. Likewise, weak commodity prices are likely to undermine new investments in commodityproducing industries, and low real estate prices will weaken FDI in the construction sector. For countries already running current account deficits, a shortfall in FDI may further undermine their ability to manage their balance of payments.
Much global FDI is directed to high-income economies and a few large middle-income economies . . .
6i
FDI net inflows to Indonesia and Malaysia declined immediately after the East Asian financial crisis hit Indonesia FDI net inflows ($ billions) GDP growth rate (%)
$ trillions 2.5
Malaysia
15
15
10
10
1.5
5
5
1.0
0
0
0.5
–5
–5
–10
–10
To other developing economies
6k
2.0 To the largest 12 developing economies
To high-income economies
0.0 1990
1995
2000
2005
2007
–15 1995 1997 1999 2001 2003 2005
Source: World Development Indicators data files.
Source: World Development Indicators data files.
. . . But as a share of GDP, FDI net inflows are a large source of private financing for low-income economies
6j
FDI net inflows to the Republic of Korea and Thailand remained resilient for several years after the plunge in GDP Korea, Rep. FDI net inflows ($ billions) GDP growth rate (%)
Share of GDP, by income group (%) 6 High-income economies
5 4 Middle-income economies
3
–15 1995 1997 1999 2001 2003 2005
2
6l
Thailand
15
15
10
10
5
5
0
0
–5
–5
–10
–10
Low-income economies
1 0 –1 1980
1985
1990
1995
2000
2007
–15 1995 1997 1999 2001 2003 2005
Source: World Development Indicators data files.
322
2009 World Development Indicators
–15 1995 1997 1999 2001 2003 2005
Source: World Development Indicators data files.
Declining portfolio equity flows
Private debt flows have become more costly
In 2007 high-income economies received $577 billion—more than 80 percent of global portfolio equity flows. Developing economies received nearly $140 billion, more than 87 percent of it going to the 12 largest developing economies (figure 6m). And low-income economies received only 1.7 percent of global equity flows. For larger middle-income economies with developed capital markets, portfolio equity flows are the second largest source of private financial flows. Equities from these economies (emerging market equities) became an attractive investment for highincome investors with appetites for risk, and their prices soared after 2005. Equity markets, declining since October 2007, fell sharply in the last quarter of 2008; investors became more risk averse and were pressed to liquidate their holdings. Developing economies now play a bigger role in international capital markets. Stock market capitalization of listed companies from developing economies accounted for 23 percent of global market capitalization in end-2007, up from 6 percent in end-2000. In more mature developing markets the stock market capitalization to GDP ratio, at its peak, approached that of high-income economies, around 120 percent. But the ratio has been volatile over the past few years and may be unsustainable for some countries (figure 6n). By December 2008 the stock market capitalization in 42 developing economies where data are available had fallen to 52 percent of GDP, down more than 59 percentage points from its October 2007 peak.
Developing economies raised net capital of $85.4 billion through bond issuance in 2007, up from $20.4 billion in 2003. The five largest bond issuers (Russian Federation, Kazakhstan, India, Turkey, and Ukraine) accounted for 74 percent. Nearly 90 percent of the bond issuance by low-income economies in 2007 was public and publicly guaranteed, while almost 70 percent by middle-income economies was private nonguaranteed. Following the financial crisis, sovereign bond spreads (the spread over 10-year U.S. Treasury notes) widened for most developing economies, raising the cost of borrowing. Corporate bond spreads jumped even more (figure 6o). By January 2009 sovereign bond spreads in 15 developing economies exceeded the “distressed debt” threshold of 1,000 basis points. Borrowing from private creditors is (after FDI) the secondlargest source of private financial flows to developing economies. It is especially important for low-income economies with limited or no access to global equity markets. The financial crisis has made cross-border borrowing more costly. And trade finance tightened in the last quarter of 2008. Most affected by the slump in debt flows is Europe and Central Asia, where net borrowing from foreign private creditors rapidly increased during the last few years, from $14.5 billion (1 percent of GDP) in 2003 to $131.2 billion (4 percent of GDP) in 2007 (figure 6p).
Net portfolio equity flows to large middle-income economies increased considerably
6m
Low-income economies Other middle-income economies The largest 12 middle-income economies
$ billions 150
Spreads on emerging market sovereign and corporate bonds have widened, increasing the cost of borrowing
6o
Spreads over U.S. Treasury notes (basis points) 1,200 Corporate Emerging Market Bond Index
900 100 600 50
300 Sovereign Emerging Market Bond Index
0 1990
1992
1994
1996
1998
2000
2002
2004
2007
Source: World Development Indicators data files.
December 2003 December 2008a
Jun-07
Jan-08
Jun-08
Jan-09
Source: JP Morgan Chase, Bloomberg, and Thomson Datastream Advance.
Stock market capitalizations declined after the financial crisis Percent of GDP 400
0 Jan-07
6n
Private lending to Europe and Central Asia increased ninefold between 2003 and 2007
6p
Peak (October 2007) Net commercial bank and other private lending ($ billions) 150 Russian Federation
120
300
Romania
90
Ukraine
200
Kazakhstan Poland
60 100
30
0 South Africa
China
India
Brazil
Russian Thailand Indonesia Federation
Turkey
a. Data are as a percentage of 2007 GDP. Source: World Development Indicators data files and Standard & Poor’s.
Mexico
Turkey Rest of Eastern Europe and Central Asia
0 2003
2004
2005
2006
2007
Source: Global Development Finance data files.
2009 World Development Indicators
323
External debt declined but the share of private debt has increased
Official development assistance— a lifeline to poor countries
External debt of low-income economies declined from 65 percent of GNI in 2000 to 29 percent in 2007, partly due to debt relief (figure 6q). Public debt from official creditors accounted for nearly 90 percent of the long-term external debt of lowincome countries in 2007, and public debt from private creditors for 7 percent. Private nonguaranteed debt was less than 1 percent of GNI. External debt of middle-income economies declined from 36 percent of GNI in 2000 to less than 25 percent in 2007. More than half their long-term external debt was private nonguaranteed debt, which amounted to 10 percent of GNI in 2007. Public debt from private creditors was 6 percent of GNI and short-term debt 5 percent of GNI, putting some middle-income economies at greater risk from tightening global credit markets. Net lending of international financial institutions to middleincome economies had been declining since 2002 (figure 6r). But with private capital flows drying up, middle-income economies are again turning to the major international financial institutions. The International Monetary Fund made new commitments to several countries totaling $45 billion as of February 2009. The World Bank created a $2 billion fast-track fund to assist the poorest countries affected by the crisis and plans to increase commitments to all eligible countries in the next few years.
Official development assistance is the main source of external financing for low-income economies (figure 6s). For some countries (Liberia, Burundi, Micronesia, Solomon Islands, Afghanistan, Guinea-Bissau, and Sierra Leone) it is equivalent to more than 30 percent of GNI. Official development assistance has risen 38 percent in constant prices since 2000, but most of the increase was for debt relief, technical assistance, and emergency relief, which do not provide long-term investment to raise productive capacity. Official development assistance to finance long-term development projects has not increased much since the 1970s (figure 6t). After reaching a record $107 billion in 2005—mainly driven by one-time debt relief—aid declined by 13 percent in 2007. Official development assistance is a small part of the budgets of the Development Assistance Committee members of the Organisation for Economic Co-operation and Development, averaging only 0.28 percent of their GNI, a small fraction of what they will spend on bailouts and fiscal stimulus in their domestic economies. But for low-income economies that depend on aid for their development, even a small decline can be devastating. To counter the financial crisis and progress toward their development goals, the poorest countries will need more assistance from external donors.
For middle-income economies nearly 80 percent of longterm debt was from private creditors while for low-income economies 90 percent was from official creditors
Aid is equivalent to 5 percent of the GNI of low-income economies
6s
6q
Low-income economies Middle-income economies Short-term debt Private nonguaranteed debt Public and publicly guaranteed debt from private creditors Percent of GNI Public and publicly guaranteed debt from official creditors 100 100
Net aid received (% of GNI) 10 8 Low-income economies
6
75
75
4
50
50
2
25
25
0 1975
Sub-Saharan African economies
Middle-income economies
0
0 1990 1995 2000 2005 2007
1990 1995 2000 2005 2007
Source: Global Development Finance data files.
Net nonconcessional lending to middle-income economies from international financial institutions, declining since 2002, recently increased Regional development banks International Monetary Fund
$ billions
1980
1985
1990
1995
2000
2007
Source: Organisation for Economic Co-operation and Development, Development Assistance Committee International Development Statistics database and World Development Indicators data files.
Aid for long-term development has remained about the same as in the 1970s
6t
6r World Bank
Net aid disbursements from Development Assistance Committee member economies (2006 $ billions) 120 Debt-related aid
50
100
Humanitarian aid Administrative costs
80
25
60 Technical cooperation
0
40 Bilateral aid for development programs and projects
20
–25
0 1970
–50 1990
1992
1994
1996
1998
Source: Global Development Finance data files.
324
2009 World Development Indicators
2000
2002
2004
Contribution to multilateral institutions
1975
1980
1985
1990
1995
2000
2007
2007 Source: Organisation for Economic Co-operation and Development, Development Assistance Committee International Development Statistics database.
Official development assistance— in historical perspective
Migration and remittances—of increased importance for developing economies
Aid flows usually decline after an economic crisis in the donor economy. After Japan’s bubble economy burst in 1990 aid flows declined continually through 1996 by 24 percent. After the U.S. savings and loan crisis in the 1980s aid flows dropped 46 percent over the next 10 years. After the Nordic banking crisis in 1991 net aid flows fell 61 percent from Finland, 14 percent from Sweden, and 7 percent from Norway (figure 6u). In Norway aid flows recovered immediately, while Sweden’s aid flows continued to decline through 1998. But donor economies can increase aid flows to developing economies even with difficult conditions at home. After the dot-com bubble burst in 2000, the United States—despite significant economic difficulties—increased its aid flows continually through 2005 (figure 6v). The increase was partly a response to international pressure to increase aid following the 2000 UN Millennium Summit and the 2002 Financing for Development Conference and partly a response to the humanitarian needs of countries at war, such as Afghanistan and Iraq.
Net migration to high-income economies totaled 18.5 million people during 2000–05, almost twice the number for 1985–90 (figure 6w). Global flows of workers’ remittances and compensation of employees also increased, from $68.6 billion in 1990 to $371.3 billion in 2007, more than 75 percent of it received by developing economies, up from 45 percent in 1990 (figure 6x). Remittances have become an important source of foreign exchange earnings for developing economies, at 2 percent of their GDP in 2007. For low-income economies remittances equaled 7 percent of GDP in 2007, up from 3 percent in 2000. And for Guyana, Lesotho, Liberia, Moldova, Seychelles, Tajikistan, and Tonga remittances accounted for more than 25 percent of GDP in 2007. The deepening global recession has reduced demand for migrant workers. As labor markets tighten, thousands of workers are losing their jobs, and some migrants are returning home. After a final surge associated with the repatriation of savings and capital assets, remittances are expected to decline, leaving many families with fewer means of support.
Aid flows declined after the Nordic banking crisis in 1991
6u
Index, 1991 = 100 120 Norway
100
Migration to high-income economies has increased Net migration (millions) 20
6w
1985–90
1990–95
1995–2000
2000–05
Sweden 7% drop
10
14% drop
80 60
0 Finland
40
61% drop
–10
20 0
–20 1985
1987
1989
1991
1993
1995
1997
Source: Organisation for Economic Co-operation and Development, Development Assistance Committee’s International Development Statistics database.
Two U.S. financial crises in the late 20th century—aid down, then up
Low-income economies
1999
6v
Middle-income economies
High-income economies
Source: United Nations Population Division and World Development Indicators data files.
More remittance flows are now going to developing economies
6x
Percentage of global workers’ remittances and compensation of employees
Index, 1988 = 100 200
1990: $68.6 billion
2007: $371.3 billion
East Asia & Pacific 5% Europe & Central Asia 5% Latin America & Caribbean 8%
150 148% increase
High income 24%
Banking crisis in 1988
East Asia & Pacific 18%
100 Stock market crash in 2000
27% drop
50
High income 55%
Middle East & North Africa 17%
46% drop
South Asia 14%
South Asia 8%
0
Europe & Central Asia 14%
Sub-Saharan Africa 5%
Latin America & Caribbean 17%
1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 Sub-Saharan Africa 3%
Source: Organisation for Economic Co-operation and Development, Development Assistance Committee’s International Development Statistics database.
Middle East & North Africa 9%
Source: World Development Indicators data files.
2009 World Development Indicators
325
Merchandise trade Goods make up 70–90 percent of most countries’ total exports and imports. Since 2006 Brazil, China, and the Russian Federation have been running current account surpluses, while Egypt, India, and South Africa have had deficits. In the last quarter of 2008 merchandise exports and imports in absolute values and as a share of GDP declined for China, India, the Russian Federation, and South Africa (figures 6y–6dd). Merchandise exports and imports also declined for Brazil, but its GDP declined more.
6y
Brazil Merchandise trade (percent of GDP) 50
Merchandise trade (percent of GDP) 50
40
40
30
30
Exports
20
20
Equities of large developing economies had become attractive investments, and their prices soared through October 2007. But equity prices fell back in late 2007 and plunged in the last quarter of 2008 (figures 6ee–6jj). Declines in equity prices undermine the values of developing country assets.
10
Imports
0 Q1-06
Q1-07
Q1-08
Q4-08
6ee
150
150
100
100
50
50
Jan-08
Jan-09
2009 World Development Indicators
0 Jan-07
Jan-08
Jan-09
Source: MSCI Barra and Thomson Datastream Advance.
6kk
Brazil
6ll
China
Spreads over U.S. Treasury notes (basis points) 2,500
Spreads over U.S. Treasury notes (basis points) 2,500
2,000
2,000
1,500
1,500
1,000
1,000
CEMBI
CEMBI
500
500 EMBI Global
0
Jan-08
Jan-09
6qq
Brazil
0 –500 Jan-07
EMBI Global
Jan-08
6rr
China Gross capital inflows over previous 12 months ($ billions) 160
120
120
80
80
40
40
Jan-08
Jan-09
Jan-09
Source: JP Morgan Chase, Bloomberg, and Thomson Datastream Advance.
Gross capital inflows over previous 12 months ($ billions) 160
0 Jan-07
Q4-08
6ff
200
Source: Dealogic.
326
Q1-08
China
200
Source: JP Morgan Chase, Bloomberg, and Thomson Datastream Advance.
In 2007 developing economies received increasing amounts of gross private debt and equity flows. But in 2008, even before the intensification of the financial crisis, credit conditions tightened, and transactions slowed. Since October 2008 there has been virtually no new equity issuance, and only a limited amount of new bond issuance and syndicated bank loan commitments (figures 6qq–6vv).
Q1-07
MSCI equity price index (January 2007 = 100) 250
–500 Jan-07
Financing through international capital markets
0 Q1-06
MSCI equity price index (January 2007 = 100) 250
Source: MSCI Barra and Thomson Datastream Advance.
Following the financial crisis, sovereign bond spreads have widened. Corporate spreads have jumped even more. Bond spreads, measured by J.P. Morgan’s Emerging Markets Bond Index Global (EMBI Global) and Corporate Emerging Markets Bond Index (CEMBI) and benchmarked against the yield of 10year U.S. Treasury notes, indicate that the cost of external borrowing for developing countries is rising (figures 6kk–6pp).
10
Source: Haver Analytics.
Brazil
0 Jan-07
Bond spreads
Imports
Exports
Source: Haver Analytics.
Equity price indices
6z
China
0 Jan-07 Source: Dealogic.
Jan-08
Jan-09
6aa
Arab Republic of Egypt
6bb
India
6cc
Russian Federation
6dd
South Africa
Merchandise trade (percent of GDP) 50
Merchandise trade (percent of GDP)
Merchandise trade (percent of GDP)
Merchandise trade (percent of GDP)
50
50
50
40
40
40
40
Imports
30
30
20
20
Exports Imports
30
30
20
20
Imports
Exports
10
Exports
10
Imports
10
10
Exports
0 Q1-06
Q1-07
Q1-08
Q4-08
Source: Haver Analytics.
0 Q1-06
Q1-07
Q1-08
Q4-08
Source: Haver Analytics.
6gg
Arab Republic of Egypt
0 Q1-06
Q1-07
Q1-08
Q4-08
Source: Haver Analytics.
6hh
India
0 Q1-06
Q1-07
6ii
Russian Federation
6jj
South Africa
MSCI equity price index (January 2007 = 100) 250
MSCI equity price index (January 2007 = 100) 250
MSCI equity price index (January 2007 = 100) 250
200
200
200
200
150
150
150
150
100
100
100
100
50
50
50
50
Jan-08
Jan-09
Source: MSCI Barra and Thomson Datastream Advance.
0 Jan-07
Jan-08
Jan-09
Source: MSCI Barra and Thomson Datastream Advance.
Arab Republic of Egypt 6mm Spreads over U.S. Treasury notes (basis points) 2,500
Jan-08
Jan-09
Source: MSCI Barra and Thomson Datastream Advance.
6nn
India
0 Jan-07
Q4-08
Source: Haver Analytics.
MSCI equity price index (January 2007 = 100) 250
0 Jan-07
Q1-08
Jan-08
Jan-09
Source: MSCI Barra and Thomson Datastream Advance.
6oo
Russian Federation
0 Jan-07
6pp
South Africa
Spreads over U.S. Treasury notes (basis points) 2,500
Spreads over U.S. Treasury notes (basis points) 2,500
Spreads over U.S. Treasury notes (basis points) 2,500
2,000
2,000
2,000
CEMBI
2,000
CEMBI
1,500
1,500
1,500
1,500
1,000
1,000
1,000
1,000
500
500
500
500
0
0
CEMBI EMBI Global
EMBI Global
0 –500 Jan-07
Jan-08
Jan-09
Source: JP Morgan Chase, Bloomberg, and Thomson Datastream Advance.
Arab Republic of Egypt
6ss
–500 Jan-07
Jan-08
Jan-09
Source: JP Morgan Chase, Bloomberg, and Thomson Datastream Advance.
6tt
India
–500 Jan-07
EMBI Global
Jan-08
Jan-09
Source: JP Morgan Chase, Bloomberg, and Thomson Datastream Advance.
Russian Federation
6uu
0 –500 Jan-07
Jan-08
6vv
South Africa
Gross capital inflows over previous 12 months ($ billions) 160
Gross capital inflows over previous 12 months ($ billions) 160
Gross capital inflows over previous 12 months ($ billions) 160
Gross capital inflows over previous 12 months ($ billions) 160
120
120
120
120
80
80
80
80
40
40
40
40
0 Jan-07 Source: Dealogic.
Jan-08
Jan-09
0 Jan-07 Source: Dealogic.
Jan-08
Jan-09
0 Jan-07 Source: Dealogic.
Jan-08
Jan-09
Jan-09
Source: JP Morgan Chase, Bloomberg, and Thomson Datastream Advance.
0 Jan-07
Jan-08
Jan-09
Source: Dealogic.
2009 World Development Indicators
327
Tables
6.1
Integration with the global economy Trade
International finance
Financing through international capital markets % of GDP Gross Merchandise Services inflows 2007
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
328
35.6 48.6 64.9 83.5 38.4 48.2 37.4 87.2 51.9 45.4 118.4 186.4 39.6 60.5 92.0 74.3 21.9 122.5 33.4 39.2 115.0 35.6 60.8 24.8 69.9 70.4 67.8 347.3 30.3 70.9 117.7 84.9 74.1 74.5 .. 137.3 65.2 57.4 61.5 33.2 62.1 38.6 126.8 34.4 70.0 45.1 72.6 51.0 63.5 71.9 80.9 31.9 60.6 50.2 65.8 32.8
2007
.. 26.0 .. 21.1 8.1 14.9 9.7 25.4 14.8 6.6 11.8 33.5 12.3 9.9 12.8 15.5 4.7 28.2 .. 21.4 29.4 10.1 10.8 .. .. 11.4 7.9 60.2 4.8 .. 50.3 20.7 17.1 32.1 .. 18.0 37.1 18.0 8.4 26.3 15.8 .. 35.7 16.1 17.2 10.7 13.7 29.7 19.9 14.4 25.3 20.2 11.0 7.5 .. 13.5
2009 World Development Indicators
2007
0.0 0.0 0.3 5.8 4.4 0.2 .. .. 2.9 1.0 0.8 .. 0.0 0.0 0.9 0.0 5.3 11.9 0.2 0.0 3.2 0.0 .. 17.8 0.0 3.4 2.9 .. 3.1 0.0 0.0 0.1 0.4 9.9 .. .. .. 2.9 0.2 5.2 0.0 0.0 2.9 0.0 .. .. 9.6 0.0 7.8 .. 15.2 .. 0.0 0.0 0.0 0.0
Movement of people
Communication
% of GDP
Emigration of Workers’ people with tertiary International International remittances education to Internet Foreign direct voice and OECD countries bandwidtha International investment traffic a compensation migrant stock % of population age bits per Net Net of employees Net migration 25 and older with % of total second minutes inflows outflows received tertiary education population thousands per capita per person 2007
2007
2007
2000–05
2005
2000
2007
2.9 4.4 1.2 –1.5 2.5 7.6 4.8 8.2 –15.2 1.0 4.0 15.9 0.9 1.6 13.9 –0.2 2.6 22.7 8.9 0.1 10.4 2.1 8.4 1.6 8.5 8.8 4.3 26.2 4.4 8.0 56.1 7.2 2.2 9.6 .. 5.3 3.8 4.6 0.4 8.9 7.5 –0.2 12.9 1.1 4.7 6.2 2.3 10.6 17.0 1.6 6.4 0.6 2.1 2.4 2.0 1.1
.. 0.1 .. 1.5 0.6 0.0 3.0 8.4 0.9 0.0 0.0 12.2 0.0 0.0 0.2 0.0 0.5 0.7 .. 0.0 0.0 –0.8 4.1 .. .. 2.3 0.5 29.5 0.4 .. .. 1.0 .. 0.5 .. 0.8 6.4 0.0 0.0 0.5 0.5 .. 7.5 0.0 3.5 8.8 0.9 .. 0.7 5.2 0.0 1.7 0.2 .. .. ..
.. 9.9 1.6b .. 0.2 9.2 0.5 0.8 4.1 9.6 0.8 1.9 4.1b 7.1 16.6 1.1 0.3 5.3 0.7b 0.0 4.2 0.8 .. .. .. 0.0b 1.0 b 0.2 2.2 .. 0.2 2.4 0.9 2.7 .. 0.8 0.3 9.3 7.0 5.9 18.2 .. 2.0 1.8 0.3 0.5 0.1b 7.4 6.8 0.3 0.8 0.8 12.6 3.3 8.1b 18.2
1,112 –110 –140 175 –100 –100 593 180 –100 –500 0 180 99 –100 115 20 –229 –43 100 192 10 6 1,041 –45 219 30 –1,900 300 –120 –237 –10 84 –339 100 –129 67 46 –148 –400 –525 –143 229 1 –140 33 722 10 31 –248 1,000 12 154 –300 –425 1 –140
.. 2.6 0.7 0.4 3.9 7.8 20.1 15.0 2.2 0.7 12.2 6.9 2.1 1.3 1.1 4.4 0.3 1.3 5.5 1.3 2.2 0.8 18.9 1.8 4.3 1.4 0.0 44.0 0.3 0.9 8.0 10.2 12.8 14.9 0.7 4.4 7.2 1.7 0.9 0.2 0.4 0.3 15.0 0.7 3.0 10.6 18.9 14.3 4.3 12.3 7.4 8.8 0.4 4.5 1.2 0.3
22.6 17.4 9.4 3.6 2.8 8.9 2.7 13.5 1.8 4.4 3.2 5.5 8.6 5.8 20.3 5.1 2.0 9.6 2.5 7.3 21.4 17.1 4.7 7.2 9.0 6.0 3.8 29.6 10.4 9.0 22.9 7.1 6.1 24.6 28.8 8.5 7.8 22.4 9.5 4.7 31.7 35.2 9.9 9.8 7.2 3.4 14.4 67.8 2.8 5.7 44.6 12.1 23.9 4.6 27.7 83.4
1 125 18 .. 3 128 .. .. .. 6 .. .. 11 80 241 93 .. 31 11 .. 10 4 .. .. .. 40 9 1,387 106 4 .. 119 .. 208 31 74 307 .. 90 42 515 7 .. 3 .. 243 74 .. .. .. 1 182 .. .. .. ..
2007
0 216 89 17 2,320 .. 5,472 20,288 701 4 264 24,945 17 42 530 43 1,041 4,909 15 1 17 11 16,193 0 1 4,086 280 15,892 971 0 0 820 16 3,380 19 7,075 34,506 154 324 143 18 2 11,925 3 17,221 29,466 150 36 745 25,654 21 4,537 187 0 1 17
Trade
International finance
Financing through international capital markets % of GDP Gross Merchandise Services inflows
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
Movement of people
GLOBAL LINKS
Integration with the global economy
6.1 Communication
% of GDP
Emigration of Workers’ people with tertiary International International remittances education to Internet Foreign direct voice and OECD countries bandwidtha International investment traffic a compensation migrant stock % of population age bits per Net Net of employees Net migration 25 and older with % of total second minutes inflows outflows received tertiary education population thousands per capita per person
2007
2007
2007
2007
2007
2007
2000–05
2005
2000
2007
2007
115.7 137.0 30.8 48.6 46.1 .. 78.6 69.0 47.4 68.6 30.4 121.3 76.8 54.0 .. 75.1 76.7 94.9 48.4 86.9 65.0 158.4 92.9 91.1 107.9 111.9 51.2 60.6 173.1 54.4 114.2 90.3 55.6 114.5 101.9 61.7 77.0 .. 90.4 36.8 136.2 42.7 83.5 40.8 57.4 55.8 91.3 35.3 41.2 121.1 82.3 44.9 75.3 71.4 58.2 ..
14.6 23.1 15.2 8.5 .. .. 70.8 23.8 11.1 43.4 6.4 43.5 14.5 17.2 .. 15.1 20.3 33.8 8.6 23.5 92.4 11.7 216.4 4.7 19.4 18.5 22.1 .. 30.1 16.8 .. 55.3 4.1 29.1 32.2 23.4 16.9 .. 15.9 12.0 23.7 13.5 16.2 11.7 9.5 20.7 13.4 8.8 36.1 29.9 10.8 7.1 11.0 12.5 16.7 ..
0.0 8.2 5.2 2.9 0.1 .. .. .. .. 33.4 .. 8.9 26.2 0.0 .. .. .. 0.0 5.3 7.8 11.8 1.2 158.1 0.2 5.3 0.5 5.7 0.2 7.9 2.6 0.0 16.2 4.3 0.0 1.9 4.1 11.0 .. 0.0 0.0 .. .. 0.0 0.0 4.3 .. 6.6 1.7 15.3 13.2 0.0 3.1 6.3 2.1 .. ..
6.7 26.9 2.0 1.6 0.3 .. 10.1 5.9 1.9 7.6 0.5 11.6 9.7 3.0 .. 0.2 0.1 5.6 7.9 8.3 11.7 8.1 17.9 8.0 5.3 4.2 13.5 1.5 4.5 5.2 5.8 5.0 2.4 11.2 8.3 3.7 5.5 .. 2.4 0.1 16.1 2.0 6.7 0.6 3.7 1.0 4.5 3.7 9.8 1.5 1.6 5.0 2.0 5.4 2.5 ..
0.0 25.8 1.1 1.1 .. .. 8.5 4.3 4.4 1.0 1.7 0.3 3.1 0.1 .. 1.6 12.2 0.0 .. 1.2 –1.0 .. 0.0 1.3 1.6 0.0 .. .. 5.9 0.0 .. 0.9 0.8 0.3 .. 0.8 0.0 .. 0.0 .. 3.5 2.1 0.0 0.0 0.3 3.2 0.9 0.1 0.0 0.1 0.1 .. 2.4 1.2 2.8 ..
21.5 0.3 3.0 b 1.4 0.4b .. 0.2 0.6 0.2 18.8 0.0 21.7 0.2 6.6b .. 0.1 .. 19.1 0.0 b 2.0 23.7 27.7 8.8 0.0 b 3.7 3.5b 0.1b 0.0b 1.0 b 3.1b 0.1b 3.2b 2.7b 34.1 4.9 b 9.0 1.3 ..b 0.2 16.8 0.3 0.5 12.9 1.9 5.6 b 0.2 0.1 4.2 0.9 0.2b 3.8 2.0 11.3 2.5 1.8 ..
–150 65 –1,350 –1,000 –1,250 –375 188 115 1,125 –100 270 130 –200 25 0 –80 264 –75 –115 –20 0 –36 –119 10 –30 –10 –5 –30 150 –134 30 0 –3,983 –250 –50 –550 –20 –99 –1 –100 110 102 –210 –28 –170 84 –150 –1,239 8 0 –45 –510 –900 –200 276 –10
0.4 3.1 0.5 0.1 2.8 .. 14.1 38.4 4.3 0.7 1.6 41.1 16.5 1.0 0.2 1.1 65.8 5.6 0.4 19.5 16.4 0.3 1.5 10.4 4.8 6.0 0.3 2.1 6.4 0.4 2.2 1.7 0.6 11.4 0.4 0.4 2.0 0.2 7.1 3.0 10.0 15.5 0.5 0.9 0.7 7.4 25.0 2.1 3.2 0.4 2.9 0.2 0.4 1.8 7.2 10.7
24.8 12.8 4.3 2.9 14.3 10.9 33.7 7.8 9.6 84.7 1.2 7.4 1.2 38.5 .. 7.5 7.1 0.9 37.2 8.5 43.8 4.1 44.3 4.3 8.3 29.4 7.7 20.9 10.5 14.7 8.5 55.8 15.5 4.1 7.4 18.0 22.5 3.9 3.4 4.0 9.5 21.8 30.2 5.4 10.5 6.2 0.4 12.7 16.7 27.8 3.8 5.8 13.5 14.2 18.9 ..
33 120 .. .. 9 .. .. 364 .. .. 46 32 47 3 .. 29 .. 30 7 67 279 18 .. 66 54 125 1 .. .. 2 5 125 185 149 5 22 13 .. .. 6 .. 310 65 .. .. 193 37 10 66 .. 35 99 .. .. 178 ..
244 4,773 32 53 153 4 15,229 2,003 10,302 19,151 3,734 164 129 9 .. 1,027 871 114 32 3,537 227 2 .. 50 4,656 17 8 5 998 17 70 226 178 931 116 814 3 2 27 5 78,159 4,544 144 2 5 26,904 142 44 15,977 1 163 2,704 114 2,748 4,790 511
2009 World Development Indicators
329
6.1
Integration with the global economy Trade
International finance
Financing through international capital markets % of GDP Gross Merchandise Services inflows 2007
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
66.4 44.8 27.4 85.0 55.1 67.7 41.4 348.6 157.9 130.5 .. 56.8 42.7 58.9 38.2 176.3 70.5 78.5 69.4 105.7 45.5 119.8 .. 85.2 108.0 97.1 42.3 103.5 43.2 77.9 150.4 38.1 23.1 44.1 57.7 50.5 159.1 .. 61.3 75.6 122.0 51.0 w 62.6 55.9 59.6 52.0 56.2 75.3 56.6 41.2 57.5 32.7 59.7 49.1 67.0
Movement of people
Communication
% of GDP
Emigration of Workers’ people with tertiary International International remittances education to Internet Foreign direct voice and OECD countries bandwidtha International investment traffic a compensation migrant stock % of population age bits per Net Net of employees Net migration 25 and older with % of total second minutes inflows outflows received tertiary education population thousands per capita per person
2007
2007
2007
2007
2007
12.4 7.6 13.5 10.1 17.7 .. 8.2 88.2 18.1 20.9 .. 10.7 15.9 13.3 7.2 33.2 24.6 23.5 16.3 20.0 19.7 28.0 .. 23.2 9.5 22.0 6.7 .. 14.2 18.3 .. 17.5 6.3 13.0 .. 4.0 18.9 .. 12.3 10.5 .. 12.0 w 12.5 9.6 10.6 8.8 9.8 10.4 10.4 6.0 .. 14.0 13.6 12.7 17.2
1.0 12.2 0.4 .. 0.6 0.0 0.0 .. 3.8 .. .. 9.9 .. 3.4 0.3 0.0 .. .. 0.0 0.0 0.5 1.1 0.0 0.0 6.1 1.6 5.5 0.0 10.1 9.6 .. .. .. 5.9 0.0 5.2 8.5 0.0 0.4 2.7 0.0 .. w 3.3 5.0 3.2 6.8 4.9 3.2 8.3 4.5 1.9 4.6 5.7 .. ..
5.7 4.3 2.0 –2.1 0.7 7.8 5.7 15.0 4.5 3.1 .. 2.0 4.2 1.9 5.2 1.3 2.7 11.7 1.8 9.7 4.0 3.9 .. 2.8 .. 4.6 3.4 6.2 4.1 7.0 .. 7.1 1.7 3.8 1.2 0.3 9.8 .. 4.1 8.7 3.0 4.0 w 4.2 3.7 3.5 3.9 3.7 4.1 5.0 3.0 3.7 2.1 3.4 4.1 6.3
0.0 3.6 –0.4 0.0 0.1 .. –0.6 7.6 0.5 3.3 .. 1.3 8.9 0.2 0.0 0.8 6.3 11.9 0.0 0.0 0.0 0.9 .. –0.6 –2.3 0.3 0.3 .. 0.0 0.5 .. 9.9 2.4 0.0 .. 1.0 0.2 .. 0.0 0.0 .. 4.3 w 0.1 1.1 0.6 1.5 1.1 0.9 1.9 0.7 .. 0.9 0.7 5.2 8.4
5.1 0.3 1.5 .. 8.3b 12.2b,c 8.9 .. 2.0 0.6 .. 0.3 0.7 7.8 3.8 3.5 0.2 0.5 2.2b 45.5 0.1 0.7 .. 9.2b 0.4b 4.9 0.2 .. 7.2 3.2 .. 0.3 0.0 0.4 .. 0.1 8.0 b 14.9 b 5.7b 0.5 .. 0.7 w 5.7 1.8 2.4 1.2 2.0 1.5 1.6 1.8 3.7 3.6 2.5 0.2 0.5
2000–05
–270 917 43 285 –100 –339 472 200 3 22 100 75 2,846 –442 –532 –6 152 100 200 –345 –345 231 100 –4 –20 –29 –30 –10 –5 –173 577 948 6,493 –104 –300 40 –200 11 –100 –82 –75 ..d s –2,858 –15,770 –11,295 –4,475 –18,629 –3,847 –1,798 –6,811 –2,618 –2,484 –1,070 18,522 6,887
2005
2000
0.6 8.4 1.3 27.5 2.8 6.4 c 2.1 43.2 2.3 8.4 3.4 2.4 11.0 1.9 1.7 4.0 12.4 22.3 5.2 4.7 2.1 1.7 0.6 2.9 2.8 0.4 1.8 4.6 1.8 14.5 78.3 9.0 13.0 2.5 4.8 3.8 0.0 48.5 1.3 2.4 3.9 3.0 w 1.7 1.3 0.8 3.6 1.4 0.2 6.8 1.0 3.0 0.8 2.1 11.2 9.5
11.2 1.4 26.3 0.9 17.1 14.6c 49.2 14.5 14.3 10.9 34.5 7.4 4.2 28.2 6.8 5.3 4.5 9.5 6.1 0.6 12.1 2.2 16.5 16.3 78.9 12.4 5.8 0.4 36.0 4.3 0.7 17.1 0.5 9.0 0.8 3.8 26.9 12.0 6.0 16.4 13.1 5.4 w 12.8 6.7 6.6 6.8 7.2 7.0 4.4 10.6 10.4 5.3 12.3 3.9 6.9
2007
2007
41 .. 11 216 26 144 .. 1,531 97 92 .. .. .. 34 7 .. .. .. 79 .. 0 14 18 5 .. 73 30 .. 7 57 .. .. .. 127 12 .. .. .. .. .. 21 .. w .. .. .. .. .. 9 .. .. 32 .. .. .. ..
2,945 573 16 510 137 2,861 .. 22,783 5,555 6,720 0 71 11,008 118 345 1 49,828 29,417 53 0 3 346 9 4 675 303 1,381 16 11 206 2,785 39,650 11,277 903 9 628 148 324 28 3 4 3,297 w 26 389 199 1,185 318 247 1,114 1,126 186 31 36 18,242 32,560
a. Data are from the International Telecommunication Union’s (ITU) World Telecommunication Development Report database. Please cite the ITU for third-party use of these data. b. World Bank estimates. c. Includes Montenegro. d. 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.
330
2009 World Development Indicators
GLOBAL LINKS
Integration with the global economy
6.1
About the data
Globalization—the integration of the world
definition of long-term loans differs across countries.
syndicated bank lending, and new equity place-
economy—has been a persistent theme of the past
However, the quality and coverage of the data are
ments. • Foreign direct investment net inflows
quarter century. Growth of cross-border economic
improving as a result of continuous efforts by inter-
are net inflows of FDI in the reporting economy. FDI
activity has changed the structure of economies and
national and national statistics agencies. See About
is the sum of equity capital, reinvestment of earn-
the political and social organization of countries. Not
the data for table 6.11 for more information.
ings, and other short- and long-term capital. • For-
all effects of globalization can be measured directly.
Workers’ remittances are current private transfers
eign direct investment net outflows are net outflows
But the scope and pace of change can be monitored
from migrant workers resident in the host country for
of investment from the reporting economy to the rest
along four key dimensions: trade in goods and ser-
more than a year, irrespective of their immigration
of the world. • Workers’ remittances and compen-
vices, fi nancial fl ows, movement of people, and
status, to recipients in their country of origin. Com-
sation of employees received are current transfers
communication.
pensation of employees is the income of migrants
by migrant workers and wages and salaries of non-
Trade data are based on gross flows that capture
who have lived in the host country for less than a
resident workers. • Net migration is the total number
the two-way flow of goods and services. In conven-
year. Migration has increased in importance, now
of immigrants minus the total number of emigrants,
tional balance of payments accounting, exports are
accounting for a substantial part of global integra-
including citizens and noncitizens, for the five-year
recorded as a credit and imports as a debit. See
tion. The estimates of the international migrant stock
period. • International migrant stock is the number of
tables 4.4 and 4.5 for data on the main trade com-
are derived from data on foreign-born population—
people born in a country other than that in which they
ponents of merchandise trade and tables 4.6 and
people who reside in one country but were born in
live, including refugees. • Emigration of people with
4.7 for the same data on services trade.
another—mainly from population censuses. See
tertiary education to OECD countries is the stock
Financing through international capital markets
About the data and Definitions for table 6.17 for
of emigrants ages 25 and older, residing in an OECD
includes gross bond issuance, bank lending, and new
more information. One negative effect of migration
country other than that in which they were born, with
equity placement as reported by Dealogic, a com-
is “brain drain”—emigration of highly educated peo-
at least one year of tertiary education. • International
pany specializing in the investment banking industry.
ple. The table shows data on emigration of people
voice traffic is the sum of international incoming and
In financial accounting inward investment is a credit
with tertiary education, drawn from Docquier, Mar-
outgoing telephone traffic (in minutes) divided by
and outward investment a debit. Gross flow is a bet-
fouk, and Lowell (2007). The study analyzes skilled
total population. • International Internet bandwidth
ter measure of integration than net flow because
migration using data from censuses and registers
is the contracted capacity of international connections
gross flow shows the total value of financial trans-
of Organisation for Economic Development and Co-
between countries for transmitting Internet traffic.
actions over a period, while net flow is the sum of
operation (OECD) countries and provides data disag-
credits and debits and represents a balance in which
gregated by gender for 1990 and 2000.
Data sources
many transactions are canceled out. Components of
Well developed communications infrastructure
Data on merchandise trade are from the World
financing through international capital markets are
attracts investments and allows investors to capi-
Trade Organization’s Annual Report. Data on trade in
reported in U.S. dollars by market sources.
talize on benefi ts offered by the digital age. See
services are from the International Monetary Fund’s
About the data for tables 5.10 and 5.11 for more
(IMF) Balance of Payments database. Data on inter-
information.
national capital market financing are based on data
Foreign direct investment (FDI) includes equity investment, reinvested earnings, and short- and
reported by Dealogic. Data on FDI are based on bal-
long-term loans between parent firms and foreign affiliates. Distinguished from other kinds of interna-
Definitions
ance of payments data reported by the IMF, supple-
tional investment, FDI establishes a lasting interest
• Trade in merchandise is the sum of merchandise
mented by staff estimates using data reported by
in or effective management control over an enter-
exports and imports. • Trade in services is the
the United Nations Conference on Trade and Devel-
prise in another country. FDI may be understated in
sum of services exports and imports. • Financ-
opment and official national sources. Data on work-
many developing countries because some countries
ing through international capital markets is the
ers’ remittances are World Bank staff estimates
fail to report reinvested earnings and because the
sum of the absolute values of new bond issuance,
based on IMF balance of payments data. Data on net migration are from the United Nations Popu-
Estimating the global emigrant stock
6.1a
lation Division’s World Population Prospects: The
Internationally comparable estimates of migrant stock by country of origin are vital for making policies on
2006 Revision. Data on international migrant stock
international migration. The World Bank’s Development Research Group and the United Nations Population
are from the United Nations Population Division’s
Division’s Department of Economic and Social Affairs are developing estimates of international migrants
Trends in Total Migrant Stock: The 2005 Revision.
by country of origin. They have created the Global Migration database, which contains all publicly available
Data on emigration of people with tertiary educa-
data on international migrants, classified by age, sex, place of birth, and country of citizenship enumer-
tion are from Frédéric Docquier, Abdeslam Marfouk,
ated by censuses, population registers, and surveys. Available at www.unmigration.org, the database
and B. Lindsay Lowell’s, “A Gendered Assessment
uses many sources of data, including the United Nations Statistics Division, the United Nations Popula-
of the Brain Drain” (2007). Data on international
tion Division, and the World Bank’s Development Research Group, in collaboration with the University
voice traffic and international Internet bandwidth are
of Nottingham and the University of Sussex. The next step is to develop an appropriate methodology for
from the International Telecommunication Union’s
estimating the emigrant stock for each country at specific points in time.
International Development Report database.
2009 World Development Indicators
331
6.2 Afghanistan Albania Algeria Angola Argentina Armenia Australiab Austriab Azerbaijan Bangladesh Belarus Belgiumb Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canadab Central African Republic Chad Chile China† Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmarkb Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland Franceb Gabon Gambia, The Georgia Germany b Ghana Greeceb Guatemala Guinea Guinea-Bissau Haiti †Data for Taiwan, China
332
Growth of merchandise trade Export volume
Import volume
Export value
Import value
Net barter terms of trade index
average annual % growth
average annual % growth
average annual % growth
average annual % growth
2000 = 100
1990–2000
2000–07a
1990–2000
2000–07a
1990–2000
2000–07a
1990–2000
2000–07a
1995
2007a
.. .. 2.8 6.2 8.4 .. 7.3 4.1 .. 12.9 .. 6.0 1.0 2.8 .. 4.8 5.1 .. 13.2 8.6 .. 0.3 9.1 20.0 .. 11.1 13.8 8.4 4.5 –1.8 6.6 14.0 5.0 .. .. .. 5.4 3.9 6.3 –0.2 2.9 –28.3 .. 10.5 .. 8.4 5.2 –11.6 .. .. 7.7 8.9 8.5 5.0 .. 12.6 3.1
.. .. 0.7 11.9 7.2 .. 5.6 8.2 .. 11.1 .. 4.7 3.6 11.2 .. 4.9 11.1 .. 15.1 –3.9 16.0 –1.6 1.1 –1.2 .. 6.7 26.2 9.6 6.5 9.4 3.0 8.3 2.0 .. –0.6 .. 3.4 1.3 10.1 8.3 2.4 –14.5 .. 7.8 .. 4.8 1.7 –7.8 .. .. 5.3 .. 4.8 –10.4 .. 8.0 7.9
.. .. –0.8 7.1 17.7 .. 9.2 1.9 .. 5.9 .. 5.7 8.2 9.1 .. 4.0 16.7 .. 3.6 4.0 .. 5.0 9.0 4.3 .. 10.7 12.8 8.9 8.5 4.6 4.9 14.9 –0.3 .. .. .. 5.8 11.6 5.9 1.8 7.6 –3.2 .. 7.3 .. 6.6 2.5 0.1 .. .. 8.6 9.3 10.0 –1.4 .. 13.3 4.8
.. .. 10.8 16.3 11.0 .. 7.1 8.0 .. 5.0 .. 5.0 0.9 5.0 .. 0.8 6.0 .. 9.6 12.0 12.0 2.0 4.6 5.0 .. 12.5 18.8 8.8 12.3 17.8 17.5 8.1 7.9 .. 4.1 .. 5.1 1.4 14.0 3.0 5.5 –2.4 .. 16.5 .. 6.3 6.2 2.5 .. .. 11.6 .. 7.1 1.0 .. 3.6 4.2
.. .. 2.1 6.1 10.1 .. 5.7 .. .. 15.7 .. 8.7 3.3 4.3 .. 4.7 5.9 .. 12.9 –4.3 26.8 –3.6 12.4 3.5 .. 9.4 14.5 8.3 7.3 –7.2 7.5 17.0 6.0 .. –1.7 .. 5.9 4.2 6.8 0.7 8.9 –31.1 .. 10.7 .. 6.8 0.8 –12.3 .. .. 9.0 8.2 10.1 0.6 .. 12.2 7.2
.. .. 18.6 31.5 12.1 .. 17.5 .. .. 11.7 .. 6.4 7.7 22.7 .. 9.1 18.3 .. 20.3 6.7 17.4 12.1 2.1 1.7 .. 23.2 27.5 9.5 14.2 19.4 20.5 8.2 13.2 .. 12.5 .. 4.7 3.9 18.8 22.8 4.2 –13.7 .. 17.0 .. 3.7 18.3 –4.0 .. .. 14.7 .. 8.3 5.5 .. 10.4 10.0
.. .. –1.3 7.8 17.0 .. 8.7 .. .. 10.4 .. 9.1 9.7 9.7 .. 2.7 12.5 .. 3.6 –6.9 25.2 2.1 11.9 0.2 .. 10.3 13.0 8.8 9.7 –0.5 8.7 13.9 2.4 .. 2.5 .. 6.8 12.0 7.9 4.7 10.9 –0.2 .. 7.3 .. 5.6 2.2 0.2 .. .. 8.3 8.2 10.4 –2.6 .. 14.4 8.5
.. .. 16.3 20.0 13.4 .. 11.8 .. .. 12.2 .. 6.8 8.5 9.9 .. 6.5 11.8 .. 16.6 17.8 16.8 10.4 3.2 11.5 .. 16.3 25.0 9.5 16.2 24.8 22.6 10.8 16.1 .. 12.7 .. 6.2 5.1 19.0 10.6 8.5 2.6 .. 23.8 .. 5.1 10.6 9.9 .. .. 17.2 .. 12.5 7.9 .. 8.5 10.1
.. .. 57.9 80.8 91.6 .. 99.4 .. .. 111.8 .. 104.3 106.6 89.4 .. 89.3 110.4 .. 131.0 163.6 .. 90.4 103.2 193.0 .. 135.6 101.9 99.1 86.8 79.8 52.0 104.6 122.0 .. .. .. 102.1 98.1 80.6 116.3 121.1 101.7 .. 151.0 .. 106.3 125.4 100.0 .. 107.5 106.7 89.6 117.9 89.6 .. 113.2 89.9
.. .. 183.8 204.1 116.2 .. 152.4 .. .. 69.1 .. 101.2 80.3 137.2 .. 89.6 107.8 .. 89.6 128.6 84.8 132.1 117.6 81.3 .. 194.4 98.9 96.8 121.4 119.2 187.1 84.9 134.5 .. 143.5 .. 98.9 94.7 114.2 130.6 95.5 70.6 .. 110.2 .. 101.4 182.8 82.5 .. 97.2 143.3 93.3 88.7 173.1 .. 83.9 80.0
2009 World Development Indicators
Honduras Hungary b India Indonesia Iran, Islamic Rep. Iraq Irelandb Israelb Italy b Jamaica Japanb Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latviab Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlandsb New Zealandb Nicaragua Niger Nigeria Norway b Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Polandb Portugalb Puerto Rico
Export volume
Import volume
Export value
Import value
average annual % growth
average annual % growth
average annual % growth
average annual % growth
GLOBAL LINKS
Growth of merchandise trade
6.2 Net barter terms of trade index
2000 = 100
1990–2000
2000–07a
1990–2000
2000–07a
1990–2000
2000–07a
1990–2000
2000–07a
1995
2007a
2.5 10.1 6.9 9.1 .. .. 15.2 9.7 4.8 2.2 2.6 4.7 .. 3.9 .. 15.8 .. .. .. 7.2 .. 13.3 .. .. .. .. 4.1 2.7 13.6 10.3 1.9 2.7 15.5 .. .. 7.5 15.2 15.5 2.4 .. 6.8 4.6 10.4 3.1 3.3 6.6 4.0 2.5 6.0 –7.7 –0.2 9.4 16.0 9.8 0.3 ..
5.8 12.5 12.4 1.9 1.9 .. 2.6 5.1 1.6 1.4 4.8 8.7 .. 6.2 .. 13.6 5.8 .. 11.4 .. 14.4 20.3 .. 5.9 .. .. 1.2 6.4 7.4 2.8 9.1 4.7 4.1 .. 5.4 2.6 17.4 6.4 7.8 –1.5 4.4 3.3 8.6 –8.7 1.8 0.6 –4.5 9.3 –0.5 –2.7 15.9 9.7 4.6 13.9 .. ..
12.7 11.6 9 2.9 .. .. 11.4 8.9 4.2 .. 5.3 3.8 .. 7.4 .. 10.0 .. .. .. .. .. 3.1 .. 0.0 .. .. 4.5 –2.4 10.6 6.4 4.2 3.4 13.2 .. .. 7.2 1.0 13.8 7.7 .. 7.0 5.9 9.3 –2.1 2.5 7.8 .. 2.4 7.8 .. 5.4 10.6 11.3 19.0 0.5 ..
8.8 9.9 17.6 5.7 10.5 .. 3.1 2.5 2.1 1.3 3.5 8.2 .. 8.6 .. 8.3 12.3 .. 6.7 .. 2.0 9.0 .. 11.4 .. .. 8.3 8.0 7.2 6.2 14.2 9.0 4.8 .. 10.3 8.1 10.7 –6.3 8.6 0.1 4.9 8.1 5.6 11.8 11.6 6.9 9.6 10.4 5.1 4.9 17.9 8.2 4.3 10.6 .. ..
7.2 10.1 5.3 7.9 1.2 .. 17.3 17.9 10.0 2.2 2.4 6.6 .. 6.3 .. 10.1 16.5 .. 15.4 11.6 4.1 12.8 .. –2.6 .. .. 9.0 0.9 12.2 6.3 –1.9 2.2 16.1 .. 0.7 7.2 10.2 14.4 0.9 10.7 7.2 3.9 10.3 0.0 1.1 8.6 5.7 4.3 9.4 3.7 1.7 9.0 18.8 9.4 –3.0 ..
7.2 19.3 21.1 10.1 18.9 .. –0.4 9.5 4.4 8.5 7.8 17.2 .. 13.1 .. 14.2 23.9 .. 20.5 .. 21.6 21.2 .. 24.4 .. .. 3.3 9.3 10.7 14.6 24.3 3.9 8.3 .. 21.8 9.5 30.1 17.3 16.9 2.8 5.9 10.9 10.9 13.9 19.6 7.4 12.3 11.6 4.4 15.1 19.2 24.5 4.8 24.7 .. ..
13.8 11.8 7.9 1.0 –4.8 .. 13.8 16.0 8.4 6.9 2.9 5.1 .. 6.0 .. 7.1 5.5 .. 12.7 .. 8.7 1.9 .. –1.4 .. .. 6.3 –0.6 9.5 4.7 –1.6 3.3 14.2 .. 0.5 5.5 1.1 22.6 3.9 9.3 7.3 5.7 11.6 0.8 3.1 7.2 6.1 3.1 8.7 –0.8 7.0 10.8 12.5 16.9 –2.6 ..
13.4 17.4 25.9 15.4 17.9 .. 1.2 7.9 5.5 9.4 9.1 18.6 .. 17.9 .. 14.5 16.3 .. 13.4 .. 9.0 14.1 .. 22.4 .. .. 15.1 14.3 10.4 14.0 20.3 10.4 8.0 .. 18.6 15.4 16.7 –0.5 12.6 8.9 5.2 13.9 11.0 20.0 18.8 7.6 15.4 20.3 10.8 12.2 21.7 13.9 7.3 19.6 .. ..
96.3 104.3 108.0 90.4 .. .. 98.9 92.1 96.8 .. 105.5 115.6 .. 103.9 .. 138.5 .. .. .. .. .. 100.0 .. .. .. .. 79.6 105.7 108.5 109.6 102.2 88.5 92.5 .. .. 89.1 151.1 214.3 82.6 .. 103.2 101.8 128.9 121.4 55.6 60.3 .. 119.2 100.0 .. 118.3 123.4 80.2 101.7 104.7 ..
80.8 95.5 99.3 104.9 160.2 .. 91.3 91.7 98.0 101.6 86.0 92.4 .. 83.8 .. 72.0 194.3 .. 114.0 .. 98.8 78.9 .. 172.0 .. .. 74.8 84.6 100.1 132.3 153.6 86.4 104.6 .. 160.3 98.4 127.6 122.8 139.3 80.0 111.7 118.1 78.3 313.9 168.4 134.0 180.8 65.5 94.0 177.2 99.2 161.9 81.7 109.4 .. ..
2009 World Development Indicators
333
6.2 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spainb Sri Lanka Sudan Swaziland Swedenb Switzerlandb Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdomb United Statesb Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe
Growth of merchandise trade Export volume
Import volume
Export value
Import value
Net barter terms of trade index
average annual % growth
average annual % growth
average annual % growth
average annual % growth
2000 = 100
1990–2000
2000–07a
1990–2000
2000–07a
1990–2000
2000–07a
1990–2000
2000–07a
1995
2007a
.. .. –8.0 2.9 10.6 .. .. 11.7 .. .. .. 4.5 11.4 7.4 12.6 4.0 8.9 3.7 2.2 .. 6.0 9.6 .. 9.1 .. 5.7 10.7 .. 17.8 .. .. 6.2 6.6 6.1 .. 5.2 .. .. .. 6.1 8.8
.. .. 5.7 2.2 2.2 .. .. 13.4 .. .. .. 2.9 4.2 4.1 8.4 10.8 3.9 4.9 –0.4 .. 5.8 9.1 .. 8.0 5.3 9.3 13.6 .. 10.8 .. 7.8 2.5 4.4 9.3 .. –1.2 13.2 .. –3.5 8.0 –6.6
.. .. 0.8 .. 4.9 .. .. 8.3 .. .. .. 7.6 9.3 8.0 8.4 3.1 6.4 4.2 .. .. –2.0 2.6 .. 6.0 .. 4.3 11.1 .. 22.4 .. .. 6.5 9.1 10.5 .. 4.8 .. .. 4.4 2.9 8.0
.. .. 8.1 12.7 7.1 .. .. 8.9 .. .. .. 10.5 7.2 2.3 23.9 8.5 2.1 3.0 12.3 .. 11.8 10.1 .. 4.3 3.3 5.1 12.1 .. 5.5 .. 16.9 5.2 5.2 4.1 .. 10.1 14.0 .. 9.1 17.6 –7.5
.. .. –4.0 3.1 4.0 .. .. 9.9 .. .. .. 2.5 14.5 11.3 14.0 5.9 7.4 4.4 0.9 .. 6.4 10.5 .. 6.6 6.8 6.0 9.2 .. 15.4 .. 6.5 6.2 7.2 5.2 .. 5.4 22.7 .. 20.6 –4.6 3.4
.. .. 17.6 21.6 10.2 .. .. 14.8 .. .. .. 14.3 5.8 6.4 26.4 14.9 10.2 6.3 12.8 .. 15.9 13.6 .. 13.7 21.1 13.7 22.0 .. 19.5 .. 21.8 9.0 6.7 12.6 .. 15.5 20.4 .. 13.4 28.3 2.4
.. .. –1.7 0.8 3.6 .. .. 7.8 .. .. .. 5.8 12.0 8.9 9.8 5.0 5.4 3.6 3.6 .. 0.1 5.0 .. 5.5 12.1 5.2 10.3 .. 21.0 .. 10.7 6.5 9.5 10.1 .. 5.3 22.7 .. 0.6 1.3 1.9
.. .. 14.7 17.6 15.8 .. .. 12.9 .. .. .. 19.1 8.3 9.9 29.5 14.7 10.3 5.1 19.8 .. 20.6 14.6 .. 16.5 12.6 11.2 21.3 .. 12.6 .. 21.7 11.0 8.7 9.9 .. 16.6 22.2 .. 17.5 24.8 2.7
.. .. 110.1 .. 156.3 .. .. 104.3 .. .. .. 106.0 104.3 99.0 100.0 100.0 109.5 96.4 .. .. 98.0 116.0 .. 99.1 .. 95.8 105.7 .. 197.2 .. .. 100.1 103.3 116.2 .. 63.4 .. .. .. 189.7 96.8
.. .. 127.2 236.5 97.4 .. .. 84.5 .. .. .. 126.7 104.7 75.8 180.7 86.6 89.3 96.4 134.4 .. 109.4 96.1 .. 85.5 132.2 91.5 94.7 .. 104.2 .. 154.0 104.5 96.6 88.8 .. 161.6 92.0 .. 154.8 193.2 94.0
a. Data for 2007 are provisional and may differ from data published elsewhere. b. Data are from the International Monetary Fund’s International Financial Statistics database.
334
2009 World Development Indicators
About the data
GLOBAL LINKS
Growth of merchandise trade
6.2
Definitions
Data on international trade in goods are available
from national and international sources such as the
• Export and import volumes are indexes of the quan-
from each country’s balance of payments and
IMF’s International Financial Statistics database, the
tity of goods traded. They are derived from UNCTAD’s
customs records. While the balance of payments
United Nations Economic Commission for Latin Amer-
quantum index series and are the ratio of the export
focuses on the financial transactions that accom-
ica and the Caribbean, the United Nations Statistics
or import value indexes to the corresponding unit
pany trade, customs data record the direction of
Division’s Monthly Bulletin of Statistics database,
value indexes. Unit value indexes are based on data
trade and the physical quantities and value of goods
the World Bank Africa Database, the U.S. Bureau
reported by countries that demonstrate consistency
entering or leaving the customs area. Customs data
of Labor Statistics, Japan Customs, and UNCTAD’s
under UNCTAD quality controls, supplemented by
may differ from data recorded in the balance of pay-
Commodity Price Statistics. The IMF also compiles
UNCTAD’s estimates using the previous year’s trade
ments because of differences in valuation and time
data on trade prices and volumes in its International
values at the Standard International Trade Classifi -
of recording. The 1993 United Nations System of
Financial Statistics (IFS) database.
cation three-digit level as weights. For economies
National Accounts and the fifth edition of the Inter-
Unless otherwise noted, the growth rates and
for which UNCTAD does not publish data, the export
national Monetary Fund’s (IMF) Balance of Payments
terms of trade in the table were calculated from
and import volume indexes (lines 72 and 73) in the
Manual (1993) attempted to reconcile definitions and
index numbers compiled by UNCTAD. The growth
IMF’s International Financial Statistics are used to
reporting standards for international trade statistics,
rates and terms of trade for selected economies
calculate the average annual growth rates. • Export
but differences in sources, timing, and national prac-
were calculated from index numbers compiled in
and import values are the current value of exports
tices limit comparability. Real growth rates derived
the IMF’s International Financial Statistics. In some
(f.o.b.) or imports (c.i.f.), converted to U.S. dollars
from trade volume indexes and terms of trade based
cases price and volume indexes from different
and expressed as a percentage of the average for
on unit price indexes may therefore differ from those
sources vary significantly as a result of differences
the base period (2000). UNCTAD’s export or import
derived from national accounts aggregates.
in estimation procedures. Because the IMF does not
value indexes are reported for most economies. For
Trade in goods, or merchandise trade, includes all
publish trade value indexes, for selected economies
selected economies for which UNCTAD does not pub-
goods that add to or subtract from an economy’s
the trade value indexes were derived from the vol-
lish data, the value indexes are derived from export
material resources. Trade data are collected on the
ume and price indexes. All indexes are rescaled to
or import volume indexes (lines 72 and 73) and cor-
basis of a country’s customs area, which in most
a 2000 base year.
responding unit value indexes of exports or imports
cases is the same as its geographic area. Goods
The terms of trade measures the relative prices of
(lines 74 and 75) in the IMF’s International Financial
provided as part of foreign aid are included, but
a country’s exports and imports. There are several
Statistics. • Net barter terms of trade index is calcu-
goods destined for extraterritorial agencies (such
ways to calculate it. The most common is the net
lated as the percentage ratio of the export unit value
as embassies) are not.
barter (or commodity) terms of trade index, or the
indexes to the import unit value indexes, measured
Collecting and tabulating trade statistics are dif-
ratio of the export price index to the import price
relative to the base year 2000.
ficult. Some developing countries lack the capacity
index. When a country’s net barter terms of trade
to report timely data, especially landlocked coun-
index increases, its exports become more valuable
tries and countries whose territorial boundaries are
or its imports cheaper.
porous. Their trade has to be estimated from the data reported by their partners. (For further discussion of the use of partner country reports, see About the data for table 6.3.) Countries that belong to common customs unions may need to collect data through direct inquiry of companies. Economic or political concerns may lead some national authorities to suppress or misrepresent data on certain trade flows, such as oil, military equipment, or the exports of a dominant producer. In other cases reported trade data may be distorted by deliberate under- or overinvoicing to affect capital transfers or avoid taxes. And in some regions smuggling and black market trading result in unreported trade flows. By international agreement customs data are reported to the United Nations Statistics Division,
Data sources
which maintains the Commodity Trade (Comtrade)
Data on trade indexes are from UNCTAD’s annual
and Monthly Bulletin of Statistics databases. The
Handbook of Statistics for most economies and
United Nations Conference on Trade and Develop-
from the IMF’s International Financial Statistics for
ment (UNCTAD) compiles international trade sta-
selected economies.
tistics, including price, value, and volume indexes,
2009 World Development Indicators
335
6.3
Direction and growth of merchandise trade
Direction of trade High-income importers
% of world trade, 2007
Source of exports High-income economies European Union Japan United States Other high-income economies Low- and middle-income economies East Asia & Pacific China Europe & Central Asia Russian Federation Latin America & Caribbean Brazil Middle East & N. Africa Algeria South Asia India Sub-Saharan Africa South Africa World
European Union
Japan
United States
Other highincome
Total
29.0 22.7 0.7 1.7 3.8 7.6 2.2 1.7 3.0 1.2 0.8 0.3 0.9 0.2 0.3 0.2 0.4 0.1 36.7
2.4 0.4 .. 0.5 1.5 1.7 1.3 0.7 0.1 0.1 0.1 0.0 0.1 0.0 0.0 0.0 0.1 0.1 4.1
7.9 2.6 1.1 .. 4.3 5.8 2.3 1.7 0.1 0.1 2.5 0.2 0.3 0.1 0.2 0.2 0.4 0.1 13.7
12.1 3.8 1.6 3.3 3.5 5.5 3.8 2.7 0.5 0.2 0.4 0.1 0.3 0.0 0.3 0.3 0.2 0.1 17.6
51.5 29.5 3.4 5.5 13.1 20.6 9.6 6.8 3.7 1.5 3.8 0.6 1.5 0.4 0.9 0.7 1.1 0.3 72.1
Low- and middle-income importers
% of world trade, 2007
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
336
East Asia & Pacific
Europe & Central Asia
Latin America & Caribbean
Middle East & N. Africa
South Asia
Sub-Saharan Africa
Total
7.5 1.0 1.3 0.7 4.5 2.5 1.4 0.5 0.2 0.1 0.3 0.1 0.2 0.0 0.2 0.1 0.2 0.0 10.0
3.9 3.1 0.1 0.2 0.4 2.5 0.6 0.5 1.7 0.7 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.0 6.5
3.1 0.7 0.2 1.7 0.5 1.6 0.4 0.4 0.1 0.0 1.0 0.3 0.0 0.0 0.0 0.0 0.1 0.0 4.8
1.1 0.7 0.0 0.1 0.3 0.7 0.2 0.2 0.2 0.1 0.1 0.0 0.2 0.0 0.0 0.0 0.0 0.0 1.8
1.1 0.4 0.1 0.2 0.5 0.7 0.4 0.3 0.1 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.0 0.0 1.8
1.0 0.5 0.1 0.1 0.3 0.7 0.2 0.2 0.0 0.0 0.1 0.0 0.0 0.0 0.1 0.1 0.2 0.1 1.6
17.7 6.4 1.8 2.9 6.6 8.7 3.2 2.0 2.3 1.0 1.6 0.5 0.5 0.1 0.5 0.4 0.6 0.1 26.4
2009 World Development Indicators
GLOBAL LINKS
Direction and growth of merchandise trade
6.3
Nominal growth of trade High-income importers
annual % growth, 1997–2007
Source of exports High-income economies European Union Japan United States Other high-income economies Low- and middle-income economies East Asia & Pacific China Europe & Central Asia Russian Federation Latin America & Caribbean Brazil Middle East & N. Africa Algeria South Asia India Sub-Saharan Africa South Africaa World
European Union
Japan
United States
Other highincome
Total
9.2 9.7 4.4 5.4 9.4 15.0 17.7 25.2 17.4 17.4 11.2 11.5 12.4 11.7 11.6 13.3 7.6 7.2 10.2
4.6 3.7 .. –0.5 7.2 9.5 9.6 12.4 10.1 9.9 6.9 3.5 15.4 16.5 4.7 7.0 9.6 9.6 6.4
6.0 8.2 2.1 .. 6.0 12.2 15.3 21.7 8.5 5.3 9.4 10.4 27.4 22.5 10.6 13.0 14.4 11.2 8.2
6.8 7.6 5.3 5.0 8.6 14.0 14.1 18.4 15.0 14.3 12.9 14.2 16.7 23.5 16.0 17.9 6.3 3.6 8.5
7.8 9.2 4.0 4.5 7.7 13.4 14.3 19.5 16.4 15.7 10.0 11.0 14.8 15.4 12.4 14.5 9.7 7.2 9.1
Low- and middle-income importers
annual % growth, 1997–2007
Source of exports High-income economies European Union Japan United States Other high-income economies Low- and middle-income economies East Asia & Pacific China Europe & Central Asia Russian Federation Latin America & Caribbean Brazil Middle East & N. Africa Algeria South Asia India Sub-Saharan Africa South Africaa World
East Asia & Pacific
Europe & Central Asia
Latin America & Caribbean
Middle East & N. Africa
South Asia
Sub-Saharan Africa
Total
11.5 10.0 9.3 8.6 13.3 19.3 18.3 22.2 13.9 13.8 22.1 18.8 24.0 59.3 22.1 23.6 25.0 14.2 13.0
13.4 13.6 17.7 8.2 12.9 16.2 30.3 33.4 14.2 14.7 14.0 15.1 12.6 8.6 13.0 14.1 13.0 8.1 14.5
6.5 6.6 5.1 6.1 8.6 12.8 22.7 27.2 13.7 14.0 9.9 10.4 10.1 10.4 20.8 23.8 19.1 5.2 8.2
9.5 8.5 6.2 7.6 13.9 18.4 22.3 28.5 18.2 22.9 10.2 13.2 21.3 23.5 18.4 21.7 8.8 10.9 12.2
11.9 11.8 8.8 14.7 11.8 19.0 22.9 29.4 17.5 16.9 20.8 15.7 12.0 14.4 18.4 16.9 4.1 11.4 14.1
9.7 9.5 8.7 8.8 11.0 17.1 24.0 27.5 17.2 18.3 18.4 20.2 20.7 3.3 17.3 18.7 11.6 7.9 12.2
10.6 11.0 8.9 7.2 12.6 16.6 21.6 27.3 14.6 15.0 12.3 12.8 17.7 14.4 18.8 20.1 15.6 9.5 12.2
a. Data for 1997 are based on imports from South Africa reported by other economies because data on exports for South Africa were not available.
2009 World Development Indicators
337
6.3
Direction and growth of merchandise trade
About the data
Definitions
The table provides estimates of the flow of trade in
using the IMF’s published period average exchange
• Merchandise trade includes all trade in goods;
goods between groups of economies. The data are
rate (series rf or rh, monthly averages of the market
trade in services is excluded. • High-income econo-
from the International Monetary Fund’s (IMF) Direc-
or official rates) for the reporting country or, if unavail-
mies are those classifi ed as such by the World
tion of Trade database. All high-income economies
able, monthly average rates in New York. Because
Bank (see inside front cover). • European Union is
and major developing economies report trade on
imports are reported at cost, insurance, and freight
defined as all high-income EU members: Austria,
a timely basis, covering about 85 percent of trade
(c.i.f.) valuations, and exports at free on board (f.o.b.)
Belgium, Cyprus, Czech Republic, Denmark, Esto-
for recent years. Trade by less timely reporters and
valuations, the IMF adjusts country reports of import
nia, Finland, France, Germany, Greece, Hungary,
by countries that do not report is estimated using
values by dividing them by 1.10 to estimate equiva-
Ireland, Italy, Luxembourg, Malta, the Netherlands,
reports of trading partner countries. Because the
lent export values. The accuracy of this approxima-
Portugal, Slovenia, Spain, Sweden, and the United
largest exporting and importing countries are reli-
tion depends on the set of partners and the items
Kingdom. • Other high-income economies include
able reporters, a large portion of the missing trade
traded. Other factors affecting the accuracy of trade
all high-income economies (both Organisation for
flows can be estimated from partner reports. Part-
data include lags in reporting, recording differences
Economic Co-operation and Development members
ner country data may introduce discrepancies due to
across countries, and whether the country reports
and others) except the high-income European Union,
smuggling, confidentiality, different exchange rates,
trade according to the general or special system of
Japan, and the United States. • Low- and middle-
overreporting of transit trade, inclusion or exclusion
trade. (For further discussion of the measurement of
income regional groupings are based on World Bank
of freight rates, and different points of valuation and
exports and imports, see About the data for tables
classifications (see inside back cover for regional
times of recording.
4.4 and 4.5.)
groupings) and may differ from those used by other
In addition, estimates of trade within the European
The regional trade flows in the table are calculated
Union (EU) have been significantly affected by changes
from current price values. The growth rates are in
in reporting methods following the creation of a cus-
nominal terms; that is, they include the effects of
toms union. The current system for collecting data on
changes in both volumes and prices.
organizations.
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 In 2007 around 70 percent of exports from low- and middle-income economies and from high-income economies were directed to high-income economies Destination of exports from low- and middle-income economies South Asia 2.3% Middle East & North Africa 2.4%
Sub-Saharan Africa 2.2% Unspecified 1.9%
Latin America & Caribbean 5.4%
6.3a
Destination of exports from high-income economies South Asia 1.5% Middle East & North Africa 1.6%
Sub-Saharan Africa 1.4% Unspecified 1.3%
Latin America & Caribbean 4.5% Europe & Central Asia 5.5%
Europe & Central Asia 8.5%
East Asia & Pacific 10.7% East Asia & Pacific 8.3%
High income 69.0%
High income 73.4%
Data sources East Asia and Pacific and Europe and Central Asia were the two largest developing region importers from
Data on the direction and growth of merchandise
both low- and middle-income economies and high-income economies.
trade were calculated using the IMF’s Direction of
Source: World Bank staff calculations based on data from the International Monetary Fund’s Direction of Trade database.
Trade database.
338
2009 World Development Indicators
GLOBAL LINKS
High-income economy trade with low- and middle-income economies
6.4
Exports to low-income economies High-income economies
European Union
Japan
United States
1997
2007
1997
2007
1997
2007
1997
2007
47.8
121.4
21.1
47.2
4.9
12.2
5.2
12.6
11.7 4.2 2.1 1.2 4.4 0.0 3.6 78.4 12.0 3.6 43.5 0.3 6.5 0.5 12.1 1.7
8.7 2.6 2.1 2.3 13.0 0.7 11.9 69.1 11.7 3.5 40.0 0.2 4.1 0.2 9.4 4.2
13.8 3.7 1.5 0.9 2.7 0.0 2.4 78.5 14.6 3.5 43.0 0.5 2.3 0.4 14.2 1.5
9.4 1.7 1.4 1.2 11.8 0.0 11.4 72.5 12.0 2.7 44.5 0.4 1.7 0.2 11.0 3.1
0.9 0.4 1.2 0.5 1.1 0.0 1.0 94.0 5.3 7.8 64.9 0.1 6.1 0.0 9.9 2.3
0.7 0.1 1.6 1.8 1.1 0.0 1.0 92.3 5.8 9.6 64.0 0.2 3.7 0.0 9.0 2.6
23.8 17.7 5.1 0.7 1.4 0.0 1.1 65.6 9.2 1.5 44.2 0.2 3.1 0.4 7.0 3.5
20.5 13.6 6.6 1.9 3.4 0.0 3.0 63.3 7.2 1.3 46.0 0.2 1.0 0.4 7.3 4.2
55.5
154.4
26.3
58.2
5.2
10.2
12.8
61.3
20.6 0.3 7.2 4.5 29.8 28.4 1.1 36.4 0.6 0.5 2.5 0.5 22.1 2.8 7.3 1.5
12.2 0.4 2.5 5.4 40.1 35.2 1.5 37.9 1.0 0.2 3.3 2.2 22.5 3.9 4.8 1.7
28.0 0.1 8.7 5.1 17.6 17.3 0.2 39.5 0.5 0.5 1.8 0.4 21.2 4.5 10.5 1.0
19.5 0.2 4.4 8.1 22.9 16.6 0.6 44.4 1.4 0.3 2.3 1.9 25.7 6.7 6.1 0.6
26.5 0.0 10.9 9.0 20.6 17.8 1.1 32.3 0.3 1.2 2.2 1.6 21.1 1.8 4.2 0.7
15.4 0.4 3.1 10.9 25.6 17.0 3.0 42.7 1.2 0.4 16.9 2.8 10.8 3.7 6.8 2.4
8.8 0.2 0.9 1.9 54.3 51.3 2.9 33.7 0.7 0.3 0.2 0.1 27.6 0.8 4.1 0.4
4.2 0.1 0.5 0.4 60.8 57.3 1.9 33.6 0.4 0.1 1.2 2.6 24.9 1.8 2.6 0.4
Simple applied tariff rates on imports from low-income economies (%)a Food 7.6 6.9 6.9 Cereals 12.9 11 41.5 Agricultural raw materials 2.5 3.7 0.2 Ores and nonferrous metals 1.5 0.9 0.3 Fuels 3.1 0.8 0.0 Crude petroleum 1.7 0.5 0.0 Petroleum products 5.0 1.1 0.0 Manufactured goods 4.9 3.6 1.2 Chemical products 3.6 2.6 1.3 Iron and steel 5.1 1.8 0.6 Machinery and transport equipment 2.3 1.5 0.3 Furniture 4.0 3.2 0.1 Textiles 8.1 5.9 3.1 Footwear 7.9 6.4 3.0 Other 3.0 2.14 0.6 Miscellaneous goods 0.8 0.3 0.0 Average 5.1 4.0 1.9
4.4 20.7 0.1 0.4 0.0 0.0 0.1 1.0 1.0 0.1 0.2 0.0 2.9 2.5 0.2 0.0 1.4
10.2 4.2 1.8 0.7 1.6 0.9 5.6 2.4 3.0 0.1 0.1 0.0 4.5 8.4 1.4 0.0 3.6
4.0 7.9 0.2 0.1 0.2 0.0 0.6 1.8 0.3 0.1 0.0 0.0 3.4 9.1 0.7 0.0 3.2
3.2 1.5 0.4 0.2 0.5 0.3 1.1 5.9 1.0 1.7 0.5 1.2 11.4 14.2 3.2 0.3 5.1
2.9 2.2 0.5 0.6 0.8 0.0 1.4 4.0 0.8 0.5 0.4 1.1 7.5 8.3 1.1 0.1 3.7
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 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
2009 World Development Indicators
339
6.4
High-income economy trade with low- and middle-income economies
Exports to middle-income economies High-income economies
Total ($ billions) % of total exports Food Cereals Agricultural raw materials Ores and nonferrous metals Fuels Crude petroleum Petroleum products Manufactured goods Chemical products Iron and steel Machinery and transport equipment Furniture Textiles Footwear Other Miscellaneous goods
European Union
Japan
United States
1997
2007
1997
2007
1997
2007
1997
2007
737.3
2,001.2
287.0
837.3
100.4
235.5
189.2
357.5
7.4 1.6 2.0 1.9 3.1 0.6 1.8 83.2 11.1 2.8 49.1 0.6 5.7 0.4 13.7 2.1
5.1 1.0 1.9 4.0 5.6 0.8 3.7 79.2 13.2 3.5 45.8 0.4 2.9 0.2 13.2 3.4
8.7 1.3 1.3 1.6 1.6 0.1 1.3 84.2 12.7 2.7 45.9 0.9 5.4 0.7 16.1 1.9
5.0 0.6 1.4 2.6 2.6 0.1 2.1 84.2 13.4 3.6 47.0 0.7 3.7 0.5 15.2 2.9
0.4 0.0 1.1 1.3 0.9 0.0 0.7 94.6 6.9 6.3 68.1 0.1 2.9 0.0 10.2 1.7
0.3 0.0 0.9 3.6 1.2 0.0 1.0 89.1 9.8 6.6 60.9 0.2 1.6 0.0 10.1 4.8
9.1 2.5 2.7 1.6 2.6 0.1 1.6 80.7 10.8 1.0 49.7 0.6 4.9 0.2 13.5 3.4
10.1 3.3 3.7 4.3 5.3 0.0 4.0 72.7 13.1 1.3 43.3 0.3 2.4 0.1 12.2 3.9
916.3
3,053.0
273.8
1,123.6
109.0
264.9
291.3
895.4
10.6 0.4 2.9 5.6 13.7 8.6 2.2 65.2 3.2 2.5 25.9 1.4 13.8 3.1 15.2 1.9
6.3 0.4 1.4 5.6 18.5 12.2 3.5 65.7 3.5 3.2 32.3 2.0 8.6 1.5 14.5 1.7
14.1 0.3 3.8 7.3 18.7 11.9 2.9 53.7 4.3 2.2 15.8 1.5 14.9 2.1 12.9 2.2
7.9 0.5 1.9 6.1 22.5 15.1 3.9 58.6 3.6 4.0 25.4 2.0 9.3 1.5 12.9 1.3
15.5 0.3 5.5 10.0 15.8 7.4 1.2 51.8 2.7 1.7 18.0 1.4 13.5 1.9 12.6 1.4
7.8 0.3 2.4 12.3 17.8 8.8 2.2 58.1 4.2 1.6 26.9 1.5 9.6 1.2 13.2 1.6
7.7 0.2 1.5 2.7 12.4 9.6 2.5 73.4 2.2 2.1 34.2 1.8 13.0 4.0 16.2 2.3
4.8 0.2 0.9 2.8 18.4 14.6 3.2 70.9 2.7 2.0 36.5 3.0 8.5 1.9 16.3 2.2
8.9 25.2 0.3 0.5 0.0 0.0 0.1 0.9 1.0 0.1 0.2 0.0 2.9 2.7 0.3 0.0 1.7
12.4 16.8 1.3 0.1 1.3 0.9 5.6 1.5 0.6 0.1 0.0 0.0 4.4 13.2 0.5 0.0 2.7
7.7 12.0 0.6 0.1 0.3 0.0 1.2 2.4 0.4 0.2 0.0 0.1 6.6 19.3 0.7 0.0 2.8
3.7 1.5 0.5 0.4 0.4 0.4 1.3 3.8 1.4 2.9 0.5 0.4 11.0 12.9 1.0 0.4 3.6
3.8 1.4 0.6 0.4 1.3 0.0 3.4 2.6 0.8 0.2 0.2 0.3 7.4 7.8 0.7 0.0 2.6
Imports from middle-income economies Total ($ billions) % of total imports Food Cereals Agricultural raw materials Ores and nonferrous metals Fuels Crude petroleum Petroleum products Manufactured goods Chemical products Iron and steel Machinery and transport equipment Furniture Textiles Footwear Other Miscellaneous goods
Simple applied tariff rates on imports from middle-income economies (%)a Food 9.9 9.1 16.4 Cereals 13.8 13.9 47.6 Agricultural raw materials 2.6 2.6 1.0 Ores and nonferrous metals 1.8 1.3 0.9 Fuels 3.3 1.4 0.0 Crude petroleum 7.1 0.6 0.0 Petroleum products 6.2 2.1 0.1 Manufactured goods 4.9 3.6 2.0 Chemical products 3.4 2.4 1.4 Iron and steel 3.5 1.8 1.1 Machinery and transport equipment 3.3 2.3 0.7 Furniture 5.4 4.3 0.3 Textiles 8.7 6.7 5.6 Footwear 9.1 8.1 4.6 Other 3.2 2.8 1.1 Miscellaneous goods 0.7 1.5 0.0 Average 5.3 4.1 3.3 a. Includes ad valorem equivalents of specific rates.
340
2009 World Development Indicators
About the data
GLOBAL LINKS
High-income economy trade with low- and middle-income economies
6.4
Definitions
Developing economies are becoming increasingly
manufactures as a share of goods imports from both
The product groups in the table are defined in accor-
important in the global trading system. Since the
low- and middle-income economies have grown. And
dance with SITC revision 3: food (0, 1, 22, and 4) and
early 1990s trade between high-income economies
trade between developing economies has grown
cereals (04); agricultural raw materials (2 excluding
and low- and middle-income economies has grown
substantially over the past decade, a result of their
22, 27, and 28); ores and nonferrous metals (27, 28,
faster than trade among high-income economies.
increasing share of world output and liberalization of
and 68); fuels (3), crude petroleum (crude petroleum
The increased trade benefi ts consumers and pro-
trade, among other influences.
oils and oils obtained from bituminous minerals;
ducers. But as was apparent at the World Trade Orga-
Yet trade barriers remain high. The table includes
333), and petroleum products (noncrude petroleum
nization’s (WTO) Ministerial Conferences in Doha,
information about tariff rates by selected product
and preparations; 334); manufactured goods (5–8
Qatar, in October 2001; Cancun, Mexico, in Sep-
groups. Applied tariff rates are the tariffs in effect
excluding 68), chemical products (5), iron and steel
tember 2003; and Hong Kong, China, in December
for partners in preferential trade agreements such
(67), machinery and transport equipment (7), furni-
2005, achieving a more pro-development outcome
as the North American Free Trade Agreement. When
ture (82), textiles (65 and 84), footwear (85), and
from trade remains a challenge. Doing so will require
these rates are unavailable, most favored nation
other manufactured goods (6 and 8 excluding 65,
strengthening international consultation. After the
rates are used. The difference between most favored
67, 68, 82, 84, and 85); and miscellaneous goods
Doha meetings negotiations were launched on ser-
nation and applied rates can be substantial. Simple
(9). • Exports are all merchandise exports by high-
vices, agriculture, manufactures, WTO rules, the
averages of applied rates are shown because they
income economies to low-income and middle-income
environment, dispute settlement, intellectual prop-
are generally a better indicator of tariff protection
economies as recorded in the United Nations Sta-
erty rights protection, and disciplines on regional
than weighted average rates are.
tistics Division’s Comtrade database. Exports are
integration. At the most recent negotiations in Hong
The data are from the United Nations Conference
recorded free on board (f.o.b.). • Imports are all
Kong, China, trade ministers agreed to eliminate
on Trade and Development (UNCTAD). Partner coun-
merchandise imports by high-income economies
subsidies of agricultural exports by 2013; to abolish
try reports by high-income economies were used for
from low-income and middle-income economies as
cotton export subsidies and grant unlimited export
both exports and imports. Because of differences in
recorded in the United Nations Statistics Division’s
access to selected cotton-growing countries in Sub-
sources of data, timing, and treatment of missing data,
Commodity Trade (Comtrade) database. Imports
Saharan Africa; to cut more domestic farm supports
the numbers in the table may not be fully comparable
include insurance and freight charges (c.i.f.). • High-,
in the European Union, Japan, and the United States;
with those used to calculate the direction of trade
middle-, and low-income economies are those
and to offer more aid to developing countries to help
statistics in table 6.3 or the aggregate flows in tables
classified as such by the World Bank (see inside
them compete in global trade.
4.4, 4.5, and 6.2. Tariff line data were matched to
front cover). • European Union is defined as all
Trade flows between high-income and low- and
Standard International Trade Classification (SITC) revi-
high-income EU members: Austria, Belgium, Cyprus,
middle-income economies reflect the changing mix of
sion 3 codes to define commodity groups. For further
Czech Republic, Denmark, Estonia, Finland, France,
exports to and imports from developing economies.
discussion of merchandise trade statistics, see About
Germany, Greece, Hungary, Ireland, Italy, Luxem-
While food and primary commodities have continued
the data for tables 4.4, 4.5, 6.2, 6.3, and 6.5, and for
bourg, Malta, the Netherlands, Portugal, Slovenia,
to fall as a share of high-income economies’ imports,
information about tariff barriers, see table 6.8.
Spain, Sweden, and the United Kingdom.
High-income economies’ tariffs on imports from low- and middle-income economies fell between 1997 and 2007 but remain high for some products Simple applied tariff rates (%)
6.4a 1997
2007
10
8 6 4 2 0 Food Agricultural Fuels Ores and Manu- Machinery Textiles raw nonferrous factured and materials metals goods transport equipment
Food Agricultural Fuels Ores and Manu- Machinery Textiles raw nonferrous factured and materials metals goods transport equipment
Imports from low-income economies
Imports from middle-income economies
Data sources Data on trade values are from United Nations Statistics Division’s Comtrade database. Data
Food and textile products are subject to higher tariff rates than other products are. And tariff rates on
on tariffs are from UNCTAD’s Trade Analysis and
agricultural raw material imports from low-income countries have increased significantly.
Information System database and are calculated by World Bank staff using the World Integrated
Source: United Nations Statistics Division’s Comtrade database and the United Nations Conference on Trade and Development’s Trade Analysis and Information System database.
Trade Solution system.
2009 World Development Indicators
341
6.5
Direction of trade of developing economies Exports
Imports
% of total merchandise exports To developing economies
East Asia & Pacific Cambodia China Indonesia Korea, Dem. Rep. Lao PDR Malaysia Mongolia Myanmar Papua New Guinea Philippines Thailand Vietnam Europe & Central Asia Albania Armenia Azerbaijan Belarus Bosnia and Herzegovina Bulgaria Croatia Georgia Kazakhstan Kyrgyz Republic Latvia Lithuania Macedonia, FYR Moldova Poland Romania Russian Federation Tajikistan Turkey Turkmenistan Ukraine Uzbekistan Latin America & Carib. Argentina Bolivia Brazil Chile Colombia Costa Rica Cuba Dominican Republic Ecuador El Salvador Guatemala Haiti Honduras Jamaica Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela, RB
342
% of total merchandise imports
Within region 1997 2007
Outside region 1997 2007
To high-income economies 1997 2007
8.5 55.5 4.9 11.2 16.8 26.2 10.1 22.5 15.4 11.2 8.4 13.6 14.4 32.1 8.1 44.1 55.0 80.7 39.3 36.3 23.6 77.3 49.2 54.7 38.4 58.6 20.4 80.5 27.8 13.6 30.4 43.3 18.4 48.2 51.2 62.8 19.5 50.8 43.5 28.7 21.5 27.7 26.1 6.1 2.9 25.9 48.0 30.1 0.4 17.8 4.3 5.1 28.6 23.6 61.0 17.9 56.6 21.9
6.9 1.0 8.4 6.7 33.5 0.0 6.5 10.8 20.2 0.7 1.2 5.4 5.4 10.0 0.1 21.8 25.0 7.3 4.9 9.1 4.8 5.6 11.7 7.5 2.3 1.0 1.2 0.5 6.1 14.4 9.9 2.7 11.7 31.7 22.5 10.1 5.8 17.6 0.3 13.0 7.6 1.9 2.1 42.7 0.4 7.3 0.8 3.5 0.0 0.0 8.1 0.3 0.1 0.2 0.4 13.5 9.6 0.6
84.6 43.5 86.6 82.1 49.7 73.8 83.5 66.6 64.4 88.2 90.4 81.0 80.2 57.8 91.8 34.2 20.0 12.0 55.9 54.6 71.6 17.1 39.1 37.8 59.3 40.4 78.4 19.0 66.1 72.0 59.7 53.9 69.9 20.2 26.3 27.1 74.7 31.6 56.2 58.2 70.9 70.4 71.8 51.2 96.7 66.8 51.2 66.4 99.6 82.2 87.6 94.6 71.3 76.2 38.6 68.6 33.9 77.4
10.6 7.3 5.5 18.9 40.7 71.0 19.7 72.0 61.3 15.3 21.1 25.1 19.8 28.3 8.6 36.0 38.2 58.9 26.9 29.5 22.9 46.8 26.2 56.6 37.0 45.2 42.4 65.0 34.4 19.5 28.7 50.9 19.1 64.6 51.1 62.8 18.2 41.1 65.2 25.3 16.9 35.4 19.5 13.0 6.4 28.3 40.3 39.6 11.9 17.5 2.0 6.0 44.6 16.9 70.0 20.5 41.9 13.3
2009 World Development Indicators
14.6 1.9 17.0 12.7 49.6 1.2 9.8 3.9 19.1 2.4 2.2 11.0 3.4 10.2 3.9 4.9 21.3 6.7 2.3 5.9 2.8 5.6 22.3 17.2 2.3 2.0 0.5 2.0 10.6 5.8 10.5 6.7 14.8 22.9 20.1 14.1 11.1 27.2 2.5 20.5 23.2 4.6 18.2 36.8 2.8 9.4 1.4 3.0 3.7 0.8 9.1 1.6 0.8 8.1 8.1 15.9 18.6 4.8
74.8 90.8 77.5 68.4 9.6 27.8 70.5 24.1 19.6 82.2 76.6 64.0 76.8 61.5 87.6 59.2 40.5 34.4 70.8 64.6 74.4 47.6 51.5 26.1 60.7 52.9 57.1 32.9 55.0 74.7 60.8 42.4 66.1 12.4 28.7 23.1 70.7 31.7 32.3 54.2 59.9 60.0 62.3 50.2 90.8 62.3 58.3 57.4 84.3 81.7 88.9 92.4 54.6 75.1 21.9 63.6 39.5 81.9
From developing economies Within region 1997 2007
Outside region 1997 2007
From high-income economies 1997 2007
9.4 34.3 6.0 8.6 43.0 91.7 10.2 14.5 42.6 7.2 10.4 11.6 13.4 27.7 10.5 41.4 69.0 73.5 33.7 38.2 9.0 55.8 61.1 68.7 30.1 38.4 28.5 69.1 21.4 19.0 33.3 66.6 9.7 71.5 65.4 46.5 17.7 31.6 43.8 22.1 29.4 25.8 32.4 10.9 21.2 34.2 38.9 31.2 14.9 30.5 10.5 2.3 46.9 30.0 53.9 30.4 50.7 22.2
7.5 0.8 10.1 8.3 18.7 0.1 4.4 38.9 3.0 1.0 5.1 7.3 3.6 7.9 0.5 12.1 7.7 3.3 0.1 9.3 7.4 2.5 3.0 7.0 1.6 2.9 6.2 1.6 7.6 7.7 9.2 2.3 12.6 4.7 3.2 6.1 5.4 7.5 1.0 10.2 8.4 3.7 4.1 24.7 1.4 4.5 1.8 2.4 4.5 0.0 2.3 3.2 0.5 1.6 2.9 2.6 7.6 1.4
84.6 43.5 86.6 82.1 49.7 73.8 83.5 66.6 64.4 88.2 90.4 81.0 80.2 57.8 91.8 34.2 20.0 12.0 55.9 54.6 71.6 17.1 39.1 37.8 59.3 40.4 78.4 19.0 66.1 72.0 59.7 53.9 69.9 20.2 26.3 27.1 74.7 31.6 56.2 58.2 70.9 70.4 71.8 51.2 96.7 66.8 51.2 66.4 99.6 82.2 87.6 94.6 71.3 76.2 38.6 68.6 33.9 77.4
16.9 59.7 10.7 27.8 51.9 85.5 26.1 34.5 63.9 18.0 19.5 25.6 33.9 27.5 23.4 42.6 45.3 72.6 37.2 35.7 20.3 56.8 48.4 66.5 35.4 39.1 37.5 67.0 36.5 20.9 20.8 70.3 22.8 41.4 45.4 52.4 20.4 44.5 71.3 17.3 35.8 31.5 27.6 39.9 28.2 36.1 42.1 33.1 16.8 27.1 29.1 5.1 58.8 24.2 53.6 38.4 48.2 43.4
13.2 1.4 17.0 10.3 38.6 0.7 5.4 34.7 4.7 1.3 3.4 7.3 4.6 14.0 10.7 15.1 11.6 5.1 1.1 7.7 9.7 8.1 23.5 16.4 3.2 4.3 4.7 2.9 23.0 7.1 19.9 17.5 22.8 17.7 12.1 14.9 16.9 18.3 4.8 27.9 21.1 15.3 7.6 18.6 6.2 13.4 7.1 9.5 10.4 5.9 5.4 15.4 0.8 7.3 10.6 18.3 21.3 10.2
69.9 38.9 72.3 61.9 9.5 13.8 68.5 30.8 31.4 80.7 77.2 67.0 61.4 58.4 65.9 42.3 43.1 22.3 61.7 56.6 70.0 35.2 28.0 17.1 61.4 56.6 57.8 30.2 40.5 72.1 59.3 12.1 54.5 40.9 42.5 32.7 62.6 37.2 23.8 54.8 43.0 53.2 64.8 41.5 65.7 50.6 50.8 57.4 72.8 66.9 65.5 79.5 40.4 68.6 35.8 43.3 30.5 46.5
Exports
Middle East & N. Africa Algeria Egypt, Arab Rep. Iran, Islamic Rep. Iraq Jordan Lebanon Libya Morocco Syrian Arab Republic Tunisia Yemen, Rep. South Asia Afghanistan Bangladesh India Nepal Pakistan Sri Lanka Sub-Saharan Africa Angola Benin Burkina Faso Burundi Cameroon Central African Republic Chad Congo, Dem. Rep. Congo, Rep. Ethiopia Gabon Gambia, The Ghana Guinea Guinea-Bissau Kenya Liberia Madagascar Malawi Mali Mauritania Mauritius Mozambique Niger Nigeria Rwanda Senegal Sierra Leone Somalia South Africa Sudan Tanzania Togo Uganda Zambia Zimbabwe
Within region 1997 2007
Outside region 1997 2007
4.6 1.2 7.0 0.3 20.2 28.4 16.9 3.5 6.2 13.5 7.3 0.3 4.4 24.0 2.5 4.8 25.9 2.9 2.8 12.3 1.3 7.8 19.9 2.5 6.6 17.0 6.5 8.6 1.1 3.0 1.0 8.5 8.2 5.7 1.6 41.5 1.4 7.5 21.6 4.0 9.6 5.8 31.0 28.5 8.1 5.8 29.6 0.0 0.3 14.2 1.3 14.6 17.4 2.4 13.7 35.1
16.9 13.5 12.9 19.6 17.2 25.4 14.7 10.9 19.4 15.6 8.3 56.5 17.5 20.6 9.9 19.6 0.4 14.1 15.1 12.4 16.4 61.1 11.0 0.0 9.3 0.1 27.9 4.6 11.6 15.3 11.2 15.3 13.0 0.5 55.0 13.5 14.0 6.3 13.1 42.7 4.6 1.2 8.0 0.3 13.7 14.2 19.5 0.0 13.1 11.7 30.5 25.8 37.5 7.0 25.8 9.6
7.5 2.5 14.7 2.7 3.0 27.5 39.6 3.1 2.7 53.4 9.5 2.1 6.4 45.9 2.8 5.2 72.8 13.0 9.3 12.3 4.6 31.5 16.3 18.4 9.6 8.7 0.5 10.5 1.4 2.4 3.1 8.4 11.6 1.9 8.9 42.8 2.3 2.7 26.1 6.6 13.0 12.1 21.0 32.8 10.2 5.5 49.6 2.9 4.0 14.2 0.3 18.7 55.7 23.4 24.1 55.7
18.5 11.2 15.3 38.0 7.0 17.1 11.9 7.1 21.1 4.3 6.5 66.8 26.5 21.1 7.1 29.2 2.8 21.3 14.3 23.3 38.4 44.6 50.1 16.6 9.5 33.3 4.6 34.1 43.1 30.1 31.1 57.4 20.3 35.3 90.2 16.1 57.6 5.8 29.1 62.4 37.2 3.1 9.8 2.8 14.0 40.0 12.2 6.2 29.3 16.0 84.2 32.4 17.3 11.1 18.5 11.6
6.5
Imports
% of total merchandise exports To developing economies
GLOBAL LINKS
Direction of trade of developing economies
% of total merchandise imports To high-income economies 1997 2007
78.5 85.3 80.1 80.1 62.6 46.3 68.4 85.7 74.4 70.9 84.4 43.2 78.1 55.4 87.6 75.5 73.8 83.0 82.0 75.4 82.3 31.1 69.1 97.5 84.1 82.9 65.7 86.9 87.3 81.7 87.8 76.2 78.8 93.8 43.4 45.0 84.5 86.2 65.3 53.3 85.8 93.0 61.0 71.2 78.2 80.0 51.0 100.0 86.6 74.0 68.2 59.5 45.1 90.6 60.5 55.4
74.0 86.4 70.1 59.3 90.0 55.4 48.5 89.8 76.2 42.3 84.0 31.1 67.1 33.0 90.1 65.6 24.4 65.7 76.4 64.4 57.0 23.9 33.6 65.0 80.9 58.0 95.0 55.3 55.5 67.4 65.8 34.2 68.1 62.8 0.9 41.1 40.0 91.6 44.8 31.0 49.8 84.9 69.2 64.3 75.8 54.4 38.2 90.9 66.7 69.8 15.5 48.9 27.0 65.5 57.5 32.7
From developing economies Within region 1997 2007
Outside region 1997 2007
4.8 3.6 1.3 0.8 25.8 17.7 7.0 9.2 4.6 4.4 4.8 3.6 3.9 9.7 15.0 0.6 28.2 2.3 12.1 13.3 9.6 15.4 26.2 27.0 19.2 23.1 22.8 40.6 12.2 2.3 16.0 10.5 22.9 12.5 13.3 13.5 1.1 11.8 68.0 36.1 4.1 15.2 64.1 27.2 3.3 30.9 11.8 21.2 13.6 3.7 3.2 16.5 23.9 44.2 55.6 50.6
20.7 15.6 24.3 29.4 28.7 19.5 16.7 10.7 21.3 26.9 11.8 25.5 21.7 33.3 23.2 22.1 8.2 21.3 21.2 13.5 7.7 15.8 12.0 6.6 10.0 6.8 7.5 9.4 6.5 18.7 5.1 19.7 13.1 16.4 20.7 14.7 9.1 25.0 5.2 6.5 26.2 22.1 7.2 16.9 20.9 7.7 20.8 9.3 66.7 11.6 41.6 23.3 8.9 9.4 5.8 5.6
8.3 3.3 4.1 0.8 39.2 8.6 16.5 11.4 6.3 21.2 7.0 3.3 4.9 41.8 16.9 1.1 66.2 3.0 25.6 12.2 7.4 9.0 41.7 27.2 19.8 24.9 22.0 53.9 4.8 2.4 9.3 21.8 24.8 12.2 25.8 9.1 2.4 14.4 55.1 43.2 6.0 10.5 47.1 23.3 6.0 46.6 9.3 19.5 10.9 6.2 2.4 20.1 6.1 40.2 59.1 67.8
31.5 31.6 33.6 46.0 31.9 25.9 22.2 25.5 20.0 30.4 15.3 34.7 32.3 28.1 31.3 33.0 18.6 33.9 24.9 28.9 27.6 59.8 15.6 16.5 25.1 10.9 14.3 6.7 27.8 42.4 13.0 51.9 33.2 33.1 19.7 29.2 17.7 37.8 18.0 14.3 35.3 44.2 19.3 17.0 28.6 11.4 27.7 33.6 66.7 27.9 52.8 30.5 47.7 17.4 11.6 11.6
From high-income economies 1997 2007
78.5 85.3 80.1 80.1 62.6 46.3 68.4 85.7 74.4 70.9 84.4 43.2 78.1 55.4 87.6 75.5 73.8 83.0 82.0 75.4 82.3 31.1 69.1 97.5 84.1 82.9 65.7 86.9 87.3 81.7 87.8 76.2 78.8 93.8 43.4 45.0 84.5 86.2 65.3 53.3 85.8 93.0 61.0 71.2 78.2 80.0 51.0 100.0 86.6 74.0 68.2 59.5 45.1 90.6 60.5 55.4
60.2 65.1 62.3 53.2 28.9 65.4 61.3 63.1 73.8 48.5 77.7 62.0 62.8 30.0 51.9 65.9 15.2 63.1 49.5 59.0 65.0 31.2 42.7 56.3 55.1 64.2 63.7 39.4 67.4 55.2 77.7 26.3 42.1 54.6 54.5 61.7 79.9 47.8 27.0 42.5 58.7 45.3 33.5 59.6 65.4 42.0 63.0 46.9 22.4 65.9 44.8 49.3 46.2 42.4 29.2 20.7
Note: Bilateral trade data are not available for Timor-Leste, Serbia, West Bank and Gaza, Botswana, Côte d’Ivoire, Eritrea, Lesotho, Namibia, and Swaziland.
2009 World Development Indicators
343
6.5
Direction of trade of developing economies
About the data
Definitions
Developing economies are an increasingly important
share of intraregional trade is increasing. Geographic
• Exports to developing economies within region
part of the global trading system. Their share of world
patterns of trade vary widely by country and commod-
are the sum of merchandise exports from the report-
trade rose from 18 percent in 1990 to 28 percent
ity. Larger shares of exports from oil- and resource-
ing economy to other developing economies in the
in 2007. And trade between high-income economies
rich economies are to high-income economies.
same World Bank region as a percentage of total
and low- and middle-income economies has grown
The relative importance of intraregional trade
merchandise exports by the economy. • Exports to
faster than trade between high-income economies.
is higher for both landlocked countries and small
developing economies outside region are the sum
This increased trade benefits both producers and con-
countries with close trade links to the largest
of merchandise exports from the reporting economy
sumers in developing and high-income economies.
regional economy. For most developing economies—
to other developing economies in other World Bank
The table shows trade in goods between develop-
especially smaller ones—there is a “geographic
regions as a percentage of total merchandise exports
ing economies in the same region and other regions
bias” favoring intraregional trade. Despite the broad
by the economy. • Exports to high-income econo-
and between developing economies and high-income
trend toward globalization and the reduction of trade
mies are the sum of merchandise exports from the
economies. Data on exports and imports are from the
barriers, the relative share of intraregional trade
reporting economy to high-income economies as a
International Monetary Fund’s (IMF) Direction of Trade
increased for most economies between 1997 and
percentage of total merchandise exports by the econ-
database and should be broadly consistent with data
2007. This is due partly to trade-related advantages,
omy. • Imports from developing economies within
from other sources, such as the United Nations Statis-
such as proximity, lower transport costs, increased
region are the sum of merchandise imports by the
tics Division’s Commodity Trade (Comtrade) database.
knowledge from repeated interaction, and cultural
reporting economy from other developing economies
Generally, data on trade between developing and high-
and historical affinity. The direction of trade is also
in the same World Bank region as a percentage of
income economies are complete. But trade flows
influenced by preferential trade agreements that a
total merchandise imports by the economy. • Imports
between many developing economies—particularly
country has made with other economies. Though
from developing economies outside region are the
those in Sub-Saharan Africa—are not well recorded,
formal agreements on trade liberalization do not
sum of merchandise imports by the reporting econ-
and the value of trade among developing economies
automatically increase trade, they nevertheless
omy from other developing economies in other World
may be understated. The table does not include some
affect the direction of trade between the participat-
Bank regions as a percentage of total merchandise
developing economies because data on their bilateral
ing economies. Table 6.7 illustrates the size of exist-
imports by the economy. • Imports from high-income
trade flows are not available. Data on the direction of
ing regional trade blocs that have formal preferential
economies are the sum of merchandise imports by
trade between selected high-income economies are
trade agreements.
the reporting economy from high-income economies
presented and discussed in tables 6.3 and 6.4. At the regional level most exports from developing economies are to high-income economies, but the
Although global integration has increased, developing economies still face trade barriers when access-
6.5a
Merchandise exports, 2007 (%)
Merchandise imports, 2007 (%)
To developing economies within region To developing economies outside region To the United States To other high-income economies 100
From From From From 100
75
75
50
50
25
25
developing economies within region developing economies outside region the United States other high-income economies
0 East Europe & Latin Middle South SubAsia & Central America East & Asia Saharan Pacific Asia & Carib. N. Africa Africa
economy.
ing other markets (see table 6.8).
Trading partners vary by region
0
as a percentage of total merchandise imports by the
East Europe & Latin Middle South SubAsia & Central America East & Asia Saharan Pacific Asia & Carib. N. Africa Africa
Data sources Data on merchandise trade flows are published in
In 2007 most developing economy merchandise trade was with high-income partners, but the degree of
the IMF’s Direction of Trade Statistics Yearbook and
dependence varied by region. Latin America and Caribbean is highly integrated with the United States
Direction of Trade Statistics Quarterly; the data in
and most likely to be affected by the U.S. recession. Most merchandise exports of Latin America and
the table were calculated using the IMF’s Direction
Caribbean and Europe and Central Asia to developing economies stayed within the same region. Most
of Trade database. Regional and income group
merchandise imports of East Asia and Pacific and South Asia from developing economies were from within
classifications are according to the World Bank
the same region, reflecting strong presence of large regional economies such as China and India.
classification of economies as of July 1, 2008 and
Source: World Bank staff calculations based on data from International Monetary Fund’s Direction of Trade database.
344
2009 World Development Indicators
are as shown on the inside covers of this report.
World Bank commodity price index (2000 = 100) Energy Nonenergy commodities Agriculture Beverages Food Fats and oils Grains Other food Raw materials Timber Other raw materials Fertilizers Metals and minerals Steel productsa Commodity prices (2000 prices) Energy Coal, Australian ($/mt) Natural gas, Europe ($/mmBtu) Natural gas, U.S. ($/mmBtu) Natural gas, liquefied, Japan ($/mmBtu) Petroleum, avg, spot ($/bbl) Beverages (cents/kg) Cocoa Coffee, Arabica Coffee, robusta Tea, avg., 3 auctions Tea, Colombo auctions Tea, Kolkata auctions Tea, Mombasa auctions Food Fats and oils ($/mt) Coconut oil Copraa Groundnut oil Palm oil Palm kernel oila Soybeans Soybean meal Soybean oil Grains ($/mt) Barley Maize Rice, Thailand, 5% Sorghuma Wheat, Canadaa Wheat, U.S., hard red winter Wheat, U.S., soft red winter a
GLOBAL LINKS
6.6
Primary commodity prices
1970
1980
1990
1995
2000
2002
2003
2004
2005
2006
2007
2008
19 183 188 230 201 237 204 151 136 97 179 82 185 0
153 177 195 273 199 196 199 205 143 92 198 177 141 134
79 115 113 117 116 105 121 124 105 88 124 98 122 131
53 117 122 136 117 126 124 101 125 105 146 110 106 118
100 100 100 100 100 100 100 100 100 100 100 100 100 100
92 105 112 124 115 115 117 114 97 92 104 98 92 92
101 108 114 117 117 129 112 105 107 91 124 110 96 100
123 121 118 109 123 134 115 117 109 90 130 125 126 153
171 135 121 125 121 120 115 129 119 100 141 148 162 170
197 172 134 130 131 123 134 140 144 113 179 151 251 162
207 190 153 144 156 177 160 126 149 116 185 203 266 154
269 215 181 165 195 219 222 140 155 119 195 452 257 228
.. .. 1 .. 4
49 5 2 7 45
39 2 2 4 22
33 2 1 3 14
26 4 4 5 28
26 3 4 4 26
25 4 5 5 28
48 4 5 5 34
43 6 8 5 48
44 8 6 6 57
56 7 6 7 60
100 11 7 10 76
233 397 321 289 217 343 307
321 427 400 205 137 253 224
123 192 115 200 182 273 144
119 277 230 124 118 145 108
91 192 91 188 179 181 203
185 142 69 157 163 153 156
170 137 79 147 150 142 150
141 161 72 153 162 156 141
140 230 101 150 167 147 134
142 225 133 168 171 157 175
165 231 162 172 214 163 141
203 243 183 191 220 178 175
1,376 779 1,312 901 .. 405 357 992
831 558 1,059 719 .. 365 323 737
327 224 937 282 .. 240 195 435
556 364 823 521 .. 215 164 519
450 305 714 310 444 212 189 338
439 278 717 407 434 222 183 474
454 291 1,207 430 445 256 205 538
600 409 1,054 428 588 278 219 559
560 376 963 383 569 249 195 495
542 360 867 427 519 240 187 535
778 514 1,145 661 753 325 260 747
964 643 1,679 747 890 412 339 991
.. 202 438 179 218 190 197
96 154 506 159 235 213 208
78 106 263 101 152 132 125
86 103 266 99 172 147 139
77 89 202 88 147 114 99
114 104 200 106 183 155 136
102 102 192 103 172 142 134
90 102 216 100 169 142 131
86 90 260 87 179 138 123
104 109 272 110 194 172 142
146 139 277 138 254 216 202
158 176 512 164 358 257 214
2009 World Development Indicators
345
6.6
Primary commodity prices
Commodity prices (continued) (2000 prices) Food (continued) Other food Bananas, U.S. ($/mt) Beef (cents/kg) Chicken meat (cents/kg) Fishmeal ($/mt)a Oranges ($/mt) Shrimp, Mexico (cents/kg) Sugar, EU domestic (cents/kg) Sugar, U.S. domestic (cents/kg) Sugar, world (cents/kg) Agricultural raw materials Cotton A index (cents/kg) Logs, Cameroon ($/cu. m)a Logs, Malaysian ($/cu. m) Rubber, Singapore (cents/kg) Plywood (cents/sheet)a Sawnwood, Malaysian ($/cu. m) Tobacco ($/mt)a Woodpulp ($/mt)a Fertilizers ($/mt) Diammonium phosphate Phosphate rock Potassium chloride Triple superphosphate Urea Metals and minerals Aluminum ($/mt) Copper ($/mt) Gold ($/toz)a Iron ore (cents/dmtu) Lead (cents/kg) Nickel ($/mt) Silver (cents/toz)a Tin (cents/kg) Zinc (cents/kg) MUV G-5 index (2000 = 100)
1970
1980
1990
1995
2000
2002
2003
2004
2005
2006
2007
2008
573 452 .. 682 582 .. 39 57 29
467 340 85 621 482 1,420 60 82 78
526 249 96 401 516 1,039 57 50 27
369 158 92 411 441 1,253 57 42 24
424 193 119 413 363 1,515 56 43 18
552 220 132 632 589 1,097 57 48 16
364 192 129 593 661 1,110 58 46 15
476 228 138 589 780 928 61 41 14
547 238 135 664 794 939 60 43 20
605 228 124 1,040 741 915 58 44 29
572 220 133 997 810 855 58 39 19
665 247 134 .. 872 840 55 37 22
219 149 149 141 357 608 3,727 615
252 310 241 176 338 489 2,806 661
177 334 172 84 345 518 3,297 792
177 282 212 131 485 614 2,194 708
130 275 190 67 448 595 2,976 664
106 246 170 80 410 549 2,864 472
136 271 182 105 419 535 2,568 510
124 301 179 118 422 528 2,488 582
110 304 184 136 462 599 2,533 577
113 285 214 188 532 670 2,653 624
118 323 227 194 543 683 2,808 650
124 415 230 206 511 701 2,801 652
187 38 109 147 ..
274 58 143 222 ..
167 39 95 128 116
180 29 98 124 155
154 44 123 138 101
164 42 118 139 98
174 37 110 145 135
201 37 113 169 159
224 38 144 183 199
233 40 156 180 199
366 60 170 287 262
762 272 449 749 388
1,926 4,895 125 34 105 9,860 614 1,273 102
1,795 2,690 750 35 112 8,037 2,544 2,068 94
1,593 2,586 373 32 79 8,614 475 591 147
1,499 2,437 319 24 52 6,830 431 516 86
1,549 1,813 279 29 45 8,638 500 544 113
1,408 1,627 323 31 47 7,066 483 424 81
1,389 1,727 353 31 50 9,346 477 475 80
1,558 2,602 372 34 80 12,551 607 773 95
1,724 3,340 404 59 89 13,387 666 670 125
2,297 6,007 540 69 115 21,675 1,034 785 293
2,235 6,030 590 72 219 31,532 1,136 1,231 275
2,027 5,481 687 111 165 16,635 1,182 1,459 148
29
81
103
120
100
96
103
110
110
112
118
127
Note: bbl = barrel, cu. m = cubic meter, dmtu = dry metric ton unit, kg = kilogram, mmBtu = million British thermal units, mt = metric ton, toz = troy ounce. a. Series not included in the nonenergy index.
346
2009 World Development Indicators
About the data
GLOBAL LINKS
Primary commodity prices
6.6
Definitions
Primary commodities—raw or partially processed
International Trade Classification (SITC) revision 3,
• Energy price index is the composite price index for
materials that will be transformed into fi nished
the Food Agriculture Organization’s FAOSTAT data-
coal, petroleum, and natural gas, weighted by exports
goods—are often developing countries’ most signifi -
base, the International Energy Agency database, BP’s
of each commodity from low- and middle-income
cant exports, and the revenues obtained from them
Statistical Review of World Energy, the World Bureau of
countries. • Nonenergy commodity price index cov-
have an important effect on living standards. Price
Metal Statistics, and World Bank staff estimates.
ers the 34 nonenergy primary commodities that make
data for primary commodities are collected from a
Each index in the table represents a fixed basket
up the agriculture, fertilizer, and metals and miner-
variety of sources, including international commodity
of primary commodity exports over time. The non-
als indexes. • Agriculture includes beverages, food,
study groups, government agencies, industry trade
energy commodity price index contains 41 price
and agricultural raw materials. • Beverages include
journals, and Bloomberg and Datastream data feed
series for 34 nonenergy commodities. The index
cocoa, coffee, and tea. • Food includes fats and oils,
systems. Prices are either compiled in U.S. dollars
in previous editions contained only 31 nonenergy
grains, and other food items. Fats and oils include
or converted to U.S. dollars when quoted in local
commodities. In response to changes in commodity
coconut oil, groundnut oil, palm oil, soybeans, soy-
currencies.
trade shares, minor adjustments have been made to
bean oil, and soybean meal. Grains include barley,
The table is based on frequently updated price
the commodities basket, with barley, poultry meat,
maize, rice, and wheat. Other food items include
reports. When available, the prices received by
and potassium and nitrogen fertilizers added and
bananas, beef, chicken, oranges, shrimp, and sugar.
exporters are used; otherwise, the prices paid by
sorghum dropped.
• Agricultural raw materials include timber and
importers or trade unit values are used. Annual
Separate indices are compiled for energy and steel
other raw materials. Timber includes tropical hard
price series are generally simple averages based on
products, which are not included in the nonenergy
logs and sawnwood. Other raw materials include
higher frequency data. The constant price series in
commodity price index. The previous petroleum index
cotton, natural rubber, and tobacco. • Fertilizers
the table are deflated using the manufactures unit
has been replaced with a new energy index that
include phosphate, phosphate rock, potassium, and
value (MUV) index for the Group of Five (G-5) coun-
includes coal, petroleum, and natural gas. The new
nitrogenous products. • Metals and minerals include
tries (see below).
and old energy indices are similar because petroleum
aluminum, copper, iron ore, lead, nickel, tin, and zinc.
exports account for almost 85 percent of total energy
• Steel products price index is the composite price
commodity exports from developing countries.
index for eight steel products based on quotations
The commodity price indices are calculated as Laspeyres index numbers, in which the fixed weights are the 2002–04 average export values for low- and
The MUV index is a composite index of prices for
free on board (f.o.b.) Japan excluding shipments to
middle-income economies (based on 2001 gross
manufactured exports from the five major (G-5) indus-
the United States for all years and to China prior
national income) rebased to 2000. As of April 2008
trial economies (France, Germany, Japan, the United
to 2001, weighted by product shares of apparent
the weights were changed from 1987–89 average
Kingdom, and the United States) to low- and middle-
combined consumption (volume of deliveries) for Ger-
export values to 2002–04 averages in order to include
income economies, valued in U.S. dollars. The index
many, Japan, and the United States. • Commodity
the most recent available complete data. Data for
covers products in groups 5–8 of SITC revision 1. To
prices—for definitions and sources, see “Commod-
exports are collected from various sources, includ-
construct the MUV G-5 index, unit value indexes for
ity price data” (also known as the “Pink Sheet”) at
ing the United Nations Statistics Division’s Commod-
each country are combined using weights determined
the World Bank Prospects for Development website
ity Trade Statistics (Comtrade) database Standard
by each country’s export share in a base year.
(www.worldbank.org/prospects, click on Products). • MUV G-5 index is the manufactures unit value
Commodity prices increased between 2000 and the last quarter of 2008— the longest boom since 1960
index for G-5 country exports to low- and middle-
6.6a
income economies.
World Bank commodity price index (2000 = 100) 300 250 200
Nonenergy commodities
150 100 50
Energy commodities
0 1960
1965
1970
1975
1980
1985
1990
1995
2000
2005 2008
Data sources The recent commodity price boom threatened to impoverish millions of people around the world. Although there have been other commodity price booms, the recent boom lasted the longest. The average price of
Data on commodity prices and the MUV G-5 index
nonenergy commodities increased 115 percent over 2000–08. The increase in energy prices was even
are compiled by the World Bank’s Development
more remarkable—169 percent. In the last quarter of 2008, commodity prices declined significantly.
Prospects Group. Monthly updates of commodity prices are available at www.worldbank.org/
Note: Dotted lines are projections for 2009. Source: World Bank commodity price data.
prospects.
2009 World Development Indicators
347
6.7
Regional trade blocs
Merchandise exports within bloc
Year of creation High-income and lowand middle-income economies 1989 APECb EEA 1994 EFTA 1960 European Union 1957 NAFTA 1994 SPARTECA 1981 Trans-Pacific SEP 2006 East Asia and Pacific and South Asia APTA 1975 ASEAN 1967 PICTA 2001 SAARC 1985 Europe, Central Asia,and Middle East CEFTA 1992 CIS 1991 COZ 2003 EAEC 1997 ECO 1985 GCC 1981 PAFTA (GAFTA) 1997 UMA 1989 Latin America and the Caribbean Andean Community 1969 CACM 1961 CARICOM 1973 LAIA (ALADI) 1980 MERCOSUR 1991 OECS 1981 Sub-Saharan Africa CEMAC 1994 COMESA 1994 EAC 1996 ECCAS 1983 ECOWAS 1975 Indian Ocean Commission 1984 SADC 1992 UEMOA 1994
Year of entry into Type force of the of most most recent recent agreement agreementa
$ millions 1990
1995
2000
2004
2005
2006
2007
1994 2002 1958 1994 1981 2006
None EIA EIA EIA, CU FTA, EIA PS EIA, FTA
1,000,616 1,688,708 2,261,791 2,924,272 3,310,461 3,775,728 4,191,536 998,015 1,330,493 1,702,877 2,643,117 2,846,278 3,218,165 3,774,508 717 925 831 1,128 1,252 1,524 2,196 951,373 1,272,211 1,630,509 2,535,600 2,714,582 3,069,912 3,596,135 249,474 394,472 676,141 737,591 824,710 902,298 951,587 5,637 9,101 8,554 13,585 15,181 15,536 18,582 1,195 2,614 1,438 2,096 2,345 2,927 3,290
1976 1992 2003 2006
PS FTA FTA FTA
5,475 32,785 5 1,013
21,728 79,544 42 2,024
37,895 98,060 65 2,680
99,369 141,931 130 5,830
127,277 165,458 122 7,266
156,957 191,392 151 8,310
198,000 216,424 187 10,222
1994 1994 2004 1997 2003 2003c 1998 1994 c
FTA FTA FTA CU PS CU FTA NNA
.. .. .. .. 1,232 4,760 10,028 1,071
534 31,529 24,398 13,556 4,746 6,832 12,948 1,109
1,038 28,753 22,985 15,467 4,518 7,954 16,088 1,041
2,009 43,425 32,629 17,292 9,982 12,532 35,328 1,448
2,434 59,423 45,973 27,297 13,936 16,635 44,511 1,934
2,819 66,689 49,695 27,930 19,053 20,693 54,827 2,478
3,641 97,545 75,700 50,079 24,584 24,728 65,818 3,076
1988 1961 1997 1981 2005 1981c
CU CU EIA PS EIA NNA
788 779 445 15,769 6,166 29
1,788 1,594 877 35,986 16,811 39
2,046 2,586 1,078 44,253 20,082 38
3,435 3,574 1,746 57,741 19,675 60
4,572 4,342 2,090 71,720 24,211 68
5,011 4,697 2,429 90,357 31,197 84
5,509 5,562 3,759 109,130 39,486 104
1999 1994 2000 2004 c 1993 2005c 2000 2000
CU FTA CU NNA PS NNA FTA CU
114 830 132 133 1,384 75 1,720 499
120 1,367 628 157 1,875 113 3,615 560
96 1,443 689 181 2,715 106 4,427 741
174 2,420 930 221 4,366 155 6,655 1,233
198 2,866 1,043 251 5,497 159 7,798 1,390
245 3,468 1,279 310 5,957 172 8,694 1,545
304 4,582 1,587 385 7,341 204 11,952 1,917
Note: Regional bloc memberships are as follows: Andean Community, Bolivia, Colombia, Ecuador, and Peru; Arab Maghreb Union (UMA), Algeria, Libyan Arab Republic, Mauritania, Morocco, and Tunisia; Asia Pacific Economic Cooperation (APEC), Australia, Brunei Darussalam, Canada, Chile, China, Hong Kong (China), Indonesia, Japan, the Republic of Korea, Malaysia, Mexico, New Zealand, Papua New Guinea, Peru, the Philippines, the Russian Federation, Singapore, Taiwan (China), Thailand, the United States, and Vietnam; Asia-Pacific Trade Agreement (APTA; formerly Bangkok Agreement), Bangladesh, China, India, the Republic of Korea, the Lao People’s Democratic Republic, and Sri Lanka; Association of South East Asian Nations (ASEAN), Brunei Darussalam, Cambodia, Indonesia, the Lao People’s Democratic Republic, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam; Caribbean Community and Common Market (CARICOM), Antigua and Barbuda, the Bahamas, Barbados, Belize, Dominica, Grenada, Guyana, Haiti, Jamaica, Montserrat, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Suriname, and Trinidad and Tobago; Central American Common Market (CACM), Costa Rica, El Salvador, Guatemala, Honduras, and Nicaragua; Central European Free Trade Area (CEFTA), Albania, Bosnia and Herzegovina, Croatia, Kosovo, Macedonia, Moldova, Montenegro, and Serbia; Common Economic Zone (COZ), Belarus, Kazakhstan, the Russian Federation, and Ukraine; Common Market for Eastern and Southern Africa (COMESA), Burundi, Comoros, the Democratic Republic of Congo, Djibouti, the Arab Republic of Egypt, Eritrea, Ethiopia, Kenya, Libyan Arab Republic, Madagascar, Malawi, Mauritius, Rwanda, Seychelles, Sudan, Swaziland, Uganda, Zambia, and Zimbabwe; Commonwealth of Independent States (CIS), Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyz Republic, Moldova, the Russian Federation, Tajikistan, Turkmenistan, Ukraine, and Uzbekistan; East African Community (EAC), Burundi, Kenya, Rwanda, Tanzania, and Uganda; Economic and Monetary Community of Central Africa (CEMAC; formerly Central African Customs and Economic Union [UDEAC]), Cameroon, the Central African Republic, Chad, the Republic of Congo, Equatorial Guinea, and Gabon; Economic Community of Central African States (ECCAS), Angola, Burundi, Cameroon, the Central African Republic, Chad, the Democratic Republic of Congo, the Republic of Congo, Equatorial Guinea, Gabon, and São Tomé and Principe; Economic Community of West African States (ECOWAS), Benin, Burkina Faso, Cape Verde, Côte d’Ivoire, the Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone, and Togo; Economic Cooperation Organization (ECO), Afghanistan, Azerbaijan, the Islamic Republic of Iran, Kazakhstan, the Kyrgyz Republic,
348
2009 World Development Indicators
GLOBAL LINKS
Regional trade blocs
6.7
Merchandise exports within bloc
Year of creation High-income and lowand middle-income economies 1989 APECb EEA 1994 EFTA 1960 European Union 1957 NAFTA 1994 SPARTECA 1981 Trans-Pacific SEP 2006 East Asia and Pacific and South Asia APTA 1975 ASEAN 1967 PICTA 2001 SAARC 1985 Europe, Central Asia, and Middle East CEFTA 1992 CIS 1991 COZ 2003 EAEC 1997 ECO 1985 GCC 1981 PAFTA (GAFTA) 1997 UMA 1989 Latin America and the Caribbean Andean Community 1969 CACM 1961 CARICOM 1973 LAIA (ALADI) 1980 MERCOSUR 1991 OECS 1981 Sub-Saharan Africa CEMAC 1994 COMESA 1994 EAC 1996 ECCAS 1983 ECOWAS 1975 Indian Ocean Commission 1984 SADC 1992 UEMOA 1994
Year of entry into Type force of the of most most recent recent agreement agreementa
% of total bloc exports 1990
1995
2000
2004
2005
2006
2007
1994 2002 1958 1994 1981 2006
None EIA EIA EIA, CU FTA, EIA PS EIA, FTA
68.7 69.4 0.7 67.8 42.2 10.5 1.5
71.7 67.3 0.7 65.8 46.2 12.9 1.7
73.1 68.6 0.6 67.3 55.7 10.7 0.8
72.2 68.9 0.5 67.6 55.9 12.1 0.8
70.8 68.4 0.5 67.0 55.8 11.4 0.8
69.5 68.6 0.6 67.2 53.9 10.2 0.8
67.4 69.0 0.7 67.5 51.3 10.5 0.8
1976 1992 2003 2006
PS FTA FTA FTA
3.3 19.8 0.2 3.6
6.8 24.5 1.0 4.4
8.0 23.0 1.7 4.2
10.6 24.9 2.3 5.7
11.0 25.3 1.8 5.6
10.9 25.0 1.9 5.2
11.2 25.2 2.0 5.3
1994 1994 2004 1997 2003 2003c 1998 1994 c
FTA FTA FTA CU PS CU FTA NNA
.. .. .. .. 3.2 5.8 8.9 3.3
7.8 28.6 23.8 14.8 7.9 6.8 9.8 3.8
15.3 20.0 17.1 12.5 5.6 4.8 7.2 2.2
15.9 17.6 14.0 8.5 6.7 5.0 10.0 2.0
16.6 18.0 14.7 9.6 7.6 4.9 9.9 2.0
16.1 16.5 13.0 8.0 8.5 5.0 9.9 2.1
16.8 19.8 16.2 11.8 9.2 5.4 10.6 2.3
1988 1961 1997 1981 2005 1981c
CU CU EIA PS EIA NNA
5.6 17.6 8.2 12.2 9.9 9.0
8.6 21.8 12.1 17.3 18.9 12.6
7.7 19.1 14.4 13.2 16.4 10.0
8.7 20.9 12.2 13.2 11.1 11.7
9.0 20.1 11.6 13.6 11.0 11.4
7.8 15.8 11.3 14.3 12.2 8.2
7.4 17.0 15.7 15.1 12.8 8.1
1999 1994 2000 2004 c 1993 2005c 2000 2000
CU FTA CU NNA PS NNA FTA CU
2.0 3.6 7.4 1.3 9.7 4.8 17.9 11.3
2.1 6.1 19.5 1.5 9.0 5.9 32.8 10.3
1.0 4.6 22.6 1.0 7.6 4.4 9.5 13.1
1.2 5.0 18.9 0.8 9.3 4.3 9.7 12.9
0.9 4.5 17.6 0.6 9.3 4.6 9.3 13.4
0.9 4.2 19.3 0.5 8.4 4.8 9.1 13.1
1.1 4.7 20.4 0.6 9.4 5.7 10.1 15.2
Pakistan, Tajikistan, Turkey, Turkmenistan, and Uzbekistan; Eurasian Economic Community (EAEC), Belarus, Kazakhstan, Kyrgyz Republic, the Russian Federation, Tajikistan, and Uzbekistan; European Economic Area (EEA), European Union plus Iceland, Liechtenstein, and Norway; European Free Trade Association (EFTA), Iceland, Liechtenstein, Norway, and Switzerland; European Union (EU; formerly European Economic Community and European Community), Austria, Belgium, Bulgaria, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Poland, Portugal, Romania, Slovak Republic, Slovenia, Spain, Sweden, and the United Kingdom; Gulf Cooperation Council (GCC), Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates; Indian Ocean Commission, Comoros, Madagascar, Mauritius, Réunion, and Seychelles; Latin American Integration Association (LAIA; formerly Latin American Free Trade Area), Argentina, Bolivia, Brazil, Chile, Colombia, Cuba, Ecuador, Mexico, Paraguay, Peru, Uruguay, and Bolivarian Republic of Venezuela; North American Free Trade Agreement (NAFTA), Canada, Mexico, and the United States; Organization of Eastern Caribbean States (OECS), Anguilla, Antigua and Barbuda, British Virgin Islands, Dominica, Grenada, Montserrat, St. Kitts and Nevis, St. Lucia, and St. Vincent and the Grenadines; Pacific Island Countries Trade Agreement (PICTA), Cook Islands, Fiji, Federated States of Micronesia, Nauru, Niue, Papua New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu, and Vanuatu; Pan-Arab Free Trade Area (PAFTA; also known as Greater Arab Trade Area [GAFTA]), Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Qatar, Saudi Arabia, Sudan, Syrian Arab Republic, Tunisia, the United Arab Emirates, and Yemen; South Asian Association for Regional Cooperation (SAARC), Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka; South Pacific Regional Trade and Economic Cooperation Agreement (SPARTECA), Australia, Cook Islands, Fiji, Kiribati, Marshall Islands, Federated States of Micronesia, Nauru, New Zealand, Niue, Papua New Guinea, Solomon Islands, Tonga, Tuvalu, Vanuatu, and Western Samoa; Southern African Development Community (SADC), Angola, Botswana, the Democratic Republic of Congo, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Swaziland, Tanzania, Zambia, and Zimbabwe; Southern Common Market (MERCOSUR), Argentina, Brazil, Paraguay, Uruguay, and Bolivarian Republic of Venezuela; Trans-Pacific Strategic Economic Partnership (Trans-Pacific SEP), Brunei Darussalam, Chile, New Zealand, and Singapore; West African Economic and Monetary Union (UEMOA), Benin, Burkina Faso, Côte d’Ivoire, Guinea-Bissau, Mali, Niger, Senegal, and Togo.
2009 World Development Indicators
349
6.7
Regional trade blocs
Merchandise exports by bloc
Year of creation High-income and lowand middle-income economies 1989 APECb EEA 1994 EFTA 1960 European Union 1957 NAFTA 1994 SPARTECA 1981 Trans-Pacific SEP 2006 East Asia and Pacific and South Asia APTA 1975 ASEAN 1967 PICTA 2001 SAARC 1985 Europe, Central Asia, and Middle East CEFTA 1992 CIS 1991 COZ 2003 EAEC 1997 ECO 1985 GCC 1981 PAFTA (GAFTA) 1997 UMA 1989 Latin America and the Caribbean Andean Community 1969 CACM 1961 CARICOM 1973 LAIA (ALADI) 1980 MERCOSUR 1991 OECS 1981 Sub-Saharan Africa CEMAC 1994 COMESA 1994 EAC 1996 ECCAS 1983 ECOWAS 1975 Indian Ocean Commission 1984 SADC 1992 UEMOA 1994
Year of entry into Type force of the of most most recent recent agreement agreementa
% of world exports 1990
1995
2000
2004
2005
2006
2007
1994 2002 1958 1994 1981 2006
None EIA EIA EIA, CU FTA, EIA PS EIA, FTA
41.7 41.1 2.8 40.1 16.9 1.5 2.3
46.3 38.9 2.4 38.1 16.8 1.4 3.0
48.5 38.9 2.2 38.0 19.0 1.3 2.7
44.4 42.0 2.3 41.0 14.5 1.2 2.8
45.1 40.2 2.3 39.1 14.3 1.3 2.9
45.5 39.2 2.3 38.2 14.0 1.3 3.0
45.0 39.6 2.3 38.6 13.4 1.3 2.9
1976 1992 2003 2006
PS FTA FTA FTA
4.8 4.7 0.1 0.8
6.3 6.4 0.1 0.9
7.4 6.7 0.1 1.0
10.3 6.2 0.1 1.1
11.2 6.3 0.1 1.3
12.0 6.4 0.1 1.3
12.8 6.2 0.1 1.4
1994 1994 2004 1997 2003 2003c 1998 1994 c
FTA FTA FTA CU PS CU FTA NNA
.. .. .. .. 1.1 2.4 3.2 0.9
0.1 2.2 2.0 1.8 1.2 2.0 2.6 0.6
0.1 2.2 2.1 1.9 1.3 2.6 3.5 0.8
0.1 2.7 2.5 2.2 1.6 2.7 3.9 0.8
0.1 3.2 3.0 2.7 1.8 3.3 4.3 0.9
0.1 3.4 3.2 2.9 1.9 3.5 4.7 1.0
0.2 3.6 3.4 3.1 1.9 3.3 4.5 1.0
1988 1961 1997 1981 2005 1981c
CU CU EIA PS EIA NNA
0.4 0.1 0.2 3.7 1.8 0.0
0.4 0.1 0.1 4.1 1.8 0.0
0.4 0.2 0.1 5.3 1.9 0.0
0.4 0.2 0.2 4.8 1.9 0.0
0.5 0.2 0.2 5.1 2.1 0.0
0.5 0.2 0.2 5.3 2.1 0.0
0.5 0.2 0.2 5.2 2.2 0.0
1999 1994 2000 2004 c 1993 2005c 2000 2000
CU FTA CU NNA PS NNA FTA CU
0.2 0.7 0.1 0.3 0.4 0.0 0.3 0.1
0.1 0.4 0.1 0.2 0.4 0.0 0.2 0.1
0.1 0.5 0.0 0.3 0.6 0.0 0.7 0.1
0.1 0.5 0.1 0.3 0.5 0.0 0.7 0.1
0.2 0.6 0.1 0.4 0.6 0.0 0.8 0.1
0.2 0.7 0.1 0.5 0.6 0.0 0.8 0.1
0.2 0.7 0.1 0.5 0.6 0.0 0.9 0.1
a. CU is customs union; EIA is economic integration agreement; FTA is free trade agreement; NNA is not notified agreement, which refers to preferential trade arrangements established among member countries that are not notified to the World Trade Organization (these agreements may be functionally equivalent to any of the other agreements); and PS is partial scope agreement. b. No preferential trade agreement c. Years of the most recent agreement are collected from the official website of the trade bloc.
350
2009 World Development Indicators
GLOBAL LINKS
Regional trade blocs
6.7
About the data
Trade blocs are groups of countries that have estab-
The table shows the value of merchandise intra-
affected by the same preferences as in recent years.
lished preferential arrangements governing trade
trade (service exports are excluded) for important
In addition, some countries belong to more than
between members. Although in some cases the pref-
regional trade blocs and the size of intratrade rela-
one trade bloc, so shares of world exports exceed
erences—such as lower tariff duties or exemptions
tive to each bloc’s exports of goods and the share
100 percent. Exports of blocs include all commod-
from quantitative restrictions—may be no greater
of the bloc’s exports in world exports. Although the
ity trade, which may include items not specified in
than those available to other trading partners, such
Asia Pacific Economic Cooperation (APEC) has no
trade bloc agreements. Differences from previously
arrangements are intended to encourage exports by
preferential arrangements, it is included because of
published estimates may be due to changes in mem-
bloc members to one another—sometimes called
the volume of trade between its members.
bership or revisions in underlying data.
intratrade.
The data on country exports are from the InterDefinitions
Most countries are members of a regional trade
national Monetary Fund’s (IMF) Direction of Trade
bloc, and more than a third of the world’s trade takes
database and should be broadly consistent with
• Merchandise exports within bloc are the sum of
place within such arrangements. While trade blocs
those from sources such as the United Nations
merchandise exports by members of a trade bloc to
vary in structure, they all have the same objective:
Statistics Division’s Commodity Trade (Comtrade)
other members of the bloc. They are shown both in
to reduce trade barriers between member countries.
database. However, trade flows between many devel-
U.S. dollars and as a percentage of total merchan-
But effective integration requires more than reducing
oping countries, particularly in Sub-Saharan Africa,
dise exports by the bloc. • Merchandise exports by
tariffs and quotas. Economic gains from competition
are not well recorded, so the value of intratrade for
bloc as a share of world exports are the bloc’s total
and scale may not be achieved unless other barri-
certain groups may be understated. Data on trade
merchandise exports (within the bloc and to the rest
ers that divide markets and impede the free flow
between developing and high-income countries are
of the world) as a share of total merchandise exports
of goods, services, and investments are lifted. For
generally complete.
by all economies in the world. • Type of most recent
example, many regional trade blocs retain contingent
Membership in the trade blocs shown is based
agreement includes customs union, under which
protections on intrabloc trade, including antidumping,
on the most recent information available (see Data
members substantially eliminate all tariff and nontariff
countervailing duties, and “emergency protection” to
sources). Other types of preferential trade agree-
barriers among themselves and establish a common
address balance of payments problems or protect an
ments may have entered into force earlier than those
external tariff for nonmembers; economic integration
industry from import surges. Other barriers include
shown in the table and may still be effective. Unless
agreement, which liberalizes trade in services among
differing product standards, discrimination in public
otherwise indicated in the footnotes, information on
members and covers a substantial number of sectors,
procurement, and cumbersome border formalities.
the type of agreement and date of enforcement are
affects a sufficient volume of trade, includes substan-
based on the World Trade Organization’s (WTO) list
tial modes of supply, and is nondiscriminatory (in the
of regional trade agreements.
sense that similarly situated service suppliers are
Membership in a regional trade bloc may reduce the frictional costs of trade, increase the credibility of reform initiatives, and strengthen security
Although bloc exports have been calculated back
treated the same); free trade agreement, under which
among partners. But making it work effectively is
to 1990 on the basis of current membership, several
members substantially eliminate all tariff and nontariff
challenging. All economic sectors may be affected,
blocs came into existence after that and membership
barriers but set tariffs on imports from nonmembers;
and some may expand while others contract, so it is
may have changed over time. For this reason, and
partial scope agreement, which is a preferential trade
important to weigh the potential costs and benefits
because systems of preferences also change over
agreement notified to the WTO that is not a free trade
of membership.
time, intratrade in earlier years may not have been
agreement, a customs union, or an economic integration agreement; and not notified agreement, which is a preferential trade arrangement established among
The number of trade agreements has increased rapidly since 1990, especially bilateral agreements Cumulative agreements 180
6.7a Bilateral
Multilateral
member countries that is not notified to the World Trade Organization (the agreement may be functionally equivalent to any of the other agreements).
150
Data sources
120
Data on merchandise trade flows are published in
90
the IMF’s Direction of Trade Statistics Yearbook and
60
Direction of Trade Statistics Quarterly; the data in
30
the table were calculated using the IMF’s Direction of Trade database. Data on trade bloc mem-
0 1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2008
bership are from the World Bank Policy Research Report Trade Blocs (2000a), UNCTAD’s Trade and
Note: Data are cumulative number of bilateral and multilateral trade agreements notified to the General Agreement on Tariffs and Trade/World Trade Organization (GATT/WTO) at the time they entered into force. Only agreements that are that are currently in force are included. Agreements on accessions of new members to an existing agreement are not included. Agreements that are in force but have not been notified to GATT/WTO may be excluded. Source: World Bank staff calculations based on the World Trade Organization’s Regional Trade Agreements Information System.
Development Report 2007, WTO’s Regional Trade Agreements Information System, and the World Bank’s International Trade Unit.
2009 World Development Indicators
351
6.8
Tariff barriers All products
Primary products
Manufactured products
% Most recent year
Afghanistan Albania Algeria Angola Antigua and Barbuda Argentina Armenia Australia Azerbaijan Bahamas, The Bahrain Bangladesh Barbados Belarus Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Brunei 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 Djibouti Dominica Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Equatorial Guinea Ethiopia European Union Fiji Gabon Gambia, The Georgia Ghana Grenada Guatemala †Data for Taiwan, China
352
2007 2007 2006 2007 2007 2006 2007 2007 2006 2007 2007 2007 2002 2007 2007 2007 2007 2007 2007 2007 2007 2007 2006 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2006 2007 2006 2007 2005 2007 2007 2006 2007 2007 2007 2007 2007 2007 2007 2007
Binding coverage
.. 100.0 .. 100.0 97.9 100.0 100.0 97.1 .. .. 73.4 15.9 97.9 .. 97.9 39.0 .. .. 100.0 .. 96.3 100.0 95.3 100.0 39.2 21.8 .. 13.3 99.7 .. .. 100.0 100.0 45.6 100.0 .. 16.1 100.0 33.1 100.0 30.9 100.0 94.8 100.0 100.0 99.3 100.0 .. .. 100.0 51.3 100.0 13.7 100.0 14.3 100.0 100.0 100.0
2009 World Development Indicators
Simple mean bound rate
.. 7.0 .. 59.2 58.7 31.9 8.5 9.9 .. .. 34.4 163.1 78.1 .. 58.2 28.6 .. .. 40.0 .. 18.9 31.4 24.3 24.6 41.9 68.3 .. 79.9 5.1 .. .. 25.1 10.0 0.0 42.8 .. 27.3 42.9 11.2 6.0 21.3 41.0 58.7 34.9 21.8 36.8 36.6 .. .. 4.2 40.1 21.4 101.8 7.2 92.5 56.8 42.2 5.9
Simple mean tariff
Weighted mean tariff
.. 5.7 16.2 7.6 11.6 10.8 3.6 2.8 8.6 28.5 4.2 14.5 15.1 11.3 11.6 13.4 18.5 18.2 6.2 6.8 8.3 12.3 3.1 4.1 12.1 13.5 12.5 18.6 4.2 17.5 17.0 2.0 8.9 0.0 10.8 13.0 18.6 6.1 13.4 2.5 11.3 30.2 11.9 9.3 10.0 19.1 5.1 18.3 18.6 2.4 .. 18.0 18.7 0.6 13.0 10.5 5.3 5.7
.. 5.7 9.9 6.5 12.6 5.7 1.8 1.8 4.0 23.9 4.8 11.0 14.8 8.9 9.3 11.8 30.2 17.8 4.3 4.9 8.9 6.8 6.1 2.1 10.3 12.3 10.0 12.5 1.6 13.6 13.6 1.8 5.1 0.0 8.8 11.2 14.7 4.1 7.2 1.2 7.4 29.1 7.9 8.5 5.9 13.3 4.6 15.6 12.0 1.8 .. 14.5 15.1 0.5 9.9 8.6 4.6 2.0
Share of tariff Share of tariff lines lines with international with specific rates peaks
.. 0.0 40.0 10.4 47.9 24.8 0.1 2.6 47.9 77.4 0.2 41.1 44.9 16.4 43.3 53.8 66.5 50.7 0.0 11.4 19.0 26.4 21.6 13.3 44.4 27.4 49.2 52.6 7.9 47.4 44.8 0.0 14.9 0.0 41.0 43.4 52.6 0.4 50.2 4.1 34.4 87.9 43.3 28.6 34.1 23.0 1.9 52.3 57.4 5.5 .. 52.0 90.7 0.0 40.8 43.0 1.1 8.9
.. 0.0 0.0 0.0 0.0 0.0 0.3 0.1 0.5 0.0 0.0 0.0 0.6 0.0 0.0 0.0 0.8 0.0 0.0 0.0 0.2 0.0 0.1 2.0 0.0 0.0 0.0 0.0 3.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6.4 .. 0.0 0.0 0.0 0.0 0.0 0.0 0.6
%
%
Simple mean tariff
Weighted mean tariff
Simple mean tariff
Weighted mean tariff
.. 7.0 16.2 11.5 13.7 7.8 5.5 0.9 10.1 24.4 5.8 15.2 26.4 11.1 15.6 13.4 14.0 43.7 6.1 3.3 4.3 7.9 0.9 9.5 11.3 12.8 14.8 21.9 5.1 19.0 20.8 2.4 9.0 0.0 9.7 14.2 22.1 8.5 15.5 4.4 10.9 23.0 19.3 12.7 8.7 86.2 6.9 21.7 18.1 6.1 .. 20.0 17.1 4.4 16.8 13.7 6.7 11.1
.. 5.2 9.2 13.1 12.3 1.3 1.5 0.3 3.7 15.1 5.6 7.3 21.9 7.1 6.5 11.9 18.6 44.9 4.4 2.0 0.9 1.2 13.2 4.8 7.6 13.4 10.5 10.8 3.2 13.8 18.3 2.2 3.0 0.0 8.2 10.9 18.6 5.1 4.1 1.9 5.0 23.1 5.7 7.3 3.3 17.7 3.5 21.5 7.8 1.8 .. 14.2 13.3 1.3 14.4 8.4 4.2 2.4
.. 5.5 16.2 6.9 11.2 11.1 3.4 3.1 8.4 29.4 3.9 14.4 13.5 11.3 11.1 13.4 19.4 15.5 6.2 7.2 8.6 12.7 3.4 3.5 12.2 13.7 12.1 18.2 4.0 17.3 16.5 1.9 8.9 0.0 10.9 12.8 18.1 5.8 13.0 2.3 11.3 31.3 10.6 8.9 10.1 12.0 4.9 17.8 18.6 1.6 .. 17.7 19.1 0.1 12.5 10.0 5.2 4.9
.. 6.0 10.1 5.0 12.7 6.4 2.0 2.3 4.1 29.7 3.4 13.0 12.3 10.3 11.0 11.7 30.9 16.0 4.3 6.3 9.5 9.4 4.5 1.3 11.0 11.5 10.0 14.6 1.2 13.5 12.7 1.5 6.3 0.0 9.0 11.3 14.1 3.8 10.7 0.9 9.5 31.0 9.3 9.0 7.1 11.7 5.3 14.3 13.9 1.8 .. 14.6 17.0 0.1 8.8 8.7 4.9 1.9
All products
Primary products
GLOBAL LINKS
Tariff barriers
6.8 Manufactured products
%
Guinea Guinea-Bissau Guyana Haiti Honduras Hungary Iceland India Indonesia Iran, Islamic Rep. Israel Jamaica Japan Jordan Kazakhstan Kenya Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Libya Lithuania Macao Macedonia, FYR Madagascar Malawi Malaysia Maldives Mali Mauritania Mauritius Mexico Moldova Mongolia Montserrat Morocco Mozambique Myanmar Namibia Nepal New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Qatar
Most recent year
Binding coverage
2005 2007 2006 2007 2007 2002 2007 2005a 2007 2007 2007 2006 2007 2007 2004 2007 2007 2007 2007 2007 2001 2007 2007 2006 2003 2007 2007 2007 2006 2007 2006 2007 2007 2007 2006 2006 2007 1999 2007 2007 2007 2007 2007 2007 2007 2007 2006 2007 2007 2007 2007 2007 2007 2007 2007 2003 2007
38.6 .. 100.0 100.0 .. 96.2 95.0 73.8 96.6 .. 75.0 100.0 99.7 100.0 .. 14.8 94.6 99.9 99.9 .. 100.0 .. .. .. 100.0 28.7 100.0 30.0 31.6 83.7 97.1 40.2 39.3 17.9 100.0 .. 100.0 .. 100.0 .. 17.4 96.3 .. 99.9 100.0 96.7 19.3 100.0 100.0 98.7 99.9 100.0 100.0 100.0 67.0 96.3 100.0
Simple mean bound rate
20.3 .. 56.7 32.4 .. 9.7 13.5 49.6 37.1 .. 21.5 49.6 2.9 16.3 .. 95.4 15.8 100.0 7.4 .. 12.8 .. .. .. 9.2 0.0 6.9 27.3 75.4 14.5 36.9 28.5 19.6 94.0 35.0 .. 17.5 .. 41.3 .. 83.6 19.2 .. 10.0 41.7 44.7 118.4 3.0 13.8 59.9 23.4 31.7 33.5 30.1 25.7 11.9 15.9
Simple mean tariff
Weighted mean tariff
14.2 14.0 11.4 2.9 5.4 8.9 4.1 17.0 5.9 21.3 2.3 9.2 4.2 10.7 2.4 12.3 8.5 4.3 2.9 5.8 3.3 5.6 9.0 0.0 1.3 0.0 8.6 12.1 12.9 5.9 21.4 12.5 12.6 4.3 8.0 4.4 4.9 18.2 13.3 11.0 4.1 6.6 12.6 3.8 5.4 12.9 10.6 4.1 3.8 14.9 7.3 5.2 8.0 8.5 5.0 4.3 3.9
12.7 14.5 6.2 3.0 4.5 7.9 2.1 13.4 3.9 17.6 1.1 8.9 3.1 5.9 1.9 7.0 8.0 3.9 1.1 8.3 2.6 4.8 13.9 0.0 0.6 0.0 5.7 8.4 8.1 3.1 21.1 8.7 10.1 2.5 2.4 1.7 5.1 13.3 10.0 7.7 3.9 1.5 13.7 2.7 3.6 10.1 9.4 2.6 3.4 11.4 7.0 1.6 3.3 5.2 3.6 2.3 4.0
Share of tariff Share of tariff lines lines with international with specific rates peaks
58.6 55.3 34.5 4.7 0.3 10.9 10.2 15.4 12.7 54.8 1.0 35.8 10.1 32.7 0.0 37.4 5.1 0.0 1.5 15.1 3.0 11.6 21.3 0.0 3.1 0.0 31.7 40.4 40.4 24.8 72.3 47.2 49.0 9.2 10.9 16.0 0.4 41.2 45.1 36.7 8.1 18.0 42.1 7.5 0.4 51.0 33.9 5.0 0.2 52.7 33.6 25.6 17.3 10.5 15.8 8.8 0.1
0.0 0.0 0.0 0.0 0.0 0.0 3.4 3.9 0.0 0.0 0.7 0.0 3.6 0.0 0.0 0.0 0.1 0.0 1.7 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 1.3 0.0 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.0 5.4 0.0 0.0 0.0 0.8 0.0 0.0 0.0 0.1 0.0
%
%
Simple mean tariff
Weighted mean tariff
Simple mean tariff
Weighted mean tariff
16.4 16.6 17.9 5.6 7.1 18.1 16.4 25.2 6.6 16.9 4.8 16.0 11.4 14.4 3.5 16.1 20.8 3.4 4.7 9.9 8.1 8.2 7.8 0.0 3.3 0.0 9.3 14.1 12.8 2.8 18.1 11.6 11.2 6.3 7.3 7.2 5.2 22.4 20.0 13.9 5.8 4.2 12.4 2.1 7.8 13.2 12.8 29.8 4.5 14.2 11.3 16.2 5.7 9.3 6.0 18.2 3.5
14.3 17.6 4.1 4.4 5.4 6.7 5.4 14.3 2.5 15.3 1.3 9.5 3.8 3.8 3.4 7.0 11.5 2.9 0.7 8.3 5.4 5.0 5.0 0.0 1.3 0.0 5.4 4.4 6.1 2.3 19.5 9.8 9.2 2.8 1.8 1.4 5.4 15.6 11.6 8.0 4.5 0.7 9.7 0.4 3.9 10.6 13.1 10.2 3.0 8.7 7.8 2.4 0.8 2.7 5.2 6.7 3.8
13.9 13.5 10.6 2.4 5.2 7.8 2.4 15.9 5.8 21.7 2.1 8.3 2.9 10.1 2.3 11.9 6.6 4.4 2.6 5.3 2.6 5.2 9.0 0.0 1.0 0.0 8.6 11.8 12.9 6.5 22.2 12.6 12.9 4.0 8.1 4.0 4.9 16.4 12.6 10.5 3.9 7.0 12.7 4.0 5.1 12.9 10.3 0.5 3.7 15.0 6.9 3.7 8.2 8.5 4.8 2.5 3.9
11.2 12.4 7.9 2.0 4.0 8.1 1.0 12.3 4.4 18.5 1.1 8.5 2.2 7.3 1.5 7.0 4.8 4.0 1.4 8.3 1.6 5.1 14.3 0.0 0.4 0.0 5.9 10.4 8.9 3.4 22.0 8.4 11.0 2.4 2.5 1.9 4.9 12.2 9.1 7.5 3.7 1.9 15.8 3.6 3.4 9.6 8.2 0.2 3.4 13.3 6.6 1.1 4.0 6.4 2.7 1.2 4.0
2009 World Development Indicators
353
6.8
Tariff barriers All products
Primary products
Manufactured products
% Most recent year
Romania Russian Federation Rwanda Saudi Arabia Serbiab Senegal Seychelles Sierra Leone Singapore Slovak Republic Solomon Islands South Africa Sri Lanka St. Kitts and Nevis St. Lucia St. Vincent & Grenadines Sudan Suriname Swaziland Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United States Uruguay Uzbekistan Vanuatu Venezuela Vietnam Yemen 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 OECD Non-OECD
2005 2007 2008 2007 2005 2007 2007 2004 2007 2002 2007 2007 2006a 2007 2007 2007 2006 2007 2007 2007 2002 2006 2007 2006 2007 2007 2006 2007 2002 2007 2006 2007 2007 2007 2007 2007 2007 2007 2006 2005 2007a
Binding coverage
.. .. 100.0 .. .. 100.0 .. 100.0 69.7 100.0 100.0 96.3 38.1 97.9 99.6 .. .. .. 96.3 99.8 .. .. 13.4 75.0 14.0 100.0 57.9 50.4 .. 15.7 .. 100.0 100.0 100.0 .. .. 99.9 .. .. 16.8 22.4 79.6 45.9 89.0 88.7 89.0 75.3 79.1 93.8 97.0 91.4 64.7 48.0 92.1 98.7 86.3
Simple mean bound rate
.. .. 89.5 .. .. 30.0 .. 47.4 7.0 5.0 78.7 19.2 29.8 75.9 61.9 .. .. .. 19.2 0.0 .. .. 120.0 25.7 80.0 55.8 57.9 28.6 .. 73.4 .. 14.7 3.6 31.6 .. .. 36.5 .. .. 106.5 89.8 32.0 52.4 32.1 31.0 33.9 36.2 32.5 11.0 41.6 36.9 52.3 43.2 21.7 7.2 33.4
Simple mean tariff
Weighted mean tariff
6.5 9.9 19.3 4.0 8.1 13.5 6.5 .. 0.0 5.0 10.3 8.1 11.3 12.1 9.6 11.3 17.1 11.5 9.9 3.9 14.7 4.9 12.5 10.8 13.9 8.8 23.0 2.5 5.4 12.1 4.9 4.2 2.9 9.5 10.8 16.9 12.3 11.7 6.7 14.6 16.6 7.0 3.9 4.6 2.9 8.2 5.3 7.9 14.0 13.7 9.3 8.6 11.8 7.9 11.9 6.5
3.1 7.2 11.6 3.9 6.0 9.3 28.3 .. 0.0 4.6 13.3 5.0 7.1 12.1 9.0 8.4 15.3 11.8 7.9 1.9 15.5 3.8 7.2 4.6 10.4 10.6 18.3 2.0 2.9 8.4 3.1 3.8 1.6 3.6 6.6 11.0 10.7 10.6 6.9 9.4 .. 3.0 1.9 1.7 1.8 4.9 4.2 4.6 11.0 8.0 5.7 5.2 9.9 5.0 7.9 4.0
Note: Tariff rates include ad valorem equivalents of specific rates whenever available. a. Rates are most favored nation rates. b. Includes Montenegro.
354
2009 World Development Indicators
Share of tariff Share of tariff lines lines with international with specific rates peaks
20.6 34.4 55.2 0.1 17.8 51.4 12.8 .. 0.1 4.3 2.5 19.1 23.5 43.9 39.9 44.4 38.1 39.4 24.0 10.6 23.3 0.1 38.0 22.9 55.1 43.6 55.5 4.7 14.8 37.4 4.8 0.2 5.5 25.9 18.8 64.8 45.6 32.2 1.8 34.5 34.3 15.7 5.6 7.1 3.4 21.5 13.0 21.7 40.2 35.8 22.9 21.8 34.3 20.0 36.4 17.0
0.0 15.2 0.0 0.0 0.0 0.0 0.0 .. 0.1 0.0 1.2 0.6 0.8 0.2 0.0 0.2 0.0 0.0 0.8 30.9 0.0 0.7 0.0 0.9 0.0 0.4 0.0 0.3 2.8 0.0 3.7 0.0 4.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.6 0.5 0.0 0.1 0.0 0.1 2.4 0.0 0.0 0.0 0.4 0.7 0.1 0.9 0.0 1.3
%
%
Simple mean tariff
Weighted mean tariff
Simple mean tariff
Weighted mean tariff
13.4 8.8 17.0 3.4 11.0 14.5 14.1 .. 0.2 5.6 16.2 5.9 17.8 13.0 12.8 15.1 23.0 17.8 10.3 20.0 14.4 5.4 16.8 13.6 14.2 17.0 32.4 14.0 14.9 15.9 5.2 4.3 3.0 5.8 10.2 19.4 11.1 14.5 9.6 15.0 17.4 8.9 13.8 10.8 12.9 8.7 11.2 9.2 8.1 9.1 24.1 16.6 13.6 5.1 3.7 5.5
7.2 9.1 8.8 2.8 4.5 7.8 50.5 .. 0.0 3.7 19.9 1.9 9.0 11.1 4.9 7.8 19.7 16.0 3.1 9.6 11.7 2.1 7.5 2.1 9.6 3.2 13.9 3.8 12.6 9.6 0.9 2.9 1.3 1.3 2.6 18.7 8.5 10.2 8.6 9.3 .. 2.5 9.2 4.0 4.0 3.9 4.3 3.2 4.8 2.9 10.7 8.2 7.0 1.7 1.7 1.6
5.6 10.1 19.5 4.1 7.8 13.3 4.8 .. 0.0 4.9 9.4 8.4 10.6 11.9 9.1 10.6 16.6 10.6 9.8 1.1 14.7 4.8 12.1 10.4 13.8 7.6 22.2 1.4 3.8 11.6 4.8 4.2 2.9 9.8 10.9 16.4 12.4 11.3 6.3 14.6 16.4 6.7 11.6 7.5 8.9 6.2 8.2 8.0 4.9 7.8 12.8 13.3 11.7 3.7 2.8 4.4
1.8 6.8 12.6 4.2 6.8 10.6 6.4 .. 0.0 4.9 7.8 6.5 6.4 12.5 12.3 8.6 14.7 10.9 8.7 0.2 17.1 5.2 7.0 5.8 10.8 18.4 20.0 1.3 1.1 7.8 4.5 4.4 1.7 4.9 7.4 9.9 11.0 11.0 5.6 9.4 .. 3.2 10.3 5.4 6.5 4.1 5.6 5.7 4.0 5.1 11.2 7.8 8.3 1.9 1.9 1.8
About the data
GLOBAL LINKS
Tariff barriers
6.8
Definitions
Poor people in developing countries work primarily in
nation or applied rates are calculated using all traded
• Binding coverage is the percentage of product
agriculture and labor-intensive manufactures, sectors
items. Weighted mean tariffs are weighted by the
lines with an agreed bound rate. • Simple mean
that confront the greatest trade barriers. Removing
value of the country’s trade with each trading part-
bound rate is the unweighted average of all the lines
barriers to merchandise trade could increase growth
ner. Simple averages are often a better indicator of
in the tariff schedule in which bound rates have been
in these countries—even more if trade in services
tariff protection than weighted averages, which are
set. • Simple mean tariff is the unweighted average
(retailing, business, financial, and telecommunica-
biased downward because higher tariffs discourage
of effectively applied rates or most favored nation
tions services) were also liberalized.
trade and reduce the weights applied to these tariffs.
rates for all products subject to tariffs calculated
In general, tariffs in high-income countries on
Bound rates result from trade negotiations incorpo-
for all traded goods. • Weighted mean tariff is the
imports from developing countries, though low, are
rated into a country’s schedule of concessions and
average of effectively applied rates or most favored
twice those collected from other high-income coun-
are thus enforceable.
nation rates weighted by the product import shares
tries. But protection is also an issue for developing
Some countries set fairly uniform tariff rates across
corresponding to each partner country. • Share of
countries, which maintain high tariffs on agricultural
all imports. Others are selective, setting high tariffs
tariff lines with international peaks is the share
commodities, labor-intensive manufactures, and
to protect favored domestic industries. The share
of lines in the tariff schedule with tariff rates that
other products and services. In some developing
of tariff lines with international peaks provides an
exceed 15 percent. • Share of tariff lines with spe-
regions new trade policies could make the difference
indication of how selectively tariffs are applied. The
cific rates is the share of lines in the tariff schedule
between achieving important Millennium Develop-
effective rate of protection—the degree to which the
that are set on a per unit basis or that combine ad
ment Goals—reducing poverty, lowering maternal
value added in an industry is protected—may exceed
valorem and per unit rates. • Primary products are
and child mortality rates, improving educational
the nominal rate if the tariff system systematically
commodities classified in SITC revision 3 sections
attainment—and falling far short.
differentiates among imports of raw materials, inter-
0–4 plus division 68 (nonferrous metals). • Manu-
mediate products, and finished goods.
factured products are commodities classified in
Countries use a combination of tariff and nontariff measures to regulate imports. The most common
The share of tariff lines with specific rates shows
form of tariff is an ad valorem duty, based on the
the extent to which countries use tariffs based on
value of the import, but tariffs may also be levied
physical quantities or other, non–ad valorem mea-
on a specific, or per unit, basis or may combine ad
sures. Some countries such as Switzerland apply
valorem and specific rates. Tariffs may be used to
mainly specific duties. To the extent possible, these
raise fiscal revenues or to protect domestic indus-
specifi c rates have been converted to their ad
tries from foreign competition—or both. Nontariff
valorem equivalent rates and have been included in
barriers, which limit the quantity of imports of a par-
the calculation of simple and weighted tariffs.
ticular good, include quotas, prohibitions, licensing
Data are classified using the Harmonized System
schemes, export restraint arrangements, and health
of trade at the six- or eight-digit level. Tariff line data
and quarantine measures. Because of the difficulty
were matched to Standard International Trade Clas-
of combining nontariff barriers into an aggregate indi-
sification (SITC) revision 3 codes to define commod-
cator, they are not included in the table.
ity groups and import weights. Import weights were
Unless specified as most favored nation rates, the
calculated using the United Nations Statistics Divi-
tariff rates used in calculating the indicators in the
sion’s Commodity Trade (Comtrade) database. Data
table are effectively applied rates. Effectively applied
are shown only for the last year for which complete
rates are those in effect for partners in preferen-
data are available.
SITC revision 3 sections 5–8 excluding division 68.
tial trade arrangements such as the North American Free Trade Agreement. The difference between most favored nation and applied rates can be substantial. As more countries report their free trade agreements, suspensions of tariffs, or other special preferences, World Development Indicators will include their effectively applied rates. All estimates
Data sources
are calculated using the most recent information,
All indicators in the table were calculated by World
which is not necessarily revised every year. As a
Bank staff using the World Integrated Trade Solu-
result, data for the same year may differ from data
tion system. Data on tariffs were provided by the
in last year’s edition.
United Nations Conference on Trade and Develop-
Three measures of average tariffs are shown: sim-
ment and the World Trade Organization. Data on
ple bound rates and the simple and the weighted
global imports are from the United Nations Statis-
tariffs. Bound rates are based on all products in a
tics Division’s Comtrade database.
country’s tariff schedule, while the most favored
2009 World Development Indicators
355
6.9
External debt Total external debt
Long-term debt
$ millions
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
1995
2007
.. 456 33,042 11,500 98,465 371 .. .. 321 15,927 1,694 .. 1,614 5,272 .. 717 160,469 10,379 1,271 1,162 2,284 10,807 .. 942 843 22,038 118,090 .. 25,044 13,239 5,896 3,802 18,899 3,830 .. .. .. 4,447 13,903 33,475 2,509 37 .. 10,308 .. .. 4,360 426 1,240 .. 5,495 .. 3,282 3,242 898 821
2,041 2,697 5,541 12,730 127,758 2,888 .. .. 3,068 21,998 9,470 .. 857 4,947 6,378 396 237,472 32,968 1,472 1,456 3,755 3,091 .. 972 1,793 58,649 373,635 .. 44,976 12,287 5,156 7,846 13,938 48,584 .. .. .. 10,312 17,525 30,444 8,790 875 .. 2,634 .. .. 5,746 732 2,257 .. 4,486 .. 6,271 3,261 744 1,598
2009 World Development Indicators
1995
.. 330 31,303 9,543 54,913 298 .. .. 206 15,106 1,301 .. 1,483 4,459 .. 707 98,260 8,808 1,140 1,099 2,110 9,477 .. 850 777 7,178 94,674 .. 13,946 9,636 4,874 3,133 11,902 1,860 .. .. .. 3,653 11,977 30,687 1,979 37 .. 9,774 .. .. 3,976 385 1,039 .. 4,200 .. 2,328 2,987 798 766
$ millions Public and publicly guaranteed IBRD loans Total and IDA credits 2007 1995 2007
1,961 1,787 3,756 10,474 66,110 1,272 .. .. 1,748 20,151 2,338 .. 852 2,150 3,014 380 79,957 5,243 1,268 1,344 3,537 2,204 .. 836 1,712 9,378 87,653 .. 27,689 10,853 4,807 3,750 11,651 14,212 .. .. .. 6,546 10,447 26,940 5,444 856 .. 2,544 .. .. 5,177 704 1,543 .. 3,047 .. 4,214 3,048 730 1,500
.. 109 2,049 81 4,913 96 .. .. 30 5,692 116 .. 498 865 472 108 6,038 444 608 591 65 1,067 .. 414 379 1,383 14,248 .. 2,559 1,413 279 303 2,386 117 .. .. .. 300 1,108 2,356 327 24 .. 1,470 .. .. 110 162 84 .. 2,434 .. 158 847 210 389
411 809 113 365 5,674 970 .. .. 682 10,077 42 .. 176 259 1,521 6 9,676 1,604 468 830 535 239 .. 403 994 357 21,912 .. 4,758 2,402 303 46 2,388 1,101 .. .. .. 482 697 2,671 411 457 .. 711 .. .. 12 219 885 .. 1,104 .. 740 1,305 315 518
Private nonguaranteed 1995 2007
.. 0 0 0 16,066 0 .. .. 0 0 0 .. 0 239 .. 0 30,830 342 0 0 0 288 .. 0 0 11,429 1,090 .. 5,553 0 0 214 2,660 1,257 .. .. .. 19 440 313 5 0 .. 0 .. .. 0 0 0 .. 27 .. 142 0 0 0
0 72 1,068 0 23,581 999 .. .. 143 0 1,125 .. 0 2,621 1,774 0 118,267 13,689 0 0 0 636 .. 0 0 35,969 82,284 .. 11,938 0 0 1,134 591 29,273 .. .. .. 845 5,184 2,053 2,376 0 .. 0 .. .. 0 0 241 .. 0 .. 78 0 0 0
Short-term debt
Use of IMF credit
$ millions 1995 2007
$ millions 1995 2007
.. 21 62 748 261 717 1,958 2,256 21,355 38,067 2 459 .. .. .. .. 14 1,074 199 1,347 110 6,007 .. .. 47 1 307 176 .. 1,587 10 16 31,238 39,248 512 14,036 56 166 15 14 102 218 991 234 .. .. 57 87 17 25 3,431 13,302 22,325 203,698 .. .. 5,545 5,349 3,118 625 1,004 312 430 2,962 3,910 1,523 492 5,099 .. .. .. .. .. .. 616 2,374 1,312 1,894 2,372 1,451 525 970 0 19 .. .. 460 90 .. .. .. .. 287 545 15 21 85 222 .. .. 620 1,272 .. .. 812 1,979 161 148 95 8 27 42
.. 65 1,478 0 6,131 70 .. .. 101 622 283 .. 84 268 48 0 142 717 75 48 72 51 .. 35 49 0 0 .. 0 485 19 24 427 221 .. .. .. 160 173 103 0 0 .. 73 .. .. 97 26 116 .. 648 .. 0 94 6 29
59 90 0 0 0 158 .. .. 103 501 0 .. 4 0 2 0 0 0 37 98 0 17 .. 49 56 0 0 .. 0 808 37 0 173 0 .. .. .. 548 0 0 0 0 .. 0 .. .. 25 6 252 .. 167 .. 0 65 5 56
Total external debt
Long-term debt
$ 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
1995
2007
4,797 .. 94,464 124,398 21,879 .. .. .. .. 4,577 .. 7,661 3,750 7,309 .. .. .. 609 2,165 463 2,966 684 2,154 .. .. 1,277 4,302 2,238 34,343 2,958 2,396 1,757 165,379 695 520 23,771 7,458 5,771 .. 2,410 .. .. 10,390 1,576 34,092 .. .. 30,229 6,099 2,506 2,574 30,833 39,379 44,080 .. ..
3,232 .. 220,956 140,783 20,058 .. .. .. .. 10,063 .. 8,397 96,133 7,327 .. .. .. 2,401 3,337 39,342 24,634 680 2,475 .. .. 3,760 1,661 870 53,717 2,018 1,704 4,253 178,108 3,203 1,596 20,293 3,123 7,372 .. 3,645 .. .. 3,399 972 9,008 .. .. 40,685 9,862 2,254 3,561 32,154 65,845 195,374 .. ..
1995
4,193 .. 80,422 65,309 15,116 .. .. .. .. 3,716 .. 6,624 2,834 5,857 .. .. .. 472 2,091 271 1,550 642 1,161 .. .. 788 3,687 2,078 16,023 2,739 2,127 1,148 93,902 450 472 23,190 5,209 5,378 .. 2,339 .. .. 8,566 1,319 28,140 .. .. 23,788 3,782 1,668 1,453 18,931 28,525 40,890 .. ..
$ millions Public and publicly guaranteed IBRD loans Total and IDA credits 2007 1995 2007
1,942 .. 74,419 68,708 11,146 .. .. .. .. 6,372 .. 7,318 1,698 6,122 .. .. .. 1,895 2,446 1,809 19,789 645 910 .. .. 1,520 1,425 807 18,441 1,989 1,437 572 105,379 779 1,566 15,670 2,533 5,516 .. 3,485 .. .. 2,144 863 3,815 .. .. 35,917 8,267 1,156 2,195 19,669 37,895 43,598 .. ..
828 .. 27,348 13,259 316 .. .. .. .. 595 .. 806 295 2,412 .. .. .. 141 285 55 113 207 269 .. .. 181 1,121 1,306 1,059 863 347 157 13,823 152 59 3,999 890 777 .. 1,023 .. .. 341 598 3,489 .. .. 6,403 175 407 189 1,729 5,185 2,067 .. ..
401 .. 33,432 8,371 699 .. .. .. .. 360 .. 908 427 2,968 .. .. .. 651 686 85 437 297 77 .. .. 587 865 178 135 452 203 90 4,540 432 331 2,595 902 793 .. 1,524 .. .. 321 235 2,309 .. .. 11,161 216 256 244 2,649 2,921 1,870 .. ..
Private nonguaranteed 1995 2007
123 .. 6,618 33,123 314 .. .. .. .. 128 .. 0 103 445 .. .. .. 0 0 0 50 0 0 .. .. 289 0 0 11,046 0 0 267 18,348 9 0 331 1,769 0 .. 0 .. .. 0 133 301 .. .. 1,593 0 711 338 1,288 4,847 1,012 .. ..
624 .. 102,876 37,132 91 .. .. .. .. 2,189 .. 0 82,690 0 .. .. .. 280 860 20,858 530 0 0 .. .. 1,044 0 0 20,026 0 0 62 63,723 1,169 5 2,673 0 0 .. 0 .. .. 361 20 175 .. .. 1,153 1,061 998 572 6,679 20,867 91,412 .. ..
GLOBAL LINKS
External debt
6.9
Short-term debt
Use of IMF credit
$ millions 1995 2007
$ millions 1995 2007
382 .. 5,049 25,966 6,449 .. .. .. .. 492 .. 785 381 634 .. .. .. 13 10 31 1,365 4 657 .. .. 143 542 44 7,274 72 169 342 37,300 6 0 198 279 393 .. 23 .. .. 1,785 72 5,651 .. .. 3,235 2,207 78 784 9,659 5,279 2,178 .. ..
634 .. 43,662 34,943 8,821 .. .. .. .. 1,502 .. 991 11,745 936 .. .. .. 76 5 16,676 4,235 0 1,214 .. .. 1,196 194 32 15,250 17 254 3,618 9,006 1,095 0 1,949 575 1,856 .. 81 .. .. 809 49 5,018 .. .. 2,233 529 100 794 5,806 7,084 60,365 .. ..
99 .. 2,374 0 0 .. .. .. .. 240 .. 251 432 374 .. .. .. 124 64 160 0 38 336 .. .. 57 73 116 0 147 100 0 15,828 230 47 52 202 0 .. 48 .. .. 39 52 0 .. .. 1,613 111 50 0 955 728 0 .. ..
2009 World Development Indicators
32 .. 0 0 0 .. .. .. .. 0 .. 88 0 269 .. .. .. 150 26 0 80 35 352 .. .. 0 43 31 0 13 13 0 0 160 26 0 15 0 .. 79 .. .. 85 40 0 .. .. 1,381 5 0 0 0 0 0 .. ..
357
6.9
External debt Total external debt
Long-term debt
$ millions 1995
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
6,832 121,401 1,029 .. 3,916 10,785a 1,220 .. .. .. 2,678 25,358 .. 8,395 17,603 249 .. .. .. 634 7,421 100,039 .. 1,476 .. 10,818 73,781 402 3,609 8,429 .. .. .. 5,318 1,799 35,538 25,428 .. 6,217 6,958 4,989 .. s 253,626 1,632,033 782,620 849,412 1,885,659 455,608 293,781 608,461 140,110 151,740 235,959
2007
85,380 3,957 15,238 844 2,604 370,172 101,582 70,396 1,524 4,292 495 971 456 512 204 .. .. .. .. .. 2,579 3,266 2,029 1,160 663 26,280 6,788a 8,224 1,252a 2,951 348 1,028 308 234 84 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 2,944 1,961 1,979 432 448 43,380 9,837 13,868 0 27 .. .. .. .. .. 14,037 7,175 11,879 1,512 2,357 19,224 9,779 12,337 1,279 1,307 393 238 357 25 23 .. .. .. .. .. .. .. .. .. .. .. .. .. 471 19 1,228 590 1,065 0 360 5,024 6,217 3,684 2,269 1,585 63,067 16,826 9,841 1,906 129 .. .. .. .. .. 1,967 1,286 1,655 541 723 .. .. .. .. .. 20,231 9,215 16,579 1,766 1,595 251,477 50,317 75,171 5,069 7,601 739 385 648 1 15 1,611 3,089 1,575 1,792 840 73,600 6,581 10,568 491 2,309 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 12,346 3,833 9,616 513 666 3,871 1,415 3,086 157 359 43,148 28,223 27,494 1,639 0 24,207 21,778 19,372 231 4,549 .. .. .. .. .. 5,930 5,528 5,343 827 2,058 2,783 5,291 1,136 1,434 323 5,290 3,462 3,735 896 977 .. s .. s .. s .. s .. s 222,664 207,566 182,906 48,542 59,927 3,242,545 1,078,726 1,129,584 131,030 145,775 1,264,349 528,904 512,685 88,208 100,440 1,978,196 549,822 616,899 42,822 45,335 3,465,210 1,286,291 1,312,490 179,572 205,702 741,426 255,393 256,942 37,604 40,778 1,262,445 231,636 268,016 13,699 32,471 825,567 371,667 403,303 38,485 33,226 136,000 123,482 106,981 12,279 11,222 304,698 129,135 149,012 42,036 59,126 195,074 174,978 128,235 35,468 28,878
a. Includes Montenegro.
358
1995
$ millions Public and publicly guaranteed IBRD loans Total and IDA credits 2007 1995 2007
2009 World Development Indicators
Private nonguaranteed 1995 2007
Short-term debt
Use of IMF credit
$ millions 1995 2007
$ millions 1995 2007
534 39,636 1,303 30,505 0 220,673 10,201 79,103 0 0 32 31 .. .. .. .. 44 200 260 323 1,773a 15,404 2,139a 2,652 0 0 27 3 .. .. .. .. .. .. .. .. .. .. .. .. 0 0 551 788 4,935 12,954 9,673 16,558 .. .. .. .. 90 259 535 1,649 496 0 6,368 6,405 0 0 11 36 .. .. .. .. .. .. .. .. .. .. .. .. 0 40 43 76 44 4 963 1,319 39,117 31,585 44,095 21,640 .. .. .. .. 0 0 85 310 .. .. .. .. 0 0 1,310 3,652 7,079 127,344 15,701 41,803 0 2 17 89 0 0 103 26 84 39,687 223 22,914 .. .. .. .. .. .. .. .. .. .. .. .. 127 357 1,336 2,373 15 594 212 191 2,013 3,954 3,063 11,700 0 0 3,272 4,672 .. .. .. .. 0 0 689 417 13 990 415 569 381 16 685 1,421 .. s .. s .. s .. s 8,185 5,945 28,143 28,452 202,778 1,283,874 300,461 818,908 95,156 357,936 147,477 390,837 107,622 925,938 152,984 428,071 210,963 1,289,819 328,604 847,360 90,050 193,785 108,828 290,485 12,497 688,158 33,902 297,721 87,303 281,527 122,859 139,932 1,008 6,415 13,443 22,250 8,301 104,287 9,051 49,121 11,804 15,647 40,522 47,850
1,038 0 9,617 0 26 8 .. .. 347 27 84 a 0 165 37 .. .. .. .. .. .. 166 177 913 0 .. .. 595 251 960 482 0 0 .. .. .. .. .. .. 0 46 197 18 0 0 .. .. 105 2 .. .. 293 0 685 7,158 0 0 417 9 1,542 431 .. .. .. .. .. .. 21 0 157 0 2,239 0 377 164 .. .. 0 169 1,239 87 461 118 .. s .. s 9,732 5,361 50,068 10,179 11,084 2,891 38,984 7,288 59,800 15,540 1,337 215 15,747 8,550 26,632 804 2,177 353 5,252 2,277 8,654 3,341
About the data
GLOBAL LINKS
External debt
6.9
Definitions
A country’s external indebtedness affects its credit-
Because debt data are normally reported in the
• Total external debt is debt owed to nonresidents
worthiness and investor perceptions. Data on the
currency of repayment, they have to be converted into
repayable in foreign currency, goods, or services. It
external debt of developing countries are gathered by
a single currency (U.S. dollars) to produce summary
is the sum of public, publicly guaranteed, and private
the World Bank through its Debtor Reporting System.
tables. Stock figures (amount of debt outstanding)
nonguaranteed long-term debt, short-term debt, and
Indebtedness is calculated using loan-by-loan reports
are converted using end-of-period exchange rates,
use of IMF credit. • Long-term debt is debt that has
submitted by countries on long-term public and pub-
as published in the IMF’s International Financial Sta-
an original or extended maturity of more than one
licly guaranteed borrowing and information on short-
tistics (line ae). Flow figures are converted at annual
year. It has three components: public, publicly guar-
term debt collected by the countries or from creditors
average exchange rates (line rf). Projected debt
anteed, and private nonguaranteed debt. • Public
through the reporting systems of the Bank for Inter-
service is converted using end-of-period exchange
and publicly guaranteed debt comprises the long-
national Settlements and the Organisation for Eco-
rates. Debt repayable in multiple currencies, goods,
term external obligations of public debtors, including
nomic Co-operation and Development. These data are
or services and debt with a provision for maintenance
the national government and political subdivisions
supplemented by information from major multilateral
of the value of the currency of repayment are shown
(or an agency of either) and autonomous public bod-
banks and official lending agencies in major creditor
at book value.
ies, and the external obligations of private debtors
countries and by estimates by World Bank and Inter-
Because flow data are converted at annual aver-
that are guaranteed for repayment by a public entity.
national Monetary Fund (IMF) staff. The table includes
age exchange rates and stock data at end-of-period
• IBRD loans and IDA credits are extended by the
data on long-term private nonguaranteed debt reported
exchange rates, year-to-year changes in debt out-
World Bank. The International Bank for Reconstruc-
to the World Bank or estimated by its staff.
standing and disbursed are sometimes not equal to
tion and Development (IBRD) lends at market rates.
The coverage, quality, and timeliness of data vary
net flows (disbursements less principal repayments);
The International Development Association (IDA) pro-
across countries. Coverage varies for both debt instru-
similarly, changes in debt outstanding, including
vides credits at concessional rates. • Private non-
ments and borrowers. The widening spectrum of debt
undisbursed debt, differ from commitments less
guaranteed debt consists of the long-term external
instruments and investors alongside the expansion of
repayments. Discrepancies are particularly notable
obligations of private debtors that are not guaranteed
private nonguaranteed borrowing makes comprehen-
when exchange rates have moved sharply during
for repayment by a public entity. • Short-term debt is
sive coverage of external debt more complex. Report-
the year. Cancellations and reschedulings of other
debt owed to nonresidents having an original matu-
ing countries differ in their capacity to monitor debt,
liabilities into long-term public debt also contribute
rity of one year or less and interest in arrears on long-
especially private nonguaranteed debt. Even data on
to the differences.
term debt. • Use of IMF credit denotes members’
public and publicly guaranteed debt are affected by
Variations in reporting rescheduled debt also affect
drawings on the IMF other than those drawn against
coverage and reporting accuracy—again because of
cross-country comparability. For example, reschedul-
the country’s reserve tranche position and includes
monitoring capacity and sometimes because of an
ing of official Paris Club creditors may be subject to
purchases and drawings under Stand-By, Extended,
unwillingness to provide information. A key part often
lags between completion of the general rescheduling
Structural Adjustment, Enhanced Structural Adjust-
underreported is military debt. Currently, 128 devel-
agreement and completion of the specific bilateral
ment, and Systemic Transformation Facility Arrange-
oping countries report to the Debtor Reporting Sys-
agreements that define the terms of the rescheduled
ments, together with Trust Fund loans.
tem. Nonreporting countries might have outstanding
debt. Other areas of inconsistency include country
debt with the World Bank, other international financial
treatment of arrears and of nonresident national
institutions, and private creditors.
deposits denominated in foreign currency.
6.9a
The levels and the composition of external debt vary by regions $ billions
Public and publicly-guaranteed long-term debt
Private nonguaranteed long-term debt
Short-term debt
1,500
Data sources Data on external debt are mainly from reports to
1,200
the World Bank through its Debtor Reporting System from member countries that have received
900
IBRD loans or IDA credits, with additional infor-
600
mation from the files of the World Bank, the IMF, 300
the African Development Bank and African Development Fund, the Asian Development Bank and
0 1990 2000 2007
1990 2000 2007
East Asia & Pacific
Europe & Central Asia
1990 2000 2007 1990 2000 2007 Latin America & Caribbean
Middle East & North Africa
1990 2000 2007
1990 2000 2007
Asian Development Fund, and the Inter-American
South Asia
Sub-Saharan Africa
Development Bank. Summary tables of the exter-
The share of private nonguaranteed debt has increased for East Asia and Pacific, Europe and Central
nal debt of developing countries are published
Asia, Latin America and Caribbean, and South Asia. Public and publicly guaranteed debt remain the main
annually in the World Bank’s Global Development
source of external borrowing for the Middle East and North Africa and Sub-Saharan Africa.
Finance and on its Global Development Finance
Source: Global Development Finance data files.
CD-ROM.
2009 World Development Indicators
359
6.10 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
Ratios for external debt Total external debt
Total debt service
Multilateral debt service
% of GNI 1995 2007
% of exports of goods and services and incomea 1995 2007
% of public and publicly guaranteed debt service 1995 2007
.. 18.4 83.5 311.9 38.9 25.3 .. .. 10.6 40.7 12.2 .. 82.1 81.2 .. 15.1 21.2 81.8 53.6 117.6 71.8 131.7 .. 85.5 58.5 32.1 16.5 .. 27.5 271.4 480.0 33.1 188.7 20.4 .. .. .. 37.8 72.1 55.8 26.7 6.3 .. 136.6 .. .. 101.6 113.0 48.2 .. 86.9 .. 22.6 89.8 380.7 28.1
.. 24.2 4.1 26.2 49.7 30.5 .. .. 11.7 29.9 21.3 .. 15.8 38.2 40.8 3.4 18.7 84.3 21.9 154.6 47.0 15.0 .. 57.1 29.1 40.3 11.6 .. 22.5 142.9 86.1 30.8 73.6 97.7 .. .. .. 29.7 41.3 23.2 44.4 64.1 .. 13.6 .. .. 56.2 122.7 21.7 .. 29.9 .. 18.7 72.5 213.6 26.1
2009 World Development Indicators
.. 1.4 .. 12.0 30.1 3.1 .. .. 1.3 13.2 3.4 .. 6.8 29.4 .. 3.1 36.6 16.5 .. 27.6 0.7 20.8 .. .. .. 24.5 9.9 .. 31.5 .. 13.1 13.8 23.1 4.8 .. .. .. 6.1 24.8 13.2 8.9 0.1 .. 18.4 .. .. 15.3 15.5 .. .. 24.0 .. 11.1 25.0 51.9 51.0
.. 4.1 .. 10.2 13.0 7.0 .. .. 0.7 3.9 3.9 .. .. 11.9 8.0 0.9 27.8 15.5 .. 42.6 0.5 9.9 .. .. .. 14.2 2.2 .. 22.0 .. 1.2 4.4 4.5 33.0 .. .. .. 8.6 18.7 4.4 11.0 .. .. 4.1 .. .. .. 12.4 4.6 .. 3.1 .. 5.2 13.1 .. 4.7
.. 11.4 17.7 0.6 21.6 69.8 .. .. 21.8 27.1 55.4 .. 54.8 75.5 .. 76.0 18.5 10.5 76.7 70.6 11.9 60.8 .. 100.0 86.1 76.2 7.6 .. 32.7 .. 21.2 50.7 59.3 73.1 .. .. .. 39.8 32.0 26.3 55.1 100.0 .. 41.7 .. .. 17.9 49.1 0.4 .. 48.4 .. 47.7 30.4 88.3 92.2
52.2 51.2 3.0 0.2 72.2 86.8 .. .. 49.2 70.0 8.3 .. 35.9 88.2 54.0 70.8 13.5 49.7 53.2 94.1 80.0 19.6 .. 100.0 83.2 7.3 28.1 .. 39.0 37.6 44.5 60.8 95.8 14.6 .. .. .. 29.1 53.7 22.2 52.9 94.4 .. 38.6 .. .. 93.6 70.1 22.3 .. 32.4 .. 49.4 50.6 49.8 81.1
Short-term debt
% of total debt 1995 2007
.. 13.7 0.8 17.0 21.7 0.6 .. .. 4.4 1.3 6.5 .. 2.9 5.8 .. 1.4 19.5 4.9 4.4 1.3 4.5 9.2 .. 6.0 2.0 15.6 18.9 .. 22.1 23.6 17.0 11.3 20.7 12.8 .. .. .. 13.8 9.4 7.1 20.9 0.0 .. 4.5 .. .. 6.6 3.5 6.9 .. 11.3 .. 24.7 5.0 10.5 3.2
1.0 27.7 12.9 17.7 29.8 15.9 .. .. 35.0 6.1 63.4 .. 0.1 3.6 24.9 4.0 16.5 42.6 11.3 0.9 5.8 7.6 .. 9.0 1.4 22.7 54.5 .. 11.9 5.1 6.0 37.8 10.9 10.5 .. .. .. 23.0 10.8 4.8 11.0 2.2 .. 3.4 .. .. 9.5 2.9 9.8 .. 28.4 .. 31.6 4.5 1.1 2.6
Present value of debt
% of total reserves 1995 2007
.. 23.5 6.3 919.7 133.6 1.9 .. .. 11.6 8.4 29.2 .. 23.7 30.5 .. 0.2 60.7 31.3 16.1 6.9 53.1 6,444.5 .. 23.8 11.6 23.1 27.8 .. 65.6 1,980.9 1,578.0 40.6 739.1 25.9 .. .. .. 165.3 73.4 13.9 55.9 0.0 .. 56.5 .. .. 187.8 14.0 43.0 .. 77.1 .. 103.7 185.6 467.0 13.4
.. 34.6 0.6 20.2 82.5 27.7 .. .. 25.1 25.5 143.8 .. 0.0 3.3 35.1 0.2 21.8 80.0 16.1 7.8 10.2 8.0 .. 95.0 2.6 79.0 13.2 .. 25.5 346.1 14.3 72.0 60.4 37.3 .. .. .. 92.7 53.8 4.5 42.1 .. .. .. .. .. 44.0 15.1 16.3 .. .. .. 45.9 .. 7.5 9.3
% of GNI 2007b
% of exports of goods, services, and incomea 2007b
18 c 22 4 32 63 38 .. .. 14 22 25 .. 12c 24 c 42 3 25 100 14 c 97c 46 5c .. 48 c 19c 45 13 .. 28 111c 93c 35 67c 109 .. .. .. 33 50 25 50 41c .. 8c .. .. 73 34 c 20 .. 22c .. 21 64 c 263c 20 c
80 c 61 9 35 219 117 .. .. 16 84 40 .. 58 c 52c 80 5 155 144 108 c 882c 63 19c .. 325c 28 c 85 32 .. 133 326c 88 c 62 123c 197 .. .. .. 70 115 60 104 660 c .. 47c .. .. 99 63c 52 .. 55c .. 54 210 c 529c 57c
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 external debt
Total debt service
Multilateral debt service
% of GNI 1995 2007
% of exports of goods and services and incomea 1995 2007
% of public and publicly guaranteed debt service 1995 2007
% of total debt 1995 2007
34.0 .. 29.7 29.9 30.2 .. .. .. .. 16.2 .. 12.4 3.9 24.7 .. .. .. 13.2 6.3 1.6 .. 6.1 .. .. .. .. 7.6 24.9 7.0 13.4 22.9 9.4 27.0 7.9 10.1 33.4 34.5 17.8 .. 7.5 .. .. 38.7 16.7 13.8 .. .. 26.5 3.4 20.8 5.6 15.9 16.1 11.0 .. ..
52.6 .. 24.3 28.4 1.3 .. .. .. .. 40.6 .. 33.5 7.8 32.5 .. .. .. 59.0 37.4 60.3 13.2 60.3 .. .. .. 99.9 74.3 51.4 15.5 45.5 49.6 34.5 19.5 79.1 2.8 30.3 17.4 15.0 .. 54.2 .. .. 30.3 95.5 45.4 .. .. 43.2 52.7 31.7 48.0 49.9 29.2 13.5 .. ..
8.0 .. 5.3 20.9 29.5 .. .. .. .. 10.7 .. 10.2 10.2 8.7 .. .. .. 2.1 0.5 6.7 46.0 0.6 30.5 .. .. 11.2 12.6 1.9 21.2 2.4 7.1 19.5 22.6 0.9 0.1 0.8 3.7 6.8 .. 0.9 .. .. 17.2 4.5 16.6 .. .. 10.7 36.2 3.1 30.4 31.3 13.4 4.9 .. ..
131.5 .. 26.8 63.4 24.3 .. .. .. .. 82.2 .. 118.8 18.6 83.8 .. .. .. 37.5 123.2 8.8 24.3 50.9 .. .. .. 29.0 143.3 165.8 40.6 122.3 175.3 46.2 60.5 40.3 43.3 75.1 360.6 .. .. 54.7 .. .. 368.3 86.1 131.7 .. .. 49.5 80.9 57.3 31.5 60.3 51.7 32.2 .. ..
27.8 .. 18.9 33.9 7.1 .. .. .. .. 101.0 .. 50.5 103.7 30.2 .. .. .. 65.0 84.4 150.3 101.8 33.7 442.1 .. .. 49.2 22.7 24.6 29.4 30.6 62.0 62.1 17.7 66.5 41.5 27.4 44.3 .. .. 35.0 .. .. 60.7 23.0 6.1 .. .. 28.0 54.3 40.2 28.6 32.6 41.9 47.7 .. ..
3.7 .. .. 10.5 .. .. .. .. .. 17.3 .. 5.7 49.6 6.0 .. .. .. 6.7 18.9 73.3 18.7 7.0 111.6 .. .. .. .. .. 4.6 .. .. 4.9 12.5 9.5 .. 11.4 1.3 .. .. 4.5 .. .. 11.7 .. 1.4 .. .. 8.9 5.3 .. 6.2 25.0 13.7 25.6 .. ..
45.4 .. 20.8 33.7 3.7 .. .. .. .. 21.7 .. 42.5 36.9 60.7 .. .. .. 83.9 91.6 49.7 4.1 29.0 100.0 .. .. 58.1 84.7 35.8 8.0 51.5 89.5 25.1 6.7 56.2 38.1 34.2 62.2 0.5 .. 73.6 .. .. 76.3 94.8 43.7 .. .. 53.7 26.6 78.2 59.2 19.4 17.7 8.1 .. ..
Short-term debt
19.6 .. 19.8 24.8 44.0 .. .. .. .. 14.9 .. 11.8 12.2 12.8 .. .. .. 3.2 0.2 42.4 17.2 0.0 49.0 .. .. 31.8 11.7 3.7 28.4 0.8 14.9 85.1 5.1 34.2 0.0 9.6 18.4 25.2 .. 2.2 .. .. 23.8 5.1 55.7 .. .. 5.5 5.4 4.4 22.3 18.1 10.8 30.9 .. ..
Present value of debt
% of total reserves 1995 2007
141.7 .. 22.1 174.2 .. .. .. .. .. 72.2 .. 34.4 23.0 164.9 .. .. .. 9.7 10.2 5.2 16.9 0.9 2,340.6 .. .. 51.9 497.1 37.8 29.5 22.2 187.9 38.5 218.8 2.3 0.3 5.1 142.8 60.4 .. 3.5 .. .. 1,256.8 75.6 330.7 .. .. 128.0 282.4 29.1 70.8 111.6 67.8 14.6 .. ..
6.10
24.9 .. 15.8 61.4 .. .. .. .. .. 80.0 .. 12.5 66.6 27.9 .. .. .. 6.5 0.7 289.5 20.6 .. 1,016.9 .. .. 52.8 22.9 14.1 15.0 1.6 122.5 197.5 10.3 82.1 0.0 7.9 37.7 .. .. .. .. .. 73.3 8.3 9.7 .. .. 14.1 27.4 4.7 32.3 20.9 21.0 91.8 .. ..
% of GNI 2007b
% of exports of goods, services, and incomea 2007b
21c .. 20 43 8 .. .. .. .. 131 .. 54 131 26 .. .. .. 43c 84 192 111 23 978 c .. .. 54 21c 9c 34 16 c 85 c 65 20 72 37 29 15c 46 .. 22c .. .. 31c 12c 6 .. .. 25 70 42 35 42 51 53 .. ..
26c .. 82 120 22 .. .. .. .. 183 .. 69 218 85 .. .. .. 65c 267 373 115 35 976c .. .. 100 70 c 37c 28 51c 150 c 94 62 98 52 66 34 c 119 .. 70 c .. .. 52c 70 c 12 .. .. 123 81 47 60 125 97 121 .. ..
2009 World Development Indicators
361
GLOBAL LINKS
Ratios for external debt
6.10 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Ratios for external debt Total external debt
Total debt service
Multilateral debt service
% of GNI 1995 2007
% of exports of goods and services and incomea 1995 2007
% of public and publicly guaranteed debt service 1995 2007
19.4 31.0 79.2 .. 82.9 .. 149.0 .. .. .. .. 17.1 .. 65.3 136.3 14.0 .. .. .. 53.6 144.6 60.6 .. 116.7 .. 63.0 30.5 16.1 63.3 17.8 .. .. .. 29.4 13.5 48.7 124.0 .. 169.0 215.1 73.5 .. w 87.1 34.7 37.6 32.3 37.7 35.5 30.0 35.9 53.6 32.0 76.1
51.5 29.4 14.9 .. 23.3 68.3 21.4 .. .. .. .. 15.8 .. 43.9 46.1 13.3 .. .. .. 34.0 31.1 26.5 .. 80.1 .. 60.8 38.8 5.9 14.0 52.9 .. .. .. 54.3 17.3 18.7 36.3 .. 27.9 27.9 .. .. w 28.9 24.8 18.7 31.5 25.1 17.0 41.5 23.7 18.9 21.1 24.9
10.5 6.3 20.5 .. 16.8 .. 53.7 .. .. .. .. 9.5 .. 8.0 6.7 1.5 .. .. .. .. 17.9 11.6 .. 6.0 .. 16.9 27.7 .. 19.8 6.6 .. .. .. 22.1 .. 22.9 .. .. 3.1 .. .. .. w 19.2 17.1 16.8 17.4 17.2 12.7 10.5 26.2 19.8 25.5 15.9
19.1 9.1 3.2 .. .. .. 2.5 .. .. .. .. 5.9 .. 6.7 3.2 1.9 .. .. .. 2.3 2.5 8.1 .. .. .. 11.3 32.1 .. 2.1 16.9 .. .. .. 19.1 .. 7.4 2.3 .. 2.7 2.5 .. .. w 4.0 10.6 6.2 16.4 10.2 4.0 18.7 16.0 5.8 12.9 5.0
21.3 9.7 99.0 .. 62.2 100.0 d 8.4 .. .. .. .. 0.0 .. 14.0 100.0 64.0 .. .. .. .. 66.7 20.9 .. 75.5 .. 43.8 20.7 1.9 69.7 13.6 .. .. .. 27.3 1.9 11.5 2.9 .. 78.3 50.6 33.6 .. w 41.7 21.4 22.0 20.8 22.9 18.2 16.5 26.2 19.3 27.4 35.0
35.3 11.1 72.9 .. 61.2 44.9 40.7 .. .. .. .. 1.5 .. 25.1 25.3 58.2 .. .. .. 59.2 37.4 16.2 .. 60.9 .. 45.7 11.8 4.7 58.4 25.3 .. .. .. 28.3 17.0 23.9 12.0 .. 59.8 91.0 5.6 .. w 50.2 19.8 25.9 15.4 21.4 23.6 16.7 21.6 19.7 28.8 22.4
Short-term debt
% of total debt 1995 2007
19.1 8.4 3.1 .. 6.6 19.8d 2.2 .. .. .. 20.6 38.1 .. 6.4 36.2 4.5 .. .. .. 6.8 13.0 44.1 .. 5.8 .. 12.1 21.3 4.3 2.8 2.6 .. .. .. 25.1 11.8 8.6 12.9 .. 11.1 6.0 13.7 .. w 11.1 18.4 18.8 18.0 17.4 23.9 11.5 20.2 9.6 6.0 17.2
Present value of debt
% of total reserves 1995 2007
35.7 49.7 21.4 56.6 6.3 32.3 .. .. 12.5 95.6 10.1 .. 1.0 77.8 .. .. .. .. .. .. 26.8 .. 38.2 216.7 .. .. 11.7 25.3 33.3 3,898.2 9.3 3.7 .. .. .. .. .. .. 6.2 .. 26.2 356.6 34.3 119.4 .. .. 15.8 65.1 .. .. 18.1 77.6 16.6 113.0 12.1 1.5 1.6 22.4 31.1 20.9 .. .. .. .. .. .. 19.2 73.7 4.9 .. 27.1 28.6 19.3 247.2 .. .. 7.0 107.9 20.5 186.2 26.9 77.2 .. w .. w 12.8 149.8 25.3 66.8 30.9 63.2 21.6 70.6 24.5 70.2 39.2 64.9 23.6 53.1 16.9 88.6 16.4 18.4 16.1 29.5 24.5 193.5
76.3 16.6 5.6 .. 19.5 18.7 1.6 .. .. .. .. 50.3 .. 45.1 464.8 4.7 .. .. .. .. 45.7 24.7 .. 70.8 .. 45.5 54.6 .. 1.0 70.5 .. .. .. 57.6 .. 34.7 19.9 .. 5.4 52.2 .. .. w 16.7 22.4 16.3 33.5 22.2 15.5 37.9 31.1 6.2 16.2 31.4
% of GNI 2007b
% of exports of goods, services, and incomea 2007b
67 39 8c .. 21c 86 10 c .. .. .. .. 19 .. 42 93c 14 .. .. .. 30 15 c 29 .. 80 c .. 65 47 7 9c 66 .. .. .. 69 20 26 35 .. 23 7c 121
175 105 69c .. 59c 198 37c .. .. .. .. 58 .. 105 382c 17 .. .. .. 33 62c 37 .. 148 c .. 106 200 10 37c 131 .. .. .. 196 51 66 45 .. 46 16c 326
a. Includes workers’ remittances. b. The numerator refers to 2007, whereas the denominator is a three year average of 2005–07 data. c. Data are from debt sustainability analyses for low-income countries. Present value estimates for these countries are for public and publicly guaranteed debt only. d. Includes Montenegro.
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2009 World Development Indicators
About the data
6.10
Definitions
A country’s external debt burden, both debt outstand-
value of external debt provides a measure of future
• Total external debt is debt owed to nonresidents
ing and debt service, affects its creditworthiness and
debt service obligations.
and comprises public, publicly guaranteed, and pri-
vulnerability. The table shows total external debt rela-
The present value of external debt is calculated
vate nonguaranteed long-term debt, short-term debt,
tive to a country’s size—gross national income (GNI).
by discounting the debt service (interest plus amor-
and use of IMF credit. It is presented as a share of
Total debt service is contrasted with countries’ ability
tization) due on long-term external debt over the
GNI. • Total debt service is the sum of principal
to obtain foreign exchange through exports of goods,
life of existing loans. Short-term debt is included at
repayments and interest actually paid on total long-
services, income, and workers’ remittances.
face value. The data on debt are in U.S. dollars con-
term debt (public and publicly guaranteed and private
Multilateral debt service (shown as a share of the
verted at official exchange rates (see About the data
nonguaranteed), use of IMF credit, and interest on
country’s total public and publicly guaranteed debt
for table 6.9). The discount rate on long-term debt
short-term debt. • Exports of goods, services, and
service) are obligations to international fi nancial
depends on the currency of repayment and is based
income refer to international transactions involv-
institutions, such as the World Bank, the Interna-
on commercial interest reference rates established
ing a change in ownership of general merchandise,
tional Monetary Fund (IMF), and regional develop-
by the Organisation for Economic Co-operation and
goods sent for processing and repairs, nonmonetary
ment banks. Multilateral debt service takes priority
Development. Loans from the International Bank
gold, services, receipts of employee compensation
over private and bilateral debt service, and bor-
for Reconstruction and Development (IBRD), cred-
for nonresident workers, investment income, and
rowers must stay current with multilateral debts
its from the International Development Association
workers’ remittances. • Multilateral debt service is
to remain creditworthy. While bilateral and private
(IDA), and obligations to the IMF are discounted using
the repayment of principal and interest to the World
creditors often write off debts, international financial
a special drawing rights reference rate. When the
Bank, regional development banks, and other multi-
institution bylaws prohibit granting debt relief or can-
discount rate is greater than the loan interest rate,
lateral and intergovernmental agencies. • Short-term
celing debts directly. However, the recent decrease
the present value is less than the nominal sum of
debt includes all debt having an original maturity of
in multilateral debt service ratios for some countries
future debt service obligations.
one year or less and interest in arrears on long-term
reflects debt relief from special programs, such as
Debt ratios are used to assess the sustainability of
debt. • Total reserves comprise holdings of mon-
the Heavily Indebted Poor Countries (HIPC) Debt
a country’s debt service obligations, but no absolute
etary gold, special drawing rights, reserves of IMF
Initiative and the Multilateral Debt Relief Initiative
rules determine what values are too high. Empirical
members held by the IMF, and holdings of foreign
(MDRI) (see table 1.4.) Other countries have accel-
analysis of developing countries’ experience and
exchange under the control of monetary authorities.
erated repayment of debt outstanding. Indebted
debt service performance shows that debt service
• Present value of debt is the sum of short-term
countries may also apply to the Paris and London
difficulties become increasingly likely when the pres-
external debt plus the discounted sum of total debt
Clubs to renegotiate obligations to public and private
ent value of debt reaches 200 percent of exports.
service payments due on public, publicly guaranteed,
creditors.
Still, what constitutes a sustainable debt burden var-
and private nonguaranteed long-term external debt
Because short-term debt poses an immediate
ies by country. Countries with fast-growing econo-
over the life of existing loans.
burden and is particularly important for monitoring
mies and exports are likely to be able to sustain
vulnerability, it is compared with the total debt and
higher debt levels.
foreign exchange reserves that are instrumental in providing coverage for such obligations. The present Data sources The burden of external debt service declined for most regions over 1995–2007
6.10a
Data on external debt are mainly from reports to the World Bank through its Debtor Reporting Sys-
Percent
Total debt service (% of exports of goods, services, and income)
Total debt service (% of GNI)
40
tem from member countries that have received IBRD loans or IDA credits, with additional information from the files of the World Bank, the IMF,
30
the African Development Bank and African Development Fund, the Asian Development Bank and
20
Asian Development Fund, and the Inter- American Development Bank. Data on GNI, exports of
10
goods and services, and total reserves are from 0 1995 2000 2007
1995 2000 2007
1995 2000 2007
1995 2000 2007
1995 2000 2007
1995 2000 2007
East Asia & Pacific
Europe & Central Asia
Latin America & Caribbean
Middle East & North Africa
South Asia
Sub-Saharan Africa
the World Bank’s national accounts files and the IMF’s Balance of Payments and International Financial Statistics databases. Summary tables
Declines in external debt service ratios for the Middle East and North Africa and Sub-Saharan Africa
of the external debt of developing countries are
were due partly to debt relief. Ratios for Europe and Central Asia in 2007 are nearly the same as in 2000
published annually in the World Bank’s Global
because debt service, GNI, and export revenue increased at a similar rate.
Development Finance and on its Global Develop-
Source: Global Development Finance data files.
ment Finance CD-ROM.
2009 World Development Indicators
363
GLOBAL LINKS
Ratios for external debt
6.11
Global private financial flows Equity flows
Debt flows
$ millions Foreign direct investment 1995
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
.. 70 0 472 5,609 25 12,026 1,901 330 2 15 10,689 a 13 393 0 70 4,859 90 10 2 151 7 9,319 6 33 2,957 35,849 .. 968 122 125 337 211 114 .. 2,568 4,139 414 452 598 38 .. 201 14 1,044 23,736 –315 8 6 11,985 107 1,053 75 1 0 7
2009 World Development Indicators
2007
288 477 1,665 –893 6,462 699 39,596 30,717 –4,749 653 1,785 72,195 48 204 2,111 –29 34,585 8,974 600 1 867 433 111,772 27 603 14,457 138,413 54,365 9,040 720 4,289 1,896 427 4,916 .. 9,294 11,858 1,698 183 11,578 1,526 –3 2,687 223 11,568 159,463 269 68 1,728 51,543 970 1,959 724 111 7 75
Portfolio equity 1995
.. 0 0 0 1,552 0 2,585 1,262 0 –15 0 .. 0 0 0 6 2,775 0 0 0 0 0 –3,077 0 0 –249 0 .. 165 0 0 0 1 4 .. 1,236 .. 0 13 0 0 0 10 0 2,027 6,823 0 0 0 –1,513 0 0 0 0 0 0
2007
0 0 0 0 1,785 0 17,104 3,684 2 153 5 3,360 0 0 0 9 26,217 101 0 0 0 –13 –42,041 0 0 404 18,510 43,625 790 0 0 0 148 435 .. –268 3,017 0 1 –3,199 0 0 260 0 5,279 –68,583 0 0 34 14,314 0 10,865 0 0 0 0
$ millions Commercial bank and other lending
Bonds 1995
2007
1995
2007
.. 0 –278 0 3,705 0 .. .. 0 0 0 .. 0 0 .. 0 2,636 –6 0 0 0 0 .. 0 0 489 317 .. 1,008 0 0 –4 0 0 .. .. .. 0 0 0 0 0 .. 0 .. .. 0 0 0 .. 0 .. 44 0 0 0
0 0 0 0 4,227 0 .. .. 0 0 19 .. 0 0 0 0 570 –87 0 0 0 0 .. 0 0 –862 1,718 .. 210 0 0 –25 0 92 .. .. .. 411 0 0 –30 0 .. 0 .. .. 1,000 0 0 .. 750 .. –150 0 0 0
.. 0 788 123 754 0 .. .. 0 –21 103 .. 0 41 .. –6 8,283 –93 0 –1 13 –65 .. 0 0 1,773 4,696 .. 1,250 0 –50 –20 14 265 .. .. .. –31 59 –311 –31 0 .. –48 .. .. –75 0 0 .. 38 .. –32 –15 0 0
0 –7 –642 1,536 –135 502 .. .. 94 –21 257 .. 0 230 –242 –2 18,629 5,079 11 0 0 –120 .. 0 –1 4,664 13,898 .. 3,068 –9 0 272 –167 5,270 .. .. .. –15 338 –103 –80 0 .. –44 .. .. 55 0 75 .. 47 .. –13 0 0 0
Equity flows
Debt flows
$ millions Foreign direct investment 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
50 4,804 2,144 4,346 17 .. 1,447 1,350 4,842 147 39 13 964 32 .. 1,776 7 96 95 180 35 275 5 –88 73 9 10 6 4,178 111 7 19 9,526 26 10 92 45 280 153 .. 12,206 3,316 89 7 1,079 2,393 46 723 223 455 103 2,557 1,478 3,659 685 ..
2007
816 37,231 22,950 6,928 754 .. 26,085 9,664 40,040 866 22,180 1,835 10,189 728 .. 1,579 119 208 324 2,247 2,845 130 132 4,689 2,017 320 997 55 8,456 360 153 339 24,686 493 328 2,807 427 428 170 6 123,609 2,753 382 27 6,087 3,788 2,376 5,333 1,907 96 196 5,343 2,928 22,959 5,534 ..
Portfolio equity
$ millions Commercial bank and other lending
Bonds
1995
2007
1995
2007
1995
2007
0 –62 1,591 1,493 0 .. 0 991 5,358 0 50,597 0 0 6 .. 4,219 0 0 0 0 0 0 0 .. 6 0 0 0 0 0 0 22 519 –1 0 20 0 0 46 0 –743 .. 0 0 0 636 0 10 0 0 0 171 0 219 –179 ..
0 –5,014 34,986b 3,559 0 .. 137,155 4,306 –14,874 0 45,455 346 841 1 .. –28,726 0 2 0 –13 791 0 0 0 –166 0 0 0 –669 0 0 50 –482 2 0 –64 0 0 5 0 –98,086 192 0 0 4,648 6,444 2,056 1,276 0 0 0 814 3,285 –453 –664 ..
–13 .. 286 2,248 0 .. .. .. .. 13 .. 0 0 0 .. .. .. 0 0 43 350 0 0 .. .. 0 0 0 2,440 0 0 150 3,758 0 0 0 0 0 .. 0 .. .. 0 0 0 .. .. 0 0 –32 0 0 1,110 250 .. ..
0 .. 8,227 2,843 0 .. .. .. .. 1,796 .. –2 10,243 0 .. .. .. 0 0 0 94 0 0 .. .. 0 0 0 –2,170 0 0 0 –514 –6 75 0 0 0 .. 0 .. .. 0 0 175 .. .. 750 930 0 0 1,003 28 2,821 .. ..
38 .. 967 58 –115 .. .. .. .. 15 .. –201 240 –163 .. .. .. 0 0 3 333 12 0 .. .. 0 –4 –23 1,231 0 0 126 1,401 24 –14 158 24 36 .. –5 .. .. –81 –24 –448 .. .. 317 –12 –311 –16 43 –215 228 .. ..
25 .. 17,704 2,317 –1,284 .. .. .. .. –65 .. –16 11,029 –14 .. .. .. –19 305 6,174 270 –5 0 .. .. 200 –1 –1 873 –1 –1 –37 8,678 364 5 –170 6 –137 .. 0 .. .. 77 –7 –487 .. .. –247 –17 103 14 2,086 2,523 15,036 .. ..
2009 World Development Indicators
365
GLOBAL LINKS
6.11
Global private financial flows
6.11
Global private financial flows Equity flows
Debt flows
$ millions Foreign direct investment 1995
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
419 2,065 2 –1,875 32 45c 7 11,535 236 150 1 1,248 8,086 56 12 52 14,939 4,158 100 10 120 2,068 .. 26 299 264 885 233 121 267 .. 21,731 57,800 157 –24 985 1,780 .. –218 97 118 328,380 s 5,713 93,118 54,387 38,731 98,831 50,798 9,558 30,181 817 2,931 4,546 229,549 78,432
2007
9,492 55,073 67 –8,069 78 3,110 94 24,137 3,363 1,483 141 5,746 60,122 603 2,426 37 12,286 49,730 .. 360 647 9,498 .. 69 .. 1,620 22,195 804 484 9,891 .. 197,766 237,541 879 262 646 6,700 .. 917 984 69 2,139,338 s 31,995 494,617 241,019 253,598 526,612 175,340 156,437 107,270 28,905 29,926 28,734 1,612,726 778,971
Portfolio equity 1995
2007
0 47 0 0 4 0c 0 –159 –16 .. 0 2,914 4,216 0 0 1 1,853 5,851 .. 0 0 2,253 .. 0 17 12 195 0 0 0 .. 8,070 16,523 0 0 270 0 .. 0 0 0 120,570 s 6 14,043 5,763 8,281 14,049 3,746 471 5,216 32 1,585 2,999 106,520 17,232
746 18,844 0 0 0 0 0 6,743 232 275 0 8,670 15,393 –322 –17 1 4,371 689 .. 0 3 4,241 .. 0 .. 30 5,138 0 –48 715 .. 31,483 197,517 –27 0 66 6,243 .. 0 4 0 715,869 s 12,429 126,002 63,710 62,292 138,431 35,168 26,232 29,569 –2,096 36,093 13,465 577,438 289,878
a. Includes Luxembourg. b. Based on data from the Reserve Bank of India. c. Includes Montenegro.
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2009 World Development Indicators
$ millions Commercial bank and other lending
Bonds 1995
0 –810 0 .. 0 0c 0 .. .. .. 0 731 .. 0 0 0 .. .. .. 0 0 2,123 .. 0 .. 588 627 0 0 –200 .. .. .. 144 0 –468 0 .. 0 0 –30 .. s .. 21,280 7,233 14,047 21,218 8,206 .. 11,311 660 286 851 .. ..
2007
0 36,505 0 .. 0 165 0 .. .. .. 0 4,645 .. 500 0 0 .. .. .. 0 0 –183 .. 0 .. 5 4,415 0 0 4,068 .. .. .. 814 0 760 –26 .. 0 0 0 .. s 1,649 83,745 18,717 65,028 85,395 2,286 58,236 8,699 97 9,477 6,600 .. ..
1995
413 444 0 .. –25 0c –28 .. .. .. 0 748 .. 103 0 0 .. .. .. 0 15 3,702 .. 0 .. –96 174 20 –9 –19 .. .. .. 39 201 –247 356 .. –2 –37 140 .. s –10 26,886 10,799 16,087 26,876 9,532 2,003 13,211 555 1,362 213 .. ..
2007
12,534 22,824 0 .. –32 4,072 0 .. .. .. 0 305 .. 283 0 –3 .. .. .. 3 8 5,840 .. 0 .. 29 34,243 –42 –1 13,975 .. .. .. –58 –222 –1,232 –60 .. 0 198 –3 .. s –776 211,782 62,434 149,348 211,006 25,832 131,197 36,949 –1,916 17,719 1,225 .. ..
6.11
About the data
Private fi nancial fl ows account for the bulk of
payments data on FDI do not include capital raised
edition includes portfolio equity flows data for non-
development finance and are split into two broad
locally, which has become an important source of
reporting countries based on data from the IMF’s
categories—equity and debt. Equity flows comprise
investment financing in some developing countries.
International Financial Statistics database. Bonds,
foreign direct investment (FDI) and portfolio equity.
In addition, FDI data capture only cross-border invest-
bank lending, and supplier credits are shown for only
Debt flows are financing raised through bond issu-
ment flows involving equity participation and thus
128 developing countries that report to the Debtor
ance, bank lending, and supplier credits.
omit nonequity crossborder transactions such as
Reporting System; nonreporting countries may also
The data on FDI and portfolio equity are based on
intrafirm flows of goods and services. For a detailed
receive debt flows.
balance of payments data reported by the Interna-
discussion of the data issues, see the World Bank’s
tional Monetary Fund (IMF). These data are supple-
World Debt Tables 1993–94 (vol. 1, chap. 3).
The volume of global private financial flows reported by the World Bank generally differs from that reported
mented by staff estimates using data from the United
Statistics on bonds, bank lending, and supplier
by other sources because of differences in sources,
Nations Conference on Trade and Development and
credits are produced by aggregating individual trans-
classifi cation of economies, and method used to
official national sources for FDI data and from market
actions of public and publicly guaranteed debt and
adjust and disaggregate reported information. In
sources for portfolio equity data.
private nonguaranteed debt. Data on public and pub-
addition, particularly for debt financing, differences
Under the internationally accepted definition of FDI,
licly guaranteed debt are reported through the Debtor
may also result based on whether particular install-
provided in the fifth edition of the IMF’s Balance of
Reporting System by World Bank member economies
ments of the transactions are included and how cer-
Payments Manual (1993), FDI has three components:
that have received either loans from the International
tain offshore issuances are treated.
equity investment, reinvested earnings, and short- and
Bank for Reconstruction and Development or cred-
long-term loans between parent firms and foreign affili-
its from the International Development Association.
ates. Distinguished from other kinds of international
These reports are cross-checked with data reported
• Foreign direct investment is net inflows of invest-
investment, FDI is made to establish a lasting interest
from market sources that also provide transactions
ment to acquire a lasting interest in or management
in or effective management control over an enterprise
data. Information on private nonguaranteed bonds
control over an enterprise operating in an economy
in another country. The IMF suggests as a guideline
and bank lending is collected from market sources,
other than that of the investor. It is the sum of equity
that investments should account for at least 10 per-
because official national sources reporting to the
capital, reinvested earnings, other long-term capital,
cent of voting stock to be counted as FDI. In practice
Debtor Reporting System are not asked to report the
and short-term capital, as shown in the balance of
many countries set a higher threshold. Also, many
breakdown between private nonguaranteed bonds
payments. • Portfolio equity includes net inflows
countries fail to report reinvested earnings, and the
and private nonguaranteed loans.
from equity securities other than those recorded
Definitions
definition of long-term loans differs among countries.
Previous editions of the table included portfolio
as direct investment and including shares, stocks,
FDI data do not give a complete picture of inter-
equity flows data only for countries that report to
depository receipts, and direct purchases of shares
national investment in an economy. Balance of
the Debtor Reporting System. The table in this year’s
in local stock markets by foreign investors • Bonds are securities issued with a fixed rate of interest for a period of more than one year. They include net flows
In 2007 middle-income economies received nearly 20 times more private capital flows than low-income economies did Middle-income economies Net inflows ($ billions) 1,000 800 600
cial bank and other lending includes net commercial
Net inflows ($ billions) 50
Commercial bank and other lending Bonds Portfolio equity Foreign direct investment Official development assistance
through cross-border public and publicly guaranteed and private nonguaranteed bond issues. • Commer-
Low-income economies
bank lending (public and publicly guaranteed and private nonguaranteed) and other private credits.
40 30
400
20
200
10
0
0
–200 1990
6.11a
–10 1990
Data sources Data on equity and debt flows are compiled from 2007
a variety of public and private sources, including
Net private flows to middle-income economies have increased since 2003, reaching $916 billion in
the World Bank’s Debtor Reporting System, the
2007—or 24 times the value of aid received. But aid remains the main source of external financing for
IMF’s International Financial Statistics and Bal-
low-income economies.
ance of Payments databases, and Dealogic. These
1995
2000
2007
1995
2000
data are also published in the World Bank’s Global Source: Global Development Finance data files and Organisation for Economic Cooperation and Development, Development Assistance Committee’s International Development Statistics.
Development Finance 2009.
2009 World Development Indicators
367
GLOBAL LINKS
Global private financial flows
6.12
Net official financial flows Total
International financial institutions
$ millions
$ millions
From From bilateral multilateral sourcesa,b sources 2007
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
368
2007
World Banka IDA 2007
IBRD 2007
IMF Concessional 2007
Nonconcessional 2007
United Nationsb,c
Regional development banksb ConcesNonOther sional concessional institutions 2007 2007 2007
$ millions UNICEF 2007
UNRWA 2007
UNTA 2007
Others 2007
6.9 6.7 –134.1 665.0 0.2 94.8
238.9 102.8 –4.3 31.8 –215.2 83.3
41.8 42.9 0.0 3.0 0.0 85.8
0.0 5.4 –5.9 0.0 –531.6 –0.7
54.8 –10.7 0.0 0.0 0.0 –13.6
0.0 3.7 0.0 0.0 0.0 0.0
94.3 0.0 0.0 1.0 0.0 0.0
0.0 8.3 0.0 –0.5 –5.2 –6.9
0.3 48.3 –4.3 0.0 316.3 7.2
26.4 0.7 1.1 12.1 0.6 0.7
0.0 0.0 0.0 0.0 0.0 0.0
3.1 0.9 2.3 2.6 2.0 1.2
18.2 3.3 2.5 13.6 2.7 9.6
3.1 –69.3 1,512.3
157.3 677.4 –10.4
52.0 408.3 0.0
16.0 0.0 –8.7
–25.6 0.0 0.0
–11.2 0.0 0.0
13.1 127.2 0.0
76.0 55.4 –5.3
24.9 21.2 0.0
1.4 16.0 0.7
0.0 0.0 0.0
0.7 7.7 0.8
10.0 41.6 2.1
–35.8 36.5 –52.4 5.1 –518.3 8.5 35.9 1.0 74.5 –31.9
121.1 71.2 42.4 –20.6 811.9 –292.7 191.2 52.9 105.2 109.9
42.5 15.7 48.0 –0.5 0.0 0.0 80.0 1.7 13.6 19.9
0.0 0.0 –23.8 –1.1 –198.6 136.6 0.0 0.0 0.0 –5.4
1.3 0.0 0.0 0.0 0.0 0.0 0.8 10.9 0.0 8.1
0.0 –14.8 –18.4 0.0 0.0 –347.0 0.0 0.0 0.0 0.0
45.3 43.8 0.0 –2.3 0.0 0.0 55.4 –0.1 52.2 17.7
0.0 –45.8 18.3 –15.2 1,031.1 –14.8 0.0 0.0 0.0 12.6
16.4 62.1 7.8 –8.2 –30.0 –67.5 24.4 3.4 1.1 35.3
5.4 1.7 0.8 1.2 2.1 .. 11.4 9.6 6.7 6.1
0.0 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0
1.9 1.4 0.9 1.7 3.8 .. 2.1 2.2 1.8 2.1
8.3 7.1 8.8 3.8 3.5 .. 17.1 25.2 29.8 13.5
0.0 6.1 –5.0 –1,038.4 .. –112.6 –138.2 –19.0 2.2 16.4 –159.6 .. ..
–6.1 25.0 35.2 1,094.3 .. 334.5 44.3 0.9 –135.6 –23.6 201.4 .. ..
–7.9 4.9 –0.7 –261.3 .. –0.7 61.3 –3.1 –0.2 –15.9 0.0 .. 0.0
0.0 –4.7 8.3 285.3 .. 132.4 0.0 0.0 –8.0 –29.7 2.3 .. –9.9
24.2 –14.8 0.0 0.0 .. 0.0 –64.3 0.0 0.0 –46.9 0.0 .. ..
–19.2 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 62.2 0.0 .. ..
–16.9 8.2 –0.2 0.0 .. –10.0 4.5 0.4 –12.3 –1.1 0.0 .. ..
–3.6 0.0 25.1 983.3 .. 476.9 –24.7 –13.0 –56.2 –37.6 25.5 .. ..
0.0 1.4 0.0 20.8 .. –272.2 –3.6 3.5 –61.5 20.6 169.1 .. ..
6.1 11.8 0.4 13.3 .. 1.6 43.3 1.8 0.6 9.7 0.3 0.8 ..
0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ..
2.1 2.1 1.5 7.8 .. 1.8 4.4 4.0 0.8 2.7 1.1 1.4 ..
9.1 16.1 0.8 45.1 .. 4.7 23.4 7.3 1.2 12.4 3.1 3.5 ..
125.7 –222.8 –1,140.8 –15.6 13.4 .. 102.5
30.8 558.3 1,212.1 –7.8 44.7 .. 425.6
–0.7 –1.1 –33.3 –0.8 19.6 0.0 132.3
34.4 –62.2 626.0 –16.3 0.0 –6.6 0.0
0.0 0.0 0.0 0.0 0.0 .. 0.0
63.0 –23.1 0.0 0.0 0.0 .. 0.0
–21.1 –26.5 0.2 –22.5 4.0 .. 137.2
–46.4 116.8 572.2 30.6 0.0 .. –18.2
–2.0 550.6 28.9 –5.1 3.5 .. 59.0
1.1 0.8 3.2 0.7 2.7 .. 51.4
0.0 0.0 0.0 0.0 0.0 .. 0.0
1.0 1.9 2.9 1.0 2.0 .. 3.8
1.5 1.1 12.0 4.6 12.9 .. 60.1
20.8 9.1 –55.3
–37.4 38.7 79.8
0.0 –3.9 65.4
–7.8 0.0 0.0
0.0 2.4 3.2
–34.0 0.0 0.0
–0.2 10.1 0.0
–26.4 0.0 1.2
27.3 21.2 0.3
0.7 1.5 0.8
0.0 0.0 0.0
1.1 1.5 1.1
1.9 5.9 7.8
–3.2 .. –40.1 –36.9 –8.7 –5.3
292.3 .. 464.1 4.7 20.4 117.4
247.5 0.0 0.0 –5.2 5.2 –14.6
0.0 0.0 98.2 0.0 0.0 0.0
0.0 .. 0.0 –10.7 –3.3 51.6
0.0 .. 0.0 0.0 0.0 –30.9
17.9 .. –17.6 13.9 1.8 92.0
–14.4 .. 199.0 –7.4 0.0 0.0
14.3 .. 176.0 –15.5 0.6 1.8
7.7 .. 1.1 5.8 2.5 5.0
0.0 .. 0.0 0.0 0.0 0.0
2.1 .. 1.3 2.1 1.7 1.4
17.2 .. 6.1 21.7 11.9 11.1
2009 World Development Indicators
Total
International financial institutions
$ millions
$ millions
From From bilateral multilateral sourcesa,b sources
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
IMF
World Banka IBRD 2007
Concessional 2007
Nonconcessional 2007
GLOBAL LINKS
6.12
Net official financial flows
United Nationsb,c
Regional development banksb ConcesNonOther sional concessional institutions 2007 2007 2007
$ millions
2007
2007
IDA 2007
UNICEF 2007
10.6 .. 525.1 –2,013.5 –152.3 ..
127.7 .. 1,385.3 192.3 147.7 ..
45.6 0.0 44.6 192.1 0.0 ..
0.0 –36.6 651.8 –601.5 139.5 ..
0.0 .. 0.0 0.0 0.0 ..
0.0 .. 0.0 0.0 0.0 ..
65.4 .. 0.0 100.9 0.0 ..
–18.8 .. 606.1 471.9 0.0 ..
20.6 .. –10.8 0.0 0.0 ..
1.1 .. 37.0 5.5 2.2 9.4
..
..
..
..
..
..
..
..
..
–52.2 .. –165.0 –4.5 48.6 .. .. .. 5.5 34.3 –0.5 –106.9 –3.1 0.0 .. .. –84.1 7.2 6.7 –1,150.4 45.5 40.4 4.0 –213.2 –11.9 10.7 240.2 –1.9 –142.2 .. –26.6
–58.1 .. –0.9 –109.8 151.2 .. .. .. 21.3 95.2 71.6 236.9 28.5 –232.9 .. .. –114.5 290.2 99.7 –326.9 205.9 81.5 24.8 844.9 48.8 44.6 604.2 372.4 38.2 .. 132.4
0.0 .. –2.6 0.0 92.8 .. 0.0 .. 13.1 16.4 0.0 0.0 8.0 –35.3 .. 0.0 –4.6 198.1 13.7 0.0 138.9 66.1 –0.6 0.0 38.2 16.4 –1.4 211.9 0.0 0.0 0.3
–28.9 .. –38.4 –76.3 0.0 .. –469.8 .. 0.0 0.0 –22.2 114.6 –3.6 –162.5 .. –58.5 –79.6 0.0 0.0 –301.8 0.0 0.0 19.8 329.0 –15.0 0.0 124.1 0.0 0.0 0.0 0.0
0.0 .. 0.0 0.0 104.5 .. .. .. –20.8 –3.0 0.0 0.0 –2.7 0.0 .. .. –10.5 12.0 10.2 0.0 4.1 12.8 0.0 0.0 27.6 –6.6 0.0 5.0 0.0 .. 33.2
0.0 .. –76.2 0.0 0.0 .. .. .. 0.0 0.0 0.0 77.7 0.0 –0.7 .. .. –46.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 –16.6 0.0 0.0 0.0 0.0 .. 0.0
–5.2 .. 0.0 –49.0 27.6 .. .. .. 26.5 57.1 0.0 0.0 3.3 –5.9 .. .. 0.0 36.7 26.7 0.0 28.4 9.9 –0.1 0.0 0.0 17.5 –0.9 73.2 0.0 .. 57.1
–32.1 .. 0.0 –10.4 –11.0 .. .. .. –6.9 0.9 –1.8 0.0 –1.0 –52.3 .. .. 23.4 4.2 –2.0 –36.5 0.0 –9.1 11.8 511.2 –4.6 0.0 113.2 7.6 0.0 .. 0.0
1.8 –0.2 68.2 .. .. –149.9 –3.1 –18.5 –40.2 –2,404.6 86.9 –2,275.1
200.7 121.1 223.5 .. .. 1,374.6 45.3 –88.6 –26.0 –34.8 202.2 –258.3
53.2 36.6 315.4 .. 0.0 867.3 0.0 –3.7 –1.5 0.0 –7.0 0.0
0.0 0.0 –175.9 .. 0.0 –109.0 30.4 –66.1 –14.4 15.3 –30.9 –258.3
18.2 11.9 0.0 .. .. –122.6 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 .. .. –29.5 –10.2 0.0 0.0 –20.5 0.0 0.0
112.1 27.9 48.0 .. .. 279.4 –7.6 –2.9 –15.1 –7.5 –27.4 0.0
–5.7 –2.5 –33.2 .. .. 422.5 1.7 –21.2 1.2 173.2 241.5 0.0
UNRWA 2007
UNTA 2007
Others 2007
0.0 .. 0.0 0.0 0.0 0.0
1.5 .. 9.6 6.8 2.6 1.9
12.3 .. 47.0 16.6 3.4 2.9
..
..
..
..
4.9 .. –0.2 21.6 –111.5 .. .. .. 2.3 3.8 95.6 –45.2 8.5 0.0 .. .. –2.5 8.1 7.6 5.7 2.3 –20.8 –9.0 0.0 7.6 5.7 365.1 29.9 –1.3 .. –3.0
0.6 .. 2.3 1.1 11.0 5.1 .. .. 1.1 2.6 .. 2.2 2.0 6.1 0.0 .. 0.8 12.5 11.9 0.5 14.5 2.4 0.0 1.8 0.7 1.2 1.4 14.3 14.3 0.8 7.7
0.0 .. 110.9 0.0 0.0 0.0 .. .. 0.0 0.0 .. 84.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.7 .. 1.6 1.0 2.8 3.4 .. .. 1.0 2.1 .. 1.3 1.4 2.1 0.9 .. 0.7 2.6 2.4 1.5 2.6 1.7 1.6 2.6 1.1 3.2 2.8 2.4 4.7 1.6 5.5
1.9 .. 1.7 2.2 35.0 2.1 .. .. 5.0 15.3 .. 2.3 12.6 15.6 0.3 .. 4.0 16.0 29.2 3.7 15.1 18.5 1.3 0.3 9.8 7.2 –0.1 28.1 20.5 4.8 31.6
8.0 12.7 0.0 .. .. 8.8 24.5 –3.1 1.1 –204.5 3.1 0.0
1.7 19.9 33.8 .. 0.0 17.0 0.4 2.2 0.9 1.4 2.6 ..
0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ..
1.7 2.6 4.8 .. 0.7 5.6 1.0 2.3 0.6 2.1 3.5 ..
11.5 12.0 30.6 .. 0.5 35.1 5.1 3.9 1.2 5.7 16.8 ..
2009 World Development Indicators
369
6.12
Net official financial flows Total
International financial institutions
$ millions
$ millions
From From bilateral multilateral sourcesa,b sources 2007
Romania –10.8 Russian Federation –850.0 Rwanda –3.4 Saudi Arabia .. Senegal 51.7 Serbia –36.0 Sierra Leone –3.0 Singapore .. Slovak Republic .. Slovenia .. Somalia 0.0 South Africa 0.0 Spain .. Sri Lanka 124.7 Sudan 266.0 Swaziland –5.2 Sweden Switzerland Syrian Arab Republic .. Tajikistan 180.6 Tanzania 9.4 Thailand –826.1 Timor-Leste .. Togo –2.0 Trinidad and Tobago .. Tunisia –90.3 Turkey –367.5 Turkmenistan –78.3 Uganda –0.6 Ukraine –246.3 United Arab Emirates .. United Kingdom United States Uruguay –13.2 Uzbekistan –75.3 Venezuela, RB 141.3 Vietnam 335.3 West Bank and Gaza .. Yemen, Rep. –23.3 Zambia 36.2 Zimbabwe 126.4 World .. s Low income 510.8 Middle income –10,875.0 Lower middle income –6,793.6 Upper middle income –4,081.4 Low & middle income –10,364.3 East Asia & Pacific –4,660.1 Europe & Central Asia –2,427.9 Latin America & Carib. –3,362.9 Middle East & N. Africa –1,595.8 South Asia 398.8 Sub-Saharan Africa 1,283.6 High income .. Euro area ..
World Banka
Concessional 2007
Nonconcessional 2007
Regional development banksb ConcesNonOther sional concessional institutions 2007 2007 2007
$ millions
2007
IDA 2007
50.3 –450.0 123.9 .. 395.0 213.0 57.5 .. .. .. 30.2 –16.5 .. 161.4 157.3 3.5
0.0 0.0 27.6 .. 132.9 47.4 14.2 .. 0.0 0.0 0.0 0.0 .. 24.9 0.0 –0.3
21.4 –510.1 0.0 .. 0.0 –17.5 0.0 .. –10.8 –13.0 0.0 –2.5 .. 0.0 0.0 –2.3
0.0 0.0 3.5 .. 0.0 0.0 0.0 .. .. .. 0.0 0.0 .. 0.0 0.0 0.0
–105.5 0.0 0.0 .. 0.0 0.0 0.0 .. .. .. 0.0 0.0 .. –5.3 –60.0 0.0
14.0 0.0 61.2 .. 50.3 0.0 0.9 .. .. .. 0.0 0.0 .. 81.3 0.0 –1.0
26.4 3.9 0.0 .. –5.3 71.2 0.0 .. .. .. 0.0 –26.3 .. –2.7 0.0 0.6
94.0 56.2 –4.7 .. 188.7 95.6 6.9 .. .. .. 0.0 0.0 .. 49.7 174.3 0.2
.. .. 9.1 0.1 4.8 0.6 10.7 .. .. .. 12.0 1.7 .. 0.8 18.4 2.4
.. 50.9 671.8 –332.4 .. 5.7 .. 68.7 –2,421.0 –3.6 498.6 –492.8 ..
–1.5 7.0 474.4 –3.4 .. 0.0 .. –2.1 –5.9 0.0 373.6 0.0 ..
0.0 0.0 0.0 –272.4 .. 0.0 .. –8.0 557.1 –5.6 0.0 –74.4 ..
.. 0.0 4.3 0.0 .. –6.7 .. 0.0 0.0 0.0 0.0 0.0 ..
.. 0.0 0.0 0.0 .. 0.0 .. 0.0 –4,016.7 0.0 0.0 –427.1 ..
.. 38.3 119.4 –3.8 .. 1.0 .. 0.0 0.0 0.0 81.2 0.0 ..
.. –0.4 –0.9 –42.3 .. –1.4 .. –131.9 0.0 0.0 –2.5 –33.9 ..
.. –4.6 20.0 –26.7 .. 0.3 .. 204.6 1,035.0 –1.9 –2.9 32.4 ..
3.8 3.0 15.0 1.1 2.2 4.0 0.0 0.7 1.2 1.6 18.5 1.2 ..
–2.7 48.1 –589.2 940.7 .. 162.3 140.9 12.7 .. s 8,409.6 5,108.9 7,488.6 –2,406.5 14,260.7 2,024.0 –3,049.3 2,731.2 3,030.8 4,032.8 4,840.8 .. ..
0.0 15.8 0.0 718.1 .. 87.9 51.2 0.0 .. s 4,849.5 539.7 481.3 58.4 5,389.2 689.9 411.5 106.9 55.1 1,399.2 2,726.5 .. ..
IBRD 2007
IMF
United Nationsb,c
13.2 0.0 0.0 –2.4 –7.4 0.0 0.0 0.2 –50.4 0.0 0.0 0.0 0.0 –25.4 0.0 159.2 .. .. .. .. 0.0 –73.0 –13.5 0.0 0.0 42.1 0.0 19.0 0.0 –0.1 0.0 0.0 .. s .. s .. s .. s –555.3 –1.2 –31.6 1,866.4 –17.5 –15.5 –5,092.9 298.0 867.9 –15.5 –652.6 364.1 –885.4 0.0 –4,440.3 –66.1 –572.8 –16.8 –5,124.4 2,164.4 –989.1 –34.9 0.0 351.5 –458.6 –50.4 –4,984.7 43.1 –244.2 69.8 –41.2 212.7 952.0 –75.8 –12.1 0.3 542.8 –34.6 –34.7 651.0 –375.7 109.2 –51.7 905.8 .. .. .. .. .. .. .. ..
–17.5 0.8 18.3 11.9 –430.1 –116.2 47.5 10.6 .. .. 0.0 136.6 5.2 –7.1 0.0 –2.4 .. s .. s 297.3 456.8 4,820.3 3,015.4 3,770.0 1,371.1 1,050.3 1,644.3 5,117.6 3,472.2 1,651.0 27.5 187.5 1,651.3 1,922.2 506.1 553.6 693.9 1,081.3 91.7 –277.9 501.7 .. .. .. ..
UNICEF 2007
0.5 2.8 1.1 4.3 6.4 5.7 10.0 4.7 984.1 s 505.5 196.0 164.5 26.9 979.7 68.8 23.5 28.6 42.0 107.1 439.6 4.4 ..
UNRWA 2007
UNTA 2007
Others 2007
.. .. 0.0 0.0 0.0 0.0 0.0 .. .. .. 0.0 0.0 .. 0.0 0.0 0.0
.. .. 1.8 0.7 2.5 1.3 2.1 .. .. .. 3.0 2.9 .. 3.7 4.3 1.3
.. .. 25.4 0.8 21.1 14.4 22.7 .. .. .. 15.2 7.7 .. 9.0 20.3 2.6
42.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ..
2.5 1.6 3.0 4.5 1.4 1.5 0.3 1.6 0.9 0.2 2.4 1.7 ..
7.6 6.0 36.6 10.6 4.5 7.0 2.5 3.8 7.4 2.1 28.3 7.3 ..
0.0 0.9 1.8 0.0 1.2 5.3 0.0 1.2 5.2 0.0 5.3 21.1 463.3 0.0 3.6 0.0 3.5 15.1 0.0 2.6 17.9 0.0 2.0 8.5 700.3 s 461.7 s 1,699.4 s 0.0 129.0 893.2 700.3 167.0 498.1 616.3 109.5 412.0 84.0 36.6 84.8 700.3 458.4 1,692.9 0.0 55.5 203.8 0.0 17.2 110.3 0.0 64.9 105.4 700.3 58.4 63.1 0.0 38.6 190.4 0.0 128.7 734.6 0.0 3.3 6.5 .. .. ..
a. Aggregates include amounts for economies that do not report to the World Bank’s Debtor Reporting System and may differ from aggregates published in Global Development Finance 2009. b. Aggregates include amounts for economies not specified elsewhere. c. World and income group aggregates include flows not allocated by country or region.
370
2009 World Development Indicators
About the data
6.12
Definitions
The table shows concessional and nonconcessional
Bank for Reconstruction and Development (IBRD) lend-
• Total net official financial flows are disbursements
financial flows from official bilateral sources and from
ing. Exceptions are also made for small island econo-
of public or publicly guaranteed loans and credits,
the major international financial institutions and UN
mies. The IBRD lends to creditworthy countries at a
less repayments of principal. • IDA is the Interna-
agencies. The international financial institutions fund
variable base rate of six-month LIBOR plus a spread,
tional Development Association, the concessional
nonconcessional lending operations primarily by sell-
either variable or fixed, for the life of the loan. The lend-
loan window of the World Bank Group. • IBRD is
ing low-interest, highly rated bonds backed by prudent
ing rate is reset every six months and applies to the
the International Bank for Reconstruction and
lending and financial policies and the strong financial
interest period beginning on that date. Although some
Development, the founding and largest member of
support of their members. Funds are then on-lent to
outstanding IBRD loans have a low enough interest
the World Bank Group. • IMF is the International
developing countries at slightly higher interest rates
rate to be classified as concessional under the DAC
Monetary Fund, which provides concessional lending
with 15- to 20-year maturities. Lending terms vary
definition, all IBRD loans in the table are classified as
through the Poverty Reduction and Growth Facility
with market conditions and institutional policies.
nonconcessional. Lending by the International Finance
and the IMF Trust Fund and nonconcessional lending
Corporation is not included.
through credit to its members, mainly for balance of
Concessional flows from international financial institutions are credits provided through concessional
The International Monetary Fund makes conces-
payments needs. • Regional development banks
lending facilities. Subsidies from donors or other
sional funds available through its Poverty Reduction
are the African Development Bank, which serves all
resources reduce the cost of these loans. Grants
and Growth Facility and the IMF Trust Fund. Eligibility
of Africa, including North Africa; the Asian Develop-
are not included in net flows. The Organisation for
is based principally on a country’s per capita income
ment Bank, which serves South and Central Asia
Economic Co-operation and Development’s (OECD)
and eligibility under IDA.
and East Asia and Pacific; the European Bank for
Development Assistance Committee (DAC) defines
Regional development banks also maintain conces-
Reconstruction and Development, which serves
concessional flows from bilateral donors as flows with
sional windows. Loans from the major regional devel-
Europe and Central Asia; and the Inter-American
a grant element of at least 25 percent, evaluated
opment banks are recorded in the table according to
Development Bank, which serves the Americas.
assuming a 10 percent nominal discount rate.
each institution’s classification and not according to
• Concessional financial flows are disbursements
the DAC definition.
made through concessional lending facilities. • Non-
World Bank concessional lending is done by the International Development Association (IDA) based
Data for flows from international financial institutions
concessional financial flows are all disbursements
on gross national income (GNI) per capita and perfor-
are available for 128 countries that report to the World
that are not concessional. • Other institutions, a
mance standards assessed by World Bank staff. The
Bank’s Debtor Reporting System. World Bank flows for
residual category in the World Bank’s Debtor Report-
cutoff for IDA eligibility, set at the beginning of the
nonreporting countries were collected from its opera-
ing System, includes other multilateral institutions
World Bank’s fiscal year, has been $1,095 since July 1,
tional records. Nonreporting countries may have net
such as the Caribbean Development Fund, Council
2008, measured in 2007 U.S. dollars using the World
flows from other international financial institutions.
of Europe, European Development Fund, Islamic
Bank Atlas method (see Users guide). In exceptional
Official flows from the United Nations are mainly con-
Development Bank, and Nordic Development Fund.
circumstances IDA extends temporary eligibility to
cessional flows classified as official development assis-
• United Nations includes the United Nations Chil-
countries above the cutoff that are undertaking major
tance but may include nonconcessional flows classified
dren’s Fund (UNICEF), United Nations Relief and
adjustments but are not creditworthy for International
as other official flows in OECD-DAC databases.
Works Agency for Palestine Refugees in the Near East (UNRWA), United Nations Regular Programme
Net nonconcessional lending from international financial institutions has declined in recent years as countries have paid off previous loans
6.12a
for Technical Assistance (UNTA), and other UN agencies, such as the International Fund for Agricultural
Net inflows ($ billions)
World Bank
International Monetary Fund
Regional development banks
Development, Joint United Nations Programme on
6
HIV/AIDS, United Nations Develop ment Programme,
3
United Nations Population Fund, United Nations
0
Refugee Agency, and World Food Programme.
–3
Data sources
–6
Data on net fi nancial fl ows from international –9 –12
financial institutions are from the World Bank’s 1990 2000 2007
1990 2000 2007
1990 2000 2007
1990 2000 2007
1990 2000 2007
1990 2000 2007
East Asia & Pacific
Europe & Central Asia
Latin America & Caribbean
Middle East & North Africa
South Asia
Sub-Saharan Africa
Debtor Reporting System and published in the World Bank’s Global Development Finance 2009 and electronically as GDF Online. Data on official
Latin America and the Caribbean paid off International Monetary Fund (IMF) loans in the early 2000s but still
flows from UN agencies are from the OECD-DAC
receives positive net disbursements from regional development banks, as do East Asia and Pacific, Middle
annual Development Cooperation Report and are
East and North Africa, and South Asia. Europe and Central Asia paid off loans from the IMF and World Bank in
available electronically on the OECD-DAC’s Inter-
2007. Nonconcessional lending to Sub-Saharan Africa is small because most borrowing is concessional.
national Development Statistics CD-ROM and at
Source: Global Development Finance data files.
www.oecd.org/dac/stats/idsonline.
2009 World Development Indicators
371
GLOBAL LINKS
Net official financial flows
6.13
Financial flows from Development Assistance Committee members
Net disbursements Total net flowsa
2007
$ 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
Official development assistancea
Total 2007
10,307 2,669 20,553 1,808 3,820 1,953 17,161 4,080 4,807 2,562 2,149 981 43,126 9,884 39,339 12,291 3,391 501 5,840 1,192 4,422 3,971 30,315 7,679 384 376 18,142 6,224 404 320 5,221 3,728 2,215 471 21,662 5,140 6,911 4,339 12,561 1,689 58,319 9,849 129,862 21,787 440,912 103,491
Other official flowsa
Bilateral grants 2007
Bilateral loans 2007
Contributions to multilateral institutions 2007
2007
2,265 1,351 1,268 3,192 1,722 575 6,690 8,091 249 824 1,252 5,983 253 4,813 247 2,624 252 3,257 2,862 1,256 6,572 19,729 75,326
3 –26 –29 –40 –72 9 –431 –141 .. .. 19 –205 .. –169 .. 258 18 82 71 18 –971 –827 –2,433
400 484 713 928 912 397 3,625 4,341 252 368 2,700 1,901 122 1,580 73 845 200 1,801 1,407 416 4,247 2,886 30,598
36 –624 –161 –4 –91 96 –1,179 –2,525 4 .. –261 211 .. .. 8 5 –237 6 –46 .. –43 –1,632 –6,438
Private flowsa
Total 2007
6,948 19,247 1,686 11,731 2,242 1,051 34,422 28,302 2,880 4,329 649 21,979 .. 11,575 26 1,488 1,980 16,516 2,541 10,368 47,846 97,545 325,350
Net grants by NGOsa
Bilateral Multilateral Foreign portfolio portfolio direct investment investment investment 2007 2007 2007
2,367 15,802 1,488 7,932 2,242 11 14,337 13,521 2,880 .. 1,353 18,037 .. –1,028 26 1,488 1,550 16,626 2,232 11,199 31,043 45,591 188,696
4,379 .. .. 2,386 .. 1,040 21,925 11,101 .. 4,329 –3,547 3,251 .. 11,951 .. .. .. 2 0 .. 16,587 59,796 133,199
.. .. .. .. .. .. .. –56 .. .. .. –1,896 .. 795 .. .. .. .. .. –833 .. –7,737 –9,727
Private export credits 2007
2007
202 3,445 198 1,413 .. .. –1,840 3,736 .. .. 2,843 2,586 .. –143 .. 0 430 –111 309 3 217 –105 13,182
655 123 342 1,355 94 20 .. 1,271 7 318 63 446 8 343 50 .. 2 .. 78 504 667 12,161 18,508
Official development assistance Commitmentsb
$ millions 2000 2007
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
1,863 1,886 863 1,695 1,297 1,968 3,013 4,244 2,461 2,116 512 948 7,181 10,651 8,287 12,854 370 446 388 1,070 2,586 3,787 14,679 14,424 202 334 5,475 6,686 199 308 1,954 3,339 681 425 2,412 4,834 1,949 3,333 1,279 1,654 6,470 10,358 14,698 26,933 78,821 114,295
Gross disbursementsb
$ millions 2000 2007
1,605 666 1,297 2,669 2,625 547 7,657 8,412 370 388 2,558 13,982 202 5,135 187 2,206 681 2,412 2,438 1,260 6,470 12,662 76,430
2,317 1,648 1,828 3,766 2,394 887 10,315 12,326 446 1,070 3,832 13,801 334 5,986 272 3,350 425 4,834 3,857 1,611 10,358 22,111 107,770
Note: Components may not sum to totals because of gaps in reporting. a. At current prices and exchange rates. b. At 2006 prices and exchange rates.
372
2009 World Development Indicators
Net disbursements
$ 2000
millionsb
1,605 662 1,262 2,632 2,597 537 6,287 7,288 370 388 2,202 11,587 202 4,989 187 2,195 443 2,077 2,438 1,257 6,398 11,604 69,210
2007
2,317 1,622 1,756 3,729 2,301 887 8,867 11,069 446 1,070 3,547 7,812 334 5,629 272 3,350 420 4,566 3,857 1,605 8,774 21,231 95,462
Per capitab $ 2000 2007
% of 2000
83 82 123 86 486 104 107 89 34 102 39 91 459 313 49 489 43 52 275 175 109 42 82
0.27 0.23 0.36 0.25 1.06 0.31 0.30 0.27 0.20 0.29 0.13 0.28 0.72 0.84 0.25 0.76 0.26 0.22 0.80 0.34 0.32 0.10 0.22
110 195 166 113 420 167 144 135 40 247 60 61 727 343 64 707 41 101 420 211 145 70 107
GNIa
% of general government disbursements a
2007
2000
2007
0.32 0.50 0.43 0.29 0.81 0.39 0.38 0.37 0.16 0.55 0.19 0.17 0.91 0.81 0.27 0.95 0.22 0.37 0.93 0.37 0.36 0.16 0.28
0.73 0.46 0.74 0.60 1.96 0.64 0.62 0.57 0.42 0.87 0.28 0.86 1.76 1.86 0.57 1.83 0.60 0.57 1.35 1.11 0.80 0.31 0.60
0.89 1.05 0.90 0.75 1.64 0.85 0.75 0.87 0.38 1.47 0.41 0.50 2.19 1.81 0.64 2.41 0.48 1.01 1.90 1.24 0.83 0.44 0.71
6.13
About the data
The flows of official and private financial resources
dropped. ODA recipients now comprise all low- and
official flows are transactions by the official sector
from the members of the Development Assistance
middle-income countries except those that are mem-
whose main objective is other than development or
Committee (DAC) of the Organisation for Economic
bers of the Group of Eight or the European Union
whose grant element is less than 25 percent. • Pri-
Co-operation and Development (OECD) to developing
(including countries with a firm date for EU acces-
vate flows are flows at market terms financed from
economies are compiled by DAC, based principally on
sion). The content and structure of tables 6.13–6.16
private sector resources in donor countries. They
reporting by DAC members using standard question-
were revised to reflect this change. Because official
include changes in holdings of private long-term
naires issued by the DAC Secretariat.
aid flows are quite small relative to ODA, the net
assets by reporting country residents. • Foreign
effect of these changes is believed to be minor.
direct investment is investment by residents of DAC
The table shows data reported by DAC member economies and does not include aid provided by the
Flows are transfers of resources, either in cash or
member countries to acquire a lasting management
Commission of the European Communities—a multi-
in the form of commodities or services measured on
interest (at least 10 percent of voting stock) in an
lateral member of DAC.
a cash basis. Short-term capital transactions (with
enterprise operating in the recipient country. The
DAC exists to help its members coordinate their
one year or less maturity) are not counted. Repay-
data reflect changes in the net worth of subsidiaries
development assistance and to encourage the
ments of the principal (but not interest) of ODA loans
in recipient countries whose parent company is in
expansion and improve the effectiveness of the
are recorded as negative flows. Proceeds from offi -
the DAC source country. • Bilateral portfolio invest-
aggregate resources flowing to recipient economies.
cial equity investments in a developing country are
ment is bank lending and the purchase of bonds,
In this capacity DAC monitors the flow of all financial
reported as ODA, while proceeds from their later sale
shares, and real estate by residents of DAC member
resources, but its main concern is official develop-
are recorded as negative flows.
countries in recipient countries. • Multilateral port-
ment assistance (ODA). Grants or loans to countries
The table is based on donor country reports and
folio investment is transactions of private banks and
and territories on the DAC list of aid recipients have
does not provide a complete picture of the resources
nonbanks in DAC member countries in the securities
to meet three criteria to be counted as ODA. They
received by developing economies for two reasons.
issued by multilateral institutions. • Private export
are undertaken by the official sector. They promote
First, flows from DAC members are only part of the
credits are loans extended to recipient countries
economic development and welfare as the main
aggregate resource flows to these economies. Sec-
by the private sector in DAC member countries to
objective. And they are provided on concessional
ond, the data that record contributions to multilateral
promote trade; they may be supported by an offi -
financial terms (loans must have a grant element of
institutions measure the flow of resources made
cial guarantee. • Net grants by nongovernmental
at least 25 percent, calculated at a discount rate of
available to those institutions by DAC members, not
organizations (NGOs) are private grants by NGOs,
10 percent). The DAC Statistical Reporting Directives
the flow of resources from those institutions to devel-
net of subsidies from the offi cial sector. • Com-
provide the most detailed explanation of this defini-
oping and transition economies.
mitments are obligations, expressed in writing and
tion and all ODA-related rules.
Aid as a share of gross national income (GNI), aid
backed by funds, undertaken by an official donor to
This definition excludes nonconcessional fl ows
per capita, and ODA as a share of the general gov-
provide specified assistance to a recipient country
from official creditors, which are classified as “other
ernment disbursements of the donor are calculated
or multilateral organization. • Gross disbursements
official flows,” and aid for military purposes. Trans-
by the OECD. The denominators used in calculating
are the international transfer of financial resources
fer payments to private individuals, such as pen-
these ratios may differ from corresponding values
and goods and services, valued at the cost to the
sions, reparations, and insurance payouts, are in
elsewhere in this book because of differences in tim-
donor.
general not counted. In addition to financial flows,
ing or definitions.
ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and
Definitions
assistance to refugees, contributions to multilateral
• Net disbursements are gross disbursements of
institutions such as the United Nations and its spe-
grants and loans minus repayments of principal on
cialized agencies, and concessional funding to multi-
earlier loans. • Total net flows are ODA or official
lateral development banks.
aid flows, other official flows, private flows, and net
A DAC revision of the list of countries and terri-
grants by nongovernmental organizations. • Official
tories counted as aid recipients has governed aid
development assistance refers to flows that meet
reporting for the three years starting in 2005. In the
the DAC definition of ODA and are made to coun-
past DAC distinguished aid going to Part I and Part II
tries and territories on the DAC list of aid recipients.
Data on financial flows are compiled by OECD-
countries. Part I countries, the recipients of ODA,
• Bilateral grants are transfers of money or in kind
DAC and published in its annual statistical report,
comprised many of the countries classified by the
for which no repayment is required. • Bilateral loans
Geographical Distribution of Financial Flows to Aid
World Bank as low- and middle-income economies.
are loans extended by governments or official agen-
Recipients, and its annual Development Coopera-
Part II countries, whose assistance was designated
cies that have a grant element of at least 25 percent
tion Report. Data are available electronically on
official aid, included the more advanced countries
(calculated at a 10 percent discount rate). • Contri-
the OECD-DAC’s International Development Sta-
of Central and Eastern Europe, countries of the for-
butions to multilateral institutions are concessional
tistics CD-ROM and at www.oecd.org/dac/stats/
mer Soviet Union, and certain advanced developing
funding received by multilateral institutions from DAC
idsonline.
countries and territories. This distinction has been
members as grants or capital subscriptions. • Other
Data sources
2009 World Development Indicators
373
GLOBAL LINKS
Financial flows from Development Assistance Committee members
6.14 6.14a
Allocation of bilateral aid from Development Assistance Committee members
Aid by purpose Net disbursements
$ millionsa 2000 2007
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
758 273 477 1,160 1,024 217 2,829 2,687 99 154 377 9,768 99 2,243 85 934 179 720 1,242 627 2,710 7,405 36,064
2,268 1,324 1,240 3,152 1,651 584 6,258 7,950 249 824 1,270 5,778 253 4,644 247 2,883 270 3,339 2,932 1,274 5,602 18,901 72,894
Share of bilateral ODA net disbursements Development projects, programs, and other resource provisions 2000 2007
28.0 36.4 36.4 39.9 66.1 40.8 30.7 19.8 69.6 79.1 52.0 61.6 84.6 43.0 39.7 58.1 35.4 70.2 61.9 58.8 49.9 15.1 42.3
27.3 26.1 28.9 58.8 70.3 28.5 26.3 25.4 28.2 67.3 61.4 34.8 77.5 67.1 51.2 60.8 37.0 70.2 65.4 48.5 67.3 71.5 53.1
% Technical cooperationb 2000 2007
Debt-related aid 2000 2007
Humanitarian assistance 2000 2007
Administrative costs 2000 2007
55.1 41.8 46.9 43.0 25.3 41.4 50.6 63.8 23.8 0.4 8.1 24.9 3.2 33.7 48.1 23.0 50.4 17.9 13.6 19.4 25.5 64.4 39.4
0.8 12.7 3.9 0.7 0.6 0.0 11.7 3.5 0.0 0.0 15.7 3.1 0.6 4.9 0.0 0.7 9.6 1.4 2.1 0.7 3.4 1.3 3.6
9.7 2.7 5.4 5.0 0.0 10.5 0.4 4.1 6.4 15.5 18.3 0.9 10.4 9.1 3.4 11.3 1.9 3.7 14.6 20.2 12.7 9.6 6.1
6.2 6.4 7.5 11.4 8.0 7.2 6.7 8.7 0.2 5.1 5.9 9.5 1.2 9.4 8.8 6.9 2.7 6.8 7.7 0.9 8.4 9.7 8.6
51.2 18.9 49.5 24.6 8.7 45.6 52.3 44.8 57.1 4.7 13.8 31.4 3.9 13.6 28.9 18.8 57.5 14.5 15.1 25.6 16.0 6.3 23.3
10.9 51.1 9.5 0.4 4.8 0.0 15.2 23.0 0.0 0.0 14.3 20.6 0.0 6.3 0.0 1.6 0.1 4.7 1.7 3.8 0.7 0.5 8.7
6.6 1.1 7.4 8.7 8.5 18.0 0.6 3.5 5.1 23.1 6.5 1.6 12.0 7.3 11.7 12.3 0.3 6.8 10.5 13.5 6.3 15.8 8.6
4.0 2.7 4.7 7.5 7.7 7.8 5.7 3.3 9.6 5.0 3.9 11.6 6.6 5.7 8.2 6.5 5.2 3.8 7.3 8.5 9.7 5.9 6.3
a. At current exchange rates and prices. b. Includes aid for promoting development awareness and aid provided to refugees in donor economies.
About the data Aid can be used in many ways. The sector to which
provide debt relief on liabilities that recipient coun-
human resources from donors or action directed to
aid goes, the form it takes, and the procurement
tries have difficulty servicing. Thus, this type of aid
human resources (such as training or advice). Also
restrictions attached to it are important influences
may not provide a full value of new resource flows
included are aid for promoting development aware-
on aid effectiveness. The data on allocation of offi -
for development, in particular for heavily indebted
ness and aid provided to refugees in the donor econ-
cial development assistance (ODA) in the table are
poor countries. Humanitarian assistance provides
omy. Assistance specifically to facilitate a capital
based principally on reporting by members of the
relief following sudden disasters and supports food
project is not included. • Debt-related aid groups
Organisation for Economic Co-operation and Devel-
programs in emergency situations. This type of aid
all actions relating to debt, including forgiveness,
opment (OECD) Development Assistance Committee
does not generally contribute to financing long-term
swaps, buybacks, rescheduling, and refinancing.
(DAC). For more detailed explanation of ODA, see
development.
• Humanitarian assistance is emergency and dis-
About the data for table 6.13. The form in which an ODA contribution reaches
tress relief (including aid to refugees and assistance Definitions
for disaster preparedness). • Administrative costs
the benefiting sector or the economy is important. A
• Net disbursements are gross disbursements of
are the total current budget outlays of institutions
distinction is made between resource provision and
grants and loans minus repayments of principal on
responsible for the formulation and implementation
technical cooperation. Resource provision involves
earlier loans • Development projects, programs,
of donor’s aid programs and other administrative
mainly cash or in-kind transfers and financing of
and other resource provisions are aid provided as
costs incurred by donors in aid delivery.
capital projects, with the deliverables being finan-
cash transfers, aid in kind, development food aid,
cial support and the provision of commodities and
and the financing of capital projects, intended to
supplies. Technical cooperation includes grants to
increase or improve the recipient’s stock of physical
nationals of aid-recipient countries receiving educa-
capital and to support recipient’s development plans
Data on aid flows are published by OECD-DAC in
tion or training at home or abroad, and payments
and other activities with finance and commodity
its annual statistical report, Geographical Distribu-
to consultants, advisers, and similar personnel and
supply. • Technical cooperation is the provision of
tion of Financial Flows to Aid Recipients, and its
to teachers and administrators serving in recipient
resources whose main aim is to augment the stock of
annual Development Cooperation Report. Data are
countries. Technical cooperation is spent mostly in
human intellectual capital, such as the level of knowl-
available electronically on the OECD-DAC’s Interna-
the donor economy.
edge, skills, and technical know-how in the recipient
tional Development Statistics CD-ROM and at www.
Two other types of aid are presented because they
country (including the cost of associated equipment).
oecd.org/dac/stats/idsonline.
serve distinctive purposes. Debt-related aid aims to
Contributions take the form mainly of the supply of
374
2009 World Development Indicators
Data sources
6.14b
6.14
Aid by sector Total sectorallocable aid
Social infrastructure and services
Economic infrastructure, Multiservices, and production sector sector or crossTransport Government Water and comand civil supply and cutting
Share of bilateral ODA commitment (%)
2007
Total 2007
Education 2007
Health 2007
Population 2007
sanitation 2007
society 2007
Total 2007
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
73.8 26.0 54.3 63.9 63.9 58.2 62.0 62.0 82.9 64.6 43.9 68.6 68.9 56.6 53.4 69.9 91.7 71.7 52.0 49.1 67.4 75.3 66.3
48.0 20.3 39.1 47.3 33.8 31.7 35.9 37.9 67.6 55.5 22.2 26.7 47.5 33.8 39.3 41.3 73.3 46.2 31.3 23.6 44.7 51.4 40.5
8.9 10.6 13.7 7.1 3.6 4.4 22.8 15.2 24.4 12.1 3.4 5.5 10.8 12.4 17.3 9.3 25.8 10.1 2.8 3.5 12.1 3.4 9.1
6.3 2.0 7.6 14.4 5.2 3.2 2.1 2.6 11.3 15.1 6.3 2.3 14.6 2.5 3.2 5.1 3.9 5.3 6.1 3.4 8.1 4.6 4.7
1.7 0.3 1.9 2.5 4.2 1.9 0.0 1.3 2.3 6.1 1.0 0.2 6.7 1.0 2.2 2.3 0.1 1.6 3.2 0.2 5.7 18.1 6.1
0.7 1.7 3.6 0.7 2.1 4.7 4.6 6.2 1.1 2.8 4.1 14.9 5.1 7.5 1.4 1.6 0.6 3.3 1.6 2.8 1.7 1.7 4.7
28.7 4.8 8.7 21.6 16.5 14.7 1.2 10.2 24.3 15.7 5.7 2.3 6.0 9.7 14.2 20.1 35.3 13.8 14.5 12.8 14.3 18.6 12.5
9.8 4.4 10.5 9.6 23.6 17.4 16.6 17.4 6.1 6.1 11.2 33.7 14.9 14.7 9.5 18.8 12.4 13.1 12.4 14.8 18.9 19.2 18.8
munication Agriculture 2007 2007
3.4 1.8 2.0 1.5 8.6 1.0 6.5 0.5 0.3 0.8 2.6 11.2 2.2 0.8 2.4 0.8 11.0 4.7 0.8 0.9 1.0 5.4 4.4
4.4 1.0 3.3 2.1 5.7 6.3 7.9 2.4 2.6 4.4 3.8 8.2 4.8 1.5 2.1 3.5 0.7 3.2 4.0 5.1 1.6 4.9 4.6
Untied aida
2007
2007
15.9 1.4 4.7 7.0 6.5 9.0 9.5 6.7 9.2 3.0 10.5 8.2 6.6 8.0 4.6 9.8 5.9 12.4 8.2 10.7 3.8 4.7 7.1
98.4 86.6 92.0 74.6 95.5 90.7 92.6 93.4 42.3b 100.0 b 59.8 95.1 100.0 b 81.1 87.8 99.9 58.0 b 89.1b 100.0 99.7 100.0b 68.5 84.6
a. Excludes technical cooperation and administrative costs. b. Gross disbursements.
About the data
Definitions
The Development Assistance Committee (DAC)
• Bilateral official development assistance (ODA)
and planning and activities promoting good gover-
records the sector classifi cation of aid using a
commitments are firm obligations, expressed in
nance and civil society. • Economic infrastructure,
three-level hierarchy. The top level is grouped by
writing and backed by the necessary funds, under-
services, and production sector group assistance
themes, such as social infrastructure and services;
taken by official bilateral donors to provide specified
for networks, utilities, services that facilitate eco-
economic infrastructure, services, and production;
assistance to a recipient country or a multilateral
nomic activity, and contributions to all directly pro-
and multisector or cross-cutting areas. The second
organization. Bilateral commitments are recorded
ductive sectors. • Transport and communication
level is more specifi c. Education and health and
in the full amount of expected transfer, irrespective
refer to road, rail, water, and air transport; post
transport and storage are examples. The third level
of the time required for completing disbursements.
and telecommunications; and television and print
comprises subsectors such as basic education and
• Total sector-allocable aid is the sum of aid that
media. • Agriculture refers to sector policy, devel-
basic health. Some contributions are reported as
can be assigned to specific sectors or multisector
opment, and inputs; crop and livestock production;
non-sector-allocable aid.
activities. • Social infrastructure and services refer
and agricultural credit, cooperatives, and research.
Reporting on the sectoral destination and the
to efforts to develop the human resources poten-
• Multisector or cross-cutting refers to support for
form of aid by donors may not be complete. Also,
tial of aid recipients. • Education refers to general
projects that straddle several sectors. • Untied aid
measures of aid allocation may differ from the per-
teaching and instruction at all levels, as well as con-
is ODA not subject to restrictions by donors on pro-
spectives of donors and recipients because of dif-
struction to improve or adapt educational establish-
curement sources.
ference in classification, available information, and
ments. Training in a particular field is reported for the
recording time.
sector concerned. • Health refers to assistance to
Data sources
The proportion of untied aid is reported because
hospitals, clinics, other medical and dental services,
Data on aid flows are published annually by the
tying arrangements may prevent recipients from
public health administration, and medical insur-
Organisation for Economic Co-operation and Devel-
obtaining the best value for their money. Tying
ance programs. • Population refers to all activities
opment (OECD) DAC in Geographical Distribution of
requires recipients to purchase goods and services
related to family planning and research into popula-
Financial Flows to Aid Recipients and Development
from the donor country or from a specified group of
tion problems. • Water supply and sanitation refer
Cooperation Report. Data are available electroni-
countries. Such arrangements prevent a recipient
to assistance for water supply and use, sanitation,
cally on the OECD-DAC’s International Develop-
from misappropriating or mismanaging aid receipts,
and water resources development (including rivers).
ment Statistics CD-ROM and at www.oecd.org/
but they may also be motivated by a desire to benefit
• Government and civil society refer to assistance
dac/stats/idsonline.
donor country suppliers.
to strengthen government administrative apparatus
2009 World Development Indicators
375
GLOBAL LINKS
Allocation of bilateral aid from Development Assistance Committee members
6.15
Aid dependency Net official development assistancea
$ millions 2000 2007
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgariab 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 Republicb Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estoniab Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
376
Aid per capita
Aid dependency ratios
$ 2000
2007
Aid as % of GNI 2000 2007
Aid as % of gross capital formation 2000 2007
Aid as % of imports of goods, services, and income 2000 2007
Aid as % of central government expense 2000 2007
136 317 201 302 53 216
3,951 305 390 241 82 352
.. 103 7 22 1 70
.. 96 12 14 2 117
.. 8.4 0.4 4.1 0.0 11.0
.. 2.7 0.3 0.5 0.0 3.7
.. 34.8 1.5 22.0 0.1 60.6
.. 9.4 0.9 2.8 0.1 10.3
.. 21.0 .. 4.1 0.1 21.2
.. 7.0 .. 0.7 0.1 8.5
.. .. 1.8 .. .. ..
167.7 .. 1.5 .. .. 23.0
139 1,172 40
225 1,502 83
17 8 4
26 9 9
2.8 2.4 0.3
0.9 2.0 0.2
12.8 10.8 1.2
3.4 9.0 0.6
5.8 11.7 0.5
1.5 7.2 0.3
.. .. 1.5
.. 21.7 0.5
241 482 737 31 232 311 338 93 396 381
470 476 443 104 297 .. 930 466 672 1,933
33 58 199 18 1 39 28 14 31 24
52 50 117 56 2 .. 63 55 46 104
10.7 5.9 12.4 0.5 0.0 2.5 13.0 12.9 10.9 4.0
8.7 3.7 2.8 0.9 0.0 .. 13.8 49.5 8.4 9.4
56.4 31.6 65.1 1.4 0.2 13.5 77.2 213.8 60.3 22.6
.. 23.9 12.9 2.1 0.1 .. .. .. 38.7 54.0
32.4 19.7 17.4 1.0 0.2 3.7 48.9 56.5 16.1 12.9
.. 10.3 4.0 2.0 0.1 .. .. 103.8 9.9 32.4
.. .. .. .. .. 7.6 .. .. .. ..
.. 16.7 7.8 .. .. .. .. .. .. ..
75 130 49 1,728 4 187 177 33 11 351 66 44 438
176 352 120 1,439 .. 731 1,217 127 53 165 164 92 ..
19 15 3 1 1 5 3 10 3 21 15 4 43
41 33 7 1 .. 17 19 34 12 9 37 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
10.4 5.7 0.1 0.0 .. 0.4 14.2 2.1 0.2 0.9 0.3 .. ..
82.4 40.4 0.3 0.4 0.0 1.3 119.1 4.6 0.4 31.2 1.8 .. 2.6
116.1 26.0 0.3 0.1 .. 1.4 67.3 6.1 0.8 9.7 1.0 .. ..
.. .. 0.2 0.6 0.0 1.1 .. 1.6 0.1 7.9 0.6 .. 1.1
.. .. 0.2 0.1 .. 1.5 .. 1.5 0.3 1.8 0.5 .. ..
.. .. 0.3 .. .. .. 15.2 .. 0.3 .. 0.8 .. 2.3
.. .. 0.4 .. .. 1.4 .. .. 0.9 4.1 0.8 .. ..
56 146 1,328 180 176 64 686
128 215 1,083 88 155 .. 2,422
6 12 20 29 48 47 10
13 16 14 13 32 .. 31
0.3 1.0 1.3 1.4 27.7 1.2 8.5
0.4 0.5 0.8 0.4 11.3 .. 12.5
1.2 4.6 6.8 8.1 116.7 4.0 41.4
1.6 2.0 4.0 2.1 106.7 .. 50.1
0.5 2.3 5.6 3.0 34.5 1.2 41.0
0.7 1.2 1.9 0.8 .. .. 34.8
.. .. .. .. .. 3.8 ..
.. .. .. 22.1 .. .. ..
12 50 169
48 72 382
10 36 36
36 42 87
0.3 12.4 5.3
0.5 12.1 3.7
1.1 67.8 20.8
1.6 48.5 10.9
0.5 .. 13.6
.. 19.6 6.0
.. .. 47.9
.. .. 16.4
600
1,151
30
49
12.4
7.7
50.2
22.5
17.3
11.2
..
26.0
263 153 80 208
450 224 123 701
23 19 59 24
34 24 73 73
1.4 5.0 39.5 5.4
1.3 5.0 35.4 11.4
7.7 24.9 329.8 20.8
6.4 39.0 200.7 40.6
4.4 15.7 .. 15.1
2.8 13.7 .. 30.0
12.5 .. .. ..
.. .. .. ..
2009 World Development Indicators
Net official development assistancea
Aid per capita
$ millions 2000 2007
Honduras Hungary b India Indonesia Iran, Islamic Rep. Iraq Ireland Israelb Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep.b Kuwaitb Kyrgyz Republic Lao PDR Latviab Lebanon Lesotho Liberia Libya Lithuaniab 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 Polandb Portugal Puerto Rico
Aid dependency ratios
$ 2000
2007
Aid as % of GNI 2000 2007
Aid as % of gross capital formation 2000 2007
Aid as % of imports of goods, services, and income 2000 2007
Aid as % of central government expense 2000 2007
449 252 1,463 1,654 130 100
464 .. 1,298 796 102 9,115
72 25 1 8 2 ..
65 .. 1 4 1 ..
6.5 0.6 0.3 1.1 0.1 ..
4.0 .. 0.1 0.2 0.0 ..
22.3 2.0 1.3 4.5 0.4 ..
11.3 .. 0.3 0.7 0.1 ..
8.9 0.6 1.8 2.5 0.7 ..
4.5 .. .. 0.6 .. ..
.. 1.3 2.0 .. 0.2 ..
16.7 .. 0.7 .. 0.2 ..
800
..
127
..
0.7
..
3.2
..
1.4
..
1.5
..
10
26
4
10
0.1
0.3
0.5
..
0.2
0.3
0.4
0.4
552 189 510 73 –198 3 215 282 91 199 37 67 14 99 251 322 446 45 360 216 20 –56 123 217 419 906 106 152 387
504 202 1,275 98 .. .. 274 396 .. 939 130 696 19 .. 213 892 735 200 1,017 364 75 121 269 228 1,090 1,777 190 205 598
115 13 16 3 –4 1 44 54 38 53 19 22 3 28 125 20 38 2 36 84 17 –1 30 91 15 50 2 81 16
88 13 34 4 .. .. 52 68 .. 229 65 187 3 .. 105 45 53 8 82 117 59 1 71 87 35 83 4 99 21
6.4 1.1 4.1 .. 0.0 0.0 16.7 16.9 1.2 1.2 3.4 17.4 .. 0.9 7.1 8.4 26.1 0.1 15.0 19.8 0.5 0.0 9.4 20.1 1.2 22.5 .. 4.4 7.0
3.0 0.2 5.3 .. .. .. 7.4 10.0 .. 3.9 6.4 124.3 0.0 .. 2.8 12.2 20.8 0.1 15.4 13.2 1.1 0.0 5.6 5.9 1.5 25.2 .. 3.0 5.7
29.2 5.7 23.1 .. –0.1 0.1 78.3 57.4 4.9 5.9 10.1 .. 0.3 4.4 31.5 55.1 188.7 0.2 60.5 103.3 1.8 0.0 39.7 68.8 4.4 68.9 .. 22.8 29.0
11.6 0.5 26.1 .. .. .. 28.1 24.2 .. 21.5 29.0 473.6 .. .. 12.0 44.2 79.3 0.5 63.7 53.1 4.1 0.0 16.0 14.4 4.5 118.9 .. 9.7 20.7
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 51.4 4.0 8.2 21.2
3.1 0.3 12.6 .. .. .. 8.3 32.1 .. 3.9 7.1 36.4 0.1 .. .. .. .. 0.1 .. .. 1.3 0.0 5.8 .. 3.0 39.9 .. 5.1 16.0
24.1 7.5 23.9 .. –0.2 .. 99.2 .. 4.1 3.8 .. .. .. 3.2 .. 78.1 .. 0.3 128.0 .. 2.2 –0.1 32.9 .. .. .. .. 14.1 ..
8.7 1.4 24.0 .. .. .. 39.8 89.7 .. 12.0 17.1 .. .. .. .. 108.1 .. .. 97.4 .. 5.0 .. 18.9 23.2 5.0 .. .. .. ..
561 208 174
834 542 2,042
110 19 1
149 38 14
15.0 11.7 0.4
14.9 12.8 1.4
47.2 101.4 ..
45.8 .. ..
23.5 43.0 1.1
17.2 .. 3.1
86.5 .. ..
76.4 108.4 ..
45 700 16 275 82 398 575 1,396
–31 2,212 –135 317 108 263 634 ..
19 5 5 51 15 15 8 36
–12 14 –40 50 18 9 7 ..
0.2 1.0 0.1 8.3 1.1 0.8 0.7 0.8
.. 1.5 –0.7 5.7 0.9 0.3 0.4 ..
1.9 5.5 0.6 35.7 6.1 3.7 3.6 3.3
.. 6.8 –2.9 25.7 4.9 1.1 2.9 ..
0.6 4.8 0.2 13.7 2.3 3.4 1.1 2.3
–0.1 5.2 –0.8 .. 1.6 0.8 0.9 ..
0.9 5.7 0.6 26.2 .. 4.2 4.3 ..
.. 9.5 .. .. 5.3 1.4 2.6 ..
2009 World Development Indicators
377
GLOBAL LINKS
6.15
Aid dependency
6.15
Aid dependency Net official development assistancea
$ millions 2000 2007
Romaniab Russian Federationb Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singaporeb Slovak Republicb Sloveniab Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emiratesb United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Aid per capita
Aid dependency ratios
$ 2000
2007
Aid as % of GNI 2000 2007
432 1,561 321 22 425 1,134 c 181 1 113 61 101 487
.. .. 713 –131 843 834 535 .. .. .. 384 794
19 11 39 1 41 151c 40 0 21 31 14 11
.. .. 73 –5 68 113 92 .. .. .. 44 17
1.2 0.6 18.7 0.0 9.2 12.6c 29.3 0.0 0.6 0.3 .. 0.4
.. .. 21.5 0.0 7.6 2.2 32.9 .. .. .. .. 0.3
276 220 13
589 2,104 63
15 7 13
29 55 55
1.7 1.9 0.9
1.8 5.0 2.1
158 124 1,035 698 231 70 –2 222 327 31 845 541 3
75 221 2,811 –312 278 121 18 310 797 28 1,728 405 ..
10 20 31 12 297 13 –1 23 5 7 34 11 1
4 33 70 –5 262 18 14 30 11 6 56 9 ..
0.9 15.8 11.6 0.6 71.6 5.4 0.0 1.2 0.1 1.2 13.9 1.8 0.0
5 8 3 22 219 14 76 14 10 w 15 6 6 9 11 5 22 9 16 3 20 3 ..
10 6 3 29 504 10 88 35 16 w 31 9 9 7 19 5 13 12 56 7 44 0 ..
0.1 1.4 0.1 5.5 13.3 3.0 25.8 2.5 0.2 w 4.6 0.5 0.6 0.3 0.9 0.5 1.1 0.2 1.0 0.7 4.1 0.0 ..
17 34 186 166 76 71 1,681 2,497 637 1,868 263 225 795 1,045 176 465 57,878 s 105,056 s 16,632 40,259 25,713 38,538 17,757 31,700 7,241 6,011 55,145 105,130 8,589 8,611 9,962 5,785 4,850 6,826 4,489 17,578 4,206 10,379 13,261 35,362 2,732 –74 .. ..
Aid as % of gross capital formation 2000 2007
6.0 3.2 101.2 0.1 44.3 150.1c 413.2 0.0 2.1 1.1 .. 2.3
Aid as % of imports of goods, services, and income 2000 2007
Aid as % of central government expense 2000 2007
.. .. 100.9 –0.2 23.2 9.0 239.5 .. .. .. .. 1.3
2.9 2.2 71.2 0.1 22.0 .. 68.8 0.0 0.7 0.5 .. 1.3
.. .. 73.4 –0.1 .. .. 84.0 .. .. .. .. 0.7
.. .. .. .. 71.2 .. 98.8 0.0 .. 0.9 .. 1.3
.. .. .. .. .. .. .. .. .. .. .. 0.9
6.0 9.7 5.1
6.7 18.8 16.7
3.2 8.5 0.9
4.3 16.1 2.3
7.3 .. ..
9.1 .. ..
0.2 6.1 17.4 –0.1 16.3 4.9 0.1 0.9 0.1 0.2 15.0 0.3 ..
4.7 125.1 64.7 2.5 285.9 29.4 –0.1 4.2 0.6 3.1 70.0 8.8 0.0
1.0 27.1 .. –0.5 .. .. 0.7 3.6 0.6 .. 65.7 1.1 ..
2.4 .. 46.4 0.9 .. 10.5 0.0 2.1 0.5 .. 53.7 2.8 ..
.. 5.9 43.3 –0.2 .. .. .. 1.3 0.4 .. 38.1 0.5 ..
.. 160.3 .. .. .. .. .. 4.1 .. .. 95.5 6.4 ..
.. .. .. –0.7 .. 27.6 .. 3.1 0.5 .. .. 0.8 ..
0.1 0.7 0.0 3.7 .. 1.1 10.5 .. 0.2 w 5.2 0.3 0.5 0.1 0.7 0.2 0.2 0.2 1.8 0.7 4.5 0.0 ..
0.6 8.3 0.3 18.2 47.4 14.3 140.8 17.5 0.8 w 23.4 1.9 2.3 1.2 3.8 1.6 5.3 1.2 4.0 3.0 23.1 0.0 ..
1.0 3.8 0.1 8.7 .. .. 38.1 .. .. w 23.3 0.9 1.2 0.4 2.5 0.5 0.7 0.8 6.7 2.0 21.6 .. ..
0.3 .. 0.3 9.3 .. 6.2 53.1 .. 0.6 w 14.0 1.5 2.2 0.8 3.0 1.4 3.1 0.9 3.4 3.6 11.0 0.0 ..
0.4 .. 0.1 3.6 .. 2.1 17.6 .. 0.5 w 11.7 0.8 1.3 0.3 2.1 0.5 0.5 0.7 5.5 .. 10.0 0.0 ..
0.3 .. 0.3 .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. .. .. ..
0.5 .. .. .. .. .. 39.9 .. .. w .. .. .. .. .. .. .. .. .. .. .. .. ..
Note: Regional aggregates include data for economies not listed in the table. World and income group totals include aid not allocated by country or region—including administrative costs, research on development issues, and aid to nongovernmental organizations. Thus regional and income group totals do not sum to the world total. a. The distinction between official aid, for countries on the Part II list of the Organisation for Economic Co-operation and Development Development Assistance Committee (DAC), and official development assistance was dropped in 2005. b. No longer on the DAC list of eligible official development assistance recipients. Data for 2000 are official aid. c. Includes Montenegro.
378
2009 World Development Indicators
6.15
About the data
provide measures of recipient country dependency
The nominal values used here may overstate the
opment assistance (ODA; see About the data for
on aid. But care must be taken in drawing policy con-
real value of aid to recipients. Changes in interna-
table 6.13). The data cover loans and grants from
clusions. For foreign policy reasons some countries
tional prices and exchange rates can reduce the pur-
Development Assistance Committee (DAC) member
have traditionally received large amounts of aid. Thus
chasing power of aid. Tying aid, still prevalent though
countries, multilateral organizations, and non-DAC
aid dependency ratios may reveal as much about
declining in importance, also tends to reduce its pur-
donors. They do not refl ect aid given by recipient
a donor’s interests as about a recipient’s needs.
chasing power (see About the data for table 6.14).
countries to other developing countries. As a result,
Ratios are generally much higher in Sub-Saharan
The aggregates refer to World Bank definitions.
some countries that are net donors (such as Saudi
Africa than in other regions, and they increased in
Therefore the ratios shown may differ from those
Arabia) are shown in the table as aid recipients (see
the 1980s. High ratios are due only in part to aid
of the Organisation for Economic Co-operation and
table 6.15a). Aid given before 2005 to countries
flows. Many African countries saw severe erosion
Development (OECD).
that were Part II recipients (see About the data
in their terms of trade in the 1980s, which, along
for table 6.13 for more information) is defined as
with weak policies, contributed to falling incomes,
Unless otherwise noted, aid includes official devel-
official aid. The table does not distinguish types of aid (program, project, or food aid; emergency assistance;
Definitions
imports, and investment. Thus the increase in aid
• Net official development assistance is flows (net
dependency ratios reflects events affecting both the
of repayment of principal) that meet the DAC defini-
numerator (aid) and the denominator (GNI).
tion of ODA and are made to countries and territories
postconflict peacekeeping assistance; or technical
Because the table relies on information from
on the DAC list of aid recipients. See About the data
cooperation), which may have different effects on the
donors, it is not necessarily consistent with infor-
for table 6.13. • Aid per capita is ODA divided by
economy. Expenditures on technical cooperation do
mation recorded by recipients in the balance of pay-
midyear population. • Aid dependency ratios are
not always directly benefit the economy to the extent
ments, which often excludes all or some technical
calculated using values in U.S. dollars converted at
that they defray costs incurred outside the country on
assistance—particularly payments to expatriates
official exchange rates. Imports of goods, services,
salaries and benefits of technical experts and over-
made directly by the donor. Similarly, grant commod-
and income refer to international transactions involv-
head costs of firms supplying technical services.
ity aid may not always be recorded in trade data or in
ing a change in ownership of general merchandise,
the balance of payments. Moreover, DAC statistics
goods sent for processing and repairs, nonmonetary
exclude purely military aid.
gold, services, receipts of employee compensation
Ratios of aid to gross national income (GNI), gross capital formation, imports, and government spending
for nonresident workers, and investment income. For
6.15a
Official development assistance from non-DAC donors, 2003–07
definitions of GNI, gross capital formation, and central government expense, see Definitions for tables
Net disbursements ($ millions) 2003
2004
2005
2006
2007
Czech Republic
91
108
135
161
179
Hungary
21
70
100
149
103
1.1, 4.8, and 4.10.
OECD members (non-DAC)
Iceland
18
21
27
41
48
366
423
752
455
699
Poland
27
118
205
297
363
Slovak Republic
15
28
56
55
67
Turkey
67
339
601
714
602
Korea, Rep.
Arab countries Kuwait
138
161
218
158
110
2,391
1,734
1,005
2,095
2,079
188
181
141
249
429
112
84
95
90
111
Aid Recipients, and in its annual Development
Taiwan, China
..
421
483
513
514
Cooperation Report. Data are available electroni-
Others
4
22
86
195
255
cally on the OECD-DAC’s International Develop-
3,436
3,712
3,905
5,172
5,560
ment Statistics CD-ROM and at www.oecd.org/
Saudi Arabia United Arab Emirates Other donors Israela
Total
Data sources Data on fi nancial fl ows are compiled by DAC and published in its annual statistical report, Geographical Distribution of Financial Flows to
Note: The table does not reflect aid provided by several major emerging non–Organisation for Economic Co-operation and Development donors because information on their aid has not been disclosed. a. Includes $68.8 million in 2003, $47.9 million in 2004, $49.2 million in 2005, $45.5 million in 2006, and $42.9 million in 2007 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.
dac/stats/idsonline. Data on population, GNI, gross capital formation, imports of goods and services, and central government expense used in computing the ratios are from World Bank and International Monetary Fund databases.
2009 World Development Indicators
379
GLOBAL LINKS
Aid dependency
6.16
Distribution of net aid by Development Assistance Committee members Ten major DAC donors
$ millions Total $ millions 2007
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
380
United States 2007
European Commission 2007
Germany 2007
France 2007
Japan 2007
United Kingdom Netherlands 2007 2007
Spain 2007
Canada 2007
Sweden 2007
Other DAC donors $ millions 2007
3,300.2 251.0 375.4 150.5 69.9 251.2
1,514.3 32.1 1.7 39.6 7.8 79.9
307.5 50.3 86.2 64.9 6.1 20.5
217.2 46.0 9.4 12.3 11.9 22.5
19.5 5.0 185.2 3.2 16.8 8.5
101.0 –1.6 7.3 23.1 15.1 85.2
268.7 6.8 0.6 10.0 1.0 7.5
88.8 6.8 0.0 –49.3 0.0 5.8
43.5 19.5 60.5 17.6 21.6 0.1
345.4 0.8 0.9 4.5 3.6 1.9
56.2 11.9 0.9 6.7 0.9 3.6
338.2 73.4 22.9 17.9 –14.9 15.7
118.8 765.3 55.7
49.0 49.1 8.1
9.0 101.5 6.9
24.3 43.1 18.9
9.5 –0.9 1.2
11.4 –6.6 0.4
0.4 245.6 0.8
0.1 99.5 0.0
0.0 12.2 0.2
1.5 60.2 0.5
1.4 11.8 10.5
12.3 150.0 8.3
319.7 396.6 352.1 101.0 295.5
25.3 122.4 31.6 44.8 3.9
81.4 43.9 63.7 37.3 25.7
29.6 39.8 29.0 2.5 76.8
56.4 7.2 5.6 9.2 112.9
6.8 36.9 5.4 –2.2 –9.9
0.0 –105.2 9.5 0.4 3.1
34.7 48.3 21.1 0.1 0.2
2.1 74.6 30.2 0.0 32.8
7.0 22.8 10.3 2.2 9.2
0.3 26.0 37.0 3.7 4.0
76.0 79.9 108.6 2.9 36.9
611.2 321.4 462.1 1,791.7
21.8 25.9 87.2 30.7
199.4 121.7 44.8 94.9
39.9 23.0 37.6 754.5
114.8 18.3 35.0 596.2
20.4 8.5 113.6 18.6
0.0 13.2 24.6 51.7
65.7 23.1 0.2 2.6
4.8 2.3 8.5 15.3
22.7 6.9 15.3 12.4
21.1 6.0 17.9 73.6
100.6 72.4 77.4 141.3
147.8 298.6 110.5 1,387.2
18.4 59.6 –1.1 40.8
30.0 75.2 12.5 56.0
5.3 29.5 27.5 289.3
54.2 47.9 10.2 132.3
2.6 9.9 8.8 435.7
5.1 5.1 0.5 162.4
6.3 6.8 0.1 24.8
1.0 4.1 6.7 67.5
5.7 11.6 3.5 32.9
7.6 5.9 0.3 10.6
11.8 43.0 41.6 135.0
702.7 946.4 97.8 56.3 179.6 155.9 59.1
403.5 132.4 9.6 –2.9 37.0 21.1 12.4
73.8 158.0 50.3 7.9 68.1 100.9 2.1
23.9 63.0 5.8 3.2 19.8 7.5 3.0
34.4 27.6 18.5 23.1 50.7 3.2 2.5
0.4 22.9 5.0 17.3 6.5 0.2 1.8
1.5 121.2 0.2 –12.0 –37.1 1.1 –4.9
28.0 50.7 0.0 1.8 1.5 0.1 0.2
64.3 17.7 1.0 10.0 3.1 0.0 24.0
20.1 33.0 2.5 3.9 3.9 0.3 8.4
18.6 33.4 3.2 1.1 5.7 6.1 0.1
34.3 286.4 1.9 2.9 20.3 15.5 9.6
132.3 215.4 995.2 96.6 81.6
4.5 42.7 462.4 39.0 1.6
107.3 34.9 208.1 25.2 36.1
8.7 22.0 153.9 9.2 3.9
16.5 4.0 77.1 5.7 0.9
3.0 3.0 –27.0 26.8 8.4
–37.4 –1.3 0.1 –96.7 5.2
0.0 0.5 14.6 0.4 4.4
27.3 71.3 11.4 61.1 0.2
2.9 3.5 17.7 4.2 1.4
0.2 0.5 2.4 4.2 2.6
–0.7 34.4 74.5 17.6 16.8
1,606.8
371.7
364.8
96.5
20.1
36.0
291.5
50.8
27.1
90.5
44.7
213.2
40.3 42.3 272.3
1.1 1.7 86.8
6.7 9.2 28.1
–1.4 0.9 38.3
32.2 0.7 4.5
0.3 6.4 7.0
0.0 5.0 8.7
0.0 10.1 7.9
0.2 4.8 0.0
1.7 1.5 4.2
0.0 0.8 10.8
–0.2 1.1 76.1
802.3
70.7
93.9
52.7
41.6
46.5
152.3
142.2
5.8
78.6
1.7
116.5
443.2 153.1 88.5 531.8
45.7 24.7 6.3 202.2
30.8 30.9 44.9 97.5
17.3 15.8 0.4 2.7
2.9 55.1 3.4 48.2
17.7 12.0 1.1 6.8
–27.6 1.1 0.1 0.0
25.2 0.0 0.0 0.0
252.9 2.3 12.6 15.4
11.2 7.1 1.0 119.2
28.7 0.4 0.0 2.3
38.5 3.8 18.9 37.4
2009 World Development Indicators
6.16
Ten major DAC donors
$ millions Total $ millions 2007
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 2007
European Commission 2007
Germany 2007
France 2007
Japan 2007
United Kingdom Netherlands 2007 2007
Spain 2007
Canada 2007
Sweden 2007
Other DAC donors $ millions 2007
330.9
71.1
41.3
26.2
1.6
20.8
0.0
0.4
110.8
13.1
19.8
25.9
992.8 418.0 77.4 9,016.1
84.9 117.3 2.2 3,749.3
89.6 55.9 10.0 24.5
128.0 31.4 42.3 2,095.0
–48.0 –84.0 18.1 759.2
99.9 –222.5 –12.1 858.8
510.5 69.7 0.5 60.2
6.9 42.4 2.2 5.9
12.6 –16.2 6.9 33.2
21.4 53.4 0.1 43.0
13.5 16.4 0.0 17.4
73.5 354.0 7.2 1,369.6
20.7
–5.5
37.7
–8.1
–1.2
–8.0
2.1
–5.3
0.5
6.8
0.2
1.4
344.8 190.2 938.1 88.3
259.5 77.7 325.2 32.5
55.4 9.4 114.0 16.6
27.9 49.6 62.5 3.1
–3.8 3.5 47.8 0.3
–28.3 43.3 57.1 0.0
0.5 0.7 111.3 1.2
0.1 0.4 11.0 0.7
10.3 0.3 44.9 1.6
8.8 0.6 22.4 2.1
0.2 0.7 45.5 7.8
14.3 4.2 96.3 22.5
138.5 230.7
39.8 1.4
19.9 8.9
25.0 23.8
1.0 35.5
15.7 81.5
13.0 1.7
0.4 0.1
0.3 0.0
0.7 4.7
7.4 19.8
15.5 53.4
524.3 79.7 265.9 16.3
127.1 19.5 102.7 4.0
60.5 17.4 39.5 1.1
32.6 6.8 10.0 3.9
102.6 –1.0 1.1 1.1
15.8 4.9 12.5 0.4
7.5 8.1 10.0 0.3
0.6 0.0 2.9 0.0
37.3 1.2 3.6 0.1
10.3 1.4 2.9 0.0
9.1 0.2 19.8 0.0
120.8 21.5 61.1 5.3
210.0 554.9 475.5 192.1 736.7 235.2 77.3 89.8 159.4 142.6 952.6 1,313.0 149.0 159.8 402.0
31.3 66.9 79.0 2.3 54.0 10.2 0.3 83.6 18.9 12.7 5.5 153.4 15.4 58.8 54.0
76.0 168.4 75.0 0.4 178.7 101.9 33.8 10.9 66.3 2.2 324.7 239.8 19.7 16.3 24.7
18.4 14.0 24.4 9.6 40.6 12.9 –0.1 28.2 9.3 30.3 142.8 61.8 5.8 21.2 48.9
3.5 142.0 0.9 –4.8 214.0 37.9 39.8 16.0 7.3 0.7 218.8 25.7 1.7 2.8 –2.4
20.2 111.2 40.3 223.0 9.7 23.5 2.8 –45.2 5.7 51.6 64.7 27.8 30.5 5.7 48.6
1.9 1.7 133.7 –20.2 0.0 0.1 0.1 2.3 6.8 1.2 0.3 115.7 18.0 0.9 88.4
9.0 11.9 6.8 0.0 64.9 0.1 0.0 –0.3 8.0 11.1 –0.3 80.7 2.4 1.0 3.3
1.4 5.4 –0.3 0.0 17.5 39.1 0.0 –17.2 0.7 9.0 84.8 53.8 0.0 28.5 0.1
0.2 3.2 16.0 1.0 55.9 1.7 0.6 6.7 0.4 2.1 8.9 57.3 0.4 1.9 14.8
14.1 0.5 20.4 0.4 26.5 0.8 0.0 0.2 17.1 2.2 1.3 103.6 11.4 4.1 –17.7
34.1 29.8 79.3 –19.6 75.0 7.0 0.2 4.6 19.1 19.4 101.1 393.5 43.8 18.7 139.3
581.8 350.2 1,558.6
76.5 41.3 240.6
87.8 117.4 173.4
30.8 21.4 25.5
2.9 56.7 11.8
30.6 28.3 26.8
–6.9 2.4 286.0
37.0 0.1 344.0
115.1 8.2 0.5
22.2 14.5 20.9
41.9 1.2 1.1
143.7 58.6 428.1
9.6 1,044.3 –136.5 307.9 105.7 236.4 581.8
7.3 433.6 7.3 0.8 24.9 94.1 84.8
0.0 67.9 3.0 20.4 23.0 65.2 34.4
0.4 62.4 1.1 –1.0 4.8 7.6 28.2
0.9 52.4 0.2 0.1 3.5 6.2 –4.1
0.9 53.2 2.0 –10.6 28.9 39.8 222.2
0.2 197.8 –162.3 1.0 –0.2 –251.0 0.6
0.0 36.2 0.1 0.0 0.1 –0.1 4.8
0.0 5.7 10.6 0.1 13.3 109.4 29.2
0.0 44.7 1.2 1.2 3.3 20.1 22.5
0.0 14.2 0.1 0.2 1.8 5.8 6.1
0.0 76.1 0.3 295.7 2.4 139.4 153.3
2009 World Development Indicators
381
GLOBAL LINKS
Distribution of net aid by Development Assistance Committee members
6.16
Distribution of net aid by Development Assistance Committee members Ten major DAC donors
$ millions
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Total $ millions 2007
United States 2007
European Commission 2007
453.1 –144.1 532.2 747.2 452.8
90.8 0.2 39.2 105.1 20.9
79.2 0.0 81.3 271.1 72.0
23.1 1.5 27.1 78.4 36.5
5.4 7.0 176.7 7.3 41.7
19.5 –154.0 32.0 7.2 30.1
95.0 0.8 11.7 15.5 88.1
27.8 0.0 22.4 3.1 47.1
335.3 741.8
58.7 227.1
78.6 144.7
13.6 101.5
6.2 105.0
3.9 4.7
26.4 –20.4
351.8 1,920.8 35.9
33.5 710.5 3.5
53.9 254.7 23.7
21.3 36.9 –5.9
6.5 13.8 0.3
44.2 51.6 7.3
49.3 121.9 2,017.8 –366.0 265.8 95.8 15.4 310.7 783.4 3.8 1,119.4 333.2
2.5 34.9 166.9 44.5 25.1 7.4 0.3 –10.9 –11.8 0.1 301.6 91.1
40.2 16.0 187.1 30.2 39.6 31.1 8.5 116.8 545.9 2.5 117.0 89.0
8.0 12.6 65.0 –2.0 6.2 12.1 0.4 27.5 –55.6 0.8 47.6 69.1
31.7 4.7 3.0 5.2 0.5 33.7 4.4 127.9 134.2 0.4 9.0 6.5
–2.9 16.5 5.6 97.6 75.2 60.8 40.7 19.5 7,949.8 s 1,600.9 4,873.1 4,394.4 393.7 7,947.5 604.4 436.1 474.0 2,746.1 544.3 2,059.4 2.3
2.9 2.8 6.8 154.5 55.9 5.8 1.1 15.5 6,258.4 s 1,702.6 3,570.5 2,441.9 1,044.0 6,242.3 431.6 216.8 355.1 1,668.1 30.0 2,869.0 16.2
29.1 0.5 9.2 112.7 19.1 10.4 63.0 10.1 18.5 1,556.1 40.6 67.7 1,369.8 212.3 533.3 185.1 19.9 17.7 834.2 165.3 121.3 423.5 139.1 52.1 83,989.0 s 18,901.2 s 11,095.5 s 27,993.9 5,405.8 4,201.8 33,464.3 8,326.2 5,104.0 27,519.4 7,536.3 3,284.8 5,182.4 743.4 1,466.8 84,053.1 18,893.2 11,072.0 6,548.1 734.8 450.2 4,608.9 900.3 1,450.3 5,805.6 1,398.1 1,042.7 14,819.7 4,958.1 1,688.3 7,029.5 2,237.2 655.9 26,801.6 4,557.6 4,409.5 –64.1 8.1 23.5
Germany 2007
France 2007
Japan 2007
United Kingdom Netherlands 2007 2007
Spain 2007
Canada 2007
Sweden 2007
8.4 0.0 41.6 2.5 3.2
9.7 0.0 47.9 8.0 5.7
21.8 0.0 0.2 33.5 1.7
72.4 0.4 52.2 215.6 105.6
12.4 44.9
2.3 1.1
12.9 14.0
25.8 19.4
94.7 100.0
11.5 206.2 2.2
14.6 202.5 0.0
14.7 28.4 0.0
30.7 70.8 1.4
23.1 68.1 0.0
97.8 277.5 3.4
–45.6 9.4 721.7 –477.4 13.1 0.5 0.1 20.6 86.6 –0.5 27.5 5.7
0.1 4.5 231.8 0.2 4.0 0.3 0.1 0.1 1.4 0.2 167.2 7.8
0.1 0.1 128.2 0.7 0.0 0.0 0.0 –2.0 –0.8 0.0 70.4 0.0
3.0 3.0 8.0 0.7 11.4 1.6 0.0 21.3 55.9 0.0 2.7 0.1
0.5 5.8 56.7 5.2 3.2 2.3 1.5 –0.8 –1.7 0.3 20.0 16.0
1.1 13.9 107.8 9.1 6.4 0.8 0.0 0.9 7.2 0.0 56.6 22.1
8.0 17.2 341.7 17.7 156.3 6.1 0.1 9.4 22.0 0.0 300.1 25.8
2.6 56.3 2.4 640.0 48.7 9.8 94.6 11.7 5,778.2 s 2,634.3 1,995.6 1,596.7 398.4 5,930.9 1,184.3 362.0 225.2 917.5 362.7 1,696.8 –152.7
0.1 0.1 0.1 97.2 22.7 25.3 74.2 94.1 5,601.5 s 2,999.3 552.4 694.3 –152.6 5,597.3 365.8 86.8 –572.6 118.2 1,323.5 2,415.5 4.3
0.0 0.1 0.0 47.7 30.3 31.7 71.5 7.1 4,643.9 s 1,616.8 717.5 530.2 170.2 4,643.9 136.1 65.8 268.8 85.0 256.6 1,663.0 0.0
12.7 0.0 15.9 31.5 72.7 0.2 0.9 0.7 3,338.9 s 452.9 1,772.4 1,500.8 215.8 3,318.7 144.4 121.7 1,181.4 386.8 89.4 520.9 20.2
2.0 0.3 1.7 0.5 0.8 6.2 1.6 0.0 2.0 28.9 47.0 303.3 42.3 54.3 222.1 1.6 0.8 11.4 23.8 53.7 187.0 13.0 19.7 51.2 3,152.2 s 2,932.2 s 14,337.2 s 1,306.1 831.3 5,242.3 707.9 697.2 5,147.4 562.6 588.4 4,389.1 112.6 103.4 686.7 3,138.9 2,932.2 14,336.3 179.9 172.9 2,143.8 51.1 207.3 710.9 450.2 203.1 779.7 136.8 113.2 2,001.5 523.7 103.5 902.9 1,164.9 995.2 4,449.9 13.3 0.0 0.9
Note: Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region.
382
2009 World Development Indicators
Other DAC donors $ millions 2007
About the data
6.16
Definitions
The table shows net bilateral aid to low- and middle-
information on such aid expenditures as development-
• Net aid refers to net bilateral official development
income economies from members of the Develop-
oriented research, stipends and tuition costs for aid-
assistance that meets the DAC definition of official
ment Assistance Committee (DAC) of the Organisa-
financed students in donor countries, and payment
development assistance and is made to countries
tion for Economic Co-operation and Development
of experts hired by donor countries. Moreover, a full
and territories on the DAC list of aid recipients. See
(OECD). The data include aid to some countries and
accounting would include donor country contributions
About the data for table 6.13 • Other DAC donors
territories not shown in the table and aid to unspeci-
to multilateral institutions, the flow of resources from
are Australia, Austria, Belgium, Denmark, Finland,
fied economies recorded only at the regional or global
multilateral institutions to recipient countries, and
Greece, Ireland, Italy, Luxembourg, New Zealand,
level. Aid to countries and territories not shown in the
flows from countries that are not members of DAC.
Norway, Portugal, and Switzerland.
table has been assigned to regional totals based
Previous editions of the table included only DAC
on the World Bank’s regional classification system.
member economies. The table also includes net
Aid to unspecified economies is included in regional
aid from the European Commission—a multilateral
totals and, when possible, income group totals. Aid
member of DAC.
not allocated by country or region—including admin-
The expenditures that countries report as official
istrative costs, research on development, and aid to
development assistance (ODA) have changed. For
nongovernmental organizations—is included in the
example, some DAC members have reported as
world total. Thus regional and income group totals
ODA the aid provided to refugees during the first 12
do not sum to the world total.
months of their stay within the donor’s borders.
The table is based on donor country reports of
Some of the aid recipients shown in the table are
bilateral programs, which may differ from reports by
also aid donors. See table 6.15a for a summary of
recipient countries. Recipients may lack access to
ODA from non-DAC countries.
Most donors increased their proportions of untied aid between 2000 and 2007
6.16a
Proportion of bilateral official development assistance commitment that was untied, 2007 (percent) 100
Australia France Denmark Belgium
DAC donors whose proportion of untied aid increased over 2000–07 Spain
Japan Germany Finland
Austria
New Zealand Netherlands
Canada
75 United States
Italy Portugal
50 Greece
25
DAC donors whose proportion of untied aid decreased over 2000–07 0 0
25
50
75
100
Data sources
Proportion of bilateral official development assistance commitment that was untied, 2000 (percent)
Six Development Assistance Committee (DAC) donor countries provided nearly 100 percent untied aid in 2007: Sweden, the United Kingdom, Norway, Luxembourg, Ireland, and Switzerland (data points at top right of figure, left to right). Tying aid may prevent recipients from getting the best value for their money. Such arrangements prevent a recipient from misappropriating or mismanaging aid receipts, but they may also be motivated by a desire to benefit donor country suppliers. On average, 85 percent of bilateral commitments by DAC donors in 2007 were untied, up from 81 percent in 2000. Note: Data for Ireland are for 2001 and 2007 and for New Zealand for 2002 and 2007. The United States did not report amount of untied aid until 2006. Source: Organisation for Economic Co-operation and Development, Development Assistance Committee.
Data on financial flows are compiled by DAC and published in its annual statistical report, Geographical Distribution of Financial Flows to Aid Recipients, and its annual Development Cooperation Report. Data are available electronically on the OECD-DAC’s International Development Statistics CD-ROM and at www.oecd.org/dac/stats/ idsonline.
2009 World Development Indicators
383
GLOBAL LINKS
Distribution of net aid by Development Assistance Committee members
6.17 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
384
Movement of people Net migration
International migrant stock
thousands 1990–95 2000–05
thousands 1995 2005
3,313 –409 –50 143 50 –500 519 262 –116 –260 0 85 105 –100 –1,000 14 –184 –349 –128 –250 150 –5 643 37 –10 90 –1,281 300 –250 1,208 14 62 214 153 –98 8 58 –129 –50 –600 –90 –359 –108 868 43 424 20 45 –560 2,688 40 470 –360 350 20 –133
35 71 299 38 1,590 455 4,068 717 292 1,006 1,269 909 146 70 73 39 730 47 464 295 116 159 5,003 67 78 136 441 2,432 108 2,049 169 228 2,314 721 90 454 250 118 88 172 26 12 309 795 103 6,089 164 148 250 9,092 1,038 549 45 870 32 22
1,112 –110 –140 175 –100 –100 593 180 –100 –500 0 180 99 –100 115 20 –229 –43 100 192 10 6 1,041 –45 219 30 –1,900 300 –120 –237 –10 84 –339 100 –129 67 46 –148 –400 –525 –143 229 1 –140 33 722 10 31 –248 1,000 12 154 –300 –425 1 –140
2009 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
By country of origin 1995 2007
2,679.1 5.8 1.5 246.7 0.3 201.4 .. 0.1 200.5 57.0 0.1 .. 0.1 0.2 769.8 .. 0.1 4.2 0.1 350.6 61.2 2.0 .. 0.2 59.7 14.3 104.7 0.2 1.9 89.7 0.2 0.2 0.2 245.6 24.9 2.0 .. .. 0.2 0.9 23.5 286.7 0.4 101.0 .. 0.1 .. 0.2 0.3 0.4 13.6 0.2 42.9 0.4 0.8 13.9
3,057.7 15.3 10.6 186.2 1.2 15.4 0.1 0.1 15.9 10.2 5.0 0.1 0.3 0.4 78.3 .. 1.6 3.3 0.6 375.7 17.7 11.5 0.5 98.1 55.7 1.0 149.1 .. 551.7 370.4 19.7 0.4 22.2 100.4 7.5 1.4 .. 0.4 1.3 6.8 6.0 208.7 0.3 59.9 .. 0.1 0.1 1.3 11.8 0.1 5.1 0.1 6.2 8.3 1.0 22.3
Refugees
Workers’ remittances and compensation of employees
thousands
$ millions Received Paid 1995 2007 1995 2007
By country of asylum 1995 2007
19.6 4.7 192.5 10.9 10.3 219.0 62.2 34.4 233.7 51.1 29.0 31.7 23.8 0.7 40.0 0.3 2.1 1.3 29.8 173.0 .. 45.8 152.1 33.9 0.1 0.3 288.3 1.5 0.2 1,433.8 19.4 24.2 297.9 198.7 1.8 2.7 64.8 1.0 0.2 5.4 0.2 1.1 .. 393.5 10.2 155.3 0.8 6.6 0.1 1,267.9 83.2 4.4 1.5 672.3 15.4 ..
.. 0.1 94.1 12.1 3.3 4.6 22.2 30.8 2.4 27.6 0.6 17.6 7.6 0.6 7.4 2.5 20.8 4.8 0.5 24.5 0.2 60.1 175.7 7.5 294.0 1.4 301.1 0.1 0.2 177.4 38.5 17.2 24.6 1.6 0.6 2.0 26.8 .. 264.9 97.6 .. 5.0 .. 85.2 6.2 151.8 8.8 14.9 1.0 578.9 35.0 2.2 0.4 25.2 7.9 ..
.. 427 1,120a 5 56 65 1,651 1,012 3 1,202 29 4,937 100a 7 .. 59 3,315 42 80a .. 12 11 .. 0 1 .. 1,053a .. 815 .. 4 123 151 544 .. 191 523 839 386 3,226 1,064 .. 1 27 74 4,640 4a 19 284 4,523 17 3,286 358 1 2a 109
.. .. 1,071 .. 2,120a .. .. 210 604 190 846 17 3,862 700 2,945 346 1,287 9 6,562 1 354 12 8,562 3,252 224a 26a 927 9 2,520 .. 141 200 4,382 347 2,086 34 50a 51a 0 5 353 52 167 22 .. .. .. 27 .. 15 7a 3a 32,833a 19 348 .. 4,523 150 .. .. 15 27 635 36 179 457 1,394 17 .. .. 1,332 101 989 209 3,414 7 3,094 4 7,656 223 3,711 1 .. .. 426 3 359 1 772 54 13,746 4,935 11a 99a 47 .. 696 12 8,570 11,270 117 5a 2,484 300 4,254 8 151 10 29a 3a 1,222 ..
.. 7 .. 603 472 176 3,559 2,985 435 3 109 3,192 67a 72 65 120 896 86 44 a 0 157 103 .. .. .. 6a 4,372 380 95 .. 102 271 19 86 .. 2,625 2,958 28 83 180 29 .. 96 15 391 4,380 186a 12 28 13,860 6a 1,460 18 119 5a 96
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 migration
International migrant stock
thousands 1990–95 2000–05
thousands 1995 2005
–120 101 –960 –725 –1,587 170 –1 484 573 –100 248 509 –1,509 222 0 –115 –598 –273 –30 –134 230 –84 –283 10 –99 –27 –7 –920 287 –260 –15 –7 –1,792 –121 –59 –450 650 –126 3 –101 190 94 –115 –3 –96 42 23 –2,611 8 0 –30 –441 –900 –77 –7 –4
31 293 6,951 219 2,478 134 264 1,919 1,483 20 1,261 1,618 3,295 366 35 584 996 482 23 713 594 5 199 506 272 114 60 325 1,135 63 118 12 467 473 7 103 246 112 124 625 1,387 732 27 139 582 231 573 4,077 73 32 183 51 214 963 528 351
–150 65 –1,350 –1,000 –1,250 –375 188 115 1,125 –100 270 130 –200 25 0 –80 264 –75 –115 –20 0 –36 –119 10 –30 –10 –5 –30 150 –134 30 0 –3,983 –250 –50 –550 –20 –99 –1 –100 110 102 –210 –28 –170 84 –150 –1,239 8 0 –45 –510 –900 –200 276 –10
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
By country of origin 1995 2007
1.2 2.3 5.0 9.8 112.4 718.7 .. 0.9 0.1 .. .. 0.5 0.1 9.3 .. .. 0.8 17.1 58.2 0.2 13.5 .. 744.6 0.6 0.1 12.9 0.1 .. 0.1 77.2 84.3 .. 0.4 0.5 .. 0.3 125.6 152.3 .. 0.1 0.1 .. 23.9 10.3 1.9 .. .. 5.3 0.2 2.0 0.1 5.9 0.5 19.7 0.2 ..
1.2 3.4 20.5 20.6 68.4 2,309.2 .. 1.5 0.1 0.8 0.5 1.8 5.2 7.5 0.6 1.2 0.7 2.3 10.0 0.7 13.1 .. 91.5 2.0 0.5 8.1 0.3 0.1 0.6 4.5 33.1 0.1 5.6 4.9 1.1 4.0 0.2 191.3 1.1 3.4 0.2 .. 1.9 0.8 13.9 .. .. 31.9 0.1 .. 0.1 7.7 1.6 2.9 0.1 ..
GLOBAL LINKS
Movement of people
6.17
Refugees
Workers’ remittances and compensation of employees
thousands
$ millions Received Paid 1995 2007 1995 2007
By country of asylum 1995 2007
0.1 11.4 227.5 0.1 2,072.0 116.7 0.4 .. 74.3 .. 5.4 1,288.9b 15.6 234.7 .. .. 3.3 13.4 .. .. 348.1b .. 120.1 4.0 .. 9.1 .. 1.0 5.3 17.9 34.4 .. 38.7 .. .. 0.1 0.1 .. 1.7 124.8 80.0 3.8 0.6 27.6 8.1 47.6 .. 1,202.5 0.9 9.6 0.1 0.6 0.8 0.6 0.3 ..
.. 8.1 161.5 0.3 963.5 42.4 9.3 1.2 38.1 .. 1.8 2,403.8 b 4.3 265.7 .. 0.1 38.2 0.7 .. .. 464.3b .. 10.5 4.1 0.7 1.2 .. 2.9 32.7 9.2 30.5 .. 1.6 0.2 .. 0.8 2.8 .. 6.5 130.7 86.6 2.7 0.2 0.3 8.5 34.5 .. 2,035.0 16.9 10.0 0.1 1.0 0.1 9.8 0.4 ..
124 152 6,223a 651 1,600a .. 347 702 2,364 653 1,151 1,441 116 298 a .. 1,080 .. 1 22a 41 1,225 411 .. .. 1 68a 14 a 1a 716a 112a 5a 132a 4,368a 1 .. 1,970 59 81a 16 57 1,359 1,858 75 8 804 a 239 39 1,712 112 16a 287 599 5,360 724 3,953 ..
2,625 413 35,262a 6,174 1,115a 389 580 1,041 3,165 2,144 1,577 3,434 223 1,588a .. 1,128 .. 715 1a 552 5,769 443 65 16a 1,427 267a 11a 1a 1,803a 212a 2a 215a 27,144a 1,498 194a 6,730 99 125a 16 1,734 2,548 650 740 78 9,221a 613 39 5,998 180 13a 469 2,131 16,291 10,496 3,945 ..
8 146 419a .. .. .. 173 1,408 1,824 74 1,820 107 503 4 .. 634 1,354 41 9a 1 .. 75 .. 222a 1 1a 11a 1a 1,329 42a 14 1 .. 1 .. 20 21 .. 11 9 2,802 584 .. 29a 5 603 1,537 4a 20 16a .. 34 151 262 527 ..
2009 World Development Indicators
2 235 1,580a 1,654 .. 781a 2,554 2,770 11,287 454 4,037 479 4,303 16 .. 4,070 3,824 220 1a 45 2,845 21 0 945a 566 18a 21a 1a 6,385 57a .. 12 .. 87 77a 52 45 32a 16 4 7,830 1,207 .. 29a 103 3,642 3,670 3a 151 135a .. 137 35 1,278 1,311 ..
385
6.17 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Movement of people Net migration
International migrant stock
thousands 1990–95 2000–05
thousands 1995 2005
–529 –270 2,263 917 –1,714 43 –500 285 –100 –100 451 –339 –380 472 250 200 9 3 38 22 –1,193 100 1,125 75 292 2,846 –256 –442 –168 –532 –38 –6 151 152 200 100 –70 200 –313 –345 591 –345 172 231 0 100 –122 –4 –24 –20 –22 –29 109 –30 50 –10 120 –5 100 –173 340 577 167 948 5,200 6,493 –20 –104 –340 –300 40 40 –256 –200 1 11 650 –100 –11 –82 –192 –75 ..d s ..d s –1,910 –2,858 –10,752 –15,770 –10,710 –11,295 –42 –4,475 –12,662 –18,629 –2,828 –3,847 –3,216 –1,798 –3,847 –6,811 –1,224 –2,618 –976 –2,484 –572 –1,070 12,645 18,522 5,099 6,887
Refugees
Workers’ remittances and compensation of employees
thousands
$ millions Received Paid 1995 2007 1995 2007
By country of origin 1995 2007
135 133 17.0 11,707 12,080 207.0 60 121 1,819.4 4,611 6,361 0.3 320 326 17.6 760c 512c 86.1c 55 119 379.5 992 1,843 .. 114 124 0.1 200 167 12.9 18 282 638.7 1,098 1,106 0.5 1,009 4,790 0.2 428 368 107.6 1,111 639 445.3 38 45 .. 906 1,117 .. 1,471 1,660 0.1 801 985 8.0 305 306 59.0 1,130 792 0.1 568 1,050 0.2 6 6 .. 169 183 93.2 46 38 .. 38 38 0.3 1,210 1,328 44.9 260 224 2.9 610 518 24.2 7,063 6,833 1.7 1,716 3,212 .. 4,198 5,408 0.1 28,522 38,355 0.2 93 84 0.3 1,474 1,268 0.1 1,019 1,010 0.5 27 21 543.5 1,201 1,680 72.8 228 265 0.4 271 275 0.1 638 511 .. 164,017 s 189,693 s 18,068.7b,e s 22,337 20,756 8,561.3 55,625 54,857 3,935.3 27,196 26,298 3,253.1 28,429 28,559 682.2 77,963 75,613 12,496.6 2,996 4,427 932.7 31,643 29,529 1,877.0 5,280 5,713 155.8 8,207 9,014 948.0 13,133 11,229 2,958.8 16,704 15,701 5,624.3 86,054 114,080 21.3 22,528 30,461 13.7
5.3 92.9 81.0 0.8 15.9 165.6 32.1 0.1 0.3 0.1 457.4 0.5 2.4 135.0 523.0 .. 0.1 .. 13.7 0.9 1.3 2.3 0.3 22.5 0.2 2.5 221.9 0.7 21.3 26.0 0.3 0.2 2.2 0.2 5.7 5.1 327.8 341.2 1.6 0.2 14.4 15,953.5b,e s 5,688.8 5,342.5 4,695.1 647.4 11,031.3 724.5 789.6 624.7 2,775.5 3,369.3 2,747.7 14.9 0.6
By country of asylum 1995 2007
0.2 1.8 9 8,533 2 351 246.7 1.7 2,503 4,100 3,939 17,716 7.8 53.6 21 51 1 68 13.2 240.7 .. .. 16,594 16,068 925a 76a 96a 66.8 20.4 146a 650.7c 98.0 105a,c 4,910a,c .. .. 4.7 8.8 24 148 .. 136 0.1 .. .. .. .. .. 2.3 0.3 1 1,483 3 73 22.3 0.3 272 284 31 207 0.6 0.9 .. .. .. .. 101.4 36.7 26 834 629 1,186 5.9 5.1 3,235 10,687 868 14,728 .. 0.2 809 2,527 16 314 674.1 222.7 346 1,769 1 2 0.7 0.8 83 100 4 8 199.2 75.1 288 775 336 1,142 82.9 45.7 1,473 2,035 10,114 16,273 1,955.3b 339a 824a 15a 235a 373.5b 0.6 1.1 .. 1,691 .. 184 829.7 435.6 1 14 1 46 106.6 125.6 1,695 1,635 .. .. .. .. .. .. .. .. 229a 5a 35a 10.9 1.3 15a .. .. 32a 92a 14 .. 0.2 0.1 680 1,716 36 15 12.8 7.0 3,327 1,209 .. 106 23.3 0.1 4 .. 7 .. 229.4 229.0 .. 849 .. 364 5.2 7.3 6 4,503 1 42 0.4 0.2 .. .. .. .. 90.9 299.7 2,469 8,234 2,581 5,048 623.3 281.2 2,179 2,972 22,181 45,643 0.1 0.1 .. 97 .. 4 2.6 1.1 .. .. .. .. 1.6 200.9 2 136 203 598 .. .. 34.4 2.4 .. 5,500a 1,793.9b 626a 598a .. 16a 1,201.0b 53.5 117.4 1,080a 1,283a 61a 120a 130.0 112.9 .. 59 59 124 0.5 4.0 44 .. 7 .. 18,068.7b,f s 15,953.5b,f s 101,562 s 371,263 s 98,648 s 248,066 s 6,402.8 4,232.4 6,207 39,940 922 2,388 8,611.2 9,530.4 51,121 241,233 9,763 51,507 7,153.7 8,579.4 32,898 161,264 1,643 12,172 1,457.6 951.1 18,223 79,970 8,120 39,335 15,014.0 13,762.8 57,328 281,174 10,685 53,895 447.0 472.4 9,701 65,340 1,618 12,909 1,420.6 166.0 7,855 50,377 4,771 25,908 94.0 530.6 13,335 63,107 1,114 3,582 5,683.1 7,943.9 13,319 31,678 702 5,673 1,625.5 2,355.0 10,005 52,086 475 2,007 5,743.8 2,294.9 3,113 18,586 2,005 3,816 3,055.6 2,190.8 44,234 90,089 87,963 194,171 1,688.3 934.2 30,072 61,551 26,446 73,962
a. World Bank estimates. b. Includes Palestinian refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East, who are not included in data from the UN Refugee Agency (UNHCR). c. Includes Montenegro. d. World totals computed by the United Nations sum to zero, but because the aggregates refer to World Bank definitions, regional and income group totals do not. e. Includes refugees without specified country of origin. f. Regional and income group totals do not sum to the world total because of rounding.
386
2009 World Development Indicators
About the data
6.17
Definitions
Movement of people, most often through migration,
registrations varies greatly. Many refugees may not
• Net migration is the net total of migrants during the
is a significant part of global integration. Migrants
be aware of the need to register or may choose not
period. It is the total number of immigrants less the
contribute to the economies of both their host coun-
to do so. And administrative records tend to over-
total number of emigrants, including both citizens and
try and their country of origin. Yet reliable statistics
estimate the number of refugees because it is eas-
noncitizens. Data are five-year estimates. • Interna-
on migration are difficult to collect and are often
ier to register than to de-register. The UN Refugee
tional migrant stock is the number of people born in
incomplete, making international comparisons a
Agency (UNHCR) collects and maintains data on
a country other than that in which they live. It includes
challenge.
refugees, except for Palestinian refugees residing
refugees. • Refugees are people who are recognized
The United Nations Population Division provides
in areas under the mandate of the United Nations
as refugees under the 1951 Convention Relating to
data on net migration and migrant stock. To derive
Relief and Works Agency for Palestine Refugees in
the Status of Refugees or its 1967 Protocol, the
estimates of net migration, the organization takes
the Near East (UNRWA). The UNRWA provides ser-
1969 Organization of African Unity Convention Gov-
into account the past migration history of a country
vices to Palestinian refugees who live in certain
erning the Specific Aspects of Refugee Problems in
or area, the migration policy of a country, and the
areas and who register with the agency. Registra-
Africa, people recognized as refugees in accordance
influx of refugees in recent periods. The data to cal-
tion is voluntary, and estimates by the UNRWA are
with the UNHCR statute, people granted refugee-like
culate these official estimates come from a variety
not an accurate count of the Palestinian refugee
humanitarian status, and people provided temporary
of sources, including border statistics, administra-
population. The table shows estimates of refugees
protection. Asylum seekers are people who have
tive records, surveys, and censuses. When no offi -
collected by the UNHCR, complemented by estimates
applied for asylum or refugee status and who have
cial estimates can be made because of insufficient
of Palestinian refugees under the UNRWA mandate.
not yet received a decision or who are registered as
data, net migration is derived through the balance
Thus, the aggregates differ from those published by
asylum seekers. Palestinian refugees are people (and
equation, which is the difference between overall
the UNHCR.
their descendants) whose residence was Palestine
population growth and the natural increase during
Workers’ remittances and compensation of employ-
between June 1946 and May 1948 and who lost their
ees are World Bank staff estimates based on data
homes and means of livelihood as a result of the
The data used to estimate the international migrant
from the International Monetary Fund’s (IMF) Balance
1948 Arab-Israeli conflict. • Country of origin refers
stock at a particular time are obtained mainly from
of Payments Statistics Yearbook. The IMF data are
to the nationality or country of citizenship of a claim-
population censuses. The estimates are derived
supplemented by World Bank staff estimates for
ant. • Country of asylum is the country where an
from the data on foreign-born population—people
missing data for countries where workers’ remit-
asylum claim was filed. • Workers’ remittances and
who have residence in one country but were born in
tances are important. The data reported here are the
compensation of employees received and paid com-
another country. When data on the foreign-born popu-
sum of three items defined in the fifth edition of the
prise current transfers by migrant workers and wages
lation are not available, data on foreign population—
IMF’s Balance of Payments Manual: workers’ remit-
and salaries earned by nonresident workers. Remit-
that is, people who are citizens of a country other
tances, compensation of employees, and migrants’
tances are classified as current private transfers from
than the country in which they reside—are used as
transfers.
migrant workers resident in the host country for more
the 1990–2000 intercensal period.
estimates.
The distinction among these three items is not
than a year, irrespective of their immigration status,
After the breakup of the Soviet Union in 1991 peo-
always consistent in the data reported by countries
to recipients in their country of origin. Migrants’ trans-
ple living in one of the newly independent countries
to the IMF. In some cases countries compile data on
fers are defined as the net worth of migrants who are
who were born in another were classified as interna-
the basis of the citizenship of migrant workers rather
expected to remain in the host country for more than
tional migrants. Estimates of migrant stock in the
than their residency status. Some countries also
one year that is transferred to another country at the
newly independent states from 1990 on are based
report remittances entirely as workers’ remittances
time of migration. Compensation of employees is the
on the 1989 census of the Soviet Union.
or compensation of employees. Following the fifth
income of migrants who have lived in the host country
For countries with information on the interna-
edition of the Balance of Payments Manual in 1993,
for less than a year.
tional migrant stock for at least two points in time,
migrants’ transfers are considered a capital transac-
interpolation or extrapolation was used to estimate
tion, but previous editions regarded them as current
the international migrant stock on July 1 of the refer-
transfers. For these reasons the figures presented in
Data on net migration are from the United Nations
ence years. For countries with only one observation,
the table take all three items into account.
Population Division’s World Population Prospects:
Data sources
estimates for the reference years were derived using
The 2006 Revision. Data on migration stock are
rates of change in the migrant stock in the years pre-
from the United Nations Population Division’s
ceding or following the single observation available.
Trends in Total Migrant Stock: The 2005 Revision.
A model was used to estimate migrants for countries
Data on refugees are from the UNHCR’s Statisti-
that had no data.
cal Yearbook 2007, complemented by statistics
Registrations, together with other sources—
on Palestinian refugees under the mandate of
including estimates and surveys—are the main
the UNRWA as published on its website. Data on
sources of refugee data. But there are difficulties
remittances are World Bank staff estimates based
in collecting accurate statistics. Although refugees
on IMF balance of payments data.
are often registered individually, the accuracy of
2009 World Development Indicators
387
GLOBAL LINKS
Movement of people
6.18
Characteristics of immigrants in selected OECD countries
Foreign-born population by country of origin Country of residence
Country of birth
Australia
United Kingdom New Zealand Italy Former Yugoslavia Germany Turkey France Italy Morocco United Kingdom China Italy Slovak Republic Former USSR Poland Turkey Former Yugoslavia Germany Former USSR Sweden Former Yugoslavia Algeria Morocco Portugal
Austria
Belgium
Canada
Czech Republic Denmark
Finland
France
% of foreign-born population 26.1 8.2 5.6 34.5 14.1 12.2 13.9 12.7 11.2 11.4 5.9 5.9 63.8 11.2 5.6 9.1 8.5 7.8 33.6 21.9 3.5 21.6 12.3 10.1
6.18a Country of residence
Country of birth
Germany
Former USSR Turkey Poland Albania Former USSR Germany Romania Slovak Republic Former Yugoslavia United Kingdom United States Former USSR Switzerland Former Yugoslavia Germany Koreab China Brazil Indonesia Turkey Morocco United Kingdom Samoa Australia
Greece
Hungary
Ireland
Italy
Japana
Netherlands
New Zealand
% of foreign-born population
Country of residence
Country of birth
% of foreign-born population
Norway
17.5 15.2 13.1 33.7 18.5 9.1 49.5 13.5 11.2 62.3 4.4 2.9 8.9 8.7 8.3 40.9 19.9 13.8 12.5 11.2 9.3 33.3 6.9 6.7
Sweden Former Yugoslavia Denmark Portugal Angola France Mozambique Slovak Czech Republic Republic Hungary Former USSR Spain Morocco Ecuador France Sweden Finland Former Yugoslavia Iraq Switzerland Former Yugoslavia Italy Germany United Ireland Kingdom India Pakistan United States Mexico Philippines Puerto Rico
9.6 7.5 7.1 28.4 14.0 12.9 63.2 15.2 8.1 14.5 9.9 7.8 18.4 13.1 5.7 16.1 15.9 12.1 11.7 10.1 6.7 26.3 4.3 4.1
a. Refers to individuals living in Japan not of Japanese nationality because data based on the country of birth are not available. b. Democratic People’s Republic of Korea and Republic of Korea combined.
Foreign-born population by gender, educational attainment, occupation, and sector of employment
Australia Austria Belgium Canada Czech Republic Denmark Finland France Germany Greece Hungary Ireland Italy Japan Netherlands New Zealand Norway Portugal Slovak Republic Spain Sweden Switzerland United Kingdom United States
6.18b
Gender
Educational attainment
Occupationa
Sector of employment
% of the foreignborn population ages 15 and older
% of the foreign-born population ages 15 and older
% of the employed foreign-born population ages 15 and older
% of the employed foreign-born population ages 15 and older
Male
Female
Primary
Secondary
Tertiary
49.4 47.9 48.1 48.1 45.5 48.6 49.6 49.5 50.3 50.1 44.1 49.6 45.6 46.8 48.6 48.1 48.9 49.1 43.7 50.3 48.6 47.8 46.7 49.6
50.6 52.1 51.9 51.9 54.5 51.4 50.4 50.5 49.7 49.9 55.9 50.4 54.4 53.2 51.4 51.9 51.1 50.9 56.3 49.7 51.4 52.2 53.3 50.4
41.3 49.4 53.3 30.1 38.6 36.9 52.6 54.8 45.8 42.7 41.1 29.6 54.3 25.9 49.2 18.7 18.3 54.7 29.3 56.3 29.5 41.6 40.6 39.2
32.8 39.3 23.8 31.9 48.7 39.2 28.5 27.2 39.3 41.4 39.1 29.3 33.5 44.2 31.6 50.4 51.2 25.9 55.0 22.6 46.2 34.7 24.5 34.7
25.8 11.3 23.0 38.0 12.8 23.9 18.9 18.1 14.9 15.9 19.8 41.1 12.2 30.0 19.2 31.0 30.5 19.3 15.7 21.1 24.3 23.7 34.8 26.1
Professionals Technicians
31.2 13.3 31.6 28.8 18.6 16.9 21.6 22.1 10.2 11.2 31.8 38.1 17.5 15.6c 25.3 33.4 20.9 21.3 23.8 15.5 19.0 23.1 34.2 28.9c
24.2 19.7 22.0 26.0 21.4 23.3 20.2 22.4 20.5 9.0 20.1 19.9 19.9 8.5c 27.6 25.3 26.0 24.8 26.1 14.0 21.2 27.2 26.0 11.2c
Operators
44.7 67.1 46.4 45.2 60.0 59.8 58.1 55.5 69.3 79.9 48.1 42.0 62.6 75.9c 47.1 41.3 53.1 53.9 50.0 70.5 59.8 49.8 39.8 59.9c
Agriculture Distributive and industry services
26.6 32.9 28.7 26.9 44.2 22.9 27.3 28.3b .. 49.7 33.3 28.0 45.2 .. 26.2 25.2 18.8 33.1 37.4 37.6 25.0 33.0 16.7 26.9
24.6 21.4 21.2 21.9 20.8 21.1 19.5 30.5b .. 15.4 24.5 17.9 15.5 .. 19.7 23.6 19.2 19.2 19.6 17.8 17.5 19.6 22.2 18.0
Personal and social services
Producer services
31.1 31.9 37.0 32.0 27.4 38.4 36.3 36.9b .. 28.8 31.8 36.0 33.3 .. 35.6 34.2 47.8 36.4 34.8 35.9 43.2 32.3 40.2 43.8
17.7 13.8 13.1 19.3 7.6 17.6 16.8 4.3b .. 6.1 10.3 18.1 6.1 .. 18.5 17.0 14.2 11.3 8.2 8.6 14.3 15.1 20.9 11.2
a. Excludes armed forces (International Standard Classification of Occupations, ISCO-88, group 0). b. Does not refer to International Standard Industrial Classification revision 3 classifications. c. Does not refer to ISCO-88 classifications.
388
2009 World Development Indicators
6.18
About the data
International migration has become an important
and Southern African countries also have a signifi -
science occupations; architecture and engineering
element of global integration over the last 20 years.
cant share of migrant workers in their labor force.
occupations; life, physical, and social science occu-
Many countries have adopted policies that encour-
Census and population register data generally
pations; community and social services occupations;
age the entrance of foreign labor. At the same time
are the most relevant sources for small population
legal occupations; education, training, and library
remittances—transfers of gifts, wages, and salaries
groups, but the data are subject to two limitations.
occupations; arts, design, entertainment, sports,
earned by migrants working abroad—have fueled
First, to ensure international comparability, immi-
and media occupations; and healthcare practitioner
private financing in developing countries. And demo-
grants are identified as people whose place of birth
and technical occupations; technicians refer to office
graphic trends in developing and high-income econo-
differs from their current country of residence. Thus,
and administrative support occupations; operators
mies are likely to influence future migration patterns.
nationals born abroad may be included in the immi-
refer to healthcare support occupations; protective
Despite the importance of international migration,
grant population. This could be an issue for countries
service occupations; food preparation and servic-
the quality and comparability of data remain limited,
with large repatriated communities (such as France
ing occupations; building and grounds cleaning and
in part because of differing national definitions of who
and Portugal) or with large expatriate communities
maintenance occupations; personal care and service
is an immigrant. Many countries define immigrants
(such as Germany and the United Kingdom). Sec-
occupations; sales and related occupations; farming,
as people with foreign nationality, but others focus
ond, coverage of undocumented migrants, short-term
fishing, and forestry occupations; construction and
on birthplace, considering all those born abroad as
migrants, and asylum seekers may be incomplete.
extraction occupations; installation, maintenance,
immigrants. The lack of comparable data hinders
Table 6.18a presents the top three countries of
the design and implementation of sound migration
birth along with their share of the foreign-born popu-
and repair occupations; production occupations; and
policies by both receiving and sending countries. Sys-
lation in the reporting country. Because censuses
tematic recording of immigration stock is difficult,
of different countries apply different rules when
especially for poor countries and countries affected
addressing countries that split, recomposed, or are
• Foreign-born population is the population ages 15
by civil disorder and natural disasters.
newly constituted, the coding from the national data
and older who were born in a country other than the
The table presents the main characteristics of
collection were maintained. For some countries peo-
country of residence when the census data were col-
immigrants in selected Organisation for Economic
ple born in the Democratic People’s Republic of Korea
lected. • Primary education refers to International
Co-operation and Development (OECD) countries,
or the Republic of Korea could not be distinguished.
Standard Classification of Education 1997 (ISCED)
using data from the Database on Immigrants in
In many OECD country censuses countries of the
levels 0–2. • Secondary education refers to ISCED
OECD Countries, the OECD’s effort, in cooperation
former Union of Soviet Socialist Republics (USSR) are
levels 3–4. • Tertiary education refers to ISCED 5–6.
with national statistical offices, to compile interna-
grouped under the name “former USSR.” The same
• Professionals refer to ISCO major groups 1 and 2.
tionally comparable data on foreign-born populations
applies to the countries of the former Yugoslavia.
• Technicians refer to ISCO major groups 3 and 4.
transportation and material moving occupations. Definitions
in OECD countries. The database provides compre-
Table 6.18b presents the main characteristics of the
• Operators refer to ISCO major groups 5–9. • Agri-
hensive information on many demographic and
foreign-born population by gender, educational attain-
culture and industry refer to International Standard
labor market characteristics of OECD immigrants.
ment, occupation, and sector of employment. The
Industrial Classification revision 3 (ISIC) major groups
Its main sources of data are population censuses
database tries to harmonize the classification of vari-
A–F. • Producer services refer to ISIC major groups
(for 22 countries), population registers (for Denmark,
ables that are not systematically collected according
J and K. • Distributive services refer to ISIC major
Finland, Norway, and Sweden), and labor force sur-
to international classifications. For example, occupa-
groups G and I. • Personal and social services refer
veys (Germany and the Netherlands). The database
tions based on national classifications were mapped
to ISIC major groups H and L–Q.
includes information on demographic characteristics
to International Standard Classification of Occupations
(age and gender), duration of stay, labor market out-
(ISCO-88) classifications, generally with help from
comes (employment status, occupations, sectors of
national statistical offices. Details of the mapping are
activity), fields of study, educational attainment, and
in OECD’s A Profile of Immigrant Populations in the 21st
place of birth. The database provides comparable
Century: Data from OECD Countries (2008).
data on foreign-born population for around 2000;
Because the database does not provide mapping
time series data are not available. Other national
for Japan and the United States, their data were
sources that contain time series data use differ-
mapped for the table. For Japan professionals refer
ent definitions of immigrant, making comparability
to managers and officials and professional and tech-
across countries difficult.
Data sources
nical workers; technicians refer to clerical and related
Data on characteristics of migrants in OECD coun-
Although the database includes data on all OECD
workers; operators refer to agricultural, forestry, and
tries are from OECD’s Database on Immigrants
countries except Iceland and the Republic of Korea,
fisheries workers; production process workers and
in OECD Countries. The methodology of data col-
the table presents selected data only for high-income
laborers; protective service workers; sales workers;
lection and compilation and the summary report
OECD countries with populations of more than 1 mil-
service workers; and workers in transport and com-
can be found in OECD’s A Profile of Immigrant
lion. More data are available from the original source.
munications. For the United States professionals refer
Populations in the 21st Century: Data from OECD
Though OECD countries are believed to receive the
to management occupations; business and financial
Countries (2008).
largest number of migrant workers, Gulf countries
operations occupations; computer and mathematical
2009 World Development Indicators
389
GLOBAL LINKS
Characteristics of immigrants in selected OECD countries
6.19
Travel and tourism International tourists
Inbound tourism expenditure
thousands Inbound Outbound 1995 2007 1995 2007
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, 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
390
.. 41a 520b,c 9 2,289 12 3,825e 17,173f .. 156 161 5,560 f 138 284 115f 521 1,991 3,466 124h 34 c .. 100h 16,932 26e 19h 1,540 20,034 7,137 1,433b 35 37h 785 188 1,485f 742e 3,381f 2,124f 1,776c,e 440b,i 2,871 235 315b,c 530 103e 2,644 60,033 125e 45 85b 14,847f 286c 10,130 563b 12e .. 145
.. .. 57a 12 1,743b,c 1,090 195 3 4,562 3,815 381 .. 5,064e 2,519 20,766f 3,713 1,010 432 289 830 105 626 7,045f 5,645 186 .. 556 249 306f .. 1,675 .. 5,026 2,600 5,151 3,524 289h .. 201c 36 1,873 31 185h .. 17,931 18,206 14e .. 25h .. 2,507 1,070 54,720 4,520 17,154 3,023 1,195b 1,057 50 47e .. .. 1,980 273 .. .. 9,307f .. 2,119e 72 6,680f .. 4,716f 5,035 3,980c,e 168 937b,i 271 10,610 2,683 1,339 348 78b,c .. 1,900 1,764 303c 120 3,519 5,147 81,940 18,686 .. 203 143 .. 1,052b 228 24,421f 55,800 497c .. 17,518 .. 1,628b 333 46e .. .. 30e 112 ..
2009 World Development Indicators
.. 2,979 1,499 .. 4,167 329 5,462 9,876 1,631 2,327 517 8,371 .. 476 .. .. 5,141 4,515 .. .. 996 .. 25,163 11 .. 3,234 40,954 80,682 1,767 .. .. 577 .. .. 194 .. 6,142 443 801 4,531 1,012 .. .. .. 5,749 22,467 .. 387 .. 70,400 .. .. 1,168 .. .. ..
$ millions 1995 2007
.. 70 32d 27 2,550 14 11,915 14,529 87 25g 28 4,548g 85g 92 257 176 1,085 662 .. 2 71 75 9,176 4d 43d 1,186 8,730 g 9,604g 887 .. 15 763 103 1,349g 1,100d 2,880 g 3,691g 1,571 g 315 2,954 152 58d 452 177 2,383 31,295 94 28g 75 24,052 30 4,182 216 1 3 90 g
.. 1,055 219d 236 4,984 343 29,065 21,292 317 76g 479 12,176 124 294 798 549 5,284 3,975 55 2 1,284 221 17,985 4 .. 2,172 41,126 18,015d 2,262 .. 54g 2,224 104 g 9,576 2,415d 7,496 5,587g 4,026g 626 10,327 1,158 60d 1,415 792 3,890 63,609 13 77 441 46,860 990 15,687 1,055g 1 3 140 g
% of exports 1995 2007
.. 23.2 .. 0.7 10.2 4.7 17.1 16.2 11.1 0.6g 0.5 2.4g 13.8g 7.5 22.9 7.3 2.1 9.8 .. 1.9 7.3 3.7 4.2 .. .. 6.1 5.9g 3.5g 7.2 .. 1.1 17.1 2.4 19.3g .. 10.2g 5.6g 27.4g 6.1 22.3 7.5 43.1d 17.6 23.1 5.0 8.6 3.2 15.8g 13.1 4.0 1.9 26.9 7.7 0.1 5.3 46.8g
.. 47.9 .. 0.5 7.5 19.3 15.9 9.8 1.4 0.5g 1.7 3.0 12.8 5.9 14.2 9.0 2.9 16.0 .. 2.7 22.8 4.5 3.6 .. .. 2.8 3.1 4.2d 6.6 .. 0.9g 17.2 1.1g 38.1 .. 5.4 3.9g 33.6g 3.9 23.3 21.0 .. 9.1 29.8 3.5 9.2 0.2 33.1 13.9 3.0 16.5 23.4 12.1 g 0.1 2.6 19.2g
Outbound tourism expenditure
$ millions 1995 2007
1 19 186d 113 4,013 12 7,260 11,686 165 234g 101 8,115g 48 72 97 153 3,982 312 .. 25g 22 140 12,658 43d 38d 934 3,688g 10,497d,g 1,162 .. 69 336 312 422g .. 1,635g 4,288g 267 331 1,371 99 .. 121 30 2,853 20,699 182 16 171 66,527 74 1,495 167 29 6 35g
.. 940 377d 473 5,071 345 19,844 12,839 381 514 724 19,095 75 325 232 281 10,434 2,597 84 106 194 476 31,365 32d .. 2,140 33,264 15,086d,g 2,093 .. 168g 731 396g 1,025 .. 3,771 7,428g 517 733 2,886 690 .. 804 107g 4,632 44,544 346 7g 277 93,515 816 3,430 736 96 16g 332
% of imports 1995 2007
.. 2.3 .. 3.2 15.4 1.7 9.7 12.7 12.8 3.1 g 1.8 4.5g 5.4 4.6 2.4 7.5 6.3 4.8 .. 9.7g 1.6 8.7 6.3 .. .. 5.1 2.7g 6.5d,g 7.3 .. 5.1 7.1 8.2 4.6g .. 5.4g 7.4g 4.4 5.8 8.0 2.7 .. 4.2 2.1 7.6 6.2 10.6 6.9 12.1 11.3 3.5 6.0 4.5 2.9 6.7 4.4g
.. 21.9 .. 1.8 9.5 9.6 9.9 6.4 4.0 2.6 2.4 4.8 5.1 8.0 2.2 6.4 6.6 7.8 .. 24.4 3.1 8.6 6.7 .. .. 4.0 3.2 3.7d,g 5.6 .. 2.6g 5.2 4.7g 3.5 .. 2.9 5.6g 3.4 4.7 5.4 7.0 .. 4.5 1.5g 4.6 6.1 14.4 2.2g 4.7 7.0 8.1 3.4 5.1 6.3 17.3g 14.3
International tourists
Inbound tourism expenditure
thousands Inbound Outbound 1995 2007 1995 2007
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 .. 2,124i 4,324 489 61b 4,818 2,215i 31,052 1,147c,e 3,345b,i 1,075 .. 896 .. 3,753b,c 72h 36 60 539 450 87 .. 56 650 147f 75e 192 7,469 42e,h .. 422 20,241c 32 108 2,602c .. 117 272 363 6,574f 1,475b 281 35 656 2,880a 279h 378 345 42 438i 479 1,760 c 19,215 9,511i 3,131e
831 8,638 5,082i 5,506 2,735 .. 8,332 2,067i 43,654 1,701c,e 8,347b,i 3,431c 3,876 1,644 .. 6,448b,c 293h 1,654 842 1,653 1,017 292 .. 149 1,486 230 f 344 e 714 20,973 164e,h .. 907 21,424 c 13 452 7,408 c 664 248 929 527 11,008f 2,434b 800 c 60 1,111 4,290 1,144h 840 1,103 104 416i 1,812 3,092c 14,975 12,321c 3,687e
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
315 18,471 9,780 4,341 .. .. 6,848 4,147 27,734 .. 17,295 2,094 4,544 .. .. 13,325 2,529 454 .. 3,398 .. .. .. .. 3,696 .. .. .. 30,761 .. .. 213 15,089 82 .. 2,320 .. .. .. 469 17,556 1,978 868 .. .. 3,395 .. .. 314 .. 242 1,857 2,745 47,561 20,989 1,441
$ millions 1995 2007
85 2,938 2,582g 5,229g 205 18d,g 2,698 3,491 30,426 1,199 4,894 973 155 590 .. 6,670 307 5g 52 37 710 29 .. 4 102 19g 106 22 5,044 26 11 g 616 6,847 71 33 1,469 49g 169 278 g 232 10,611 2,318g 51 7g 47 2,730 193g 582 372 25g 162 521 1,141 6,927 5,646 1,828 d
559 5,693 10,729g 5,833 1,834 170 8,863 3,712 46,144 2,137 12,422 2,755 1,213 1,507 .. 8,947 512 392 189g 880 5,573 43g 131g 244 1,192 219 506 48 16,798 175 .. 1,663 14,072 221 261 8,307 182 59 542 234 19,981 5,406g 255 39 340 5,021 902 900 1,796 4 121 2,222 5,518 11,686 12,917 3,414 d
% of exports 1995 2007
5.2 14.9 6.8g 9.9g 1.1 .. 5.5 12.7 10.3 35.3 1.0 28.0 2.6 16.7 .. 4.5 2.2 1.1 g 12.8 1.8 .. 14.6 .. 0.1 3.2 2.7 14.2 4.7 6.1 4.9 2.2g 26.2 7.7 8.0 6.5 16.2 10.2g 12.9 16.0 g 22.5 4.4 13.0 g 7.7 2.2g 0.4 4.9 2.5g 5.7 4.9 0.8g 3.4 7.9 4.3 19.4 17.5 ..
8.8 5.1 4.5g 4.5 .. 0.6 4.3 5.2 7.5 43.4 1.5 30.2 2.3 22.1 .. 2.0 0.7 19.4 15.7g 7.4 33.6 4.9g 24.2g 0.6 5.6 5.2 21.8 .. 8.2 9.4 .. 37.5 4.9 11.0 12.9 30.4 6.3 1.2 15.4 16.3 3.6 14.8g 9.5 6.5 0.5 2.8 3.5 4.1 12.6 0.1 1.9 7.1 9.5 6.7 17.2 ..
6.19
GLOBAL LINKS
Travel and tourism
Outbound tourism expenditure
$ millions 1995 2007
99 1,501 996g 2,172g 247 117d,g 2,034g 2,626 17,219 173 46,966 719 296 183g .. 6,947 2,514 7g 34 62 .. 17 .. 98 107 27g 79 53 2,722 74 30 184 3,587 73 22 356 68 18 90 g 167 13,151 1,259g 56 26 939 4,481 47g 654 181 58g 173 428 551 5,865 2,540 1,155d
385 3,468 9,296 6,120 6,526 526 8,811 4,250 32,754 340 37,261 1,024 1,355 262g .. 23,359 6,678 193 .. 1,021 3,914 24 48 915 1,167 147 94g 84 6,245 196 .. 388 9,843 270 212 1,418 209 40 132g 402 19,475 3,066g 195 42 3,494 14,109 944 2,043 457 56 184 1,274 2,007 8,341 4,836 1,743d
% of imports 1995 2007
5.3 7.5 2.1g 4.0 g 1.6 .. 4.8g 7.4 6.9 4.6 11.2 14.7 4.9 3.1g .. 4.5 19.9 1.0 g 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 6.6 0.9 4.3g 10.3 6.1 7.3g 4.9 5.7 7.3 9.6 0.9g 4.6 2.3 3.0 g 3.3 4.5 1.7 17.3 6.4 ..
2009 World Development Indicators
4.0 3.2 4.0 5.6 .. 2.2 4.9 5.8 5.3 4.2 5.1 6.6 3.0 2.7g .. 5.4 19.8 6.0 .. 5.7 17.9 1.4 2.8 5.8 4.4 2.6 3.9g .. 3.7 9.1 .. 7.5 3.2 6.3 11.3 4.1 5.7 1.4 3.7g 11.0 4.0 8.0 g 4.2 3.9 7.6 12.1 4.9 5.4 3.1 2.1 2.8 5.3 3.1 4.5 5.4 ..
391
6.19
Travel and tourism International tourists
thousands Inbound Outbound 1995 2007 1995 2007
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
5,445b 7,722b 10,290 b 22,909 b .. .. 3,325 11,531 .. 866 .. 696f 32e 38e 6,070 7,957 903f 1,685f 1,751f 732f .. .. 4,488 9,091 34,920 58,973 494i 403i 436c 29i 300a 870h 3,434f 2,310 f 8,448h 6,946h .. 4,566 .. .. 285 692 6,952c 14,464i .. .. 86h 53h 449e 260e 6,758i 4,120i 7,083 22,248 218 8 160 642 3,716 23,122 7,126a,c 2,315a,c 21,719 30,870 43,490 55,986 2,022 1,752 92 281 700 771 4,244b 1,351b 264h 220h 379h 61h 163 897 1,416b 2,508b 535,972 t 911,470 t 9,007 25,153 156,729 336,249 59,900 165,468 97,095 171,571 168,466 365,821 43,654 108,445 55,679 122,293 38,965 57,814 13,329 39,685 3,819 8,088 12,871 29,688 362,515 540,487 202,533 297,435
Inbound tourism expenditure
$ millions 1995 2007
5,737 10,980 689 1,922 21,329 34,285 4,312g 12,587 .. .. 4 66 .. 4,817 .. 6,020d .. .. 168 329 .. .. .. 1,011d 6 71 57g 22g 8,680 g 2,867 6,024 7,611g 218 23,837 630 2,352 .. 2,496 1,128 2,400 .. .. .. .. 2,520 4,433 2,655 9,890 3,648 11,276 27,369 65,136 504 862 367 750 195 .. 8g 262g .. 1,130 54 32 10,127 12,681 4,390 13,706 11,148 .. 11,354 14,777 2,113 1,746 4,042 1,258g .. .. .. 16 157 .. 502g 1,053 1,820 4,018 9,257 20,623 .. .. .. .. .. .. 13g 23 261 .. 232 693 1,778 2,302 1,838 3,373 20,649 3,981 8,938 4,957g 21 38 13 .. 359 148 272 78g 5,317 6,552 16,875 191g .. .. 632d 4,972d 41,345 69,450 27,577 47,109 51,285 64,052 93,700 144,808 562 635 725 927 43d 246 893 15d 534 1,410 995 894 .. .. .. 3,200d .. .. 255g 121g .. .. 50 g 425g 138g .. .. 29g 256 .. 145d 365d 579,267 t 1,100,372 t 486,148 t 1,028,350 t .. .. 4,632 15,333 164,054 341,298 89,012 279,099 36,644 112,566 42,143 139,002 121,149 203,223 46,881 140,091 195,054 415,707 93,858 295,275 36,055 81,138 31,197 96,536 87,492 151,898 20,529 76,692 21,780 40,970 21,591 50,739 13,236 25,636 9,771 35,786 5,151 15,408 4,016 13,343 .. .. 6,729 22,231 336,254 601,882 392,250 733,064 140,127 225,488 164,023 329,568
% of exports 1995 2007
7.3 4.6g 5.4 .. 11.2 .. 44.4g 4.8g 5.7 10.9 .. 7.7 20.4 7.9 1.2g 5.3 4.6 9.2 21.9g .. 39.7g 13.2 .. 2.8g 8.3 23.0 13.6g 0.7 11.7g 1.1g .. 8.6 11.8 20.7 .. 4.8 .. .. 2.3g 2.4g .. 7.6 w 6.2 8.3 8.7 7.9 8.2 7.8 8.7 7.5 12.8 6.8 7.6 7.5 7.8
3.8 3.2 18.2 2.5d 13.7 .. 6.6g 2.3g 3.6 7.3 .. 11.0 16.9 7.9 2.8g 1.5 5.9 5.5 16.0 0.9 26.7 11.4 .. 2.4 5.6 16.8 14.3 .. 16.4 8.3 .. 6.5 8.8 13.6 .. 1.3 7.1d .. 5.5g 2.8g .. 5.9 w 5.7 6.3 5.8 6.8 6.2 4.8 7.1 5.6 24.6 5.3 7.1 5.8 6.5
Outbound tourism expenditure
$ millions 1995 2007
749 1,719 11,599g 24,289 13 69 .. 6,279d 154 139 .. 1,194 d 51 17 4,663g 11,844g 338 1,825 606 1,219 .. .. 2,414 6,103 5,826 24,179 279 709 43g 1,477g 45 63 6,816 15,696 9,478 12,449 498g 585 .. 7g 360 g 666 4,791 6,887 .. .. 40 42 91 206 294 530 911g 3,720 74 .. 80 g 200 210 g 3,743 .. 8,827d 30,749 88,478 60,924 109,578 332 349 .. .. 1,852 2,101 .. .. 162g 265g 76g 247 83 98 .. 106d 458,208 t 918,692 t 6,137 16,856 63,726 186,323 20,688 90,554 43,323 95,178 69,343 201,988 14,769 57,505 22,793 57,564 18,751 39,665 4,422 15,481 2,393 12,922 6,761 19,233 388,174 717,338 154,993 276,680
% of imports 1995 2007
6.6 14.0 g 3.5 .. 8.5 .. 19.4 3.2g 3.2 5.6 .. 7.2 4.3 4.7 3.5g 3.5 8.4 8.7 9.0 g .. 16.8g 5.8 .. 6.0 4.3 3.3 2.3g 4.1 5.4g 1.1g .. 9.4 6.8 9.3 .. 11.0 .. .. 3.1 g 6.2 .. 7.4 w 5.4 5.7 3.9 7.4 5.7 3.5 9.3 6.5 5.2 3.0 6.7 7.8 7.8
2.3 8.6 7.6 5.5d 3.4 .. 3.5 3.6g 2.8 3.6 .. 6.2 5.0 5.6 13.9g 2.5 7.8 5.6 4.9 0.2g 10.5 4.2 .. 2.8 3.7 2.5 2.1 .. 4.8 5.2 .. 10.7 4.7 5.1 .. 4.0 .. .. 2.6 2.2 .. 5.5 w 5.5 4.5 3.9 5.1 4.5 3.5 5.1 4.8 6.4 5.1 6.0 5.8 5.6
Note: Aggregates are based on World Bank country classifications and differ from those of the World Tourism Organization. Regional and income group totals include countries not shown in the table for which data are available. a. Arrivals in hotels only. b. Arrivals of nonresident visitors at national borders. c. Includes nationals residing abroad. d. Country estimates. e. Arrivals by air only. f. Arrivals in all types of accommodation establishments. g. Expenditure of travel related items only; excludes passenger transport items. h. Arrivals in hotels and similar establishments. i. Excludes nationals residing abroad.
392
2009 World Development Indicators
About the data
6.19
GLOBAL LINKS
Travel and tourism Definitions
Tourism is defined as the activities of people trav-
the arrivals of international visitors, which include
• International inbound tourists (overnight visitors)
eling to and staying in places outside their usual
tourists, same-day visitors, cruise passengers, and
are the number of tourists who travel to a country
environment for no more than one year for leisure,
crew members.
other than that in which they usually reside, and out-
business, and other purposes not related to an activ-
Sources and collection methods for arrivals differ
side their usual environment, for a period not exceed-
ity remunerated from within the place visited. The
across countries. In some cases data are from bor-
ing 12 months and whose main purpose in visiting
social and economic phenomenon of tourism has
der statistics (police, immigration, and the like) and
is other than an activity remunerated from within the
grown substantially over the past quarter century.
supplemented by border surveys. In other cases data
country visited. When data on number of tourists are
Statistical information on tourism is based mainly
are from tourism accommodation establishments. For
not available, the number of visitors, which includes
on data on arrivals and overnight stays along with
some countries number of arrivals is limited to arriv-
tourists, same-day visitors, cruise passengers, and
balance of payments information. These data do not
als by air and for others to arrivals staying in hotels.
crew members, is shown instead. • International out-
completely capture the economic phenomenon of
Some countries include arrivals of nationals residing
bound tourists are the number of departures that
tourism or provide the information needed for effec-
abroad while others do not. Caution should thus be
people make from their country of usual residence
tive public policies and efficient business operations.
used in comparing arrivals across countries.
to any other country for any purpose other than an
Data are needed on the scale and significance of
The World Tourism Organization is improving its
activity remunerated in the country visited. • Inbound
tourism. Information on the role of tourism in national
coverage of tourism expenditure data, using balance
tourism expenditure is expenditures by international
economies is particularly defi cient. Although the
of payments data from the International Monetary
inbound visitors, including payments to national carri-
World Tourism Organization reports progress in har-
Fund (IMF) supplemented by data from individual
ers for international transport. These receipts include
monizing definitions and measurement, differences
countries. These data, shown in the table, include
any other prepayment made for goods or services
in national practices still prevent full comparability.
travel and passenger transport items as defined in
received in the destination country. They may include
The data in the table are from the World Tourism
the IMF’s (1993) Balance of Payments Manual. When
receipts from same-day visitors, except when these
Organization, a United Nations agency. The data on
the IMF does not report data on passenger transport
are important enough to justify separate classifica-
inbound and outbound tourists refer to the number of
items, expenditure data for travel items are shown.
tion. For some countries they do not include receipts
arrivals and departures, not to the number of people
The aggregates are calculated using the World
for passenger transport items. Their share in exports
traveling. Thus a person who makes several trips to
Bank’s weighted aggregation methodology (see Sta-
is calculated as a ratio to exports of goods and ser-
a country during a given period is counted each time
tistical methods) and differ from the World Tourism
vices (all transactions between residents of a coun-
as a new arrival. Unless otherwise indicated in the
Organization’s aggregates.
try and the rest of the world involving a change of
footnotes, the data on inbound tourism show the
ownership from residents to nonresidents of general
arrivals of nonresident tourists (overnight visitors)
merchandise, goods sent for processing and repairs,
at national borders. When data on international tour-
nonmonetary gold, and services). • Outbound tour-
ists are unavailable or incomplete, the table shows
ism expenditure is expenditures of international outbound visitors in other countries, including payments to foreign carriers for international transport. These
High-income economies remain the main destination for international travelers, but the share of tourists visiting developing economies is rising
6.19a
expenditures may include those by residents traveling abroad as same-day visitors, except when these
1995 Total international tourist arrivals: 536 million Middle East & North Africa 2.5%
South Asia 0.7% Sub-Saharan Africa 2.4%
Latin America & Caribbean 7.3%
2007 Total international tourist arrivals: 911 million Middle East & North Africa 4.4%
South Asia 0.9% Sub-Saharan Africa 3.3%
tion. For some countries they do not include expenditures for passenger transport items. Their share in imports is calculated as a ratio to imports of goods
Latin America & Caribbean 6.4%
and services (all transactions between residents of a
Europe & Central Asia 10.5%
East Asia & Pacific 8.2%
are important enough to justify separate classifica-
country and the rest of the world involving a change of ownership from nonresidents to residents of general
High income 68.3%
merchandise, goods sent for processing and repairs,
Europe & Central Asia 13.5% East Asia & Pacific 12.0%
nonmonetary gold, and services). High income 59.6%
Data sources Data on visitors and tourism expenditure are from the World Tourism Organization’s Yearbook of Tourism Statistics and Compendium of Tourism Statistics 2009. Data in the table are updated
Although nearly 60 percent of international tourists traveled to high-income economies in 2007, the share
from electronic files provided by the World Tour-
traveling to developing economies has increased since 1995. The share of tourists going to East Asia
ism Organization. Data on exports and imports
and Pacific and Europe and Central Asia increased the most—about 3 percentage points.
are from the IMF’s Balance of Payments Statistics
Source: World Bank staff calculations based on World Tourism Organization data.
Yearbook and data files.
2009 World Development Indicators
393 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 enterprise 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 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 are central to implementing poverty reduction strategies and lie 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.
2009 World Development Indicators
395
PRIMARY DATA DOCUMENTATION Currency
National accounts
SNA System of price Reference National Accounts valuation year
Base year
Afghanistan Albania Algeria American Samoa Andorra Angola Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas, The Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Brunei Darussalam Bulgaria
Afghan afghani 2002/03 a Albanian lek Algerian dinar 1980 U.S. dollar Euro Angolan kwanza 1997 Eastern Caribbean dollar 1990 Argentine peso 1993 a Armenian dram Aruban florins 1995 a Australian dollar Euro 2000 a New Azeri manat Bahamian dollar 1991 Dinar 1985 Bangladesh taka 1995/96 Barbados dollar 1974 a Belarusian rubel Euro 2000 Belize dollar 2000 CFA franc 1985 Bermuda dollar 1996 Ngultrum 2000 Boliviano 1990 a Konvertible mark Botswana pula 1993/94 Brazilian real 2000 Brunei dollar 2000 a Bulgarian lev
Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Channel Islands
CFA franc Burundi franc Cambodian riel CFA franc Canadian dollar Escudos Cayman Islands dollar CFA franc CFA franc Jersey pound & Guernsey pound Chilean peso Chinese yuan Hong Kong dollar Colombian peso CFA franc Congo franc CFA franc Costa Rican colon CFA franc Croatian kuna Cuban peso Euro Czech koruna Danish krone CFA franc
Chile China Hong Kong, China Colombia Comoros Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Djibouti
396
2009 World Development Indicators
1996
b
1996
b
2007
b b
2003
a
b b
b
2000
b b b
b b
1996
b b b
2002
1999 1980 2000 2000 2000 1980 2000 1995 2007 & 2003 2003 2000 2006 2000 1990 1987 1978 1991 1996
b
b
b b
b
2007
b
b b b b
b
b
1997
b
1984 a
2000 2000 1990
2000 1995
b b
Balance of payments and trade
Balance of Payments Manual in use
External debt
System of trade
Accounting concept
2005
BPM5 BPM5
Preliminary Actual Actual
G G S
C C B
1991–96
2005
1971–84 1990–95
2005 2005
BPM5 BPM5 BPM5 BPM5
1992–95
2005 2005 2005
Alternative conversion factor
VAB VAB VAB
VAP VAB VAB VAB VAB VAB VAB VAB VAP VAB VAB VAB VAB VAB VAP VAB VAB VAB VAB VAB VAB VAP VAB VAB VAB VAB VAB VAB VAP
PPP survey year
2005 2005 1990–95
2005 2005
1992
2005
1960–85
1978–89, 1991–92 1992–93
VAB VAB VAB VAB VAP VAB VAB VAP VAB VAP VAB VAP VAB VAP VAB VAB VAB VAB
Government IMF data finance dissemination standard
1978–93 1992–94 1999–2001 1993
BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5
Actual Actual Actual
Actual
Preliminary Actual Actual Preliminary
2005 2005 2005 2005 2005 2005 2005
BPM5 BPM5 BPM5 BPM5
Actual Actual Actual Preliminary Actual
BPM5
Actual
2005 2005 2005 2005 2005 2005
BPM4 BPM5 BPM5 BPM5 BPM5 BPM5
Actual Actual Actual Actual
2005 2005
2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005
G S G S S S G S G G G G G G S G S
S G G S G G
C C C C C B C C C C C B C C C B C C
G G
G G S S S S G G G G G S S G G
G G S G S
B C C B C
Actual
G S G S G S
BPM4 BPM5
Preliminary Actual
S S
B C
G G
BPM5 BPM5 BPM5 BPM5
Actual Preliminary
S S G S
C B C B
S G S S
S S S S G G G G G
C C C C C
G G S G S
C C C
S S
BPM5 BPM5 BPM5 BPM5 BPM5
Actual Preliminary Estimate Preliminary Actual Actual Actual
BPM5 BPM5 BPM5 Actual
G G G S G
PRIMARY DATA DOCUMENTATION Latest population census
Latest demographic, education, or health household survey
Afghanistan Albania Algeria American Samoa Andorra Angola Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas, The Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Brunei Darussalam Bulgaria
1979 2001 2008 2000 2000 1970 2001 2001 2001 2000 2006 2001 1999 2000 2001 2001 2000 1999 2001 2000 2002 2000 2005 2001 1991 2001 2000 2001 2001
MICS, 2003 MICS, 2005 MICS, 2006
Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Channel Islands
2006 1990 2008 1987 2006 2000 1999 2003 1993 2001
Chile China Hong Kong, China Colombia Comoros Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Djibouti
2002 2000 2006 2005 2003 1984 1996 2000 1998 2001 2002 2001 2001 2001 1983
Source of most recent income and expenditure data
LSMS, 2005 IHS, 1995
Vital Latest Latest registration agricultural industrial complete census data
Yes
1998 2001
2005
Yes Yes MICS, 2001
IHS, 2000
1964–65
DHS, 2005
IHS, 2005 IHS, 2003
Yes Yes Yes
DHS, 2006
ES/BS, 1994 IS 2000 ES/BS, 2005
Yes Yes Yes
MICS 2006
IHS, 2005
MICS, 2005
ES/BS 2005 IHS, 2000
2002
2002
2001 1999–2000
2005 2004 2005 1998
2005
1998
Yes
MICS, 2006 DHS, 2006
CWIQ, 2003
DHS, 2003 MICS, 2006 MICS, 2000 DHS, 1996
IHS, 2003 IHS, 2005 LSMS, 2004 ES/BS, 1993/94 IHS, 2007
Yes Yes Yes
1994 1999–2000c
2004
1992
Latest trade data
Latest water withdrawal data
1977 2007 2006
2000 2000 2000
2006 1991 2007 2006 2006 2007 2006 2006 2006 2007 2007 2006 2007 2007 2006 2007 2005
2000 1990 2000 2000 2000 2000 2005 2003 2000 2000 2000 2000 2001
Yes
ES/BS, 2003 DHS, 2003 MICS, 2000 DHS, 2005 MICS, 2006
MICS, 2006 DHS, 2004
CWIQ, 2003 CWIQ, 2006 IHS, 2004 PS, 2001 LFS, 2000 ES/BS, 2001
2000 1984–88
2001
1993 1996
2005 2004
Yes
Yes Yes
2005 1993 2000
Yes Yes Yes
PS, 2003 PS, 2002
1984 1996/2001 2004
2002
1985
2006 2007 2007 2006 2006 2006
2000 2000 2000 2000 2000
2004 2007 2004 2006 2006 2007
2000 2000 2000 2000 2000
2005 1995
2000 2000
2006 2006 2006 2006 2007 1986 1995 2006 2007 2006 2006 2006 2006 2006
2000 2000
Yes
NSS, 2006
IHS, 2006 IHS, 2003
Yes
1997 1997
2005 2005
2001
2005
Yes DHS, 2005 MICS, 2000 DHS 2007 DHS, 2005 RHS, 1993 MICS, 2006
IHS, 2006 IHS, 2004 1–2-3, 2005 CWIQ/ PS, 2005 LFS, 2005 IHS, 2002 ES/BS, 2005
MICS, 2006 RHS, 1993 MICS, 2006
IS 1996/97 ITR 1997 PS, 2002
Yes Yes Yes Yes Yes Yes
1990 1985–86 1973 2001 2003
2000 1999–2000
2005 2004 2004
2009 World Development Indicators
2000 2000 2002 2000
2000 2000 2000 2000 2000
397
PRIMARY DATA DOCUMENTATION Currency
National accounts
Base year
Dominica Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Faeroe Islands Fiji Finland France French Polynesia Gabon Gambia, The Georgia Germany Ghana Greece Greenland Grenada Guam Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hungary Iceland India Indonesia Iran, Islamic Rep. Iraq Ireland Isle of Man Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kiribati Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia
398
Eastern Caribbean dollar 1990 Dominican peso 1990 U.S. dollar 2000 Egyptian pound 1991/92 Salvadoran colon 1990 CFA franc 2000 Eritrean nakfa 1992 Estonian kroon 2000 Ethiopian birr 1999/2000 Danish krone Fijian dollar 1995 Euro 2000 a Euro CFP franc CFA franc 1991 Gambian dalasi 1987 a Georgian lari Euro 2000 Ghanaian cedi 1975 a Euro Danish krone Eastern Caribbean dollar 1990 U.S. dollar Guatemalan quetzal 2001 Guinean franc 1996 CFA franc 1986 Guyana dollar 1988 Haitian gourde 1975/76 Honduran lempira 2000 a Hungarian forint Iceland kronur 2000 Indian rupee 1999/ 2000 Indonesian rupiah 2000 Iranian rial 1997/98 Iraqi dinar 1997 Euro 2000 Manx pound 2005 Israeli new shekel 2005 Euro 2000 Jamaica dollar 1996 Japanese yen 2000 Jordan dinar 1994 a Kazakh tenge Kenya shilling 2001 Australian dollar 1991 Democratic Republic of Korea won Korean won 2000 Kuwaiti dinar 1995 a Kyrgyz som Lao kip 1990 Latvian lat 2000 Lebanese pound 2005 Lesotho loti 1995 Liberian dollar 1992
2009 World Development Indicators
SNA System of price Reference National Accounts valuation year b
b
b b
b
2000
1996
b
b b
2000
VAB VAP VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAP VAB VAB VAB VAP VAB
Balance of payments and trade
Alternative conversion factor
Balance of Payments Manual in use
External debt
System of trade
Accounting concept
BPM5 BPM5 BPM5 BPM5 BPM5
Actual Actual Actual Actual Actual
G G S S S
C B B C
G G S S S
Actual
S G
B C C
G S S
2005 2005 2005 2005 2005 2005
BPM5 BPM5 BPM5 BPM5 BPM5 BPM5
Preliminary Estimate Actual
B B C C B C
G G G S G S
BPM5
Actual
G G G G G G S G S G G S G S G G
C C
2005 2005 2005
BPM4 BPM5 BPM5 BPM5 BPM4 BPM5 BPM5
Actual Estimate Preliminary Actual Preliminary Actual
2005 2005 2005
BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5
2005 2005 2005 2005
BPM5 BPM5 BPM5 BPM5
Actual Actual
2005 2005
BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5
PPP survey year
2005 2005 1965–84
2005
1987–95
2005 2005
1993 1990–95 1973–87
VAB b
b
2000
b
b
b
VAP VAB VAB VAB VAB VAB VAB VAB VAB VAP VAB VAB VAB
2005 2005 1991 1988–89
1980–02 1997, 2004
Government IMF data finance dissemination standard
Actual Actual
Preliminary
Actual
S S G S G S S G G
G B B
G G G
B C C C
G S S S
S G S G
C C
S
C
S
S S G G G G G G
C C C C B C B
S S G S G S G G
S S G G S G G
C C C
S G S
C B C
S G G G
2003 b b
1995
b b
VAP VAB VAB VAB VAB VAB VAB VAB
1987–95
2005 2005 2005 2005
Actual Actual Actual Actual
BPM5 b
1995
b
b
b
VAB VAP VAB VAB VAB VAB VAB VAB
1990–95 1987–95
2005 2005 2005 2005 2005 2005 2005 2005
BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5
Actual Preliminary Actual Actual Actual Estimate
PRIMARY DATA DOCUMENTATION Latest population census
Dominica Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Faeroe Islands Fiji Finland France French Polynesia Gabon Gambia, The Georgia Germany Ghana Greece Greenland Grenada Guam Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hungary Iceland India
2001 2002 2001 2006 2007 2002 1984 2000 2007 2002 2007 2000 2006 2007 2003 2003 2002 2004 2000 2001 2000 2001 2000 2002 1996 1991 2002 2003 2001 2001 2000 2001
Indonesia Iran, Islamic Rep. Iraq Ireland Isle of Man Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kiribati Korea, Dem. Rep.
2000 2006 1997 2006 2006 1995 2001 2001 2005 2004 1999 1999 2005 1993
Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia
2005 2005 1999 2005 2000 1970 2006 2008
Latest demographic, education, or health household survey
Source of most recent income and expenditure data
Vital Latest Latest registration agricultural industrial complete census data
Yes DHS, 2007 RHS, 2004 DHS, 2005, SPA 2004 RHS, 2002/03
IHS, 2005 LFS, 2007 ES/BS, 2005 IHS, 2005
Yes Yes
1971 1999–2000 1999–2000 1970–71
DHS, 2002 DHS, 2005
ES/BS, 2004 ES/BS, 2004–05
IS, 2000 ES/BS, 1994/95 DHS, 2000 MICS, 2005/06 MICS, 2005 MICS, 2006
RHS, 2002 DHS, 2005 MICS, 2006 MICS, 2006 DHS, 2005 DHS, 2005
CWIQ/ IHS, 2005 IHS, 2003/04 IHS, 2005 IHS, 2000 LSMS, 2005 IHS, 2000
LSMS, 2006 CWIQ/, 2002/03 CWIQ, 2002 IHS, 1998 IHS, 2001 IHS, 2006 ES/BS, 2004
DHS, 2005/06
IHS, 2004/05
DHS, 2002/03 DHS, 2000 MICS, 2006
IHS, 2005 ES/BS, 2005 IHS, 2000
MICS 2005
ES/BS, 2001 ES/BS, 2000 LSMS, 2004
DHS, 2007 MICS, 2006 DHS, 2003, SPA, 2004
ES/BS, 2004 ES/BS, 2003 IHS, 2005
2005 2002
2001 2001–02
2005 2005 2005
Yes Yes Yes Yes
1999–2000 1999–2000
2004 2004
Yes Yes
1974–75 2001–02 2004 1999–2000 1984 1999–2000
Yes
Yes Yes Yes Yes Yes
Yes Yes
Yes Yes Yes Yes Yes Yes
2005 2004 2003 1998
2003 2000–01 1988
Latest trade data
2006 2001 2007 2007 2006 2003 2006 2007 2006 2007 2006 2006 2007 2006 2007 2007 2006 2007 2006 2006 2006
2000 2000 2000 2000 2000 2005 2000 2000 2000
2004 2005 2004
2000 2000 2000 2000 2000 2000 2000 2000 2000
2003 2004 1998 2004
2007 2006 2007 2006
2000 2004 2000 2000
1981 2000 1996 2000 1997
2004 2004
1977–79
2005
2006 2006 2007 2006 2006 2007 2007 2005
2004 2000 2000 2000 2005 2000 2003
1971 1993 2000 1995–96/ 2000–01 2003 2003 1981 2000
2005 2005
Yes
2000 ES/BS, 1998/99
MICS, 2000 DHS, 2004 DHS, 2007
2000 2000 2000 2000 2000 2004 2000 2002
2006 2002 1995 2006 1997 2006 2006 2006 2007
MICS, 2000
FHS, 1996 MICS 2005/06 MICS, 2006
Latest water withdrawal data
ES/BS, 2004 ES/BS, 2002 IHS, 2004 ES/BS, 2002–03
Yes Yes Yes Yes
2000 1970 2002 1998–99 2001 1998–99 1999–2000
2005 2005 1999 2005 1998
2006 2007 2006 1975 2006 2004 2004 1984
2009 World Development Indicators
2000 2002 2000 2000 2000 2005 2000 2000
399
PRIMARY DATA DOCUMENTATION Currency
National accounts
SNA System of price Reference National Accounts valuation year
Base year
Libya Liechtenstein Lithuania Luxembourg Macao, China Macedonia, FYR Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Mauritania Mauritius Mayotte Mexico Micronesia, Fed. Sts. Moldova Monaco Mongolia Montenegro Morocco Mozambique
Libyan dinar Swiss franc Lithuanian litas Euro Pataca Macedonian denar Malagasy ariary Malawi kwacha Malaysian ringgit Rufiyaa CFA franc Euro U.S. dollar Mauritanian ouguiya Mauritian rupee
Mexican new peso U.S. dollar Moldovan leu Euro Mongolian tugrik Euro Moroccan dirham Mozambican metical (New) Myanmar Myanmar kyat Namibia Namibia dollar Nepal Nepalese rupee Netherlands Antilles Netherlands Antilles guilder Netherlands Euro New Caledonia CFP franc New Zealand New Zealand dollar Nicaragua Nicaraguan gold cordoba Niger CFA franc Nigeria Nigerian naira Northern Mariana Islands U.S. dollar Norway Norwegian krone Oman Rial Omani Pakistan Pakistan rupee Palau Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar Romania
U.S. dollar Panamanian balboa Papua New Guinea kina Paraguayan guarani Peruvian new sol Philippine peso Polish zloty Euro U.S. dollar Qatar riyals New Romanian leu
Russian Federation Rwanda
Russian ruble Rwanda franc
400
2009 World Development Indicators
1999 b
2000 2000 2002 1997 1984 1994 2000 1995 1987 1973 1991 1998 1997/98
1995
b
2003 1998 a
1996
b
b
2005 2000 1998 2003
b
1985/86 1995/96 2000/01
a
b
b
2000
2000/01 1994
b
b
1987 2002 a
2000
1988 1999/ 2000 1995 1996 1998 1994 1994 1985 a
b
b
2002
2000 1995
b b
2000 1954 2001 a
b
2005
b
b
VAB VAB VAB VAB VAB VAB VAB VAB VAP VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB
Balance of payments and trade
Alternative conversion factor
PPP survey year
1986 1990–95
Balance of Payments Manual in use
External debt
BPM5
System of trade
Government IMF data finance dissemination standard
Accounting concept
G S G S G G S G G G G G
C B C C B C
G G
2005 2005 2005 2005 2005 2005 2005 2005 2005 2005
BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM4 BPM5
2005 2005
BPM4 BPM5
Actual Actual
G G
C
G G
2005
BPM5
Actual
G
C
S
1990–95
2005
BPM5
Actual
G
C
S
BPM5
Estimate Actual Actual Preliminary
S
C
G
S S
C
1992–95
2005 2005 2005 2005
S G
G G S S
C B C
G G
C
S
C B
G
BPM5 BPM5
Actual Actual Actual Preliminary Actual Actual
S S G G G G S
VAP VAB VAB
2005 2005
BPM5 BPM5 BPM5 BPM5
VAB
2005
BPM5
2005
BPM5 BPM5
Actual
S S G S
2005 2005
BPM5 BPM5
Preliminary Preliminary
S G
2005 2005 2005
BPM5 BPM5 BPM5
Actual
G G G
C B C
S G G
Actual Actual Actual Actual Actual Actual
C B C C B C C
G S S S S
2005 2005
BPM5
Actual
S G S S G S S G G S
G
2005 2005 2005 2005 2005
BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5
B C
G S
2005 2005
BPM5 BPM5
Preliminary Preliminary
G G
C C
S G
VAB VAB
1965–95
VAP VAB
1993 1971–98
VAB VAP VAB VAB VAB VAB VAP VAB VAP VAB VAB VAP VAP VAB VAB VAP
1989 1985–90
1987–89, 1992 1987–95 1994
Estimate
C C C
Actual
G G
PRIMARY DATA DOCUMENTATION Latest population census
Latest demographic, education, or health household survey
Libya Liechtenstein Lithuania Luxembourg Macao, China Macedonia, FYR Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Mauritania Mauritius Mayotte Mexico Micronesia, Fed. Sts. Moldova Monaco Mongolia Montenegro Morocco Mozambique
1995 2000 2001 2001 2006 2002 1993 2008 2000 2006 1998 2005 1999 2000 2000 2007 2005 2000 2004 2000 2000 2003 2004 2007
MICS, 2000
Myanmar Namibia Nepal Netherlands Antilles
1983 2001 2001 2001
Netherlands New Caledonia New Zealand Nicaragua
2001 2004 2006 2005
Niger Nigeria Northern Mariana Islands Norway Oman Pakistan
2001 2006 2000 2001 2003 1998
Palau Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar Romania
2005 2000 2000 2002 2007 2007 2002 2001 2000 2004 2002
Russian Federation Rwanda
2002 2002
Source of most recent income and expenditure data
MICS, 2001 DHS, 2006
ES/BS, 2003 PS 2005 LSMS, 2004 ES/BS, 2004/05
Yes Yes Yes Yes Yes
2003 1999–2000c 1994 2004 1993
Yes Yes
IHS, 2006 Yes
DHS, 2000/01
Latest trade data
Latest water withdrawal data
2004
2000 2000
2005
2006 2007 2007 2007 2007 2006 2006 2007 2007 2007
2004
2006 2007
2000 2003
2000
2007
2000
2001 ES/BS, 2004
MICS, 2005 DHS, 2003/04 MICS 2006
Vital Latest Latest registration agricultural industrial complete census data
IHS, 2000
1984 2001
2005 2003 2001 2005 2001 2004
1984–85 Yes 1991
2000 2000 2000 2000 2000
ENPF, 1995
LFS, 2006
DHS, 2005
ES/BS, 2004
Yes
2005
2007
2000
MICS, 2005 MICS, 2005/06 DHS, 2003/04 DHS, 2003
LSMS, 2005
Yes Yes
2000
2006
2000
ES/BS, 2007 ES/BS, 2002/03
1996 1999–2000
2005
2006 2007
2000 2000
MICS, 2000 DHS, 2006/07 DHS, 2006
ES/BS, 1993/94 LSMS, 2003/04
2003 1996–97 2002
1992 2006 2007
2000 2000 2000 2000
Yes IHS, 1999
Yes Yes Yes
1999–2000c
DHS, 2001
LSMS, 2005
2002 2001
DHS/MICS, 2006 MICS, 2007
IHS, 2003
1980 1960
IS, 2000 FHS, 1995 DHS, 2006/07
Yes
LSMS, 2004/05
2002 2004 2004 2004
2006 2006 2006 2007
2000 2000
2007 2006
2000 2000
1999 1978–79 2000
2004 2005
2006 2007 2007
2000 2003 2000
2001
2001 2001 2002 2005 2003 2004 2004
2006 2004 2006 2006 2006 2007 2006
2000 2000 2000 2000 2000 2000 2000
2004 2005
2006 2007
2005 2000
2005 1999
2007 2005
2000 2000
Yes LSMS, 2003 DHS, 1996 RHS, 2004 DHS, 2004 DHS, 2003
LFS, 2006 IHS, 1996 IHS, 2007 LSMS, 2006 ES/BS, 2006 ES/BS, 2005
RHS, 1995/96 RHS, 1999
LFS, 2005
RHS, 1996 DHS, 2005
IHS, 2005 IHS, 1999
Yes Yes Yes Yes Yes Yes Yes
1991 1994 2002 1996/2002 1999 1997/2002 2000–01 2002 1994–95 1984
2009 World Development Indicators
401
PRIMARY DATA DOCUMENTATION Currency
National accounts
SNA System of price Reference National Accounts valuation year
Base year
Samoa San Marino São Tomé and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovak Republic Slovenia Solomon Islands Somalia South Africa Spain Sri Lanka St. Kitts and Nevis St. Lucia St. Vincent & Grenadines Sudan Suriname Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan
U.S. dollar 2002 Euro 1995 Dobras 2001 Saudi Arabian riyal 1999 CFA franc 1999 a Serbian dinar Seychelles rupees 1986 Sierra Leonean leone 1990 Singapore dollar 2000 Slovak koruna 2000 a Euro Solomon Islands dollar 1990 Somali shilling 1985 South African rand 2000 Euro 2000 Sri Lankan rupee 2002 Eastern Caribbean dollar 1990 Eastern Caribbean dollar 1990 Eastern Caribbean dollar 1990 Sudanese pound 1981/82d Suriname guilder 1990 Lilangeni 2000 a Swedish krona Swiss franc 2000 Syrian pound 2000 a Tajik somoni Tanzania shilling 1992 Thai baht 1988 U.S. dollar 2000 CFA franc 1978 Pa’anga 2000/01 Trinidad and 2000 Tobago dollar Tunisian dinar 1990 Turkish lira 1998 a Turkmen manat
Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Vanuatu Venezuela, RB Vietnam Virgin Islands (U.S.) West Bank and Gaza Yemen, Rep. Zambia Zimbabwe
Uganda shilling Ukrainian hryvnia U.A.E. dirham Pound sterling U.S. dollar Uruguayan peso Uzbek sum Vatu Bolivar fuerte Vietnamese dong U.S. dollar Israeli new shekel Yemen rial Zambian kwacha Zimbabwe dollar
402
2009 World Development Indicators
2000
1987 2002
b
b b
b b
1995 2000
b b
b b
b
1996 b
2000
2000
b
b
1987
b
2003
b
2001/02 a
1995 2000 a
b
2000
1983 a
1983 1997 1994 1982 1997 1990 1994 1990
1997
b
b
VAB VAB VAP VAP VAB VAB VAP VAB VAB VAB VAB VAB VAB VAB VAB VAP VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAP VAP VAP VAB VAB VAP VAB VAB VAB VAB VAB VAB VAB VAB VAB VAP VAB VAP VAB VAP VAB VAB
Balance of payments and trade
Alternative conversion factor
PPP survey year
Balance of Payments Manual in use
External debt
System of trade
BPM5
Actual
G S S G S S G S G G S
2005 2005 2005 2005
Actual BPM4 BPM5
2005 2005 2005 2005
BPM5 BPM5 BPM5 BPM5 BPM5 BPM5
1977–90 2005 2005 2005
2005
1970–07 1990–95
2005 2005 2005 2005 2005 2005 2005 2005
BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5
Preliminary Actual Actual Preliminary
Actual Estimate Preliminary Actual Actual Actual Actual Actual Actual
BPM5 BPM5 BPM5 BPM5 BPM5 BPM5
Preliminary Estimate Preliminary
BPM5 BPM5 BPM5
Actual Actual
2005 2005
BPM5 BPM5 BPM5
2005 2005
G S G G G G G G G G S S G S G
Government IMF data finance dissemination standard
Accounting concept
C
B C C B C C C
C C B C
B C C C C C C
G G G G G G S S S
S S G G G G G G G S S G G G S
S
B
S
C
G G G
Actual Actual Actual
G S G
C B
S S
Actual Preliminary
G G G G G S G
B C C C C C
G S G S S S
1991
2005 2005
BPM5 BPM5 BPM4 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM4
G G G
C C
G G G
1990–96 1990–92 1991, 1998
2005 2005 2005
BPM5 BPM5 BPM5
Actual Preliminary Actual
B B B C
G G G G
1987–95, 1997–07 1987–95
2005 2005 2005 1990–95
Actual Estimate Actual Actual Estimate
G G G
PRIMARY DATA DOCUMENTATION Latest population census
Samoa San Marino São Tomé and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovak Republic Slovenia Solomon Islands Somalia South Africa Spain Sri Lanka St. Kitts and Nevis St. Lucia St. Vincent & Grenadines Sudan Suriname Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Tonga Trinidad and Tobago
2006 2000 2001 2004 2002 2002 2002 2004 2000 2001 2002 1999 1987 2001 2001 2001 2001 2001 2001 1993 2004 2007 2005 2000 2004 2000 2002 2000 2004 1981 2006 2000
Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Vanuatu Venezuela, RB Vietnam Virgin Islands (U.S.) West Bank and Gaza Yemen, Rep. Zambia Zimbabwe
Latest demographic, education, or health household survey
Source of most recent income and expenditure data
Vital Latest Latest registration agricultural industrial complete census data
1999
MICS, 2005, DHS 2008 General household, 2005
PS 2005 Yes Yes IHS, 2002/03 IS, 1997 ES/BS, 2004
MICS, 2006 DHS, 1998 DHS, 1987
ES/BS, 2000 IHS, 2000 ES/BS, 2002 IHS, 1995
MICS-PAPFAM 2006 MICS, 2000 DHS, 2006
MICS, 2006 MICS, 2005 DHS, 2004, SPA, 2006 MICS 2005/06 DGHS, 2003 MICS, 2006
1999 1998–1999
Yes Yes Yes
Yes Yes Yes Yes Yes
ES/BS, 1999 ES/BS, 2000/01 IS, 2000 ES/BS, 2000
Yes
LSMS, 2004 ES/BS, 2000/01 IHS, 2004 LSMS, 2001 CWIQ, 2006
Yes
Yes Yes
Yes Yes
MICS, 2006
IHS, 1992
2004 2000 1995
MICS, 2006 DHS, 2003 DHS,2006
IHS, 2000 LFS, 2005 LSMS, 1998
Yes
2002 2001 2005 2001 2000 2004 1989 1999 2001 1999 2000 2007 2004 2000 2002
DHS, 2006 DHS, 2007
PS, 2005 ES/BS, 2005
Yes
MICS, 2006
Yes Yes Yes Yes
MICS, 2000 MICS, 2006
IHS, 2006 IHS, 2006
Yes
2004 2004
2005 2005 2006 2006 2006 2007 2006 2006 2006
2006 2002 2003 2000
1982 2007 2006 2005 2006 2007
2003 2000 2000 2000
2000
2006 2001 2007 2007 2006 2007 2000 2007 2006
2000 2000 2000 2000 2000 2003 2000 2002 2000
1996 2001 2004
2002
2005
2007 2007 2006
2000
2004 2001
2001
2006 2006 2000
2000 2003 2000
2000 1999 2002
2005 2004
2001 2004 2003 1999–2000 2000 1981 1994 2002–2003 2003
1991
IS, 1999 LFS 2000 IHS, 2006 ES/BS, 2003
CPS(monthly)
2002
1998 1984–1985 2001 2000
Latest water withdrawal data
2005
Yes Yes Demographic survey, 2007 DHS, 2005 MICS, 2005/06
Latest trade data
1998 1999–2000c 1997/2002 2000
1997 2001
2004 2003
2000 2004 2004 2004 2004
2006 2007 2006 2006 2007 2006
2000
2006 2006 2006
2000
2006 2006 2006
2000 2000 2002
2000 2005 2000 2000 2000 2000
Yes PAPFAM, 2006 MICS, 2006 DHS, 2001/02, SPA, 2005 DHS, 2005/06
ES/BS, 2005 IHS, 2004
1971 2002 1990 1960
2004 1996
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.
2009 World Development Indicators
403
Primary data documentation notes • Base year is the base or pricing period used for
system (G) or special trade system (S). Under the gen-
Faeroe Islands, Greenland, Iceland, San Marino, and
constant price calculations in the country’s national
eral trade system goods entering directly for domestic
Sweden. These countries produce similar census
accounts. Price indices derived from national accounts
consumption and goods entered into customs storage
tables every 5 or 10 years instead of conducting tra-
aggregates, such as the implicit deflator for gross
are recorded as imports at arrival. Under the special
ditional censuses. A rare case, France has been con-
domestic product (GDP), express the price level rela-
trade system goods are recorded as imports when
ducting a rolling census every year since 2004; the
tive to base year prices. • Reference year is the year
declared for domestic consumption whether at time
1999 general population census was the last to cover
in which the local currency, constant price series of a
of entry or on withdrawal from customs storage.
the entire population simultaneously (www.insee.fr/
country is valued. The reference year is usually the
Exports under the general system comprise outward-
en/recensement/page_accueil_rp.htm). • Latest
same as the base year used to report the constant
moving goods: (a) national goods wholly or partly pro-
demographic, education, or health household survey
price series. However, when the constant price data
duced in the country; (b) foreign goods, neither trans-
indicates the household surveys used to compile the
are chain linked, the base year is changed annually,
formed nor declared for domestic consumption in the
demographic, education, and health data in section
so the data are rescaled to a specific reference year
country, that move outward from customs storage;
2. CPS is Current Population Survey, DGHS is Demo-
to provide a consistent time series. When the country
and (c) nationalized goods that have been declared for
graphic and General Health Survey, DHS is Demo-
has not rescaled following a change in base year,
domestic consumption and move outward without
graphic and Health Survey, ENPF is National Family
World Bank staff rescale the data to maintain a longer
being transformed. Under the special system of trade,
Planning Survey (Encuesta Nacional de Planificacion
historical series. To allow for cross-country compari-
exports are categories a and c. In some compilations
Familiar), FHS is Family Health Survey, LSMS is Living
son and data aggregation, constant price data
categories b and c are classified as re-exports. Direct
Standards Measurement Survey, MICS is Multiple
reported in World Development Indicators are rescaled
transit trade—goods entering or leaving for transport
Indicator Cluster Survey, NSS is National Sample Sur-
to a common reference year (2000) and currency (U.S.
only—is excluded from both import and export statis-
vey on Population Change, PAPFAM is Pan Arab Project
dollars). • System of National Accounts identifies
tics. See About the data for tables 4.4, 4.5, and 6.2
for Family Health, RHS is Reproductive Health Survey,
countries that use the 1993 System of National
for further discussion. • Government finance account-
and SPA is Service Provision Assessments. Detailed
Accounts (1993 SNA), the terminology applied in
ing concept is the accounting basis for reporting cen-
information for DHS and SPA are available at www.
World Development Indicators since 2001, to compile
tral government financial data. For most countries
measuredhs.com/aboutsurveys; for MICS at www.
national accounts. Although more countries are adopt-
government finance data have been consolidated (C)
childinfo.org; and for RHS at www.cdc.gov/
ing the 1993 SNA, many still follow the 1968 SNA,
into one set of accounts capturing all central govern-
reproductivehealth/surveys. • Source of most recent
and some low-income countries use concepts from
ment fiscal activities. Budgetary central government
income and expenditure data shows household sur-
the 1953 SNA. • SNA price valuation shows whether
accounts (B) exclude some central government units.
veys that collect income and expenditure data. Names
value added in the national accounts is reported at
See About the data for tables 4.10, 4.11, and 4.12 for
and detailed information on household surveys can
basic prices (VAB) or producer prices (VAP). Producer
further details. • IMF data dissemination standard
be found on the website of the International House-
prices include taxes paid by producers and thus tend
shows the countries that subscribe to the IMF’s Spe-
hold Survey Network (www.surveynetwork.org). Core
to overstate the actual value added in production.
cial Data Dissemination Standard (SDDS) or General
Welfare Indicator Questionnaire Surveys (CWIQ),
However, VAB can be higher than VAP in countries with
Data Dissemination System (GDDS). S refers to coun-
developed by the World Bank, measure changes in key
high agricultural subsidies. See About the data for
tries that subscribe to the SDDS and have posted data
social indicators for different population groups—
tables 4.1 and 4.2 for further discussion of national
on the Dissemination Standards Bulletin Board at
specifically indicators of access, utilization, and sat-
accounts valuation. • Alternative conversion factor
http://dsbb.imf.org. G refers to countries that sub-
isfaction with core social and economic services.
identifies the countries and years for which a World
scribe to the GDDS. The SDDS was established for
Expenditure survey/budget surveys (ES/BS) collect
Bank–estimated conversion factor has been used in
member countries that have or might seek access to
detailed information on household consumption as
place of the official exchange rate (line rf in the Inter-
international capital markets to guide them in provid-
well as on general demographic, social, and economic
national Monetary Fund’s [IMF] International Financial
ing their economic and financial data to the public.
characteristics. Integrated household surveys (IHS)
Statistics). See Statistical methods for further discus-
The GDDS helps countries disseminate comprehen-
collect detailed information on a wide variety of topics,
sion of alternative conversion factors. • Purchasing
sive, timely, accessible, and reliable economic, finan-
including health, education, economic activities, hous-
power parity (PPP) survey year is the latest available
cial, and sociodemographic statistics. IMF member
ing, and utilities. Income surveys (IS) collect informa-
survey year for the International Comparison Pro-
countries elect to participate in either the SDDS or
tion on the income and wealth of households as well
gram’s estimates of PPPs. See About the data for
the GDDS. Both standards enhance the availability of
as various social and economic characteristics. Labor
table 1.1 for a more detailed description of PPPs.
timely and comprehensive data and therefore contrib-
force surveys (LFS) collect information on employ-
• Balance of Payments Manual in use refers to the
ute to the pursuit of sound macroeconomic policies.
ment, unemployment, hours of work, income, and
classification system used to compile and report data
The SDDS is also expected to improve the functioning
wages. Living Standards Measurement Studies
on balance of payments items in table 4.15. BPM4
of financial markets. • Latest population census
(LSMS), developed by the World Bank, provide a com-
refers to the 4th edition of the IMF’s Balance of Pay-
shows the most recent year in which a census was
prehensive picture of household welfare and the fac-
ments Manual (1977), and BPM5 to the 5th edition
conducted and in which at least preliminary results
tors that affect it; they typically incorporate data col-
(1993). • External debt shows debt reporting status
have been released. The preliminary results from the
lection at the individual, household, and community
for 2007 data. Actual indicates that data are as
very recent censuses could be reflected in timely revi-
levels. Priority surveys (PS) are a light monitoring sur-
reported, preliminary that data are preliminary and
sions if basic data are available, such as population
vey, designed by the World Bank, for collecting data
include an element of staff estimation, and estimate
by age and sex, as well as the detailed definition of
from a large number of households cost-effectively
that data are World Bank staff estimates. • System
counting, coverage and completeness. The census
and quickly. Income tax registers (ITR) provide infor-
of trade refers to the United Nations general trade
includes registration-based censuses for Andorra,
mation on a population’s income and allowance, such
404
2009 World Development Indicators
Primary data documentation notes as gross income, taxable income, and taxes by socio-
Economies with exceptional reporting periods
economic group. 1-2-3 surveys (1-2-3) are imple-
of Geography and Statistics revised its national
Reporting period for national accounts data
accounts data. Among the changes are new sources
FY
National accounts value added and expenditure
mented in three phases and collect sociodemographic
Economy
Fiscal year end
and employment data, data on the informal sector,
Afghanistan
Mar. 20
and information on living conditions and household
Australia
Jun. 30
FY
data have been revised for 1985–2006 according to
consumption. • Vital registration complete identifies
Bangladesh
Jun. 30
FY
recently released data from the Ministry of Economy
countries judged to have at least 90 percent complete
Botswana
Jun. 30
FY
and Finance. Constant price series have been linked
registries of vital (birth and death) statistics by the
Canada
Mar. 31
CY
back since 1984. Valuation is value added at basic
United Nations Statistics Division and reported in
Egypt, Arab Rep.
Jun. 30
FY
prices, and the new base year is 1999. • Chile. Data
Population and Vital Statistics Reports. Countries with
Ethiopia
Jul. 7
FY
from 2003 onward reflect the Central Bank’s new
complete vital statistics registries may have more
Gambia, The
Jun. 30
CY
series using 2003 as the base year. • China. The
accurate and more timely demographic indicators
Haiti
Sep. 30
FY
base year for constant price data changed from 1990
than other countries. • Latest agricultural census
India
Mar. 31
FY
to 2000. • Côte d’Ivoire. Data for 1999–2006 were
shows the most recent year in which an agricultural
Indonesia
Mar. 31
CY
revised using data from the IMF, national authorities,
census was conducted and reported to the Food and
Iran, Islamic Rep.
Mar. 20
FY
and World Bank staff estimates. • Egypt. Constant
Agriculture Organization of the United Nations. • Lat-
Japan
Mar. 31
CY
price data are updated from official published national
est industrial data show the most recent year for
Kenya
Jun. 30
CY
accounts. Constant price import and export data
which manufacturing value added data at the three-
Kuwait
Jun. 30
CY
have been revised based on data from the Central
digit level of the International Standard Industrial Clas-
Lesotho
Mar. 31
CY
Bank website (www.cbe.org.eg), which lists the con-
Malawi
Mar. 31
CY
Mauritius
Jun. 30
FY
Myanmar
Mar. 31
FY
Namibia
Mar. 31
CY
Nepal
Jul. 14
FY
New Zealand
Mar. 31
FY
Pakistan
Jun. 30
FY
Puerto Rico
Jun. 30
FY
Sierra Leone
Jun. 30
CY
Singapore
Mar. 31
CY
South Africa
Mar. 31
CY
sification (ISIC, revision 2 or 3) are available in the United Nations Industrial Development Organization database. • Latest trade data show the most recent year for which structure of merchandise trade data from the United Nations Statistics Division’s Commodity Trade (Comtrade) database are available. • Latest water withdrawal data show the most recent year for which data on freshwater withdrawals have been compiled from a variety of sources. See About the data for table 3.5 for more information.
and a change in base year to 2000. • Burkina Faso.
stant price expenditure components of GDP. • Fiji. Data revisions reflect changes in sources. Data for 1996–2005 were revised using data from the Asian Development Bank’s Key Indicators 2007. • India. In May 2007 the Central Statistical Organization published revised national accounts data for 1951–99 consistent with the new series of national accounts statistics released on January 31, 2006. • Jordan. Data have been revised by the Central Bank and the Department of Statistics. • Lebanon. Data have been
Swaziland
Mar. 31
CY
revised by the Central Bank. • Malawi. The National
Exceptional reporting periods
Sweden
Jun. 30
CY
Statistical Offi ce, with assistance from Norway,
In most economies the fiscal year is concurrent with
Thailand
Sep. 30
CY
revised its national accounts data. The initial out-
the calendar year. Exceptions are shown in the table
Uganda
Jun. 30
FY
come is that GDP will increase by approximately 37
at right. The ending date reported here is for the fis-
United States
Sep. 30
CY
percent. • Morocco. The government revised national
cal year of the central government. Fiscal years for
Zimbabwe
Jun. 30
CY
accounts data from 1998 onward. National accounts
other levels of government and reporting years for
value added data switched from producer prices to
statistical surveys may differ. And some countries
Revisions to national accounts data
basic prices. The new base year is 1998. • São Tomé
that follow a fiscal year report their national accounts
National accounts data are revised by national sta-
and Principe. Data have been revised by the National
data on a calendar year basis as shown in the report-
tistical offices when methodologies change or data
Statistics Institute. Revised GDP estimates are much
ing period column.
sources improve. National accounts data in World
higher (47.5 percent for the new base year 2001) than
The reporting period for national accounts data
Development Indicators are also revised when data
those of the previous series and reflect improvements
is designated as either calendar year basis (CY) or
sources change. The following notes, while not com-
in coverage. • Senegal. National accounts data have
fiscal year basis (FY). Most economies report their
prehensive, provide information on revisions from
been revised to conform to 1993 SNA methodology,
national accounts and balance of payments data
previous data.
and the base year has changed to 1999. Value added
using calendar years, but some use fiscal years. In
• Bhutan. Data revisions refl ect changes in
data are now in basic prices. Agricultural sector data
World Development Indicators fiscal year data are
sources. Current and constant price value added data
are entered in the year of production (N) in the 1999
assigned to the calendar year that contains the larger
from 1980 to 2006 are from the government of Bhu-
base year of the SNA as opposed to the year follow-
share of the fiscal year. If a country’s fiscal year ends
tan. Current price expenditure data for 1989–2005
ing the year of production (N+1) in base year 1987.
before June 30, data are shown in the first year of
and constant price expenditure data for 2000–05
• Sudan. Expenditure items in both current and con-
the fiscal period; if the fiscal year ends on or after
are from the Asian Development Bank’s Key Indica-
stant prices for 1988–95 were revised using recent
June 30, data are shown in the second year of the
tors 2007. • Botswana. Large changes in constant
United Nations Statistics Division and IMF World
period. Balance of payments data are reported in
price consumption indicators from 1998–2006 are
Economic Outlook estimates. • Tanzania. National
World Development Indicators by calendar year and
due to statistical discrepancy. The Central Statisti-
accounts expenditure data in current and constant
so are not comparable to the national accounts data
cal Office published large-scale revisions of constant
prices have been revised from 1995 onward. Data
of the countries that report their national accounts
price discrepancies in GDP for 1996/97–2004/05
are from IMF and World Bank staff estimates and
on a fiscal year basis.
in April 2006 and May 2007. • Brazil. The Institute
Tanzanian authorities.
2009 World Development Indicators
405
STATISTICAL METHODS This section describes some of the statistical procedures used in preparing the World
indicator as a weight) and denoted by a u when calculated as unweighted
Development Indicators. It covers the methods employed for calculating regional and
averages. The aggregate ratios are based on available data, including data
income group aggregates and for calculating growth rates, and it describes the World
for economies not shown in the main tables. Missing values are assumed
Bank Atlas method for deriving the conversion factor used to estimate gross national
to have the same average value as the available data. No aggregate is cal-
income (GNI) and GNI per capita in U.S. dollars. Other statistical procedures and
culated if missing data account for more than a third of the value of weights
calculations are described in the About the data sections 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 econo-
•
Aggregate growth rates are denoted by a w when calculated as a weighted
mies appear at the end of most tables. The countries included in these classifica-
average of growth rates. In a few cases growth rates may be computed from
tions are shown on the flaps on the front and back covers of the book. Most tables
time series of group totals. Growth rates are not calculated if more than half
also include the aggregate euro area. This aggregate includes the member states of
the observations in a period are missing. For further discussion of methods of computing growth rates see below.
the Economic and Monetary Union (EMU) of the European Union that have adopted the euro as their currency: Austria, Belgium, Cyprus, Finland, France, Germany,
•
Aggregates denoted by an m are medians of the values shown in the table.
Greece, Ireland, Italy, Luxembourg, Malta, Netherlands, Portugal, Slovak Republic,
No value is shown if more than half the observations for countries with a
Slovenia, and Spain. Other classifications, such as the European Union and regional
population of more than 1 million are missing.
trade 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 153 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 fitting 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 if missing data account for more than a third of the total in the benchmark
ln Xt = a + bt
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
of the ratios (using the value of the denominator or, in some cases, another
average annual growth rate, r, is obtained as [exp(b*) – 1] and is multiplied by 100
406
2009 World Development Indicators
for expression as a percentage. The calculated growth rate is an average rate that
The inflation rate for Japan, the United Kingdom, the United States, and the
is representative of the available observations over the entire period. It does not
euro area, representing international inflation, is measured by the change in the
necessarily match the actual growth rate between any two periods.
“SDR deflator”. (Special drawing rights, or SDRs, are the International Monetary Fund’s unit of account.) The SDR deflator is calculated as a weighted average of
Exponential growth rate. The growth rate between two points in time for cer-
these countries’ GDP deflators in SDR terms, the weights being the amount of
tain demographic indicators, notably labor force and population, is calculated
each country’s currency in one SDR unit. Weights vary over time because both
from the equation
the composition of the SDR and the relative exchange rates for each currency change. The SDR deflator is calculated in SDR terms first and then converted r = ln(pn/p 0)/n
to U.S. dollars using the SDR to dollar Atlas conversion factor. The Atlas conversion factor is then applied to a country’s GNI. The resulting GNI in U.S. dollars is
where pn and p 0 are the last and first observations in the period, n is the number
divided by the midyear population to derive GNI per capita.
of years in the period, and ln is the natural logarithm operator. This growth rate is
When official exchange rates are deemed to be unreliable or unrepresenta-
based on a model of continuous, exponential growth between two points in time.
tive of the effective exchange rate during a period, an alternative estimate of the
It does not take into account the intermediate values of the series. Nor does it
exchange rate is used in the Atlas formula (see below).
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 intervals, in which case the compound growth model is appropriate. The average
and the calculation of GNI per capita in U.S. dollars for year t:
growth rate over n periods is calculated as Yt$ = (Yt/Nt)/et* r = exp[ln(pn/p 0)/n] – 1. where et* is the Atlas conversion factor (national currency to the U.S. dollar) for Like the exponential growth rate, it does not take into account intermediate
year t, et is the average annual exchange rate (national currency to the U.S. dollar)
values of the series.
for year t, pt is the GDP deflator for year t, ptS$ is the SDR deflator in U.S. dollar terms for year t, Yt$ is the Atlas GNI per capita in U.S. dollars in year t, Yt is current
World Bank Atlas method
GNI (local currency) for year t, and Nt is the midyear population for year t.
In calculating GNI and GNI per capita in U.S. dollars for certain operational purposes, the World Bank uses the Atlas conversion factor. The purpose of the
Alternative conversion factors
Atlas conversion factor is to reduce the impact of exchange rate fluctuations in
The World Bank systematically assesses the appropriateness of official exchange
the cross-country comparison of national incomes.
rates as conversion factors. An alternative conversion factor is used when the
The Atlas conversion factor for any year is the average of a country’s exchange
official exchange rate is judged to diverge by an exceptionally large margin from
rate (or alternative conversion factor) for that year and its exchange rates for
the rate effectively applied to domestic transactions of foreign currencies and
the two preceding years, adjusted for the difference between the rate of infla-
traded products. This applies to only a small number of countries, as shown
tion in the country and that in Japan, the United Kingdom, the United States,
in Primary data documentation. Alternative conversion factors are used in the
and the euro area. A country’s inflation rate is measured by the change in its
Atlas methodology and elsewhere in World Development Indicators as single-year
GDP deflator.
conversion factors.
2009 World Development Indicators
407
CREDITS World Development Indicators 2009 draws on a wide range of World Bank reports
and Seyed Mehran Hosseini (tuberculosis); Omar Shafey of the American Cancer
and numerous external sources, listed in the bibliography following this section.
Society (tobacco); Delice Gan of the International Diabetes Federation (diabetes),
Many people inside and outside the World Bank helped in writing and producing
and Nyein Nyein Lwin of the United Nations Children’s Fund (health). Valuable com-
the book. The team would like to particularly acknowledge the help and encour-
ments and inputs at all stages of the production process came from Eric Swanson
agement of Justin Lin, Senior Vice President and Chief Economist of the World
and on the introduction from Sarwar Lateef.
Bank, and Shaida Badiee, Director, Development Data Group. The team is also grateful to the people who provided valuable comments on the entire book. This
3. Environment
note identifies many of those who made specific contributions. Many others,
Section 3 was prepared by Mehdi Akhlaghi and M.H. Saeed Ordoubadi in partner-
too numerous to acknowledge here, helped in many ways for which the team is
ship with the World Bank’s Sustainable Development Network. Important contri-
extremely grateful.
butions were made by Carola Fabi and Edward Gillin of the Food and Agriculture Organization of the United Nations; Ricardo Quercioli of the International Energy
1. World view
Agency; Amy Cassara, Christian Layke, Daniel Prager, and Robin White of the
The introduction to section 1 was prepared by Sarwar Lateef, Soong Sup Lee,
World Resources Institute; Laura Battlebury of the World Conservation Monitor-
and Eric Swanson. Valuable suggestions were provided by Amar Bhattacharya,
ing Centre; and Gerhard Metchies of German Technical Cooperation (GTZ). The
Milan Brahmbhatt, Mansoor Dailami, Alan Gelb, and Claudia Paz Sepulveda.
World Bank’s Environment Department devoted substantial staff resources to
Maurizio Bussolo and Rafael de Hoyos of the Development Economics Pros-
the book, for which the team is very grateful. M.H. Saeed Ordoubadi wrote the
pects Group helped in com puting the inequality estimates. Changqing Sun
introduction with valuable comments from Sarwar Lateef, Bruce Ross-Larson, and
prepared the estimates of gross national income in purchasing power parity
Eric Swanson. Other contributions were made by Susmita Dasgupta, Kirk Hamil-
(PPP) terms. K.M. Vijayalakshmi prepared tables 1.1 and 1.6. Uranbileg Bat-
ton, Craig Meisner, Kiran Pandey, Giovanni Ruta, and Jana Stover.
jargal prepared table 1.4, with valuable assistance from Azita Amjadi. Tables 1.2, 1.3, and 1.5 were prepared by Masako Hiraga. Juan Pedro Schmid and
4. Economy
Mona Prasad of the World Bank’s Economic Policy and Debt Department pro-
Section 4 was prepared by K.M. Vijayalakshmi in close collaboration with the Sus-
vided the estimates of debt relief for the Heavily Indebted Poor Countries
tainable Development and Economic Data Team of the World Bank’s Development
Debt Initiative and Multilateral Debt Relief Initiative.
Data Group, led by Soong Sup Lee. K.M. Vijayalakshmi wrote the introduction with valuable suggestions from Sarwar Lateef, Soong Sup Lee, and Eric Swanson.
2. People
Useful comments were provided by Hinh Dinh and Alice Kuegler. Contributions to
Section 2 was prepared by Masako Hiraga and Sulekha Patel in partnership with
the section were provided by Azita Amjadi (trade). The national accounts data for
the World Bank’s Human Development Network and the Development Research
low- and middle-income economies were gathered by the World Bank’s regional
Group in the Development Economics Vice Presidency. Shota Hatakeyama and
staff through the annual Unified Survey. Maja Bresslauer, Mahyar Eshragh-Tabary,
William Prince provided invaluable assistance in data and table preparation,
Victor Gabor, Bala Bhaskar Naidu Kalimili, and Soong Sup Lee worked on updat-
and Kiyomi Horiuchi prepared the demographic estimates and projections. The
ing, estimating, and validating the databases for national accounts. The team
introduction was written by the International Labour Organization’s (ILO) Employ-
is grateful to the International Monetary Fund, Organisation for Economic Co-
ment Trends Team. The poverty estimates were prepared by Shaohua Chen
operation and Development, United Nations Industrial Development Organization,
and Prem Sangraula of the World Bank’s Poverty Monitoring Group and
and World Trade Organization for access to their databases.
Changquin Sun. The data on children at work were prepared by Lorenzo Guarcello and Furio Rosati from the Understanding Children’s Work project. The data on
5. States and markets
health gaps by income and gender were based on data prepared by Darcy Gallucio
Section 5 was prepared by David Cieslikowski and Raymond Muhula, in partner-
and Davidson Gwatkin of the Human Development Network. Other contribu-
ship with the World Bank’s Financial and Private Sector Development Network,
tions were provided by Eduard Bos, Charu Garg, Inez Mikkelsen-Lopez, and
Poverty Reduction and Economic Management Network, Sustainable Devel-
Emi Suzuki (population, health, and nutrition); Montserrat Pallares-Miralles
opment Network, International Finance Corporation, and external partners.
(pension); Lawrence Jeffrey Johnson of the ILO (labor force); Juan Cruz Perusia
David Cieslikowski wrote the introduction with input from Sarwar Lateef and
and Olivier Labeof the United Nations Educational, Scientific, and Cultural Orga-
Eric Swanson. Other contributors include Ada Karina Izaguirre (privatization and
nization Institute for Statistics (education and literacy); the World Health Orga-
infrastructure projects); Leora Klapper (business registration); Federica Saliola
nization’s Chandika Indikadahena (health expenditure), Monika Bloessner and
(Enterprise Surveys); Svetlana Bagaudinova (Doing Business); Alka Banerjee and
Mercedes de Onis (malnutrition and overweight), Neeru Gupta (health workers),
Isilay Cabuk (Standard & Poor’s global stock market indexes); Satish Mannan
Mie Inoue and Jessica Ho (hospital beds), Rifat Hossain (water and sanitation),
(public policies and institutions); Nigel Adderley of the International Institute
408
2009 World Development Indicators
for Strategic Studies (military personnel); Bjorn Hagelin and Petter Stålenheim
Design, production, and editing
of the Stockholm International Peace Research Institute (military expenditures
Richard Fix and Beatriz Prieto-Oramas coordinated all stages of production with
and arms transfers); Imed Ben Hamadi of the International Road Federation,
Communications Development Incorporated, which provided overall design direc-
Ananthanaryan Sainarayan of the International Civil Aviation Organization, and
tion, editing, and layout, led by Meta de Coquereaumont, Bruce Ross-Larson,
Helene Stephan (transport); Jane Degerlund of Containerisation International
and Christopher Trott. Elaine Wilson created the graphics and typeset the book.
(ports); Vanessa Grey and Esperanza Magpantay of the International Telecom-
Joseph Caponio and Amye Kenall provided proofreading and production assis-
munication Union; Ernesto Fernandez Polcuch of the United Nations Educa-
tance. Communications Development’s London partner, Peter Grundy of Peter
tional, Scientific, and Cultural Organization Institute for Statistics (research and
Grundy Art & Design, provided art direction and design. Staff from External Affairs
development, researchers, and technicians); and Anders Halvorsen of the World
oversaw printing and dissemination of the book.
Information Technology and Services Alliance (information and communication technology expenditures).
Client services The Development Data Group’s Client Services and Communications Team
6. Global links
(Azita Amjadi, Richard Fix, Buyant Erdene Khaltarkhuu, Alison Kwong, and
Section 6 was prepared by Uranbileg Batjargal in partnership with the Finan-
Beatriz Prieto-Oramas) contributed to the design and planning of World Development
cial Data Team of the World Bank’s Development Data Group, Development
Indicators 2009 and helped coordinate work with the Office of the Publisher.
Research Group (trade), Prospects Group (commodity prices), and external partners. Uranbileg Batjargal wrote the introduction, with valuable comments
Administrative assistance and office technology support
from Sarwar Lateef and Eric Swanson. Olga Akcadag and Nino Kostava pro-
Awatif Abuzeid and Estela Zamora provided administrative assistance. Jean-
vided research assistance. Substantial input for the data and tables came from
Pierre Djomalieu, Gytis Kanchas, Nacer Megherbi, and Shahin Outadi provided
Azita Amjadi (trade and tariffs) and Nino Kostava (external debt and financial
information technology support.
data). Eric Swanson provided guidance on table contents and organization. Other contributors include Frederic Docquier (emigration rates), Flavine Creppy
Publishing and dissemination
and Yumiko Mochizuki of the United Nations Conference on Trade and Develop-
The Offi ce of the Publisher, under the direction of Carlos Rossel, provided
ment, and Francis Ng (trade); Betty Dow (commodity prices); Dilek Aykut (for-
valu able assistance throughout the production process. Denise Bergeron,
eign direct investment flows); Eung Ju Kim (financing through capital markets);
Stephen McGroarty, and Nora Ridolfi coordinated printing and supervised mar-
Olga Akcadag (Bloomberg, external debt, and financial data); Yasmin Ahmad
keting and distribution. Merrell Tuck-Primdahl of the Development Economics
and Cecile Sangare of the Organisation for Economic Co-operation and Devel-
Vice President’s Office managed the communications strategy.
opment (aid); Ibrahim Levent and Alagiriswamy Venkatesan (external debt); Henrik Pilgaard of the United Nations Refugee Agency (refugees); Bela Hovy of the
World Development Indicators CD-ROM
United Nations Population Division (migration); K.M. Vijayalakshmi (remittances);
Programming and testing were carried out under the coordination of Reza Farivari
and Teresa Ciller of the World Tourism Organization (tourism). Ramgopal Erabelly,
by Azita Amjadi, Shelley Fu, Buyant Erdene Khaltarkhuu, Vilas K. Mandlekar,
Shelley Lai Fu, and William Prince provided valuable technical assistance.
Nacer Megherbi, William Prince, and Malarvizhi Veerappan. Masako Hiraga produced the social indicators tables. Kiyomi Horiuchi produced the population projection
Other parts of the book
tables. William Prince coordinated user interface design and overall production and
Jeff Lecksell of the World Bank’s Map Design Unit coordinated preparation of
provided quality assurance. Photo credits belong to the World Bank photo library.
the maps on the inside covers. David Cieslikowski prepared the Users guide. Eric Swanson wrote Statistical methods. K.M. Vijayalakshmi coordinated preparation
WDI Online
of Primary data documentation, and Awatif Abuzeid and Buyant Erdene Khaltarkhuu
Design, programming, and testing were carried out by Reza Farivari and his team:
assisted in updating the Primary data documentation table. Richard Fix and
Azita Amjadi, Ramgopal Erabelly, Shelley Fu, Buyant Erdene Khaltarkhuu, and
Beatriz Prieto-Oramas prepared Partners and Index of indicators.
Shahin Outadi. William Prince coordinated production and provided quality assurance. Valentina Kalk and Malika Khek of the Office of the Publisher were responsible
Database management
for implementation of WDI Online and management of the subscription service.
Mehdi Akhlaghi and William Prince coordinated management of the integrated World Development Indicators database. Development and operation of the
Client feedback
database management system was made possible by the Data and Informa-
The team is grateful to the many people who have taken the time to provide
tion Systems Team, which included Ying Chi, Ramgopal Erabelly, Shelley Fu,
assistance on its publications. Their feedback and suggestions have helped
Shahin Outadi, and Atsushi Shimo, under the leadership of Reza Farivari.
improve this year’s edition.
2009 World Development Indicators
409
BIBLIOGRAPHY Aminian, Nathalie, K.C. Fung, and Francis Ng. 2008. “Integration of Markets vs. Integration by Agreements.” Policy Research Working Paper 4546. World Bank, Development Research Group, Washington, D.C. An, Feng, and Amanda Sauer. 2004. “Comparison of Passenger Vehicle Fuel Economy and Greenhouse Gas Emission Standards around the World.” Pew Center on Global Climate Change, Arlington, Va. Anthoff, David, Robert J. Nichols, Richard S. J. Tol, and Athanasios T. Vafeidis. 2006. “Global and Regional Exposure to Large Rises in Sea-level: A Sensitivity Analysis.” Working Paper 96. Tyndall Centre for Climate Change Research, University of East Anglia, Norwich, U.K.
Caiola, Marcello. 1995. A Manual for Country Economists. Training Series 1, Vol. 1. Washington, D.C.: International Monetary Fund. Caprio, Gerard, Asli Demirgüç-Kunt, and Edward J. Kane. 2008. “The 2007 Meltdown in Structured Securitization, Searching for Lessons, Not Scapegoats.” Policy Research Working Paper 4756. World Bank, Washington, D.C. Caprio, Gerard and Daniel Klingebiel. 1996 “Bank Insolvency: Bad Luck, Bad Policy or Bad Banking?” Paper presented at Annual World Bank Conference on Development Economics 1996, Washington, D.C. ———. 2003. Episodes of Systemic and Borderline Financial Crisis. Washington, D.C.: World Bank.
Ashford, Lori S., Davidson R. Gwatkin, and Abdo S. Yazbeck. 2006. Designing
Chen, Shaohua, and Martin Ravallion. 2008. “The Developing World Is Poorer
Health and Population Programs to Reach the Poor. Washington, D.C.: Popula-
than We Thought, but No Less Successful in the Fight Against Poverty?” Policy
tion Reference Bureau. Ball, Nicole. 1984. “Measuring Third World Security Expenditure: A Research Note.” World Development 12 (2): 157–64.
Research Working Paper 4703. World Bank, Washington, D.C. Chomitz, Kenneth M., Piet Buys, and Timothy S. Thomas. 2005. “Quantifying the Rural-Urban Gradient in Latin America and the Caribbean.” Policy Research Work-
Bank for International Settlements. 2007. Triennial Central Bank Survey, December
ing Paper 3634. World Bank, Development Research Group, Washington, D.C.
2007. Foreign Exchange and Derivatives Market Activity in 2007. Basel, Switzer-
CIESIN (Center for International Earth Science Information Network). 2005. Grid-
land: Bank for International Settlements. ———. 2008. “OTC Derivatives Market Activity in the First Half of 2008.” Bank for International Settlements, Monetary and Economic Department, Basel, Switzerland. [www.bis.org/publ/otc_hy0811.pdf].
ded Population of the World. Columbia University and Centro Internacional de Agricultura Tropical. [http://sedac.ciesin.columbia.edu/gpw/]. Claessens, Stijn, Daniela Klingebiel, and Sergio L. Schmukler. 2002. “Explaining the Migration of Stocks from Exchanges in Emerging Economies to International
———. 2009. Capital Flows and Emerging Market Economies. Committee on the
Centers.” Policy Research Working Paper 2816. World Bank, Washington, D.C.
Global Financial System Paper 33. Report submitted by a working group chaired
Claessens, Stijn, M. Ayhan Kose, and Marco E. Terrones. 2008. “What Happens
by Rakesh Mohan of the Reserve Bank of India. Basel, Switzerland: Bank for
During Recessions, Crunches and Busts?” IMF Working Paper 274. International
International Settlements.
Monetary Fund, Research Department, Washington, D.C.
Beck, Thorsten, and Ross Levine. 2001. “Stock Markets, Banks, and Growth:
Commission of the European Communities, IMF (International Monetary Fund),
Correlation or Causality?” Policy Research Working Paper 2670. World Bank,
OECD (Organisation for Economic Co-operation and Development), United
Development Research Group, Washington, D.C.
Nations, and World Bank. 2002. System of Environmental and Economic
Bhattacharya, Amar. 2008. “Global Financial Governance: From Where….to Where?” Paper prepared for Conference on Governance of a Globalizing World: Whither Asia and the West?, December 4–6, National University of Singapore.
Accounts: SEEA 2000. New York. Containerisation International. 2008. Containerisation International Yearbook 2008. London: Informa Maritime & Transport.
Bhattacharya, Amar, Kemal Dervi , and José Antonio Ocampo. 2008. “Respond-
Corrao, Marlo Ann, G. Emmanuel Guindon, Namita Sharma, and Dorna Fakhra-
ing to the Financial Crisis, an Agenda for Global Action.” Paper for the Global
badi Shokoohi. 2000. Tobacco Control Country Profiles. Atlanta, Ga.: American
Financial Crisis Meeting, November 13, Columbia University, New York. Blankfein, Lloyd. 2009. “Do Not Destroy the Essential Catalyst of Risk.” Financial Times. February 9, p. 7. Board of Governors of the Federal Reserve System. n.d. “Statistics and Historical Data.” [www.federalreserve.gov/econresdata/releases/statisticsdata.htm] Brahmbhatt, Milan. 2008. “Low Income Countries and the Financial Crisis, Vulnerabilities and Policy Options.” World Bank, Poverty Reduction and Economic Management, Washington, D.C. Brunnermeier, Markus K. 2009. “Deciphering the Liquidity and Credit Crunch 2007–08.” Journal of Economic Perspectives 23 (1, winter): 77–100. Burton, Ian, Elliot Diringer, and Joel Smith. 2006. “Climate Change: International Policy Options.” Pew Center on Global Climate Change, Arlington, Va.
410
2009 World Development Indicators
Cancer Society. Dasgupta, Susmita, Denoit Laplante, Craig Meisner, David Wheeler, and Jianping Yan. 2007. “The Impact of Sea Level Rise on Developing Countries: A Comparative Analysis.” Policy Research Working Paper 4136. World Bank, Development Research Group, Washington, D.C. De Grauwe, Paul. 2008. “Lessons from the Banking Crisis: A Return to Narrow Banking.” University of Leuven and CESifo. De Onis, Mercedes, and Monika Blossner. 2000. “The WHO Global Database on Child Growth and Malnutrition: Methodology and Applications.” International Journal of Epidemiology 32. 518–26. De Onis, Mercedes, Adelheid W. Onyango, Elaine Borghi, Cutberto Garza, and Hong Yang. 2006. “Comparison of the World Health Organization (WHO) Child Growth
Standards and the National Center for Health Statistics/WHO International
Geithner, Timothy F. 2008. “Reducing Systemic Risk in a Dynamic Financial
Growth Reference: Implications for Child Health Programmes.” Public Health
System.” Speech given at Federal Reserve Bank of New York, June 9. [www.
Nutrition 9 (7): 942–47.
newyorkfed.org/newsevents/speeches/2008/tfg080609.html].
Demirgüç-Kunt, Asli, and Ross Levine. 1996. “Stock Market Development and
Glasier A., A. M. Gulmezoglu, G. Schmid, C. Garcia Moreno, and P.F.A. van Look.
Financial Intermediaries: Stylized Facts.” World Bank Economic Review 10 (2):
2006. “Sexual and Reproductive Health: A Matter of Life and Death.” Lancet
291–321. Deutch, John, Anne Lauvergeon, and Widhyawan Prawiraatmadja. 2007. Energy Security and Climate Change. Task Force Report 61. Washington, D.C.: The Trilateral Commission. Docquier, Frédéric, Abdeslam Marfouk, and B. Lindsay Lowell. 2007. “A Gendered Assessment of the Brain Drain.” Policy Research Working Paper 4613. World Bank, Washington, D.C. El-Erian, Mohamed A. 2008. “A Crisis to Remember.” Finance and Development 45 (4): 15–17. Eurostat (Statistical Office of the European Communities). Various years. Demographic Statistics. Luxembourg. ———. Various years. Statistical Yearbook. Luxembourg. Faiz, Asif, Christopher S. Weaver, and Michael P. Walsh. 1996. Air Pollution from Motor Vehicles: Standards and Technologies for Controlling Emissions. Washington, D.C.: World Bank. Fankhauser, Samuel. 1995. Valuing Climate Change: The Economics of the Greenhouse. London: Earthscan. FAO (Food and Agriculture Organization). 2005. Global Forest Resources Assessment 2005. Rome: Food and Agriculture Organization. ———. 2008. The State of Food and Agriculture 2008. Rome: Food and Agriculture Organization.
368(9547): 1595–1607. Group of Thirty. 2009. Financial Reform: A Framework for Financial Stability. Washington, D.C. Gwatkin, Davidson R., Shea Rutstein, Kiersten Johnson, Eldaw Suliman, Adam Wagstaff, and Agbessi Amouzou. 2007. Socio Economic Differences in Health, Nutrition, and Population. Washington, D.C.: World Bank. Hamilton, Kirk, and Michael Clemens. 1999. “Genuine Savings Rates in Developing Countries.” World Bank Economic Review 13 (2): 333–56. Hamilton, Kirk, and Giovanni Ruta. 2008. “Wealth Accounting, Exhaustible Resources and Social Welfare.” Environmental and Resource Economics 42 (1): 53–64. Hanushek, Eric. 2002. The Long-Run Importance of School Quality. NBER Working Paper 9071. Cambridge, Mass.: National Bureau of Economic Research. Happe, Nancy, and John Wakeman-Linn. 1994. “Military Expenditures and Arms Trade: Alternative Data Sources.” IMF Working Paper 94/69. International Monetary Fund, Policy Development and Review Department, Washington, D.C. Hatzichronoglou, Thomas. 1997. “Revision of the High-Technology Sector and Product Classification.” STI Working Paper 1997/2. Organisation for Economic Co-operation and Development, Directorate for Science, Technology, and Industry, Paris. Heston, Alan. 1994. “A Brief Review of Some Problems in Using National Accounts
———. n.d. FAOSTAT database. [http://faostat.fao.org/default.aspx].
Data in Level of Output Comparisons and Growth Studies.” Journal of Develop-
———. Various years. Fertilizer Yearbook. FAO Statistics Series. Rome: Food and
ment Economics 44 (1): 29–52.
Agriculture Organization. ———. Various years. Production Yearbook. FAO Statistics Series. Rome: Food and Agriculture Organization. ———. Various years. State of Food Insecurity in the World. Rome: Food and Agriculture Organization. ———. Various years. Trade Yearbook. FAO Statistics Series. Rome: Food and Agriculture Organization. Frankhauser, Pierre. 1994. “Fractales, tissus urbains et reseaux de transport.” Revue d’economie politique 104: 435–55. Fredricksen, Birger. 1993. Statistics of Education in Developing Countries: An Intro-
Hettige, Hemamala, Muthukumara Mani, and David Wheeler. 1998. “Industrial Pollution in Economic Development: Kuznets Revisited.” Policy Research Working Paper 1876. World Bank, Development Research Group, Washington, D.C. Hinz, Richard Paul, and Montserrat Pallares-Miralles. Forthcoming. “International Patterns of Pension Provision II.” Social Protection Discussion Paper. World Bank, Washington, D.C. Houser T., R. Bradley, B. Childs, J. Werksman, and R. Heilmayr. 2008. Leveling the Carbon Playing Field: International Competition and US Climate Policy Design. Washington, D.C.: Peterson Institute for International Economics and World Resources Institute.
duction to Their Collection and Analysis. Paris: United Nations Educational, Sci-
IEA (International Energy Agency). 2006. World Energy Outlook. Paris.
entific, and Cultural Organization.
———. 2007. World Energy Outlook: China and India Insights. Paris: International
Gardner-Outlaw, Tom, and Robert Engelman. 1997. “Sustaining Water, Easing Scarcity: A Second Update.” Population Action International, Washington, D.C. GEF (Global Environmental Facility). 2007. “Report on Pledging Meeting for Climate Change Funds.” GEF Secretariat, Washington, D.C.
Energy Agency. ———. 2008a. World Energy Outlook. Paris: International Energy Agency. ———. 2008b. “Energy Technology Perspectives: Scenarios and Strategies to 2050.” International Energy Agency, Paris.
2009 World Development Indicators
411
BIBLIOGRAPHY ———. 2008c. “Energy Efficiency Indicators for Public Electricity Production From Fossil Fuels.” IEA Information Paper. Organisation for Economic Co-operation/ International Energy Agency, Paris. ———. 2008d. Worldwide Trends in Energy Use and Efficiency: Key Insight from IEA Indicator Analysis. Paris: International Energy Agency. ———. 2008e. Towards a Sustainable Energy Future: IEA Programme of Work on Climate Change, Clean Energy and Sustainable Development. Paris: International Energy Agency. ———. 2008f. Deploying Renewables: Principles for Effective Policies. Paris: International Energy Agency. ———. 2008g. Energy Efficiency Policy Recommendations. Paris: International Energy Agency. ———. 2008h. IEA Work for G-8: 2008 Messages. Paris: International Energy Agency.
D.C.: International Monetary Fund. ———. 2008a. Global Financial Stability Report October 2008: Financial Stress and Deleveraging Macrofinancial Implications and Policy. Washington, D.C.: International Monetary Fund. ———. 2008b. World Economic Outlook: Financial Stress, Downturns, and Recoveries. Washington, D.C.: International Monetary Fund. ———. Various issues. Direction of Trade Statistics Quarterly. Washington, D.C.: International Monetary Fund. ———. Various issues. International Financial Statistics. Washington, D.C.: International Monetary Fund. ———. Various years. Balance of Payments Statistics Yearbook. Washington, D.C.: International Monetary Fund. ———. Various years. Direction of Trade Statistics Yearbook. Washington, D.C.:
———. 2008i. “Sectoral Approach for Greenhouse Gas Mitigation.” Organisation for Economic Co-operation and Development / International Energy Agency, Paris. ———. Various years. Energy Balances of OECD Countries. Paris: International Energy Agency. ———. Various years. Energy Statistics and Balances of Non-OECD Countries. Paris: International Energy Agency. ———. Various years. Energy Statistics of OECD Countries. Paris: International Energy Agency. ILO (International Labour Organization). 1990. “International Standard Classification of Occupations: ISCO-88.” International Labour Organization, Geneva. [http://www.ilo.org/public/english/bureau/stat/isco/isco88/index.htm]. ———. 2008. Trends Econometric Models. Geneva: International Labour Organization. ———. 2009. Global Employment Trends. Geneva: International Labour Organization. ———. Forthcoming. Guide to the New MDG Employment Indicators. Geneva: International Labour Organization. ———. Various years. Key Indicators of the Labour Market. Geneva: International Labour Organization. ———. Various years. Yearbook of Labour Statistics. Geneva: International Labour Organization. IMF (International Monetary Fund). 1977. Balance of Payments Manual. Washington, D.C.: International Monetary Fund. ———. 1993. Balance of Payments Manual. Washington, D.C.: International Monetary Fund. ———. 1995. Balance of Payments Compilation Guide. Washington, D.C.: International Monetary Fund. ———. 1996. Balance of Payments Textbook. Washington, D.C.: International Monetary Fund. ———. 2000. Monetary and Financial Statistics Manual. Washington, D.C.: International Monetary Fund. ———. 2001. Government Finance Statistics Manual. Washington, D.C.: International Monetary Fund.
412
———. 2004. Compilation Guide on Financial Soundness Indicators. Washington,
2009 World Development Indicators
International Monetary Fund. ———. Various years. Government Finance Statistics Yearbook. Washington, D.C.: International Monetary Fund. ———. Various years. International Financial Statistics Yearbook. Washington, D.C.: International Monetary Fund. International Civil Aviation Organization. 2008. Civil Aviation Statistics of the World. Montreal: International Civil Aviation Organization. International Diabetes Federation. Various years. Diabetes Atlas. Brussels: International Diabetes Federation. International Institute for Strategic Studies. 2009. The Military Balance 2009. London: Oxford University Press. International Road Federation. 2008. World Road Statistics 2008. Geneva: International Road Federation. International Trade Center, UNCTAD (United Nations Conference on Trade and Development), and WTO (World Trade Organization). n.d. The Millennium Development Goals. Online database. [www.mdg-trade.org/]. International Working Group of External Debt Compilers (Bank for International Settlements, International Monetary Fund, Organisation for Economic Cooperation and Development, and World Bank). 1987. External Debt Definitions. Washington, D.C. IPCC (Intergovernmental Panel on Climate Change). 2007a. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, U.K.: Cambridge University Press. ———. 2007b. “Summary for Policymakers.” In Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, U.K.: Cambridge University Press. ———. 2007c. “Technical Summary.” In S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H. L. Miller, eds., Climate Change 2007: Climate Change Impacts, Adaptation and Vulnerability. WorkingGroup II
Contribution to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, U.K.: Cambridge University Press. ITU (International Telecommunication Union). 2008. World Telecommunication Indicators database. Geneva: International Telecommunication Union. IUCN (World Conservation Union). 2008. 2008 IUCN Red List of Threatened Species. Gland, Switzerland: World Conservation Union.
National Science Board. 2008. Science and Engineering Indicators 2008. Arlington, Va.: National Science Foundation. Netcraft. 2008. “Netcraft Secure Server Survey.” [www.netcraft.com]. NREL (National Renewable Energy Laboratory) Energy Analysis Office. 2005. “Renewable Energy Cost Trends.” [www.nrel.gov/analysis/docs/cost_ curves_2005.ppt].
Ivanic, Maros, and Will Martin. 2008. “Implications of Higher Global Food Prices
OECD (Organisation for Economic Co-operation and Development). 2005. Guide
for Poverty in Low-Income Countries.” Policy Research Working Paper 4594.
to Measuring the Information Society. DSTI/ICCP/ISS (2005)/6. Paris: Organisa-
World Bank, Development Research Group, Washington, D.C. Kharas, Homi. 2007. “Trends and Issues in Development Aid.” Wolfensohn Center for Development Working Paper. The Brookings Institution, Washington, D.C. Krugman, Paul. 2008. The Return of Depression Economics and the Crisis of 2008. New York: W.W. Norton & Company.
tion for Economic Co-operation and Development. ———. 2006. OECD Health Data 2006. Paris: Organisation for Economic Co-operation and Development. ———. 2008a. A Profile of Immigrant Populations in the 21st Century: Data from OECD Countries. Paris: Organisation for Economic Co-operation and Development.
Kunte, Arundhati, Kirk Hamilton, John Dixon, and Michael Clemens. 1998. “Esti-
———. 2008b. Agricultural Policies in OECD Countries: Monitoring and Evalua-
mating National Wealth: Methodology and Results.” Environmental Economics
tion—At a Glance 2008. Paris: Organisation for Economic Co-operation and
Series 57. World Bank, Environment Department, Washington, D.C. Laeven, Luc, and Fabian Valencia. 2008. “Systemic Banking Crises: A New Database.” IMF Working Paper 08/224. International Monetary Fund, Washington, D.C. Lanjouw, Jean O., and Peter Lanjouw. 2001. “The Rural Non-Farm Sector: Issues and Evidence from Developing Countries.” Agricultural Economics 26 (1): 1–23. Lanjouw, Peter, and Gershon Feder. 2001. “Rural Nonfarm Activities and Rural Development: From Experience toward Strategy.” Rural Strategy Discussion Paper 4. World Bank, Washington, D.C. Lin, Justin Yifu. 2009. “How to Solve the Global Economic Crisis: Making Fiscal Stimulus Packages Work across the World.” World Bank, Washington, D.C. Lopez, Alan D., Colin D. Mathers, Majad Ezzati, Dean T. Jamison, and Christopher J.L. Murray, eds. 2006. Global Burden of Disease and Risk Factors. Washington, D.C.: World Bank. Lovei, Magdolna. 1997. “Toward Effective Pollution Management.” Environment Matters (fall): 52–3. Macro International. Various years. Demographic and Health Surveys. [www. measuredhs.com]. Mani, Muthukumara, and David Wheeler. 1997. “In Search of Pollution Havens? Dirty Industry in the World Economy, 1960–95.” World Bank, Policy Research Department, Washington, D.C. McKinsey Global Institute. 2008. Mapping Global Capital Markets: Fifth Annual Report. New York. Mitchell, Donald. 2008. “A Note on Rising Food Prices.” Policy Research Working Paper 4682. World Bank, Washington, D.C.
Development. ———. 2008c. OECD Information Technology Outlook 2008. Paris: Organisation for Economic Co-operation and Development. ———. 2008d. Producer and Consumer Support Estimates, 1986–2007. OECD Online Database. [www.oecd.org/tad/support/psecse]. ———. 2009. “Trade Flows Weaken in Third Quarter 2008.” OECD International Trade Statistics News Release. January 27. [www.oecd.org/ dataoecd/14/55/42058488.pdf]. ———. n.d. Database on Immigrants in OECD countries. Online database. [http:// stats.oecd.org, select Demography and Population, then select Migration Statistics]. ———. Various issues. Main Economic Indicators. Paris: Organisation for Economic Co-operation and Development. ———. Various years. National Accounts. Vol. 1, Main Aggregates. Paris: Organisation for Economic Co-operation and Development. ———. Various years. National Accounts. Vol. 2, Detailed Tables. Paris: Organisation for Economic Co-operation and Development. OECD (Organisation for Economic Co-operation and Development) DAC (Development Assistance Committee). Various years. International Development Statistics Online. Online database. [www.oecd.org/dac/stats/idsonline]. ———. Various years. Development Cooperation Report. Paris: Organisation for Economic Co-operation and Development. ———. Various years. Geographical Distribution of Financial Flows to Aid Recipients. Paris: Organisation for Economic Co-operation and Development. Pandey, Kiran D., Piet Buys, Kenneth Chomitz, and David Wheeler. 2006a. “Bio-
Morgan Stanley Capital International Barra. 2009. MSCI Global Investable Mar-
diversity Conservation Indicators: New Tools for Priority Setting at the Global
ket Indices Methodology. [www.mscibarra.com/products/indices/equity/
Environmental Facility.” World Bank, Development Economics Research Group
methodology.jsp]. Morgenstern, Oskar. 1963. On the Accuracy of Economic Observations. Princeton, N.J.: Princeton University Press.
and Environment Department, Washington, D.C. Pandey, Kiran D., David Wheeler, Bart Ostro, Uwe Deichmann, Kirk Hamilton, and Katie Bolt. 2006b. “Ambient Particulate Matter Concentrations in Residential
2009 World Development Indicators
413
BIBLIOGRAPHY and Pollution Hotspots of World Cities: New Estimates Based on the Global
Smith, Lisa, and Laurence Haddad. 2000. “Overcoming Child Malnutrition in Devel-
Model of Ambient Particulates (GMAPS).” World Bank, Development Economics
oping Countries: Past Achievements and Future Choices.” 2020 Brief 64. Inter-
Research Group and Environment Department, Washington, D.C. Pandey, Kiran D., Katharine Bolt, Uwe Deichmann, Kirk Hamilton, Bart Ostro, and David Wheeler. 2006c. “The Human Cost of Air Pollution: New Estimates for Developing Countries.” World Bank, Development Research Group and Environment Department, Washington, D.C. Partnership on Measuring ICT for Development. 2008. The Global Information Society: A Statistical View. Santiago, Chile: United Nations. Pew Center on Global Climate Change. 2007. “A Look at Emission Targets.” Pew Center on Global Climate Change. [www.pewclimate.org/what_s_being_done/ targets].
national Food Policy Research Institute, Washington D.C. SPC (Secretariat of the Pacific Community). n.d. Online Statistics and Demography. [www.spc.int]. Srinivasan, T. N. 1994. “Database for Development Analysis: An Overview.” Journal of Development Economics 44 (1): 3–28. Standard & Poor’s. 2000. The S&P Emerging Market Indices: Methodology, Definitions, and Practices. New York. ———. 2008. Global Stock Markets Factbook 2008. New York. Stern, Nicholas. 2006. The Economics of Climate Change: The Stern Review. London: Cambridge University Press.
Pricewaterhouse Coopers. 2006. Worldwide Tax Summaries Online. New York. [www.
Summers, Lawrence H. 2006. “Reflections on Global Account Imbalances
pwc.com/extweb/pwcpublications.nsf/docid/9B2B76032544964C852571
and Emerging Markets Reserve Accumulation.” L.K. Jha memorial lecture,
7E00606CBD].
Reserve Bank of India, Mumbai, India, March 24. [http://ksghome.harvard.
Ratha, Dilip and William Shaw. 2007. “South-South Migration and Remittances.” World Bank, Development Prospects Group, Washington, D.C. Ravallion, Martin, and Shaohua Chen. 1996. “What Can New Survey Data Tell Us
edu/~lsummer/speeches/2006/0324_rbi.html]. UCW (Understanding Children’s Work). n.d. Online database. [www.ucw-project.org] UNAIDS (Joint United Nations Programme on HIV/AIDS) and WHO (World Health
about the Recent Changes in Living Standards in Developing and Transitional
Organization). Various years. Report on the Global AIDS Epidemic. Geneva.
Economies?” Policy Research Working Paper 16943. World Bank, Development
UNCTAD (United Nations Conference on Trade and Development). 2007. Trade and
Research Group, Washington, D.C. Ravallion, Martin, Shaohua Chen, and Prem Sangraula. 2008. “Dollar a Day Revisited?” Policy Research Working Paper 4620. World Bank, Development Research Group, Washington, D.C. Ravallion, Martin, Gaurav Datt, and Dominique van de Walle. 1991. “Quantifying Absolute Poverty in the Developing World.” Review of Income and Wealth 37(4): 345–61. Reinhart, Carmen M., and Kenneth S. Rogoff. 2008. Banking Crises: An Equal Opportunity Menace. NBER Working Paper 14587. Cambridge, Mass.: National Bureau of Economic Research. ———. 2009. The Aftermath of Financial Crises. NBER Working Paper 14656. Cambridge, Mass.: National Bureau of Economic Research.
Development Report, 2007: Regional Cooperation for Development. New York and Geneva: United Nations Conference on Trade and Development. ———. 2008a. Trade and Development Report 2008: Commodity Prices, Capital Flows and the Financing of Investment. New York and Geneva: United Nations Conference on Trade and Development. ———. 2008b. World Investment Report 2008: Transnational Corporations and the Infrastructure Challenge. New York and Geneva: United Nations Conference on Trade and Development. ———. Various years. Handbook of International Trade and Development Statistics. Geneva: United Nations Conference on Trade and Development. ———. Various years. Handbook of Statistics. Geneva: United Nations Conference on Trade and Development.
Rosenzweig C., D. Karoly, M. Vicarelli, P. Neofotis, Q. Wu, G. Casassa, A. Menzel,
UNDP (United Nations Development Programme). 2006. Human Development
T.L. Root, N. Estrella, B. Seguin, P. Tryjanowski, C. Liu, S. Rawlins, and A.
Report 2006: Beyond Scarcity: Power, Poverty and Global Water Crisis. New
Imeson. 2008. “Attributing Physical and Biological Impacts to Anthropogenic Climate Change.” Nature 453: 353–57. Ruggles, Robert. 1994. “Issues Relating to the UN System of National Accounts and Developing Countries.” Journal of Development Economics 44 (1): 77–85. Ryten, Jacob. 1998. “Fifty Years of ISIC: Historical Origins and Future Perspectives.” ECA/STAT.AC. 63/22. United Nations Statistics Division, New York. Shafey, O., M. Eriksen, H. Ross, and J. Mackay. 2009. The Tobacco Atlas. Third edition. Atlanta, Ga.: American Cancer Society and World Lung Foundation. SIPRI (Stockholm International Peace Research Institute). 2008. SIPRI Yearbook 2008: Armaments, Disarmament, and International Security. Oxford, U.K.: Oxford University Press.
414
2009 World Development Indicators
York: Palgrave Macmillan ———. 2007. Human Development Report 2007/2008: Fighting Climate Change: Human Solidarity in a Divided World. New York: United Nations Development Programme. UNEP (United Nations Environment Programme). 2002. Global Environment Outlook 3. London: Earthscan. UNESCO (United Nations Educational, Scientific, and Cultural Organization). 1997. “International Standard Classification of Education.” United Nations Educational, Scientific, and Cultural Organization, Paris. ———. Various years. EFA Global Monitoring Report. Paris: United Nations Educational, Scientific, and Cultural Organization.
UNESCO (United Nations Educational, Scientific and Cultural Organization) Institute for Statistics. Various years. Global Education Digest. Paris: United Nations Educational, Scientific, and Cultural Organization. ———. Online database. [http://stats.uis.unesco.org/unesco/TableViewer/document.aspx?ReportId=143&IF_Language=eng]. UNHCR (United Nations High Commissioner for Refugees). Various years. Statistical Yearbook. Geneva: United Nations High Commissioner for Refugees. UNICEF (United Nations Children’s Fund). n.d. Child Mortality Database. Online database. Childinfo. [www.childmortality.org]. ———. Various years. State of the World’s Children. New York: Oxford University Press. ———. Various years. “Multiple Indicator Cluster Surveys.” Childinfo. [http://www. childinfo.org/mics.html].
———. Various years. National Accounts Statistics: Main Aggregates and Detailed Tables. Parts 1 and 2. New York: United Nations. ———. Various years. National Income Accounts. New York: United Nations. ———. Various years. Population and Vital Statistics Report. New York: United Nations. ———. Various years. World Urbanization Prospects. New York: United Nations. ———. Various years. Statistical Yearbook. New York: United Nations. University of California, Berkeley, and Max Planck Institute for Demographic Research. n.d. Human Mortality Database. [www.mortality.org or www.humanmortality.de]. U.S. Census Bureau. n.d. International Data Base (IDB). [www.census.gov/ipc/ www/idb/] U.S. Census Bureau, Foreign Trade Statistics. 2009. “FT900: U.S. International
UNICEF (United Nations Children’s Fund), WHO (World Health Organization), World
Trade in Goods and Services. December 2008.” U.S. Census Bureau of Eco-
Bank, and United Nations Population Division. 2007. “Levels and Trends of
nomic Analysis News. February 11. [http://www.census.gov/foreign-trade/
Child Mortality in 2006: Estimates Developed by the Inter-Agency Group for Child Mortality Estimation.” Working Paper. United Nations, New York. UNIDO (United Nations Industrial Development Organization). Various years. International Yearbook of Industrial Statistics. Vienna: United Nations Industrial Development Organization. UNIFEM (United Nations Development Fund for Women). 2005. Progress of the World’s Women. New York: United Nations Development Fund for Women. United Nations. 1947. Measurement of National Income and the Construction of Social Accounts. Geneva: United Nations. ———. 1968. A System of National Accounts: Studies and Methods. Series F, No. 2, Rev. 3. New York: United Nations.
Press-Release/current_press_release/press.html]. U.S. Center for Disease Control and Prevention. Various years. International Reproductive Health Surveys. [www.cdc.gov/reproductivehealth/surveys]. U.S. Department of Treasury. 2008. Report on U.S. Portfolio Holdings of Foreign Securities. Washington, D.C.: U.S. Department of Treasury. [http://www.ustreas. gov/press/reports.html]. U.S. Environmental Protection Agency. 1995. National Air Quality and Emissions Trends Report 1995. Washington, D.C.: U.S. Environmental Protection Agency. Walsh, Michael P. 1994. “Motor Vehicle Pollution Control: an Increasingly Critical Issue for Developing Countries.” World Bank, Washington, D.C. Watson, Robert, John A. Dixon, Steven P. Hamburg, Anthony C. Janetos, and
———. 1990. International Standard Industrial Classification of All Economic Activi-
Richard H. Moss. 1998. Protecting Our Planet, Securing Our Future: Linkages
ties, Third Revision. Statistical Papers Series M, No. 4, Rev. 3. New York: United
among Global Environmental Issues and Human Needs. Nairobi and Washington,
Nations..
D.C.: United Nations Environment Programme, U.S. National Aeronautics and
———. 1993. System of National Accounts. New York: United Nations. United Nations Population Division. 2006. Trends in Total Migrant Stock: 2005 Revision. New York. [http://esa.un.org/migration/].
Space Administration, and World Bank. Watson, Jim, Gordon MacKerron, David Ockwell, and Tao Wang. 2007. “Technology and Carbon Mitigation in Developing Countries: Are Cleaner Coal Technolo-
———. 2007. World Population Prospects: The 2006 Revision. New York: United
gies a Viable Option?” Background paper for United Nations Development Pro-
Nations, Department of Economic and Social Affairs. [http://esa.un.org/
gramme’s Human Development Report 2007. [http://hdr.undp.org/en/reports/
unpp/]. ———. Various years. World Urbanization Prospects. New York: United Nations, Department of Economic and Social Affairs. United Nations Statistics Division. 1985. National Accounts Statistics: Compendium of Income Distribution Statistics. New York: United Nations. ———. n.d. International Standard Industrial Classification of All Economic Activities, Third Revision (ISIC, Rev. 3). Geneva. [http://unstats.un.org/unsd/cr/registry/ regcst.asp?Cl=2 /] ———. Various issues. Monthly Bulletin of Statistics. New York: United Nations. ———. Various years. Energy Statistics Yearbook. New York: United Nations. ———. Various years. International Trade Statistics Yearbook. New York: United Nations.
global/hdr2007-2008/papers/watson_mackerron_ockwell_wang.pdf]. WHO (World Health Organization). 2000. World Health Report 2000: Health Systems: Improving Performance. Geneva: World Health Organization. ———. 2006. World Health Report 2006. Geneva: World Health Organization. ———. 2008a. “Measuring Health System Strengthening and Trends: A Toolkit for Countries.” World Health Organization, Geneva. [www.who.int/healthinfo/ statistics/toolkit_hss/en/index.html] ———. 2008b. Worldwide Prevalence of Anemia 1993–2005. Geneva: World Health Organization. ———. Various years. Global Tuberculosis Control Report. Geneva: World Health Organization. ———. Various years. World Health Statistics. Geneva: World Health Organization.
2009 World Development Indicators
415
BIBLIOGRAPHY WHO (World Health Organization) and UNICEF (United Nations Children’s Fund). 2008. Progress on Drinking Water and Sanitation. Geneva. ———. Various years. WHO-UNICEF Estimates of National Immunization Coverage Database. [www.who.int/immunization_monitoring/routine/immunization_ coverage/en/index4.html]. WHO (World Health Organization), UNICEF (United Nations Children’s Fund), UNFPA (United Nations Population Fund), and World Bank. 2007. Maternal Mortality in 2005: Estimates Developed by WHO, UNICEF, UNFPA, and the World Bank. Geneva. WIPO (World Intellectual Property Organization). 2008. WIPO Patent Report: Statistics on Worldwide Patent Activity. Geneva: World Intellectual Property Organization. WITSA (World Information Technology and Services Alliance). 2008. Digital Planet 2008: The Global Information Economy. Vienna, Va.: World Information Technology and Services Alliance. WMO (World Meteorological Organization). 2006. Statement on the Status of the Global Climate in 2005. Geneva: World Meteorological Organization. ———. 2007. “Observing Stations.” Publication 9, Volume A, (July 9). [www.wmo. int/pages/prog/www/ois/volumea/vola-home.htm]. Wolf, Holger C. 1997. Patterns of Intra- and Inter-State Trade. NBER Working Paper 5939. Cambridge, Mass.: National Bureau of Economic Research. Wolf, Martin. 2008. “Fixing Global Finance.” Baltimore, Md.: John Hopkins University Press. World Bank. 1990. World Development Report 1990: Poverty. New York: Oxford University Press. ———. 1992. World Development Report 1992: Development and the Environment. New York: Oxford University Press. ———. 1995. World Development Report 1995: Workers in an Integrating World. New York: Oxford University Press. ———. 1996a. Environment Matters. Discussion Paper 363. Washington, D.C.: World Bank. ———. 1996b. Livable Cities for the 21st Century: A Directions in Development Book. Washington, D.C.: World Bank. ———. 1996c. National Environmental Strategies: Learning from Experience. Washington, D.C. :World Bank. ———. 1997a. Expanding the Measure of Wealth: Indicators of Environmentally Sustainable Development. Environmentally Sustainable Development Studies and Monographs Series Report 17. Washington, D.C.: World Bank. ———. 1997b. Rural Development: From Vision to Action. Environmentally Sustainable Development Studies and Monographs Series Report 12. Washington, D.C.: World Bank. ———. 1999a. Fuel for Thought: Environmental Strategy for the Energy Sector. Washington, D.C.: World Bank. ———. 1999b. Greening Industry: New Roles for Communities, Markets, and Governments. New York: Oxford University Press. ———. 2000a. Trade Blocs. New York: Oxford University Press.
416
2009 World Development Indicators
———. 2002. World Development Report 2003: Sustainable Development in a Dynamic World. New York: Oxford University Press. ———. 2006a. Where is the Wealth of Nations? Measuring Capital for the 21st Century. Washington, D.C.: World Bank. ———. 2006b. “Managing Climate Risk: Integrating Adaptation into World Bank Group Operations.” Working Paper 37462. World Bank, Washington, D. C. ———. 2007a. “Clean Energy for Development: The World Bank Group Action.” World Bank, Sustainable Development Network, Washington, D.C. ———. 2007b. “An Investment Framework for Clean Energy and Development. A Platform for Convergence of Public and Private Investment.” World Bank, Washington D.C. ———. 2007c. “Healthy Development: The World Bank Strategy for Health, Nutrition, and Population Results.” World Bank, Washington, D.C. ———. 2008a. “Development and Climate Change: A Strategic Framework for the World Bank Group.” Report to Development Committee. World Bank, Washington D.C. ———. 2008b. Doing Business 2009. Washington, D.C.: World Bank. ———. 2008c. East Asia: Navigating the Perfect Storm A World Bank Economic Update for the East Asia and Pacific Region. Washington, D.C.: World Bank. ———. 2008d. Global Development Finance. Washington, D.C.: World Bank. ———. 2008e. “Global Financial Crisis: Responding Today, Securing Tomorrow.” Background paper prepared for the G-20 Summit on Financial Markets and the World Economy November 15, Washington, D.C. ———. 2008f. Global Monitoring Report: MDGs and the Environment. Washington, D.C.: World Bank. ———. 2008g. Performance Assessments and Allocation of IDA Resources. Online database. [http://web.worldbank.org/WBSITE/EXTERNAL/EXTABOUTUS/IDA/0 ,,contentMDK:20052347~menuPK:2607525~pagePK:51236175~piPK:4373 94~theSitePK:73154,00.html]. ———. 2008h. Private Participation in Infrastructure Project. Online database. [http://ppi.worldbank.org/]. ———. 2008i. “Weathering the Storm: Economic Policy Responses to the Financial Crisis.” World Bank, Washington, D.C. [http://siteresources.worldbank.org/ NEWS/Resources/weatheringstorm.pdf]. ———. 2009a. “Crisis Hitting Poor Hard in Developing World, World Bank Says.” Press Release 2009/220/EXC. World Bank, Washington, D.C. ———. 2009b. Global Economic Prospects 2009: Commodities at the Crossroads. Washington, D.C.: World Bank. ———. Forthcoming. Information and Communications for Development 2009: Scaling up the Impact. Washington, D.C.: World Bank. ———. n.d. Enterprise Surveys Online. [www.enterprisesurveys.org]. ———. Various issues. “Commodity Market Review.” World Bank, Development Prospects Group, Washington, D.C. ———. Various issues. “Commodity Price Data (Pink sheet).” World Bank, Development Prospects Group, Washington, D.C.
———. Various issues. “Migration and Development Brief.” World Bank, Development Prospects Group, Washington, D.C. ———. Various years. Global Development Finance: Volume I and Volume II. Washington, D.C.: World Bank. ———. Various years. World Development Indicators. Washington, D.C.: World Bank.
———. 2007. Climate Analysis Indicators Tool (CAIT). Online database. [www.wri. org/climate/project_description2.cfm?pid=93]. ———. 2008a. Earth Trends, the Environmental Information Portal. Online database. ———. 2008b. “Energy and Climate Action Plan in China.” WRI Fact Sheet. World Resources Institute, Washington D.C. WRI (World Resources Institute), UNEP (United Nations Environment Programme),
———. Various years. World Debt Tables. Washington, D.C.: World Bank.
UNDP (United Nations Development Programme), and World Bank. Various
World Bank and IMF (International Monetary Fund). 2006. “Fiscal Policy for
years. World Resources: A Guide to the Global Environment. New York: Oxford
Growth and Development: an Interim Report to the Development Committee.” Report DC-0003 April 6, 2006. International Monetary Fund, World Bank, Washington D.C. ———. 2008a. Global Monitoring Report 2008. Washington, D.C. ———. 2008b. “Heavily Indebted Poor Countries (HIPC) Initiative and Multilateral Debt Relief Initiative (MDRI)—Status of Implementation.” International Monetary Fund, World Bank, Washington, D.C. World Federation of Exchanges. n.d. WFE Online Database. [http://www.worldexchanges.org/statistics] WRI (World Resources Institute). 2005. “Navigating the Numbers.” World Resources Institute, Washington, D.C.
University Press. World Tourism Organization. Various years. Compendium of Tourism Statistics. Madrid: World Tourism Organization. ———. Various years. Yearbook of Tourism Statistics. Vols. 1 and 2. Madrid: World Tourism Organization. WTO (World Trade Organization). Various years. Annual Report. Geneva: World Trade Organization. ———. n.d. “Regional Trade Agreements Information System.” Online database. [http://rtais.wto.org/]. ———. n.d. “Regional Trade Agreements Gateway.” World Trace Organization, Geneva. [www.wto.org/english/tratop_e/region_e/region_e.htm].
2009 World Development Indicators
417
INDEX OF INDICATORS References are to table numbers. per worker
A
3.3
Aid
Agriculture
by recipient
agricultural raw materials commodity prices
aid dependency ratios 6.6
exports as share of total exports
4.4
from high-income economies as share of total exports
6.4
imports as share of total imports
4.4
by high-income economies as share of total exports
6.4
tariff rates applied by high-income countries
6.4
cereal
6.15
total
6.15
net concessional flows from international financial institutions
6.12
from UN agencies
6.12
official development assistance by DAC members administrative costs, as share of net bilateral ODA disbursements bilateral aid
6.14a 6.14a, 6.14b, 6.16
area under production
3.2
by purpose
exports from high-income economies as share of total exports
6.4
by sector
imports, by high-income economies as share of total imports
6.4
commitments
tariff rates applied by high-income countries
6.4
debt-related aid, as share of net bilateral ODA disbursements
yield
3.3
development projects, programs, and other resource provisions,
employment, as share of total
3.2
fertilizer
6.14a 6.14b 6.13, 6.14b 6.14a
as share of net bilateral ODA disbursements
6.14a
for basic social services, as share of sector-allocable bilateral
commodity prices
6.6
ODA commitments
1.4
consumption, per hectare of arable land
3.2
gross disbursements
6.13
food
humanitarian assistance, as share of net bilateral ODA
beverages and tobacco
4.3
disbursements
commodity prices
6.6
net disbursements
exports from high-income economies as share of total exports
4.4, 6.4
as share of general government disbursements
imports by high-income economies as share of total imports
4.5, 6.4
as share of GNI of donor country
tariff rates applied by high-income countries freshwater withdrawals for, as share of total
6.14a
6.13 1.4, 6.13
6.4
from major donors, by recipient
6.16
3.5
per capita of donor country
6.13
land
total
6.13, 6.14a
agricultural, as share of land area
3.2
arable, as share of land area
3.1
arable, per 100 people
3.1
total sector allocable, as share of bilateral ODA commitments
area under cereal production
3.2
untied aid
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
crop
technical cooperation, as share of net bilateral ODA disbursements
official development assistance by non-DAC members
6.14a 6.14b 6.14b 6.15a
AIDS—see HIV, prevalence 3.2
production indexes
Air pollution—see Pollution 3.3
food
3.3
livestock
3.3
value added
418
6.15
per capita
annual growth
4.1
as share of GDP
4.2
2009 World Development Indicators
Air transport air freight
5.9
passengers carried
5.9
registered carrier departures worldwide
5.9
Animal species
closing a business
threatened
3.4
total known
3.4
time to resolve insolvency
5.3
corruption informal payments to public officials
Asylum seekers—see Migration; Refugees
crime
B
customs
Balance of payments
dealing with construction permits to build a warehouse
losses due to theft, robbery, vandalism, and arson
average time to clear exports
5.2
5.2
5.2
current account balance
4.15
number of procedures
5.3
exports and imports of goods and services
4.15
time required
5.3
net current transfers
4.15
net income
4.15
total reserves
4.15
employing workers rigidity of employment index
5.3
enforcing contracts
See also Exports; Imports; Investment; Private financial flows; Trade
number of procedures
5.3
time required
5.3
finance
Beverages commodity prices
firms using banks to finance investment
6.6
5.2
gender female participation in ownership
Biodiversity—see Biological diversity
5.2
informality firms that do not report all sales for tax purposes
Biological diversity assessment, date prepared, by country GEF benefits index
3.4
threatened species
3.4
animal
3.4
higher plants
3.4
treaty
value lost due to electrical outages
ISO certification ownership
5.2
permits and licenses time required to obtain operating license protecting investors disclosure, index
Births attended by skilled health staff
5.2
innovation
3.15
Birth rate, crude
5.2
infrastructure
3.15
5.2 5.3
registering property
2.1
2.18, 2.21
number of procedures
5.3
time to register
5.3
regulation and tax Birthweight, low
2.19
average number of times firms spend meeting with tax officials
5.2
time dealing with officials
5.2
starting a business
Bonds—see Debt flows; Private financial flows
Brain drain—see Emigration of people with tertiary education to OECD
cost to start a business
5.3
number of start-up procedures
5.3
time to start a business
countries
5.3
workforce, firms offering formal training Breastfeeding, exclusive
5.2
2.19, 2.21
C
Business environment businesses registered
Carbon dioxide
new
5.1
total
5.1
damage
3.16
2009 World Development Indicators
419
INDEX OF INDICATORS emissions per 2005 PPP dollar of GDP
See also Purchasing power parity (PPP) 3.8
per capita
1.3, 3.8
total
1.6, 3.8
intensity
3.8
Contraceptive prevalence rate
1.3, 2.17, 2.20
Contract enforcement
Children at work
number of procedures
5.3
time required for
5.3
by economic activity
2.6
male and female
2.6
study and work
2.6
status in employment
2.6
total
2.6
management; Social inclusion and equity policies; Public sector management
work only
2.6
and institutions; Structural policies
Cities
Corruption, informal payments to public officials
5.2
Country Policy and Institutional Assessment (CPIA)—see Economic
Credit
air pollution
3.14
getting credit
population
depth of credit information index
5.5
in largest city
3.11
strength of legal rights index
5.5
in selected cities
3.14
private credit registry coverage
5.5
in urban agglomerations of more than 1 million
3.11
public credit registry coverage
5.5
urban population
3.11
provided by banking sector
5.5
to private sector
5.1
Crime, losses due to
5.2
See also Urban environment
Closing a business—see Business environment
Commercial bank and other lending
6.11
Current account balance
See also Debt flows; Private financial flows
Commodity prices and price indexes
4.15
See also Balance of payments
6.6
Communications—see Internet; Newspapers, daily; Telephones; Television, households with
Customs, average time to clear
5.2
D
Compensation of government employees
4.11
DAC (Development Assistance Committee)—see Aid
Computers (personal) per 100 people
5.11
Death rate, crude
2.1
See also Mortality rate Consumption distribution—see Income distribution fixed capital
Debt, external 3.16
government, general annual growth as share of GDP
4.9
per capita as share of GDP
420
2009 World Development Indicators
6.10
debt ratios
6.10
debt service
4.8
multilateral, as share of public and publicly guaranteed debt
4.9
total, as share of exports of goods and services and income
household average annual growth
as share of GNI
service
4.9 4.8
IMF credit, use of
6.10 6.10 6.9
long-term
out of school children, male and female
private nonguaranteed
6.9
primary completion rate
IBRD loans and IDA credits
6.9
progression
total
6.9
public and publicly guaranteed
2.12, 2.15 1.2, 2.14, 2.15
male and female
present value as share of GNI
6.10
as share of exports of goods and services and income
6.10
short-term
6.9
as share of total debt
6.10
as share of total reserves
6.10
total total
2.14, 2.15
share of cohort reaching grade 5, male and female
2.13
share of cohort reaching last grade of primary, male and female
2.13
public expenditure on as share of GDP
2.11
as share of total government expenditure
2.11
per student, as share of GDP per capita, by level
2.11
pupil-teacher ratio, primary level
2.11
6.9
repeaters, primary level, male and female
2.13
6.9
teachers, primary, trained
2.11
transition to secondary school, male and female
2.13
unemployment by level of educational attainment
Debt flows bonds
6.11
commercial banks and other lending
6.11
See also Private financial flows
years of schooling, average
2.5 2.15
Electricity consumption
Deforestation, average annual
3.4
Density—see Population, density
5.10
production share of total
3.10
sources
3.10
transmissions and distribution losses value lost due to outages
Dependency ratio—See Population
5.10 5.2
Development assistance—see Aid
Emigration of people with tertiary education to OECD countries
Disease—see Health risks
Emissions
6.1
carbon dioxide Distribution of income or consumption—see Income distribution
E
average annual growth
3.9
intensity
3.8
per capita
3.8
total
3.8
methane
Economic management (Country Policy and Institutional Assessment) debt policy
5.8
agricultural as share of total
economic management cluster average
5.8
industrial as share of total
3.9
fiscal policy
5.8
total
3.9
macroeconomic management
5.8
nitrous oxide agricultural as share of total
Education enrollment ratio girls to boys enrollment in primary and secondary schools
1.2
gross, by level
2.12
net, by level
2.12
adjusted net, primary
2.12
gross intake rate, grade 1 gross primary participation rate
2.13, 2.15 2.15
3.9
3.9
industrial as share of total
3.9
total
3.9
other greenhouse gases
3.9
Employment children in employment
2.6
in agriculture as share of total employment
2009 World Development Indicators
3.2
421
INDEX OF INDICATORS 2.3
ratio of PPP conversion factor to official exchange rate
4.14
in industry, male and female
male and female
2.3
real effective
4.14
in informal sector, urban, male and female
2.9
See also Purchasing power parity (PPP)
in services, male and female
2.3
rigidity index
5.3
to population ratio
2.4
vulnerable
2.4
See also Labor force; Unemployment
Export credits private, from DAC members
Exports arms
Employing workers rigidity of employment index
6.13
5.7
goods and services 5.3
as share of GDP average annual growth
Endangered species—see Animal species; Biological diversity; Plants, higher
total
4.8 4.9 4.15
high-technology Energy commodity prices depletion, as share of GNI
6.6
share of manufactured exports
5.12
total
5.12
merchandise
3.16
annual growth
emissions—see Pollution
6.2, 6.3
imports, net
3.8
by high-income countries, by product
6.4
production
3.7
by developing countries, by partner
6.5
by regional trade blocs
6.7
2005 PPP dollar of GDP per unit
3.8
direction of trade
6.3
average annual growth
3.8
structure
4.4
clean energy consumption as share of total
3.7
total
4.4
combustible renewables and waste as share of total
3.7
value, average annual growth
6.2
fossil fuel consumption as share of total
3.7
volume, average annual growth
6.2
total
3.7
use
services
See also Electricity; Fuels
structure
4.6
total
4.6
transport
Enforcing contracts—see Business environment
travel See also Trade
Enrollment—see Education
F
Entry regulations for business—see Business environment
Environmental strategy, year adopted
4.6 4.6, 6.19
3.15
Equity flows
Female-headed households
2.9
Fertility rate
foreign direct investment, net inflows
6.11
adolescent
portfolio equity
6.11
total
2.18 2.18, 2.21
See also Private financial flows Finance, firms using banks to finance investment
5.2
European Commission distribution of net aid from
6.16
Exchange rates official, local currency units to U.S. dollar
422
2009 World Development Indicators
4.14
Financial access, stability, and efficiency bank capital to asset ratio
5.5
bank nonperforming loans
5.5
Financial flows, net from DAC members
Fuels 6.13
consumption
official
road sector
3.13
transportation sector
3.13
from bilateral sources
6.12
from international financial institutions
6.12
from multilateral sources
6.12
as share of total exports
from UN agencies
6.12
crude petroleum, from high-income economies, as share of total
total
6.12
exports 4.4
exports
official development assistance and official aid
6.4
from high-income economies, as share of total exports
grants from NGOs
6.13
other official flows
6.13
private
6.13
total
6.13
total exports
6.4
imports as share of total imports
See also Aid
4.4
crude petroleum, by high-income economies, as share of total imports
Financing through international capital markets
6.4
petroleum products, from high-income economies, as share of
6.4
by high-income economies, as share of total imports
6.1
6.4
petroleum products, by high-income economies, as share of total
See also Private financial flows
imports
6.4
prices
Food—see Agriculture, production indexes; Commodity prices and price
3.13
tariff rates applied by high-income countries
indexes
6.4
by country of origin
6.18a
G
by gender
6.18b
GEF benefits index for biodiversity
3.4
by education attainment
6.18b
by occupation
6.18b
Gender, female participation in ownership
5.2
by sector of employment
6.18b
Foreign-born population in OECD countries
Gender differences Foreign direct investment, net—see Investment; Private financial flows
in children in employment
2.6
in education Forest
enrollment, primary and secondary
area, as share of total land area
3.1
in employment
deforestation, average annual
3.4
in HIV prevalence
net depletion
3.16
Freshwater
1.2, 2.12, 2.13, 2.14 2.3 2.20
in labor force participation
2.2
in life expectancy at birth
1.5
in literacy
annual withdrawals
adult
2.14
youth
2.14
amount
3.5
as share of internal resources
3.5
for agriculture
3.5
adult
for domestic use
3.5
child
for industry
3.5
renewable internal resources
in mortality 2.22 2.22
in smoking
2.20
in survival to age 65
2.22
flows
3.5
in youth unemployment
2.10
per capita
3.5
unpaid family workers
1.5
women in nonagricultural sector
1.5
women in parliaments
1.5
See also Water, access to improved source of
2009 World Development Indicators
423
INDEX OF INDICATORS Gini index
2.9
Gross savings
Government, central cash surplus or deficit
as share of GDP
4.8
as share of GNI
3.16
4.10
as share of GDP
4.10
H
interest, as share of revenue
4.10
Health care
interest, as share of total expenses
4.11
debt
expense as share of GDP
4.10
by economic type
4.11
military
5.7
net incurrence of liabilities, as share of GDP domestic
4.10
foreign 4.10revenues, current
children sleeping under treated bednets
2.17
children with acute respiratory infection taken to health provider
2.17
children with diarrhea who received oral rehydration and continued feeding
2.17
children with fever receiving antimalarial drugs
2.17
hospital beds per 1,000 people
2.16
immunization
2.17, 2.21
newborns protected against tetanus
2.18
4.10
physicians, nurses, and midwives
2.16
grants and other
4.12
outpatient visits per capita
2.16
social contributions
4.12
physicians per 1,000 people
2.16
as share of GDP
tax, as share of GDP tax, by source
5.6 4.12
pregnant women receiving prenatal care
anemia, prevalence of, pregnant women Greenhouse gases—see Emissions
1.2, 2.18, 2.21
contraceptive prevalence rate
1.3, 2.18, 2.21
annual growth
4.9
adolescent
as share of GDP
4.8
total low-birthweight babies maternal mortality ratio
Gross domestic product (GDP) 1.1, 1.6, 4.1
total
unmet need for contraception
2.18 2.18, 2.21 2.19 1.3, 2.18 2.18
tuberculosis
implicit deflator—see Prices per capita, annual growth
2.19
births attended by skilled health staff
fertility rate
Gross capital formation
annual growth
1.5, 2.18, 2.21
reproductive
1.1, 1.6 4.2
DOTS detection rate incidence treatment success rate
2.17 1.3, 2.20 2.17
Gross enrollment—see Education 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
2.16
out of pocket
2.16
per capita
2.16
public
2.16
total
2.16
year last national health account completed
2.16
Health surveys year of most recent health survey
total
424
as share of GDP
PPP dollars
1.1, 1.6
U.S. dollars
1.1, 1.6
2009 World Development Indicators
2.16
Health risks
Imports
anemia, prevalence of children ages under 5 pregnant women child malnutrition, prevalence
arms
5.7
2.19
energy, net, as share of total energy use
3.8
2.19
goods and services
1.2, 2.19, 2.21
as share of GDP
condom use
2.20
average annual growth
diabetes, prevalence
2.20
total
HIV prevalence
1.3, 2.20
4.8 4.9 4.15
merchandise
overweight children, prevalence
2.19
annual growth
smoking, prevalence
2.20
by high-income countries, by product
6.4
by developing countries, by partner
6.5
tuberculosis, incidence undernourishment, prevalence
1.3, 2.20 2.19
6.3
structure
4.5
tariffs Heavily indebted poor countries (HIPCs) assistance
1.4
completion point
1.4
decision point
1.4
Multilateral Debt Relief Initiative (MDRI) assistance
1.4
HIV prevalence
1.3, 2.20
female
2.20
population ages 15–24, male and female
2.20
total
2.20
total
4.5
value, average annual growth
6.2
volume, average annual growth
6.2
services structure
4.7
total
4.7
transport
4.7
travel
4.7, 6.17
See also Trade
Income distribution Gini index
prevention condom use, male and female
6.4, 6.8
2.20
2.9
percentage of
1.2, 2.9
Industry
Hospital beds—see Health care
Housing conditions, national and urban durable dwelling units
3.12
home ownership
3.12
household size
3.12
multiunit dwellings
3.12
overcrowding
3.12
vacancy rate
3.12
annual growth
4.1
as share of GDP
4.2
employment, male and female
2.3
Inflation—see Prices
Informal economy, firms that do not report all sales for tax purposes
5.2
Information and communications technology expenditures
I
as share of GDP
IDA Resource Allocation Index (IRAI)
Immigrants in selected OECD countries
5.8
6.18
Innovation, ISO certification ownership
5.2
Integration, global economic, indicators
6.1
Interest payments—see Government, central, debt
Immunization rate, child DPT, share of children ages 12–23 months
2.17, 2.21
measles, share of children ages 12–23 months
2.17, 2.21
tetanus, newborns protected against
5.11
2.18
Interest rates deposit
4.13
2009 World Development Indicators
425
INDEX OF INDICATORS lending
4.13
real
4.13
risk premium on lending
5.5
spread
5.5
International Bank for Reconstruction and Development (IBRD) IBRD loans and IDA credits net financial flows from
6.9 6.12
IBRD loans and IDA credits
Labor force annual growth
2.2
armed forces
5.7
children at work
2.6
female
2.2
participation of population ages 15+, male female
2.2
total
2.2
See also Employment; Migration; Unemployment
International Development Association (IDA)
net concessional flows from
L
6.9 6.12
Land area arable—see Agriculture, land; Land use See also Protected areas; Surface area
International migrant stock as share of total population total
6.1 6.17
See also Migration
International Monetary Fund (IMF) net financial flows from use of IMF credit
6.12 6.9
Internet broadband subscribers
Land use 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
irrigated land
3.2
permanent cropland, as share of total land
3.1
total area
3.1
5.11
fixed broadband access tariff
5.11
Life expectancy at birth
secure servers
5.11
male and female
users
5.11
total
international Internet bandwidth
1.5 1.6, 2.22
5.11, 6.1 Literacy
Investment foreign direct, net inflows as share of GDP
1.6, 2.14
youth, male and female
1.6, 2.14
6.1
from DAC members
6.13
total
6.11
foreign direct, net outflows as share of GDP
adult, male and female
M Malnutrition, in children under age 5
1.2, 2.19, 2.21
6.1
infrastructure, private participation in
Malaria
energy
5.1
children sleeping under treated bednets
2.17
telecommunications
5.1
children with fever receiving antimalarial drugs
2.17
transport
5.1
water and sanitation
5.1
Management time dealing with officials
5.2
See also Gross capital formation; Private financial flows Manufacturing Iodized salt, consumption of
2.19
chemicals exports food imports
426
2009 World Development Indicators
4.3 4.4, 6.4 4.3 4.5, 6.4
machinery
4.3
food
structure
4.3
footwear
6.4
textile
4.3
fuels
4.5
value added
4.5
furniture
6.4
annual growth
4.1
information and communications technology goods
as share of GDP
4.2
iron and steel
total
4.3
machinery and transport equipment
6.4
manufactures
4.5
ores and metals
4.5
See also Merchandise
Market access to high-income countries
5.11 6.4
ores and nonferrous materials
6.4 6.4
goods admitted free of tariffs
1.4
petroleum products
support to agriculture
1.4
textiles
6.4
total
4.5
tariffs on exports from low- and middle-income countries agricultural products
1.4
value, average annual growth
6.2
textiles and clothing
1.4
volume, average annual growth
6.2
trade Merchandise exports
as share of GDP
6.1
by developing countries, by partner
6.5
direction
6.3
by regional trade blocs
6.7
growth
6.3
by developing countries, by partner
6.5
regional trade blocs
6.7
cereals
6.4
chemicals
6.4
crude petroleum
6.4
agricultural raw materials
food
4.4, 6.4
Metals and minerals commodity prices and price index
6.6
4.4, 6.4
footwear
6.4
fuels
4.4
agricultural as share of total
furniture
6.4
industrial as share of total
3.9
total
3.9
information and communications technology goods
5.11
information and communications technology services
5.11
Methane emissions 3.9
iron and steel
6.4
machinery and transport equipment
6.4
characteristics of immigrants in OECD countries
manufactures
4.4
emigration of people with tertiary education to OECD countries
ores and metals
4.4
international migrant stock
Migration
ores and nonferrous materials
6.4
as share of total population
petroleum products
6.4
total
textiles
6.4
net
total
4.4
See also Refugees; Remittances
value, average annual growth
6.2
volume, average annual growth
6.2
within regional trade blocs
6.7
imports
6.18 6.1
6.1 6.17 6.1, 6.17
Military armed forces personnel as share of labor force
5.7
total
5.7
agricultural raw materials
4.5
by developing countries, by partner
6.5
arms transfers
cereals
6.4
exports
5.7
chemicals
6.4
imports
5.7
crude petroleum
6.4
2009 World Development Indicators
427
INDEX OF INDICATORS military expenditure
unmet need for contraception
as share of central government expenditure
5.7
vulnerable employment
as share of GDP
5.7
women in wage employment in the nonagricultural sector
Millennium Development Goals, indicators for
Minerals, depletion of
access to improved sanitation facilities
1.3, 2.17
access to improved water source
2.17, 3.5
1.5
3.15
Monetary indicators
aid as share of GNI of donor country
2.18 1.2, 2.4
1.4, 6.13
claims on governments and other public entities
4.13
claims on private sector
4.13
for basic social services as share of total sector allocable ODA commitments
1.4
births attended by skilled health staff carbon dioxide emissions per capita
1.3, 3.8
children sleeping under treated bednets contraceptive prevalence rate
Money and quasi money, annual growth
4.13
2.18 Mortality rate
2.17
adult, male and female
1.3, 2.18
child, male and female
employment to population ratio
2.4
enrollment ratio, net, primary
children under age 5
2.12
female to male enrollments, primary and secondary
infant
1.2
fertility rate, adolescent
maternal
2.22 2.22 1.2, 2.21, 2.22 2.22 1.3, 2.18
2.18
heavily indebted poor countries (HIPCs)
Motor vehicles
completion point
1.4
passenger cars
3.13
decision point
1.4
per 1,000 people
3.13
nominal debt service relief
1.4
per kilometer of road
3.13
road density
3.13
immunization DPT
2.17, 2.21
measles
2.17, 2.21
income or consumption, national share of poorest quintile infant mortality rate
1.2, 2.9
MUV G-5 index
6.6
2.21, 2.22
labor productivity, GDP per person employed
2.4
literacy rate of 15- to 24-year-olds malnutrition, prevalence
See also Roads; Traffic
2.14 1.2, 2.19, 2.21
N Net enrollment—see Education
malaria children under age 5 sleeping under insecticide treated bednets
2.17
Net national savings
3.16
2.17
Newspapers, daily
5.11
children under age 5 with fever who are treated with appropriate antimalarial drugs maternal mortality ratio national parliament seats held by women poverty gap pregnant women receiving prenatal care share of cohort reaching last grade of primary telephone lines, fixed-line and mobile
1.3, 2.18 1.5 2.7, 2.8 1.5, 2.18, 2.21 2.13
incidence treatment success rate under-five mortality rate undernourishment, prevalence
428
2009 World Development Indicators
agricultural as share of total
3.9
industrial as share of total
3.9
total
3.9
1.3, 5.10
tuberculosis DOTS detection rate
Nitrous oxide emissions
Nutrition 2.17 1.3, 2.20 2.17 1.2, 2.22 2.19
anemia, prevalence of children ages under 5 pregnant women breastfeeding iodized salt consumption
2.19 2.19 2.19, 2.21 2.19
malnutrition, child
1.2, 2.19, 2.11
per 2005 PPP dollar of GDP
3.8
overweight children, prevalence
2.19
per capita
3.8
undernourishment, prevalence
2.19
total
3.8
vitamin A supplementation
2.19
methane emissions agricultural as share of total
O
3.9
total
3.9
nitrogen dioxide, selected cities
Official development assistance—see Aid
3.9
industrial as share of total
3.14
nitrous oxide emissions agricultural as share of total
Official flows net from bilateral sources
6.12
3.9
industrial as share of total
3.9
total
3.9
organic water pollutants, emissions
from international financial institutions
6.12
from multilateral sources
6.12
by industry
from United Nations
6.12
per day
3.6
6.13
per worker
3.6
other
3.6
particulate matter, selected cities
P Passenger cars per 1,000 people
3.14
sulfur dioxide, selected cities
3.14
urban-population-weighted PM10
3.13
3.13 Population
Particulate matter
age dependency ratio
2.1 2.1
emission damage
3.16
annual growth
selected cities
3.14
by age group
urban-population-weighted PM10
3.13
0–14
2.11
5–64
2.1
Patent applications filed
5.12
65 and older
2.1
density female, as share of total
Pension average, as share of per capita income
1.1, 1.6
2.10
contributors
1.5
rural annual growth
3.1
as share of total
3.1
as share of labor force
2.10
as share of working age population
2.10
total
public expenditure on, as share of GDP
2.10
urban
1.1, 1.6, 2.1
as share of total Permits and licenses, time required to obtain operating license
5.2
Physicians—see Health care
Plants, higher species
3.4
threatened species
3.4
3.11
average annual growth
3.11
in largest city
3.11
in selected cities
3.14
in urban agglomerations
3.11
total
3.11
See also Migration
Portfolio—see Equity flows; Private financial flows Pollution Ports, container traffic in
carbon dioxide damage, as share of GNI
5.9
3.16
emissions
2009 World Development Indicators
429
INDEX OF INDICATORS Poverty
national
international poverty line local currency
2.8
as share of total land area
3.4
total
3.4
population living below $1.25 a day
2.8
$2 a day
2.8
national poverty line
Protecting investors disclosure index
5.3
Public sector management and institutions (Country Policy and Institutional
population living below
2.7
Assessment)
national
2.7
efficiency of revenue mobilization
5.8
rural
2.7
property rights and rule-based governance
5.8
urban
2.7
public sector management and institutions cluster average
5.8
quality of budgetary and financial management
5.8
Power—see Electricity, production
Prenatal care, pregnant women receiving
quality of public administration
5.8
transparency, accountability, and corruption in the public sector
5.8
1.5, 2.18, 2.21 Purchasing power parity (PPP) conversion factor
Prices commodity prices and price indexes
6.6
consumer, annual growth
4.14
fuel
3.18
GDP implicit deflator, annual growth
4.14
terms of trade wholesale, annual growth
6.2 4.14
Primary education—see Education
Private financial flows
bonds
6.11
commercial bank and other lending
6.11
equity flows
R Railways goods hauled by
5.9
lines, total
5.9
passengers carried
5.9
by country of asylum
6.17
by country of origin
6.17
Regional development banks, net financial flows from
foreign direct investment, net inflows
6.11
portfolio equity
6.11
from DAC members
4.14 1.1, 1.6
Refugees
debt flows
financing through international capital markets, as share of GDP
gross national income
6.12
Regional trade agreements—see Trade blocs, regional
6.1 6.13
See also Investment
Registering property number of procedures
5.3
time to register
5.3
Productivity in agriculture value added per worker
Regulation and tax administration 3.3
management time dealing with officials
5.2
labor productivity, GDP per person employed
2.4
meeting with tax officials, number of times
5.2
water productivity, total
3.5 Relative prices (PPP)—see Purchasing power parity (PPP)
Protected areas marine
430
Remittances
as share of total surface area
3.4
total
3.4
2009 World Development Indicators
workers’ remittances and compensation of employees as share of GDP
6.1
paid
6.17
received
6.17
Secondary education—see Education
Services Research and development
employment, male and female
2.3
expenditures
5.12
exports
researchers
5.12
structure
4.6
technicians
5.12
total
4.6
imports Reserves, gross international—see Balance of payments
structure
4.7
total
4.7
trade, as share of GDP
Roads
6.1
value added
goods hauled by
5.9
passengers carried
5.9
annual growth
4.1
paved, as share of total
5.9
as share of GDP
4.2
total network traffic
5.9 3.13
2.20
Social inclusion and equity policies (Country Policy and Institutional
Royalty and license fees payments
5.12
receipts
5.12
Rural environment access to improved sanitation facilities
Smoking, prevalence, male and female
3.11
population annual growth
3.1
as share of total
3.1
Assessment) building human resources
5.8
equity of public resource use
5.8
gender equity
5.8
policy and institutions for environmental sustainability
5.8
social inclusion and equity cluster average
5.8
social protection and labor
5.8
Starting a business—see Business environment
S
Stock markets
S&P/EMDB Indexes
5.4
listed domestic companies
5.4
market capitalization Sanitation, access to improved facilities, population with rural
3.11
total
1.3, 2.17
urban
3.11
as share of GDP
5.4
total
5.4
market liquidity
5.4
S&P/EMDB Indices
5.4
turnover ratio
5.4
Savings gross, as share of GDP
4.8
gross, as share of GNI
3.16
net
3.16
Schooling—see Education
Science and technology scientific and technical journal articles See also Research and development
Steel products, commodity prices and price index
6.6
Structural policies (Country Policy and Institutional Assessment) business regulating environment
5.8
financial sector
5.8
structural policies cluster average
5.8
trade
5.8
5.12 Sulfur dioxide emissions—see Pollution
2009 World Development Indicators
431
INDEX OF INDICATORS Surface area
1.1, 1.6
international voice traffic
See also Land use
5.10, 6.1
per 100 people
5.10
mobile cellular Survival to age 65, male and female
per 100 people
2.22
1.3, 5.10
population covered prepaid tariff
Suspended particulate matter—see Pollution
T
5.10
mobile cellular and fixed-line subscribers per employee
5.10
total revenue
5.10
Television, households with
Tariffs
5.10
5.11
all products binding coverage
6.8
simple mean board rate
6.8
simple mean tariff
6.8
weighted mean tariff
6.8
applied rates on imports from low- and middle-income economies
Terms of trade, net barter
6.2
Tertiary education—see Education
6.4
Tetanus vaccinations, newborns protected against
simple mean tariff
6.8
Threatened species—see Animal species; Biological diversity; Plants, higher
weighted mean tariff
6.8
2.18
manufactured products
on exports of least developed countries
1.4
primary products
Tourism, international expenditures in the country
simple mean tariff
6.8
as share of exports
6.19
weighted mean tariff
6.8
total
6.19
expenditures in other countries Taxes and tax policies business taxes
as share of imports
6.19
total
6.19
average number of times firms spent meeting tax officials
5.2
inbound tourists, by country
6.19
number of payments
5.6
outbound tourists, by country
6.19
time to prepare, file, and pay
5.6
total tax rate, percent profit goods and services taxes, domestic
5.6 4.12
highest marginal tax rate
Trade arms
5.7
merchandise
corporate
5.6
as share of GDP
6.1
individual
5.6
direction of, by developing countries
6.5 6.3
income, profit, and capital gains taxes as share of revenue
4.12
direction of, by region
international trade taxes
4.12
high-income economy with low- and middle-income economies,
other taxes
4.12
social contributions
4.12
tax revenue, as share of GDP
5.6
by product
6.4
nominal growth, by region
6.3
regional trading blocs
6.7
services Technology—see Computers; Exports, high-technology; Internet; Research and development; Science and technology
Telephones fixed line
432
per 100 people
5.10
residential tariff
5.10
2009 World Development Indicators
as share of GDP
6.1
computer, information, communications, and other
4.6, 4.7
insurance and financial
4.6, 4.7
transport
4.6, 4.7
travel
4.6, 4.7
See also Balance of payments; Exports; Imports; Manufacturing; Merchandise; Terms of trade; Trade blocs
Trade blocs, regional exports within bloc
6.7
total exports, by bloc
6.7
type of agreement
6.7
year of creation
6.7
year of entry into force of the most recent agreement
6.7
Trademark applications filed
UNICEF, net official financial flows from
6.12
UNTA, net official financial flows from
6.12
UNRWA net official financial flows from
6.12
refugees under the mandate of
6.17
5.12 Urban environment
Trade policies—see Tariffs
access to sanitation
3.11
employment, informal sector Traffic
2.8
population
road traffic
3.13
as share of total
road traffic injury and mortality
2.18
average annual growth
3.11
in largest city
3.11
in urban agglomerations
3.11
total
3.11
See also Roads
Transport—see Air transport; Railways; Roads; Traffic; Urban environment
3.11
selected cities nitrogen dioxide
Travel—see Tourism, international
Treaties, participation in biological diversity
3.15
CFC control
3.15
3.14
particulate matter
3.14
population
3.14
sulfur dioxide
3.14
See also Pollution; Population; Sanitation; Water
climate change
3.15
Convention on International Trade on Endangered Species (CITES)
3.15
Convention to Combat Desertification (CCD)
3.15
V
Kyoto Protocol
3.15
Value added
Law of the Sea
3.15
ozone layer
3.15
in agriculture
4.2
Stockholm Convention on Persistent Organic Pollutants
3.15
in industry
4.2
in manufacturing
4.2
in services
4.2
Tuberculosis, incidence
as share of GDP
1.3, 2.20
growth
U
in agriculture
4.1
in industry
4.1
UN agencies, net official financial flows from
in manufacturing
4.1
in services
4.1
Undernourishment, prevalence of
6.12
2.19
per worker in agriculture
Unemployment incidence of long-term, total, male, and female
2.5
by level of educational attainment, primary, secondary, tertiary
2.5
total, male, and female youth, male, and female
UNHCR, refugees under the mandate of
3.3
total, in manufacturing
4.3
Vulnerable employment
1.2, 2.4
2.5 1.3, 2.10
6.17
W Water access to improved source of, population with
2009 World Development Indicators
1.3, 2.17
433
INDEX OF INDICATORS pollution—see Pollution, organic water pollutants productivity
women in parliaments
1.5
3.5 Workforce, firms offering formal training
5.2
Women in development female-headed households
2.10
World Bank commodity price index
female population
1.5
energy
life expectancy at birth
1.5
nonenergy commodities
6.6
pregnant women receiving prenatal care
1.5
steel products
6.6
teenage mothers
1.5
unpaid family workers
1.5
vulnerable employment
2.4
women in nonagricultural sector
1.5
434
2009 World Development Indicators
World Bank, net financial flows from See also International Bank for Reconstruction and Development; International Development Association
6.6
6.12
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
The World Bank 1818 H Street N.W.
ISBN 978-0-8213-7829-8
Washington, D.C. 20433 USA Telephone: 202 473 1000 Fax: 202 477 6391 Web site: www.worldbank.org Email:
[email protected]
SKU 17829
The World Development Indicators t Includes more than 800 indicators for 153 economies t Provides definitions, sources, and other information about the data t Organizes the data into six thematic areas
1 4
WORLD VIEW Living standards and development progress
ECONOMY New opportunities for growth
2 5
PEOPLE Gender, health, and employment
STATES & MARKETS Elements of a good investment climate
3 6
ENVIRONMENT Natural resources and environmental changes
GLOBAL LINKS Evidence on globalization
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